Package: a4
Version: 1.55.0
Depends: a4Base, a4Preproc, a4Classif, a4Core, a4Reporting
Suggests: MLP, nlcv, ALL, Cairo, Rgraphviz, GOstats, hgu95av2.db
License: GPL-3
MD5sum: 62328ffcea69c884d330b102c101d1ed
NeedsCompilation: no
Title: Automated Affymetrix Array Analysis Umbrella Package
Description: Umbrella package is available for the entire Automated
Affymetrix Array Analysis suite of package.
biocViews: Microarray
Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud
[cre]
Maintainer: Laure Cougnaud <laure.cougnaud@openanalytics.eu>
git_url: https://git.bioconductor.org/packages/a4
git_branch: devel
git_last_commit: c714e25
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/a4_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/a4_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/a4_1.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/a4_1.55.0.tgz
vignettes: vignettes/a4/inst/doc/a4vignette.pdf
vignetteTitles: a4vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/a4/inst/doc/a4vignette.R
dependencyCount: 87
Package: a4Base
Version: 1.55.0
Depends: a4Preproc, a4Core
Imports: methods, graphics, grid, Biobase, annaffy, mpm, genefilter,
limma, multtest, glmnet, gplots
Suggests: Cairo, ALL, hgu95av2.db, nlcv
Enhances: gridSVG, JavaGD
License: GPL-3
MD5sum: 3212771821859920375603eac3f30d05
NeedsCompilation: no
Title: Automated Affymetrix Array Analysis Base Package
Description: Base utility functions are available for the Automated
Affymetrix Array Analysis set of packages.
biocViews: Microarray
Author: Willem Talloen [aut], Tine Casneuf [aut], An De Bondt [aut],
Steven Osselaer [aut], Hinrich Goehlmann [aut], Willem
Ligtenberg [aut], Tobias Verbeke [aut], Laure Cougnaud [cre]
Maintainer: Laure Cougnaud <laure.cougnaud@openanalytics.eu>
git_url: https://git.bioconductor.org/packages/a4Base
git_branch: devel
git_last_commit: d78b7ff
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/a4Base_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/a4Base_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/a4Base_1.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/a4Base_1.55.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: a4
suggestsMe: epimutacions
dependencyCount: 78
Package: a4Classif
Version: 1.55.0
Depends: a4Core, a4Preproc
Imports: methods, Biobase, ROCR, pamr, glmnet, varSelRF, utils,
graphics, stats
Suggests: ALL, hgu95av2.db, knitr, rmarkdown
License: GPL-3
MD5sum: b7af5109bfba4f239d13ee4ce5e7b36a
NeedsCompilation: no
Title: Automated Affymetrix Array Analysis Classification Package
Description: Functionalities for classification of Affymetrix
microarray data, integrating within the Automated Affymetrix
Array Analysis set of packages.
biocViews: Microarray, GeneExpression, Classification
Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud
[cre]
Maintainer: Laure Cougnaud <laure.cougnaud@openanalytics.eu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/a4Classif
git_branch: devel
git_last_commit: 37f77ac
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/a4Classif_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/a4Classif_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/a4Classif_1.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/a4Classif_1.55.0.tgz
vignettes: vignettes/a4Classif/inst/doc/a4Classif-vignette.html
vignetteTitles: a4Classif package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/a4Classif/inst/doc/a4Classif-vignette.R
dependsOnMe: a4
dependencyCount: 33
Package: a4Core
Version: 1.55.0
Imports: Biobase, glmnet, methods, stats
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: d2d0ce867df2a7200bb7c0d600fda437
NeedsCompilation: no
Title: Automated Affymetrix Array Analysis Core Package
Description: Utility functions for the Automated Affymetrix Array
Analysis set of packages.
biocViews: Microarray, Classification
Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud
[cre]
Maintainer: Laure Cougnaud <laure.cougnaud@openanalytics.eu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/a4Core
git_branch: devel
git_last_commit: 85fe6a4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/a4Core_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/a4Core_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/a4Core_1.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/a4Core_1.55.0.tgz
vignettes: vignettes/a4Core/inst/doc/a4Core-vignette.html
vignetteTitles: a4Core package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/a4Core/inst/doc/a4Core-vignette.R
dependsOnMe: a4, a4Base, a4Classif, nlcv
dependencyCount: 20
Package: a4Preproc
Version: 1.55.0
Imports: BiocGenerics, Biobase
Suggests: ALL, hgu95av2.db, knitr, rmarkdown
License: GPL-3
MD5sum: 8f055b54a4e208d211798f8e4a856e44
NeedsCompilation: no
Title: Automated Affymetrix Array Analysis Preprocessing Package
Description: Utility functions to pre-process data for the Automated
Affymetrix Array Analysis set of packages.
biocViews: Microarray, Preprocessing
Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud
[cre]
Maintainer: Laure Cougnaud <laure.cougnaud@openanalytics.eu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/a4Preproc
git_branch: devel
git_last_commit: 2b1a86f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/a4Preproc_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/a4Preproc_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/a4Preproc_1.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/a4Preproc_1.55.0.tgz
vignettes: vignettes/a4Preproc/inst/doc/a4Preproc-vignette.html
vignetteTitles: a4Preproc package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/a4Preproc/inst/doc/a4Preproc-vignette.R
dependsOnMe: a4, a4Base, a4Classif
suggestsMe: graphite
dependencyCount: 7
Package: a4Reporting
Version: 1.55.0
Imports: methods, xtable
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: aa19f45ff368856ba2f39588ce2caa92
NeedsCompilation: no
Title: Automated Affymetrix Array Analysis Reporting Package
Description: Utility functions to facilitate the reporting of the
Automated Affymetrix Array Analysis Reporting set of packages.
biocViews: Microarray
Author: Tobias Verbeke [aut], Laure Cougnaud [cre]
Maintainer: Laure Cougnaud <laure.cougnaud@openanalytics.eu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/a4Reporting
git_branch: devel
git_last_commit: d4dd26b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/a4Reporting_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/a4Reporting_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/a4Reporting_1.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/a4Reporting_1.55.0.tgz
vignettes: vignettes/a4Reporting/inst/doc/a4reporting-vignette.html
vignetteTitles: a4Reporting package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/a4Reporting/inst/doc/a4reporting-vignette.R
dependsOnMe: a4
dependencyCount: 4
Package: ABarray
Version: 1.75.0
Imports: Biobase, graphics, grDevices, methods, multtest, stats, tcltk,
utils
Suggests: limma, LPE
License: GPL
MD5sum: b91d31a146a163cdbc7793d6637c94ee
NeedsCompilation: no
Title: Microarray QA and statistical data analysis for Applied
Biosystems Genome Survey Microrarray (AB1700) gene expression
data.
Description: Automated pipline to perform gene expression analysis for
Applied Biosystems Genome Survey Microarray (AB1700) data
format. Functions include data preprocessing, filtering,
control probe analysis, statistical analysis in one single
function. A GUI interface is also provided. The raw data,
processed data, graphics output and statistical results are
organized into folders according to the analysis settings used.
biocViews: Microarray, OneChannel, Preprocessing
Author: Yongming Andrew Sun
Maintainer: Yongming Andrew Sun <sunya@appliedbiosystems.com>
git_url: https://git.bioconductor.org/packages/ABarray
git_branch: devel
git_last_commit: efb1f10
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ABarray_1.75.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ABarray_1.75.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ABarray_1.75.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ABarray_1.75.0.tgz
vignettes: vignettes/ABarray/inst/doc/ABarray.pdf,
vignettes/ABarray/inst/doc/ABarrayGUI.pdf
vignetteTitles: ABarray gene expression, ABarray gene expression GUI
interface
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 17
Package: abseqR
Version: 1.25.0
Depends: R (>= 3.5.0)
Imports: ggplot2, RColorBrewer, circlize, reshape2, VennDiagram, plyr,
flexdashboard, BiocParallel (>= 1.1.25), png, grid, gridExtra,
rmarkdown, knitr, vegan, ggcorrplot, ggdendro, plotly,
BiocStyle, stringr, utils, methods, grDevices, stats, tools,
graphics
Suggests: testthat
License: GPL-3 | file LICENSE
MD5sum: cbcbd1e25dac5dcad144c35023f81e0d
NeedsCompilation: no
Title: Reporting and data analysis functionalities for Rep-Seq datasets
of antibody libraries
Description: AbSeq is a comprehensive bioinformatic pipeline for the
analysis of sequencing datasets generated from antibody
libraries and abseqR is one of its packages. abseqR empowers
the users of abseqPy (https://github.com/malhamdoosh/abseqPy)
with plotting and reporting capabilities and allows them to
generate interactive HTML reports for the convenience of
viewing and sharing with other researchers. Additionally,
abseqR extends abseqPy to compare multiple repertoire analyses
and perform further downstream analysis on its output.
biocViews: Sequencing, Visualization, ReportWriting, QualityControl,
MultipleComparison
Author: JiaHong Fong [cre, aut], Monther Alhamdoosh [aut]
Maintainer: JiaHong Fong <jiahfong@gmail.com>
URL: https://github.com/malhamdoosh/abseqR
SystemRequirements: pandoc (>= 1.19.2.1)
VignetteBuilder: knitr
BugReports: https://github.com/malhamdoosh/abseqR/issues
git_url: https://git.bioconductor.org/packages/abseqR
git_branch: devel
git_last_commit: 5c67450
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/abseqR_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/abseqR_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/abseqR_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/abseqR_1.25.0.tgz
vignettes: vignettes/abseqR/inst/doc/abseqR.pdf
vignetteTitles: Introduction to abseqR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/abseqR/inst/doc/abseqR.R
dependencyCount: 110
Package: ABSSeq
Version: 1.61.0
Depends: R (>= 2.10), methods
Imports: locfit, limma
Suggests: edgeR
License: GPL (>= 3)
MD5sum: c0631aebbe2996b0cb40d0238b9a9360
NeedsCompilation: no
Title: ABSSeq: a new RNA-Seq analysis method based on modelling
absolute expression differences
Description: Inferring differential expression genes by absolute counts
difference between two groups, utilizing Negative binomial
distribution and moderating fold-change according to
heterogeneity of dispersion across expression level.
biocViews: DifferentialExpression
Author: Wentao Yang
Maintainer: Wentao Yang <wyang@zoologie.uni-kiel.de>
git_url: https://git.bioconductor.org/packages/ABSSeq
git_branch: devel
git_last_commit: 15bbde7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ABSSeq_1.61.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ABSSeq_1.61.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ABSSeq_1.61.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ABSSeq_1.61.0.tgz
vignettes: vignettes/ABSSeq/inst/doc/ABSSeq.pdf
vignetteTitles: ABSSeq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ABSSeq/inst/doc/ABSSeq.R
importsMe: metaseqR2
dependencyCount: 10
Package: acde
Version: 1.37.0
Depends: R(>= 3.3), boot(>= 1.3)
Imports: stats, graphics
Suggests: BiocGenerics, RUnit
License: GPL-3
MD5sum: 1fdbcd7b2160ac4e33fe02fbe835e187
NeedsCompilation: no
Title: Artificial Components Detection of Differentially Expressed
Genes
Description: This package provides a multivariate inferential analysis
method for detecting differentially expressed genes in gene
expression data. It uses artificial components, close to the
data's principal components but with an exact interpretation in
terms of differential genetic expression, to identify
differentially expressed genes while controlling the false
discovery rate (FDR). The methods on this package are described
in the vignette or in the article 'Multivariate Method for
Inferential Identification of Differentially Expressed Genes in
Gene Expression Experiments' by J. P. Acosta, L. Lopez-Kleine
and S. Restrepo (2015, pending publication).
biocViews: DifferentialExpression, TimeCourse, PrincipalComponent,
GeneExpression, Microarray, mRNAMicroarray
Author: Juan Pablo Acosta, Liliana Lopez-Kleine
Maintainer: Juan Pablo Acosta <jpacostar@unal.edu.co>
git_url: https://git.bioconductor.org/packages/acde
git_branch: devel
git_last_commit: 863c5e5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/acde_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/acde_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/acde_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/acde_1.37.0.tgz
vignettes: vignettes/acde/inst/doc/acde.pdf
vignetteTitles: Identification of Differentially Expressed Genes with
Artificial Components
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/acde/inst/doc/acde.R
dependencyCount: 3
Package: ACE
Version: 1.25.0
Depends: R (>= 3.4)
Imports: Biobase, QDNAseq, ggplot2, grid, stats, utils, methods,
grDevices, GenomicRanges
Suggests: knitr, rmarkdown, BiocStyle
License: GPL-2
MD5sum: f824ec9c06fde87add1827987bfb7efa
NeedsCompilation: no
Title: Absolute Copy Number Estimation from Low-coverage Whole Genome
Sequencing
Description: Uses segmented copy number data to estimate tumor cell
percentage and produce copy number plots displaying absolute
copy numbers.
biocViews: CopyNumberVariation, DNASeq, Coverage, WholeGenome,
Visualization, Sequencing
Author: Jos B Poell
Maintainer: Jos B Poell <j.poell@amsterdamumc.nl>
URL: https://github.com/tgac-vumc/ACE
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ACE
git_branch: devel
git_last_commit: 0696f37
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ACE_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ACE_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ACE_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ACE_1.25.0.tgz
vignettes: vignettes/ACE/inst/doc/ACE_vignette.html
vignetteTitles: ACE vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ACE/inst/doc/ACE_vignette.R
dependencyCount: 88
Package: aCGH
Version: 1.85.0
Depends: R (>= 2.10), cluster, survival, multtest
Imports: Biobase, grDevices, graphics, methods, stats, splines, utils
License: GPL-2
Archs: x64
MD5sum: 2064ffce40ff66812e8e0ddfe976ce13
NeedsCompilation: yes
Title: Classes and functions for Array Comparative Genomic
Hybridization data
Description: Functions for reading aCGH data from image analysis output
files and clone information files, creation of aCGH S3 objects
for storing these data. Basic methods for accessing/replacing,
subsetting, printing and plotting aCGH objects.
biocViews: CopyNumberVariation, DataImport, Genetics
Author: Jane Fridlyand <jfridlyand@cc.ucsf.edu>, Peter Dimitrov
<dimitrov@stat.Berkeley.EDU>
Maintainer: Peter Dimitrov <dimitrov@stat.Berkeley.EDU>
git_url: https://git.bioconductor.org/packages/aCGH
git_branch: devel
git_last_commit: 061926d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/aCGH_1.85.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/aCGH_1.85.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/aCGH_1.85.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/aCGH_1.85.0.tgz
vignettes: vignettes/aCGH/inst/doc/aCGH.pdf
vignetteTitles: aCGH Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/aCGH/inst/doc/aCGH.R
dependsOnMe: CRImage
importsMe: ADaCGH2
dependencyCount: 17
Package: ACME
Version: 2.63.0
Depends: R (>= 2.10), Biobase (>= 2.5.5), methods, BiocGenerics
Imports: graphics, stats
License: GPL (>= 2)
MD5sum: d8506f8f4d510bd085bd48ac6be7f8a6
NeedsCompilation: yes
Title: Algorithms for Calculating Microarray Enrichment (ACME)
Description: ACME (Algorithms for Calculating Microarray Enrichment) is
a set of tools for analysing tiling array ChIP/chip, DNAse
hypersensitivity, or other experiments that result in regions
of the genome showing "enrichment". It does not rely on a
specific array technology (although the array should be a
"tiling" array), is very general (can be applied in experiments
resulting in regions of enrichment), and is very insensitive to
array noise or normalization methods. It is also very fast and
can be applied on whole-genome tiling array experiments quite
easily with enough memory.
biocViews: Technology, Microarray, Normalization
Author: Sean Davis <sdavis2@mail.nih.gov>
Maintainer: Sean Davis <sdavis2@mail.nih.gov>
URL: http://watson.nci.nih.gov/~sdavis
git_url: https://git.bioconductor.org/packages/ACME
git_branch: devel
git_last_commit: e012f98
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ACME_2.63.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ACME_2.63.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ACME_2.63.0.tgz
vignettes: vignettes/ACME/inst/doc/ACME.pdf
vignetteTitles: ACME
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ACME/inst/doc/ACME.R
suggestsMe: oligo
dependencyCount: 7
Package: ADaCGH2
Version: 2.47.1
Depends: R (>= 3.2.0), parallel, ff
Imports: bit, DNAcopy, tilingArray, waveslim, cluster, aCGH
Suggests: CGHregions, Cairo, limma
Enhances: Rmpi, GLAD
License: GPL (>= 3)
Archs: x64
MD5sum: 82409b70b28cfc374f664bb4ad176fda
NeedsCompilation: yes
Title: Analysis of big data from aCGH experiments using parallel
computing and ff objects
Description: Analysis and plotting of array CGH data. Allows usage of
Circular Binary Segementation, wavelet-based smoothing (both as
in Liu et al., and HaarSeg as in Ben-Yaacov and Eldar), HMM,
GLAD, CGHseg. Most computations are parallelized (either via
forking or with clusters, including MPI and sockets clusters)
and use ff for storing data.
biocViews: Microarray, CopyNumberVariants
Author: Ramon Diaz-Uriarte <rdiaz02@gmail.com> and Oscar M. Rueda
<rueda.om@gmail.com>. Wavelet-based aCGH smoothing code from Li
Hsu <lih@fhcrc.org> and Douglas Grove <dgrove@fhcrc.org>.
Imagemap code from Barry Rowlingson
<B.Rowlingson@lancaster.ac.uk>. HaarSeg code from Erez
Ben-Yaacov; downloaded from
<http://www.ee.technion.ac.il/people/YoninaEldar/Info/software/HaarSeg.htm>.
Code from ffbase <https://github.com/edwindj/ffbase> by Edwin
de Jonge <edwindjonge@gmail.com>, Jan Wijffels, Jan van der
Laan.
Maintainer: Ramon Diaz-Uriarte <rdiaz02@gmail.com>
URL: https://github.com/rdiaz02/adacgh2
git_url: https://git.bioconductor.org/packages/ADaCGH2
git_branch: devel
git_last_commit: 7003a29
git_last_commit_date: 2025-02-19
Date/Publication: 2025-02-20
source.ver: src/contrib/ADaCGH2_2.47.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ADaCGH2_2.47.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ADaCGH2_2.47.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ADaCGH2_2.47.1.tgz
vignettes: vignettes/ADaCGH2/inst/doc/ADaCGH2-long-examples.pdf,
vignettes/ADaCGH2/inst/doc/ADaCGH2.pdf,
vignettes/ADaCGH2/inst/doc/benchmarks.pdf
vignetteTitles: ADaCGH2-long-examples.pdf, ADaCGH2 Overview,
benchmarks.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ADaCGH2/inst/doc/ADaCGH2.R
dependencyCount: 95
Package: ADAM
Version: 1.23.0
Depends: R(>= 3.5), stats, utils, methods
Imports: Rcpp (>= 0.12.18), GO.db (>= 3.6.0), KEGGREST (>= 1.20.2),
knitr, pbapply (>= 1.3-4), dplyr (>= 0.7.6), DT (>= 0.4),
stringr (>= 1.3.1), SummarizedExperiment (>= 1.10.1)
LinkingTo: Rcpp
Suggests: testthat, rmarkdown, BiocStyle
License: GPL (>= 2)
Archs: x64
MD5sum: dbaeb5e5dc6fde0fc81ca76bd1168a3f
NeedsCompilation: yes
Title: ADAM: Activity and Diversity Analysis Module
Description: ADAM is a GSEA R package created to group a set of genes
from comparative samples (control versus experiment) belonging
to different species according to their respective functions
(Gene Ontology and KEGG pathways as default) and show their
significance by calculating p-values referring togene diversity
and activity. Each group of genes is called GFAG (Group of
Functionally Associated Genes).
biocViews: GeneSetEnrichment, Pathways, KEGG, GeneExpression,
Microarray
Author: André Luiz Molan [aut], Giordano Bruno Sanches Seco [ctb],
Agnes Takeda [ctb], Jose Rybarczyk Filho [ctb, cre, ths]
Maintainer: Jose Luiz Rybarczyk Filho <jose.luiz@unesp.br>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ADAM
git_branch: devel
git_last_commit: 6c1c9e3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ADAM_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ADAM_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ADAM_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ADAM_1.23.0.tgz
vignettes: vignettes/ADAM/inst/doc/ADAM.html
vignetteTitles: "Using ADAM"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ADAM/inst/doc/ADAM.R
dependsOnMe: ADAMgui
dependencyCount: 93
Package: ADAMgui
Version: 1.23.0
Depends: R(>= 3.6), stats, utils, methods, ADAM
Imports: GO.db (>= 3.5.0), dplyr (>= 0.7.6), shiny (>= 1.1.0), stringr
(>= 1.3.1), stringi (>= 1.2.4), varhandle (>= 2.0.3), ggplot2
(>= 3.0.0), ggrepel (>= 0.8.0), ggpubr (>= 0.1.8), ggsignif (>=
0.4.0), reshape2 (>= 1.4.3), RColorBrewer (>= 1.1-2),
colorRamps (>= 2.3), DT (>= 0.4), data.table (>= 1.11.4),
gridExtra (>= 2.3), shinyjs (>= 1.0), knitr, testthat
Suggests: markdown, BiocStyle
License: GPL (>= 2)
MD5sum: 632bb08472109113b822cb93159fc85d
NeedsCompilation: no
Title: Activity and Diversity Analysis Module Graphical User Interface
Description: ADAMgui is a Graphical User Interface for the ADAM
package. The ADAMgui package provides 2 shiny-based
applications that allows the user to study the output of the
ADAM package files through different plots. It's possible, for
example, to choose a specific GFAG and observe the gene
expression behavior with the plots created with the
GFAGtargetUi function. Features such as differential expression
and foldchange can be easily seen with aid of the plots made
with GFAGpathUi function.
biocViews: GeneSetEnrichment, Pathways, KEGG
Author: Giordano Bruno Sanches Seco [aut], André Luiz Molan [ctb],
Agnes Takeda [ctb], Jose Rybarczyk Filho [ctb, cre, ths]
Maintainer: Jose Luiz Rybarczyk Filho <jose.luiz@unesp.br>
URL: TBA
VignetteBuilder: knitr
BugReports: https://github.com/jrybarczyk/ADAMgui/issues
git_url: https://git.bioconductor.org/packages/ADAMgui
git_branch: devel
git_last_commit: 9cd8b2c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ADAMgui_1.23.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ADAMgui_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ADAMgui_1.23.0.tgz
vignettes: vignettes/ADAMgui/inst/doc/ADAMgui.html
vignetteTitles: "Using ADAMgui"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ADAMgui/inst/doc/ADAMgui.R
dependencyCount: 165
Package: ADAPT
Version: 1.1.0
Depends: R (>= 4.1.0)
Imports: Rcpp (>= 1.0.8), RcppArmadillo (>= 0.10.8), RcppParallel (>=
5.1.5), phyloseq (>= 1.39.0), methods, stats, ggplot2 (>=
3.4.1), ggrepel (>= 0.9.1)
LinkingTo: Rcpp, RcppArmadillo, RcppParallel
Suggests: rmarkdown (>= 2.11), knitr (>= 1.37), testthat (>= 3.0.0)
License: MIT + file LICENSE
Archs: x64
MD5sum: 187724739fdd239f0f4b5de0dce215c5
NeedsCompilation: yes
Title: Analysis of Microbiome Differential Abundance by Pooling Tobit
Models
Description: ADAPT carries out differential abundance analysis for
microbiome metagenomics data in phyloseq format. It has two
innovations. One is to treat zero counts as left censored and
use Tobit models for log count ratios. The other is an
innovative way to find non-differentially abundant taxa as
reference, then use the reference taxa to find the
differentially abundant ones.
biocViews: DifferentialExpression, Microbiome, Normalization,
Sequencing, Metagenomics, Software, MultipleComparison
Author: Mukai Wang [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1413-1904>), Simon Fontaine [ctb],
Hui Jiang [ctb], Gen Li [aut, ctb]
Maintainer: Mukai Wang <wangmk@umich.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ADAPT
git_branch: devel
git_last_commit: ec4beea
git_last_commit_date: 2024-10-29
Date/Publication: 2025-01-23
source.ver: src/contrib/ADAPT_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ADAPT_1.1.0.zip
vignettes: vignettes/ADAPT/inst/doc/ADAPT-manual.html
vignetteTitles: ADAPT Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ADAPT/inst/doc/ADAPT-manual.R
dependencyCount: 85
Package: adductomicsR
Version: 1.23.0
Depends: R (>= 3.6), adductData, ExperimentHub, AnnotationHub
Imports: parallel (>= 3.3.2), data.table (>= 1.10.4), OrgMassSpecR (>=
0.4.6), foreach (>= 1.4.3), mzR (>= 2.14.0), ade4 (>= 1.7.6),
rvest (>= 0.3.2), pastecs (>= 1.3.18), reshape2 (>= 1.4.2),
pracma (>= 2.0.4), DT (>= 0.2), fpc (>= 2.1.10), doSNOW (>=
1.0.14), fastcluster (>= 1.1.22), RcppEigen (>= 0.3.3.3.0),
bootstrap (>= 2017.2), smoother (>= 1.1), dplyr (>= 0.7.5), zoo
(>= 1.8), stats (>= 3.5.0), utils (>= 3.5.0), graphics (>=
3.5.0), grDevices (>= 3.5.0), methods (>= 3.5.0), datasets (>=
3.5.0)
Suggests: knitr (>= 1.15.1), rmarkdown (>= 1.5), Rdisop (>= 1.34.0),
testthat
License: Artistic-2.0
MD5sum: 99db89d82b1f61e0659c477b5ba9378d
NeedsCompilation: no
Title: Processing of adductomic mass spectral datasets
Description: Processes MS2 data to identify potentially adducted
peptides from spectra that has been corrected for mass drift
and retention time drift and quantifies MS1 level mass spectral
peaks.
biocViews:
MassSpectrometry,Metabolomics,Software,ThirdPartyClient,DataImport,
GUI
Author: Josie Hayes <jlhayes1982@gmail.com>
Maintainer: Josie Hayes <jlhayes1982@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/JosieLHayes/adductomicsR/issues
git_url: https://git.bioconductor.org/packages/adductomicsR
git_branch: devel
git_last_commit: 3c1217c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/adductomicsR_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/adductomicsR_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/adductomicsR_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/adductomicsR_1.23.0.tgz
vignettes: vignettes/adductomicsR/inst/doc/adductomicsRWorkflow.html
vignetteTitles: Adductomics workflow
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/adductomicsR/inst/doc/adductomicsRWorkflow.R
dependencyCount: 136
Package: ADImpute
Version: 1.17.0
Depends: R (>= 4.0)
Imports: checkmate, BiocParallel, data.table, DrImpute, kernlab, MASS,
Matrix, methods, rsvd, S4Vectors, SAVER, SingleCellExperiment,
stats, SummarizedExperiment, utils
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: GPL-3 + file LICENSE
MD5sum: 3af750cdbed698a4793c355a1cd64aa9
NeedsCompilation: no
Title: Adaptive Dropout Imputer (ADImpute)
Description: Single-cell RNA sequencing (scRNA-seq) methods are
typically unable to quantify the expression levels of all genes
in a cell, creating a need for the computational prediction of
missing values (‘dropout imputation’). Most existing dropout
imputation methods are limited in the sense that they
exclusively use the scRNA-seq dataset at hand and do not
exploit external gene-gene relationship information. Here we
propose two novel methods: a gene regulatory network-based
approach using gene-gene relationships learnt from external
data and a baseline approach corresponding to a sample-wide
average. ADImpute can implement these novel methods and also
combine them with existing imputation methods (currently
supported: DrImpute, SAVER). ADImpute can learn the best
performing method per gene and combine the results from
different methods into an ensemble.
biocViews: GeneExpression, Network, Preprocessing, Sequencing,
SingleCell, Transcriptomics
Author: Ana Carolina Leote [cre, aut] (ORCID:
<https://orcid.org/0000-0003-0879-328X>)
Maintainer: Ana Carolina Leote <anacarolinaleote@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/anacarolinaleote/ADImpute/issues
git_url: https://git.bioconductor.org/packages/ADImpute
git_branch: devel
git_last_commit: 1b9a5c8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ADImpute_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ADImpute_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ADImpute_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ADImpute_1.17.0.tgz
vignettes: vignettes/ADImpute/inst/doc/ADImpute_tutorial.html
vignetteTitles: ADImpute tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ADImpute/inst/doc/ADImpute_tutorial.R
dependencyCount: 65
Package: adSplit
Version: 1.77.0
Depends: R (>= 2.1.0), methods (>= 2.1.0)
Imports: AnnotationDbi, Biobase (>= 1.5.12), cluster (>= 1.9.1), GO.db
(>= 1.8.1), graphics, grDevices, KEGGREST (>= 1.30.1), multtest
(>= 1.6.0), stats (>= 2.1.0)
Suggests: golubEsets (>= 1.0), vsn (>= 1.5.0), hu6800.db (>= 1.8.1)
License: GPL (>= 2)
Archs: x64
MD5sum: 46c1eec6c7802ba3e57455ab6d21be48
NeedsCompilation: yes
Title: Annotation-Driven Clustering
Description: This package implements clustering of microarray gene
expression profiles according to functional annotations. For
each term genes are annotated to, splits into two subclasses
are computed and a significance of the supporting gene set is
determined.
biocViews: Microarray, Clustering
Author: Claudio Lottaz, Joern Toedling
Maintainer: Claudio Lottaz <Claudio.Lottaz@klinik.uni-regensburg.de>
URL: http://compdiag.molgen.mpg.de/software/adSplit.shtml
git_url: https://git.bioconductor.org/packages/adSplit
git_branch: devel
git_last_commit: 7c97ce7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/adSplit_1.77.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/adSplit_1.77.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/adSplit_1.77.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/adSplit_1.77.0.tgz
vignettes: vignettes/adSplit/inst/doc/tr_2005_02.pdf
vignetteTitles: Annotation-Driven Clustering
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/adSplit/inst/doc/tr_2005_02.R
dependencyCount: 54
Package: adverSCarial
Version: 1.5.0
Depends: R (>= 3.5.0)
Imports: gtools, S4Vectors, methods, DelayedArray
Suggests: knitr, RUnit, BiocGenerics, TENxPBMCData, CHETAH, stringr,
LoomExperiment
License: MIT + file LICENSE
MD5sum: 6bbbd73bec9bcd776c6ae522a26fa844
NeedsCompilation: no
Title: adverSCarial, generate and analyze the vulnerability of
scRNA-seq classifier to adversarial attacks
Description: adverSCarial is an R Package designed for generating and
analyzing the vulnerability of scRNA-seq classifiers to
adversarial attacks. The package is versatile and provides a
format for integrating any type of classifier. It offers
functions for studying and generating two types of attacks,
single gene attack and max change attack. The single-gene
attack involves making a small modification to the input to
alter the classification. The max-change attack involves making
a large modification to the input without changing its
classification. The package provides a comprehensive solution
for evaluating the robustness of scRNA-seq classifiers against
adversarial attacks.
biocViews: Software, SingleCell, Transcriptomics, Classification
Author: Ghislain FIEVET [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0337-7327>), Sébastien HERGALANT
[aut] (ORCID: <https://orcid.org/0000-0001-8456-7992>)
Maintainer: Ghislain FIEVET <ghislain.fievet@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/adverSCarial
git_branch: devel
git_last_commit: 9d206a6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/adverSCarial_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/adverSCarial_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/adverSCarial_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/adverSCarial_1.5.0.tgz
vignettes: vignettes/adverSCarial/inst/doc/vign01_adverSCarial.html,
vignettes/adverSCarial/inst/doc/vign02_overView_analysis.html,
vignettes/adverSCarial/inst/doc/vign03_adapt_classifier.html,
vignettes/adverSCarial/inst/doc/vign04_advRandWalkMinChange.html
vignetteTitles: Vign01_adverSCarial, Vign02_overView_analysis,
Vign03_adapt_classifiers, Vign04_advRandWalkMinChange
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/adverSCarial/inst/doc/vign01_adverSCarial.R,
vignettes/adverSCarial/inst/doc/vign02_overView_analysis.R
dependencyCount: 23
Package: AffiXcan
Version: 1.25.0
Depends: R (>= 3.6), SummarizedExperiment
Imports: MultiAssayExperiment, BiocParallel, crayon
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-3
MD5sum: 44f4b48e88792591ae07616c16f3f4f5
NeedsCompilation: no
Title: A Functional Approach To Impute Genetically Regulated Expression
Description: Impute a GReX (Genetically Regulated Expression) for a set
of genes in a sample of individuals, using a method based on
the Total Binding Affinity (TBA). Statistical models to impute
GReX can be trained with a training dataset where the real
total expression values are known.
biocViews: GeneExpression, Transcription, GeneRegulation,
DimensionReduction, Regression, PrincipalComponent
Author: Alessandro Lussana [aut, cre]
Maintainer: Alessandro Lussana <alessandro.lussana@protonmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/AffiXcan
git_branch: devel
git_last_commit: 091b100
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/AffiXcan_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AffiXcan_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AffiXcan_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AffiXcan_1.25.0.tgz
vignettes: vignettes/AffiXcan/inst/doc/AffiXcan.html
vignetteTitles: AffiXcan
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AffiXcan/inst/doc/AffiXcan.R
dependencyCount: 66
Package: affxparser
Version: 1.79.1
Depends: R (>= 2.14.0)
Suggests: R.oo (>= 1.22.0), R.utils (>= 2.7.0), AffymetrixDataTestFiles
License: LGPL (>= 2)
Archs: x64
MD5sum: dfa850058d2219107347a6a6115e1f5e
NeedsCompilation: yes
Title: Affymetrix File Parsing SDK
Description: Package for parsing Affymetrix files (CDF, CEL, CHP,
BPMAP, BAR). It provides methods for fast and memory efficient
parsing of Affymetrix files using the Affymetrix' Fusion SDK.
Both ASCII- and binary-based files are supported. Currently,
there are methods for reading chip definition file (CDF) and a
cell intensity file (CEL). These files can be read either in
full or in part. For example, probe signals from a few
probesets can be extracted very quickly from a set of CEL files
into a convenient list structure.
biocViews: Infrastructure, DataImport, Microarray,
ProprietaryPlatforms, OneChannel
Author: Henrik Bengtsson [aut], James Bullard [aut], Robert Gentleman
[ctb], Kasper Daniel Hansen [aut, cre], Jim Hester [ctb],
Martin Morgan [ctb]
Maintainer: Kasper Daniel Hansen <kasperdanielhansen@gmail.com>
URL: https://github.com/HenrikBengtsson/affxparser
BugReports: https://github.com/HenrikBengtsson/affxparser/issues
git_url: https://git.bioconductor.org/packages/affxparser
git_branch: devel
git_last_commit: d2f1dc2
git_last_commit_date: 2025-01-06
Date/Publication: 2025-01-08
source.ver: src/contrib/affxparser_1.79.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/affxparser_1.79.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/affxparser_1.79.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/affxparser_1.79.1.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: ITALICS, pdInfoBuilder
importsMe: affyILM, cn.farms, EventPointer, GeneRegionScan, ITALICS,
oligo
suggestsMe: TIN, aroma.affymetrix, aroma.apd
dependencyCount: 0
Package: affy
Version: 1.85.1
Depends: R (>= 2.8.0), BiocGenerics (>= 0.1.12), Biobase (>= 2.5.5)
Imports: affyio (>= 1.13.3), BiocManager, graphics, grDevices, methods,
preprocessCore, stats, utils
LinkingTo: preprocessCore
Suggests: tkWidgets (>= 1.19.0), affydata, widgetTools, hgu95av2cdf
License: LGPL (>= 2.0)
Archs: x64
MD5sum: b7d02386a18f81fffcb6f64bf293d4d2
NeedsCompilation: yes
Title: Methods for Affymetrix Oligonucleotide Arrays
Description: The package contains functions for exploratory
oligonucleotide array analysis. The dependence on tkWidgets
only concerns few convenience functions. 'affy' is fully
functional without it.
biocViews: Microarray, OneChannel, Preprocessing
Author: Rafael A. Irizarry <rafa@ds.dfci.harvard.edu>, Laurent Gautier
<lgautier@gmail.com>, Benjamin Milo Bolstad
<bmb@bmbolstad.com>, and Crispin Miller
<cmiller@picr.man.ac.uk> with contributions from Magnus Astrand
<Magnus.Astrand@astrazeneca.com>, Leslie M. Cope
<cope@mts.jhu.edu>, Robert Gentleman, Jeff Gentry, Conrad
Halling <challing@agilixcorp.com>, Wolfgang Huber, James
MacDonald <jmacdon@u.washington.edu>, Benjamin I. P.
Rubinstein, Christopher Workman <workman@cbs.dtu.dk>, John
Zhang
Maintainer: Robert D. Shear <rshear@ds.dfci.harvard.edu>
URL: https://bioconductor.org/packages/affy
BugReports: https://github.com/rafalab/affy/issues
git_url: https://git.bioconductor.org/packages/affy
git_branch: devel
git_last_commit: 38ca5fd
git_last_commit_date: 2025-01-10
Date/Publication: 2025-01-10
source.ver: src/contrib/affy_1.85.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/affy_1.85.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/affy_1.85.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/affy_1.85.1.tgz
vignettes: vignettes/affy/inst/doc/affy.pdf,
vignettes/affy/inst/doc/builtinMethods.pdf,
vignettes/affy/inst/doc/customMethods.pdf,
vignettes/affy/inst/doc/vim.pdf
vignetteTitles: 1. Primer, 2. Built-in Processing Methods, 3. Custom
Processing Methods, 4. Import Methods
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/affy/inst/doc/affy.R,
vignettes/affy/inst/doc/builtinMethods.R,
vignettes/affy/inst/doc/customMethods.R,
vignettes/affy/inst/doc/vim.R
dependsOnMe: affyContam, affyPLM, AffyRNADegradation, altcdfenvs,
arrayMvout, Cormotif, DrugVsDisease, ExiMiR, frmaTools, gcrma,
maskBAD, panp, prebs, qpcrNorm, RPA, SCAN.UPC, webbioc,
affydata, ALLMLL, AmpAffyExample, bronchialIL13, CLL,
curatedBladderData, ecoliLeucine, MAQCsubset, mvoutData,
PREDAsampledata, SpikeIn, SpikeInSubset, XhybCasneuf,
RobLoxBioC
importsMe: affycoretools, affyILM, affylmGUI, arrayQualityMetrics,
bnem, CAFE, ChIPXpress, Cormotif, Doscheda, ffpe, frma, gcrma,
GEOsubmission, Harshlight, iCheck, lumi, makecdfenv, mimager,
MSnbase, PECA, plier, puma, pvac, Rnits, STATegRa, tilingArray,
TurboNorm, vsn, rat2302frmavecs, DeSousa2013,
signatureSearchData, bapred
suggestsMe: AnnotationForge, ArrayExpress, autonomics, beadarray,
BiocGenerics, Biostrings, BufferedMatrixMethods,
categoryCompare, ecolitk, factDesign, GeneRegionScan, limma,
made4, piano, PREDA, qcmetrics, runibic, siggenes,
TCGAbiolinks, ath1121501frmavecs, estrogen, ffpeExampleData,
arrays, aroma.affymetrix, hexbin, isatabr, maGUI
dependencyCount: 11
Package: affycomp
Version: 1.83.0
Depends: R (>= 2.13.0), methods, Biobase (>= 2.3.3)
Suggests: splines, affycompData
License: GPL (>= 2)
MD5sum: e7caeee2cfffbe3e4efb6295d80da4d5
NeedsCompilation: no
Title: Graphics Toolbox for Assessment of Affymetrix Expression
Measures
Description: The package contains functions that can be used to compare
expression measures for Affymetrix Oligonucleotide Arrays.
biocViews: OneChannel, Microarray, Preprocessing
Author: Rafael A. Irizarry <rafa@ds.dfci.harvard.edu> and Zhijin Wu
<zwu@stat.brown.edu> with contributions from Simon Cawley
<simon_cawley@affymetrix.com>
Maintainer: Robert D. Shear <rshear@ds.dfci.harvard.edu>
URL: https://bioconductor.org/packages/affycomp
BugReports: https://github.com/rafalab/affycomp/issues
git_url: https://git.bioconductor.org/packages/affycomp
git_branch: devel
git_last_commit: 0f0f8ff
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/affycomp_1.83.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/affycomp_1.83.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/affycomp_1.83.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/affycomp_1.83.0.tgz
vignettes: vignettes/affycomp/inst/doc/affycomp.pdf
vignetteTitles: affycomp primer
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/affycomp/inst/doc/affycomp.R
dependsOnMe: affycompData
dependencyCount: 7
Package: affyContam
Version: 1.65.0
Depends: R (>= 2.7.0), tools, methods, utils, Biobase, affy, affydata
Suggests: hgu95av2cdf
License: Artistic-2.0
MD5sum: 5f69f9c8d81c1ae9b131af2a78a3a0c3
NeedsCompilation: no
Title: structured corruption of affymetrix cel file data
Description: structured corruption of cel file data to demonstrate QA
effectiveness
biocViews: Infrastructure
Author: V. Carey
Maintainer: V. Carey <stvjc@channing.harvard.edu>
git_url: https://git.bioconductor.org/packages/affyContam
git_branch: devel
git_last_commit: c1f4c9d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/affyContam_1.65.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/affyContam_1.65.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/affyContam_1.65.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/affyContam_1.65.0.tgz
vignettes: vignettes/affyContam/inst/doc/affyContam.pdf
vignetteTitles: affy contamination tools
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/affyContam/inst/doc/affyContam.R
importsMe: arrayMvout
dependencyCount: 14
Package: affycoretools
Version: 1.79.0
Depends: Biobase, methods
Imports: affy, limma, GOstats, gcrma, splines, xtable, AnnotationDbi,
ggplot2, gplots, oligoClasses, ReportingTools, hwriter,
lattice, S4Vectors, edgeR, RSQLite, BiocGenerics, DBI, Glimma
Suggests: affydata, hgfocuscdf, BiocStyle, knitr, hgu95av2.db, rgl,
rmarkdown
License: Artistic-2.0
MD5sum: 02f716e8ea8953abdebb5fca14376e95
NeedsCompilation: no
Title: Functions useful for those doing repetitive analyses with
Affymetrix GeneChips
Description: Various wrapper functions that have been written to
streamline the more common analyses that a core Biostatistician
might see.
biocViews: ReportWriting, Microarray, OneChannel, GeneExpression
Author: James W. MacDonald
Maintainer: James W. MacDonald <jmacdon@u.washington.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/affycoretools
git_branch: devel
git_last_commit: b82bcab
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/affycoretools_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/affycoretools_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/affycoretools_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/affycoretools_1.79.0.tgz
vignettes:
vignettes/affycoretools/inst/doc/RefactoredAffycoretools.html
vignetteTitles: Creating annotated output with \Biocpkg{affycoretools}
and ReportingTools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/affycoretools/inst/doc/RefactoredAffycoretools.R
suggestsMe: EnMCB
dependencyCount: 197
Package: affyILM
Version: 1.59.0
Depends: R (>= 2.10.0), methods, gcrma
Imports: affxparser (>= 1.16.0), affy, graphics, Biobase
Suggests: AffymetrixDataTestFiles, hgfocusprobe
License: GPL-3
MD5sum: 899577820895ea4a6f024dd6eb354010
NeedsCompilation: no
Title: Linear Model of background subtraction and the Langmuir isotherm
Description: affyILM is a preprocessing tool which estimates gene
expression levels for Affymetrix Gene Chips. Input from
physical chemistry is employed to first background subtract
intensities before calculating concentrations on behalf of the
Langmuir model.
biocViews: Microarray, OneChannel, Preprocessing
Author: K. Myriam Kroll, Fabrice Berger, Gerard Barkema, Enrico Carlon
Maintainer: Myriam Kroll and Fabrice Berger <fabrice.berger@gmail.com>
git_url: https://git.bioconductor.org/packages/affyILM
git_branch: devel
git_last_commit: 3485abc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/affyILM_1.59.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/affyILM_1.59.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/affyILM_1.59.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/affyILM_1.59.0.tgz
vignettes: vignettes/affyILM/inst/doc/affyILM.pdf
vignetteTitles: affyILM1.3.0
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/affyILM/inst/doc/affyILM.R
dependencyCount: 33
Package: affyio
Version: 1.77.3
Depends: R (>= 2.6.0)
Imports: methods
License: LGPL (>= 2)
Archs: x64
MD5sum: 339220563ba8d6d3f5fa1f4a2c2e3a3d
NeedsCompilation: yes
Title: Tools for parsing Affymetrix data files
Description: Routines for parsing Affymetrix data files based upon file
format information. Primary focus is on accessing the CEL and
CDF file formats.
biocViews: Microarray, DataImport, Infrastructure
Author: Ben Bolstad <bmb@bmbolstad.com>
Maintainer: Ben Bolstad <bmb@bmbolstad.com>
URL: https://github.com/bmbolstad/affyio
git_url: https://git.bioconductor.org/packages/affyio
git_branch: devel
git_last_commit: dff48aa
git_last_commit_date: 2025-01-24
Date/Publication: 2025-01-24
source.ver: src/contrib/affyio_1.77.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/affyio_1.77.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/affyio_1.77.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/affyio_1.77.3.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: makecdfenv, SCAN.UPC
importsMe: affy, affylmGUI, crlmm, ExiMiR, gcrma, oligo, oligoClasses,
puma
suggestsMe: BufferedMatrixMethods
dependencyCount: 1
Package: affylmGUI
Version: 1.81.0
Imports: grDevices, graphics, stats, utils, tcltk, tkrplot, limma,
affy, affyio, affyPLM, gcrma, BiocGenerics, AnnotationDbi,
BiocManager, R2HTML, xtable
License: GPL (>=2)
MD5sum: c72be9ad085cb64042a95827f248dae4
NeedsCompilation: no
Title: GUI for limma Package with Affymetrix Microarrays
Description: A Graphical User Interface (GUI) for analysis of
Affymetrix microarray gene expression data using the affy and
limma packages.
biocViews: GUI, GeneExpression, Transcription, DifferentialExpression,
DataImport, Bayesian, Regression, TimeCourse, Microarray,
mRNAMicroarray, OneChannel, ProprietaryPlatforms, BatchEffect,
MultipleComparison, Normalization, Preprocessing,
QualityControl
Author: James Wettenhall [cre,aut], Gordon Smyth [aut], Ken Simpson
[aut], Keith Satterley [ctb]
Maintainer: Gordon Smyth <smyth@wehi.edu.au>
URL: http://bioinf.wehi.edu.au/affylmGUI/
git_url: https://git.bioconductor.org/packages/affylmGUI
git_branch: devel
git_last_commit: cc3b06c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/affylmGUI_1.81.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/affylmGUI_1.81.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/affylmGUI_1.81.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/affylmGUI_1.81.0.tgz
vignettes: vignettes/affylmGUI/inst/doc/affylmGUI.pdf,
vignettes/affylmGUI/inst/doc/extract.pdf,
vignettes/affylmGUI/inst/doc/about.html,
vignettes/affylmGUI/inst/doc/CustMenu.html,
vignettes/affylmGUI/inst/doc/index.html,
vignettes/affylmGUI/inst/doc/windowsFocus.html
vignetteTitles: affylmGUI Vignette, Extracting affy and limma objects
from affylmGUI files, about.html, CustMenu.html, index.html,
windowsFocus.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/affylmGUI/inst/doc/affylmGUI.R
dependencyCount: 58
Package: affyPLM
Version: 1.83.3
Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), affy (>= 1.11.0),
Biobase (>= 2.17.8), gcrma, stats, preprocessCore (>= 1.5.1)
Imports: graphics, grDevices, methods
LinkingTo: preprocessCore
Suggests: affydata, MASS, hgu95av2cdf
License: GPL (>= 2)
Archs: x64
MD5sum: cccf0c90de104e30d879208c4c81c00a
NeedsCompilation: yes
Title: Methods for fitting probe-level models
Description: A package that extends and improves the functionality of
the base affy package. Routines that make heavy use of compiled
code for speed. Central focus is on implementation of methods
for fitting probe-level models and tools using these models.
PLM based quality assessment tools.
biocViews: Microarray, OneChannel, Preprocessing, QualityControl
Author: Ben Bolstad <bmb@bmbolstad.com>
Maintainer: Ben Bolstad <bmb@bmbolstad.com>
URL: https://github.com/bmbolstad/affyPLM
git_url: https://git.bioconductor.org/packages/affyPLM
git_branch: devel
git_last_commit: d5e983d
git_last_commit_date: 2025-01-29
Date/Publication: 2025-01-29
source.ver: src/contrib/affyPLM_1.83.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/affyPLM_1.83.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/affyPLM_1.83.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/affyPLM_1.83.3.tgz
vignettes: vignettes/affyPLM/inst/doc/AffyExtensions.pdf,
vignettes/affyPLM/inst/doc/MAplots.pdf,
vignettes/affyPLM/inst/doc/QualityAssess.pdf,
vignettes/affyPLM/inst/doc/ThreeStep.pdf
vignetteTitles: affyPLM: Fitting Probe Level Models, affyPLM: Advanced
use of the MAplot function, affyPLM: Model Based QC Assessment
of Affymetrix GeneChips, affyPLM: the threestep function
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/affyPLM/inst/doc/AffyExtensions.R,
vignettes/affyPLM/inst/doc/MAplots.R,
vignettes/affyPLM/inst/doc/QualityAssess.R,
vignettes/affyPLM/inst/doc/ThreeStep.R
dependsOnMe: bapred
importsMe: affylmGUI, arrayQualityMetrics, mimager
suggestsMe: arrayMvout, BiocGenerics, frmaTools, metahdep, piano,
aroma.affymetrix
dependencyCount: 32
Package: AffyRNADegradation
Version: 1.53.0
Depends: R (>= 2.9.0), methods, affy
Suggests: AmpAffyExample, hgu133acdf
License: GPL-2
MD5sum: 3599f6abb051706653aff1ce0a2ea6f3
NeedsCompilation: no
Title: Analyze and correct probe positional bias in microarray data due
to RNA degradation
Description: The package helps with the assessment and correction of
RNA degradation effects in Affymetrix 3' expression arrays. The
parameter d gives a robust and accurate measure of RNA
integrity. The correction removes the probe positional bias,
and thus improves comparability of samples that are affected by
RNA degradation.
biocViews: GeneExpression, Microarray, OneChannel, Preprocessing,
QualityControl
Author: Mario Fasold
Maintainer: Mario Fasold <fasold@izbi.uni-leipzig.de>
git_url: https://git.bioconductor.org/packages/AffyRNADegradation
git_branch: devel
git_last_commit: 7b43dc2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/AffyRNADegradation_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AffyRNADegradation_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AffyRNADegradation_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AffyRNADegradation_1.53.0.tgz
vignettes: vignettes/AffyRNADegradation/inst/doc/vignette.pdf
vignetteTitles: AffyRNADegradation Example
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AffyRNADegradation/inst/doc/vignette.R
dependencyCount: 12
Package: AGDEX
Version: 1.55.0
Depends: R (>= 2.10), Biobase, GSEABase
Imports: stats
License: GPL Version 2 or later
MD5sum: ba8c9fb26f509d903503a8ef8bd21748
NeedsCompilation: no
Title: Agreement of Differential Expression Analysis
Description: A tool to evaluate agreement of differential expression
for cross-species genomics
biocViews: Microarray, Genetics, GeneExpression
Author: Stan Pounds <stanley.pounds@stjude.org>; Cuilan Lani Gao
<cuilan.gao@stjude.org>
Maintainer: Cuilan lani Gao <cuilan.gao@stjude.org>
git_url: https://git.bioconductor.org/packages/AGDEX
git_branch: devel
git_last_commit: 8b30309
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/AGDEX_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AGDEX_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AGDEX_1.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AGDEX_1.55.0.tgz
vignettes: vignettes/AGDEX/inst/doc/AGDEX.pdf
vignetteTitles: AGDEX.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AGDEX/inst/doc/AGDEX.R
dependencyCount: 50
Package: aggregateBioVar
Version: 1.17.0
Depends: R (>= 4.0)
Imports: stats, methods, S4Vectors, SummarizedExperiment,
SingleCellExperiment, Matrix, tibble, rlang
Suggests: BiocStyle, magick, knitr, rmarkdown, testthat, BiocGenerics,
DESeq2, magrittr, dplyr, ggplot2, cowplot, ggtext,
RColorBrewer, pheatmap, viridis
License: GPL-3
MD5sum: a77febdade5c51413201a8ce965d4b8e
NeedsCompilation: no
Title: Differential Gene Expression Analysis for Multi-subject
scRNA-seq
Description: For single cell RNA-seq data collected from more than one
subject (e.g. biological sample or technical replicates), this
package contains tools to summarize single cell gene expression
profiles at the level of subject. A SingleCellExperiment object
is taken as input and converted to a list of
SummarizedExperiment objects, where each list element
corresponds to an assigned cell type. The SummarizedExperiment
objects contain aggregate gene-by-subject count matrices and
inter-subject column metadata for individual subjects that can
be processed using downstream bulk RNA-seq tools.
biocViews: Software, SingleCell, RNASeq, Transcriptomics,
Transcription, GeneExpression, DifferentialExpression
Author: Jason Ratcliff [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7079-334X>), Andrew Thurman [aut],
Michael Chimenti [ctb], Alejandro Pezzulo [ctb]
Maintainer: Jason Ratcliff <jason-ratcliff@uiowa.edu>
URL: https://github.com/jasonratcliff/aggregateBioVar
VignetteBuilder: knitr
BugReports: https://github.com/jasonratcliff/aggregateBioVar/issues
git_url: https://git.bioconductor.org/packages/aggregateBioVar
git_branch: devel
git_last_commit: 76d82e6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/aggregateBioVar_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/aggregateBioVar_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/aggregateBioVar_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/aggregateBioVar_1.17.0.tgz
vignettes:
vignettes/aggregateBioVar/inst/doc/multi-subject-scRNA-seq.html
vignetteTitles: Multi-subject scRNA-seq Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/aggregateBioVar/inst/doc/multi-subject-scRNA-seq.R
dependencyCount: 48
Package: agilp
Version: 3.39.0
Depends: R (>= 2.14.0)
License: GPL-3
MD5sum: 179d01c4b27c42c27006960a75ccc1ed
NeedsCompilation: no
Title: Agilent expression array processing package
Description: More about what it does (maybe more than one line)
Author: Benny Chain <b.chain@ucl.ac.uk>
Maintainer: Benny Chain <b.chain@ucl.ac.uk>
git_url: https://git.bioconductor.org/packages/agilp
git_branch: devel
git_last_commit: 669a614
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/agilp_3.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/agilp_3.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/agilp_3.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/agilp_3.39.0.tgz
vignettes: vignettes/agilp/inst/doc/agilp_manual.pdf
vignetteTitles: An R Package for processing expression microarray data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/agilp/inst/doc/agilp_manual.R
dependencyCount: 0
Package: AgiMicroRna
Version: 2.57.0
Depends: R (>= 2.10),methods,Biobase,limma,affy (>=
1.22),preprocessCore,affycoretools
Imports: Biobase
Suggests: geneplotter,marray,gplots,gtools,gdata,codelink
License: GPL-3
MD5sum: 20fe00fa29c7140dd6e8091be3df4b06
NeedsCompilation: no
Title: Processing and Differential Expression Analysis of Agilent
microRNA chips
Description: Processing and Analysis of Agilent microRNA data
biocViews: Microarray, AgilentChip, OneChannel, Preprocessing,
DifferentialExpression
Author: Pedro Lopez-Romero <plopez@cnic.es>
Maintainer: Pedro Lopez-Romero <plopez@cnic.es>
git_url: https://git.bioconductor.org/packages/AgiMicroRna
git_branch: devel
git_last_commit: 0da1667
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/AgiMicroRna_2.57.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AgiMicroRna_2.57.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AgiMicroRna_2.57.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AgiMicroRna_2.57.0.tgz
vignettes: vignettes/AgiMicroRna/inst/doc/AgiMicroRna.pdf
vignetteTitles: AgiMicroRna
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AgiMicroRna/inst/doc/AgiMicroRna.R
dependencyCount: 198
Package: AHMassBank
Version: 1.7.1
Depends: R (>= 4.2)
Imports: AnnotationHubData (>= 1.5.24)
Suggests: BiocStyle, knitr, AnnotationHub (>= 2.7.13), rmarkdown,
methods, CompoundDb (>= 1.1.4)
License: Artistic-2.0
MD5sum: 8fb13304274fefd51c11ccf65e62ba50
NeedsCompilation: no
Title: MassBank Annotation Resources for AnnotationHub
Description: Supplies AnnotationHub with MassBank metabolite/compound
annotations bundled in CompDb SQLite databases. CompDb SQLite
databases contain general compound annotation as well as
fragment spectra representing fragmentation patterns of
compounds' ions. MassBank data is retrieved from
https://massbank.eu/MassBank and processed using helper
functions from the CompoundDb Bioconductor package into
redistributable SQLite databases.
biocViews: MassSpectrometry, AnnotationHubSoftware
Author: Johannes Rainer [cre] (ORCID:
<https://orcid.org/0000-0002-6977-7147>)
Maintainer: Johannes Rainer <Johannes.Rainer@eurac.edu>
URL: https://github.com/jorainer/AHMassBank
VignetteBuilder: knitr
BugReports: https://github.com/jorainer/AHMassBank/issues
git_url: https://git.bioconductor.org/packages/AHMassBank
git_branch: devel
git_last_commit: 948955c
git_last_commit_date: 2025-02-25
Date/Publication: 2025-02-25
source.ver: src/contrib/AHMassBank_1.7.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AHMassBank_1.7.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AHMassBank_1.7.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AHMassBank_1.7.1.tgz
vignettes: vignettes/AHMassBank/inst/doc/creating-MassBank-CompDbs.html
vignetteTitles: Provide EnsDb databases for AnnotationHub
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AHMassBank/inst/doc/creating-MassBank-CompDbs.R
dependencyCount: 123
Package: AIMS
Version: 1.39.0
Depends: R (>= 2.10), e1071, Biobase
Suggests: breastCancerVDX, hgu133a.db, RUnit, BiocGenerics
License: Artistic-2.0
MD5sum: 2d090c5f9fbd79113cf5af9852d2a97d
NeedsCompilation: no
Title: AIMS : Absolute Assignment of Breast Cancer Intrinsic Molecular
Subtype
Description: This package contains the AIMS implementation. It contains
necessary functions to assign the five intrinsic molecular
subtypes (Luminal A, Luminal B, Her2-enriched, Basal-like,
Normal-like). Assignments could be done on individual samples
as well as on dataset of gene expression data.
biocViews: ImmunoOncology, Classification, RNASeq, Microarray,
Software, GeneExpression
Author: Eric R. Paquet, Michael T. Hallett
Maintainer: Eric R Paquet <eric.r.paquet@gmail.com>
URL: http://www.bci.mcgill.ca/AIMS
git_url: https://git.bioconductor.org/packages/AIMS
git_branch: devel
git_last_commit: 0d17e7f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/AIMS_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AIMS_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AIMS_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AIMS_1.39.0.tgz
vignettes: vignettes/AIMS/inst/doc/AIMS.pdf
vignetteTitles: AIMS An Introduction (HowTo)
hasREADME: TRUE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AIMS/inst/doc/AIMS.R
dependsOnMe: genefu
dependencyCount: 12
Package: airpart
Version: 1.15.0
Depends: R (>= 4.1)
Imports: SingleCellExperiment, SummarizedExperiment, S4Vectors, scater,
stats, smurf, apeglm (>= 1.13.3), emdbook, mclust, clue,
dynamicTreeCut, matrixStats, dplyr, plyr, ggplot2,
ComplexHeatmap, forestplot, RColorBrewer, rlang, lpSolve, grid,
grDevices, graphics, utils, pbapply
Suggests: knitr, rmarkdown, roxygen2 (>= 6.0.0), testthat (>= 3.0.0),
gplots, tidyr
License: GPL-2
MD5sum: 34d66622d66550a67adf5f672ab270e4
NeedsCompilation: no
Title: Differential cell-type-specific allelic imbalance
Description: Airpart identifies sets of genes displaying differential
cell-type-specific allelic imbalance across cell types or
states, utilizing single-cell allelic counts. It makes use of a
generalized fused lasso with binomial observations of allelic
counts to partition cell types by their allelic imbalance.
Alternatively, a nonparametric method for partitioning cell
types is offered. The package includes a number of
visualizations and quality control functions for examining
single cell allelic imbalance datasets.
biocViews: SingleCell, RNASeq, ATACSeq, ChIPSeq, Sequencing,
GeneRegulation, GeneExpression, Transcription,
TranscriptomeVariant, CellBiology, FunctionalGenomics,
DifferentialExpression, GraphAndNetwork, Regression,
Clustering, QualityControl
Author: Wancen Mu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5061-7581>), Michael Love [aut,
ctb] (ORCID: <https://orcid.org/0000-0001-8401-0545>)
Maintainer: Wancen Mu <wancen@live.unc.edu>
URL: https://github.com/Wancen/airpart
VignetteBuilder: knitr
BugReports: https://github.com/Wancen/airpart/issues
git_url: https://git.bioconductor.org/packages/airpart
git_branch: devel
git_last_commit: 68bab81
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/airpart_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/airpart_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/airpart_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/airpart_1.15.0.tgz
vignettes: vignettes/airpart/inst/doc/airpart.html
vignetteTitles: Differential allelic imbalance with airpart
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/airpart/inst/doc/airpart.R
dependencyCount: 142
Package: alabaster
Version: 1.7.0
Depends: alabaster.base
Imports: alabaster.matrix, alabaster.ranges, alabaster.se,
alabaster.sce, alabaster.spatial, alabaster.string,
alabaster.vcf, alabaster.bumpy, alabaster.mae
Suggests: knitr, rmarkdown, BiocStyle
License: MIT + file LICENSE
MD5sum: 3498f82efcc02b1de884a3f017420dfa
NeedsCompilation: no
Title: Umbrella for the Alabaster Framework
Description: Umbrella for the alabaster suite, providing a single-line
import for all alabaster.* packages. Installing this package
ensures that all known alabaster.* packages are also installed,
avoiding problems with missing packages when a staging method
or loading function is dynamically requested. Obviously, this
comes at the cost of needing to install more packages, so
advanced users and application developers may prefer to install
the required alabaster.* packages individually.
biocViews: DataRepresentation, DataImport
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/alabaster
git_branch: devel
git_last_commit: 1516307
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/alabaster_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/alabaster_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/alabaster_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/alabaster_1.7.0.tgz
vignettes: vignettes/alabaster/inst/doc/userguide.html
vignetteTitles: alabaster umbrella
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/alabaster/inst/doc/userguide.R
dependencyCount: 120
Package: alabaster.base
Version: 1.7.8
Imports: alabaster.schemas, methods, utils, S4Vectors, rhdf5 (>=
2.47.6), jsonlite, jsonvalidate, Rcpp
LinkingTo: Rcpp, assorthead (>= 1.1.2), Rhdf5lib
Suggests: BiocStyle, rmarkdown, knitr, testthat, digest, Matrix,
alabaster.matrix
License: MIT + file LICENSE
Archs: x64
MD5sum: 1435cd1d7d4c4e9f3a4b54240988d76c
NeedsCompilation: yes
Title: Save Bioconductor Objects to File
Description: Save Bioconductor data structures into file artifacts, and
load them back into memory. This is a more robust and portable
alternative to serialization of such objects into RDS files.
Each artifact is associated with metadata for further
interpretation; downstream applications can enrich this
metadata with context-specific properties.
biocViews: DataRepresentation, DataImport
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://github.com/ArtifactDB/alabaster.base
SystemRequirements: C++17, GNU make
VignetteBuilder: knitr
BugReports: https://github.com/ArtifactDB/alabaster.base/issues
git_url: https://git.bioconductor.org/packages/alabaster.base
git_branch: devel
git_last_commit: 1677a4d
git_last_commit_date: 2025-03-05
Date/Publication: 2025-03-06
source.ver: src/contrib/alabaster.base_1.7.8.tar.gz
win.binary.ver: bin/windows/contrib/4.5/alabaster.base_1.7.8.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.base_1.7.8.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/alabaster.base_1.7.8.tgz
vignettes: vignettes/alabaster.base/inst/doc/userguide.html
vignetteTitles: Saving and loading artifacts
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/alabaster.base/inst/doc/userguide.R
dependsOnMe: alabaster, alabaster.bumpy, alabaster.mae,
alabaster.matrix, alabaster.ranges, alabaster.sce,
alabaster.se, alabaster.sfe, alabaster.spatial,
alabaster.string, alabaster.vcf
importsMe: chevreulShiny, celldex, scRNAseq
dependencyCount: 19
Package: alabaster.bumpy
Version: 1.7.0
Depends: BumpyMatrix, alabaster.base
Imports: methods, rhdf5, Matrix, BiocGenerics, S4Vectors, IRanges
Suggests: BiocStyle, rmarkdown, knitr, testthat, jsonlite
License: MIT + file LICENSE
MD5sum: 89b42726caf0473e08a8cd1e210f8f64
NeedsCompilation: no
Title: Save and Load BumpyMatrices to/from file
Description: Save BumpyMatrix objects into file artifacts, and load
them back into memory. This is a more portable alternative to
serialization of such objects into RDS files. Each artifact is
associated with metadata for further interpretation; downstream
applications can enrich this metadata with context-specific
properties.
biocViews: DataImport, DataRepresentation
Author: Aaron Lun [cre, aut]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/alabaster.bumpy
git_branch: devel
git_last_commit: ab0a1f1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/alabaster.bumpy_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/alabaster.bumpy_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.bumpy_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/alabaster.bumpy_1.7.0.tgz
vignettes: vignettes/alabaster.bumpy/inst/doc/userguide.html
vignetteTitles: Saving and loading BumpyMatrices
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/alabaster.bumpy/inst/doc/userguide.R
importsMe: alabaster
dependencyCount: 26
Package: alabaster.files
Version: 1.5.0
Depends: alabaster.base,
Imports: methods, S4Vectors, BiocGenerics, Rsamtools
Suggests: BiocStyle, rmarkdown, knitr, testthat, VariantAnnotation,
rtracklayer, Biostrings
License: MIT + file LICENSE
MD5sum: 8f875b47d8e22a030d50ff7d8b1faea7
NeedsCompilation: no
Title: Wrappers to Save Common File Formats
Description: Save common bioinformatics file formats within the
alabaster framework. This includes BAM, BED, VCF, bigWig,
bigBed, FASTQ, FASTA and so on. We save and load additional
metadata for each file, and we support linkage between each
file and its corresponding index.
biocViews: DataRepresentation, DataImport
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/alabaster.files
git_branch: devel
git_last_commit: 2563297
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/alabaster.files_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/alabaster.files_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.files_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/alabaster.files_1.5.0.tgz
vignettes: vignettes/alabaster.files/inst/doc/userguide.html
vignetteTitles: Saving common file formats
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/alabaster.files/inst/doc/userguide.R
dependencyCount: 48
Package: alabaster.mae
Version: 1.7.0
Depends: MultiAssayExperiment, alabaster.base
Imports: methods, alabaster.se, S4Vectors, jsonlite, rhdf5
Suggests: testthat, knitr, SummarizedExperiment, BiocParallel,
BiocStyle, rmarkdown
License: MIT + file LICENSE
MD5sum: 5a08ba1123e9fa889e6f35d0a0d993c8
NeedsCompilation: no
Title: Load and Save MultiAssayExperiments
Description: Save MultiAssayExperiments into file artifacts, and load
them back into memory. This is a more portable alternative to
serialization of such objects into RDS files. Each artifact is
associated with metadata for further interpretation; downstream
applications can enrich this metadata with context-specific
properties.
biocViews: DataImport, DataRepresentation
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/alabaster.mae
git_branch: devel
git_last_commit: f6e4c7b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/alabaster.mae_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/alabaster.mae_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.mae_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/alabaster.mae_1.7.0.tgz
vignettes: vignettes/alabaster.mae/inst/doc/userguide.html
vignetteTitles: Saving and loading MultiAssayExperiments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/alabaster.mae/inst/doc/userguide.R
importsMe: alabaster
dependencyCount: 71
Package: alabaster.matrix
Version: 1.7.8
Depends: alabaster.base
Imports: methods, BiocGenerics, S4Vectors, DelayedArray (>= 0.33.3),
S4Arrays, SparseArray (>= 1.5.22), rhdf5 (>= 2.47.1),
HDF5Array, Matrix, Rcpp
LinkingTo: Rcpp
Suggests: testthat, knitr, BiocStyle, chihaya, BiocSingular,
ResidualMatrix
License: MIT + file LICENSE
Archs: x64
MD5sum: efa67720ea08e22c55bed192fab7f9dd
NeedsCompilation: yes
Title: Load and Save Artifacts from File
Description: Save matrices, arrays and similar objects into file
artifacts, and load them back into memory. This is a more
portable alternative to serialization of such objects into RDS
files. Each artifact is associated with metadata for further
interpretation; downstream applications can enrich this
metadata with context-specific properties.
biocViews: DataImport, DataRepresentation
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/alabaster.matrix
git_branch: devel
git_last_commit: 0b8582e
git_last_commit_date: 2025-03-05
Date/Publication: 2025-03-06
source.ver: src/contrib/alabaster.matrix_1.7.8.tar.gz
win.binary.ver: bin/windows/contrib/4.5/alabaster.matrix_1.7.8.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.matrix_1.7.8.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/alabaster.matrix_1.7.8.tgz
vignettes: vignettes/alabaster.matrix/inst/doc/userguide.html
vignetteTitles: Saving and loading arrays
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/alabaster.matrix/inst/doc/userguide.R
importsMe: alabaster, alabaster.se, celldex, scMultiome, scRNAseq
suggestsMe: alabaster.base
dependencyCount: 36
Package: alabaster.ranges
Version: 1.7.0
Depends: GenomicRanges, alabaster.base
Imports: methods, S4Vectors, BiocGenerics, IRanges, GenomeInfoDb, rhdf5
Suggests: testthat, knitr, BiocStyle, jsonlite
License: MIT + file LICENSE
MD5sum: 9e13e0cdd4626ea45ba1fb995246811b
NeedsCompilation: no
Title: Load and Save Ranges-related Artifacts from File
Description: Save GenomicRanges, IRanges and related data structures
into file artifacts, and load them back into memory. This is a
more portable alternative to serialization of such objects into
RDS files. Each artifact is associated with metadata for
further interpretation; downstream applications can enrich this
metadata with context-specific properties.
biocViews: DataImport, DataRepresentation
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/alabaster.ranges
git_branch: devel
git_last_commit: 2f66649
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/alabaster.ranges_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/alabaster.ranges_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.ranges_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/alabaster.ranges_1.7.0.tgz
vignettes: vignettes/alabaster.ranges/inst/doc/userguide.html
vignetteTitles: Saving and loading genomic ranges
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/alabaster.ranges/inst/doc/userguide.R
importsMe: alabaster, alabaster.se
dependencyCount: 32
Package: alabaster.sce
Version: 1.7.0
Depends: SingleCellExperiment, alabaster.base
Imports: methods, alabaster.se, jsonlite
Suggests: knitr, testthat, BiocStyle, rmarkdown
License: MIT + file LICENSE
MD5sum: adc1d44686eba44b850ea9886760c9db
NeedsCompilation: no
Title: Load and Save SingleCellExperiment from File
Description: Save SingleCellExperiment into file artifacts, and load
them back into memory. This is a more portable alternative to
serialization of such objects into RDS files. Each artifact is
associated with metadata for further interpretation; downstream
applications can enrich this metadata with context-specific
properties.
biocViews: DataImport, DataRepresentation
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/alabaster.sce
git_branch: devel
git_last_commit: 458bd1e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/alabaster.sce_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/alabaster.sce_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.sce_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/alabaster.sce_1.7.0.tgz
vignettes: vignettes/alabaster.sce/inst/doc/userguide.html
vignetteTitles: Saving and loading SingleCellExperiments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/alabaster.sce/inst/doc/userguide.R
importsMe: alabaster, alabaster.sfe, alabaster.spatial, scRNAseq
dependencyCount: 51
Package: alabaster.schemas
Version: 1.7.0
Suggests: knitr, rmarkdown, BiocStyle
License: MIT + file LICENSE
MD5sum: cf4f1861a12577e03c67957b3681b9fc
NeedsCompilation: no
Title: Schemas for the Alabaster Framework
Description: Stores all schemas required by various alabaster.*
packages. No computation should be performed by this package,
as that is handled by alabaster.base. We use a separate package
instead of storing the schemas in alabaster.base itself, to
avoid conflating management of the schemas with code
maintenence.
biocViews: DataRepresentation, DataImport
Author: Aaron Lun [cre, aut]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/alabaster.schemas
git_branch: devel
git_last_commit: 6197bd9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/alabaster.schemas_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/alabaster.schemas_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.schemas_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/alabaster.schemas_1.7.0.tgz
vignettes: vignettes/alabaster.schemas/inst/doc/userguide.html
vignetteTitles: Metadata schemas for Bioconductor
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
importsMe: alabaster.base
dependencyCount: 0
Package: alabaster.se
Version: 1.7.0
Depends: SummarizedExperiment, alabaster.base
Imports: methods, alabaster.ranges, alabaster.matrix, BiocGenerics,
S4Vectors, IRanges, GenomicRanges, jsonlite
Suggests: rmarkdown, knitr, testthat, BiocStyle
License: MIT + file LICENSE
MD5sum: e51c4d196f906972636979db7a27b6af
NeedsCompilation: no
Title: Load and Save SummarizedExperiments from File
Description: Save SummarizedExperiments into file artifacts, and load
them back into memory. This is a more portable alternative to
serialization of such objects into RDS files. Each artifact is
associated with metadata for further interpretation; downstream
applications can enrich this metadata with context-specific
properties.
biocViews: DataImport, DataRepresentation
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/alabaster.se
git_branch: devel
git_last_commit: a745efa
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/alabaster.se_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/alabaster.se_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.se_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/alabaster.se_1.7.0.tgz
vignettes: vignettes/alabaster.se/inst/doc/userguide.html
vignetteTitles: Saving and loading SummarizedExperiments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/alabaster.se/inst/doc/userguide.R
importsMe: alabaster, alabaster.mae, alabaster.sce, alabaster.vcf,
celldex
dependencyCount: 49
Package: alabaster.sfe
Version: 0.99.4
Depends: R (>= 4.1.0), SpatialFeatureExperiment (>= 1.9.3),
alabaster.base
Imports: alabaster.sce, alabaster.spatial (>= 1.5.2), EBImage,
jsonlite, methods, RBioFormats, S4Vectors, sfarrow,
SingleCellExperiment, spatialreg, spdep, SummarizedExperiment,
terra, xml2
Suggests: BiocStyle, fs, knitr, rmarkdown, scater, sf, SFEData,
testthat (>= 3.0.0), Voyager (>= 1.9.1)
License: MIT + file LICENSE
MD5sum: b31f2d58657f71312409c28baf1218d1
NeedsCompilation: no
Title: Language agnostic on disk serialization of
SpatialFeatureExperiment
Description: Builds upon the existing ArtifactDB project, expending
alabaster.spatial for language agnostic on disk serialization
of SpatialFeatureExperiment.
biocViews: DataRepresentation, Spatial
Author: Lambda Moses [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7092-9427>)
Maintainer: Lambda Moses <dl3764@columbia.edu>
URL: https://pachterlab.github.io/alabaster.sfe/
VignetteBuilder: knitr
BugReports: https://github.com/pachterlab/alabaster.sfe/issues
git_url: https://git.bioconductor.org/packages/alabaster.sfe
git_branch: devel
git_last_commit: e344d90
git_last_commit_date: 2025-02-06
Date/Publication: 2025-03-17
source.ver: src/contrib/alabaster.sfe_0.99.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/alabaster.sfe_0.99.4.zip
vignettes: vignettes/alabaster.sfe/inst/doc/Overview.html
vignetteTitles: Overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/alabaster.sfe/inst/doc/Overview.R
dependencyCount: 173
Package: alabaster.spatial
Version: 1.7.1
Depends: SpatialExperiment, alabaster.base
Imports: methods, utils, grDevices, S4Vectors, alabaster.sce, rhdf5
Suggests: testthat, knitr, rmarkdown, BiocStyle, DropletUtils, magick,
png, digest
License: MIT + file LICENSE
MD5sum: 8ded4b4af5eaa74f04c88e89f38c41bb
NeedsCompilation: no
Title: Save and Load Spatial 'Omics Data to/from File
Description: Save SpatialExperiment objects and their images into file
artifacts, and load them back into memory. This is a more
portable alternative to serialization of such objects into RDS
files. Each artifact is associated with metadata for further
interpretation; downstream applications can enrich this
metadata with context-specific properties.
biocViews: DataImport, DataRepresentation
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/alabaster.spatial
git_branch: devel
git_last_commit: f82c877
git_last_commit_date: 2024-11-09
Date/Publication: 2024-11-10
source.ver: src/contrib/alabaster.spatial_1.7.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/alabaster.spatial_1.7.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.spatial_1.7.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/alabaster.spatial_1.7.1.tgz
vignettes: vignettes/alabaster.spatial/inst/doc/userguide.html
vignetteTitles: Saving spatial experiments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/alabaster.spatial/inst/doc/userguide.R
importsMe: alabaster, alabaster.sfe
dependencyCount: 86
Package: alabaster.string
Version: 1.7.0
Depends: Biostrings, alabaster.base
Imports: utils, methods, S4Vectors
Suggests: BiocStyle, rmarkdown, knitr, testthat
License: MIT + file LICENSE
MD5sum: dce38c1eecc7fce7496bd9ef69bf499f
NeedsCompilation: no
Title: Save and Load Biostrings to/from File
Description: Save Biostrings objects to file artifacts, and load them
back into memory. This is a more portable alternative to
serialization of such objects into RDS files. Each artifact is
associated with metadata for further interpretation; downstream
applications can enrich this metadata with context-specific
properties.
biocViews: DataImport, DataRepresentation
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/alabaster.string
git_branch: devel
git_last_commit: 383a583
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/alabaster.string_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/alabaster.string_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.string_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/alabaster.string_1.7.0.tgz
vignettes: vignettes/alabaster.string/inst/doc/userguide.html
vignetteTitles: Saving and loading XStringSets
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/alabaster.string/inst/doc/userguide.R
importsMe: alabaster, alabaster.vcf
dependencyCount: 34
Package: alabaster.vcf
Version: 1.7.0
Depends: alabaster.base, VariantAnnotation
Imports: methods, S4Vectors, alabaster.se, alabaster.string, Rsamtools
Suggests: knitr, rmarkdown, BiocStyle, testthat
License: MIT + file LICENSE
MD5sum: 8eca20e77f60ae14ca434fc28f6b27e7
NeedsCompilation: no
Title: Save and Load Variant Data to/from File
Description: Save variant calling SummarizedExperiment to file and load
them back as VCF objects. This is a more portable alternative
to serialization of such objects into RDS files. Each artifact
is associated with metadata for further interpretation;
downstream applications can enrich this metadata with
context-specific properties.
biocViews: DataImport, DataRepresentation
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/alabaster.vcf
git_branch: devel
git_last_commit: 1e871d0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/alabaster.vcf_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/alabaster.vcf_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/alabaster.vcf_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/alabaster.vcf_1.7.0.tgz
vignettes: vignettes/alabaster.vcf/inst/doc/userguide.html
vignetteTitles: Saving and loading VCFs
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/alabaster.vcf/inst/doc/userguide.R
importsMe: alabaster
dependencyCount: 94
Package: ALDEx2
Version: 1.39.0
Depends: methods, stats, zCompositions, lattice, latticeExtra
Imports: Rfast, BiocParallel, GenomicRanges, IRanges, S4Vectors,
SummarizedExperiment, multtest, directlabels
Suggests: testthat, BiocStyle, knitr, rmarkdown, purrr, ggpattern,
ggplot2, cowplot, tidyverse, magick
License: GPL (>=3)
MD5sum: 636e554a88f263e444e1cf898ab7f6b7
NeedsCompilation: no
Title: Analysis Of Differential Abundance Taking Sample and Scale
Variation Into Account
Description: A differential abundance analysis for the comparison of
two or more conditions. Useful for analyzing data from standard
RNA-seq or meta-RNA-seq assays as well as selected and
unselected values from in-vitro sequence selections. Uses a
Dirichlet-multinomial model to infer abundance from counts,
optimized for three or more experimental replicates. The method
infers biological and sampling variation to calculate the
expected false discovery rate, given the variation, based on a
Wilcoxon Rank Sum test and Welch's t-test (via aldex.ttest), a
Kruskal-Wallis test (via aldex.kw), a generalized linear model
(via aldex.glm), or a correlation test (via aldex.corr). All
tests report predicted p-values and posterior
Benjamini-Hochberg corrected p-values. ALDEx2 also calculates
expected standardized effect sizes for paired or unpaired study
designs. ALDEx2 can now be used to estimate the effect of scale
on the results and report on the scale-dependent robustness of
results.
biocViews: DifferentialExpression, RNASeq, Transcriptomics,
GeneExpression, DNASeq, ChIPSeq, Bayesian, Sequencing,
Software, Microbiome, Metagenomics, ImmunoOncology, Scale
simulation, Posterior p-value
Author: Greg Gloor, Andrew Fernandes, Jean Macklaim, Arianne Albert,
Matt Links, Thomas Quinn, Jia Rong Wu, Ruth Grace Wong, Brandon
Lieng, Michelle Nixon
Maintainer: Greg Gloor <ggloor@uwo.ca>
URL: https://github.com/ggloor/ALDEx_bioc
VignetteBuilder: knitr
BugReports: https://github.com/ggloor/ALDEx_bioc/issues
git_url: https://git.bioconductor.org/packages/ALDEx2
git_branch: devel
git_last_commit: f7a6017
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ALDEx2_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ALDEx2_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ALDEx2_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ALDEx2_1.39.0.tgz
vignettes: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.html,
vignettes/ALDEx2/inst/doc/scaleSim_vignette.html
vignetteTitles: ANOVA-Like Differential Expression tool for high
throughput sequencing data, Incorporating Scale Uncertainty
into ALDEx2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.R,
vignettes/ALDEx2/inst/doc/scaleSim_vignette.R
dependsOnMe: omicplotR
importsMe: benchdamic, aIc
suggestsMe: dar, pctax
dependencyCount: 67
Package: alevinQC
Version: 1.23.1
Depends: R (>= 4.0)
Imports: rmarkdown (>= 2.5), tools, methods, ggplot2 (>= 3.4.0),
GGally, dplyr, rjson, shiny, shinydashboard, DT, stats, utils,
tximport (>= 1.17.4), cowplot, rlang, Rcpp
LinkingTo: Rcpp
Suggests: knitr, BiocStyle, testthat (>= 3.0.0), BiocManager
License: MIT + file LICENSE
Archs: x64
MD5sum: 20e53e85b938e7a835089b28b8a70591
NeedsCompilation: yes
Title: Generate QC Reports For Alevin Output
Description: Generate QC reports summarizing the output from an alevin,
alevin-fry, or simpleaf run. Reports can be generated as html
or pdf files, or as shiny applications.
biocViews: QualityControl, SingleCell
Author: Charlotte Soneson [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3833-2169>), Avi Srivastava [aut],
Rob Patro [aut], Dongze He [aut]
Maintainer: Charlotte Soneson <charlottesoneson@gmail.com>
URL: https://github.com/csoneson/alevinQC
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/csoneson/alevinQC/issues
git_url: https://git.bioconductor.org/packages/alevinQC
git_branch: devel
git_last_commit: 59a281c
git_last_commit_date: 2024-12-14
Date/Publication: 2024-12-15
source.ver: src/contrib/alevinQC_1.23.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/alevinQC_1.23.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/alevinQC_1.23.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/alevinQC_1.23.1.tgz
vignettes: vignettes/alevinQC/inst/doc/alevinqc.html
vignetteTitles: alevinQC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/alevinQC/inst/doc/alevinqc.R
dependencyCount: 90
Package: AllelicImbalance
Version: 1.45.0
Depends: R (>= 4.0.0), grid, GenomicRanges (>= 1.31.8),
SummarizedExperiment (>= 0.2.0), GenomicAlignments (>= 1.15.6)
Imports: methods, BiocGenerics, AnnotationDbi, BSgenome (>= 1.47.3),
VariantAnnotation (>= 1.25.11), Biostrings (>= 2.47.6),
S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Rsamtools (>=
1.99.3), GenomicFeatures (>= 1.31.3), Gviz, lattice,
latticeExtra, gridExtra, seqinr, GenomeInfoDb, nlme
Suggests: testthat, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene,
SNPlocs.Hsapiens.dbSNP144.GRCh37, BiocStyle, knitr, rmarkdown
License: GPL-3
MD5sum: c9b02c68aba1cbeb935264381056b295
NeedsCompilation: no
Title: Investigates Allele Specific Expression
Description: Provides a framework for allelic specific expression
investigation using RNA-seq data.
biocViews: Genetics, Infrastructure, Sequencing
Author: Jesper R Gadin, Lasse Folkersen
Maintainer: Jesper R Gadin <j.r.gadin@gmail.com>
URL: https://github.com/pappewaio/AllelicImbalance
VignetteBuilder: knitr
BugReports: https://github.com/pappewaio/AllelicImbalance/issues
git_url: https://git.bioconductor.org/packages/AllelicImbalance
git_branch: devel
git_last_commit: ffd47ab
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/AllelicImbalance_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AllelicImbalance_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AllelicImbalance_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AllelicImbalance_1.45.0.tgz
vignettes:
vignettes/AllelicImbalance/inst/doc/AllelicImbalance-vignette.pdf
vignetteTitles: AllelicImbalance Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AllelicImbalance/inst/doc/AllelicImbalance-vignette.R
dependencyCount: 162
Package: AlphaBeta
Version: 1.21.0
Depends: R (>= 3.6.0)
Imports: dplyr (>= 0.7), data.table (>= 1.10), stringr (>= 1.3), utils
(>= 3.6.0), gtools (>= 3.8.0), optimx (>= 2018-7.10), expm (>=
0.999-4), stats (>= 3.6), BiocParallel (>= 1.18), igraph (>=
1.2.4), graphics (>= 3.6), ggplot2 (>= 3.2), grDevices (>=
3.6), plotly (>= 4.9)
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: 559dd5e89c1c1c25c8dc14e595a98718
NeedsCompilation: no
Title: Computational inference of epimutation rates and spectra from
high-throughput DNA methylation data in plants
Description: AlphaBeta is a computational method for estimating
epimutation rates and spectra from high-throughput DNA
methylation data in plants. The method has been specifically
designed to: 1. analyze 'germline' epimutations in the context
of multi-generational mutation accumulation lines (MA-lines).
2. analyze 'somatic' epimutations in the context of plant
development and aging.
biocViews: Epigenetics, FunctionalGenomics, Genetics,
MathematicalBiology
Author: Yadollah Shahryary Dizaji [cre, aut], Frank Johannes [aut],
Rashmi Hazarika [aut]
Maintainer: Yadollah Shahryary Dizaji <shahryary@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/AlphaBeta
git_branch: devel
git_last_commit: 3bef2e9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/AlphaBeta_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AlphaBeta_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AlphaBeta_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AlphaBeta_1.21.0.tgz
vignettes: vignettes/AlphaBeta/inst/doc/AlphaBeta.pdf
vignetteTitles: AlphaBeta
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/AlphaBeta/inst/doc/AlphaBeta.R
dependencyCount: 94
Package: AlphaMissenseR
Version: 1.3.0
Depends: R (>= 4.3.0), dplyr
Imports: rjsoncons (>= 1.0.1), DBI, duckdb (>= 0.9.1), rlang, curl,
BiocFileCache, spdl, memoise, BiocBaseUtils, utils, stats,
tools, methods, whisker, ggplot2
Suggests: BiocManager, BiocGenerics, GenomicRanges, GenomeInfoDb,
AnnotationHub, ExperimentHub, ensembldb, httr, tidyr, r3dmol,
bio3d, shiny, shiny.gosling, ggdist, gghalves, colorspace,
knitr, rmarkdown, testthat (>= 3.0.0)
License: Artistic-2.0
MD5sum: e772d272577cfefc887cc990dfe3c214
NeedsCompilation: no
Title: Accessing AlphaMissense Data Resources in R
Description: The AlphaMissense publication
<https://www.science.org/doi/epdf/10.1126/science.adg7492>
outlines how a variant of AlphaFold / DeepMind was used to
predict missense variant pathogenicity. Supporting data on
Zenodo <https://zenodo.org/record/10813168> include, for
instance, 71M variants across hg19 and hg38 genome builds. The
'AlphaMissenseR' package allows ready access to the data,
downloading individual files to DuckDB databases for
exploration and integration into *R* and *Bioconductor*
workflows.
biocViews: SNP, Annotation, FunctionalGenomics, StructuralPrediction,
Transcriptomics, VariantAnnotation, GenePrediction,
ImmunoOncology
Author: Martin Morgan [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5874-8148>), Tram Nguyen [aut]
(ORCID: <https://orcid.org/0000-0003-4809-6227>), Tyrone Lee
[ctb], Nitesh Turaga [ctb], Chan Zuckerberg Initiative DAF
CZF2019-002443 [fnd], NIH NCI ITCR U24CA180996 [fnd], NIH NCI
IOTN U24CA232979 [fnd], NIH NCI ARTNet U24CA274159 [fnd]
Maintainer: Martin Morgan <mtmorgan.xyz@gmail.com>
URL: https://mtmorgan.github.io/AlphaMissenseR/
VignetteBuilder: knitr
BugReports: https://github.com/mtmorgan/AlphaMissenseR/issues
git_url: https://git.bioconductor.org/packages/AlphaMissenseR
git_branch: devel
git_last_commit: 2fc3e2c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/AlphaMissenseR_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AlphaMissenseR_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AlphaMissenseR_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AlphaMissenseR_1.3.0.tgz
vignettes: vignettes/AlphaMissenseR/inst/doc/alphafold.html,
vignettes/AlphaMissenseR/inst/doc/benchmarking.html,
vignettes/AlphaMissenseR/inst/doc/clinvar.html,
vignettes/AlphaMissenseR/inst/doc/introduction.html,
vignettes/AlphaMissenseR/inst/doc/issues.html
vignetteTitles: B. AlphaFold Integration, D. Benchmarking with
ProteinGym, C. ClinVar Integration, A. Introduction, E. Issues
& Solutions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AlphaMissenseR/inst/doc/alphafold.R,
vignettes/AlphaMissenseR/inst/doc/benchmarking.R,
vignettes/AlphaMissenseR/inst/doc/clinvar.R,
vignettes/AlphaMissenseR/inst/doc/introduction.R,
vignettes/AlphaMissenseR/inst/doc/issues.R
dependencyCount: 70
Package: AlpsNMR
Version: 4.9.0
Depends: R (>= 4.2)
Imports: utils, generics, graphics, stats, grDevices, cli, magrittr (>=
1.5), dplyr (>= 1.1.0), signal (>= 0.7-6), rlang (>= 0.3.0.1),
scales (>= 1.2.0), stringr (>= 1.3.1), tibble(>= 1.3.4), tidyr
(>= 1.0.0), tidyselect, readxl (>= 1.1.0), purrr (>= 0.2.5),
glue (>= 1.2.0), reshape2 (>= 1.4.3), mixOmics (>= 6.22.0),
matrixStats (>= 0.54.0), fs (>= 1.2.6), rmarkdown (>= 1.10),
speaq (>= 2.4.0), htmltools (>= 0.3.6), pcaPP (>= 1.9-73),
ggplot2 (>= 3.1.0), baseline (>= 1.2-1), vctrs (>= 0.3.0),
BiocParallel (>= 1.34.0)
Suggests: ASICS, BiocStyle, ChemoSpec, cowplot, curl, DT (>= 0.5),
GGally (>= 1.4.0), ggrepel (>= 0.8.0), gridExtra, knitr,
NMRphasing, plotly (>= 4.7.1), progressr, SummarizedExperiment,
S4Vectors, testthat (>= 2.0.0), writexl (>= 1.0), zip (>=
2.0.4)
License: MIT + file LICENSE
MD5sum: 7c81600d1049b38a8e56b0211adb2045
NeedsCompilation: no
Title: Automated spectraL Processing System for NMR
Description: Reads Bruker NMR data directories both zipped and
unzipped. It provides automated and efficient signal processing
for untargeted NMR metabolomics. It is able to interpolate the
samples, detect outliers, exclude regions, normalize, detect
peaks, align the spectra, integrate peaks, manage metadata and
visualize the spectra. After spectra proccessing, it can apply
multivariate analysis on extracted data. Efficient plotting
with 1-D data is also available. Basic reading of 1D ACD/Labs
exported JDX samples is also available.
biocViews: Software, Preprocessing, Visualization, Classification,
Cheminformatics, Metabolomics, DataImport
Author: Ivan Montoliu Roura [aut], Sergio Oller Moreno [aut, cre]
(ORCID: <https://orcid.org/0000-0002-8994-1549>), Francisco
Madrid Gambin [aut] (ORCID:
<https://orcid.org/0000-0001-9333-0014>), Luis Fernandez [aut]
(ORCID: <https://orcid.org/0000-0001-9790-6287>), Laura López
Sánchez [ctb], Héctor Gracia Cabrera [aut], Santiago Marco
Colás [aut] (ORCID: <https://orcid.org/0000-0003-2663-2965>),
Nestlé Institute of Health Sciences [cph], Institute for
Bioengineering of Catalonia [cph], Miller Jack [ctb] (ORCID:
<https://orcid.org/0000-0002-6258-1299>, Autophase wrapper,
ASICS export)
Maintainer: Sergio Oller Moreno <sergioller@gmail.com>
URL: https://sipss.github.io/AlpsNMR/, https://github.com/sipss/AlpsNMR
VignetteBuilder: knitr
BugReports: https://github.com/sipss/AlpsNMR/issues
git_url: https://git.bioconductor.org/packages/AlpsNMR
git_branch: devel
git_last_commit: f8b70f5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/AlpsNMR_4.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AlpsNMR_4.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AlpsNMR_4.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AlpsNMR_4.9.0.tgz
vignettes:
vignettes/AlpsNMR/inst/doc/Vig01-introduction-to-alpsnmr.pdf,
vignettes/AlpsNMR/inst/doc/Vig01b-introduction-to-alpsnmr-old-api.pdf,
vignettes/AlpsNMR/inst/doc/Vig02-handling-metadata-and-annotations.pdf
vignetteTitles: Vignette 01: Introduction to AlpsNMR (start here),
Older Introduction to AlpsNMR (soft-deprecated API), Vignette
02: Handling metadata and annotations
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/AlpsNMR/inst/doc/Vig01-introduction-to-alpsnmr.R,
vignettes/AlpsNMR/inst/doc/Vig01b-introduction-to-alpsnmr-old-api.R,
vignettes/AlpsNMR/inst/doc/Vig02-handling-metadata-and-annotations.R
dependencyCount: 131
Package: altcdfenvs
Version: 2.69.0
Depends: R (>= 2.7), methods, BiocGenerics (>= 0.1.0), S4Vectors (>=
0.9.25), Biobase (>= 2.15.1), affy, makecdfenv, Biostrings,
hypergraph
Suggests: plasmodiumanophelescdf, hgu95acdf, hgu133aprobe, hgu133a.db,
hgu133acdf, Rgraphviz, RColorBrewer
License: GPL (>= 2)
MD5sum: 0989cc77613dbcdda34a14b05d1dbd7c
NeedsCompilation: no
Title: alternative CDF environments (aka probeset mappings)
Description: Convenience data structures and functions to handle
cdfenvs
biocViews: Microarray, OneChannel, QualityControl, Preprocessing,
Annotation, ProprietaryPlatforms, Transcription
Author: Laurent Gautier <lgautier@gmail.com>
Maintainer: Laurent Gautier <lgautier@gmail.com>
git_url: https://git.bioconductor.org/packages/altcdfenvs
git_branch: devel
git_last_commit: 6869520
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/altcdfenvs_2.69.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/altcdfenvs_2.69.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/altcdfenvs_2.69.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/altcdfenvs_2.69.0.tgz
vignettes: vignettes/altcdfenvs/inst/doc/altcdfenvs.pdf,
vignettes/altcdfenvs/inst/doc/modify.pdf,
vignettes/altcdfenvs/inst/doc/ngenomeschips.pdf
vignetteTitles: altcdfenvs, Modifying existing CDF environments to make
alternative CDF environments, Alternative CDF environments for
2(or more)-genomes chips
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/altcdfenvs/inst/doc/altcdfenvs.R,
vignettes/altcdfenvs/inst/doc/modify.R,
vignettes/altcdfenvs/inst/doc/ngenomeschips.R
importsMe: Harshlight
dependencyCount: 33
Package: AMARETTO
Version: 1.23.0
Depends: R (>= 3.6), impute, doParallel, grDevices, dplyr, methods,
ComplexHeatmap
Imports: callr (>= 3.0.0.9001), Matrix, Rcpp, BiocFileCache, DT,
MultiAssayExperiment, circlize, curatedTCGAData, foreach,
glmnet, httr, limma, matrixStats, readr, reshape2, tibble,
rmarkdown, graphics, grid, parallel, stats, knitr, ggplot2,
gridExtra, utils
LinkingTo: Rcpp
Suggests: testthat, MASS, knitr, BiocStyle
License: Apache License (== 2.0) + file LICENSE
MD5sum: 5eb58c200e109e726ccef47a351fe306
NeedsCompilation: no
Title: Regulatory Network Inference and Driver Gene Evaluation using
Integrative Multi-Omics Analysis and Penalized Regression
Description: Integrating an increasing number of available multi-omics
cancer data remains one of the main challenges to improve our
understanding of cancer. One of the main challenges is using
multi-omics data for identifying novel cancer driver genes. We
have developed an algorithm, called AMARETTO, that integrates
copy number, DNA methylation and gene expression data to
identify a set of driver genes by analyzing cancer samples and
connects them to clusters of co-expressed genes, which we
define as modules. We applied AMARETTO in a pancancer setting
to identify cancer driver genes and their modules on multiple
cancer sites. AMARETTO captures modules enriched in
angiogenesis, cell cycle and EMT, and modules that accurately
predict survival and molecular subtypes. This allows AMARETTO
to identify novel cancer driver genes directing canonical
cancer pathways.
biocViews:
StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray,Transcription,Preprocessing,BatchEffect,DataImport,mRNAMicroarray,MicroRNAArray,Regression,Clustering,RNASeq,CopyNumberVariation,Sequencing,Microarray,Normalization,Network,Bayesian,ExonArray,OneChannel,TwoChannel,ProprietaryPlatforms,AlternativeSplicing,DifferentialExpression,DifferentialSplicing,GeneSetEnrichment,MultipleComparison,QualityControl,TimeCourse
Author: Jayendra Shinde, Celine Everaert, Shaimaa Bakr, Mohsen Nabian,
Jishu Xu, Vincent Carey, Nathalie Pochet and Olivier Gevaert
Maintainer: Olivier Gevaert <olivier.gevaert@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/AMARETTO
git_branch: devel
git_last_commit: 6b4ead5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/AMARETTO_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AMARETTO_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AMARETTO_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AMARETTO_1.23.0.tgz
vignettes: vignettes/AMARETTO/inst/doc/amaretto.html
vignetteTitles: "1. Introduction"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/AMARETTO/inst/doc/amaretto.R
dependencyCount: 153
Package: AMOUNTAIN
Version: 1.33.0
Depends: R (>= 3.3.0)
Imports: stats
Suggests: BiocStyle, qgraph, knitr, rmarkdown
License: GPL (>= 2)
Archs: x64
MD5sum: 5c60b53754b1a87ba32e4f5154adb4bd
NeedsCompilation: yes
Title: Active modules for multilayer weighted gene co-expression
networks: a continuous optimization approach
Description: A pure data-driven gene network, weighted gene
co-expression network (WGCN) could be constructed only from
expression profile. Different layers in such networks may
represent different time points, multiple conditions or various
species. AMOUNTAIN aims to search active modules in multi-layer
WGCN using a continuous optimization approach.
biocViews: GeneExpression, Microarray, DifferentialExpression, Network
Author: Dong Li, Shan He, Zhisong Pan and Guyu Hu
Maintainer: Dong Li <dxl466@cs.bham.ac.uk>
SystemRequirements: gsl
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/AMOUNTAIN
git_branch: devel
git_last_commit: 03af056
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/AMOUNTAIN_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AMOUNTAIN_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AMOUNTAIN_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AMOUNTAIN_1.33.0.tgz
vignettes: vignettes/AMOUNTAIN/inst/doc/AMOUNTAIN.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AMOUNTAIN/inst/doc/AMOUNTAIN.R
importsMe: MODA
dependencyCount: 1
Package: amplican
Version: 1.29.0
Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.22.0), Biostrings
(>= 2.44.2), pwalign, data.table (>= 1.10.4-3)
Imports: Rcpp, utils (>= 3.4.1), S4Vectors (>= 0.14.3), ShortRead (>=
1.34.0), IRanges (>= 2.10.2), GenomicRanges (>= 1.28.4),
GenomeInfoDb (>= 1.12.2), BiocParallel (>= 1.10.1), gtable (>=
0.2.0), gridExtra (>= 2.2.1), ggplot2 (>= 3.3.4), ggthemes (>=
3.4.0), waffle (>= 0.7.0), stringr (>= 1.2.0), stats (>=
3.4.1), matrixStats (>= 0.52.2), Matrix (>= 1.2-10), dplyr (>=
0.7.2), rmarkdown (>= 1.6), knitr (>= 1.16), cluster (>= 2.1.4)
LinkingTo: Rcpp
Suggests: testthat, BiocStyle, GenomicAlignments
License: GPL-3
Archs: x64
MD5sum: dc731008fd6965c3dbf708433c4a1621
NeedsCompilation: yes
Title: Automated analysis of CRISPR experiments
Description: `amplican` performs alignment of the amplicon reads,
normalizes gathered data, calculates multiple statistics (e.g.
cut rates, frameshifts) and presents results in form of
aggregated reports. Data and statistics can be broken down by
experiments, barcodes, user defined groups, guides and
amplicons allowing for quick identification of potential
problems.
biocViews: ImmunoOncology, Technology, Alignment, qPCR, CRISPR
Author: Kornel Labun [aut], Eivind Valen [cph, cre]
Maintainer: Eivind Valen <eivind.valen@gmail.com>
URL: https://github.com/valenlab/amplican
VignetteBuilder: knitr
BugReports: https://github.com/valenlab/amplican/issues
git_url: https://git.bioconductor.org/packages/amplican
git_branch: devel
git_last_commit: 555fe1e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/amplican_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/amplican_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/amplican_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/amplican_1.29.0.tgz
vignettes: vignettes/amplican/inst/doc/amplicanFAQ.html,
vignettes/amplican/inst/doc/amplicanOverview.html,
vignettes/amplican/inst/doc/example_amplicon_report.html,
vignettes/amplican/inst/doc/example_barcode_report.html,
vignettes/amplican/inst/doc/example_group_report.html,
vignettes/amplican/inst/doc/example_guide_report.html,
vignettes/amplican/inst/doc/example_id_report.html,
vignettes/amplican/inst/doc/example_index.html
vignetteTitles: amplican FAQ, amplican overview, example
amplicon_report report, example barcode_report report, example
group_report report, example guide_report report, example
id_report report, example index report
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/amplican/inst/doc/amplicanOverview.R,
vignettes/amplican/inst/doc/example_amplicon_report.R,
vignettes/amplican/inst/doc/example_barcode_report.R,
vignettes/amplican/inst/doc/example_group_report.R,
vignettes/amplican/inst/doc/example_guide_report.R,
vignettes/amplican/inst/doc/example_id_report.R,
vignettes/amplican/inst/doc/example_index.R
dependencyCount: 127
Package: Anaquin
Version: 2.31.0
Depends: R (>= 3.3), ggplot2 (>= 2.2.0)
Imports: ggplot2, ROCR, knitr, qvalue, locfit, methods, stats, utils,
plyr, DESeq2
Suggests: RUnit, rmarkdown
License: BSD_3_clause + file LICENSE
MD5sum: 0e856eb1dadc4b595d4b250aaa1a1b7b
NeedsCompilation: no
Title: Statistical analysis of sequins
Description: The project is intended to support the use of sequins
(synthetic sequencing spike-in controls) owned and made
available by the Garvan Institute of Medical Research. The goal
is to provide a standard open source library for quantitative
analysis, modelling and visualization of spike-in controls.
biocViews: ImmunoOncology, DifferentialExpression, Preprocessing,
RNASeq, GeneExpression, Software
Author: Ted Wong
Maintainer: Ted Wong <t.wong@garvan.org.au>
URL: www.sequin.xyz
VignetteBuilder: knitr
BugReports: https://github.com/student-t/RAnaquin/issues
git_url: https://git.bioconductor.org/packages/Anaquin
git_branch: devel
git_last_commit: 6b70356
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Anaquin_2.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Anaquin_2.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Anaquin_2.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Anaquin_2.31.0.tgz
vignettes: vignettes/Anaquin/inst/doc/Anaquin.pdf
vignetteTitles: Anaquin - Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Anaquin/inst/doc/Anaquin.R
dependencyCount: 92
Package: ANCOMBC
Version: 2.9.2
Depends: R (>= 4.5.0)
Imports: stats, CVXR, DescTools, Hmisc, MASS, Matrix, Rdpack,
doParallel, doRNG, energy, foreach, gtools, lme4, lmerTest,
multcomp, nloptr, parallel, utils
Suggests: mia (>= 1.6.0), DT, S4Vectors, SingleCellExperiment,
SummarizedExperiment, TreeSummarizedExperiment, dplyr, knitr,
magrittr, microbiome, phyloseq, rmarkdown, testthat, tidyr,
tidyverse
License: Artistic-2.0
MD5sum: 32b53b7d2dd02c74cf8221b9d0228973
NeedsCompilation: no
Title: Microbiome differential abudance and correlation analyses with
bias correction
Description: ANCOMBC is a package containing differential abundance
(DA) and correlation analyses for microbiome data.
Specifically, the package includes Analysis of Compositions of
Microbiomes with Bias Correction 2 (ANCOM-BC2), Analysis of
Compositions of Microbiomes with Bias Correction (ANCOM-BC),
and Analysis of Composition of Microbiomes (ANCOM) for DA
analysis, and Sparse Estimation of Correlations among
Microbiomes (SECOM) for correlation analysis. Microbiome data
are typically subject to two sources of biases: unequal
sampling fractions (sample-specific biases) and differential
sequencing efficiencies (taxon-specific biases). Methodologies
included in the ANCOMBC package are designed to correct these
biases and construct statistically consistent estimators.
biocViews: DifferentialExpression, Microbiome, Normalization,
Sequencing, Software
Author: Huang Lin [cre, aut] (ORCID:
<https://orcid.org/0000-0002-4892-7871>)
Maintainer: Huang Lin <huanglinfrederick@gmail.com>
URL: https://github.com/FrederickHuangLin/ANCOMBC
VignetteBuilder: knitr
BugReports: https://github.com/FrederickHuangLin/ANCOMBC/issues
git_url: https://git.bioconductor.org/packages/ANCOMBC
git_branch: devel
git_last_commit: 4e4d565
git_last_commit_date: 2025-03-16
Date/Publication: 2025-03-17
source.ver: src/contrib/ANCOMBC_2.9.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ANCOMBC_2.9.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ANCOMBC_2.9.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ANCOMBC_2.9.2.tgz
vignettes: vignettes/ANCOMBC/inst/doc/ANCOM.html,
vignettes/ANCOMBC/inst/doc/ANCOMBC.html,
vignettes/ANCOMBC/inst/doc/ANCOMBC2.html,
vignettes/ANCOMBC/inst/doc/data_sanity_check.html,
vignettes/ANCOMBC/inst/doc/SECOM.html
vignetteTitles: ANCOM Tutorial, ANCOM-BC Tutorial, ANCOM-BC2 Tutorial,
Tutorial on Data Sanity and Integrity Checks, SECOM Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ANCOMBC/inst/doc/ANCOM.R,
vignettes/ANCOMBC/inst/doc/ANCOMBC.R,
vignettes/ANCOMBC/inst/doc/ANCOMBC2.R,
vignettes/ANCOMBC/inst/doc/data_sanity_check.R,
vignettes/ANCOMBC/inst/doc/SECOM.R
importsMe: benchdamic
suggestsMe: dar, MiscMetabar
dependencyCount: 138
Package: AneuFinder
Version: 1.35.0
Depends: R (>= 3.5), GenomicRanges, ggplot2, cowplot, AneuFinderData
Imports: methods, utils, grDevices, graphics, stats, foreach,
doParallel, BiocGenerics (>= 0.31.6), S4Vectors, GenomeInfoDb,
IRanges, Rsamtools, bamsignals, DNAcopy, ecp, Biostrings,
GenomicAlignments, reshape2, ggdendro, ggrepel, mclust
Suggests: knitr, BiocStyle, testthat, BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Mmusculus.UCSC.mm10
License: Artistic-2.0
MD5sum: b44169ab3c45d8d56cd9409a9e8e34ba
NeedsCompilation: yes
Title: Analysis of Copy Number Variation in Single-Cell-Sequencing Data
Description: AneuFinder implements functions for copy-number detection,
breakpoint detection, and karyotype and heterogeneity analysis
in single-cell whole genome sequencing and strand-seq data.
biocViews: ImmunoOncology, Software, Sequencing, SingleCell,
CopyNumberVariation, GenomicVariation, HiddenMarkovModel,
WholeGenome
Author: Aaron Taudt, Bjorn Bakker, David Porubsky
Maintainer: Aaron Taudt <aaron.taudt@gmail.com>
URL: https://github.com/ataudt/aneufinder.git
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/AneuFinder
git_branch: devel
git_last_commit: 42b7837
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/AneuFinder_1.35.0.tar.gz
vignettes: vignettes/AneuFinder/inst/doc/AneuFinder.pdf
vignetteTitles: A quick introduction to AneuFinder
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AneuFinder/inst/doc/AneuFinder.R
dependencyCount: 94
Package: ANF
Version: 1.29.0
Imports: igraph, Biobase, survival, MASS, stats, RColorBrewer
Suggests: ExperimentHub, SNFtool, knitr, rmarkdown, testthat
License: GPL-3
MD5sum: 29bdbbbfac526413f9b46da404d69136
NeedsCompilation: no
Title: Affinity Network Fusion for Complex Patient Clustering
Description: This package is used for complex patient clustering by
integrating multi-omic data through affinity network fusion.
biocViews: Clustering, GraphAndNetwork, Network
Author: Tianle Ma, Aidong Zhang
Maintainer: Tianle Ma <tianlema@buffalo.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ANF
git_branch: devel
git_last_commit: 25ef886
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ANF_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ANF_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ANF_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ANF_1.29.0.tgz
vignettes: vignettes/ANF/inst/doc/ANF.html
vignetteTitles: Cancer Patient Clustering with ANF
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ANF/inst/doc/ANF.R
suggestsMe: HarmonizedTCGAData
dependencyCount: 24
Package: animalcules
Version: 1.23.0
Depends: R (>= 4.3.0)
Imports: ape, assertthat, caret, covr, DESeq2, dplyr, DT, forcats,
ggforce, ggplot2, GUniFrac, lattice, limma, magrittr, Matrix,
methods, MultiAssayExperiment, plotly, rentrez, reshape2,
ROCit, S4Vectors (>= 0.23.19), scales, shiny, shinyjs, stats,
SummarizedExperiment, tibble, tidyr, tsne, umap, utils, vegan,
XML
Suggests: BiocStyle, biomformat, devtools, glmnet, knitr, rmarkdown,
testthat, usethis
License: Artistic-2.0
MD5sum: ae926f45efd1b1e112d25ba7e99074a6
NeedsCompilation: no
Title: Interactive microbiome analysis toolkit
Description: animalcules is an R package for utilizing up-to-date data
analytics, visualization methods, and machine learning models
to provide users an easy-to-use interactive microbiome analysis
framework. It can be used as a standalone software package or
users can explore their data with the accompanying interactive
R Shiny application. Traditional microbiome analysis such as
alpha/beta diversity and differential abundance analysis are
enhanced, while new methods like biomarker identification are
introduced by animalcules. Powerful interactive and dynamic
figures generated by animalcules enable users to understand
their data better and discover new insights.
biocViews: Microbiome, Metagenomics, Coverage, Visualization
Author: Jessica McClintock [cre], Yue Zhao [aut] (ORCID:
<https://orcid.org/0000-0001-5257-5103>), Anthony Federico
[aut] (ORCID: <https://orcid.org/0000-0002-9200-1689>), W. Evan
Johnson [aut] (ORCID: <https://orcid.org/0000-0002-6247-6595>)
Maintainer: Jessica McClintock <jessica.mcclintock@rutgers.edu>
URL: https://github.com/wejlab/animalcules
VignetteBuilder: knitr
BugReports: https://github.com/wejlab/animalcules/issues
git_url: https://git.bioconductor.org/packages/animalcules
git_branch: devel
git_last_commit: 1f256fc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/animalcules_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/animalcules_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/animalcules_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/animalcules_1.23.0.tgz
vignettes: vignettes/animalcules/inst/doc/animalcules.html
vignetteTitles: animalcules
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/animalcules/inst/doc/animalcules.R
importsMe: LegATo
suggestsMe: MetaScope
dependencyCount: 193
Package: annaffy
Version: 1.79.0
Depends: R (>= 2.5.0), methods, Biobase, BiocManager, GO.db
Imports: AnnotationDbi (>= 0.1.15), DBI
Suggests: hgu95av2.db, multtest, tcltk
License: LGPL
MD5sum: 96c539beb1dc9ff298ce8e4115cf0e37
NeedsCompilation: no
Title: Annotation tools for Affymetrix biological metadata
Description: Functions for handling data from Bioconductor Affymetrix
annotation data packages. Produces compact HTML and text
reports including experimental data and URL links to many
online databases. Allows searching biological metadata using
various criteria.
biocViews: OneChannel, Microarray, Annotation, GO, Pathways,
ReportWriting
Author: Colin A. Smith <colin@colinsmith.org>
Maintainer: Colin A. Smith <colin@colinsmith.org>
git_url: https://git.bioconductor.org/packages/annaffy
git_branch: devel
git_last_commit: 5617a63
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/annaffy_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/annaffy_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/annaffy_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/annaffy_1.79.0.tgz
vignettes: vignettes/annaffy/inst/doc/annaffy.pdf
vignetteTitles: annaffy Primer
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/annaffy/inst/doc/annaffy.R
dependsOnMe: webbioc
importsMe: a4Base
suggestsMe: metaMA
dependencyCount: 47
Package: annmap
Version: 1.49.0
Depends: R (>= 2.15.0), methods, GenomicRanges
Imports: DBI, RMySQL (>= 0.6-0), digest, Biobase, grid, lattice,
Rsamtools, genefilter, IRanges, BiocGenerics
Suggests: RUnit, rjson, Gviz
License: GPL-2
MD5sum: 7fb24c28a01bf519188c5b4b2c573640
NeedsCompilation: no
Title: Genome annotation and visualisation package pertaining to
Affymetrix arrays and NGS analysis.
Description: annmap provides annotation mappings for Affymetrix exon
arrays and coordinate based queries to support deep sequencing
data analysis. Database access is hidden behind the API which
provides a set of functions such as genesInRange(),
geneToExon(), exonDetails(), etc. Functions to plot gene
architecture and BAM file data are also provided. Underlying
data are from Ensembl. The annmap database can be downloaded
from:
https://figshare.manchester.ac.uk/account/articles/16685071
biocViews: Annotation, Microarray, OneChannel, ReportWriting,
Transcription, Visualization
Author: Tim Yates <Tim.Yates@cruk.manchester.ac.uk>
Maintainer: Chris Wirth <Christopher.Wirth@cruk.manchester.ac.uk>
URL: https://github.com/cruk-mi/annmap
git_url: https://git.bioconductor.org/packages/annmap
git_branch: devel
git_last_commit: da770e6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/annmap_1.49.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/annmap_1.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/annmap_1.49.0.tgz
vignettes: vignettes/annmap/inst/doc/annmap.pdf,
vignettes/annmap/inst/doc/cookbook.pdf,
vignettes/annmap/inst/doc/INSTALL.pdf
vignetteTitles: annmap primer, The Annmap Cookbook, annmap installation
instruction
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
dependencyCount: 71
Package: annotate
Version: 1.85.0
Depends: R (>= 2.10), AnnotationDbi (>= 1.27.5), XML
Imports: Biobase, DBI, xtable, graphics, utils, stats, methods,
BiocGenerics (>= 0.13.8), httr
Suggests: hgu95av2.db, genefilter, Biostrings (>= 2.25.10), IRanges,
rae230a.db, rae230aprobe, tkWidgets, GO.db, org.Hs.eg.db,
org.Mm.eg.db, humanCHRLOC, Rgraphviz, RUnit, BiocStyle, knitr
License: Artistic-2.0
MD5sum: 28437ee87b94a9df3e1ffd22e5cae322
NeedsCompilation: no
Title: Annotation for microarrays
Description: Using R enviroments for annotation.
biocViews: Annotation, Pathways, GO
Author: R. Gentleman
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/annotate
git_branch: devel
git_last_commit: 732426e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/annotate_1.85.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/annotate_1.85.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/annotate_1.85.0.tgz
vignettes: vignettes/annotate/inst/doc/annotate.pdf,
vignettes/annotate/inst/doc/GOusage.pdf,
vignettes/annotate/inst/doc/prettyOutput.pdf,
vignettes/annotate/inst/doc/query.pdf,
vignettes/annotate/inst/doc/useProbeInfo.pdf,
vignettes/annotate/inst/doc/chromLOC.html,
vignettes/annotate/inst/doc/useDataPkgs.html
vignetteTitles: Annotation Overview, Basic GO Usage, HowTo: Get HTML
Output, HOWTO: Use the online query tools, Using Affymetrix
Probe Level Data, HowTo: Build and use chromosomal information,
Using Bioconductor's Annotation Libraries
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/annotate/inst/doc/annotate.R,
vignettes/annotate/inst/doc/chromLOC.R,
vignettes/annotate/inst/doc/GOusage.R,
vignettes/annotate/inst/doc/prettyOutput.R,
vignettes/annotate/inst/doc/query.R,
vignettes/annotate/inst/doc/useDataPkgs.R,
vignettes/annotate/inst/doc/useProbeInfo.R
dependsOnMe: ChromHeatMap, geneplotter, GSEABase, idiogram, MineICA,
MLInterfaces, phenoTest, PREDA, sampleClassifier, SemDist,
Neve2006, PREDAsampledata
importsMe: CAFE, Category, categoryCompare, CNEr, codelink, debrowser,
DrugVsDisease, genefilter, GlobalAncova, globaltest, GOstats,
lumi, methylumi, MGFR, phenoTest, qpgraph, SGCP, tigre,
UMI4Cats, geneExpressionFromGEO, GOxploreR
suggestsMe: BiocGenerics, GenomicRanges, GSAR, GSEAlm, hmdbQuery,
metagenomeSeq, MLP, pageRank, PhosR, RnBeads, siggenes,
SummarizedExperiment, systemPipeR, adme16cod.db, ag.db,
ath1121501.db, bovine.db, canine.db, canine2.db, celegans.db,
chicken.db, clariomdhumanprobeset.db,
clariomdhumantranscriptcluster.db,
clariomshumanhttranscriptcluster.db,
clariomshumantranscriptcluster.db,
clariomsmousehttranscriptcluster.db,
clariomsmousetranscriptcluster.db,
clariomsrathttranscriptcluster.db,
clariomsrattranscriptcluster.db, drosgenome1.db,
drosophila2.db, ecoli2.db, GGHumanMethCancerPanelv1.db,
h10kcod.db, h20kcod.db, hcg110.db, hgfocus.db, hgu133a.db,
hgu133a2.db, hgu133b.db, hgu133plus2.db, hgu219.db, hgu95a.db,
hgu95av2.db, hgu95b.db, hgu95c.db, hgu95d.db, hgu95e.db,
hguatlas13k.db, hgubeta7.db, hguDKFZ31.db, hgug4100a.db,
hgug4101a.db, hgug4110b.db, hgug4111a.db, hgug4112a.db,
hgug4845a.db, hguqiagenv3.db, hi16cod.db, hs25kresogen.db,
Hs6UG171.db, HsAgilentDesign026652.db, hta20probeset.db,
hta20transcriptcluster.db, hthgu133a.db, hthgu133b.db,
hthgu133plusa.db, hthgu133plusb.db, hthgu133pluspm.db,
htmg430a.db, htmg430b.db, htmg430pm.db, htrat230pm.db,
htratfocus.db, hu35ksuba.db, hu35ksubb.db, hu35ksubc.db,
hu35ksubd.db, hu6800.db, huex10stprobeset.db,
huex10sttranscriptcluster.db, hugene10stprobeset.db,
hugene10sttranscriptcluster.db, hugene11stprobeset.db,
hugene11sttranscriptcluster.db, hugene20stprobeset.db,
hugene20sttranscriptcluster.db, hugene21stprobeset.db,
hugene21sttranscriptcluster.db, HuO22.db, hwgcod.db,
IlluminaHumanMethylation27k.db, illuminaHumanv1.db,
illuminaHumanv2.db, illuminaHumanv2BeadID.db,
illuminaHumanv3.db, illuminaHumanv4.db,
illuminaHumanWGDASLv3.db, illuminaHumanWGDASLv4.db,
illuminaMousev1.db, illuminaMousev1p1.db, illuminaMousev2.db,
illuminaRatv1.db, indac.db, JazaeriMetaData.db, LAPOINTE.db,
lumiHumanAll.db, lumiMouseAll.db, lumiRatAll.db, m10kcod.db,
m20kcod.db, mgu74a.db, mgu74av2.db, mgu74b.db, mgu74bv2.db,
mgu74c.db, mgu74cv2.db, mguatlas5k.db, mgug4104a.db,
mgug4120a.db, mgug4121a.db, mgug4122a.db, mi16cod.db,
miRBaseVersions.db, mm24kresogen.db, MmAgilentDesign026655.db,
moe430a.db, moe430b.db, moex10stprobeset.db,
moex10sttranscriptcluster.db, mogene10stprobeset.db,
mogene10sttranscriptcluster.db, mogene11stprobeset.db,
mogene11sttranscriptcluster.db, mogene20stprobeset.db,
mogene20sttranscriptcluster.db, mogene21stprobeset.db,
mogene21sttranscriptcluster.db, mouse4302.db, mouse430a2.db,
mpedbarray.db, mta10probeset.db, mta10transcriptcluster.db,
mu11ksuba.db, mu11ksubb.db, Mu15v1.db, mu19ksuba.db,
mu19ksubb.db, mu19ksubc.db, Mu22v3.db, mwgcod.db, Norway981.db,
nugohs1a520180.db, nugomm1a520177.db, OperonHumanV3.db,
org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db, org.Ce.eg.db,
org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcK12.eg.db,
org.EcSakai.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db,
org.Mmu.eg.db, org.Pf.plasmo.db, org.Pt.eg.db, org.Rn.eg.db,
org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, Orthology.eg.db,
PartheenMetaData.db, pedbarrayv10.db, pedbarrayv9.db,
POCRCannotation.db, porcine.db, r10kcod.db, rae230a.db,
rae230b.db, raex10stprobeset.db, raex10sttranscriptcluster.db,
ragene10stprobeset.db, ragene10sttranscriptcluster.db,
ragene11stprobeset.db, ragene11sttranscriptcluster.db,
ragene20stprobeset.db, ragene20sttranscriptcluster.db,
ragene21stprobeset.db, ragene21sttranscriptcluster.db,
rat2302.db, rgu34a.db, rgu34b.db, rgu34c.db, rguatlas4k.db,
rgug4105a.db, rgug4130a.db, rgug4131a.db, ri16cod.db,
RnAgilentDesign028282.db, rnu34.db, Roberts2005Annotation.db,
rta10probeset.db, rta10transcriptcluster.db, rtu34.db,
rwgcod.db, SHDZ.db, SomaScan.db, u133x3p.db, xlaevis.db,
yeast2.db, ygs98.db, zebrafish.db, clValid, limorhyde, maGUI
dependencyCount: 47
Package: AnnotationDbi
Version: 1.69.0
Depends: R (>= 2.7.0), methods, stats4, BiocGenerics (>= 0.29.2),
Biobase (>= 1.17.0), IRanges
Imports: DBI, RSQLite, S4Vectors (>= 0.9.25), stats, KEGGREST
Suggests: utils, hgu95av2.db, GO.db, org.Sc.sgd.db, org.At.tair.db,
RUnit, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db,
reactome.db, AnnotationForge, graph, EnsDb.Hsapiens.v75,
BiocStyle, knitr
License: Artistic-2.0
MD5sum: d5469c79e59a01c6e7fb8b2ad598601f
NeedsCompilation: no
Title: Manipulation of SQLite-based annotations in Bioconductor
Description: Implements a user-friendly interface for querying
SQLite-based annotation data packages.
biocViews: Annotation, Microarray, Sequencing, GenomeAnnotation
Author: Hervé Pagès, Marc Carlson, Seth Falcon, Nianhua Li
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://bioconductor.org/packages/AnnotationDbi
VignetteBuilder: knitr
Video: https://www.youtube.com/watch?v=8qvGNTVz3Ik
BugReports: https://github.com/Bioconductor/AnnotationDbi/issues
git_url: https://git.bioconductor.org/packages/AnnotationDbi
git_branch: devel
git_last_commit: faea5ea
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/AnnotationDbi_1.69.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AnnotationDbi_1.69.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AnnotationDbi_1.69.0.tgz
vignettes: vignettes/AnnotationDbi/inst/doc/AnnotationDbi.pdf,
vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf
vignetteTitles: 2. (Deprecated) How to use bimaps from the ".db"
annotation packages, 1. Introduction To Bioconductor Annotation
Packages
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AnnotationDbi/inst/doc/AnnotationDbi.R,
vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.R
dependsOnMe: annotate, AnnotationForge, ASpli, attract, Category,
ChromHeatMap, customProDB, DEXSeq, EGSEA, EpiTxDb,
GenomicFeatures, goProfiles, GSReg, ipdDb, miRNAtap,
OrganismDbi, pathRender, proBAMr, safe, SemDist, topGO,
adme16cod.db, ag.db, agprobe, anopheles.db0, arabidopsis.db0,
ath1121501.db, ath1121501probe, barley1probe, bovine.db,
bovine.db0, bovineprobe, bsubtilisprobe, canine.db, canine.db0,
canine2.db, canine2probe, canineprobe, celegans.db,
celegansprobe, chicken.db, chicken.db0, chickenprobe,
chimp.db0, citrusprobe, clariomdhumanprobeset.db,
clariomdhumantranscriptcluster.db,
clariomshumanhttranscriptcluster.db,
clariomshumantranscriptcluster.db,
clariomsmousehttranscriptcluster.db,
clariomsmousetranscriptcluster.db,
clariomsrathttranscriptcluster.db,
clariomsrattranscriptcluster.db, cottonprobe, DO.db,
drosgenome1.db, drosgenome1probe, drosophila2.db,
drosophila2probe, ecoli2.db, ecoli2probe, ecoliasv2probe,
ecoliK12.db0, ecoliprobe, ecoliSakai.db0, fly.db0,
GGHumanMethCancerPanelv1.db, GO.db, h10kcod.db, h20kcod.db,
hcg110.db, hcg110probe, hgfocus.db, hgfocusprobe, hgu133a.db,
hgu133a2.db, hgu133a2probe, hgu133aprobe, hgu133atagprobe,
hgu133b.db, hgu133bprobe, hgu133plus2.db, hgu133plus2probe,
hgu219.db, hgu219probe, hgu95a.db, hgu95aprobe, hgu95av2.db,
hgu95av2probe, hgu95b.db, hgu95bprobe, hgu95c.db, hgu95cprobe,
hgu95d.db, hgu95dprobe, hgu95e.db, hgu95eprobe, hguatlas13k.db,
hgubeta7.db, hguDKFZ31.db, hgug4100a.db, hgug4101a.db,
hgug4110b.db, hgug4111a.db, hgug4112a.db, hgug4845a.db,
hguqiagenv3.db, hi16cod.db, Homo.sapiens, hs25kresogen.db,
Hs6UG171.db, HsAgilentDesign026652.db, hta20probeset.db,
hta20transcriptcluster.db, hthgu133a.db, hthgu133aprobe,
hthgu133b.db, hthgu133bprobe, hthgu133plusa.db,
hthgu133plusb.db, hthgu133pluspm.db, hthgu133pluspmprobe,
htmg430a.db, htmg430aprobe, htmg430b.db, htmg430bprobe,
htmg430pm.db, htmg430pmprobe, htrat230pm.db, htrat230pmprobe,
htratfocus.db, htratfocusprobe, hu35ksuba.db, hu35ksubaprobe,
hu35ksubb.db, hu35ksubbprobe, hu35ksubc.db, hu35ksubcprobe,
hu35ksubd.db, hu35ksubdprobe, hu6800.db, hu6800probe,
huex10stprobeset.db, huex10sttranscriptcluster.db,
HuExExonProbesetLocation, HuExExonProbesetLocationHg18,
HuExExonProbesetLocationHg19, hugene10stprobeset.db,
hugene10sttranscriptcluster.db, hugene10stv1probe,
hugene11stprobeset.db, hugene11sttranscriptcluster.db,
hugene20stprobeset.db, hugene20sttranscriptcluster.db,
hugene21stprobeset.db, hugene21sttranscriptcluster.db,
human.db0, HuO22.db, hwgcod.db, IlluminaHumanMethylation27k.db,
IlluminaHumanMethylation450kprobe, illuminaHumanv1.db,
illuminaHumanv2.db, illuminaHumanv2BeadID.db,
illuminaHumanv3.db, illuminaHumanv4.db,
illuminaHumanWGDASLv3.db, illuminaHumanWGDASLv4.db,
illuminaMousev1.db, illuminaMousev1p1.db, illuminaMousev2.db,
illuminaRatv1.db, indac.db, JazaeriMetaData.db, LAPOINTE.db,
lumiHumanAll.db, lumiHumanIDMapping, lumiMouseAll.db,
lumiMouseIDMapping, lumiRatAll.db, lumiRatIDMapping,
m10kcod.db, m20kcod.db, maizeprobe, malaria.db0, medicagoprobe,
mgu74a.db, mgu74aprobe, mgu74av2.db, mgu74av2probe, mgu74b.db,
mgu74bprobe, mgu74bv2.db, mgu74bv2probe, mgu74c.db,
mgu74cprobe, mgu74cv2.db, mgu74cv2probe, mguatlas5k.db,
mgug4104a.db, mgug4120a.db, mgug4121a.db, mgug4122a.db,
mi16cod.db, mirbase.db, mirna10probe, mm24kresogen.db,
MmAgilentDesign026655.db, moe430a.db, moe430aprobe, moe430b.db,
moe430bprobe, moex10stprobeset.db,
moex10sttranscriptcluster.db, MoExExonProbesetLocation,
mogene10stprobeset.db, mogene10sttranscriptcluster.db,
mogene10stv1probe, mogene11stprobeset.db,
mogene11sttranscriptcluster.db, mogene20stprobeset.db,
mogene20sttranscriptcluster.db, mogene21stprobeset.db,
mogene21sttranscriptcluster.db, mouse.db0, mouse4302.db,
mouse4302probe, mouse430a2.db, mouse430a2probe, mpedbarray.db,
mta10probeset.db, mta10transcriptcluster.db, mu11ksuba.db,
mu11ksubaprobe, mu11ksubb.db, mu11ksubbprobe, Mu15v1.db,
mu19ksuba.db, mu19ksubb.db, mu19ksubc.db, Mu22v3.db,
Mus.musculus, mwgcod.db, Norway981.db, nugohs1a520180.db,
nugohs1a520180probe, nugomm1a520177.db, nugomm1a520177probe,
OperonHumanV3.db, org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db,
org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db,
org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Hs.eg.db,
org.Mm.eg.db, org.Mmu.eg.db, org.Mxanthus.db, org.Pf.plasmo.db,
org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db,
org.Xl.eg.db, Orthology.eg.db, paeg1aprobe,
PartheenMetaData.db, pedbarrayv10.db, pedbarrayv9.db, PFAM.db,
pig.db0, plasmodiumanophelesprobe, POCRCannotation.db,
poplarprobe, porcine.db, porcineprobe, primeviewprobe,
r10kcod.db, rae230a.db, rae230aprobe, rae230b.db, rae230bprobe,
raex10stprobeset.db, raex10sttranscriptcluster.db,
RaExExonProbesetLocation, ragene10stprobeset.db,
ragene10sttranscriptcluster.db, ragene10stv1probe,
ragene11stprobeset.db, ragene11sttranscriptcluster.db,
ragene20stprobeset.db, ragene20sttranscriptcluster.db,
ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat.db0,
rat2302.db, rat2302probe, rattoxfxprobe, Rattus.norvegicus,
reactome.db, rgu34a.db, rgu34aprobe, rgu34b.db, rgu34bprobe,
rgu34c.db, rgu34cprobe, rguatlas4k.db, rgug4105a.db,
rgug4130a.db, rgug4131a.db, rhesus.db0, rhesusprobe,
ri16cod.db, riceprobe, RnAgilentDesign028282.db, rnu34.db,
rnu34probe, Roberts2005Annotation.db, rta10probeset.db,
rta10transcriptcluster.db, rtu34.db, rtu34probe, rwgcod.db,
saureusprobe, SHDZ.db, SomaScan.db, soybeanprobe,
sugarcaneprobe, targetscan.Hs.eg.db, targetscan.Mm.eg.db,
test3probe, tomatoprobe, u133x3p.db, u133x3pprobe,
vitisviniferaprobe, wheatprobe, worm.db0, xenopus.db0,
xenopuslaevisprobe, xlaevis.db, xlaevis2probe,
xtropicalisprobe, yeast.db0, yeast2.db, yeast2probe, ygs98.db,
ygs98probe, zebrafish.db, zebrafish.db0, zebrafishprobe,
tinesath1probe, rnaseqGene, convertid
importsMe: adSplit, affycoretools, affylmGUI, AllelicImbalance,
annaffy, AnnotationHub, AnnotationHubData, annotatr, artMS,
beadarray, bioCancer, BiocSet, biomaRt, BioNAR, BioNet,
biovizBase, bumphunter, BUSpaRse, categoryCompare, ccmap,
cellity, chimeraviz, chipenrich, ChIPpeakAnno, ChIPseeker,
clusterProfiler, CoCiteStats, Cogito, compEpiTools,
consensusDE, CoSIA, crisprDesign, CrispRVariants, cTRAP,
Damsel, debrowser, derfinder, DominoEffect, DOSE,
doubletrouble, EasyCellType, EDASeq, EnrichmentBrowser,
ensembldb, EpiMix, epimutacions, erma, esATAC, FRASER,
funOmics, GA4GHshiny, gage, gDNAx, genefilter, geneplotter,
GeneTonic, geneXtendeR, GenomicInteractionNodes, GenVisR,
ggbio, gINTomics, GlobalAncova, globaltest, GmicR, GOfuncR,
GOpro, GOSemSim, goseq, goSTAG, GOstats, goTools, graphite,
GSEABase, GSEABenchmarkeR, Gviz, gwascat, ideal, IMAS,
interactiveDisplay, isomiRs, IVAS, karyoploteR, keggorthology,
LRBaseDbi, lumi, magpie, mastR, MCbiclust, MeSHDbi, meshes,
MesKit, MetaboSignal, methylGSA, methylumi, MineICA, MiRaGE,
mirIntegrator, MIRit, miRNAmeConverter, missMethyl, MLP,
MOSClip, mosdef, MSnID, multiGSEA, multiMiR, NanoMethViz,
NetSAM, ORFik, Organism.dplyr, OutSplice, PADOG, pathview,
pcaExplorer, phantasus, phenoTest, proActiv, psichomics,
qpgraph, QuasR, RAIDS, ReactomePA, REDseq, regutools, rGREAT,
rgsepd, ribosomeProfilingQC, RNAAgeCalc, rrvgo, rTRM, SBGNview,
scanMiRApp, scPipe, scruff, scTensor, SGSeq, signatureSearch,
signifinder, simplifyEnrichment, SMITE, SubCellBarCode, SVMDO,
TCGAutils, tenXplore, TFutils, tigre, trackViewer, TRESS,
tricycle, txcutr, txdbmaker, tximeta, Ularcirc, UniProt.ws,
VariantAnnotation, VariantFiltering, ViSEAGO, adme16cod.db,
ag.db, agcdf, ath1121501.db, ath1121501cdf, barley1cdf,
bovine.db, bovinecdf, bsubtiliscdf, canine.db, canine2.db,
canine2cdf, caninecdf, celegans.db, celeganscdf, chicken.db,
chickencdf, citruscdf, clariomdhumanprobeset.db,
clariomdhumantranscriptcluster.db,
clariomshumanhttranscriptcluster.db,
clariomshumantranscriptcluster.db,
clariomsmousehttranscriptcluster.db,
clariomsmousetranscriptcluster.db,
clariomsrathttranscriptcluster.db,
clariomsrattranscriptcluster.db, cottoncdf, cyp450cdf, DO.db,
drosgenome1.db, drosgenome1cdf, drosophila2.db, drosophila2cdf,
ecoli2.db, ecoli2cdf, ecoliasv2cdf, ecolicdf,
FDb.FANTOM4.promoters.hg19, FDb.InfiniumMethylation.hg18,
FDb.InfiniumMethylation.hg19, FDb.UCSC.snp135common.hg19,
FDb.UCSC.snp137common.hg19, FDb.UCSC.tRNAs, GenomicState,
GGHumanMethCancerPanelv1.db, gp53cdf, h10kcod.db, h20kcod.db,
hcg110.db, hcg110cdf, HDO.db, hgfocus.db, hgfocuscdf,
hgu133a.db, hgu133a2.db, hgu133a2cdf, hgu133acdf,
hgu133atagcdf, hgu133b.db, hgu133bcdf, hgu133plus2.db,
hgu133plus2cdf, hgu219.db, hgu219cdf, hgu95a.db, hgu95acdf,
hgu95av2.db, hgu95av2cdf, hgu95b.db, hgu95bcdf, hgu95c.db,
hgu95ccdf, hgu95d.db, hgu95dcdf, hgu95e.db, hgu95ecdf,
hguatlas13k.db, hgubeta7.db, hguDKFZ31.db, hgug4100a.db,
hgug4101a.db, hgug4110b.db, hgug4111a.db, hgug4112a.db,
hgug4845a.db, hguqiagenv3.db, hi16cod.db, hivprtplus2cdf,
Homo.sapiens, HPO.db, hs25kresogen.db, Hs6UG171.db,
HsAgilentDesign026652.db, Hspec, hspeccdf, hta20probeset.db,
hta20transcriptcluster.db, hthgu133a.db, hthgu133acdf,
hthgu133b.db, hthgu133bcdf, hthgu133plusa.db, hthgu133plusb.db,
hthgu133pluspm.db, hthgu133pluspmcdf, htmg430a.db, htmg430acdf,
htmg430b.db, htmg430bcdf, htmg430pm.db, htmg430pmcdf,
htrat230pm.db, htrat230pmcdf, htratfocus.db, htratfocuscdf,
hu35ksuba.db, hu35ksubacdf, hu35ksubb.db, hu35ksubbcdf,
hu35ksubc.db, hu35ksubccdf, hu35ksubd.db, hu35ksubdcdf,
hu6800.db, hu6800cdf, hu6800subacdf, hu6800subbcdf,
hu6800subccdf, hu6800subdcdf, huex10stprobeset.db,
huex10sttranscriptcluster.db, hugene10stprobeset.db,
hugene10sttranscriptcluster.db, hugene10stv1cdf,
hugene11stprobeset.db, hugene11sttranscriptcluster.db,
hugene20stprobeset.db, hugene20sttranscriptcluster.db,
hugene21stprobeset.db, hugene21sttranscriptcluster.db,
HuO22.db, hwgcod.db, IlluminaHumanMethylation27k.db,
illuminaHumanv1.db, illuminaHumanv2.db,
illuminaHumanv2BeadID.db, illuminaHumanv3.db,
illuminaHumanv4.db, illuminaHumanWGDASLv3.db,
illuminaHumanWGDASLv4.db, illuminaMousev1.db,
illuminaMousev1p1.db, illuminaMousev2.db, illuminaRatv1.db,
indac.db, JazaeriMetaData.db, LAPOINTE.db, lumiHumanAll.db,
lumiHumanIDMapping, lumiMouseAll.db, lumiMouseIDMapping,
lumiRatAll.db, lumiRatIDMapping, m10kcod.db, m20kcod.db,
maizecdf, medicagocdf, mgu74a.db, mgu74acdf, mgu74av2.db,
mgu74av2cdf, mgu74b.db, mgu74bcdf, mgu74bv2.db, mgu74bv2cdf,
mgu74c.db, mgu74ccdf, mgu74cv2.db, mgu74cv2cdf, mguatlas5k.db,
mgug4104a.db, mgug4120a.db, mgug4121a.db, mgug4122a.db,
mi16cod.db, mirbase.db, miRBaseVersions.db, mirna102xgaincdf,
mirna10cdf, mirna20cdf, miRNAtap.db, mm24kresogen.db,
MmAgilentDesign026655.db, moe430a.db, moe430acdf, moe430b.db,
moe430bcdf, moex10stprobeset.db, moex10sttranscriptcluster.db,
mogene10stprobeset.db, mogene10sttranscriptcluster.db,
mogene10stv1cdf, mogene11stprobeset.db,
mogene11sttranscriptcluster.db, mogene20stprobeset.db,
mogene20sttranscriptcluster.db, mogene21stprobeset.db,
mogene21sttranscriptcluster.db, mouse4302.db, mouse4302cdf,
mouse430a2.db, mouse430a2cdf, mpedbarray.db, MPO.db,
mta10probeset.db, mta10transcriptcluster.db, mu11ksuba.db,
mu11ksubacdf, mu11ksubb.db, mu11ksubbcdf, Mu15v1.db,
mu19ksuba.db, mu19ksubacdf, mu19ksubb.db, mu19ksubbcdf,
mu19ksubc.db, mu19ksubccdf, Mu22v3.db, mu6500subacdf,
mu6500subbcdf, mu6500subccdf, mu6500subdcdf, Mus.musculus,
mwgcod.db, Norway981.db, nugohs1a520180.db, nugohs1a520180cdf,
nugomm1a520177.db, nugomm1a520177cdf, OperonHumanV3.db,
paeg1acdf, PartheenMetaData.db, pedbarrayv10.db,
pedbarrayv9.db, plasmodiumanophelescdf, POCRCannotation.db,
PolyPhen.Hsapiens.dbSNP131, poplarcdf, porcine.db, porcinecdf,
primeviewcdf, r10kcod.db, rae230a.db, rae230acdf, rae230b.db,
rae230bcdf, raex10stprobeset.db, raex10sttranscriptcluster.db,
ragene10stprobeset.db, ragene10sttranscriptcluster.db,
ragene10stv1cdf, ragene11stprobeset.db,
ragene11sttranscriptcluster.db, ragene20stprobeset.db,
ragene20sttranscriptcluster.db, ragene21stprobeset.db,
ragene21sttranscriptcluster.db, rat2302.db, rat2302cdf,
rattoxfxcdf, Rattus.norvegicus, reactome.db, rgu34a.db,
rgu34acdf, rgu34b.db, rgu34bcdf, rgu34c.db, rgu34ccdf,
rguatlas4k.db, rgug4105a.db, rgug4130a.db, rgug4131a.db,
rhesuscdf, ri16cod.db, ricecdf, RmiR.Hs.miRNA, RmiR.hsa,
RnAgilentDesign028282.db, rnu34.db, rnu34cdf,
Roberts2005Annotation.db, rta10probeset.db,
rta10transcriptcluster.db, rtu34.db, rtu34cdf, rwgcod.db,
saureuscdf, SHDZ.db, SIFT.Hsapiens.dbSNP132,
SIFT.Hsapiens.dbSNP137, soybeancdf, sugarcanecdf,
targetscan.Hs.eg.db, targetscan.Mm.eg.db, test1cdf, test2cdf,
test3cdf, tomatocdf, TxDb.Athaliana.BioMart.plantsmart22,
TxDb.Athaliana.BioMart.plantsmart25,
TxDb.Athaliana.BioMart.plantsmart28,
TxDb.Athaliana.BioMart.plantsmart51,
TxDb.Btaurus.UCSC.bosTau8.refGene,
TxDb.Btaurus.UCSC.bosTau9.refGene,
TxDb.Celegans.UCSC.ce11.ensGene,
TxDb.Celegans.UCSC.ce11.refGene,
TxDb.Celegans.UCSC.ce6.ensGene,
TxDb.Cfamiliaris.UCSC.canFam3.refGene,
TxDb.Cfamiliaris.UCSC.canFam4.refGene,
TxDb.Cfamiliaris.UCSC.canFam5.refGene,
TxDb.Cfamiliaris.UCSC.canFam6.refGene,
TxDb.Dmelanogaster.UCSC.dm3.ensGene,
TxDb.Dmelanogaster.UCSC.dm6.ensGene,
TxDb.Drerio.UCSC.danRer10.refGene,
TxDb.Drerio.UCSC.danRer11.refGene,
TxDb.Ggallus.UCSC.galGal4.refGene,
TxDb.Ggallus.UCSC.galGal5.refGene,
TxDb.Ggallus.UCSC.galGal6.refGene, TxDb.Hsapiens.BioMart.igis,
TxDb.Hsapiens.UCSC.hg18.knownGene,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts,
TxDb.Hsapiens.UCSC.hg38.knownGene,
TxDb.Hsapiens.UCSC.hg38.refGene,
TxDb.Mmulatta.UCSC.rheMac10.refGene,
TxDb.Mmulatta.UCSC.rheMac3.refGene,
TxDb.Mmulatta.UCSC.rheMac8.refGene,
TxDb.Mmusculus.UCSC.mm10.ensGene,
TxDb.Mmusculus.UCSC.mm10.knownGene,
TxDb.Mmusculus.UCSC.mm39.knownGene,
TxDb.Mmusculus.UCSC.mm39.refGene,
TxDb.Mmusculus.UCSC.mm9.knownGene,
TxDb.Ptroglodytes.UCSC.panTro4.refGene,
TxDb.Ptroglodytes.UCSC.panTro5.refGene,
TxDb.Ptroglodytes.UCSC.panTro6.refGene,
TxDb.Rnorvegicus.BioMart.igis,
TxDb.Rnorvegicus.UCSC.rn4.ensGene,
TxDb.Rnorvegicus.UCSC.rn5.refGene,
TxDb.Rnorvegicus.UCSC.rn6.ncbiRefSeq,
TxDb.Rnorvegicus.UCSC.rn6.refGene,
TxDb.Rnorvegicus.UCSC.rn7.refGene,
TxDb.Scerevisiae.UCSC.sacCer2.sgdGene,
TxDb.Scerevisiae.UCSC.sacCer3.sgdGene,
TxDb.Sscrofa.UCSC.susScr11.refGene,
TxDb.Sscrofa.UCSC.susScr3.refGene, u133aaofav2cdf, u133x3p.db,
u133x3pcdf, vitisviniferacdf, wheatcdf, xenopuslaeviscdf,
xlaevis.db, xlaevis2cdf, xtropicaliscdf, ye6100subacdf,
ye6100subbcdf, ye6100subccdf, ye6100subdcdf, yeast2.db,
yeast2cdf, ygs98.db, ygs98cdf, zebrafish.db, zebrafishcdf,
celldex, chipenrich.data, DeSousa2013, msigdb, scRNAseq,
ExpHunterSuite, aliases2entrez, BiSEp, CAMML, DIscBIO, g3viz,
jetset, Mega2R, netgsa, pathfindR, prioGene, RCPA, RobLoxBioC,
WGCNA
suggestsMe: APAlyzer, ASURAT, autonomics, bambu, BiocGenerics,
CellTrails, cicero, cola, csaw, DAPAR, DEGreport, edgeR, eisaR,
enrichplot, esetVis, FELLA, FGNet, fgsea, fishpond,
GA4GHclient, gatom, gCrisprTools, GeDi, GeneRegionScan,
GenomicPlot, GenomicRanges, ggkegg, gsean, hpar, iNETgrate,
iSEEu, limma, MutationalPatterns, NetActivity, oligo, ontoProc,
OUTRIDER, pathlinkR, piano, Pigengene, plotgardener, pRoloc,
ProteoDisco, quantiseqr, R3CPET, recount, scDotPlot, simona,
SingleCellAlleleExperiment, sparrow, spatialHeatmap, SpliceWiz,
SummarizedExperiment, systemPipeR, tidybulk, topconfects,
weitrix, wiggleplotr, BioPlex, BloodCancerMultiOmics2017,
curatedAdipoChIP, RforProteomics, bulkAnalyseR, CALANGO, conos,
dnapath, easylabel, genekitr, goat, MARVEL, pagoda2, Platypus,
rliger, scITD, SCpubr
dependencyCount: 44
Package: AnnotationFilter
Version: 1.31.0
Depends: R (>= 3.4.0)
Imports: utils, methods, GenomicRanges, lazyeval
Suggests: BiocStyle, knitr, testthat, RSQLite, org.Hs.eg.db, rmarkdown
License: Artistic-2.0
MD5sum: ca8b2772a588d11e94749ede832c6134
NeedsCompilation: no
Title: Facilities for Filtering Bioconductor Annotation Resources
Description: This package provides class and other infrastructure to
implement filters for manipulating Bioconductor annotation
resources. The filters will be used by ensembldb,
Organism.dplyr, and other packages.
biocViews: Annotation, Infrastructure, Software
Author: Martin Morgan [aut], Johannes Rainer [aut], Joachim Bargsten
[ctb], Daniel Van Twisk [ctb], Bioconductor Package Maintainer
[cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://github.com/Bioconductor/AnnotationFilter
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/AnnotationFilter/issues
git_url: https://git.bioconductor.org/packages/AnnotationFilter
git_branch: devel
git_last_commit: 164eaea
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/AnnotationFilter_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AnnotationFilter_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AnnotationFilter_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AnnotationFilter_1.31.0.tgz
vignettes: vignettes/AnnotationFilter/inst/doc/AnnotationFilter.html
vignetteTitles: Facilities for Filtering Bioconductor Annotation
resources
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AnnotationFilter/inst/doc/AnnotationFilter.R
dependsOnMe: chimeraviz, CompoundDb, ensembldb, Organism.dplyr
importsMe: biovizBase, BUSpaRse, CleanUpRNAseq, drugTargetInteractions,
ggbio, QFeatures, RAIDS, RITAN, scanMiRApp, TVTB,
GenomicDistributionsData, locuszoomr, RNAseqQC
suggestsMe: GenomicDistributions, GenomicFeatures, TFutils, wiggleplotr
dependencyCount: 24
Package: AnnotationForge
Version: 1.49.1
Depends: R (>= 3.5.0), methods, utils, BiocGenerics (>= 0.15.10),
Biobase (>= 1.17.0), AnnotationDbi (>= 1.33.14)
Imports: DBI, RSQLite, XML, S4Vectors, RCurl
Suggests: biomaRt, httr, GenomeInfoDb (>= 1.17.1), Biostrings, affy,
hgu95av2.db, human.db0, org.Hs.eg.db, Homo.sapiens, GO.db,
rmarkdown, BiocStyle, knitr, BiocManager, BiocFileCache, RUnit
License: Artistic-2.0
MD5sum: c510358f714aedb972f5f634265a16c4
NeedsCompilation: no
Title: Tools for building SQLite-based annotation data packages
Description: Provides code for generating Annotation packages and their
databases. Packages produced are intended to be used with
AnnotationDbi.
biocViews: Annotation, Infrastructure
Author: Marc Carlson [aut], Hervé Pagès [aut], Madelyn Carlson [ctb]
('Creating probe packages' vignette translation from Sweave to
Rmarkdown / HTML), Bioconductor Package Maintainer [cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://bioconductor.org/packages/AnnotationForge
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/AnnotationForge/issues
git_url: https://git.bioconductor.org/packages/AnnotationForge
git_branch: devel
git_last_commit: 88f8f39
git_last_commit_date: 2025-03-13
Date/Publication: 2025-03-17
source.ver: src/contrib/AnnotationForge_1.49.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AnnotationForge_1.49.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AnnotationForge_1.49.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AnnotationForge_1.49.1.tgz
vignettes:
vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.pdf,
vignettes/AnnotationForge/inst/doc/SQLForge.pdf,
vignettes/AnnotationForge/inst/doc/makeProbePackage.html,
vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.html
vignetteTitles: AnnotationForge: Creating select Interfaces for custom
Annotation resources, SQLForge: An easy way to create a new
annotation package with a standard database schema., Creating
probe packages, Making New Organism Packages
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AnnotationForge/inst/doc/makeProbePackage.R,
vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.R,
vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.R,
vignettes/AnnotationForge/inst/doc/SQLForge.R
importsMe: AnnotationHubData, GOstats, ViSEAGO,
GGHumanMethCancerPanelv1.db
suggestsMe: AnnotationDbi, AnnotationHub
dependencyCount: 48
Package: AnnotationHub
Version: 3.15.0
Depends: BiocGenerics (>= 0.15.10), BiocFileCache (>= 1.5.1)
Imports: utils, methods, grDevices, RSQLite, BiocManager, BiocVersion,
curl, rappdirs, AnnotationDbi (>= 1.31.19), S4Vectors, httr,
yaml, dplyr
Suggests: IRanges, GenomicRanges, GenomeInfoDb, VariantAnnotation,
Rsamtools, rtracklayer, BiocStyle, knitr, AnnotationForge,
rBiopaxParser, RUnit, txdbmaker, MSnbase, mzR, Biostrings,
CompoundDb, keras, ensembldb, SummarizedExperiment,
ExperimentHub, gdsfmt, rmarkdown, HubPub
Enhances: AnnotationHubData
License: Artistic-2.0
MD5sum: 19e24ec71967bcdb4d997d297b840382
NeedsCompilation: yes
Title: Client to access AnnotationHub resources
Description: This package provides a client for the Bioconductor
AnnotationHub web resource. The AnnotationHub web resource
provides a central location where genomic files (e.g., VCF,
bed, wig) and other resources from standard locations (e.g.,
UCSC, Ensembl) can be discovered. The resource includes
metadata about each resource, e.g., a textual description,
tags, and date of modification. The client creates and manages
a local cache of files retrieved by the user, helping with
quick and reproducible access.
biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient
Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut],
Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb],
Valerie Oberchain [ctb], Kayla Morrell [ctb], Lori Shepherd
[aut]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/AnnotationHub/issues
git_url: https://git.bioconductor.org/packages/AnnotationHub
git_branch: devel
git_last_commit: 7a750df
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/AnnotationHub_3.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AnnotationHub_3.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AnnotationHub_3.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AnnotationHub_3.15.0.tgz
vignettes: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.html,
vignettes/AnnotationHub/inst/doc/AnnotationHub.html,
vignettes/AnnotationHub/inst/doc/TroubleshootingTheHubs.html
vignetteTitles: AnnotationHub: AnnotationHub HOW TO's, AnnotationHub:
Access the AnnotationHub Web Service, Troubleshooting The Hubs
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.R,
vignettes/AnnotationHub/inst/doc/AnnotationHub.R,
vignettes/AnnotationHub/inst/doc/TroubleshootingTheHubs.R
dependsOnMe: adductomicsR, AnnotationHubData, ExperimentHub, hipathia,
ipdDb, LRcell, octad, AlphaMissense.v2023.hg19,
AlphaMissense.v2023.hg38, cadd.v1.6.hg19, cadd.v1.6.hg38,
EpiTxDb.Hs.hg38, EpiTxDb.Mm.mm10, EpiTxDb.Sc.sacCer3, EuPathDB,
GenomicState, org.Mxanthus.db, phastCons30way.UCSC.hg38,
phastCons35way.UCSC.mm39, phyloP35way.UCSC.mm39,
rGenomeTracksData, synaptome.data, UCSCRepeatMasker,
MetaGxBreast, MetaGxOvarian, NestLink, scMultiome, sesameData,
tartare, sequencing, OSCA.basic, OSCA.workflows, SingleRBook
importsMe: annotatr, atena, BiocHubsShiny, circRNAprofiler, coMethDMR,
cTRAP, customCMPdb, DMRcate, dmrseq, EpiCompare, EpiMix,
epimutacions, epiregulon, gDNAx, GenomicScores, GRaNIE,
GSEABenchmarkeR, gwascat, iSEEhub, MACSr, meshes,
MetaboAnnotation, methodical, MethReg, Moonlight2R, MSnID,
OGRE, ontoProc, orthos, partCNV, psichomics, regutools, REMP,
scanMiRApp, scAnnotatR, scmeth, scTensor, singleCellTK,
SpliceWiz, TENET, tximeta, Ularcirc, xCell2, AHLRBaseDbs,
AHMeSHDbs, AHPathbankDbs, AHPubMedDbs, AHWikipathwaysDbs,
alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38,
EPICv2manifest, grasp2db, HPO.db, metaboliteIDmapping, MPO.db,
synaptome.db, TENET.AnnotationHub, adductData, BioImageDbs,
biscuiteerData, celldex, chipseqDBData, crisprScoreData,
curatedMetagenomicData, curatedPCaData, curatedTBData,
curatedTCGAData, depmap, DropletTestFiles, easierData,
FieldEffectCrc, FlowSorted.Blood.EPIC,
FlowSorted.CordBloodCombined.450k, GenomicDistributionsData,
HCAData, HiBED, HiContactsData, HMP16SData, HMP2Data,
mcsurvdata, MerfishData, MetaGxPancreas, MouseAgingData,
msigdb, orthosData, scpdata, scRNAseq, SFEData,
SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData,
TENxBrainData, TENxBUSData, TENxPBMCData, tuberculosis,
RNAseqQC, SeedMatchR
suggestsMe: AHMassBank, AlphaMissenseR, autonomics, BgeeCall, Chicago,
ChIPpeakAnno, CINdex, clusterProfiler, CNVRanger, COCOA,
crisprViz, DNAshapeR, dupRadar, ELMER, ensembldb, epiNEM,
EpiTxDb, epivizrChart, epivizrData, factR, GenomicRanges,
Glimma, GOSemSim, HiCool, LRBaseDbi, lute, maser, MIRA,
motifTestR, MSnbase, multicrispr, nullranges, OrganismDbi,
plotgardener, raer, recountmethylation, satuRn, scTensor,
simona, TCGAbiolinks, TCGAutils, tidyCoverage,
VariantAnnotation, xcore, AHEnsDbs, CTCF, ENCODExplorerData,
excluderanges, gwascatData, ontoProcData, BioPlex, CoSIAdata,
HarmonizedTCGAData, homosapienDEE2CellScore, ProteinGymR,
GeneSelectR, locuszoomr
dependencyCount: 64
Package: AnnotationHubData
Version: 1.37.2
Depends: R (>= 3.2.2), methods, utils, S4Vectors (>= 0.7.21), IRanges
(>= 2.3.23), GenomicRanges, AnnotationHub (>= 2.15.15)
Imports: GenomicFeatures, Rsamtools, rtracklayer, BiocGenerics,
jsonlite, BiocManager, biocViews, BiocCheck, graph,
AnnotationDbi, Biobase, Biostrings, DBI, GenomeInfoDb (>=
1.15.4), OrganismDbi, RSQLite, AnnotationForge, futile.logger
(>= 1.3.0), XML, RCurl
Suggests: RUnit, knitr, BiocStyle, grasp2db, GenomeInfoDbData,
rmarkdown, HubPub
License: Artistic-2.0
MD5sum: 91b6e7ea617c04f4111d2c207f7863ff
NeedsCompilation: no
Title: Transform public data resources into Bioconductor Data
Structures
Description: These recipes convert a wide variety and a growing number
of public bioinformatic data sets into easily-used standard
Bioconductor data structures.
biocViews: DataImport
Author: Martin Morgan [ctb], Marc Carlson [ctb], Dan Tenenbaum [ctb],
Sonali Arora [ctb], Paul Shannon [ctb], Lori Shepherd [ctb],
Bioconductor Package Maintainer [cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/AnnotationHubData
git_branch: devel
git_last_commit: 92d37c8
git_last_commit_date: 2025-03-20
Date/Publication: 2025-03-21
source.ver: src/contrib/AnnotationHubData_1.37.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AnnotationHubData_1.37.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AnnotationHubData_1.37.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AnnotationHubData_1.37.2.tgz
vignettes:
vignettes/AnnotationHubData/inst/doc/IntroductionToAnnotationHubData.html
vignetteTitles: Introduction to AnnotationHubData
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: ExperimentHubData
importsMe: AHMassBank, AHEnsDbs, EuPathDB
suggestsMe: HubPub, EPICv2manifest, GenomicState, TENET.AnnotationHub,
homosapienDEE2CellScore, humanHippocampus2024, smokingMouse
dependencyCount: 122
Package: annotationTools
Version: 1.81.0
Imports: Biobase, stats
Suggests: BiocStyle
License: GPL
MD5sum: 4d1d3f136db70fbe1e2ac925ecb2efc7
NeedsCompilation: no
Title: Annotate microarrays and perform cross-species gene expression
analyses using flat file databases
Description: Functions to annotate microarrays, find orthologs, and
integrate heterogeneous gene expression profiles using
annotation and other molecular biology information available as
flat file database (plain text files).
biocViews: Microarray, Annotation
Author: Alexandre Kuhn <alexandre.m.kuhn@gmail.com>
Maintainer: Alexandre Kuhn <alexandre.m.kuhn@gmail.com>
git_url: https://git.bioconductor.org/packages/annotationTools
git_branch: devel
git_last_commit: ad67b95
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/annotationTools_1.81.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/annotationTools_1.81.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/annotationTools_1.81.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/annotationTools_1.81.0.tgz
vignettes: vignettes/annotationTools/inst/doc/annotationTools.pdf
vignetteTitles: annotationTools: Overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/annotationTools/inst/doc/annotationTools.R
importsMe: CoSIA
dependencyCount: 7
Package: annotatr
Version: 1.33.0
Depends: R (>= 3.5.0)
Imports: AnnotationDbi, AnnotationHub, dplyr, GenomicFeatures,
GenomicRanges, GenomeInfoDb (>= 1.10.3), ggplot2, IRanges,
methods, readr, regioneR, reshape2, rtracklayer, S4Vectors (>=
0.23.10), stats, utils
Suggests: BiocStyle, devtools, knitr, org.Dm.eg.db, org.Gg.eg.db,
org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, rmarkdown, roxygen2,
testthat, TxDb.Dmelanogaster.UCSC.dm3.ensGene,
TxDb.Dmelanogaster.UCSC.dm6.ensGene,
TxDb.Ggallus.UCSC.galGal5.refGene,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Hsapiens.UCSC.hg38.knownGene,
TxDb.Mmusculus.UCSC.mm9.knownGene,
TxDb.Mmusculus.UCSC.mm10.knownGene,
TxDb.Rnorvegicus.UCSC.rn4.ensGene,
TxDb.Rnorvegicus.UCSC.rn5.refGene,
TxDb.Rnorvegicus.UCSC.rn6.refGene
License: GPL-3
MD5sum: d41adc950c49f9d23983a7dc0d86c8e6
NeedsCompilation: no
Title: Annotation of Genomic Regions to Genomic Annotations
Description: Given a set of genomic sites/regions (e.g. ChIP-seq peaks,
CpGs, differentially methylated CpGs or regions, SNPs, etc.) it
is often of interest to investigate the intersecting genomic
annotations. Such annotations include those relating to gene
models (promoters, 5'UTRs, exons, introns, and 3'UTRs), CpGs
(CpG islands, CpG shores, CpG shelves), or regulatory sequences
such as enhancers. The annotatr package provides an easy way to
summarize and visualize the intersection of genomic
sites/regions with genomic annotations.
biocViews: Software, Annotation, GenomeAnnotation, FunctionalGenomics,
Visualization
Author: Raymond G. Cavalcante [aut, cre], Maureen A. Sartor [ths]
Maintainer: Raymond G. Cavalcante <rcavalca@umich.edu>
VignetteBuilder: knitr
BugReports: https://www.github.com/rcavalcante/annotatr/issues
git_url: https://git.bioconductor.org/packages/annotatr
git_branch: devel
git_last_commit: d81bd7b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/annotatr_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/annotatr_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/annotatr_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/annotatr_1.33.0.tgz
vignettes: vignettes/annotatr/inst/doc/annotatr-vignette.html
vignetteTitles: annotatr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/annotatr/inst/doc/annotatr-vignette.R
importsMe: dmrseq, methodical, scmeth, SOMNiBUS, ExpHunterSuite
suggestsMe: borealis, ramr
dependencyCount: 122
Package: anota
Version: 1.55.0
Depends: qvalue
Imports: multtest, qvalue
License: GPL-3
MD5sum: 19a80e33348fe2d21e93636d83e909e1
NeedsCompilation: no
Title: ANalysis Of Translational Activity (ANOTA).
Description: Genome wide studies of translational control is emerging
as a tool to study verious biological conditions. The output
from such analysis is both the mRNA level (e.g. cytosolic mRNA
level) and the levl of mRNA actively involved in translation
(the actively translating mRNA level) for each mRNA. The
standard analysis of such data strives towards identifying
differential translational between two or more sample classes -
i.e. differences in actively translated mRNA levels that are
independent of underlying differences in cytosolic mRNA levels.
This package allows for such analysis using partial variances
and the random variance model. As 10s of thousands of mRNAs are
analyzed in parallell the library performs a number of tests to
assure that the data set is suitable for such analysis.
biocViews: GeneExpression, DifferentialExpression, Microarray,
Sequencing
Author: Ola Larsson <ola.larsson@ki.se>, Nahum Sonenberg
<nahum.sonenberg@mcgill.ca>, Robert Nadon
<robert.nadon@mcgill.ca>
Maintainer: Ola Larsson <ola.larsson@ki.se>
git_url: https://git.bioconductor.org/packages/anota
git_branch: devel
git_last_commit: c705686
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/anota_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/anota_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/anota_1.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/anota_1.55.0.tgz
vignettes: vignettes/anota/inst/doc/anota.pdf
vignetteTitles: ANalysis Of Translational Activity (anota)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/anota/inst/doc/anota.R
dependsOnMe: tRanslatome
dependencyCount: 48
Package: anota2seq
Version: 1.29.0
Depends: R (>= 3.4.0), methods
Imports: multtest,qvalue,limma,DESeq2,edgeR,RColorBrewer, grDevices,
graphics, stats, utils, SummarizedExperiment
Suggests: BiocStyle,knitr
License: GPL-3
MD5sum: b139eaca9dca1bd1200684276ee51103
NeedsCompilation: no
Title: Generally applicable transcriptome-wide analysis of
translational efficiency using anota2seq
Description: anota2seq provides analysis of translational efficiency
and differential expression analysis for polysome-profiling and
ribosome-profiling studies (two or more sample classes)
quantified by RNA sequencing or DNA-microarray.
Polysome-profiling and ribosome-profiling typically generate
data for two RNA sources; translated mRNA and total mRNA.
Analysis of differential expression is used to estimate changes
within each RNA source (i.e. translated mRNA or total mRNA).
Analysis of translational efficiency aims to identify changes
in translation efficiency leading to altered protein levels
that are independent of total mRNA levels (i.e. changes in
translated mRNA that are independent of levels of total mRNA)
or buffering, a mechanism regulating translational efficiency
so that protein levels remain constant despite fluctuating
total mRNA levels (i.e. changes in total mRNA that are
independent of levels of translated mRNA). anota2seq applies
analysis of partial variance and the random variance model to
fulfill these tasks.
biocViews: ImmunoOncology, GeneExpression, DifferentialExpression,
Microarray,GenomeWideAssociation, BatchEffect, Normalization,
RNASeq, Sequencing, GeneRegulation, Regression
Author: Christian Oertlin <christian.oertlin@ki.se>, Julie Lorent
<julie.lorent@ki.se>, Ola Larsson <ola.larsson@ki.se>
Maintainer: Christian Oertlin <christian.oertlin@ki.se>, Ola Larsson
<ola.larsson@ki.se>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/anota2seq
git_branch: devel
git_last_commit: 9a745aa
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/anota2seq_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/anota2seq_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/anota2seq_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/anota2seq_1.29.0.tgz
vignettes: vignettes/anota2seq/inst/doc/anota2seq.pdf
vignetteTitles: Generally applicable transcriptome-wide analysis of
translational efficiency using anota2seq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/anota2seq/inst/doc/anota2seq.R
dependencyCount: 86
Package: antiProfiles
Version: 1.47.0
Depends: R (>= 3.0), matrixStats (>= 0.50.0), methods (>= 2.14), locfit
(>= 1.5)
Suggests: antiProfilesData, RColorBrewer
License: Artistic-2.0
MD5sum: 682ab8563cc1ede647a99c9bbb627000
NeedsCompilation: no
Title: Implementation of gene expression anti-profiles
Description: Implements gene expression anti-profiles as described in
Corrada Bravo et al., BMC Bioinformatics 2012, 13:272
doi:10.1186/1471-2105-13-272.
biocViews: GeneExpression,Classification
Author: Hector Corrada Bravo, Rafael A. Irizarry and Jeffrey T. Leek
Maintainer: Hector Corrada Bravo <hcorrada@gmail.com>
URL: https://github.com/HCBravoLab/antiProfiles
git_url: https://git.bioconductor.org/packages/antiProfiles
git_branch: devel
git_last_commit: d930a6c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/antiProfiles_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/antiProfiles_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/antiProfiles_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/antiProfiles_1.47.0.tgz
vignettes: vignettes/antiProfiles/inst/doc/antiProfiles.pdf
vignetteTitles: Introduction to antiProfiles
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/antiProfiles/inst/doc/antiProfiles.R
dependencyCount: 9
Package: AnVIL
Version: 1.19.9
Depends: R (>= 4.1.0), dplyr, AnVILBase
Imports: stats, utils, methods, futile.logger, jsonlite, httr,
rapiclient (>= 0.1.3), yaml, tibble, tidyselect, tidyr, rlang,
shiny, DT, miniUI, htmltools, BiocBaseUtils
Suggests: parallel, knitr, rmarkdown, testthat, withr, readr,
BiocStyle, devtools, AnVILAz, AnVILGCP, lifecycle
License: Artistic-2.0
MD5sum: c5d8b1aa7778fc7b00fb26c6de24f7f8
NeedsCompilation: no
Title: Bioconductor on the AnVIL compute environment
Description: The AnVIL is a cloud computing resource developed in part
by the National Human Genome Research Institute. The AnVIL
package provides end-user and developer functionality. For the
end-user, AnVIL provides fast binary package installation,
utitlities for working with Terra / AnVIL table and data
resources, and convenient functions for file movement to and
from Google cloud storage. For developers, AnVIL provides
programatic access to the Terra, Leonardo, Rawls, and Dockstore
RESTful programming interface, including helper functions to
transform JSON responses to formats more amenable to
manipulation in R.
biocViews: Infrastructure
Author: Marcel Ramos [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3242-0582>), Martin Morgan [aut]
(ORCID: <https://orcid.org/0000-0002-5874-8148>), Kayla
Interdonato [aut], Yubo Cheng [aut], Nitesh Turaga [aut], BJ
Stubbs [ctb], Vincent Carey [ctb], Sehyun Oh [ctb], Sweta
Gopaulakrishnan [ctb], Valerie Obenchain [ctb]
Maintainer: Marcel Ramos <marcel.ramos@sph.cuny.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/AnVIL
git_branch: devel
git_last_commit: 5aea435
git_last_commit_date: 2025-02-21
Date/Publication: 2025-02-21
source.ver: src/contrib/AnVIL_1.19.9.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AnVIL_1.19.9.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AnVIL_1.19.9.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AnVIL_1.19.9.tgz
vignettes: vignettes/AnVIL/inst/doc/BiocDockstore.html,
vignettes/AnVIL/inst/doc/Introduction.html,
vignettes/AnVIL/inst/doc/RunningWorkflow.html
vignetteTitles: Dockstore and Bioconductor for AnVIL, Introduction to
the AnVIL package, Running an AnVIL workflow within R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AnVIL/inst/doc/BiocDockstore.R,
vignettes/AnVIL/inst/doc/Introduction.R,
vignettes/AnVIL/inst/doc/RunningWorkflow.R
dependsOnMe: cBioPortalData
importsMe: AnVILPublish, AnVILWorkflow, bedbaser, terraTCGAdata
suggestsMe: AnVILBase, AnVILGCP
dependencyCount: 75
Package: AnVILAz
Version: 1.1.0
Imports: AnVILBase, BiocBaseUtils, curl, httr2, jsonlite, methods,
rjsoncons, tibble, utils
Suggests: BiocStyle, dplyr, knitr, readr, rmarkdown, tinytest
License: Artistic-2.0
MD5sum: 8bd1154025900ddfdc7b49d26ff3c6aa
NeedsCompilation: no
Title: R / Bioconductor Support for the AnVIL Azure Platform
Description: The AnVIL is a cloud computing resource developed in part
by the National Human Genome Research Institute. The AnVILAz
package supports end-users and developers using the AnVIL
platform in the Azure cloud. The package provides a
programmatic interface to AnVIL resources, including
workspaces, notebooks, tables, and workflows. The package also
provides utilities for managing resources, including copying
files to and from Azure Blob Storage, and creating shared
access signatures (SAS) for secure access to Azure resources.
biocViews: Software, Infrastructure, ThirdPartyClient
Author: Martin Morgan [aut, ctb] (ORCID:
<https://orcid.org/0000-0002-5874-8148>), Marcel Ramos [aut,
cre] (ORCID: <https://orcid.org/0000-0002-3242-0582>)
Maintainer: Marcel Ramos <marcel.ramos@sph.cuny.edu>
URL: https://github.com/Bioconductor/AnVILAz
SystemRequirements: az, azcopy
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/AnVILAz/issues
git_url: https://git.bioconductor.org/packages/AnVILAz
git_branch: devel
git_last_commit: 3d01105
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/AnVILAz_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AnVILAz_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AnVILAz_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AnVILAz_1.1.0.tgz
vignettes: vignettes/AnVILAz/inst/doc/AnVILAzWorkspaces.html,
vignettes/AnVILAz/inst/doc/IntroductionToAnVILAz.html
vignetteTitles: Working with Workspaces on AnVIL Azure, Introduction to
the AnVILAz package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AnVILAz/inst/doc/AnVILAzWorkspaces.R,
vignettes/AnVILAz/inst/doc/IntroductionToAnVILAz.R
suggestsMe: AnVIL, AnVILBase
dependencyCount: 34
Package: AnVILBase
Version: 1.1.0
Imports: httr, httr2, dplyr, jsonlite, methods, tibble
Suggests: AnVIL, AnVILAz, AnVILGCP, BiocStyle, knitr, rmarkdown,
testthat (>= 3.0.0), tinytest
License: Artistic-2.0
MD5sum: 9345b146bff0fdecb9dcb0c1ca4af3ea
NeedsCompilation: no
Title: Generic functions for interacting with the AnVIL ecosystem
Description: Provides generic functions for interacting with the AnVIL
ecosystem. Packages that use either GCP or Azure in AnVIL are
built on top of AnVILBase. Extension packages will provide
methods for interacting with other cloud providers.
biocViews: Software, Infrastructure
Author: Marcel Ramos [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3242-0582>), Martin Morgan [aut,
ctb] (ORCID: <https://orcid.org/0000-0002-5874-8148>), NIH
NHGRI U24HG004059 [fnd]
Maintainer: Marcel Ramos <marcel.ramos@sph.cuny.edu>
URL: https://github.com/Bioconductor/AnVILBase
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/AnVILBase/issues
git_url: https://git.bioconductor.org/packages/AnVILBase
git_branch: devel
git_last_commit: 95da536
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/AnVILBase_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AnVILBase_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AnVILBase_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AnVILBase_1.1.0.tgz
vignettes: vignettes/AnVILBase/inst/doc/AnVILBaseIntroduction.html
vignetteTitles: Introduction to AnVILBase
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AnVILBase/inst/doc/AnVILBaseIntroduction.R
dependsOnMe: AnVIL, AnVILWorkflow
importsMe: AnVILAz, AnVILGCP, UniProt.ws
suggestsMe: terraTCGAdata
dependencyCount: 30
Package: AnVILBilling
Version: 1.17.0
Depends: R (>= 4.1)
Imports: methods, DT, shiny, bigrquery, shinytoastr, DBI, magrittr,
dplyr, lubridate, plotly, ggplot2
Suggests: testthat, knitr, BiocStyle, rmarkdown
License: Artistic-2.0
MD5sum: 7061c6f5c9868455060600043cfb01dc
NeedsCompilation: no
Title: Provide functions to retrieve and report on usage expenses in
NHGRI AnVIL (anvilproject.org).
Description: AnVILBilling helps monitor AnVIL-related costs in R, using
queries to a BigQuery table to which costs are exported daily.
Functions are defined to help categorize tasks and associated
expenditures, and to visualize and explore expense profiles
over time. This package will be expanded to help users estimate
costs for specific task sets.
biocViews: Infrastructure, Software
Author: BJ Stubbs [aut], Vince Carey [aut, cre]
Maintainer: Vince Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
BugReports: https://github.com/vjcitn/AnVILBilling/issues
git_url: https://git.bioconductor.org/packages/AnVILBilling
git_branch: devel
git_last_commit: 67011bd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/AnVILBilling_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AnVILBilling_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AnVILBilling_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AnVILBilling_1.17.0.tgz
vignettes: vignettes/AnVILBilling/inst/doc/billing.html
vignetteTitles: Software for reckoning AnVIL/terra usage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AnVILBilling/inst/doc/billing.R
dependencyCount: 98
Package: AnVILGCP
Version: 1.1.3
Imports: AnVILBase, BiocBaseUtils, dplyr, httr, jsonlite, methods,
rlang, stats, tibble, tidyr, utils
Suggests: AnVIL, BiocStyle, knitr, rmarkdown, testthat, withr
License: Artistic-2.0
MD5sum: 3d31b19ebc05d11b80427fefda51d176
NeedsCompilation: no
Title: The GCP R Client for the AnVIL
Description: The package provides a set of functions to interact with
the Google Cloud Platform (GCP) services on the AnVIL platform.
The package is designed to work with the AnVIL package.
User-level interaction with this package should be minimal.
biocViews: Software, Infrastructure, ThirdPartyClient, DataImport
Author: Marcel Ramos [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3242-0582>), Nitesh Turaga [aut],
Martin Morgan [aut] (ORCID:
<https://orcid.org/0000-0002-5874-8148>)
Maintainer: Marcel Ramos <marcel.ramos@sph.cuny.edu>
URL: https://github.com/Bioconductor/AnVILGCP
SystemRequirements: gsutil, gcloud
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/AnVILGCP/issues
git_url: https://git.bioconductor.org/packages/AnVILGCP
git_branch: devel
git_last_commit: 72ea99c
git_last_commit_date: 2025-01-23
Date/Publication: 2025-01-24
source.ver: src/contrib/AnVILGCP_1.1.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AnVILGCP_1.1.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AnVILGCP_1.1.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AnVILGCP_1.1.3.tgz
vignettes: vignettes/AnVILGCP/inst/doc/AnVILGCPIntroduction.html
vignetteTitles: Working with AnVIL on GCP
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AnVILGCP/inst/doc/AnVILGCPIntroduction.R
dependsOnMe: AnVILWorkflow, terraTCGAdata
importsMe: AnVILPublish
suggestsMe: AnVIL, AnVILBase
dependencyCount: 38
Package: AnVILPublish
Version: 1.17.0
Imports: AnVIL, AnVILGCP, BiocBaseUtils, BiocManager, httr, jsonlite,
rmarkdown, yaml, readr, whisker, tools, utils, stats
Suggests: knitr, BiocStyle, testthat (>= 3.0.0)
License: Artistic-2.0
MD5sum: 647bc3017ad65d68da1f7d914070113e
NeedsCompilation: no
Title: Publish Packages and Other Resources to AnVIL Workspaces
Description: Use this package to create or update AnVIL workspaces from
resources such as R / Bioconductor packages. The metadata about
the package (e.g., select information from the package
DESCRIPTION file and from vignette YAML headings) are used to
populate the 'DASHBOARD'. Vignettes are translated to python
notebooks ready for evaluation in AnVIL.
biocViews: Infrastructure, Software
Author: Marcel Ramos [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3242-0582>), Martin Morgan [aut]
(ORCID: <https://orcid.org/0000-0002-5874-8148>), Kayla
Interdonato [aut], Vincent Carey [ctb] (ORCID:
<https://orcid.org/0000-0003-4046-0063>)
Maintainer: Marcel Ramos <marcel.ramos@sph.cuny.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/AnVILPublish
git_branch: devel
git_last_commit: ff5bfe9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/AnVILPublish_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AnVILPublish_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AnVILPublish_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AnVILPublish_1.17.0.tgz
vignettes: vignettes/AnVILPublish/inst/doc/AnVILPublishIntro.html
vignetteTitles: Publishing R / Bioconductor packages to AnVIL
Workspaces
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AnVILPublish/inst/doc/AnVILPublishIntro.R
dependencyCount: 88
Package: AnVILWorkflow
Version: 1.7.4
Depends: R (>= 4.4.0), AnVILGCP, AnVILBase, httr
Imports: AnVIL, dplyr, jsonlite, rlang, tibble, tidyr, utils, methods,
plyr, stringr
Suggests: knitr, BiocStyle
License: Artistic-2.0
MD5sum: 5952b20f605990f42721c2eb83b57e2e
NeedsCompilation: no
Title: Run workflows implemented in Terra/AnVIL workspace
Description: The AnVIL is a cloud computing resource developed in part
by the National Human Genome Research Institute. The main
cloud-based genomics platform deported by the AnVIL project is
Terra. The AnVILWorkflow package allows remote access to Terra
implemented workflows, enabling end-user to utilize Terra/
AnVIL provided resources - such as data, workflows, and
flexible/scalble computing resources - through the conventional
R functions.
biocViews: Infrastructure, Software
Author: Sehyun Oh [aut, cre] (ORCID:
<https://orcid.org/0000-0002-9490-3061>), Marcel Ramos [ctb]
(ORCID: <https://orcid.org/0000-0002-3242-0582>), Kai
Gravel-Pucillo [aut]
Maintainer: Sehyun Oh <shbrief@gmail.com>
URL: https://github.com/shbrief/AnVILWorkflow
VignetteBuilder: knitr
BugReports: https://github.com/shbrief/AnVILWorkflow/issues
git_url: https://git.bioconductor.org/packages/AnVILWorkflow
git_branch: devel
git_last_commit: ec32a08
git_last_commit_date: 2025-02-17
Date/Publication: 2025-02-17
source.ver: src/contrib/AnVILWorkflow_1.7.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AnVILWorkflow_1.7.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AnVILWorkflow_1.7.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AnVILWorkflow_1.7.4.tgz
vignettes: vignettes/AnVILWorkflow/inst/doc/salmon.html
vignetteTitles: Quickstart - RNAseq analysis using salmon
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AnVILWorkflow/inst/doc/salmon.R
dependencyCount: 78
Package: APAlyzer
Version: 1.21.0
Depends: R (>= 3.5.0)
Imports: GenomicRanges, GenomicFeatures, GenomicAlignments, DESeq2,
ggrepel, SummarizedExperiment, Rsubread, stats, ggplot2,
methods, rtracklayer, VariantAnnotation, dplyr, tidyr, repmis,
Rsamtools, HybridMTest
Suggests: knitr, rmarkdown, BiocStyle, org.Mm.eg.db, AnnotationDbi,
TBX20BamSubset, testthat, pasillaBamSubset
License: LGPL-3
MD5sum: 766fc387726984bb62e761772eb56146
NeedsCompilation: no
Title: A toolkit for APA analysis using RNA-seq data
Description: Perform 3'UTR APA, Intronic APA and gene expression
analysis using RNA-seq data.
biocViews: Sequencing, RNASeq, DifferentialExpression, GeneExpression,
GeneRegulation, Annotation, DataImport, Software
Author: Ruijia Wang [cre, aut] (ORCID:
<https://orcid.org/0000-0002-4211-5207>), Bin Tian [aut],
Wei-Chun Chen [aut]
Maintainer: Ruijia Wang <rjwang.bioinfo@gmail.com>
URL: https://github.com/RJWANGbioinfo/APAlyzer/
VignetteBuilder: knitr
BugReports: https://github.com/RJWANGbioinfo/APAlyzer/issues
git_url: https://git.bioconductor.org/packages/APAlyzer
git_branch: devel
git_last_commit: 7a59a45
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/APAlyzer_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/APAlyzer_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/APAlyzer_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/APAlyzer_1.21.0.tgz
vignettes: vignettes/APAlyzer/inst/doc/APAlyzer.html
vignetteTitles: APAlyzer: toolkit for RNA-seq APA analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/APAlyzer/inst/doc/APAlyzer.R
dependencyCount: 122
Package: apComplex
Version: 2.73.0
Depends: R (>= 2.10), graph, RBGL
Imports: Rgraphviz, stats, org.Sc.sgd.db
License: LGPL
MD5sum: 968189ac370600e04608e3128e110923
NeedsCompilation: no
Title: Estimate protein complex membership using AP-MS protein data
Description: Functions to estimate a bipartite graph of protein complex
membership using AP-MS data.
biocViews: ImmunoOncology, NetworkInference, MassSpectrometry,
GraphAndNetwork
Author: Denise Scholtens <dscholtens@northwestern.edu>
Maintainer: Denise Scholtens <dscholtens@northwestern.edu>
git_url: https://git.bioconductor.org/packages/apComplex
git_branch: devel
git_last_commit: f895ff9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/apComplex_2.73.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/apComplex_2.73.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/apComplex_2.73.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/apComplex_2.73.0.tgz
vignettes: vignettes/apComplex/inst/doc/apComplex.pdf
vignetteTitles: apComplex
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/apComplex/inst/doc/apComplex.R
dependencyCount: 51
Package: apeglm
Version: 1.29.0
Imports: emdbook, SummarizedExperiment, GenomicRanges, methods, stats,
utils, Rcpp
LinkingTo: Rcpp, RcppEigen, RcppNumerical
Suggests: DESeq2, airway, knitr, rmarkdown, testthat
License: GPL-2
Archs: x64
MD5sum: bfadc0ed7ad9917e3b012df2ba06bb31
NeedsCompilation: yes
Title: Approximate posterior estimation for GLM coefficients
Description: apeglm provides Bayesian shrinkage estimators for effect
sizes for a variety of GLM models, using approximation of the
posterior for individual coefficients.
biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression,
GeneExpression, Bayesian
Author: Anqi Zhu [aut, cre], Joshua Zitovsky [ctb], Joseph Ibrahim
[aut], Michael Love [aut]
Maintainer: Anqi Zhu <anqizhu@live.unc.edu>
VignetteBuilder: knitr, rmarkdown
git_url: https://git.bioconductor.org/packages/apeglm
git_branch: devel
git_last_commit: 9318bfd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/apeglm_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/apeglm_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/apeglm_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/apeglm_1.29.0.tgz
vignettes: vignettes/apeglm/inst/doc/apeglm.html
vignetteTitles: Effect size estimation with apeglm
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/apeglm/inst/doc/apeglm.R
dependsOnMe: rnaseqGene
importsMe: airpart, debrowser, DiffBind, ERSSA, phantasus, Rmmquant,
TEKRABber
suggestsMe: bambu, dar, DESeq2, extraChIPs, fishpond, terapadog,
NanoporeRNASeq, RNAseqQC
dependencyCount: 47
Package: APL
Version: 1.11.2
Depends: R (>= 4.4.0)
Imports: Matrix, RSpectra, ggrepel, ggplot2, viridisLite, plotly,
SeuratObject, SingleCellExperiment, magrittr,
SummarizedExperiment, topGO, methods, stats, utils,
org.Hs.eg.db, org.Mm.eg.db, rlang
Suggests: BiocStyle, knitr, rmarkdown, scRNAseq, scater, scran,
sparseMatrixStats, testthat
License: GPL (>= 3)
MD5sum: e3d117025e223f053b527febdfa3d508
NeedsCompilation: no
Title: Association Plots
Description: APL is a package developed for computation of Association
Plots (AP), a method for visualization and analysis of single
cell transcriptomics data. The main focus of APL is the
identification of genes characteristic for individual clusters
of cells from input data. The package performs correspondence
analysis (CA) and allows to identify cluster-specific genes
using Association Plots. Additionally, APL computes the
cluster-specificity scores for all genes which allows to rank
the genes by their specificity for a selected cell cluster of
interest.
biocViews: StatisticalMethod, DimensionReduction, SingleCell,
Sequencing, RNASeq, GeneExpression
Author: Clemens Kohl [cre, aut], Elzbieta Gralinska [aut], Martin
Vingron [aut]
Maintainer: Clemens Kohl <kohl.clemens@gmail.com>
URL: https://vingronlab.github.io/APL/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/APL
git_branch: devel
git_last_commit: e04cb82
git_last_commit_date: 2024-11-04
Date/Publication: 2024-11-05
source.ver: src/contrib/APL_1.11.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/APL_1.11.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/APL_1.11.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/APL_1.11.2.tgz
vignettes: vignettes/APL/inst/doc/APL.html
vignetteTitles: Analyzing data with APL
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/APL/inst/doc/APL.R
dependencyCount: 128
Package: appreci8R
Version: 1.25.0
Imports: shiny, shinyjs, DT, VariantAnnotation, BSgenome,
BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene,
Homo.sapiens, SNPlocs.Hsapiens.dbSNP144.GRCh37,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, Biostrings,
MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.hs37d5,
MafDb.gnomADex.r2.1.hs37d5, COSMIC.67, rentrez,
PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137, seqinr,
openxlsx, Rsamtools, stringr, stats, GenomicRanges, S4Vectors,
GenomicFeatures, IRanges, GenomicScores, SummarizedExperiment
Suggests: GO.db, org.Hs.eg.db, utils
License: LGPL-3
MD5sum: b134a7e941b76e387e22d21beaaf56ce
NeedsCompilation: no
Title: appreci8R: an R/Bioconductor package for filtering SNVs and
short indels with high sensitivity and high PPV
Description: The appreci8R is an R version of our appreci8-algorithm -
A Pipeline for PREcise variant Calling Integrating 8 tools.
Variant calling results of our standard appreci8-tools (GATK,
Platypus, VarScan, FreeBayes, LoFreq, SNVer, samtools and
VarDict), as well as up to 5 additional tools is combined,
evaluated and filtered.
biocViews: VariantDetection, GeneticVariability, SNP,
VariantAnnotation, Sequencing,
Author: Sarah Sandmann
Maintainer: Sarah Sandmann <sarah.sandmann@uni-muenster.de>
git_url: https://git.bioconductor.org/packages/appreci8R
git_branch: devel
git_last_commit: 2946ad7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/appreci8R_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/appreci8R_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/appreci8R_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/appreci8R_1.25.0.tgz
vignettes: vignettes/appreci8R/inst/doc/appreci8R.pdf
vignetteTitles: Using appreci8R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/appreci8R/inst/doc/appreci8R.R
dependencyCount: 165
Package: aroma.light
Version: 3.37.0
Depends: R (>= 2.15.2)
Imports: stats, R.methodsS3 (>= 1.7.1), R.oo (>= 1.23.0), R.utils (>=
2.9.0), matrixStats (>= 0.55.0)
Suggests: princurve (>= 2.1.4)
License: GPL (>= 2)
MD5sum: 5b69addf90fa7b82878c0aa94cf00532
NeedsCompilation: no
Title: Light-Weight Methods for Normalization and Visualization of
Microarray Data using Only Basic R Data Types
Description: Methods for microarray analysis that take basic data types
such as matrices and lists of vectors. These methods can be
used standalone, be utilized in other packages, or be wrapped
up in higher-level classes.
biocViews: Infrastructure, Microarray, OneChannel, TwoChannel,
MultiChannel, Visualization, Preprocessing
Author: Henrik Bengtsson [aut, cre, cph], Pierre Neuvial [ctb], Aaron
Lun [ctb]
Maintainer: Henrik Bengtsson <henrikb@braju.com>
URL: https://github.com/HenrikBengtsson/aroma.light,
https://www.aroma-project.org
BugReports: https://github.com/HenrikBengtsson/aroma.light/issues
git_url: https://git.bioconductor.org/packages/aroma.light
git_branch: devel
git_last_commit: 8bc7bbb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/aroma.light_3.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/aroma.light_3.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/aroma.light_3.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/aroma.light_3.37.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
importsMe: EDASeq, scone, PSCBS
suggestsMe: TIN, aroma.affymetrix, aroma.cn, aroma.core
dependencyCount: 8
Package: ArrayExpress
Version: 1.67.1
Depends: R (>= 2.9.0), Biobase (>= 2.4.0)
Imports: oligo, limma, httr, utils, jsonlite, rlang, tools, methods
Suggests: affy
License: Artistic-2.0
MD5sum: a4fc1dfa13c8acd170fd90f5fdccae27
NeedsCompilation: no
Title: Access the ArrayExpress Collection at EMBL-EBI Biostudies and
build Bioconductor data structures: ExpressionSet, AffyBatch,
NChannelSet
Description: Access the ArrayExpress Collection at EMBL-EBI Biostudies
and build Bioconductor data structures: ExpressionSet,
AffyBatch, NChannelSet.
biocViews: Microarray, DataImport, OneChannel, TwoChannel
Author: Audrey Kauffmann, Ibrahim Emam, Michael Schubert, Jose Marugan
Maintainer: Jose Marugan <jcmarca@ebi.ac.uk>
git_url: https://git.bioconductor.org/packages/ArrayExpress
git_branch: devel
git_last_commit: 167ed06
git_last_commit_date: 2025-03-07
Date/Publication: 2025-03-10
source.ver: src/contrib/ArrayExpress_1.67.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ArrayExpress_1.67.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ArrayExpress_1.67.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ArrayExpress_1.67.1.tgz
vignettes: vignettes/ArrayExpress/inst/doc/ArrayExpress.pdf
vignetteTitles: ArrayExpress: Import and convert ArrayExpress data sets
into R object
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ArrayExpress/inst/doc/ArrayExpress.R
dependsOnMe: DrugVsDisease, maEndToEnd
suggestsMe: bapred
dependencyCount: 66
Package: arrayMvout
Version: 1.65.0
Depends: R (>= 2.6.0), tools, methods, utils, parody, Biobase, affy
Imports: mdqc, affyContam, lumi
Suggests: MAQCsubset, mvoutData, lumiBarnes, affyPLM, affydata,
hgu133atagcdf
License: Artistic-2.0
MD5sum: 876c688ac8b6e581baef58b25b8b3453
NeedsCompilation: no
Title: multivariate outlier detection for expression array QA
Description: This package supports the application of diverse quality
metrics to AffyBatch instances, summarizing these metrics via
PCA, and then performing parametric outlier detection on the
PCs to identify aberrant arrays with a fixed Type I error rate
biocViews: Infrastructure, Microarray, QualityControl
Author: Z. Gao, A. Asare, R. Wang, V. Carey
Maintainer: V. Carey <stvjc@channing.harvard.edu>
git_url: https://git.bioconductor.org/packages/arrayMvout
git_branch: devel
git_last_commit: 18d3025
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/arrayMvout_1.65.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/arrayMvout_1.65.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/arrayMvout_1.65.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/arrayMvout_1.65.0.tgz
vignettes: vignettes/arrayMvout/inst/doc/arrayMvout.pdf
vignetteTitles: arrayMvout -- multivariate outlier algorithm for
expression arrays
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/arrayMvout/inst/doc/arrayMvout.R
dependencyCount: 171
Package: arrayQuality
Version: 1.85.0
Depends: R (>= 2.2.0)
Imports: graphics, grDevices, grid, gridBase, hexbin, limma, marray,
methods, RColorBrewer, stats, utils
Suggests: mclust, MEEBOdata, HEEBOdata
License: LGPL
MD5sum: 6bce6f31b56b8c436237b9aee286ea92
NeedsCompilation: no
Title: Assessing array quality on spotted arrays
Description: Functions for performing print-run and array level quality
assessment.
biocViews: Microarray,TwoChannel,QualityControl,Visualization
Author: Agnes Paquet and Jean Yee Hwa Yang <yeehwa@stat.berkeley.edu>
Maintainer: Agnes Paquet <paquetagnes@yahoo.com>
URL: http://arrays.ucsf.edu/
git_url: https://git.bioconductor.org/packages/arrayQuality
git_branch: devel
git_last_commit: b1c3550
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/arrayQuality_1.85.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/arrayQuality_1.85.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/arrayQuality_1.85.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/arrayQuality_1.85.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 13
Package: arrayQualityMetrics
Version: 3.63.0
Imports: affy, affyPLM (>= 1.27.3), beadarray, Biobase, genefilter,
graphics, grDevices, grid, gridSVG (>= 1.4-3), Hmisc, hwriter,
lattice, latticeExtra, limma, methods, RColorBrewer, setRNG,
stats, utils, vsn (>= 3.23.3), XML, svglite
Suggests: ALLMLL, CCl4, BiocStyle, knitr
License: LGPL (>= 2)
MD5sum: 384ed988d0ed3372cdb59bfa5f3b3d70
NeedsCompilation: no
Title: Quality metrics report for microarray data sets
Description: This package generates microarray quality metrics reports
for data in Bioconductor microarray data containers
(ExpressionSet, NChannelSet, AffyBatch). One and two color
array platforms are supported.
biocViews: Microarray, QualityControl, OneChannel, TwoChannel,
ReportWriting
Author: Audrey Kauffmann, Wolfgang Huber
Maintainer: Mike Smith <mike.smith@embl.de>
VignetteBuilder: knitr
BugReports: https://github.com/grimbough/arrayQualityMetrics/issues
git_url: https://git.bioconductor.org/packages/arrayQualityMetrics
git_branch: devel
git_last_commit: c608533
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/arrayQualityMetrics_3.63.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/arrayQualityMetrics_3.63.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/arrayQualityMetrics_3.63.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/arrayQualityMetrics_3.63.0.tgz
vignettes: vignettes/arrayQualityMetrics/inst/doc/aqm.pdf,
vignettes/arrayQualityMetrics/inst/doc/arrayQualityMetrics.pdf
vignetteTitles: Advanced topics: Customizing arrayQualityMetrics
reports and programmatic processing of the output,
Introduction: microarray quality assessment with
arrayQualityMetrics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/arrayQualityMetrics/inst/doc/aqm.R,
vignettes/arrayQualityMetrics/inst/doc/arrayQualityMetrics.R
dependsOnMe: maEndToEnd
dependencyCount: 135
Package: ARRmNormalization
Version: 1.47.0
Depends: R (>= 2.15.1), ARRmData
License: Artistic-2.0
MD5sum: 98f1a1b6dd113b1cbb1ca42b316334e6
NeedsCompilation: no
Title: Adaptive Robust Regression normalization for Illumina
methylation data
Description: Perform the Adaptive Robust Regression method (ARRm) for
the normalization of methylation data from the Illumina
Infinium HumanMethylation 450k assay.
biocViews: DNAMethylation, TwoChannel, Preprocessing, Microarray
Author: Jean-Philippe Fortin, Celia M.T. Greenwood, Aurelie Labbe.
Maintainer: Jean-Philippe Fortin <jfortin@jhsph.edu>
git_url: https://git.bioconductor.org/packages/ARRmNormalization
git_branch: devel
git_last_commit: 5281457
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ARRmNormalization_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ARRmNormalization_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ARRmNormalization_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ARRmNormalization_1.47.0.tgz
vignettes: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.pdf
vignetteTitles: ARRmNormalization
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.R
dependencyCount: 1
Package: artMS
Version: 1.25.0
Depends: R (>= 4.1.0)
Imports: AnnotationDbi, bit64, circlize, cluster, corrplot, data.table,
dplyr, getopt, ggdendro, ggplot2, gplots, ggrepel, graphics,
grDevices, grid, limma, MSstats, openxlsx, org.Hs.eg.db,
pheatmap, plotly, plyr, RColorBrewer, scales, seqinr, stats,
stringr, tidyr, UpSetR, utils, VennDiagram, yaml
Suggests: BiocStyle, ComplexHeatmap, factoextra, FactoMineR, gProfileR,
knitr, PerformanceAnalytics, org.Mm.eg.db, rmarkdown, testthat
License: GPL (>= 3) + file LICENSE
MD5sum: 17efac8bcded7804837f19c96fe3262c
NeedsCompilation: no
Title: Analytical R tools for Mass Spectrometry
Description: artMS provides a set of tools for the analysis of
proteomics label-free datasets. It takes as input the MaxQuant
search result output (evidence.txt file) and performs quality
control, relative quantification using MSstats, downstream
analysis and integration. artMS also provides a set of
functions to re-format and make it compatible with other
analytical tools, including, SAINTq, SAINTexpress, Phosfate,
and PHOTON. Check [http://artms.org](http://artms.org) for
details.
biocViews: Proteomics, DifferentialExpression, BiomedicalInformatics,
SystemsBiology, MassSpectrometry, Annotation, QualityControl,
GeneSetEnrichment, Clustering, Normalization, ImmunoOncology,
MultipleComparison
Author: David Jimenez-Morales [aut, cre] (ORCID:
<https://orcid.org/0000-0003-4356-6461>), Alexandre Rosa Campos
[aut, ctb] (ORCID: <https://orcid.org/0000-0003-3988-7764>),
John Von Dollen [aut], Nevan Krogan [aut] (ORCID:
<https://orcid.org/0000-0003-4902-337X>), Danielle Swaney [aut,
ctb] (ORCID: <https://orcid.org/0000-0001-6119-6084>)
Maintainer: David Jimenez-Morales <biodavidjm@gmail.com>
URL: http://artms.org
VignetteBuilder: knitr
BugReports: https://github.com/biodavidjm/artMS/issues
git_url: https://git.bioconductor.org/packages/artMS
git_branch: devel
git_last_commit: 10716cd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/artMS_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/artMS_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/artMS_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/artMS_1.25.0.tgz
vignettes: vignettes/artMS/inst/doc/artMS_vignette.html
vignetteTitles: Learn to use artMS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/artMS/inst/doc/artMS_vignette.R
dependencyCount: 148
Package: ASAFE
Version: 1.33.0
Depends: R (>= 3.2)
Suggests: knitr, testthat
License: Artistic-2.0
MD5sum: 78adca6885dd4645bd03fc5276c379b3
NeedsCompilation: no
Title: Ancestry Specific Allele Frequency Estimation
Description: Given admixed individuals' bi-allelic SNP genotypes and
ancestry pairs (where each ancestry can take one of three
values) for multiple SNPs, perform an EM algorithm to deal with
the fact that SNP genotypes are unphased with respect to
ancestry pairs, in order to estimate ancestry-specific allele
frequencies for all SNPs.
biocViews: SNP, GenomeWideAssociation, LinkageDisequilibrium,
BiomedicalInformatics, Genetics, ExperimentalDesign
Author: Qian Zhang <qszhang@uw.edu>
Maintainer: Qian Zhang <qszhang@uw.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ASAFE
git_branch: devel
git_last_commit: b28315b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ASAFE_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ASAFE_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ASAFE_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ASAFE_1.33.0.tgz
vignettes: vignettes/ASAFE/inst/doc/ASAFE.pdf
vignetteTitles: ASAFE (Ancestry Specific Allele Frequency Estimation)
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ASAFE/inst/doc/ASAFE.R
dependencyCount: 0
Package: ASEB
Version: 1.51.0
Depends: R (>= 2.8.0), methods
Imports: graphics, methods, utils
License: GPL (>= 3)
Archs: x64
MD5sum: 07aef7e57dfe97cfabf7ac0f3cde18d2
NeedsCompilation: yes
Title: Predict Acetylated Lysine Sites
Description: ASEB is an R package to predict lysine sites that can be
acetylated by a specific KAT-family.
biocViews: Proteomics
Author: Likun Wang <wanglk@hsc.pku.edu.cn> and Tingting Li
<litt@hsc.pku.edu.cn>.
Maintainer: Likun Wang <wanglk@hsc.pku.edu.cn>
git_url: https://git.bioconductor.org/packages/ASEB
git_branch: devel
git_last_commit: 951d216
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ASEB_1.51.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ASEB_1.51.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ASEB_1.51.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ASEB_1.51.0.tgz
vignettes: vignettes/ASEB/inst/doc/ASEB.pdf
vignetteTitles: ASEB
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ASEB/inst/doc/ASEB.R
dependencyCount: 3
Package: ASGSCA
Version: 1.41.0
Imports: Matrix, MASS
Suggests: BiocStyle
License: GPL-3
MD5sum: a184d8d96778d7a748f28db22e7da39c
NeedsCompilation: no
Title: Association Studies for multiple SNPs and multiple traits using
Generalized Structured Equation Models
Description: The package provides tools to model and test the
association between multiple genotypes and multiple traits,
taking into account the prior biological knowledge. Genes, and
clinical pathways are incorporated in the model as latent
variables. The method is based on Generalized Structured
Component Analysis (GSCA).
biocViews: StructuralEquationModels
Author: Hela Romdhani, Stepan Grinek , Heungsun Hwang and Aurelie
Labbe.
Maintainer: Hela Romdhani <hela.romdhani@mcgill.ca>
git_url: https://git.bioconductor.org/packages/ASGSCA
git_branch: devel
git_last_commit: 3ac7ee1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ASGSCA_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ASGSCA_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ASGSCA_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ASGSCA_1.41.0.tgz
vignettes: vignettes/ASGSCA/inst/doc/ASGSCA.pdf
vignetteTitles: Association Studies using Generalized Structured
Equation Models.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ASGSCA/inst/doc/ASGSCA.R
dependencyCount: 9
Package: ASICS
Version: 2.23.0
Depends: R (>= 3.5)
Imports: BiocParallel, ggplot2, glmnet, grDevices, gridExtra, methods,
mvtnorm, PepsNMR, plyr, quadprog, ropls, stats,
SummarizedExperiment, utils, Matrix, zoo
Suggests: knitr, rmarkdown, BiocStyle, testthat, ASICSdata
License: GPL (>= 2)
MD5sum: 5fe9b743a1dc45048f6aa15074829dd6
NeedsCompilation: no
Title: Automatic Statistical Identification in Complex Spectra
Description: With a set of pure metabolite reference spectra, ASICS
quantifies concentration of metabolites in a complex spectrum.
The identification of metabolites is performed by fitting a
mixture model to the spectra of the library with a sparse
penalty. The method and its statistical properties are
described in Tardivel et al. (2017)
<doi:10.1007/s11306-017-1244-5>.
biocViews: Software, DataImport, Cheminformatics, Metabolomics
Author: Gaëlle Lefort [aut, cre], Rémi Servien [aut], Patrick Tardivel
[aut], Nathalie Vialaneix [aut]
Maintainer: Gaëlle Lefort <gaelle.lefort@inrae.fr>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ASICS
git_branch: devel
git_last_commit: 034bc20
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ASICS_2.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ASICS_2.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ASICS_2.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ASICS_2.23.0.tgz
vignettes: vignettes/ASICS/inst/doc/ASICS.html,
vignettes/ASICS/inst/doc/ASICSUsersGuide.html
vignetteTitles: ASICS, ASICS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ASICS/inst/doc/ASICS.R,
vignettes/ASICS/inst/doc/ASICSUsersGuide.R
suggestsMe: AlpsNMR
dependencyCount: 130
Package: ASpli
Version: 2.17.0
Depends: methods, grDevices, stats, utils, parallel, edgeR, limma,
AnnotationDbi
Imports: GenomicRanges, GenomicFeatures, BiocGenerics, IRanges,
GenomicAlignments, Gviz, S4Vectors, Rsamtools, BiocStyle,
igraph, htmltools, data.table, UpSetR, tidyr, DT, MASS, grid,
graphics, pbmcapply, txdbmaker
License: GPL
MD5sum: 157f0527f269762c874416e83220e86e
NeedsCompilation: no
Title: Analysis of Alternative Splicing Using RNA-Seq
Description: Integrative pipeline for the analysis of alternative
splicing using RNAseq.
biocViews: ImmunoOncology, GeneExpression, Transcription,
AlternativeSplicing, Coverage, DifferentialExpression,
DifferentialSplicing, TimeCourse, RNASeq, GenomeAnnotation,
Sequencing, Alignment
Author: Estefania Mancini, Andres Rabinovich, Javier Iserte, Marcelo
Yanovsky and Ariel Chernomoretz
Maintainer: Ariel Chernomoretz <algo107@gmail.com>
git_url: https://git.bioconductor.org/packages/ASpli
git_branch: devel
git_last_commit: 5b28a12
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ASpli_2.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ASpli_2.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ASpli_2.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ASpli_2.17.0.tgz
vignettes: vignettes/ASpli/inst/doc/ASpli.pdf
vignetteTitles: ASpli
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ASpli/inst/doc/ASpli.R
importsMe: saseR
dependencyCount: 173
Package: AssessORF
Version: 1.25.0
Depends: R (>= 3.5.0), DECIPHER (>= 2.10.0)
Imports: Biostrings, GenomicRanges, IRanges, graphics, grDevices,
methods, stats, utils
Suggests: AssessORFData, BiocStyle, knitr, rmarkdown, RSQLite (>= 1.1)
License: GPL-3
MD5sum: 039e462f05d0e7a327fb116bab8207f4
NeedsCompilation: no
Title: Assess Gene Predictions Using Proteomics and Evolutionary
Conservation
Description: In order to assess the quality of a set of predicted genes
for a genome, evidence must first be mapped to that genome.
Next, each gene must be categorized based on how strong the
evidence is for or against that gene. The AssessORF package
provides the functions and class structures necessary for
accomplishing those tasks, using proteomic hits and
evolutionarily conserved start codons as the forms of evidence.
biocViews: ComparativeGenomics, GenePrediction, GenomeAnnotation,
Genetics, Proteomics, QualityControl, Visualization
Author: Deepank Korandla [aut, cre], Erik Wright [aut]
Maintainer: Deepank Korandla <dkorandl@alumni.cmu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/AssessORF
git_branch: devel
git_last_commit: 504f577
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/AssessORF_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AssessORF_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AssessORF_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AssessORF_1.25.0.tgz
vignettes: vignettes/AssessORF/inst/doc/UsingAssessORF.pdf
vignetteTitles: Using AssessORF
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AssessORF/inst/doc/UsingAssessORF.R
suggestsMe: AssessORFData
dependencyCount: 28
Package: ASSET
Version: 2.25.0
Depends: R (>= 3.5.0), stats, graphics
Imports: MASS, msm, rmeta
Suggests: RUnit, BiocGenerics, knitr
License: GPL-2 + file LICENSE
MD5sum: 124b4fd01505ebdb16c9ac72a63bf7d8
NeedsCompilation: no
Title: An R package for subset-based association analysis of
heterogeneous traits and subtypes
Description: An R package for subset-based analysis of heterogeneous
traits and disease subtypes. The package allows the user to
search through all possible subsets of z-scores to identify the
subset of traits giving the best meta-analyzed z-score.
Further, it returns a p-value adjusting for the
multiple-testing involved in the search. It also allows for
searching for the best combination of disease subtypes
associated with each variant.
biocViews: StatisticalMethod, SNP, GenomeWideAssociation,
MultipleComparison
Author: Samsiddhi Bhattacharjee [aut, cre], Guanghao Qi [aut], Nilanjan
Chatterjee [aut], William Wheeler [aut]
Maintainer: Samsiddhi Bhattacharjee <sb1@nibmg.ac.in>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ASSET
git_branch: devel
git_last_commit: 081f9f7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ASSET_2.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ASSET_2.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ASSET_2.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ASSET_2.25.0.tgz
vignettes: vignettes/ASSET/inst/doc/vignette.pdf
vignetteTitles: ASSET Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ASSET/inst/doc/vignette.R
dependsOnMe: REBET
dependencyCount: 27
Package: ASSIGN
Version: 1.43.0
Depends: R (>= 3.4)
Imports: gplots, graphics, grDevices, msm, Rlab, stats, sva, utils,
ggplot2, yaml
Suggests: testthat, BiocStyle, lintr, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: ffef0fd0ccdeced68898a695c76e60b0
NeedsCompilation: no
Title: Adaptive Signature Selection and InteGratioN (ASSIGN)
Description: ASSIGN is a computational tool to evaluate the pathway
deregulation/activation status in individual patient samples.
ASSIGN employs a flexible Bayesian factor analysis approach
that adapts predetermined pathway signatures derived either
from knowledge-based literature or from perturbation
experiments to the cell-/tissue-specific pathway signatures.
The deregulation/activation level of each context-specific
pathway is quantified to a score, which represents the extent
to which a patient sample encompasses the pathway
deregulation/activation signature.
biocViews: Software, GeneExpression, Pathways, Bayesian
Author: Ying Shen, Andrea H. Bild, W. Evan Johnson, and Mumtehena
Rahman
Maintainer: Ying Shen <yshen3@bu.edu>, W. Evan Johnson <wej@bu.edu>,
David Jenkins <dfj@bu.edu>, Mumtehena Rahman
<moom.rahman@utah.edu>
URL: https://compbiomed.github.io/ASSIGN/
VignetteBuilder: knitr
BugReports: https://github.com/compbiomed/ASSIGN/issues
git_url: https://git.bioconductor.org/packages/ASSIGN
git_branch: devel
git_last_commit: e4797e8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ASSIGN_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ASSIGN_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ASSIGN_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ASSIGN_1.43.0.tgz
vignettes: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.html
vignetteTitles: Primer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.R
importsMe: TBSignatureProfiler
dependencyCount: 99
Package: assorthead
Version: 1.1.16
Suggests: knitr, rmarkdown, BiocStyle
License: MIT + file LICENSE
MD5sum: 990f132da6ab14a1813ab12679a4ff21
NeedsCompilation: no
Title: Assorted Header-Only C++ Libraries
Description: Vendors an assortment of useful header-only C++ libraries.
Bioconductor packages can use these libraries in their own C++
code by LinkingTo this package without introducing any
additional dependencies. The use of a central repository avoids
duplicate vendoring of libraries across multiple R packages,
and enables better coordination of version updates across
cohorts of interdependent C++ libraries.
biocViews: SingleCell, QualityControl, Normalization,
DataRepresentation, DataImport, DifferentialExpression,
Alignment
Author: Aaron Lun [cre, aut]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://github.com/LTLA/assorthead
VignetteBuilder: knitr
BugReports: https://github.com/LTLA/assorthead/issues
git_url: https://git.bioconductor.org/packages/assorthead
git_branch: devel
git_last_commit: d0c6146
git_last_commit_date: 2025-03-03
Date/Publication: 2025-03-06
source.ver: src/contrib/assorthead_1.1.16.tar.gz
win.binary.ver: bin/windows/contrib/4.5/assorthead_1.1.16.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/assorthead_1.1.16.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/assorthead_1.1.16.tgz
vignettes: vignettes/assorthead/inst/doc/userguide.html
vignetteTitles: User's Guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/assorthead/inst/doc/userguide.R
linksToMe: alabaster.base, beachmat, beachmat.hdf5, beachmat.tiledb,
BiocNeighbors, BiocSingular, bluster, bsseq, epiregulon,
glmGamPoi, scrapper, SingleR
dependencyCount: 0
Package: ASURAT
Version: 1.11.0
Depends: R (>= 4.0.0)
Imports: SingleCellExperiment, SummarizedExperiment, S4Vectors, Rcpp
(>= 1.0.7), cluster, utils, plot3D, ComplexHeatmap, circlize,
grid, grDevices, graphics
LinkingTo: Rcpp
Suggests: ggplot2, TENxPBMCData, dplyr, Rtsne, Seurat, AnnotationDbi,
BiocGenerics, stringr, org.Hs.eg.db, knitr, rmarkdown, testthat
(>= 3.0.0)
License: GPL-3 + file LICENSE
Archs: x64
MD5sum: 8dbf2451372b8c87c754210855ce6a16
NeedsCompilation: yes
Title: Functional annotation-driven unsupervised clustering for
single-cell data
Description: ASURAT is a software for single-cell data analysis. Using
ASURAT, one can simultaneously perform unsupervised clustering
and biological interpretation in terms of cell type, disease,
biological process, and signaling pathway activity. Inputting a
single-cell RNA-seq data and knowledge-based databases, such as
Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms
gene expression tables into original multivariate tables,
termed sign-by-sample matrices (SSMs).
biocViews: GeneExpression, SingleCell, Sequencing, Clustering,
GeneSignaling
Author: Keita Iida [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1076-830X>), Johannes Nicolaus
Wibisana [ctb]
Maintainer: Keita Iida <kiida@protein.osaka-u.ac.jp>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ASURAT
git_branch: devel
git_last_commit: 433833d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ASURAT_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ASURAT_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ASURAT_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ASURAT_1.11.0.tgz
vignettes: vignettes/ASURAT/inst/doc/ASURAT.html
vignetteTitles: ASURAT
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ASURAT/inst/doc/ASURAT.R
dependencyCount: 58
Package: ATACseqQC
Version: 1.31.1
Depends: R (>= 3.5.0), BiocGenerics, S4Vectors
Imports: BSgenome, Biostrings, ChIPpeakAnno, IRanges, GenomicRanges,
GenomicAlignments, GenomeInfoDb, GenomicScores, graphics, grid,
limma, Rsamtools (>= 1.31.2), randomForest, rtracklayer, stats,
motifStack, preseqR, utils, KernSmooth, edgeR, BiocParallel
Suggests: BiocStyle, knitr, BSgenome.Hsapiens.UCSC.hg19,
TxDb.Hsapiens.UCSC.hg19.knownGene, phastCons100way.UCSC.hg19,
MotifDb, trackViewer, testthat, rmarkdown
License: GPL (>= 2)
MD5sum: 7910131180b7cc2fde47b47fc66e2cf7
NeedsCompilation: no
Title: ATAC-seq Quality Control
Description: ATAC-seq, an assay for Transposase-Accessible Chromatin
using sequencing, is a rapid and sensitive method for chromatin
accessibility analysis. It was developed as an alternative
method to MNase-seq, FAIRE-seq and DNAse-seq. Comparing to the
other methods, ATAC-seq requires less amount of the biological
samples and time to process. In the process of analyzing
several ATAC-seq dataset produced in our labs, we learned some
of the unique aspects of the quality assessment for ATAC-seq
data.To help users to quickly assess whether their ATAC-seq
experiment is successful, we developed ATACseqQC package
partially following the guideline published in Nature Method
2013 (Greenleaf et al.), including diagnostic plot of fragment
size distribution, proportion of mitochondria reads, nucleosome
positioning pattern, and CTCF or other Transcript Factor
footprints.
biocViews: Sequencing, DNASeq, ATACSeq, GeneRegulation, QualityControl,
Coverage, NucleosomePositioning, ImmunoOncology
Author: Jianhong Ou, Haibo Liu, Feng Yan, Jun Yu, Michelle Kelliher,
Lucio Castilla, Nathan Lawson, Lihua Julie Zhu
Maintainer: Jianhong Ou <jou@morgridge.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ATACseqQC
git_branch: devel
git_last_commit: f740f70
git_last_commit_date: 2025-01-03
Date/Publication: 2025-01-03
source.ver: src/contrib/ATACseqQC_1.31.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ATACseqQC_1.31.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ATACseqQC_1.31.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ATACseqQC_1.31.1.tgz
vignettes: vignettes/ATACseqQC/inst/doc/ATACseqQC.html
vignetteTitles: ATACseqQC Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ATACseqQC/inst/doc/ATACseqQC.R
suggestsMe: ATACseqTFEA
dependencyCount: 174
Package: ATACseqTFEA
Version: 1.9.1
Depends: R (>= 4.2)
Imports: BiocGenerics, S4Vectors, IRanges, Matrix, GenomicRanges,
GenomicAlignments, GenomeInfoDb, SummarizedExperiment,
Rsamtools, motifmatchr, TFBSTools, stats, pracma, ggplot2,
ggrepel, dplyr, limma, methods, rtracklayer
Suggests: BSgenome.Drerio.UCSC.danRer10, knitr, testthat, ATACseqQC,
rmarkdown, BiocStyle
License: GPL-3
MD5sum: 2e4931b6d316f484fbaf9883557cc041
NeedsCompilation: no
Title: Transcription Factor Enrichment Analysis for ATAC-seq
Description: Assay for Transpose-Accessible Chromatin using sequencing
(ATAC-seq) is a technique to assess genome-wide chromatin
accessibility by probing open chromatin with hyperactive mutant
Tn5 Transposase that inserts sequencing adapters into open
regions of the genome. ATACseqTFEA is an improvement of the
current computational method that detects differential activity
of transcription factors (TFs). ATACseqTFEA not only uses the
difference of open region information, but also (or emphasizes)
the difference of TFs footprints (cutting sites or insertion
sites). ATACseqTFEA provides an easy, rigorous way to broadly
assess TF activity changes between two conditions.
biocViews: Sequencing, DNASeq, ATACSeq, MNaseSeq, GeneRegulation
Author: Jianhong Ou [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8652-2488>)
Maintainer: Jianhong Ou <jou@morgridge.org>
URL: https://github.com/jianhong/ATACseqTFEA
VignetteBuilder: knitr
BugReports: https://github.com/jianhong/ATACseqTFEA/issues
git_url: https://git.bioconductor.org/packages/ATACseqTFEA
git_branch: devel
git_last_commit: 9b544c8
git_last_commit_date: 2025-01-03
Date/Publication: 2025-01-03
source.ver: src/contrib/ATACseqTFEA_1.9.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ATACseqTFEA_1.9.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ATACseqTFEA_1.9.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ATACseqTFEA_1.9.1.tgz
vignettes: vignettes/ATACseqTFEA/inst/doc/ATACseqTFEA.html
vignetteTitles: ATACseqTFEA Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ATACseqTFEA/inst/doc/ATACseqTFEA.R
dependencyCount: 110
Package: atena
Version: 1.13.0
Depends: R (>= 4.3.0), SummarizedExperiment
Imports: methods, stats, Matrix, BiocGenerics, MatrixGenerics,
BiocParallel, S4Vectors, IRanges, GenomicFeatures,
GenomicRanges, GenomicAlignments, Rsamtools, GenomeInfoDb,
SQUAREM, sparseMatrixStats, AnnotationHub, matrixStats, cli
Suggests: covr, BiocStyle, knitr, rmarkdown, RUnit,
TxDb.Dmelanogaster.UCSC.dm6.ensGene, RColorBrewer
License: Artistic-2.0
MD5sum: 1fab4bf559cf56d156964892c504500a
NeedsCompilation: no
Title: Analysis of Transposable Elements
Description: Quantify expression of transposable elements (TEs) from
RNA-seq data through different methods, including ERVmap,
TEtranscripts and Telescope. A common interface is provided to
use each of these methods, which consists of building a
parameter object, calling the quantification function with this
object and getting a SummarizedExperiment object as output
container of the quantified expression profiles. The
implementation allows one to quantify TEs and gene transcripts
in an integrated manner.
biocViews: Transcription, Transcriptomics, RNASeq, Sequencing,
Preprocessing, Software, GeneExpression, Coverage,
DifferentialExpression, FunctionalGenomics
Author: Beatriz Calvo-Serra [aut], Robert Castelo [aut, cre]
Maintainer: Robert Castelo <robert.castelo@upf.edu>
URL: https://github.com/rcastelo/atena
VignetteBuilder: knitr
BugReports: https://github.com/rcastelo/atena/issues
git_url: https://git.bioconductor.org/packages/atena
git_branch: devel
git_last_commit: 0778c4e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/atena_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/atena_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/atena_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/atena_1.13.0.tgz
vignettes: vignettes/atena/inst/doc/atena.html
vignetteTitles: An introduction to the atena package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/atena/inst/doc/atena.R
dependencyCount: 99
Package: atSNP
Version: 1.23.0
Depends: R (>= 3.6)
Imports: BSgenome, BiocFileCache, BiocParallel, Rcpp, data.table,
ggplot2, grDevices, graphics, grid, motifStack, rappdirs,
stats, testthat, utils, lifecycle
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-2
Archs: x64
MD5sum: 6656fa02ff06add1a723bde24144336e
NeedsCompilation: yes
Title: Affinity test for identifying regulatory SNPs
Description: atSNP performs affinity tests of motif matches with the
SNP or the reference genomes and SNP-led changes in motif
matches.
biocViews: Software, ChIPSeq, GenomeAnnotation, MotifAnnotation,
Visualization
Author: Chandler Zuo [aut], Sunyoung Shin [aut, cre], Sunduz Keles
[aut]
Maintainer: Sunyoung Shin <sunyoung.shin@utdallas.edu>
URL: https://github.com/sunyoungshin/atSNP
VignetteBuilder: knitr
BugReports: https://github.com/sunyoungshin/atSNP/issues
git_url: https://git.bioconductor.org/packages/atSNP
git_branch: devel
git_last_commit: 38e28cc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/atSNP_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/atSNP_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/atSNP_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/atSNP_1.23.0.tgz
vignettes: vignettes/atSNP/inst/doc/atsnp-vignette.html
vignetteTitles: atsnp-vignette.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/atSNP/inst/doc/atsnp-vignette.R
dependencyCount: 145
Package: attract
Version: 1.59.0
Depends: R (>= 3.4.0), AnnotationDbi
Imports: Biobase, limma, cluster, GOstats, graphics, stats,
reactome.db, KEGGREST, org.Hs.eg.db, utils, methods
Suggests: illuminaHumanv1.db
License: LGPL (>= 2.0)
MD5sum: 264fdbcea205735ab3a1381afdb3454e
NeedsCompilation: no
Title: Methods to Find the Gene Expression Modules that Represent the
Drivers of Kauffman's Attractor Landscape
Description: This package contains the functions to find the gene
expression modules that represent the drivers of Kauffman's
attractor landscape. The modules are the core attractor
pathways that discriminate between different cell types of
groups of interest. Each pathway has a set of synexpression
groups, which show transcriptionally-coordinated changes in
gene expression.
biocViews: ImmunoOncology, KEGG, Reactome, GeneExpression, Pathways,
GeneSetEnrichment, Microarray, RNASeq
Author: Jessica Mar
Maintainer: Samuel Zimmerman <samuel.e.zimmerman@gmail.com>
git_url: https://git.bioconductor.org/packages/attract
git_branch: devel
git_last_commit: 33404ba
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/attract_1.59.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/attract_1.59.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/attract_1.59.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/attract_1.59.0.tgz
vignettes: vignettes/attract/inst/doc/attract.pdf
vignetteTitles: Tutorial on How to Use the Functions in the
\texttt{attract} Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/attract/inst/doc/attract.R
dependencyCount: 72
Package: AUCell
Version: 1.29.0
Imports: DelayedArray, DelayedMatrixStats, data.table, graphics,
grDevices, GSEABase, Matrix, methods, mixtools, R.utils, stats,
SummarizedExperiment, BiocGenerics, utils
Suggests: Biobase, BiocStyle, doSNOW, dynamicTreeCut, DT, GEOquery,
knitr, NMF, plyr, R2HTML, rmarkdown, reshape2, plotly, Rtsne,
testthat, zoo
Enhances: doMC, doRNG, doParallel, foreach
License: GPL-3
MD5sum: 103051bd69a9ca5c471c19bd390de490
NeedsCompilation: no
Title: AUCell: Analysis of 'gene set' activity in single-cell RNA-seq
data (e.g. identify cells with specific gene signatures)
Description: AUCell allows to identify cells with active gene sets
(e.g. signatures, gene modules...) in single-cell RNA-seq data.
AUCell uses the "Area Under the Curve" (AUC) to calculate
whether a critical subset of the input gene set is enriched
within the expressed genes for each cell. The distribution of
AUC scores across all the cells allows exploring the relative
expression of the signature. Since the scoring method is
ranking-based, AUCell is independent of the gene expression
units and the normalization procedure. In addition, since the
cells are evaluated individually, it can easily be applied to
bigger datasets, subsetting the expression matrix if needed.
biocViews: SingleCell, GeneSetEnrichment, Transcriptomics,
Transcription, GeneExpression, WorkflowStep, Normalization
Author: Sara Aibar, Stein Aerts. Laboratory of Computational Biology.
VIB-KU Leuven Center for Brain & Disease Research. Leuven,
Belgium.
Maintainer: Gert Hulselmans <Gert.Hulselmans@kuleuven.be>
URL: http://scenic.aertslab.org
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/AUCell
git_branch: devel
git_last_commit: 1ef38fc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/AUCell_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AUCell_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AUCell_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AUCell_1.29.0.tgz
vignettes: vignettes/AUCell/inst/doc/AUCell.html
vignetteTitles: AUCell: Identifying cells with active gene sets
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AUCell/inst/doc/AUCell.R
dependsOnMe: OSCA.basic
importsMe: epiregulon, escape, RcisTarget, scFeatures
suggestsMe: decoupleR, scDiagnostics, SCpubr
dependencyCount: 120
Package: autonomics
Version: 1.15.143
Depends: R (>= 4.0)
Imports: abind, BiocFileCache, BiocGenerics, bit64, cluster,
codingMatrices, colorspace, data.table, dplyr, edgeR, ggforce,
ggplot2, ggrepel, graphics, grDevices, grid, gridExtra, limma,
magrittr, matrixStats, methods, MultiAssayExperiment, parallel,
RColorBrewer, rlang, R.utils, readxl, S4Vectors, scales, stats,
stringi, SummarizedExperiment, survival, tidyr, tidyselect,
tools, utils, vsn
Suggests: affy, AnnotationDbi, AnnotationHub, apcluster, Biobase,
BiocManager, BiocStyle, Biostrings, coin, diagram, DBI, e1071,
ensembldb, GenomicDataCommons, GenomicRanges, GEOquery, ggtext,
hgu95av2.db, ICSNP, jsonlite, knitr, lme4, lmerTest, MASS,
patchwork, mixOmics, mpm, nlme, OlinkAnalyze, org.Hs.eg.db,
org.Mm.eg.db, pcaMethods, pheatmap, progeny, propagate, RCurl,
RSQLite, remotes, rmarkdown, ropls, Rsubread, readODS,
rtracklayer, statmod, testthat, UniProt.ws, writexl, XML
License: GPL-3
MD5sum: 5a5233f1bd32950f298847642a97c08b
NeedsCompilation: no
Title: Unified Statistical Modeling of Omics Data
Description: This package unifies access to Statistal Modeling of Omics
Data. Across linear modeling engines (lm, lme, lmer, limma, and
wilcoxon). Across coding systems (treatment, difference,
deviation, etc). Across model formulae (with/without intercept,
random effect, interaction or nesting). Across omics platforms
(microarray, rnaseq, msproteomics, affinity proteomics,
metabolomics). Across projection methods (pca, pls, sma, lda,
spls, opls). Across clustering methods (hclust, pam, cmeans).
It provides a fast enrichment analysis implementation. And an
intuitive contrastogram visualisation to summarize contrast
effects in complex designs.
biocViews: Software, DataImport, Preprocessing, DimensionReduction,
PrincipalComponent, Regression, DifferentialExpression,
GeneSetEnrichment, Transcriptomics, Transcription,
GeneExpression, RNASeq, Microarray, Proteomics, Metabolomics,
MassSpectrometry,
Author: Aditya Bhagwat [aut, cre], Richard Cotton [ctb], Shahina Hayat
[ctb], Laure Cougnaud [ctb], Witold Szymanski [ctb], Vanessa
Beutgen [ctb], Willem Ligtenberg [ctb], Hinrich Goehlmann
[ctb], Karsten Suhre [ctb], Johannes Graumann [aut, sad]
Maintainer: Aditya Bhagwat <aditya.bhagwat@uni-marburg.de>
VignetteBuilder: knitr
BugReports:
https://gitlab.uni-marburg.de/fb20/ag-graumann/software/autonomics/issues
git_url: https://git.bioconductor.org/packages/autonomics
git_branch: devel
git_last_commit: d6a7703
git_last_commit_date: 2025-01-28
Date/Publication: 2025-01-28
source.ver: src/contrib/autonomics_1.15.143.tar.gz
win.binary.ver: bin/windows/contrib/4.5/autonomics_1.15.143.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/autonomics_1.15.143.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/autonomics_1.15.143.tgz
vignettes:
vignettes/autonomics/inst/doc/autonomics_platformaware_analysis.html
vignetteTitles: autonomics_platformaware_analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/autonomics/inst/doc/autonomics_platformaware_analysis.R
dependencyCount: 115
Package: AWFisher
Version: 1.21.0
Depends: R (>= 3.6)
Imports: edgeR, limma, stats
Suggests: knitr, tightClust
License: GPL-3
Archs: x64
MD5sum: 0602334dbc6589da5167f454a8b0c648
NeedsCompilation: yes
Title: An R package for fast computing for adaptively weighted fisher's
method
Description: Implementation of the adaptively weighted fisher's method,
including fast p-value computing, variability index, and
meta-pattern.
biocViews: StatisticalMethod, Software
Author: Zhiguang Huo
Maintainer: Zhiguang Huo <zhuo@ufl.edu>
VignetteBuilder: knitr
BugReports: https://github.com/Caleb-Huo/AWFisher/issues
git_url: https://git.bioconductor.org/packages/AWFisher
git_branch: devel
git_last_commit: 83d328b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/AWFisher_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/AWFisher_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/AWFisher_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/AWFisher_1.21.0.tgz
vignettes: vignettes/AWFisher/inst/doc/AWFisher.html
vignetteTitles: AWFisher
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/AWFisher/inst/doc/AWFisher.R
dependencyCount: 11
Package: awst
Version: 1.15.0
Imports: stats, methods, SummarizedExperiment
Suggests: airway, ggplot2, testthat, EDASeq, knitr, BiocStyle,
RefManageR, sessioninfo, rmarkdown
License: MIT + file LICENSE
MD5sum: 0d6a16c233e2cf9864f31c3541db9641
NeedsCompilation: no
Title: Asymmetric Within-Sample Transformation
Description: We propose an Asymmetric Within-Sample Transformation
(AWST) to regularize RNA-seq read counts and reduce the effect
of noise on the classification of samples. AWST comprises two
main steps: standardization and smoothing. These steps
transform gene expression data to reduce the noise of the lowly
expressed features, which suffer from background effects and
low signal-to-noise ratio, and the influence of the highly
expressed features, which may be the result of amplification
bias and other experimental artifacts.
biocViews: Normalization, GeneExpression, RNASeq, Software,
Transcriptomics, Sequencing, SingleCell
Author: Davide Risso [aut, cre, cph] (ORCID:
<https://orcid.org/0000-0001-8508-5012>), Stefano Pagnotta
[aut, cph] (ORCID: <https://orcid.org/0000-0002-8298-9777>)
Maintainer: Davide Risso <risso.davide@gmail.com>
URL: https://github.com/drisso/awst
VignetteBuilder: knitr
BugReports: https://github.com/drisso/awst/issues
git_url: https://git.bioconductor.org/packages/awst
git_branch: devel
git_last_commit: d18c8ed
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/awst_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/awst_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/awst_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/awst_1.15.0.tgz
vignettes: vignettes/awst/inst/doc/awst_intro.html
vignetteTitles: Introduction to awst
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/awst/inst/doc/awst_intro.R
dependencyCount: 36
Package: BaalChIP
Version: 1.33.0
Depends: R (>= 3.3.1), GenomicRanges, IRanges, Rsamtools,
Imports: GenomicAlignments, GenomeInfoDb, doParallel, parallel, doBy,
reshape2, scales, coda, foreach, ggplot2, methods, utils,
graphics, stats
Suggests: RUnit, BiocGenerics, knitr, rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: b6079c678f35e00f366764f6d8bea167
NeedsCompilation: no
Title: BaalChIP: Bayesian analysis of allele-specific transcription
factor binding in cancer genomes
Description: The package offers functions to process multiple ChIP-seq
BAM files and detect allele-specific events. Computes allele
counts at individual variants (SNPs/SNVs), implements extensive
QC steps to remove problematic variants, and utilizes a
bayesian framework to identify statistically significant
allele- specific events. BaalChIP is able to account for copy
number differences between the two alleles, a known
phenotypical feature of cancer samples.
biocViews: Software, ChIPSeq, Bayesian, Sequencing
Author: Ines de Santiago, Wei Liu, Ke Yuan, Martin O'Reilly, Chandra SR
Chilamakuri, Bruce Ponder, Kerstin Meyer, Florian Markowetz
Maintainer: Ines de Santiago <inesdesantiago@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BaalChIP
git_branch: devel
git_last_commit: 6f46c69
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BaalChIP_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BaalChIP_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BaalChIP_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BaalChIP_1.33.0.tgz
vignettes: vignettes/BaalChIP/inst/doc/BaalChIP.html
vignetteTitles: Analyzing ChIP-seq and FAIRE-seq data with the BaalChIP
package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BaalChIP/inst/doc/BaalChIP.R
dependencyCount: 98
Package: bacon
Version: 1.35.0
Depends: R (>= 3.3), methods, stats, ggplot2, graphics, BiocParallel,
ellipse
Suggests: BiocStyle, knitr, rmarkdown, testthat, roxygen2
License: GPL (>= 2)
Archs: x64
MD5sum: 06f4df3cdb3d32f4e4208ac8e422e6cc
NeedsCompilation: yes
Title: Controlling bias and inflation in association studies using the
empirical null distribution
Description: Bacon can be used to remove inflation and bias often
observed in epigenome- and transcriptome-wide association
studies. To this end bacon constructs an empirical null
distribution using a Gibbs Sampling algorithm by fitting a
three-component normal mixture on z-scores.
biocViews: ImmunoOncology, StatisticalMethod, Bayesian, Regression,
GenomeWideAssociation, Transcriptomics, RNASeq,
MethylationArray, BatchEffect, MultipleComparison
Author: Maarten van Iterson [aut, cre], Erik van Zwet [ctb]
Maintainer: Maarten van Iterson <mviterson@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/bacon
git_branch: devel
git_last_commit: 22a0e4e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/bacon_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/bacon_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/bacon_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/bacon_1.35.0.tgz
vignettes: vignettes/bacon/inst/doc/bacon.html
vignetteTitles: Controlling bias and inflation in association studies
using the empirical null distribution
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bacon/inst/doc/bacon.R
dependencyCount: 46
Package: BADER
Version: 1.45.0
Suggests: pasilla (>= 0.2.10)
License: GPL-2
Archs: x64
MD5sum: 920547d8385d607bd44ab521b2194a7d
NeedsCompilation: yes
Title: Bayesian Analysis of Differential Expression in RNA Sequencing
Data
Description: For RNA sequencing count data, BADER fits a Bayesian
hierarchical model. The algorithm returns the posterior
probability of differential expression for each gene between
two groups A and B. The joint posterior distribution of the
variables in the model can be returned in the form of posterior
samples, which can be used for further down-stream analyses
such as gene set enrichment.
biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression,
Software, SAGE
Author: Andreas Neudecker, Matthias Katzfuss
Maintainer: Andreas Neudecker <a.neudecker@arcor.de>
git_url: https://git.bioconductor.org/packages/BADER
git_branch: devel
git_last_commit: 2798fff
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BADER_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BADER_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BADER_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BADER_1.45.0.tgz
vignettes: vignettes/BADER/inst/doc/BADER.pdf
vignetteTitles: Analysing RNA-Seq data with the "BADER" package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BADER/inst/doc/BADER.R
dependencyCount: 0
Package: BadRegionFinder
Version: 1.35.3
Imports: VariantAnnotation, Rsamtools, biomaRt, GenomicRanges,
S4Vectors, utils, stats, grDevices, graphics
Suggests: BSgenome.Hsapiens.UCSC.hg19
License: LGPL-3
MD5sum: 98b4c53ba3d24106baad2099b17e9ba2
NeedsCompilation: no
Title: BadRegionFinder: an R/Bioconductor package for identifying
regions with bad coverage
Description: BadRegionFinder is a package for identifying regions with
a bad, acceptable and good coverage in sequence alignment data
available as bam files. The whole genome may be considered as
well as a set of target regions. Various visual and textual
types of output are available.
biocViews: Coverage, Sequencing, Alignment, WholeGenome, Classification
Author: Sarah Sandmann
Maintainer: Sarah Sandmann <sarah.sandmann@uni-muenster.de>
git_url: https://git.bioconductor.org/packages/BadRegionFinder
git_branch: devel
git_last_commit: 4fac2c3
git_last_commit_date: 2025-01-29
Date/Publication: 2025-01-30
source.ver: src/contrib/BadRegionFinder_1.35.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BadRegionFinder_1.35.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BadRegionFinder_1.35.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BadRegionFinder_1.35.3.tgz
vignettes: vignettes/BadRegionFinder/inst/doc/BadRegionFinder.pdf
vignetteTitles: Using BadRegionFinder
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BadRegionFinder/inst/doc/BadRegionFinder.R
dependencyCount: 102
Package: BAGS
Version: 2.47.0
Depends: R (>= 2.10), breastCancerVDX, Biobase
License: Artistic-2.0
Archs: x64
MD5sum: 0518901a34fd1ce02204c70610a24643
NeedsCompilation: yes
Title: A Bayesian Approach for Geneset Selection
Description: R package providing functions to perform geneset
significance analysis over simple cross-sectional data between
2 and 5 phenotypes of interest.
biocViews: Bayesian
Author: Alejandro Quiroz-Zarate
Maintainer: Alejandro Quiroz-Zarate <aquiroz@jimmy.harvard.edu>
git_url: https://git.bioconductor.org/packages/BAGS
git_branch: devel
git_last_commit: 323f3fe
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BAGS_2.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BAGS_2.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BAGS_2.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BAGS_2.47.0.tgz
vignettes: vignettes/BAGS/inst/doc/BAGS.pdf
vignetteTitles: BAGS: A Bayesian Approach for Geneset Selection.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BAGS/inst/doc/BAGS.R
dependencyCount: 8
Package: ballgown
Version: 2.39.0
Depends: R (>= 3.5.0), methods
Imports: GenomicRanges (>= 1.17.25), IRanges (>= 1.99.22), S4Vectors
(>= 0.9.39), RColorBrewer, splines, sva, limma, rtracklayer (>=
1.29.25), Biobase (>= 2.25.0), GenomeInfoDb
Suggests: testthat, knitr, markdown
License: Artistic-2.0
MD5sum: 5a7b7ddfe9719786ea7426b55d35004a
NeedsCompilation: no
Title: Flexible, isoform-level differential expression analysis
Description: Tools for statistical analysis of assembled
transcriptomes, including flexible differential expression
analysis, visualization of transcript structures, and matching
of assembled transcripts to annotation.
biocViews: ImmunoOncology, RNASeq, StatisticalMethod, Preprocessing,
DifferentialExpression
Author: Jack Fu [aut], Alyssa C. Frazee [aut, cre], Leonardo
Collado-Torres [aut], Andrew E. Jaffe [aut], Jeffrey T. Leek
[aut, ths]
Maintainer: Jack Fu <jmfu@jhsph.edu>
VignetteBuilder: knitr
BugReports: https://github.com/alyssafrazee/ballgown/issues
git_url: https://git.bioconductor.org/packages/ballgown
git_branch: devel
git_last_commit: 01a0bbb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ballgown_2.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ballgown_2.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ballgown_2.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ballgown_2.39.0.tgz
vignettes: vignettes/ballgown/inst/doc/ballgown.html
vignetteTitles: Flexible isoform-level differential expression analysis
with Ballgown
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ballgown/inst/doc/ballgown.R
dependsOnMe: VaSP
suggestsMe: variancePartition
dependencyCount: 89
Package: bambu
Version: 3.9.3
Depends: R(>= 4.1), SummarizedExperiment(>= 1.1.6), S4Vectors(>=
0.22.1), BSgenome, IRanges
Imports: BiocGenerics, BiocParallel, data.table, dplyr, tidyr,
GenomeInfoDb, GenomicAlignments, GenomicFeatures,
GenomicRanges, stats, Rsamtools, methods, Rcpp, xgboost
LinkingTo: Rcpp, RcppArmadillo
Suggests: AnnotationDbi, Biostrings, rmarkdown, BiocFileCache, ggplot2,
ComplexHeatmap, circlize, ggbio, gridExtra, knitr, testthat,
BSgenome.Hsapiens.NCBI.GRCh38,
TxDb.Hsapiens.UCSC.hg38.knownGene, ExperimentHub (>= 1.15.3),
DESeq2, NanoporeRNASeq, purrr, apeglm, utils, DEXSeq
Enhances: parallel
License: GPL-3 + file LICENSE
Archs: x64
MD5sum: 0cdc58450019a22a00324b0aafb92fd1
NeedsCompilation: yes
Title: Context-Aware Transcript Quantification from Long Read RNA-Seq
data
Description: bambu is a R package for multi-sample transcript discovery
and quantification using long read RNA-Seq data. You can use
bambu after read alignment to obtain expression estimates for
known and novel transcripts and genes. The output from bambu
can directly be used for visualisation and downstream analysis
such as differential gene expression or transcript usage.
biocViews: Alignment, Coverage, DifferentialExpression,
FeatureExtraction, GeneExpression, GenomeAnnotation,
GenomeAssembly, ImmunoOncology, LongRead, MultipleComparison,
Normalization, RNASeq, Regression, Sequencing, Software,
Transcription, Transcriptomics
Author: Ying Chen [cre, aut], Andre Sim [aut], Yuk Kei Wan [aut],
Jonathan Goeke [aut]
Maintainer: Ying Chen <chen_ying@gis.a-star.edu.sg>
URL: https://github.com/GoekeLab/bambu
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/bambu
git_branch: devel
git_last_commit: 29752b8
git_last_commit_date: 2025-02-11
Date/Publication: 2025-02-11
source.ver: src/contrib/bambu_3.9.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/bambu_3.9.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/bambu_3.9.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/bambu_3.9.3.tgz
vignettes: vignettes/bambu/inst/doc/bambu.html
vignetteTitles: bambu
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/bambu/inst/doc/bambu.R
importsMe: FLAMES
suggestsMe: NanoporeRNASeq
dependencyCount: 94
Package: bamsignals
Version: 1.39.0
Depends: R (>= 3.5.0)
Imports: methods, BiocGenerics, Rcpp (>= 0.10.6), IRanges,
GenomicRanges, zlibbioc
LinkingTo: Rcpp, Rhtslib (>= 1.13.1), zlibbioc
Suggests: testthat (>= 0.9), Rsamtools, BiocStyle, knitr, rmarkdown
License: GPL-2
Archs: x64
MD5sum: 21b24310dd7ac2802c46f29229a635e1
NeedsCompilation: yes
Title: Extract read count signals from bam files
Description: This package allows to efficiently obtain count vectors
from indexed bam files. It counts the number of reads in given
genomic ranges and it computes reads profiles and coverage
profiles. It also handles paired-end data.
biocViews: DataImport, Sequencing, Coverage, Alignment
Author: Alessandro Mammana [aut, cre], Johannes Helmuth [aut]
Maintainer: Johannes Helmuth <johannes.helmuth@laborberlin.com>
URL: https://github.com/lamortenera/bamsignals
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/lamortenera/bamsignals/issues
git_url: https://git.bioconductor.org/packages/bamsignals
git_branch: devel
git_last_commit: df2b237
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/bamsignals_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/bamsignals_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/bamsignals_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/bamsignals_1.39.0.tgz
vignettes: vignettes/bamsignals/inst/doc/bamsignals.html
vignetteTitles: Introduction to the bamsignals package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bamsignals/inst/doc/bamsignals.R
importsMe: AneuFinder, DNAfusion, epigraHMM, karyoploteR, normr,
segmenter, hoardeR
dependencyCount: 26
Package: BANDITS
Version: 1.23.0
Depends: R (>= 4.3.0)
Imports: Rcpp, doRNG, MASS, data.table, R.utils, doParallel, parallel,
foreach, methods, stats, graphics, ggplot2, DRIMSeq,
BiocParallel
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, testthat, tximport, BiocStyle,
GenomicFeatures, Biostrings
License: GPL (>= 3)
Archs: x64
MD5sum: 9d982541e644148f58eed67401af3168
NeedsCompilation: yes
Title: BANDITS: Bayesian ANalysis of DIfferenTial Splicing
Description: BANDITS is a Bayesian hierarchical model for detecting
differential splicing of genes and transcripts, via
differential transcript usage (DTU), between two or more
conditions. The method uses a Bayesian hierarchical framework,
which allows for sample specific proportions in a
Dirichlet-Multinomial model, and samples the allocation of
fragments to the transcripts. Parameters are inferred via
Markov chain Monte Carlo (MCMC) techniques and a DTU test is
performed via a multivariate Wald test on the posterior
densities for the average relative abundance of transcripts.
biocViews: DifferentialSplicing, AlternativeSplicing, Bayesian,
Genetics, RNASeq, Sequencing, DifferentialExpression,
GeneExpression, MultipleComparison, Software, Transcription,
StatisticalMethod, Visualization
Author: Simone Tiberi [aut, cre].
Maintainer: Simone Tiberi <simone.tiberi@uzh.ch>
URL: https://github.com/SimoneTiberi/BANDITS
SystemRequirements: C++17
VignetteBuilder: knitr
BugReports: https://github.com/SimoneTiberi/BANDITS/issues
git_url: https://git.bioconductor.org/packages/BANDITS
git_branch: devel
git_last_commit: 46f9bab
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BANDITS_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BANDITS_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BANDITS_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BANDITS_1.23.0.tgz
vignettes: vignettes/BANDITS/inst/doc/BANDITS.html
vignetteTitles: BANDITS: Bayesian ANalysis of DIfferenTial Splicing
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BANDITS/inst/doc/BANDITS.R
importsMe: DifferentialRegulation
dependencyCount: 84
Package: bandle
Version: 1.11.0
Depends: R (>= 4.1), S4Vectors, Biobase, MSnbase, pRoloc
Imports: Rcpp (>= 1.0.4.6), pRolocdata, lbfgs, ggplot2, dplyr, plyr,
knitr, methods, BiocParallel, robustbase, BiocStyle,
ggalluvial, ggrepel, tidyr, circlize, graphics, stats, utils,
grDevices, rlang, RColorBrewer, gtools, gridExtra, coda (>=
0.19-4)
LinkingTo: Rcpp, RcppArmadillo, BH
Suggests: testthat, interp, fields, pheatmap, viridis, rmarkdown,
spelling
License: Artistic-2.0
MD5sum: 5440ced877a2fce71e7e80523fc6be15
NeedsCompilation: yes
Title: An R package for the Bayesian analysis of differential
subcellular localisation experiments
Description: The Bandle package enables the analysis and visualisation
of differential localisation experiments using
mass-spectrometry data. Experimental methods supported include
dynamic LOPIT-DC, hyperLOPIT, Dynamic Organellar Maps, Dynamic
PCP. It provides Bioconductor infrastructure to analyse these
data.
biocViews: Bayesian, Classification, Clustering, ImmunoOncology,
QualityControl,DataImport, Proteomics, MassSpectrometry
Author: Oliver M. Crook [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5669-8506>), Lisa Breckels [aut]
(ORCID: <https://orcid.org/0000-0001-8918-7171>)
Maintainer: Oliver M. Crook <oliver.crook@stats.ox.ac.uk>
URL: http://github.com/ococrook/bandle
VignetteBuilder: knitr
BugReports: https://github.com/ococrook/bandle/issues
git_url: https://git.bioconductor.org/packages/bandle
git_branch: devel
git_last_commit: ca58277
git_last_commit_date: 2024-11-01
Date/Publication: 2024-11-05
source.ver: src/contrib/bandle_1.11.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/bandle_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/bandle_1.11.0.tgz
vignettes: vignettes/bandle/inst/doc/v01-getting-started.html,
vignettes/bandle/inst/doc/v02-workflow.html
vignetteTitles: Analysing differential localisation experiments with
BANDLE: Vignette 1, Analysing differential localisation
experiments with BANDLE: Vignette 2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bandle/inst/doc/v01-getting-started.R,
vignettes/bandle/inst/doc/v02-workflow.R
dependencyCount: 238
Package: Banksy
Version: 1.3.0
Depends: R (>= 4.4.0)
Imports: aricode, data.table, dbscan, SpatialExperiment,
SingleCellExperiment, SummarizedExperiment, S4Vectors, stats,
matrixStats, mclust, igraph, irlba, leidenAlg (>= 1.1.0),
utils, uwot, RcppHungarian
Suggests: knitr, rmarkdown, pals, scuttle, scater, scran, cowplot,
ggplot2, testthat (>= 3.0.0), harmony, Seurat, ExperimentHub,
spatialLIBD, BiocStyle
License: file LICENSE
MD5sum: 70aad5f8c35fa430d4207cca69e4a028
NeedsCompilation: no
Title: Spatial transcriptomic clustering
Description: Banksy is an R package that incorporates spatial
information to cluster cells in a feature space (e.g. gene
expression). To incorporate spatial information, BANKSY
computes the mean neighborhood expression and azimuthal Gabor
filters that capture gene expression gradients. These features
are combined with the cell's own expression to embed cells in a
neighbor-augmented product space which can then be clustered,
allowing for accurate and spatially-aware cell typing and
tissue domain segmentation.
biocViews: Clustering, Spatial, SingleCell, GeneExpression,
DimensionReduction
Author: Vipul Singhal [aut], Joseph Lee [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4983-4714>)
Maintainer: Joseph Lee <joseph.lee@u.nus.edu>
URL: https://github.com/prabhakarlab/Banksy
VignetteBuilder: knitr
BugReports: https://github.com/prabhakarlab/Banksy/issues
git_url: https://git.bioconductor.org/packages/Banksy
git_branch: devel
git_last_commit: abf3265
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-01
source.ver: src/contrib/Banksy_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Banksy_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Banksy_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Banksy_1.3.0.tgz
vignettes: vignettes/Banksy/inst/doc/batch-correction.html,
vignettes/Banksy/inst/doc/domain-segment.html,
vignettes/Banksy/inst/doc/multi-sample.html,
vignettes/Banksy/inst/doc/parameter-selection.html
vignetteTitles: Spatial data integration with Harmony (10x Visium Human
DLPFC), Domain segmentation (STARmap PLUS mouse brain),
Multi-sample analysis (10x Visium Human DLPFC), Parameter
selection (VeraFISH Mouse Hippocampus)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Banksy/inst/doc/batch-correction.R,
vignettes/Banksy/inst/doc/domain-segment.R,
vignettes/Banksy/inst/doc/multi-sample.R,
vignettes/Banksy/inst/doc/parameter-selection.R
dependencyCount: 110
Package: banocc
Version: 1.31.0
Depends: R (>= 3.5.1), rstan (>= 2.17.4)
Imports: coda (>= 0.18.1), mvtnorm, stringr
Suggests: knitr, rmarkdown, methods, testthat, BiocStyle
License: MIT + file LICENSE
MD5sum: 38a45c0d9b5eb383145f94a89f33cf7b
NeedsCompilation: no
Title: Bayesian ANalysis Of Compositional Covariance
Description: BAnOCC is a package designed for compositional data, where
each sample sums to one. It infers the approximate covariance
of the unconstrained data using a Bayesian model coded with
`rstan`. It provides as output the `stanfit` object as well as
posterior median and credible interval estimates for each
correlation element.
biocViews: ImmunoOncology, Metagenomics, Software, Bayesian
Author: Emma Schwager [aut, cre], Curtis Huttenhower [aut]
Maintainer: George Weingart <george.weingart@gmail.com>, Curtis
Huttenhower <chuttenh@hsph.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/banocc
git_branch: devel
git_last_commit: eef3ada
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/banocc_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/banocc_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/banocc_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/banocc_1.31.0.tgz
vignettes: vignettes/banocc/inst/doc/banocc-vignette.html
vignetteTitles: BAnOCC (Bayesian Analysis of Compositional Covariance)
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/banocc/inst/doc/banocc-vignette.R
dependencyCount: 66
Package: barcodetrackR
Version: 1.15.0
Depends: R (>= 4.1)
Imports: cowplot, circlize, dplyr, ggplot2, ggdendro, ggridges,
graphics, grDevices, magrittr, plyr, proxy, RColorBrewer,
rlang, scales, shiny, stats, SummarizedExperiment, S4Vectors,
tibble, tidyr, vegan, viridis, utils
Suggests: BiocStyle, knitr, magick, rmarkdown, testthat
License: file LICENSE
MD5sum: 30804183e779bef73e3f0ffa6dc834c3
NeedsCompilation: no
Title: Functions for Analyzing Cellular Barcoding Data
Description: barcodetrackR is an R package developed for the analysis
and visualization of clonal tracking data. Data required is
samples and tag abundances in matrix form. Usually from
cellular barcoding experiments, integration site retrieval
analyses, or similar technologies.
biocViews: Software, Visualization, Sequencing
Author: Diego Alexander Espinoza [aut, cre], Ryland Mortlock [aut]
Maintainer: Diego Alexander Espinoza
<diego.espinoza@pennmedicine.upenn.edu>
URL: https://github.com/dunbarlabNIH/barcodetrackR
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/barcodetrackR
git_branch: devel
git_last_commit: c0c5333
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/barcodetrackR_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/barcodetrackR_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/barcodetrackR_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/barcodetrackR_1.15.0.tgz
vignettes:
vignettes/barcodetrackR/inst/doc/Introduction_to_barcodetrackR.html
vignetteTitles: barcodetrackR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles:
vignettes/barcodetrackR/inst/doc/Introduction_to_barcodetrackR.R
dependencyCount: 102
Package: basecallQC
Version: 1.31.0
Depends: R (>= 3.4), stats, utils, methods, rmarkdown, knitr,
prettydoc, yaml
Imports: ggplot2, stringr, XML, raster, dplyr, data.table, tidyr,
magrittr, DT, lazyeval, ShortRead
Suggests: testthat, BiocStyle
License: GPL (>= 3)
MD5sum: 9f94546a5874cd0dd73ea7e27e4a05e8
NeedsCompilation: no
Title: Working with Illumina Basecalling and Demultiplexing input and
output files
Description: The basecallQC package provides tools to work with
Illumina bcl2Fastq (versions >= 2.1.7) software.Prior to
basecalling and demultiplexing using the bcl2Fastq software,
basecallQC functions allow the user to update Illumina sample
sheets from versions <= 1.8.9 to >= 2.1.7 standards, clean
sample sheets of common problems such as invalid sample names
and IDs, create read and index basemasks and the bcl2Fastq
command. Following the generation of basecalled and
demultiplexed data, the basecallQC packages allows the user to
generate HTML tables, plots and a self contained report of
summary metrics from Illumina XML output files.
biocViews: Sequencing, Infrastructure, DataImport, QualityControl
Author: Thomas Carroll and Marian Dore
Maintainer: Thomas Carroll <tc.infomatics@gmail.com>
SystemRequirements: bcl2Fastq (versions >= 2.1.7)
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/basecallQC
git_branch: devel
git_last_commit: 0ca2dc6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/basecallQC_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/basecallQC_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/basecallQC_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/basecallQC_1.31.0.tgz
vignettes: vignettes/basecallQC/inst/doc/basecallQC.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/basecallQC/inst/doc/basecallQC.R
dependencyCount: 125
Package: BaseSpaceR
Version: 1.51.0
Depends: R (>= 2.15.0), RCurl, RJSONIO
Imports: methods
Suggests: RUnit, IRanges, Rsamtools
License: Apache License 2.0
MD5sum: 60a85ce955f7f16cd0ba3877739ec5f1
NeedsCompilation: no
Title: R SDK for BaseSpace RESTful API
Description: A rich R interface to Illumina's BaseSpace cloud computing
environment, enabling the fast development of data analysis and
visualisation tools.
biocViews: Infrastructure, DataRepresentation, ConnectTools, Software,
DataImport, HighThroughputSequencing, Sequencing, Genetics
Author: Adrian Alexa
Maintainer: Jared O'Connell <joconnell@illumina.com>
git_url: https://git.bioconductor.org/packages/BaseSpaceR
git_branch: devel
git_last_commit: 835c641
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BaseSpaceR_1.51.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BaseSpaceR_1.51.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BaseSpaceR_1.51.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BaseSpaceR_1.51.0.tgz
vignettes: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.pdf
vignetteTitles: BaseSpaceR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.R
dependencyCount: 4
Package: Basic4Cseq
Version: 1.43.0
Depends: R (>= 3.4), Biostrings, GenomicAlignments, caTools,
GenomicRanges, grDevices, graphics, stats, utils
Imports: methods, RCircos, BSgenome.Ecoli.NCBI.20080805
Suggests: BSgenome.Hsapiens.UCSC.hg19
License: LGPL-3
MD5sum: 02b070751ffdef4ac7ff5ab2275ce5ac
NeedsCompilation: no
Title: Basic4Cseq: an R/Bioconductor package for analyzing 4C-seq data
Description: Basic4Cseq is an R/Bioconductor package for basic
filtering, analysis and subsequent visualization of 4C-seq
data. Virtual fragment libraries can be created for any
BSGenome package, and filter functions for both reads and
fragments and basic quality controls are included. Fragment
data in the vicinity of the experiment's viewpoint can be
visualized as a coverage plot based on a running median
approach and a multi-scale contact profile.
biocViews: ImmunoOncology, Visualization, QualityControl, Sequencing,
Coverage, Alignment, RNASeq, SequenceMatching, DataImport
Author: Carolin Walter
Maintainer: Carolin Walter <carolin.walter@uni-muenster.de>
git_url: https://git.bioconductor.org/packages/Basic4Cseq
git_branch: devel
git_last_commit: 1f73086
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Basic4Cseq_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Basic4Cseq_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Basic4Cseq_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Basic4Cseq_1.43.0.tgz
vignettes: vignettes/Basic4Cseq/inst/doc/vignette.pdf
vignetteTitles: Basic4Cseq: an R/Bioconductor package for the analysis
of 4C-seq data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Basic4Cseq/inst/doc/vignette.R
dependencyCount: 62
Package: BASiCS
Version: 2.19.0
Depends: R (>= 4.2), SingleCellExperiment
Imports: Biobase, BiocGenerics, coda, cowplot, ggExtra, ggplot2,
graphics, grDevices, MASS, methods, Rcpp (>= 0.11.3),
S4Vectors, scran, scuttle, stats, stats4, SummarizedExperiment,
viridis, utils, Matrix (>= 1.5.0), matrixStats, assertthat,
reshape2, BiocParallel, posterior, hexbin
LinkingTo: Rcpp, RcppArmadillo
Suggests: BiocStyle, knitr, rmarkdown, testthat, scRNAseq, magick
License: GPL-3
Archs: x64
MD5sum: 47d03ece2932df001918e7b196663328
NeedsCompilation: yes
Title: Bayesian Analysis of Single-Cell Sequencing data
Description: Single-cell mRNA sequencing can uncover novel cell-to-cell
heterogeneity in gene expression levels in seemingly
homogeneous populations of cells. However, these experiments
are prone to high levels of technical noise, creating new
challenges for identifying genes that show genuine
heterogeneous expression within the population of cells under
study. BASiCS (Bayesian Analysis of Single-Cell Sequencing
data) is an integrated Bayesian hierarchical model to perform
statistical analyses of single-cell RNA sequencing datasets in
the context of supervised experiments (where the groups of
cells of interest are known a priori, e.g. experimental
conditions or cell types). BASiCS performs built-in data
normalisation (global scaling) and technical noise
quantification (based on spike-in genes). BASiCS provides an
intuitive detection criterion for highly (or lowly) variable
genes within a single group of cells. Additionally, BASiCS can
compare gene expression patterns between two or more
pre-specified groups of cells. Unlike traditional differential
expression tools, BASiCS quantifies changes in expression that
lie beyond comparisons of means, also allowing the study of
changes in cell-to-cell heterogeneity. The latter can be
quantified via a biological over-dispersion parameter that
measures the excess of variability that is observed with
respect to Poisson sampling noise, after normalisation and
technical noise removal. Due to the strong mean/over-dispersion
confounding that is typically observed for scRNA-seq datasets,
BASiCS also tests for changes in residual over-dispersion,
defined by residual values with respect to a global
mean/over-dispersion trend.
biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software,
GeneExpression, Transcriptomics, SingleCell,
DifferentialExpression, Bayesian, CellBiology, ImmunoOncology
Author: Catalina Vallejos [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3638-1960>), Nils Eling [aut],
Alan O'Callaghan [aut], Sylvia Richardson [ctb], John Marioni
[ctb]
Maintainer: Catalina Vallejos <catalina.vallejos@igmm.ed.ac.uk>
URL: https://github.com/catavallejos/BASiCS
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/catavallejos/BASiCS/issues
git_url: https://git.bioconductor.org/packages/BASiCS
git_branch: devel
git_last_commit: 055feb8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BASiCS_2.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BASiCS_2.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BASiCS_2.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BASiCS_2.19.0.tgz
vignettes: vignettes/BASiCS/inst/doc/BASiCS.html
vignetteTitles: Introduction to BASiCS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BASiCS/inst/doc/BASiCS.R
dependsOnMe: BASiCStan
suggestsMe: splatter
dependencyCount: 141
Package: BASiCStan
Version: 1.9.0
Depends: R (>= 4.2), BASiCS, rstan (>= 2.18.1)
Imports: methods, glmGamPoi, scran, scuttle, stats, utils,
SingleCellExperiment, SummarizedExperiment, Rcpp (>= 0.12.0),
RcppParallel (>= 5.0.1), rstantools (>= 2.1.1)
LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0),
RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>=
2.18.0)
Suggests: testthat (>= 3.0.0), knitr, rmarkdown
License: GPL-3
Archs: x64
MD5sum: 4bb22cfad535d8e851a31be1229b75d5
NeedsCompilation: yes
Title: Stan implementation of BASiCS
Description: Provides an interface to infer the parameters of BASiCS
using the variational inference (ADVI), Markov chain Monte
Carlo (NUTS), and maximum a posteriori (BFGS) inference engines
in the Stan programming language. BASiCS is a Bayesian
hierarchical model that uses an adaptive Metropolis within
Gibbs sampling scheme. Alternative inference methods provided
by Stan may be preferable in some situations, for example for
particularly large data or posterior distributions with
difficult geometries.
biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software,
GeneExpression, Transcriptomics, SingleCell,
DifferentialExpression, Bayesian, CellBiology
Author: Alan O'Callaghan [aut, cre], Catalina Vallejos [aut]
Maintainer: Alan O'Callaghan <alan.ocallaghan@outlook.com>
URL: https://github.com/Alanocallaghan/BASiCStan
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/Alanocallaghan/BASiCStan/issues
git_url: https://git.bioconductor.org/packages/BASiCStan
git_branch: devel
git_last_commit: a071873
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BASiCStan_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BASiCStan_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BASiCStan_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BASiCStan_1.9.0.tgz
vignettes: vignettes/BASiCStan/inst/doc/BASiCStan.html
vignetteTitles: An introduction to BASiCStan
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BASiCStan/inst/doc/BASiCStan.R
dependencyCount: 163
Package: BasicSTARRseq
Version: 1.35.0
Depends: GenomicRanges,GenomicAlignments
Imports: S4Vectors,methods,IRanges,GenomeInfoDb,stats
Suggests: knitr
License: LGPL-3
MD5sum: fd028ff1976d44806e2f5baadd9c3df7
NeedsCompilation: no
Title: Basic peak calling on STARR-seq data
Description: Basic peak calling on STARR-seq data based on a method
introduced in "Genome-Wide Quantitative Enhancer Activity Maps
Identified by STARR-seq" Arnold et al. Science. 2013 Mar
1;339(6123):1074-7. doi: 10.1126/science. 1232542. Epub 2013
Jan 17.
biocViews: PeakDetection, GeneRegulation, FunctionalPrediction,
FunctionalGenomics, Coverage
Author: Annika Buerger
Maintainer: Annika Buerger <annika.buerger@ukmuenster.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BasicSTARRseq
git_branch: devel
git_last_commit: 2f45ad6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BasicSTARRseq_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BasicSTARRseq_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BasicSTARRseq_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BasicSTARRseq_1.35.0.tgz
vignettes: vignettes/BasicSTARRseq/inst/doc/BasicSTARRseq.pdf
vignetteTitles: BasicSTARRseq.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BasicSTARRseq/inst/doc/BasicSTARRseq.R
dependencyCount: 51
Package: basilisk
Version: 1.19.3
Depends: reticulate
Imports: utils, methods, parallel, dir.expiry, basilisk.utils (>=
1.15.1)
Suggests: knitr, rmarkdown, BiocStyle, testthat, callr
License: GPL-3
MD5sum: 15da29e40c185aa0af859890418846bf
NeedsCompilation: no
Title: Freezing Python Dependencies Inside Bioconductor Packages
Description: Installs a self-contained conda instance that is managed
by the R/Bioconductor installation machinery. This aims to
provide a consistent Python environment that can be used
reliably by Bioconductor packages. Functions are also provided
to enable smooth interoperability of multiple Python
environments in a single R session.
biocViews: Infrastructure
Author: Aaron Lun [aut, cre, cph], Vince Carey [ctb]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/LTLA/basilisk/issues
git_url: https://git.bioconductor.org/packages/basilisk
git_branch: devel
git_last_commit: 2732c8b
git_last_commit_date: 2025-03-20
Date/Publication: 2025-03-21
source.ver: src/contrib/basilisk_1.19.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/basilisk_1.19.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/basilisk_1.19.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/basilisk_1.19.3.tgz
vignettes: vignettes/basilisk/inst/doc/motivation.html
vignetteTitles: Motivation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/basilisk/inst/doc/motivation.R
dependsOnMe: scviR
importsMe: BiocHail, BiocSklearn, cbpManager, cfTools, crisprScore,
densvis, DNAcycP2, ELViS, FLAMES, HiCool, MACSr, MOFA2,
ontoProc, orthos, Pirat, Rcwl, recountmethylation, ReUseData,
scifer, scPipe, SimBu, sketchR, snifter, spatialDE,
velociraptor, zellkonverter
suggestsMe: basilisk.utils, CuratedAtlasQueryR
dependencyCount: 23
Package: basilisk.utils
Version: 1.19.1
Imports: utils, methods, tools, dir.expiry
Suggests: reticulate, knitr, rmarkdown, BiocStyle, testthat, basilisk
License: GPL-3
MD5sum: 2a0b5f610e0487a78bb3cd97322e815d
NeedsCompilation: no
Title: Basilisk Installation Utilities
Description: Implements utilities for installation of the basilisk
package, primarily for creation of the underlying Conda
instance. This allows us to avoid re-writing the same R code in
both the configure script (for centrally administered R
installations) and in the lazy installation mechanism (for
distributed package binaries). It is highly unlikely that
developers - or, heaven forbid, end-users! - will need to
interact with this package directly; they should be using the
basilisk package instead.
biocViews: Infrastructure
Author: Aaron Lun [aut, cre, cph]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/basilisk.utils
git_branch: devel
git_last_commit: 8058e30
git_last_commit_date: 2025-02-11
Date/Publication: 2025-02-12
source.ver: src/contrib/basilisk.utils_1.19.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/basilisk.utils_1.19.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/basilisk.utils_1.19.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/basilisk.utils_1.19.1.tgz
vignettes: vignettes/basilisk.utils/inst/doc/purpose.html
vignetteTitles: _basilisk_ installation utilities
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/basilisk.utils/inst/doc/purpose.R
importsMe: basilisk, crisprScore, LimROTS, scifer
dependencyCount: 5
Package: batchelor
Version: 1.23.1
Depends: SingleCellExperiment
Imports: SummarizedExperiment, S4Vectors, BiocGenerics, Rcpp, stats,
methods, utils, igraph, BiocNeighbors, BiocSingular, Matrix,
SparseArray, DelayedArray (>= 0.31.5), DelayedMatrixStats,
BiocParallel, scuttle, ResidualMatrix, ScaledMatrix, beachmat
LinkingTo: Rcpp
Suggests: testthat, BiocStyle, knitr, rmarkdown, scran, scater,
bluster, scRNAseq
License: GPL-3
Archs: x64
MD5sum: 8cf959ac376973d8183067ae02b51a00
NeedsCompilation: yes
Title: Single-Cell Batch Correction Methods
Description: Implements a variety of methods for batch correction of
single-cell (RNA sequencing) data. This includes methods based
on detecting mutually nearest neighbors, as well as several
efficient variants of linear regression of the log-expression
values. Functions are also provided to perform global rescaling
to remove differences in depth between batches, and to perform
a principal components analysis that is robust to differences
in the numbers of cells across batches.
biocViews: Sequencing, RNASeq, Software, GeneExpression,
Transcriptomics, SingleCell, BatchEffect, Normalization
Author: Aaron Lun [aut, cre], Laleh Haghverdi [ctb]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/batchelor
git_branch: devel
git_last_commit: 72f37b8
git_last_commit_date: 2025-03-13
Date/Publication: 2025-03-17
source.ver: src/contrib/batchelor_1.23.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/batchelor_1.23.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/batchelor_1.23.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/batchelor_1.23.1.tgz
vignettes: vignettes/batchelor/inst/doc/correction.html,
vignettes/batchelor/inst/doc/extension.html
vignetteTitles: 1. Correcting batch effects, 2. Extending methods
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/batchelor/inst/doc/correction.R,
vignettes/batchelor/inst/doc/extension.R
dependsOnMe: OSCA.intro, OSCA.multisample, OSCA.workflows
importsMe: chevreulProcess, ChromSCape, mumosa, scMerge, singleCellTK,
SCIntRuler, scPipeline
suggestsMe: TSCAN, Canek, RaceID
dependencyCount: 67
Package: BatchQC
Version: 2.3.5
Depends: R (>= 4.4.0)
Imports: data.table, DESeq2, dplyr, EBSeq, ggdendro, ggnewscale,
ggplot2, limma, matrixStats, pheatmap, RColorBrewer, reader,
reshape2, scran, shiny, shinyjs, shinythemes, stats,
SummarizedExperiment, sva, S4Vectors, tibble, tidyr, tidyverse,
umap, utils
Suggests: BiocManager, BiocStyle, bladderbatch, devtools, knitr, lintr,
plotly, rmarkdown, spelling, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: 5f2183381565cf2bd649dddbfbd0d088
NeedsCompilation: no
Title: Batch Effects Quality Control Software
Description: Sequencing and microarray samples often are collected or
processed in multiple batches or at different times. This often
produces technical biases that can lead to incorrect results in
the downstream analysis. BatchQC is a software tool that
streamlines batch preprocessing and evaluation by providing
interactive diagnostics, visualizations, and statistical
analyses to explore the extent to which batch variation impacts
the data. BatchQC diagnostics help determine whether batch
adjustment needs to be done, and how correction should be
applied before proceeding with a downstream analysis. Moreover,
BatchQC interactively applies multiple common batch effect
approaches to the data and the user can quickly see the
benefits of each method. BatchQC is developed as a Shiny App.
The output is organized into multiple tabs and each tab
features an important part of the batch effect analysis and
visualization of the data. The BatchQC interface has the
following analysis groups: Summary, Differential Expression,
Median Correlations, Heatmaps, Circular Dendrogram, PCA
Analysis, Shape, ComBat and SVA.
biocViews: BatchEffect, GraphAndNetwork, Microarray, Normalization,
PrincipalComponent, Sequencing, Software, Visualization,
QualityControl, RNASeq, Preprocessing, DifferentialExpression,
ImmunoOncology
Author: Jessica Anderson [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0542-9872>), W. Evan Johnson [aut]
(ORCID: <https://orcid.org/0000-0002-6247-6595>), Solaiappan
Manimaran [aut], Heather Selby [ctb], Claire Ruberman [ctb],
Kwame Okrah [ctb], Hector Corrada Bravo [ctb], Michael
Silverstein [ctb], Regan Conrad [ctb], Zhaorong Li [ctb], Evan
Holmes [aut], Solomon Joseph [ctb]
Maintainer: Jessica Anderson <anderson.jessica@rutgers.edu>
URL: https://github.com/wejlab/BatchQC
VignetteBuilder: knitr
BugReports: https://github.com/wejlab/BatchQC/issues
git_url: https://git.bioconductor.org/packages/BatchQC
git_branch: devel
git_last_commit: fc9ba94
git_last_commit_date: 2025-03-18
Date/Publication: 2025-03-19
source.ver: src/contrib/BatchQC_2.3.5.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BatchQC_2.3.5.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BatchQC_2.3.5.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BatchQC_2.3.5.tgz
vignettes: vignettes/BatchQC/inst/doc/BatchQC_examples.html,
vignettes/BatchQC/inst/doc/BatchQC_Intro.html
vignetteTitles: BatchQC Examples, Introdution to BatchQC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BatchQC/inst/doc/BatchQC_examples.R,
vignettes/BatchQC/inst/doc/BatchQC_Intro.R
dependencyCount: 212
Package: BayesKnockdown
Version: 1.33.0
Depends: R (>= 3.3)
Imports: stats, Biobase
License: GPL-3
MD5sum: 2b10851e3e118c3688b7dd4c3dd422d6
NeedsCompilation: no
Title: BayesKnockdown: Posterior Probabilities for Edges from Knockdown
Data
Description: A simple, fast Bayesian method for computing posterior
probabilities for relationships between a single predictor
variable and multiple potential outcome variables,
incorporating prior probabilities of relationships. In the
context of knockdown experiments, the predictor variable is the
knocked-down gene, while the other genes are potential targets.
Can also be used for differential expression/2-class data.
biocViews: NetworkInference, GeneExpression, GeneTarget, Network,
Bayesian
Author: William Chad Young
Maintainer: William Chad Young <wmchad@uw.edu>
git_url: https://git.bioconductor.org/packages/BayesKnockdown
git_branch: devel
git_last_commit: ee329ba
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BayesKnockdown_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BayesKnockdown_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BayesKnockdown_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BayesKnockdown_1.33.0.tgz
vignettes: vignettes/BayesKnockdown/inst/doc/BayesKnockdown.pdf
vignetteTitles: BayesKnockdown.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BayesKnockdown/inst/doc/BayesKnockdown.R
dependencyCount: 7
Package: BayesSpace
Version: 1.17.0
Depends: R (>= 4.0.0), SingleCellExperiment
Imports: Rcpp (>= 1.0.4.6), stats, methods, purrr, scater, scran,
SummarizedExperiment, coda, rhdf5, S4Vectors, Matrix, magrittr,
assertthat, arrow, mclust, RCurl, DirichletReg, xgboost, utils,
dplyr, rlang, ggplot2, tibble, rjson, tidyr, scales,
microbenchmark, BiocFileCache, BiocSingular, BiocParallel
LinkingTo: Rcpp, RcppArmadillo, RcppDist, RcppProgress
Suggests: testthat, knitr, rmarkdown, igraph, spatialLIBD, viridis,
patchwork, RColorBrewer, Seurat
License: MIT + file LICENSE
MD5sum: 88f1b1cbc590dca239a4e62a56c017df
NeedsCompilation: yes
Title: Clustering and Resolution Enhancement of Spatial Transcriptomes
Description: Tools for clustering and enhancing the resolution of
spatial gene expression experiments. BayesSpace clusters a
low-dimensional representation of the gene expression matrix,
incorporating a spatial prior to encourage neighboring spots to
cluster together. The method can enhance the resolution of the
low-dimensional representation into "sub-spots", for which
features such as gene expression or cell type composition can
be imputed.
biocViews: Software, Clustering, Transcriptomics, GeneExpression,
SingleCell, ImmunoOncology, DataImport
Author: Edward Zhao [aut], Senbai Kang [aut], Matt Stone [aut, cre],
Xing Ren [ctb], Raphael Gottardo [ctb]
Maintainer: Matt Stone <mstone@fredhutch.org>
URL: edward130603.github.io/BayesSpace
SystemRequirements: C++17
VignetteBuilder: knitr
BugReports: https://github.com/edward130603/BayesSpace/issues
git_url: https://git.bioconductor.org/packages/BayesSpace
git_branch: devel
git_last_commit: 30b68f9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BayesSpace_1.17.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BayesSpace_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BayesSpace_1.17.0.tgz
vignettes: vignettes/BayesSpace/inst/doc/BayesSpace.html
vignetteTitles: BayesSpace
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BayesSpace/inst/doc/BayesSpace.R
importsMe: RegionalST
dependencyCount: 155
Package: bayNorm
Version: 1.25.0
Depends: R (>= 3.5),
Imports: Rcpp (>= 0.12.12), BB, foreach, iterators, doSNOW, Matrix,
parallel, MASS, locfit, fitdistrplus, stats, methods, graphics,
grDevices, SingleCellExperiment, SummarizedExperiment,
BiocParallel, utils
LinkingTo: Rcpp, RcppArmadillo,RcppProgress
Suggests: knitr, rmarkdown, BiocStyle, devtools, testthat
License: GPL (>= 2)
Archs: x64
MD5sum: 23021fdb29ca4125b43ef68365859b66
NeedsCompilation: yes
Title: Single-cell RNA sequencing data normalization
Description: bayNorm is used for normalizing single-cell RNA-seq data.
biocViews: ImmunoOncology, Normalization, RNASeq, SingleCell,Sequencing
Author: Wenhao Tang [aut, cre], Fran<U+00E7>ois Bertaux [aut], Philipp
Thomas [aut], Claire Stefanelli [aut], Malika Saint [aut],
Samuel Marguerat [aut], Vahid Shahrezaei [aut]
Maintainer: Wenhao Tang <wt215@ic.ac.uk>
URL: https://github.com/WT215/bayNorm
VignetteBuilder: knitr
BugReports: https://github.com/WT215/bayNorm/issues
git_url: https://git.bioconductor.org/packages/bayNorm
git_branch: devel
git_last_commit: 43039ec
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/bayNorm_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/bayNorm_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/bayNorm_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/bayNorm_1.25.0.tgz
vignettes: vignettes/bayNorm/inst/doc/bayNorm.html
vignetteTitles: Introduction to bayNorm
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bayNorm/inst/doc/bayNorm.R
dependencyCount: 61
Package: baySeq
Version: 2.41.0
Depends: R (>= 2.3.0), methods
Imports: edgeR, GenomicRanges, abind, parallel, graphics, stats, utils
Suggests: BiocStyle, BiocGenerics
License: GPL-3
MD5sum: a96b38865d70d03e6168939f728da2d9
NeedsCompilation: no
Title: Empirical Bayesian analysis of patterns of differential
expression in count data
Description: This package identifies differential expression in
high-throughput 'count' data, such as that derived from
next-generation sequencing machines, calculating estimated
posterior likelihoods of differential expression (or more
complex hypotheses) via empirical Bayesian methods.
biocViews: Sequencing, DifferentialExpression, MultipleComparison,
SAGE, Bayesian, Coverage
Author: Thomas J. Hardcastle [aut], Samuel Granjeaud [cre] (ORCID:
<https://orcid.org/0000-0001-9245-1535>)
Maintainer: Samuel Granjeaud <samuel.granjeaud@inserm.fr>
URL: https://github.com/samgg/baySeq
BugReports: https://github.com/samgg/baySeq/issues
git_url: https://git.bioconductor.org/packages/baySeq
git_branch: devel
git_last_commit: cf527bc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/baySeq_2.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/baySeq_2.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/baySeq_2.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/baySeq_2.41.0.tgz
vignettes: vignettes/baySeq/inst/doc/baySeq_generic.pdf,
vignettes/baySeq/inst/doc/baySeq.pdf
vignetteTitles: Advanced baySeq analyses, baySeq
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/baySeq/inst/doc/baySeq_generic.R,
vignettes/baySeq/inst/doc/baySeq.R
dependsOnMe: clusterSeq, segmentSeq
importsMe: riboSeqR
dependencyCount: 32
Package: BBCAnalyzer
Version: 1.37.0
Imports: SummarizedExperiment, VariantAnnotation, Rsamtools, grDevices,
GenomicRanges, IRanges, Biostrings
Suggests: BSgenome.Hsapiens.UCSC.hg19
License: LGPL-3
MD5sum: 9779167737fbabaa62a3f5b0bb2fa146
NeedsCompilation: no
Title: BBCAnalyzer: an R/Bioconductor package for visualizing base
counts
Description: BBCAnalyzer is a package for visualizing the relative or
absolute number of bases, deletions and insertions at defined
positions in sequence alignment data available as bam files in
comparison to the reference bases. Markers for the relative
base frequencies, the mean quality of the detected bases, known
mutations or polymorphisms and variants called in the data may
additionally be included in the plots.
biocViews: Sequencing, Alignment, Coverage, GeneticVariability, SNP
Author: Sarah Sandmann
Maintainer: Sarah Sandmann <sarah.sandmann@uni-muenster.de>
git_url: https://git.bioconductor.org/packages/BBCAnalyzer
git_branch: devel
git_last_commit: 3cc65d4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BBCAnalyzer_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BBCAnalyzer_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BBCAnalyzer_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BBCAnalyzer_1.37.0.tgz
vignettes: vignettes/BBCAnalyzer/inst/doc/BBCAnalyzer.pdf
vignetteTitles: Using BBCAnalyzer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BBCAnalyzer/inst/doc/BBCAnalyzer.R
dependencyCount: 79
Package: BCRANK
Version: 1.69.0
Depends: methods
Imports: Biostrings
Suggests: seqLogo
License: GPL-2
Archs: x64
MD5sum: 11000adaf73797a7e630b4d625555877
NeedsCompilation: yes
Title: Predicting binding site consensus from ranked DNA sequences
Description: Functions and classes for de novo prediction of
transcription factor binding consensus by heuristic search
biocViews: MotifDiscovery, GeneRegulation
Author: Adam Ameur <Adam.Ameur@genpat.uu.se>
Maintainer: Adam Ameur <Adam.Ameur@genpat.uu.se>
git_url: https://git.bioconductor.org/packages/BCRANK
git_branch: devel
git_last_commit: 537aaff
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BCRANK_1.69.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BCRANK_1.69.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BCRANK_1.69.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BCRANK_1.69.0.tgz
vignettes: vignettes/BCRANK/inst/doc/BCRANK.pdf
vignetteTitles: BCRANK
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BCRANK/inst/doc/BCRANK.R
dependencyCount: 25
Package: bcSeq
Version: 1.29.0
Depends: R (>= 3.4.0)
Imports: Rcpp (>= 0.12.12), Matrix, Biostrings
LinkingTo: Rcpp, Matrix
Suggests: knitr
License: GPL (>= 2)
MD5sum: dcf650b96ba37c187a19acb2243ba4a5
NeedsCompilation: yes
Title: Fast Sequence Mapping in High-Throughput shRNA and CRISPR
Screens
Description: This Rcpp-based package implements a highly efficient data
structure and algorithm for performing alignment of short reads
from CRISPR or shRNA screens to reference barcode library.
Sequencing error are considered and matching qualities are
evaluated based on Phred scores. A Bayes' classifier is
employed to predict the originating barcode of a read. The
package supports provision of user-defined probability models
for evaluating matching qualities. The package also supports
multi-threading.
biocViews: ImmunoOncology, Alignment, CRISPR, Sequencing,
SequenceMatching, MultipleSequenceAlignment, Software, ATACSeq
Author: Jiaxing Lin [aut, cre], Jeremy Gresham [aut], Jichun Xie [aut],
Kouros Owzar [aut], Tongrong Wang [ctb], So Young Kim [ctb],
James Alvarez [ctb], Jeffrey S. Damrauer [ctb], Scott Floyd
[ctb], Joshua Granek [ctb], Andrew Allen [ctb], Cliburn Chan
[ctb]
Maintainer: Jiaxing Lin <jiaxing.lin@duke.edu>
URL: https://github.com/jl354/bcSeq
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org
git_url: https://git.bioconductor.org/packages/bcSeq
git_branch: devel
git_last_commit: 5cae63a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/bcSeq_1.29.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/bcSeq_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/bcSeq_1.29.0.tgz
vignettes: vignettes/bcSeq/inst/doc/bcSeq.pdf
vignetteTitles: bcSeq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bcSeq/inst/doc/bcSeq.R
dependencyCount: 29
Package: beachmat
Version: 2.23.7
Imports: methods, DelayedArray (>= 0.27.2), SparseArray, BiocGenerics,
Matrix, Rcpp
LinkingTo: Rcpp, assorthead
Suggests: testthat, BiocStyle, knitr, rmarkdown, rcmdcheck,
BiocParallel, HDF5Array, beachmat.hdf5
License: GPL-3
Archs: x64
MD5sum: 4c30b196839f418ef39c78cac98642e0
NeedsCompilation: yes
Title: Compiling Bioconductor to Handle Each Matrix Type
Description: Provides a consistent C++ class interface for reading from
a variety of commonly used matrix types. Ordinary matrices and
several sparse/dense Matrix classes are directly supported,
along with a subset of the delayed operations implemented in
the DelayedArray package. All other matrix-like objects are
supported by calling back into R.
biocViews: DataRepresentation, DataImport, Infrastructure
Author: Aaron Lun [aut, cre], Hervé Pagès [aut], Mike Smith [aut]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://github.com/tatami-inc/beachmat
SystemRequirements: C++17
VignetteBuilder: knitr
BugReports: https://github.com/tatami-inc/beachmat/issues
git_url: https://git.bioconductor.org/packages/beachmat
git_branch: devel
git_last_commit: 4bf9647
git_last_commit_date: 2025-03-13
Date/Publication: 2025-03-17
source.ver: src/contrib/beachmat_2.23.7.tar.gz
win.binary.ver: bin/windows/contrib/4.5/beachmat_2.23.7.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/beachmat_2.23.7.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/beachmat_2.23.7.tgz
vignettes: vignettes/beachmat/inst/doc/linking.html
vignetteTitles: Developer guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/beachmat/inst/doc/linking.R
importsMe: batchelor, beachmat.hdf5, beachmat.tiledb, BiocSingular,
bsseq, DropletUtils, epiregulon, glmGamPoi, mumosa, scater,
scran, scrapper, scuttle, SingleR
suggestsMe: mbkmeans, PCAtools, scCB2
linksToMe: beachmat.hdf5, beachmat.tiledb, BiocSingular, bsseq,
dreamlet, DropletUtils, epiregulon, glmGamPoi, mbkmeans,
PCAtools, scran, scrapper, scuttle, SingleR
dependencyCount: 24
Package: beachmat.hdf5
Version: 1.5.1
Imports: methods, beachmat, HDF5Array, DelayedArray, Rcpp
LinkingTo: Rcpp, assorthead, beachmat, Rhdf5lib
Suggests: testthat, BiocStyle, knitr, rmarkdown, rhdf5, Matrix
License: GPL-3
Archs: x64
MD5sum: a841100d53c8f80932640d341fc76dff
NeedsCompilation: yes
Title: beachmat bindings for HDF5-backed matrices
Description: Extends beachmat to support initialization of tatami
matrices from HDF5-backed arrays. This allows C++ code in
downstream packages to directly call the HDF5 C/C++ library to
access array data, without the need for block processing via
DelayedArray. Some utilities are also provided for direct
creation of an in-memory tatami matrix from a HDF5 file.
biocViews: DataRepresentation, DataImport, Infrastructure
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
SystemRequirements: C++17, GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/beachmat.hdf5
git_branch: devel
git_last_commit: cd7e813
git_last_commit_date: 2024-11-08
Date/Publication: 2024-11-08
source.ver: src/contrib/beachmat.hdf5_1.5.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/beachmat.hdf5_1.5.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/beachmat.hdf5_1.5.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/beachmat.hdf5_1.5.1.tgz
vignettes: vignettes/beachmat.hdf5/inst/doc/userguide.html
vignetteTitles: User guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/beachmat.hdf5/inst/doc/userguide.R
suggestsMe: beachmat, epiregulon, sketchR
dependencyCount: 30
Package: beachmat.tiledb
Version: 0.99.1
Imports: methods, beachmat, tiledb, TileDBArray, DelayedArray, Rcpp
LinkingTo: Rcpp, assorthead, beachmat
Suggests: testthat, BiocStyle, knitr, rmarkdown, Matrix
License: GPL-3
MD5sum: 64285ec026fdff4d51244f3e273a12ae
NeedsCompilation: yes
Title: beachmat bindings for TileDB-backed matrices
Description: Extends beachmat to initialize tatami matrices from
TileDB-backed arrays. This allows C++ code in downstream
packages to directly call the TileDB C/C++ library to access
array data, without the need for block processing via
DelayedArray. Developers only need to import this package to
automatically extend the capabilities of
beachmat::initializeCpp to TileDBArray instances.
biocViews: DataRepresentation, DataImport, Infrastructure
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://github.com/tatami-inc/beachmat.tiledb
SystemRequirements: C++17
VignetteBuilder: knitr
BugReports: https://github.com/tatami-inc/beachmat.tiledb/issues
git_url: https://git.bioconductor.org/packages/beachmat.tiledb
git_branch: devel
git_last_commit: ddcfd81
git_last_commit_date: 2024-12-17
Date/Publication: 2025-01-02
source.ver: src/contrib/beachmat.tiledb_0.99.1.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/beachmat.tiledb_0.99.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/beachmat.tiledb_0.99.1.tgz
vignettes: vignettes/beachmat.tiledb/inst/doc/userguide.html
vignetteTitles: User guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/beachmat.tiledb/inst/doc/userguide.R
dependencyCount: 37
Package: beadarray
Version: 2.57.0
Depends: R (>= 2.13.0), BiocGenerics (>= 0.3.2), Biobase (>= 2.17.8),
hexbin
Imports: BeadDataPackR, limma, AnnotationDbi, stats4, reshape2,
GenomicRanges, IRanges, illuminaio, methods, ggplot2
Suggests: lumi, vsn, affy, hwriter, beadarrayExampleData,
illuminaHumanv3.db, gridExtra, BiocStyle,
TxDb.Hsapiens.UCSC.hg19.knownGene, ggbio, Nozzle.R1, knitr
License: MIT + file LICENSE
Archs: x64
MD5sum: 02956ab2acc1faa0badbca5bd8551e59
NeedsCompilation: yes
Title: Quality assessment and low-level analysis for Illumina BeadArray
data
Description: The package is able to read bead-level data (raw TIFFs and
text files) output by BeadScan as well as bead-summary data
from BeadStudio. Methods for quality assessment and low-level
analysis are provided.
biocViews: Microarray, OneChannel, QualityControl, Preprocessing
Author: Mark Dunning, Mike Smith, Jonathan Cairns, Andy Lynch, Matt
Ritchie
Maintainer: Mark Dunning <m.j.dunning@sheffield.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/beadarray
git_branch: devel
git_last_commit: 21fcf33
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/beadarray_2.57.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/beadarray_2.57.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/beadarray_2.57.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/beadarray_2.57.0.tgz
vignettes: vignettes/beadarray/inst/doc/beadarray.html,
vignettes/beadarray/inst/doc/beadlevel.html,
vignettes/beadarray/inst/doc/beadsummary.html,
vignettes/beadarray/inst/doc/ImageProcessing.html
vignetteTitles: Introduction to beadarray, Analysis of Bead-level Data
using beadarray, Analysis of bead-summary data, Image Analysis
with beadarray
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/beadarray/inst/doc/beadarray.R,
vignettes/beadarray/inst/doc/beadlevel.R,
vignettes/beadarray/inst/doc/beadsummary.R,
vignettes/beadarray/inst/doc/ImageProcessing.R
dependsOnMe: beadarrayExampleData
importsMe: arrayQualityMetrics, blima, epigenomix, BeadArrayUseCases,
RobLoxBioC
suggestsMe: lumi, blimaTestingData, maGUI
dependencyCount: 80
Package: BeadDataPackR
Version: 1.59.0
Imports: stats, utils
Suggests: BiocStyle, knitr
License: GPL-2
Archs: x64
MD5sum: 5a8b747f2f49e7de530875c8317317a0
NeedsCompilation: yes
Title: Compression of Illumina BeadArray data
Description: Provides functionality for the compression and
decompression of raw bead-level data from the Illumina
BeadArray platform.
biocViews: Microarray
Author: Mike Smith, Andy Lynch
Maintainer: Mike Smith <grimbough@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BeadDataPackR
git_branch: devel
git_last_commit: c04cfa9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BeadDataPackR_1.59.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BeadDataPackR_1.59.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BeadDataPackR_1.59.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BeadDataPackR_1.59.0.tgz
vignettes: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.pdf
vignetteTitles: BeadDataPackR.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.R
importsMe: beadarray
dependencyCount: 2
Package: BEAT
Version: 1.45.0
Depends: R (>= 2.13.0)
Imports: GenomicRanges, ShortRead, Biostrings, BSgenome
License: LGPL (>= 3.0)
MD5sum: 2f497cd941fbe43419b072852d902603
NeedsCompilation: no
Title: BEAT - BS-Seq Epimutation Analysis Toolkit
Description: Model-based analysis of single-cell methylation data
biocViews: ImmunoOncology, Genetics, MethylSeq, Software,
DNAMethylation, Epigenetics
Author: Kemal Akman <akman@mpipz.mpg.de>
Maintainer: Kemal Akman <akman@mpipz.mpg.de>
git_url: https://git.bioconductor.org/packages/BEAT
git_branch: devel
git_last_commit: 72f59ed
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BEAT_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BEAT_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BEAT_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BEAT_1.45.0.tgz
vignettes: vignettes/BEAT/inst/doc/BEAT.pdf
vignetteTitles: Analysing single-cell BS-Seq data with the "BEAT"
package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BEAT/inst/doc/BEAT.R
dependencyCount: 71
Package: BEclear
Version: 2.23.0
Depends: BiocParallel (>= 1.14.2)
Imports: futile.logger, Rdpack, Matrix, data.table (>= 1.11.8), Rcpp,
abind, stats, graphics, utils, methods, dixonTest, ids
LinkingTo: Rcpp
Suggests: testthat, BiocStyle, knitr, rmarkdown, pander, seewave
License: GPL-3
Archs: x64
MD5sum: 5849131ee57651736825fcdda68ef89e
NeedsCompilation: yes
Title: Correction of batch effects in DNA methylation data
Description: Provides functions to detect and correct for batch effects
in DNA methylation data. The core function is based on latent
factor models and can also be used to predict missing values in
any other matrix containing real numbers.
biocViews: BatchEffect, DNAMethylation, Software, Preprocessing,
StatisticalMethod
Author: Livia Rasp [aut, cre] (ORCID:
<https://orcid.org/0000-0003-0164-2163>), Markus Merl [aut],
Ruslan Akulenko [aut]
Maintainer: Livia Rasp <livia.rasp@gmail.com>
URL: https://github.com/uds-helms/BEclear
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/uds-helms/BEclear/issues
git_url: https://git.bioconductor.org/packages/BEclear
git_branch: devel
git_last_commit: 4a3e2f4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BEclear_2.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BEclear_2.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BEclear_2.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BEclear_2.23.0.tgz
vignettes: vignettes/BEclear/inst/doc/BEclear.html
vignetteTitles: BEclear tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BEclear/inst/doc/BEclear.R
dependencyCount: 30
Package: bedbaser
Version: 0.99.23
Imports: AnVIL (>= 1.16.0), BiocFileCache, dplyr, GenomeInfoDb,
GenomicRanges, httr, methods, purrr, rtracklayer, rlang,
R.utils, stats, stringr, tibble, tidyr, tools, utils
Suggests: BiocStyle, knitr, liftOver, testthat (>= 3.0.0)
License: Artistic License 2.0
MD5sum: 9dc65e49e527aaa91bb810c6088aa353
NeedsCompilation: no
Title: A BEDbase client
Description: A client for BEDbase. bedbaser provides access to the API
at api.bedbase.org. It also includes convenience functions to
import BED files into GRanges objects and BEDsets into
GRangesLists.
biocViews: Software, DataImport, ThirdPartyClient
Author: Andres Wokaty [aut, cre] (ORCID:
<https://orcid.org/0009-0008-0900-8793>), Levi Waldron [aut]
(ORCID: <https://orcid.org/0000-0003-2725-0694>)
Maintainer: Andres Wokaty <jennifer.wokaty@sph.cuny.edu>
URL: https://github.com/waldronlab/bedbaser
VignetteBuilder: knitr
BugReports: https://github.com/waldronlab/bedbaser/issues
git_url: https://git.bioconductor.org/packages/bedbaser
git_branch: devel
git_last_commit: 466392b
git_last_commit_date: 2025-01-29
Date/Publication: 2025-02-19
source.ver: src/contrib/bedbaser_0.99.23.tar.gz
win.binary.ver: bin/windows/contrib/4.5/bedbaser_0.99.23.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/bedbaser_0.99.23.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/bedbaser_0.99.23.tgz
vignettes: vignettes/bedbaser/inst/doc/bedbaser.html
vignetteTitles: bedbaser
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bedbaser/inst/doc/bedbaser.R
dependencyCount: 124
Package: beer
Version: 1.11.0
Depends: R (>= 4.2.0), PhIPData (>= 1.1.1), rjags
Imports: cli, edgeR, BiocParallel, methods, progressr, stats,
SummarizedExperiment, utils
Suggests: testthat (>= 3.0.0), BiocStyle, covr, codetools, knitr,
rmarkdown, dplyr, ggplot2, spelling
License: MIT + file LICENSE
MD5sum: 264840163396abbe89cc55d4dc26e671
NeedsCompilation: no
Title: Bayesian Enrichment Estimation in R
Description: BEER implements a Bayesian model for analyzing
phage-immunoprecipitation sequencing (PhIP-seq) data. Given a
PhIPData object, BEER returns posterior probabilities of
enriched antibody responses, point estimates for the relative
fold-change in comparison to negative control samples, and
more. Additionally, BEER provides a convenient implementation
for using edgeR to identify enriched antibody responses.
biocViews: Software, StatisticalMethod, Bayesian, Sequencing, Coverage
Author: Athena Chen [aut, cre] (ORCID:
<https://orcid.org/0000-0001-6900-2264>), Rob Scharpf [aut],
Ingo Ruczinski [aut]
Maintainer: Athena Chen <achen70@jhu.edu>
URL: https://github.com/athchen/beer/
SystemRequirements: JAGS (4.3.0)
VignetteBuilder: knitr
BugReports: https://github.com/athchen/beer/issues
git_url: https://git.bioconductor.org/packages/beer
git_branch: devel
git_last_commit: a025d0b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/beer_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/beer_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/beer_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/beer_1.11.0.tgz
vignettes: vignettes/beer/inst/doc/beer.html
vignetteTitles: beer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/beer/inst/doc/beer.R
dependencyCount: 85
Package: benchdamic
Version: 1.13.2
Depends: R (>= 4.3.0)
Imports: stats, stats4, utils, methods, phyloseq,
TreeSummarizedExperiment, BiocParallel, zinbwave, edgeR,
DESeq2, limma, ALDEx2, corncob, SummarizedExperiment, MAST,
Seurat, ANCOMBC, microbiome, mixOmics, lme4, NOISeq, dearseq,
MicrobiomeStat, Maaslin2, GUniFrac, metagenomeSeq, MGLM,
ggplot2, RColorBrewer, plyr, reshape2, ggdendro, ggridges,
graphics, cowplot, grDevices, tidytext
Suggests: knitr, rmarkdown, kableExtra, BiocStyle, magick, SPsimSeq,
testthat
License: Artistic-2.0
MD5sum: f02adb16bab9acb310d7d3c1e9c43932
NeedsCompilation: no
Title: Benchmark of differential abundance methods on microbiome data
Description: Starting from a microbiome dataset (16S or WMS with
absolute count values) it is possible to perform several
analysis to assess the performances of many differential
abundance detection methods. A basic and standardized version
of the main differential abundance analysis methods is supplied
but the user can also add his method to the benchmark. The
analyses focus on 4 main aspects: i) the goodness of fit of
each method's distributional assumptions on the observed count
data, ii) the ability to control the false discovery rate, iii)
the within and between method concordances, iv) the
truthfulness of the findings if any apriori knowledge is given.
Several graphical functions are available for result
visualization.
biocViews: Metagenomics, Microbiome, DifferentialExpression,
MultipleComparison, Normalization, Preprocessing, Software
Author: Matteo Calgaro [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3056-518X>), Chiara Romualdi [aut]
(ORCID: <https://orcid.org/0000-0003-4792-9047>), Davide Risso
[aut] (ORCID: <https://orcid.org/0000-0001-8508-5012>), Nicola
Vitulo [aut] (ORCID: <https://orcid.org/0000-0002-9571-0747>)
Maintainer: Matteo Calgaro <mcalgaro93@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/mcalgaro93/benchdamic/issues
git_url: https://git.bioconductor.org/packages/benchdamic
git_branch: devel
git_last_commit: 9f9c589
git_last_commit_date: 2024-11-26
Date/Publication: 2025-01-23
source.ver: src/contrib/benchdamic_1.13.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/benchdamic_1.13.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/benchdamic_1.13.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/benchdamic_1.13.2.tgz
vignettes: vignettes/benchdamic/inst/doc/intro.html
vignetteTitles: Intro
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/benchdamic/inst/doc/intro.R
dependencyCount: 372
Package: BERT
Version: 1.3.6
Depends: R (>= 4.3.0)
Imports: cluster, comprehenr, foreach (>= 1.5.2), invgamma, iterators
(>= 1.0.14), janitor (>= 2.2.0), limma (>= 3.46.0), logging (>=
0.10-108), sva (>= 3.38.0), SummarizedExperiment, methods,
BiocParallel
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle
License: GPL-3
MD5sum: 227b4e2eaa8968f2cf5bc5bd60460838
NeedsCompilation: no
Title: High Performance Data Integration for Large-Scale Analyses of
Incomplete Omic Profiles Using Batch-Effect Reduction Trees
(BERT)
Description: Provides efficient batch-effect adjustment of data with
missing values. BERT orders all batch effect correction to a
tree of pairwise computations. BERT allows parallelization over
sub-trees.
biocViews: BatchEffect, Preprocessing, ExperimentalDesign,
QualityControl
Author: Yannis Schumann [aut, cre] (ORCID:
<https://orcid.org/0000-0002-2379-200X>), Simon Schlumbohm
[aut] (ORCID: <https://orcid.org/0000-0002-0083-5142>)
Maintainer: Yannis Schumann <yannis.schumann@desy.de>
URL: https://github.com/HSU-HPC/BERT/
VignetteBuilder: knitr
BugReports: https://github.com/HSU-HPC/BERT/issues
git_url: https://git.bioconductor.org/packages/BERT
git_branch: devel
git_last_commit: f785e17
git_last_commit_date: 2025-01-29
Date/Publication: 2025-01-29
source.ver: src/contrib/BERT_1.3.6.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BERT_1.3.6.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BERT_1.3.6.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BERT_1.3.6.tgz
vignettes: vignettes/BERT/inst/doc/BERT-Vignette.html
vignetteTitles: BERT-Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BERT/inst/doc/BERT-Vignette.R
dependencyCount: 101
Package: betaHMM
Version: 1.3.1
Depends: R (>= 4.3.0), SummarizedExperiment, S4Vectors, GenomicRanges
Imports: stats, ggplot2, scales, methods, pROC, foreach, doParallel,
parallel, cowplot, dplyr, tidyr, tidyselect, stringr, utils
Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle
License: GPL-3
MD5sum: 24f94326460ff3888ef708cc385be7e0
NeedsCompilation: no
Title: A Hidden Markov Model Approach for Identifying Differentially
Methylated Sites and Regions for Beta-Valued DNA Methylation
Data
Description: A novel approach utilizing a homogeneous hidden Markov
model. And effectively model untransformed beta values. To
identify DMCs while considering the spatial. Correlation of the
adjacent CpG sites.
biocViews: DNAMethylation, DifferentialMethylation, ImmunoOncology,
BiomedicalInformatics, MethylationArray, Software,
MultipleComparison, Sequencing, Spatial, Coverage, GeneTarget,
HiddenMarkovModel, Microarray
Author: Koyel Majumdar [cre, aut] (ORCID:
<https://orcid.org/0000-0001-6469-488X>), Romina Silva [aut],
Antoinette Sabrina Perry [aut], Ronald William Watson [aut],
Isobel Claire Gorley [aut] (ORCID:
<https://orcid.org/0000-0001-7713-681X>), Thomas Brendan Murphy
[aut] (ORCID: <https://orcid.org/0000-0002-5668-7046>),
Florence Jaffrezic [aut], Andrea Rau [aut] (ORCID:
<https://orcid.org/0000-0001-6469-488X>)
Maintainer: Koyel Majumdar <koyel.majumdar@ucdconnect.ie>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/betaHMM
git_branch: devel
git_last_commit: 83d4444
git_last_commit_date: 2024-12-17
Date/Publication: 2024-12-18
source.ver: src/contrib/betaHMM_1.3.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/betaHMM_1.3.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/betaHMM_1.3.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/betaHMM_1.3.1.tgz
vignettes: vignettes/betaHMM/inst/doc/betaHMM.html
vignetteTitles: betaHMM
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/betaHMM/inst/doc/betaHMM.R
dependencyCount: 78
Package: bettr
Version: 1.3.0
Depends: R (>= 4.4.0)
Imports: dplyr (>= 1.0), tidyr, ggplot2 (>= 3.4.1), shiny (>= 1.6),
tibble, ComplexHeatmap, bslib, rlang, circlize, stats, grid,
methods, cowplot, Hmisc, sortable, shinyjqui, grDevices,
scales, DT, SummarizedExperiment, S4Vectors
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle
License: MIT + file LICENSE
MD5sum: 5d351b8e5102f9b105538147bbcf5a6b
NeedsCompilation: no
Title: A Better Way To Explore What Is Best
Description: bettr provides a set of interactive visualization methods
to explore the results of a benchmarking study, where typically
more than a single performance measures are computed. The user
can weight the performance measures according to their
preferences. Performance measures can also be grouped and
aggregated according to additional annotations.
biocViews: Visualization, ShinyApps, GUI
Author: Federico Marini [aut] (ORCID:
<https://orcid.org/0000-0003-3252-7758>), Charlotte Soneson
[aut, cre] (ORCID: <https://orcid.org/0000-0003-3833-2169>)
Maintainer: Charlotte Soneson <charlottesoneson@gmail.com>
URL: https://github.com/federicomarini/bettr
VignetteBuilder: knitr
BugReports: https://github.com/federicomarini/bettr/issues
git_url: https://git.bioconductor.org/packages/bettr
git_branch: devel
git_last_commit: 717bbe5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/bettr_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/bettr_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/bettr_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/bettr_1.3.0.tgz
vignettes: vignettes/bettr/inst/doc/bettr.html
vignetteTitles: bettr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/bettr/inst/doc/bettr.R
dependencyCount: 136
Package: BG2
Version: 1.7.0
Depends: R (>= 4.2.0)
Imports: GA (>= 3.2), caret (>= 6.0-86), memoise (>= 1.1.0), Matrix (>=
1.2-18), MASS (>= 7.3-58.1), stats (>= 4.2.2)
Suggests: BiocStyle, knitr, rmarkdown, formatR, rrBLUP, testthat (>=
3.0.0)
License: GPL-3 + file LICENSE
MD5sum: d06334428c613f24a3a048a96ed80a4a
NeedsCompilation: no
Title: Performs Bayesian GWAS analysis for non-Gaussian data using BG2
Description: This package is built to perform GWAS analysis for
non-Gaussian data using BG2. The BG2 method uses penalized
quasi-likelihood along with nonlocal priors in a two step
manner to identify SNPs in GWAS analysis. The research related
to this package was supported in part by National Science
Foundation awards DMS 1853549 and DMS 2054173.
biocViews: Bayesian, AssayDomain, SNP, GenomeWideAssociation
Author: Jacob Williams [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6425-1365>), Shuangshuang Xu
[aut], Marco Ferreira [aut] (ORCID:
<https://orcid.org/0000-0002-4705-5661>)
Maintainer: Jacob Williams <jwilliams@vt.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BG2
git_branch: devel
git_last_commit: edfff8c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BG2_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BG2_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BG2_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BG2_1.7.0.tgz
vignettes: vignettes/BG2/inst/doc/BG2.html
vignetteTitles: BG2
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BG2/inst/doc/BG2.R
dependencyCount: 92
Package: BgeeCall
Version: 1.23.0
Depends: R (>= 3.6)
Imports: GenomicFeatures, tximport, Biostrings, rtracklayer, biomaRt,
jsonlite, methods, dplyr, data.table, sjmisc, grDevices,
graphics, stats, utils, rslurm, rhdf5, txdbmaker
Suggests: knitr, testthat, rmarkdown, AnnotationHub, httr
License: GPL-3 + file LICENSE
MD5sum: 8da90e529bd64c17c33576be7dc45561
NeedsCompilation: no
Title: Automatic RNA-Seq present/absent gene expression calls
generation
Description: BgeeCall allows to generate present/absent gene expression
calls without using an arbitrary cutoff like TPM<1. Calls are
generated based on reference intergenic sequences. These
sequences are generated based on expression of all RNA-Seq
libraries of each species integrated in Bgee
(https://bgee.org).
biocViews: Software, GeneExpression, RNASeq
Author: Julien Wollbrett [aut, cre], Sara Fonseca Costa [aut], Julien
Roux [aut], Marc Robinson Rechavi [ctb], Frederic Bastian [aut]
Maintainer: Julien Wollbrett <julien.wollbrett@unil.ch>
URL: https://github.com/BgeeDB/BgeeCall
SystemRequirements: kallisto
VignetteBuilder: knitr
BugReports: https://github.com/BgeeDB/BgeeCall/issues
git_url: https://git.bioconductor.org/packages/BgeeCall
git_branch: devel
git_last_commit: 6e9ba9e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BgeeCall_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BgeeCall_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BgeeCall_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BgeeCall_1.23.0.tgz
vignettes: vignettes/BgeeCall/inst/doc/bgeecall-manual.html
vignetteTitles: automatic RNA-Seq present/absent gene expression calls
generation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BgeeCall/inst/doc/bgeecall-manual.R
dependencyCount: 112
Package: BgeeDB
Version: 2.33.0
Depends: R (>= 3.6.0), topGO, tidyr
Imports: R.utils, data.table, curl, RCurl, digest, methods, stats,
utils, dplyr, RSQLite, graph, Biobase, zellkonverter, anndata,
HDF5Array, bread
Suggests: knitr, BiocStyle, testthat, rmarkdown, markdown
License: GPL-3 + file LICENSE
MD5sum: 7f1c95bcf8dc4719a6a047de1f46dc3f
NeedsCompilation: no
Title: Annotation and gene expression data retrieval from Bgee
database. TopAnat, an anatomical entities Enrichment Analysis
tool for UBERON ontology
Description: A package for the annotation and gene expression data
download from Bgee database, and TopAnat analysis: GO-like
enrichment of anatomical terms, mapped to genes by expression
patterns.
biocViews: Software, DataImport, Sequencing, GeneExpression,
Microarray, GO, GeneSetEnrichment
Author: Andrea Komljenovic [aut, cre], Julien Roux [aut, cre]
Maintainer: Julien Wollbrett <julien.wollbrett@unil.ch>, Julien Roux
<julien.roux@unibas.ch>, Andrea Komljenovic
<andreakomljenovic@gmail.com>, Frederic Bastian
<bgee@sib.swiss>
URL: https://github.com/BgeeDB/BgeeDB_R
VignetteBuilder: knitr
BugReports: https://github.com/BgeeDB/BgeeDB_R/issues
git_url: https://git.bioconductor.org/packages/BgeeDB
git_branch: devel
git_last_commit: 13c7c42
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BgeeDB_2.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BgeeDB_2.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BgeeDB_2.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BgeeDB_2.33.0.tgz
vignettes: vignettes/BgeeDB/inst/doc/BgeeDB_Manual.html
vignetteTitles: BgeeDB,, an R package for retrieval of curated
expression datasets and for gene list enrichment tests
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BgeeDB/inst/doc/BgeeDB_Manual.R
importsMe: RITAN
suggestsMe: RITAN
dependencyCount: 100
Package: BicARE
Version: 1.65.0
Depends: R (>= 1.8.0), Biobase (>= 2.5.5), multtest, GSEABase, GO.db
Imports: methods
Suggests: hgu95av2
License: GPL-2
Archs: x64
MD5sum: eac5a310d8a0fc33b9987f2a3dc5dc8d
NeedsCompilation: yes
Title: Biclustering Analysis and Results Exploration
Description: Biclustering Analysis and Results Exploration.
biocViews: Microarray, Transcription, Clustering
Author: Pierre Gestraud
Maintainer: Pierre Gestraud <pierre.gestraud@curie.fr>
URL: http://bioinfo.curie.fr
git_url: https://git.bioconductor.org/packages/BicARE
git_branch: devel
git_last_commit: 70d5604
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BicARE_1.65.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BicARE_1.65.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BicARE_1.65.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BicARE_1.65.0.tgz
vignettes: vignettes/BicARE/inst/doc/BicARE.pdf
vignetteTitles: BicARE
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BicARE/inst/doc/BicARE.R
dependsOnMe: RcmdrPlugin.BiclustGUI
importsMe: miRSM
dependencyCount: 58
Package: BiFET
Version: 1.27.0
Depends: R (>= 3.5.0)
Imports: stats, poibin, GenomicRanges
Suggests: rmarkdown, testthat, knitr
License: GPL-3
Archs: x64
MD5sum: c3dd5495961c83e465ef09090940abe1
NeedsCompilation: no
Title: Bias-free Footprint Enrichment Test
Description: BiFET identifies TFs whose footprints are over-represented
in target regions compared to background regions after
correcting for the bias arising from the imbalance in read
counts and GC contents between the target and background
regions. For a given TF k, BiFET tests the null hypothesis that
the target regions have the same probability of having
footprints for the TF k as the background regions while
correcting for the read count and GC content bias. For this, we
use the number of target regions with footprints for TF k, t_k
as a test statistic and calculate the p-value as the
probability of observing t_k or more target regions with
footprints under the null hypothesis.
biocViews: ImmunoOncology, Genetics, Epigenetics, Transcription,
GeneRegulation, ATACSeq, DNaseSeq, RIPSeq, Software
Author: Ahrim Youn [aut, cre], Eladio Marquez [aut], Nathan Lawlor
[aut], Michael Stitzel [aut], Duygu Ucar [aut]
Maintainer: Ahrim Youn <Ahrim.Youn@jax.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BiFET
git_branch: devel
git_last_commit: 68a0c10
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BiFET_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BiFET_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BiFET_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BiFET_1.27.0.tgz
vignettes: vignettes/BiFET/inst/doc/BiFET.html
vignetteTitles: "A Guide to using BiFET"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BiFET/inst/doc/BiFET.R
dependencyCount: 24
Package: BiGGR
Version: 1.43.0
Depends: R (>= 2.14.0), rsbml, hyperdraw, LIM,stringr
Imports: hypergraph, limSolve
License: file LICENSE
MD5sum: eda2ca1b253d1ebdf99aa8220d34cf0f
NeedsCompilation: no
Title: Constraint based modeling in R using metabolic reconstruction
databases
Description: This package provides an interface to simulate metabolic
reconstruction from the BiGG database(http://bigg.ucsd.edu/)
and other metabolic reconstruction databases. The package
facilitates flux balance analysis (FBA) and the sampling of
feasible flux distributions. Metabolic networks and estimated
fluxes can be visualized with hypergraphs.
biocViews: Systems Biology,Pathway,Network,GraphAndNetwork,
Visualization,Metabolomics
Author: Anand K. Gavai, Hannes Hettling
Maintainer: Anand K. Gavai <anand.gavai@bioinformatics.nl>, Hannes
Hettling <hannes.hettling@naturalis.nl>
URL: http://www.bioconductor.org/
git_url: https://git.bioconductor.org/packages/BiGGR
git_branch: devel
git_last_commit: bac514a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BiGGR_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BiGGR_1.43.0.zip
vignettes: vignettes/BiGGR/inst/doc/BiGGR.pdf
vignetteTitles: BiGGR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BiGGR/inst/doc/BiGGR.R
dependencyCount: 30
Package: bigmelon
Version: 1.33.0
Depends: R (>= 3.3), wateRmelon (>= 1.25.0), gdsfmt (>= 1.0.4),
methods, minfi (>= 1.21.0), Biobase, methylumi
Imports: stats, utils, GEOquery, graphics, BiocGenerics, illuminaio
Suggests: BiocGenerics, RUnit, BiocStyle, minfiData, parallel,
IlluminaHumanMethylation450kanno.ilmn12.hg19,
IlluminaHumanMethylationEPICanno.ilm10b2.hg19, bumphunter
License: GPL-3
MD5sum: 34b6cebd14e45318d7eb5b1c8e4e07b0
NeedsCompilation: no
Title: Illumina methylation array analysis for large experiments
Description: Methods for working with Illumina arrays using gdsfmt.
biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing,
QualityControl, MethylationArray, DataImport, CpGIsland
Author: Tyler J. Gorrie-Stone [aut], Ayden Saffari [aut], Karim Malki
[aut], Leonard C. Schalkwyk [cre, aut]
Maintainer: Leonard C. Schalkwyk <lschal@essex.ac.uk>
git_url: https://git.bioconductor.org/packages/bigmelon
git_branch: devel
git_last_commit: 9e460f8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/bigmelon_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/bigmelon_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/bigmelon_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/bigmelon_1.33.0.tgz
vignettes: vignettes/bigmelon/inst/doc/bigmelon.pdf
vignetteTitles: The bigmelon Package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bigmelon/inst/doc/bigmelon.R
dependencyCount: 174
Package: BindingSiteFinder
Version: 2.5.4
Depends: GenomicRanges, R (>= 4.2)
Imports: tidyr, tibble, plyr, matrixStats, stats, ggplot2, methods,
rtracklayer, S4Vectors, ggforce, GenomeInfoDb, ComplexHeatmap,
RColorBrewer, lifecycle, rlang, forcats, dplyr,
GenomicFeatures, IRanges, kableExtra, ggdist
Suggests: testthat, BiocStyle, knitr, rmarkdown, GenomicAlignments,
scales, Gviz, xlsx, GGally, patchwork, viridis, ggplotify,
SummarizedExperiment, DESeq2, ggpointdensity, ggrastr, ashr,
txdbmaker, ggrepel, stringr
License: Artistic-2.0
MD5sum: b69fd648ed931399ad4e097b7505e157
NeedsCompilation: no
Title: Binding site defintion based on iCLIP data
Description: Precise knowledge on the binding sites of an RNA-binding
protein (RBP) is key to understand (post-) transcriptional
regulatory processes. Here we present a workflow that describes
how exact binding sites can be defined from iCLIP data. The
package provides functions for binding site definition and
result visualization. For details please see the vignette.
biocViews: Sequencing, GeneExpression, GeneRegulation,
FunctionalGenomics, Coverage, DataImport
Author: Mirko Brüggemann [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1778-0248>), Melina Klostermann
[aut] (ORCID: <https://orcid.org/0000-0003-3122-1095>), Kathi
Zarnack [aut] (ORCID: <https://orcid.org/0000-0003-3527-3378>)
Maintainer: Mirko Brüggemann <mirko.brueggemann@mail.de>
VignetteBuilder: knitr
BugReports: https://github.com/ZarnackGroup/BindingSiteFinder/issues
git_url: https://git.bioconductor.org/packages/BindingSiteFinder
git_branch: devel
git_last_commit: 9b597ef
git_last_commit_date: 2025-03-19
Date/Publication: 2025-03-20
source.ver: src/contrib/BindingSiteFinder_2.5.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BindingSiteFinder_2.5.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BindingSiteFinder_2.5.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BindingSiteFinder_2.5.4.tgz
vignettes: vignettes/BindingSiteFinder/inst/doc/vignette.html
vignetteTitles: Definition of binding sites from iCLIP signal
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BindingSiteFinder/inst/doc/vignette.R
dependencyCount: 144
Package: bioassayR
Version: 1.45.0
Depends: R (>= 3.5.0), DBI (>= 0.3.1), RSQLite (>= 1.0.0), methods,
Matrix, rjson, BiocGenerics (>= 0.13.8)
Imports: XML, ChemmineR
Suggests: BiocStyle, RCurl, biomaRt, knitr, knitcitations,
knitrBootstrap, testthat, ggplot2, rmarkdown
License: Artistic-2.0
MD5sum: 3f731a6c089f39ca23d0e77312b07cc8
NeedsCompilation: no
Title: Cross-target analysis of small molecule bioactivity
Description: bioassayR is a computational tool that enables
simultaneous analysis of thousands of bioassay experiments
performed over a diverse set of compounds and biological
targets. Unique features include support for large-scale
cross-target analyses of both public and custom bioassays,
generation of high throughput screening fingerprints (HTSFPs),
and an optional preloaded database that provides access to a
substantial portion of publicly available bioactivity data.
biocViews: ImmunoOncology, MicrotitrePlateAssay, CellBasedAssays,
Visualization, Infrastructure, DataImport, Bioinformatics,
Proteomics, Metabolomics
Author: Tyler Backman, Ronly Schlenk, Thomas Girke
Maintainer: Thomas Girke <tgirke@citrus.ucr.edu>
URL: https://github.com/girke-lab/bioassayR
VignetteBuilder: knitr
BugReports: https://github.com/girke-lab/bioassayR/issues
git_url: https://git.bioconductor.org/packages/bioassayR
git_branch: devel
git_last_commit: 248466e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/bioassayR_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/bioassayR_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/bioassayR_1.45.0.tgz
vignettes: vignettes/bioassayR/inst/doc/bioassayR.html
vignetteTitles: bioassayR Introduction and Examples
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bioassayR/inst/doc/bioassayR.R
dependencyCount: 84
Package: Biobase
Version: 2.67.0
Depends: R (>= 2.10), BiocGenerics (>= 0.27.1), utils
Imports: methods
Suggests: tools, tkWidgets, ALL, RUnit, golubEsets, BiocStyle, knitr,
limma
License: Artistic-2.0
MD5sum: bb1f78e974248f618f28dfbc26539935
NeedsCompilation: yes
Title: Biobase: Base functions for Bioconductor
Description: Functions that are needed by many other packages or which
replace R functions.
biocViews: Infrastructure
Author: R. Gentleman [aut], V. Carey [aut], M. Morgan [aut], S. Falcon
[aut], Haleema Khan [ctb] ('esApply' and 'BiobaseDevelopment'
vignette translation from Sweave to Rmarkdown / HTML),
Bioconductor Package Maintainer [cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://bioconductor.org/packages/Biobase
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/Biobase/issues
git_url: https://git.bioconductor.org/packages/Biobase
git_branch: devel
git_last_commit: 06c9ac2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Biobase_2.67.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Biobase_2.67.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Biobase_2.67.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Biobase_2.67.0.tgz
vignettes: vignettes/Biobase/inst/doc/ExpressionSetIntroduction.pdf,
vignettes/Biobase/inst/doc/BiobaseDevelopment.html,
vignettes/Biobase/inst/doc/esApply.html
vignetteTitles: An introduction to Biobase and ExpressionSets, Notes
for eSet developers, esApply Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Biobase/inst/doc/BiobaseDevelopment.R,
vignettes/Biobase/inst/doc/esApply.R,
vignettes/Biobase/inst/doc/ExpressionSetIntroduction.R
dependsOnMe: ACME, affy, affycomp, affyContam, affycoretools, affyPLM,
AGDEX, AIMS, altcdfenvs, annaffy, AnnotationDbi,
AnnotationForge, ArrayExpress, arrayMvout, BAGS, bandle,
beadarray, BicARE, bigmelon, BioMVCClass, BioQC, BLMA,
borealis, CAMERA, cancerclass, casper, Category,
categoryCompare, CCPROMISE, CGHbase, CGHcall, CGHregions,
clippda, clusterStab, CMA, cn.farms, codelink, convert, copa,
covEB, covRNA, CytoMDS, DEXSeq, DFP, diggit, doppelgangR, DSS,
dyebias, EBarrays, EDASeq, edge, EGSEA, epigenomix,
epivizrData, ExiMiR, ExpressionAtlas, fabia, factDesign,
fastseg, flowBeads, frma, gaga, GeneMeta, geneplotter,
geneRecommender, GeneRegionScan, GeneSelectMMD,
geNetClassifier, GeoDiff, GEOexplorer, GeomxTools, GEOquery,
GOexpress, goProfiles, GOstats, GSEABase, GSEABenchmarkeR,
GSEAlm, GWASTools, hapFabia, HELP, hopach, HybridMTest, iCheck,
IdeoViz, idiogram, INSPEcT, isobar, iterativeBMA, IVAS, lumi,
made4, massiR, MEAL, metagenomeSeq, MethPed, methylumi, Mfuzz,
MiChip, microbiomeExplorer, mimager, MineICA, MiRaGE, miRcomp,
MLInterfaces, MMDiff2, monocle, MSnbase, Mulcom, MultiDataSet,
multtest, NanoStringDiff, NanoStringNCTools, NanoTube, NOISeq,
normalize450K, NormqPCR, octad, oligo, omicRexposome,
OrderedList, OTUbase, pandaR, panp, pcaMethods, pdInfoBuilder,
pepStat, phenoTest, PLPE, POWSC, PREDA, pRolocGUI, PROMISE,
qpcrNorm, qPLEXanalyzer, R453Plus1Toolbox, RbcBook1, rbsurv,
rcellminer, ReadqPCR, rexposome, Rmagpie, Rnits, RTopper,
RUVSeq, safe, SCAN.UPC, SeqGSEA, SigCheck, siggenes,
singleCellTK, SpeCond, SPEM, spkTools, splineTimeR,
SummarizedExperiment, tigre, tilingArray, topGO, TPP,
tRanslatome, UNDO, VegaMC, viper, vsn, wateRmelon, webbioc,
XDE, yarn, EuPathDB, affycompData, ALL, bcellViper,
beadarrayExampleData, bladderbatch, brgedata, cancerdata, CCl4,
CLL, colonCA, CRCL18, curatedBreastData, curatedOvarianData,
davidTiling, diggitdata, DLBCL, dressCheck, etec16s, fabiaData,
fibroEset, gaschYHS, golubEsets, GSE103322, GSE13015, GSE62944,
GSVAdata, harbChIP, HumanAffyData, humanStemCell, Iyer517,
kidpack, leeBamViews, leukemiasEset, lumiBarnes,
lungExpression, MAQCsubset, MetaGxBreast, MetaGxOvarian,
miRNATarget, msd16s, mvoutData, Neve2006, PREDAsampledata,
ProData, prostateCancerCamcap, prostateCancerGrasso,
prostateCancerStockholm, prostateCancerTaylor,
prostateCancerVarambally, pumadata, rcellminerData,
RUVnormalizeData, SpikeInSubset, TCGAcrcmiRNA, TCGAcrcmRNA,
tweeDEseqCountData, yeastCC, maEndToEnd, countTransformers,
crmn, dGAselID, eLNNpairedCov, GWASbyCluster, heatmapFlex,
InteRD, lmQCM, MM2Sdata, MMDvariance, propOverlap, statVisual
importsMe: a4Base, a4Classif, a4Core, a4Preproc, ABarray, ACE, aCGH,
adSplit, affyILM, AgiMicroRna, ANF, annmap, annotate,
AnnotationHubData, annotationTools, arrayQualityMetrics,
attract, ballgown, BASiCS, BayesKnockdown, BgeeDB, biobroom,
bioCancer, biocViews, BioNet, biosigner, biscuiteer, BiSeq,
blima, bnem, BSgenomeForge, bsseq, BubbleTree, CAFE, canceR,
Cardinal, CellScore, CellTrails, cfdnakit, CGHnormaliter,
ChIPQC, ChIPXpress, ChromHeatMap, cicero, clipper, CluMSID,
cn.mops, COCOA, cogena, combi, CompoundDb,
ConsensusClusterPlus, consensusDE, consensusOV, coRdon, CoreGx,
crlmm, cummeRbund, cyanoFilter, cycle, cydar, CytoML, DAPAR,
ddCt, debCAM, DEGreport, DESeq2, destiny, DExMA, discordant,
easyRNASeq, EBarrays, ecolitk, EGAD, ensembldb, EpiMix, erma,
esetVis, ExiMiR, ffpe, findIPs, flowClust, flowCore, flowFP,
flowMatch, flowMeans, flowSpecs, flowStats, flowViz,
flowWorkspace, FRASER, frma, frmaTools, gCrisprTools, gcrma,
gemma.R, geneClassifiers, GeneExpressionSignature, genefilter,
GeneMeta, geneRecommender, GeneRegionScan, GENESIS,
GenomicInteractions, GenomicScores, GenomicSuperSignature,
GEOsubmission, gep2pep, ggbio, girafe, GlobalAncova,
globaltest, gmapR, GSRI, GSVA, Gviz, Harshlight, HEM, hermes,
HTSFilter, infinityFlow, IsoformSwitchAnalyzeR, isomiRs,
katdetectr, kissDE, LiquidAssociation, LRBaseDbi, lute,
makecdfenv, MAPFX, maSigPro, MAST, mastR, mBPCR, MeSHDbi,
metaseqR2, MethylAid, methylCC, methylclock, methylumi, mfa,
MiChip, microbiomeDASim, minfi, MinimumDistance, MiPP, MIRA,
miRSM, missMethyl, MLSeq, mogsa, Moonlight2R, MoonlightR,
MSnID, MultiAssayExperiment, MultiRNAflow, multiscan, mzR,
npGSEA, nucleR, oligoClasses, omicade4, omicsViewer, ontoProc,
openCyto, oposSOM, oppar, OrganismDbi, panp, phantasus,
phantasusLite, PharmacoGx, phenomis, phyloseq, piano, plgem,
plier, PLSDAbatch, podkat, prebs, PrInCE, progeny, pRoloc,
PROMISE, PRONE, PROPS, Prostar, protGear, ptairMS, puma,
PureCN, pvac, pvca, qcmetrics, QDNAseq, QFeatures, qpgraph,
quantiseqr, quantro, QuasR, qusage, RadioGx, randPack,
ReactomeGSA, RIVER, Rmagpie, RMassBank, RNAseqCovarImpute,
roastgsa, rols, ropls, ROTS, rqubic, rScudo, Rtpca, Rtreemix,
RUVnormalize, scmap, scTGIF, SeqVarTools, shinyMethyl,
ShortRead, SigsPack, sigsquared, singscore, sitadela, sketchR,
SomaticSignatures, SpatialDecon, SpatialFeatureExperiment,
SpatialOmicsOverlay, spkTools, SplineDV, SPONGE, standR,
STATegRa, subSeq, synapter, TDbasedUFEadv, TEQC, TFBSTools,
tidyFlowCore, timecourse, TMixClust, TnT, topdownr, ToxicoGx,
tradeSeq, TTMap, twilight, txdbmaker, uSORT, VanillaICE,
variancePartition, VariantAnnotation, VariantFiltering,
VariantTools, vidger, vulcan, wateRmelon, wpm, xcms, Xeva,
BloodCancerMultiOmics2017, DeSousa2013, DExMAdata,
Fletcher2013a, GSE13015, hgu133plus2CellScore, IHWpaper,
KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO, mcsurvdata,
pRolocdata, seqc, signatureSearchData, ExpHunterSuite,
ExpressionNormalizationWorkflow, GeoMxWorkflows, AnnoProbe,
bapred, BisqueRNA, CIARA, ClassComparison, ClassDiscovery,
FMradio, geneExpressionFromGEO, GSEMA, IntegratedJM, maGUI,
nlcv, NMF, PCAPAM50, RCPA, RobLox, RobLoxBioC, RPPanalyzer,
SCdeconR, seAMLess, ssizeRNA, TailRank
suggestsMe: AUCell, autonomics, BiocGenerics, CellMapper, clustComp,
coseq, cypress, dar, DART, dcanr, dearseq, edgeR, EnMCB,
EpiDISH, epivizr, epivizrChart, epivizrStandalone, genefu,
GENIE3, GenomicPlot, GenomicRanges, GSAR, GSgalgoR, Heatplus,
interactiveDisplay, kebabs, les, limma, M3Drop, mCSEA, messina,
MOSim, msa, multiClust, OSAT, PCAtools, RcisTarget,
ribosomeProfilingQC, ROC, RTCGA, scater, scmeth, SeqArray,
sparrow, spatialHeatmap, stageR, survcomp, TargetScore,
TCGAbiolinks, TFutils, tidytof, tkWidgets, TOP, vbmp,
widgetTools, biotmleData, breastCancerMAINZ, breastCancerNKI,
breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP,
breastCancerVDX, dorothea, dyebiasexamples, HMP16SData,
HMP2Data, homosapienDEE2CellScore, mammaPrintData, RegParallel,
rheumaticConditionWOLLBOLD, seventyGeneData, yeastExpData,
yeastRNASeq, amap, aroma.affymetrix, BaseSet, clValid,
CrossValidate, distrDoc, GenAlgo, hexbin, HTSCluster, isatabr,
MetabolSSMF, mi4p, Modeler, multiclassPairs, NACHO,
ordinalbayes, Patterns, rsconnect, Seurat, sigminer,
SomaDataIO, tinyarray
dependencyCount: 6
Package: biobroom
Version: 1.39.0
Depends: R (>= 3.0.0), broom
Imports: dplyr, tidyr, Biobase
Suggests: limma, DESeq2, airway, ggplot2, plyr, GenomicRanges,
testthat, magrittr, edgeR, qvalue, knitr, data.table, MSnbase,
rmarkdown, SummarizedExperiment
License: LGPL
Archs: x64
MD5sum: 718605b97a56dec6715663add12cb227
NeedsCompilation: no
Title: Turn Bioconductor objects into tidy data frames
Description: This package contains methods for converting standard
objects constructed by bioinformatics packages, especially
those in Bioconductor, and converting them to tidy data. It
thus serves as a complement to the broom package, and follows
the same the tidy, augment, glance division of tidying methods.
Tidying data makes it easy to recombine, reshape and visualize
bioinformatics analyses.
biocViews: MultipleComparison, DifferentialExpression, Regression,
GeneExpression, Proteomics, DataImport
Author: Andrew J. Bass, David G. Robinson, Steve Lianoglou, Emily
Nelson, John D. Storey, with contributions from Laurent Gatto
Maintainer: John D. Storey <jstorey@princeton.edu> and Andrew J. Bass
<ajbass@emory.edu>
URL: https://github.com/StoreyLab/biobroom
VignetteBuilder: knitr
BugReports: https://github.com/StoreyLab/biobroom/issues
git_url: https://git.bioconductor.org/packages/biobroom
git_branch: devel
git_last_commit: 360bc04
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/biobroom_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/biobroom_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/biobroom_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/biobroom_1.39.0.tgz
vignettes: vignettes/biobroom/inst/doc/biobroom_vignette.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biobroom/inst/doc/biobroom_vignette.R
importsMe: TPP
dependencyCount: 31
Package: biobtreeR
Version: 1.19.0
Imports: httr, httpuv, stringi,jsonlite,methods,utils
Suggests: BiocStyle, knitr,testthat,rmarkdown,markdown
License: MIT + file LICENSE
MD5sum: 25b9527aa216a5e828aae6799b75bbde
NeedsCompilation: no
Title: Using biobtree tool from R
Description: The biobtreeR package provides an interface to
[biobtree](https://github.com/tamerh/biobtree) tool which
covers large set of bioinformatics datasets and allows search
and chain mappings functionalities.
biocViews: Annotation
Author: Tamer Gur
Maintainer: Tamer Gur <tgur@ebi.ac.uk>
URL: https://github.com/tamerh/biobtreeR
VignetteBuilder: knitr
BugReports: https://github.com/tamerh/biobtreeR/issues
git_url: https://git.bioconductor.org/packages/biobtreeR
git_branch: devel
git_last_commit: 3685204
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/biobtreeR_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/biobtreeR_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/biobtreeR_1.19.0.tgz
vignettes: vignettes/biobtreeR/inst/doc/biobtreeR.html
vignetteTitles: The biobtreeR users guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/biobtreeR/inst/doc/biobtreeR.R
dependencyCount: 20
Package: bioCancer
Version: 1.35.0
Depends: R (>= 3.6.0), radiant.data (>= 0.9.1), cBioPortalData, XML(>=
3.98)
Imports: R.oo, R.methodsS3, DT (>= 0.3), dplyr (>= 0.7.2), tidyr, shiny
(>= 1.0.5), AlgDesign (>= 1.1.7.3), import (>= 1.1.0), methods,
AnnotationDbi, shinythemes, Biobase, geNetClassifier,
org.Hs.eg.db, org.Bt.eg.db, DOSE, clusterProfiler, reactome.db,
ReactomePA, DiagrammeR(<= 1.01), visNetwork, htmlwidgets, plyr,
tibble, GO.db
Suggests: BiocStyle, prettydoc, rmarkdown, knitr, testthat (>= 0.10.0)
License: AGPL-3 | file LICENSE
MD5sum: 43e9a8e6f696a8f0790a34dbf65373e5
NeedsCompilation: no
Title: Interactive Multi-Omics Cancers Data Visualization and Analysis
Description: This package is a Shiny App to visualize and analyse
interactively Multi-Assays of Cancer Genomic Data.
biocViews: GUI, DataRepresentation, Network, MultipleComparison,
Pathways, Reactome, Visualization,GeneExpression,GeneTarget
Author: Karim Mezhoud [aut, cre]
Maintainer: Karim Mezhoud <kmezhoud@gmail.com>
URL: https://kmezhoud.github.io/bioCancer/
VignetteBuilder: knitr
BugReports: https://github.com/kmezhoud/bioCancer/issues
git_url: https://git.bioconductor.org/packages/bioCancer
git_branch: devel
git_last_commit: 94ee9bf
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/bioCancer_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/bioCancer_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/bioCancer_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/bioCancer_1.35.0.tgz
vignettes: vignettes/bioCancer/inst/doc/bioCancer.html
vignetteTitles: bioCancer: Interactive Multi-OMICS Cancers Data
Visualization and Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/bioCancer/inst/doc/bioCancer.R
dependencyCount: 257
Package: BioCartaImage
Version: 1.5.0
Depends: R (>= 4.3.0)
Imports: magick, grid, stats, grDevices, utils
Suggests: testthat, knitr, BiocStyle, ragg
License: MIT + file LICENSE
MD5sum: 2391621ba4351ffa2c100c084b2b60d4
NeedsCompilation: no
Title: BioCarta Pathway Images
Description: The core functionality of the package is to provide
coordinates of genes on the BioCarta pathway images and to
provide methods to add self-defined graphics to the genes of
interest.
biocViews: Software, Pathways, BioCarta, Visualization
Author: Zuguang Gu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7395-8709>)
Maintainer: Zuguang Gu <z.gu@dkfz.de>
URL: https://github.com/jokergoo/BioCartaImage
VignetteBuilder: knitr
BugReports: https://github.com/jokergoo/BioCartaImage/issues
git_url: https://git.bioconductor.org/packages/BioCartaImage
git_branch: devel
git_last_commit: 46b0932
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BioCartaImage_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BioCartaImage_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BioCartaImage_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BioCartaImage_1.5.0.tgz
vignettes: vignettes/BioCartaImage/inst/doc/BioCartaImage.html
vignetteTitles: Customize BioCarta Pathway Images
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BioCartaImage/inst/doc/BioCartaImage.R
dependencyCount: 9
Package: BiocBaseUtils
Version: 1.9.0
Depends: R (>= 4.2.0)
Imports: methods, utils
Suggests: knitr, rmarkdown, BiocStyle, tinytest
License: Artistic-2.0
MD5sum: dac17bda4b452bb7e95cd3ecb2c90aa3
NeedsCompilation: no
Title: General utility functions for developing Bioconductor packages
Description: The package provides utility functions related to package
development. These include functions that replace slots, and
selectors for show methods. It aims to coalesce the various
helper functions often re-used throughout the Bioconductor
ecosystem.
biocViews: Software, Infrastructure
Author: Marcel Ramos [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3242-0582>), Martin Morgan [ctb],
Hervé Pagès [ctb]
Maintainer: Marcel Ramos <marcel.ramos@sph.cuny.edu>
VignetteBuilder: knitr
BugReports: https://www.github.com/Bioconductor/BiocBaseUtils/issues
git_url: https://git.bioconductor.org/packages/BiocBaseUtils
git_branch: devel
git_last_commit: d643df4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BiocBaseUtils_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BiocBaseUtils_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BiocBaseUtils_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BiocBaseUtils_1.9.0.tgz
vignettes: vignettes/BiocBaseUtils/inst/doc/BiocBaseUtils.html
vignetteTitles: BiocBaseUtils Quick Start
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BiocBaseUtils/inst/doc/BiocBaseUtils.R
importsMe: AlphaMissenseR, AnVIL, AnVILAz, AnVILGCP, AnVILPublish,
BiocCheck, BiocFHIR, cBioPortalData, DNAfusion, iSEEfier,
MultiAssayExperiment, RaggedExperiment, TCGAutils, TENxIO,
UniProt.ws, VisiumIO, visiumStitched, XeniumIO,
SingleCellMultiModal
suggestsMe: scifer
dependencyCount: 2
Package: BiocBook
Version: 1.5.0
Depends: R (>= 4.3)
Imports: BiocGenerics, available, cli, glue, gert, gh, gitcreds, httr,
usethis, dplyr, purrr, tibble, methods, rprojroot, stringr,
yaml, tools, utils, rlang, quarto, renv
Suggests: BiocStyle, knitr, testthat (>= 3.0.0), rmarkdown
License: MIT + file LICENSE
MD5sum: cf02774cbbb7cf39e16679ea96bff0c0
NeedsCompilation: no
Title: Write, containerize, publish and version Quarto books with
Bioconductor
Description: A BiocBook can be created by authors (e.g. R developers,
but also scientists, teachers, communicators, ...) who wish to
1) write (compile a body of biological and/or bioinformatics
knowledge), 2) containerize (provide Docker images to reproduce
the examples illustrated in the compendium), 3) publish (deploy
an online book to disseminate the compendium), and 4) version
(automatically generate specific online book versions and
Docker images for specific Bioconductor releases).
biocViews: Infrastructure, ReportWriting, Software
Author: Jacques Serizay [aut, cre]
Maintainer: Jacques Serizay <jacquesserizay@gmail.com>
URL: https://bioconductor.org/packages/BiocBook
VignetteBuilder: knitr
BugReports: https://github.com/js2264/BiocBook/issues
git_url: https://git.bioconductor.org/packages/BiocBook
git_branch: devel
git_last_commit: 0148dc3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-12-20
source.ver: src/contrib/BiocBook_1.5.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BiocBook_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BiocBook_1.5.0.tgz
vignettes: vignettes/BiocBook/inst/doc/BiocBook.html
vignetteTitles: BiocBook
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BiocBook/inst/doc/BiocBook.R
dependencyCount: 84
Package: BiocCheck
Version: 1.43.12
Depends: R (>= 4.4.0)
Imports: BiocBaseUtils, BiocFileCache, BiocManager, biocViews, callr,
cli, codetools, graph, httr2, knitr, methods, rvest,
stringdist, tools, utils
Suggests: BiocStyle, devtools, gert, jsonlite, rmarkdown, tinytest,
usethis
License: Artistic-2.0
MD5sum: 074bae5e8e64c31d54d5e91fe9ba30a5
NeedsCompilation: no
Title: Bioconductor-specific package checks
Description: BiocCheck guides maintainers through Bioconductor best
practicies. It runs Bioconductor-specific package checks by
searching through package code, examples, and vignettes.
Maintainers are required to address all errors, warnings, and
most notes produced.
biocViews: Infrastructure
Author: Bioconductor Package Maintainer [aut], Lori Shepherd [aut],
Daniel von Twisk [ctb], Kevin Rue [ctb], Marcel Ramos [aut,
cre] (ORCID: <https://orcid.org/0000-0002-3242-0582>), Leonardo
Collado-Torres [ctb], Federico Marini [ctb]
Maintainer: Marcel Ramos <marcel.ramos@sph.cuny.edu>
URL: https://github.com/Bioconductor/BiocCheck
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/BiocCheck/issues
git_url: https://git.bioconductor.org/packages/BiocCheck
git_branch: devel
git_last_commit: 3e6bc8d
git_last_commit_date: 2025-02-20
Date/Publication: 2025-02-21
source.ver: src/contrib/BiocCheck_1.43.12.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BiocCheck_1.43.12.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BiocCheck_1.43.12.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BiocCheck_1.43.12.tgz
vignettes: vignettes/BiocCheck/inst/doc/BiocCheck.html
vignetteTitles: BiocCheck: Ensuring Bioconductor package guidelines
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BiocCheck/inst/doc/BiocCheck.R
importsMe: AnnotationHubData, gDRstyle, methodical
suggestsMe: GEOfastq, packFinder, preciseTAD, ReducedExperiment,
SpectralTAD, HMP16SData, HMP2Data, scpdata,
MainExistingDatasets
dependencyCount: 75
Package: BiocFHIR
Version: 1.9.0
Depends: R (>= 4.2)
Imports: DT, shiny, jsonlite, graph, tidyr, visNetwork, dplyr, utils,
methods, BiocBaseUtils
Suggests: knitr, testthat, rjsoncons, igraph, BiocStyle
License: Artistic-2.0
MD5sum: b1f1d73fbbd0d77985664635c8b78cf1
NeedsCompilation: no
Title: Illustration of FHIR ingestion and transformation using R
Description: FHIR R4 bundles in JSON format are derived from
https://synthea.mitre.org/downloads. Transformation inspired by
a kaggle notebook published by Dr Alexander Scarlat,
https://www.kaggle.com/code/drscarlat/fhir-starter-parse-healthcare-bundles-into-tables.
This is a very limited illustration of some basic parsing and
reorganization processes. Additional tooling will be required
to move beyond the Synthea data illustrations.
biocViews: Infrastructure, DataImport, DataRepresentation
Author: Vincent Carey [aut, cre] (ORCID:
<https://orcid.org/0000-0003-4046-0063>)
Maintainer: Vincent Carey <stvjc@channing.harvard.edu>
URL: https://github.com/vjcitn/BiocFHIR
VignetteBuilder: knitr
BugReports: https://github.com/vjcitn/BiocFHIR/issues
git_url: https://git.bioconductor.org/packages/BiocFHIR
git_branch: devel
git_last_commit: 9b190a5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BiocFHIR_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BiocFHIR_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BiocFHIR_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BiocFHIR_1.9.0.tgz
vignettes: vignettes/BiocFHIR/inst/doc/A_upper.html,
vignettes/BiocFHIR/inst/doc/B_handling.html,
vignettes/BiocFHIR/inst/doc/BiocFHIR.html,
vignettes/BiocFHIR/inst/doc/C_tables.html,
vignettes/BiocFHIR/inst/doc/D_linking.html
vignetteTitles: Upper level FHIR concepts, Handling FHIR documents with
BiocFHIR, BiocFHIR -- infrastructure for parsing and analyzing
FHIR data, Transforming FHIR documents to tables with BiocFHIR,
Linking information between FHIR resources
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BiocFHIR/inst/doc/A_upper.R,
vignettes/BiocFHIR/inst/doc/B_handling.R,
vignettes/BiocFHIR/inst/doc/BiocFHIR.R,
vignettes/BiocFHIR/inst/doc/C_tables.R,
vignettes/BiocFHIR/inst/doc/D_linking.R
dependencyCount: 66
Package: BiocFileCache
Version: 2.15.1
Depends: R (>= 3.4.0), dbplyr (>= 1.0.0)
Imports: methods, stats, utils, dplyr, RSQLite, DBI, filelock, curl,
httr
Suggests: testthat, knitr, BiocStyle, rmarkdown, rtracklayer
License: Artistic-2.0
MD5sum: 891bf06111b2e6ad923535e602ebc18c
NeedsCompilation: no
Title: Manage Files Across Sessions
Description: This package creates a persistent on-disk cache of files
that the user can add, update, and retrieve. It is useful for
managing resources (such as custom Txdb objects) that are
costly or difficult to create, web resources, and data files
used across sessions.
biocViews: DataImport
Author: Lori Shepherd [aut, cre], Martin Morgan [aut]
Maintainer: Lori Shepherd <lori.shepherd@roswellpark.org>
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/BiocFileCache/issues
git_url: https://git.bioconductor.org/packages/BiocFileCache
git_branch: devel
git_last_commit: 4b81e18
git_last_commit_date: 2025-01-17
Date/Publication: 2025-01-17
source.ver: src/contrib/BiocFileCache_2.15.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BiocFileCache_2.15.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BiocFileCache_2.15.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BiocFileCache_2.15.1.tgz
vignettes:
vignettes/BiocFileCache/inst/doc/BiocFileCache_Troubleshooting.html,
vignettes/BiocFileCache/inst/doc/BiocFileCache_UseCases.html,
vignettes/BiocFileCache/inst/doc/BiocFileCache.html
vignetteTitles: 3. BiocFileCache Troubleshooting, 2. BiocFileCache: Use
Cases, 1. BiocFileCache Overview: Managing File Resources
Across Sessions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/BiocFileCache/inst/doc/BiocFileCache_Troubleshooting.R,
vignettes/BiocFileCache/inst/doc/BiocFileCache_UseCases.R,
vignettes/BiocFileCache/inst/doc/BiocFileCache.R
dependsOnMe: AnnotationHub, easylift, ExperimentHub, RcwlPipelines,
JASPAR2022, JASPAR2024, scATAC.Explorer, TMExplorer, csawBook,
OSCA.basic, OSCA.intro, OSCA.workflows
importsMe: AlphaMissenseR, AMARETTO, atSNP, autonomics, BayesSpace,
bedbaser, BiocCheck, BiocHail, BiocPkgTools, biodb, biomaRt,
brendaDb, bugsigdbr, BulkSignalR, cbaf, cBioPortalData,
CBNplot, CellBench, CTDquerier, customCMPdb, CytoPipeline,
easyRNASeq, enhancerHomologSearch, EnMCB, EnrichmentBrowser,
EpiTxDb, fenr, fgga, GeDi, GenomicScores,
GenomicSuperSignature, ggkegg, GSEABenchmarkeR, gwascat, hca,
iSEEindex, MBQN, MIRit, motifbreakR, MotifPeeker,
MsBackendMetaboLights, OmicsMLRepoR, ontoProc, ORFik,
Organism.dplyr, OSTA.data, PhIPData, psichomics, rBLAST,
recount3, recountmethylation, regutools, ReUseData, RiboDiPA,
rpx, scviR, sesame, signeR, SpatialExperiment,
SpatialOmicsOverlay, SpliceWiz, SurfR, tenXplore,
terraTCGAdata, TFutils, tomoseqr, tximeta, UMI4Cats,
UniProt.ws, waddR, xenLite, geneplast.data, HPO.db, MPO.db,
org.Mxanthus.db, PANTHER.db, BioPlex, bugphyzz, depmap,
DNAZooData, fourDNData, HiContactsData, MetaScope,
MicrobiomeBenchmarkData, NxtIRFdata, orthosData, SFEData,
SingleCellMultiModal, spatialLIBD, SingscoreAMLMutations,
convertid
suggestsMe: AnnotationForge, bambu, BiocSet, ChIPpeakAnno, CoGAPS,
CRISPRseek, dominoSignal, EpiCompare, fastreeR, FLAMES, GRaNIE,
HicAggR, HiCDCPlus, HiCExperiment, HiCool, MethReg, MetMashR,
Nebulosa, nipalsMCIA, progeny, qsvaR, seqsetvis,
spatialHeatmap, structToolbox, TREG, visiumStitched, XeniumIO,
zellkonverter, emtdata, HighlyReplicatedRNASeq, MethylSeqData,
msigdb, TENxBrainData, TENxPBMCData, chipseqDB, fluentGenomics,
simpleSingleCell, scCustomize
dependencyCount: 45
Package: BiocGenerics
Version: 0.53.6
Depends: R (>= 4.0.0), methods, utils, graphics, stats, generics
Imports: methods, utils, graphics, stats
Suggests: Biobase, S4Vectors, IRanges, S4Arrays, SparseArray,
DelayedArray, HDF5Array, GenomicRanges, pwalign, Rsamtools,
AnnotationDbi, affy, affyPLM, DESeq2, flowClust, MSnbase,
annotate, MultiAssayExperiment, RUnit
License: Artistic-2.0
MD5sum: 5d64187ab455e60bf3263480bfe62772
NeedsCompilation: no
Title: S4 generic functions used in Bioconductor
Description: The package defines many S4 generic functions used in
Bioconductor.
biocViews: Infrastructure
Author: The Bioconductor Dev Team [aut], Hervé Pagès [aut, cre] (ORCID:
<https://orcid.org/0009-0002-8272-4522>), Laurent Gatto [ctb]
(ORCID: <https://orcid.org/0000-0002-1520-2268>), Nathaniel
Hayden [ctb], James Hester [ctb], Wolfgang Huber [ctb], Michael
Lawrence [ctb], Martin Morgan [ctb] (ORCID:
<https://orcid.org/0000-0002-5874-8148>), Valerie Obenchain
[ctb]
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/BiocGenerics
BugReports: https://github.com/Bioconductor/BiocGenerics/issues
git_url: https://git.bioconductor.org/packages/BiocGenerics
git_branch: devel
git_last_commit: 111836e
git_last_commit_date: 2025-01-26
Date/Publication: 2025-01-27
source.ver: src/contrib/BiocGenerics_0.53.6.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BiocGenerics_0.53.6.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BiocGenerics_0.53.6.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BiocGenerics_0.53.6.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: ACME, affy, affyPLM, altcdfenvs, amplican, AnnotationDbi,
AnnotationForge, AnnotationHub, ATACseqQC, beadarray,
bioassayR, Biobase, Biostrings, bnbc, BSgenome, BSgenomeForge,
bsseq, Cardinal, Category, categoryCompare, chipseq, ChIPseqR,
ChromHeatMap, clusterExperiment, codelink, consensusDE,
consensusSeekeR, CoreGx, CRISPRseek, cummeRbund, DelayedArray,
ensembldb, ExperimentHub, ExperimentHubData, GDSArray,
geneplotter, GenomeInfoDb, genomeIntervals, GenomicAlignments,
GenomicFeatures, GenomicFiles, GenomicRanges, GenomicScores,
ggbio, girafe, graph, GSEABase, GUIDEseq, h5mread, HelloRanges,
interactiveDisplay, interactiveDisplayBase, IRanges, ISLET,
lute, MBASED, MGnifyR, MineICA, minfi, MLInterfaces, MotifDb,
mpra, MSnbase, multtest, NADfinder, ngsReports, oligo,
OrganismDbi, plyranges, profileplyr, pwalign, PWMEnrich,
QSutils, RareVariantVis, Rarr, REDseq, Repitools, RnBeads, RPA,
rsbml, S4Arrays, S4Vectors, ShortRead, soGGi, SparseArray,
spqn, StructuralVariantAnnotation, svaNUMT, svaRetro, TEQC,
tigre, topdownr, topGO, txdbmaker, UNDO, updateObject,
VanillaICE, VariantAnnotation, VariantFiltering, VCFArray,
XVector, yamss, ChAMPdata, liftOver, rsolr
importsMe: a4Preproc, affycoretools, affylmGUI, alabaster.bumpy,
alabaster.files, alabaster.matrix, alabaster.ranges,
alabaster.se, AllelicImbalance, AneuFinder, annmap, annotate,
AnnotationHubData, ASpli, ATACseqTFEA, atena, AUCell,
autonomics, bambu, bamsignals, BASiCS, batchelor, beachmat,
bigmelon, BiocBook, biocGraph, BiocHail, BiocIO, BiocSingular,
biotmle, biovizBase, biscuiteer, BiSeq, blima, breakpointR,
BrowserViz, BubbleTree, bumphunter, BUSpaRse, CAGEfightR,
CAGEr, casper, celaref, CellBench, CellMixS, CellTrails,
cfDNAPro, cghMCR, ChemmineOB, ChemmineR, chipenrich,
ChIPpeakAnno, ChIPQC, ChIPseeker, chipseq, chromVAR, cicero,
CircSeqAlignTk, CleanUpRNAseq, clusterSeq, cn.mops, CNEr,
CNVPanelizer, CNVRanger, COCOA, cola, compEpiTools, CompoundDb,
concordexR, crisprBase, crisprBowtie, crisprBwa, crisprDesign,
crisprScore, crisprShiny, crisprViz, crlmm, csaw, CTexploreR,
cummeRbund, CuratedAtlasQueryR, cydar, dada2, dagLogo,
DAMEfinder, dandelionR, ddCt, decompTumor2Sig, deconvR, DegCre,
DEGreport, DelayedDataFrame, demuxSNP, derfinder, DEScan2,
DESeq2, DESpace, destiny, DEWSeq, DEXSeq, DFplyr, diffcoexp,
diffHic, dinoR, DirichletMultinomial, DiscoRhythm, DNAfusion,
dreamlet, DRIMSeq, DropletUtils, DrugVsDisease, easyRNASeq,
EBImage, EDASeq, eiR, eisaR, ELViS, enhancerHomologSearch,
EnrichDO, epialleleR, EpiCompare, epigenomix, epimutacions,
epistack, EpiTxDb, epivizrChart, epivizrStandalone, erma,
esATAC, factR, FamAgg, fastseg, ffpe, FindIT2, FLAMES, flowBin,
flowClust, flowCore, flowFP, FlowSOM, flowSpecs, flowStats,
flowWorkspace, fmcsR, FRASER, frma, GA4GHclient, GA4GHshiny,
gcapc, gDNAx, geneAttribution, geneClassifiers, GENESIS,
GenomAutomorphism, GenomicAlignments, GenomicInteractions,
GenomicPlot, GenomicTuples, GenVisR, geomeTriD, GeomxTools,
gINTomics, glmGamPoi, gmapR, gmoviz, goseq, GOTHiC, gpuMagic,
Gviz, HDF5Array, heatmaps, hermes, HicAggR, HiCDOC,
HiCExperiment, HiContacts, HiCParser, HiLDA, hiReadsProcessor,
hopach, icetea, igvR, igvShiny, IHW, IMAS, infercnv, INSPEcT,
intansv, InteractionSet, IntEREst, IONiseR, iSEE,
IsoformSwitchAnalyzeR, isomiRs, IVAS, KCsmart, ldblock, lefser,
lemur, LinTInd, lisaClust, LOLA, maaslin3, mariner, maser,
MAST, matter, MEAL, meshr, MetaboAnnotation, metaMS, metaseqR2,
methInheritSim, MethylAid, methylPipe, methylumi, mia, miaViz,
miloR, mimager, MinimumDistance, MIRA, MiRaGE, missMethyl,
mist, mobileRNA, Modstrings, mogsa, monaLisa, monocle,
Moonlight2R, Motif2Site, motifbreakR, msa, MsBackendSql,
MsExperiment, MSnID, MultiAssayExperiment, multicrispr,
MultiDataSet, multiMiR, MultimodalExperiment, mumosa,
MutationalPatterns, mzR, NanoStringNCTools, nearBynding,
npGSEA, nucleR, oligoClasses, openCyto, openPrimeR, ORFik,
OUTRIDER, parglms, pcaMethods, PDATK, pdInfoBuilder,
PharmacoGx, PhIPData, PhosR, phyloseq, piano, PING, PIPETS,
plyinteractions, podkat, pram, primirTSS, proDA,
profileScoreDist, pRoloc, pRolocGUI, ProteoDisco, PSMatch,
PureCN, QFeatures, qPLEXanalyzer, qsea, QTLExperiment, QuasR,
R3CPET, R453Plus1Toolbox, RadioGx, raer, RaggedExperiment,
ramr, ramwas, RCAS, RcisTarget, RCy3, RCyjs, recoup,
ReducedExperiment, REMP, ReportingTools, RGSEA, RiboCrypt,
RiboDiPA, RiboProfiling, ribosomeProfilingQC,
RJMCMCNucleosomes, rnaEditr, RNAmodR, RNAmodR.AlkAnilineSeq,
RNAmodR.ML, RNAmodR.RiboMethSeq, RNAseqCovarImpute, roar, rols,
Rqc, rqubic, Rsamtools, rsbml, rScudo, RTCGAToolbox,
rtracklayer, saseR, SC3, SCArray.sat, scater, scDblFinder,
scDotPlot, scmap, SCnorm, SCOPE, scPipe, scran, scruff,
scuttle, SeqVarTools, sevenC, SGSeq, SharedObject, shinyMethyl,
signatureSearch, signeR, signifinder, simPIC,
SingleCellExperiment, SingleMoleculeFootprinting, sitadela,
SNPhood, snpStats, sparrow, SpatialExperiment,
SpatialFeatureExperiment, spatzie, Spectra, splatter,
SpliceWiz, SplicingGraphs, SplineDV, SQLDataFrame, sRACIPE,
sscu, StabMap, standR, strandCheckR, Streamer, Structstrings,
SummarizedExperiment, SVP, SynMut, systemPipeR, tadar, TAPseq,
target, TCGAutils, TCseq, TENxIO, TFBSTools,
tidySpatialExperiment, TitanCNA, ToxicoGx, trackViewer,
transcriptR, transite, TreeSummarizedExperiment, tRNA,
tRNAscanImport, TVTB, txcutr, Ularcirc, UMI4Cats,
unifiedWMWqPCR, UniProt.ws, universalmotif, uSORT,
VariantTools, velociraptor, VisiumIO, visiumStitched,
wavClusteR, weitrix, xcms, XDE, XeniumIO, XVector, zitools,
SNPlocs.Hsapiens.dbSNP144.GRCh37,
SNPlocs.Hsapiens.dbSNP144.GRCh38,
SNPlocs.Hsapiens.dbSNP149.GRCh38,
SNPlocs.Hsapiens.dbSNP150.GRCh38,
SNPlocs.Hsapiens.dbSNP155.GRCh37,
SNPlocs.Hsapiens.dbSNP155.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh37,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, chipenrich.data,
curatedOvarianData, gDNAinRNAseqData, homosapienDEE2CellScore,
IHWpaper, KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO,
microbiomeDataSets, MouseGastrulationData, MouseThymusAgeing,
raerdata, scRNAseq, spatialLIBD, systemPipeRdata, TENxBUSData,
VariantToolsData, ExpHunterSuite, GeoMxWorkflows,
crispRdesignR, DCLEAR, decompDL, EEMDlstm, geno2proteo,
locuszoomr, oncoPredict, pathwayTMB, RNAseqQC, RobLoxBioC,
SCRIP, Signac, spectralAnalysis, TaxaNorm, toxpiR, treediff,
TSdeeplearning
suggestsMe: acde, adverSCarial, aggregateBioVar, AIMS, AlphaMissenseR,
ASSET, ASURAT, BaalChIP, baySeq, bigmelon, BiocParallel,
BiocStyle, biocViews, biosigner, BiRewire, BLMA,
BloodGen3Module, bnem, borealis, BUScorrect, BUSseq, CAFE,
CAMERA, CausalR, ccrepe, CDI, cellmigRation, CexoR, chihaya,
ChIPanalyser, ChIPXpress, CHRONOS, CINdex, cleanUpdTSeq,
clipper, ClustAll, clustComp, CNORfeeder, CNORfuzzy, consensus,
cosmiq, COSNet, cpvSNP, cypress, DEsubs, DExMA, DMRcaller,
DMRcate, DNAcycP2, EnhancedVolcano, ENmix, EpiMix, epiNEM,
EventPointer, fCCAC, fcScan, fgga, FGNet, flowCut, flowTime,
fmrs, GateFinder, gCrisprTools, gdsfmt, GEM,
GeneNetworkBuilder, GeneOverlap, geneplast, geneRxCluster,
geNetClassifier, genomation, GEOquery, GeoTcgaData, ginmappeR,
GMRP, GOstats, GrafGen, GreyListChIP, GSVA, GWASTools, h5vc,
Harman, hiAnnotator, HiCDCPlus, hierGWAS, HIREewas, HPiP,
hypergraph, iCARE, iClusterPlus, IFAA, illuminaio,
immunotation, INPower, IPO, kebabs, KEGGREST, LACE, MAGAR,
magpie, massiR, MatrixQCvis, MatrixRider, MBttest, mCSEA,
Mergeomics, MetaboSignal, metagene2, metagenomeSeq, MetCirc,
methylCC, methylInheritance, MetNet, microbiome,
miRBaseConverter, miRcomp, mirIntegrator, mnem, mosbi, MOSClip,
motifStack, MsQuality, multiClust, MultiMed, MultiRNAflow,
MungeSumstats, MWASTools, ncRNAtools, nempi, NetSAM, nucleoSim,
OMICsPCA, OncoScore, PAA, panelcn.mops, Path2PPI, PathNet,
pathview, PCAtools, pepXMLTab, phenomis, PhenStat, powerTCR,
proBAMr, qpgraph, quantro, RBGL, rBiopaxParser, rcellminer,
rCGH, REBET, RESOLVE, rfaRm, RGraph2js, Rgraphviz, rgsepd,
riboSeqR, ROntoTools, ropls, ROSeq, RTN, RTNduals, RTNsurvival,
rTRM, SAIGEgds, sangerseqR, SANTA, sarks, SCArray, scDataviz,
scmeth, scp, screenCounter, scry, segmentSeq, SeqArray,
seqPattern, seqTools, SICtools, sigFeature, sigsquared, SIMAT,
similaRpeak, SIMLR, singleCellTK, SingleR, slingshot,
SNPRelate, SparseSignatures, spatialHeatmap, specL, STATegRa,
STRINGdb, SUITOR, systemPipeTools, TCC, TFEA.ChIP, tidytof,
TIN, transcriptogramer, traseR, TreeAndLeaf, tripr,
tRNAdbImport, TRONCO, Uniquorn, variancePartition, VERSO,
XAItest, xcore, zenith, ENCODExplorerData, geneplast.data,
ConnectivityMap, FieldEffectCrc, grndata, HarmanData,
healthyControlsPresenceChecker, microRNAome, RegParallel,
scMultiome, sesameData, xcoredata, adjclust, aroma.affymetrix,
asteRisk, gkmSVM, GSEMA, MarZIC, NutrienTrackeR, openSkies,
pagoda2, Platypus, polyRAD, Rediscover, Seurat
dependencyCount: 5
Package: biocGraph
Version: 1.69.0
Depends: Rgraphviz, graph
Imports: Rgraphviz, geneplotter, graph, BiocGenerics, methods
Suggests: fibroEset, geneplotter, hgu95av2.db
License: Artistic-2.0
MD5sum: a574b9d9496b25fbe3b7e824762b3cb0
NeedsCompilation: no
Title: Graph examples and use cases in Bioinformatics
Description: This package provides examples and code that make use of
the different graph related packages produced by Bioconductor.
biocViews: Visualization, GraphAndNetwork
Author: Li Long <li.long@isb-sib.ch>, Robert Gentleman
<rgentlem@fhcrc.org>, Seth Falcon <sethf@fhcrc.org> Florian
Hahne <fhahne@fhcrc.org>
Maintainer: Florian Hahne <florian.hahne@novartis.com>
git_url: https://git.bioconductor.org/packages/biocGraph
git_branch: devel
git_last_commit: f12102d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/biocGraph_1.69.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/biocGraph_1.69.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/biocGraph_1.69.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/biocGraph_1.69.0.tgz
vignettes: vignettes/biocGraph/inst/doc/biocGraph.pdf,
vignettes/biocGraph/inst/doc/layingOutPathways.pdf
vignetteTitles: Examples of plotting graphs Using Rgraphviz, HOWTO
layout pathways
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biocGraph/inst/doc/biocGraph.R,
vignettes/biocGraph/inst/doc/layingOutPathways.R
suggestsMe: EnrichmentBrowser
dependencyCount: 54
Package: BiocHail
Version: 1.7.1
Depends: R (>= 4.3.0), graphics, stats, utils
Imports: reticulate, basilisk, BiocFileCache, methods, dplyr,
BiocGenerics
Suggests: knitr, testthat, BiocStyle, ggplot2, DT
License: Artistic-2.0
MD5sum: e87088a34de49f92f991cd95c85d7b23
NeedsCompilation: no
Title: basilisk and hail
Description: Use hail via basilisk when appropriate, or via reticulate.
This package can be used in terra.bio to interact with UK
Biobank resources processed by hail.is.
biocViews: Infrastructure
Author: Vincent Carey [aut, cre] (ORCID:
<https://orcid.org/0000-0003-4046-0063>)
Maintainer: Vincent Carey <stvjc@channing.harvard.edu>
URL: https://github.com/vjcitn/BiocHail
VignetteBuilder: knitr
BugReports: https://github.com/vjcitn/BiocHail/issues
git_url: https://git.bioconductor.org/packages/BiocHail
git_branch: devel
git_last_commit: 3bfea67
git_last_commit_date: 2024-11-20
Date/Publication: 2024-11-20
source.ver: src/contrib/BiocHail_1.7.1.tar.gz
vignettes: vignettes/BiocHail/inst/doc/gwas_tut.html,
vignettes/BiocHail/inst/doc/large_t2t.html,
vignettes/BiocHail/inst/doc/ukbb.html
vignetteTitles: 01 BiocHail -- GWAS tutorial, 02 Working with larger
VCF: T2T by chromosome, 03 Working with UK Biobank summary
statistics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BiocHail/inst/doc/gwas_tut.R,
vignettes/BiocHail/inst/doc/large_t2t.R,
vignettes/BiocHail/inst/doc/ukbb.R
dependencyCount: 61
Package: BiocHubsShiny
Version: 1.7.5
Depends: R (>= 4.3.0), shiny
Imports: AnnotationHub, ExperimentHub, DT, htmlwidgets, rclipboard,
S4Vectors, shinyAce, shinyjs, shinythemes, shinytoastr, utils
Suggests: BiocManager, BiocStyle, knitr, rmarkdown, sessioninfo,
shinytest2
License: Artistic-2.0
MD5sum: 140e1dbeda86e7935878b9324a9959f9
NeedsCompilation: no
Title: View AnnotationHub and ExperimentHub Resources Interactively
Description: A package that allows interactive exploration of
AnnotationHub and ExperimentHub resources. It uses DT /
DataTable to display resources for multiple organisms. It
provides template code for reproducibility and for downloading
resources via the indicated Hub package.
biocViews: Software, ShinyApps
Author: Marcel Ramos [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3242-0582>), Vincent Carey [ctb]
Maintainer: Marcel Ramos <marcel.ramos@sph.cuny.edu>
URL: https://github.com/Bioconductor/BiocHubsShiny
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/BiocHubsShiny/issues
git_url: https://git.bioconductor.org/packages/BiocHubsShiny
git_branch: devel
git_last_commit: 5f762bb
git_last_commit_date: 2025-03-04
Date/Publication: 2025-03-05
source.ver: src/contrib/BiocHubsShiny_1.7.5.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BiocHubsShiny_1.7.5.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BiocHubsShiny_1.7.5.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BiocHubsShiny_1.7.5.tgz
vignettes: vignettes/BiocHubsShiny/inst/doc/BiocHubsShiny.html
vignetteTitles: BiocHubsShiny Overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BiocHubsShiny/inst/doc/BiocHubsShiny.R
dependencyCount: 97
Package: BiocIO
Version: 1.17.1
Depends: R (>= 4.3.0)
Imports: BiocGenerics, S4Vectors, methods, tools
Suggests: testthat, knitr, rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: a25edfe5502c5521120d50323558fbe3
NeedsCompilation: no
Title: Standard Input and Output for Bioconductor Packages
Description: The `BiocIO` package contains high-level abstract classes
and generics used by developers to build IO funcionality within
the Bioconductor suite of packages. Implements `import()` and
`export()` standard generics for importing and exporting
biological data formats. `import()` supports whole-file as well
as chunk-wise iterative import. The `import()` interface
optionally provides a standard mechanism for 'lazy' access via
`filter()` (on row or element-like components of the file
resource), `select()` (on column-like components of the file
resource) and `collect()`. The `import()` interface optionally
provides transparent access to remote (e.g. via https) as well
as local access. Developers can register a file extension,
e.g., `.loom` for dispatch from character-based URIs to
specific `import()` / `export()` methods based on classes
representing file types, e.g., `LoomFile()`.
biocViews: Annotation,DataImport
Author: Martin Morgan [aut], Michael Lawrence [aut], Daniel Van Twisk
[aut], Marcel Ramos [cre] (ORCID:
<https://orcid.org/0000-0002-3242-0582>)
Maintainer: Marcel Ramos <marcel.ramos@sph.cuny.edu>
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/BiocIO/issues
git_url: https://git.bioconductor.org/packages/BiocIO
git_branch: devel
git_last_commit: a2a0962
git_last_commit_date: 2024-11-21
Date/Publication: 2024-11-21
source.ver: src/contrib/BiocIO_1.17.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BiocIO_1.17.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BiocIO_1.17.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BiocIO_1.17.1.tgz
vignettes: vignettes/BiocIO/inst/doc/BiocIO.html
vignetteTitles: BiocIO
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BiocIO/inst/doc/BiocIO.R
dependsOnMe: BSgenome, HelloRanges, LoomExperiment
importsMe: BiocSet, BSgenomeForge, HiCExperiment, HiContacts, HiCool,
rtracklayer, TENxIO, tidyCoverage, txdbmaker, VisiumIO,
XeniumIO
dependencyCount: 9
Package: biocmake
Version: 0.99.0
Imports: utils, tools, dir.expiry
Suggests: knitr, rmarkdown, BiocStyle, testthat
License: MIT + file LICENSE
MD5sum: 04ecbe093e41cfa89f67f163f53edde9
NeedsCompilation: no
Title: CMake for Bioconductor
Description: Manages the installation of CMake for building
Bioconductor packages. This avoids the need for end-users to
manually install CMake on their system. No action is performed
if a suitable version of CMake is already available.
biocViews: Infrastructure
Author: Aaron Lun [cre, aut]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://github.com/LTLA/biocmake
VignetteBuilder: knitr
BugReports: https://github.com/LTLA/biocmake/issues
git_url: https://git.bioconductor.org/packages/biocmake
git_branch: devel
git_last_commit: 8ab06a5
git_last_commit_date: 2025-01-30
Date/Publication: 2025-02-03
source.ver: src/contrib/biocmake_0.99.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/biocmake_0.99.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/biocmake_0.99.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/biocmake_0.99.0.tgz
vignettes: vignettes/biocmake/inst/doc/userguide.html
vignetteTitles: Cmake for Bioconductor
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/biocmake/inst/doc/userguide.R
linksToMe: Rigraphlib
dependencyCount: 4
Package: BiocNeighbors
Version: 2.1.3
Imports: Rcpp, methods
LinkingTo: Rcpp, assorthead
Suggests: BiocParallel, testthat, BiocStyle, knitr, rmarkdown
License: GPL-3
MD5sum: d969a8cfdc1ef1f32a8c067fe79a3c7a
NeedsCompilation: yes
Title: Nearest Neighbor Detection for Bioconductor Packages
Description: Implements exact and approximate methods for nearest
neighbor detection, in a framework that allows them to be
easily switched within Bioconductor packages or workflows.
Exact searches can be performed using the k-means for k-nearest
neighbors algorithm or with vantage point trees. Approximate
searches can be performed using the Annoy or HNSW libraries.
Searching on either Euclidean or Manhattan distances is
supported. Parallelization is achieved for all methods by using
BiocParallel. Functions are also provided to search for all
neighbors within a given distance.
biocViews: Clustering, Classification
Author: Aaron Lun [aut, cre, cph]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
SystemRequirements: C++17
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BiocNeighbors
git_branch: devel
git_last_commit: 25e7813
git_last_commit_date: 2025-03-05
Date/Publication: 2025-03-05
source.ver: src/contrib/BiocNeighbors_2.1.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BiocNeighbors_2.1.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BiocNeighbors_2.1.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BiocNeighbors_2.1.3.tgz
vignettes: vignettes/BiocNeighbors/inst/doc/userguide.html
vignetteTitles: Finding neighbors in high-dimensional space
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BiocNeighbors/inst/doc/userguide.R
dependsOnMe: OSCA.workflows, SingleRBook
importsMe: batchelor, bluster, CellMixS, concordexR, cydar, GeDi,
imcRtools, lemur, miloR, mumosa, poem, scater, scDblFinder,
scMerge, scrapper, SingleR, SpatialFeatureExperiment,
SpotSweeper, StabMap, SVP, UCell
suggestsMe: ClassifyR, TrajectoryUtils, TSCAN
linksToMe: scrapper, SingleR
dependencyCount: 4
Package: BioCor
Version: 1.31.0
Depends: R (>= 3.4.0)
Imports: BiocParallel, GSEABase, Matrix, methods
Suggests: airway, BiocStyle, boot, DESeq2, ggplot2 (>= 3.4.1),
GOSemSim, Hmisc, knitr (>= 1.35), org.Hs.eg.db, reactome.db,
rmarkdown, spelling, targetscan.Hs.eg.db, testthat (>= 3.0.0),
WGCNA
License: MIT + file LICENSE
Archs: x64
MD5sum: c993714b686a5c2ecca3bf1eb7654573
NeedsCompilation: no
Title: Functional similarities
Description: Calculates functional similarities based on the pathways
described on KEGG and REACTOME or in gene sets. These
similarities can be calculated for pathways or gene sets,
genes, or clusters and combined with other similarities. They
can be used to improve networks, gene selection, testing
relationships...
biocViews: StatisticalMethod, Clustering, GeneExpression, Network,
Pathways, NetworkEnrichment, SystemsBiology
Author: LluÃs Revilla Sancho [aut, cre] (ORCID:
<https://orcid.org/0000-0001-9747-2570>), Pau Sancho-Bru [ths]
(ORCID: <https://orcid.org/0000-0001-5569-9259>), Juan José
Salvatella Lozano [ths] (ORCID:
<https://orcid.org/0000-0001-7613-3908>)
Maintainer: LluÃs Revilla Sancho <lluis.revilla@gmail.com>
URL: https://bioconductor.org/packages/BioCor,
https://llrs.github.io/BioCor/
VignetteBuilder: knitr
BugReports: https://github.com/llrs/BioCor/issues
git_url: https://git.bioconductor.org/packages/BioCor
git_branch: devel
git_last_commit: 59fbed3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BioCor_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BioCor_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BioCor_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BioCor_1.31.0.tgz
vignettes: vignettes/BioCor/inst/doc/BioCor_1_basics.html,
vignettes/BioCor/inst/doc/BioCor_2_advanced.html
vignetteTitles: About BioCor, Advanced usage of BioCor
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BioCor/inst/doc/BioCor_1_basics.R,
vignettes/BioCor/inst/doc/BioCor_2_advanced.R
dependencyCount: 62
Package: BiocParallel
Version: 1.41.2
Depends: methods, R (>= 3.5.0)
Imports: stats, utils, futile.logger, parallel, snow, codetools
LinkingTo: BH, cpp11
Suggests: BiocGenerics, tools, foreach, BBmisc, doParallel,
GenomicRanges, RNAseqData.HNRNPC.bam.chr14,
TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation,
Rsamtools, GenomicAlignments, ShortRead, RUnit, BiocStyle,
knitr, batchtools, data.table
Enhances: Rmpi
License: GPL-2 | GPL-3
MD5sum: 84f5965eb529425b983c59a413ccfccd
NeedsCompilation: yes
Title: Bioconductor facilities for parallel evaluation
Description: This package provides modified versions and novel
implementation of functions for parallel evaluation, tailored
to use with Bioconductor objects.
biocViews: Infrastructure
Author: Martin Morgan [aut, cre], Jiefei Wang [aut], Valerie Obenchain
[aut], Michel Lang [aut], Ryan Thompson [aut], Nitesh Turaga
[aut], Aaron Lun [ctb], Henrik Bengtsson [ctb], Madelyn Carlson
[ctb] (Translated 'Random Numbers' vignette from Sweave to
RMarkdown / HTML.), Phylis Atieno [ctb] (Translated
'Introduction to BiocParallel' vignette from Sweave to
Rmarkdown / HTML.), Sergio Oller [ctb] (Improved bpmapply()
efficiency., ORCID: <https://orcid.org/0000-0002-8994-1549>)
Maintainer: Martin Morgan <mtmorgan.bioc@gmail.com>
URL: https://github.com/Bioconductor/BiocParallel
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/BiocParallel/issues
git_url: https://git.bioconductor.org/packages/BiocParallel
git_branch: devel
git_last_commit: efcc9a7
git_last_commit_date: 2025-02-18
Date/Publication: 2025-02-19
source.ver: src/contrib/BiocParallel_1.41.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BiocParallel_1.41.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BiocParallel_1.41.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BiocParallel_1.41.2.tgz
vignettes:
vignettes/BiocParallel/inst/doc/BiocParallel_BatchtoolsParam.pdf,
vignettes/BiocParallel/inst/doc/Errors_Logs_And_Debugging.pdf,
vignettes/BiocParallel/inst/doc/Introduction_To_BiocParallel.html,
vignettes/BiocParallel/inst/doc/Random_Numbers.html
vignetteTitles: 2. Introduction to BatchtoolsParam, 3. Errors,, Logs
and Debugging, 1. Introduction to BiocParallel, 4. Random
Numbers in BiocParallel
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BiocParallel/inst/doc/BiocParallel_BatchtoolsParam.R,
vignettes/BiocParallel/inst/doc/Errors_Logs_And_Debugging.R,
vignettes/BiocParallel/inst/doc/Introduction_To_BiocParallel.R,
vignettes/BiocParallel/inst/doc/Random_Numbers.R
dependsOnMe: bacon, BEclear, Cardinal, CardinalIO, ChIPQC, ClassifyR,
clusterSeq, consensusSeekeR, DEWSeq, DEXSeq, DMCFB, DMCHMM,
doppelgangR, DSS, extraChIPs, FEAST, FRASER, GenomicFiles,
hiReadsProcessor, INSPEcT, iPath, ISLET, matter, MBASED,
metagene2, metapone, ncGTW, Oscope, OUTRIDER, PCAN,
periodicDNA, pRoloc, RedisParam, Rqc, ShortRead, SigCheck,
Spectra, sva, variancePartition, xcms, sequencing,
OSCA.workflows, SingleRBook
importsMe: abseqR, ADImpute, AffiXcan, ALDEx2, AlphaBeta, AlpsNMR,
amplican, ASICS, ATACseqQC, atena, atSNP, bambu, BANDITS,
bandle, BASiCS, batchelor, BayesSpace, bayNorm, beer,
benchdamic, BERT, BioCor, BiocSingular, BioNERO, biotmle,
biscuiteer, bluster, brendaDb, bsseq, CAGEfightR, CAGEr,
CARDspa, ccImpute, CDI, cellbaseR, CellBench, CelliD, CellMixS,
censcyt, Cepo, ChIPexoQual, ChromSCape, chromVAR,
ClusterFoldSimilarity, CNVMetrics, CNVRanger, CoGAPS, comapr,
coMethDMR, CompoundDb, concordexR, condiments, consensusDE,
consensusOV, consICA, CoreGx, coseq, cpvSNP, CrispRVariants,
csaw, CTSV, cydar, cypress, CytoGLMM, cytoKernel, cytomapper,
CytoMDS, CytoPipeline, dcGSA, debCAM, decoupleR, DeepTarget,
DegCre, DepInfeR, derfinder, DEScan2, DESeq2, DEsingle,
DESpace, DiffBind, Dino, dmrseq, DOSE, dreamlet, DRIMSeq,
DropletUtils, Dune, easier, easyRNASeq, EMDomics,
enhancerHomologSearch, epimutacions, epiregulon, epistasisGA,
erma, ERSSA, escape, EWCE, factR, faers, fgsea, findIPs,
FindIT2, FLAMES, flowcatchR, flowSpecs, GDCRNATools, gDNAx,
gDRcore, gDRutils, GeDi, GENESIS, GenomAutomorphism,
GenomicAlignments, gINTomics, GloScope, gmapR, gscreend,
GSEABenchmarkeR, GSVA, h5vc, HicAggR, HiCBricks, HiCcompare,
HiCDOC, HiCExperiment, HiContacts, HTSFilter, HybridExpress,
iasva, icetea, ideal, IMAS, imcRtools, IntEREst, IONiseR, IPO,
IsoformSwitchAnalyzeR, jazzPanda, katdetectr, KinSwingR,
LimROTS, lisaClust, loci2path, LRcell, Macarron, magpie,
magrene, mariner, mbkmeans, MCbiclust, MetaboAnnotation,
MetaboCoreUtils, metabomxtr, metaseqR2, methodical, MethylAid,
methylGSA, methyLImp2, methylInheritance, methylscaper, MetNet,
mia, miaViz, microSTASIS, MICSQTL, miloR, minfi, MIRit, mist,
mixOmics, MOGAMUN, MoleculeExperiment, monaLisa, motifbreakR,
MotifPeeker, MPAC, MPRAnalyze, MsBackendMassbank, MsBackendMgf,
MsBackendMsp, MsBackendRawFileReader, MsBackendSql, MSnbase,
msqrob2, MsQuality, multiHiCcompare, mumosa, muscat, NBAMSeq,
nnSVG, NPARC, ORFik, orthos, OVESEG, PAIRADISE, pairedGSEA,
PCAtools, PDATK, pengls, PharmacoGx, pipeComp, poem, pram,
proActiv, profileplyr, ProteoDisco, PSMatch, qpgraph, qsea,
QuasR, RadioGx, raer, rawDiag, Rcwl, recount,
ReducedExperiment, RegEnrich, REMP, RiboCrypt,
RJMCMCNucleosomes, RNAmodR, RNAseqCovarImpute, ROTS, Rsamtools,
RUVcorr, saseR, satuRn, scanMiR, scanMiRApp, SCArray,
SCArray.sat, scater, scBubbletree, scClassify, scDblFinder,
scDD, scDDboost, scde, scDesign3, SCFA, scFeatures, scHOT,
scMerge, scMultiSim, SCnorm, scone, scoreInvHap, scPCA, scran,
scRecover, screenCounter, scruff, scShapes, scTHI, scuttle,
seqArchR, seqArchRplus, sesame, SEtools, sigFeature,
signatureSearch, SimBu, simpleSeg, SingleCellAlleleExperiment,
singleCellTK, SingleR, singscore, SNPhood, soGGi, sparrow,
SpatialFeatureExperiment, SpectralTAD, spicyR, splatter,
SpliceWiz, SplicingGraphs, spoon, SpotSweeper, srnadiff,
StabMap, Statial, SUITOR, SVP, syntenet, TAPseq,
TBSignatureProfiler, ternarynet, TFBSTools, tidyCoverage,
TMixClust, ToxicoGx, TPP2D, tpSVG, tradeSeq,
TreeSummarizedExperiment, Trendy, TVTB, txcutr, UCell,
VariantFiltering, VariantTools, VDJdive, velociraptor, Voyager,
waddR, weitrix, xCell2, zinbwave, CytoMethIC, IHWpaper,
JohnsonKinaseData, ExpHunterSuite, causalBatch, DCLEAR, DTSEA,
DysPIA, enviGCMS, GSEMA, Holomics, LDM, minSNPs, oosse, robin,
scGate
suggestsMe: alabaster.mae, beachmat, BiocNeighbors, cliqueMS,
DelayedArray, EpiCompare, GenomicDataCommons, ggsc, glmGamPoi,
GRaNIE, h5mread, HDF5Array, ISAnalytics, MungeSumstats,
netSmooth, omicsPrint, plyinteractions, PureCN, randRotation,
RcisTarget, rebook, rhdf5, S4Arrays, scGPS, SeqArray,
spatialHeatmap, survClust, TFutils, TileDBArray,
TrajectoryUtils, TSCAN, universalmotif, xcore, MethylAidData,
Single.mTEC.Transcriptomes, TENxBrainData, TENxPBMCData,
CAGEWorkflow, clustermq, conos, pagoda2, phase1RMD, RaMS,
SpatialDDLS, survBootOutliers, wrTopDownFrag
dependencyCount: 12
Package: BiocPkgTools
Version: 1.25.8
Depends: htmlwidgets, R (>= 4.1.0)
Imports: BiocFileCache, BiocManager, biocViews, tibble, methods, rlang,
stringr, stats, rvest, dplyr, xml2, readr, httr, htmltools, DT,
tools, utils, igraph, jsonlite, gh, RBGL, graph, rorcid, curl,
glue, lubridate, purrr, tidyr, yaml
Suggests: BiocStyle, knitr, rmarkdown, testthat, tm, networkD3,
visNetwork, clipr, blastula, kableExtra, DiagrammeR,
SummarizedExperiment
License: MIT + file LICENSE
Archs: x64
MD5sum: 96bb42b4ee064e9aca47fc7a6ad1f2de
NeedsCompilation: no
Title: Collection of simple tools for learning about Bioconductor
Packages
Description: Bioconductor has a rich ecosystem of metadata around
packages, usage, and build status. This package is a simple
collection of functions to access that metadata from R. The
goal is to expose metadata for data mining and value-added
functionality such as package searching, text mining, and
analytics on packages.
biocViews: Software, Infrastructure
Author: Shian Su [aut, ctb], Lori Shepherd [ctb], Marcel Ramos [aut,
ctb] (ORCID: <https://orcid.org/0000-0002-3242-0582>), Felix
G.M. Ernst [ctb], Jennifer Wokaty [ctb], Charlotte Soneson
[ctb], Martin Morgan [ctb], Vince Carey [ctb], Sean Davis [aut,
cre]
Maintainer: Sean Davis <seandavi@gmail.com>
URL: https://github.com/seandavi/BiocPkgTools
SystemRequirements: mailsend-go
VignetteBuilder: knitr
BugReports: https://github.com/seandavi/BiocPkgTools/issues/new
git_url: https://git.bioconductor.org/packages/BiocPkgTools
git_branch: devel
git_last_commit: adf2e7d
git_last_commit_date: 2025-03-04
Date/Publication: 2025-03-06
source.ver: src/contrib/BiocPkgTools_1.25.8.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BiocPkgTools_1.25.8.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BiocPkgTools_1.25.8.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BiocPkgTools_1.25.8.tgz
vignettes: vignettes/BiocPkgTools/inst/doc/BiocPkgTools.html
vignetteTitles: Overview of BiocPkgTools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BiocPkgTools/inst/doc/BiocPkgTools.R
suggestsMe: rworkflows
dependencyCount: 111
Package: biocroxytest
Version: 1.3.0
Depends: R (>= 4.4.0)
Imports: cli, glue, roxygen2, stringr
Suggests: BiocStyle, here, knitr, rmarkdown, testthat (>= 3.0.0)
License: GPL (>= 3)
MD5sum: 7ffcf9a9655ceec87ad93009348ffbed
NeedsCompilation: no
Title: Handle Long Tests in Bioconductor Packages
Description: This package provides a roclet for roxygen2 that
identifies and processes code blocks in your documentation
marked with `@longtests`. These blocks should contain tests
that take a long time to run and thus cannot be included in the
regular test suite of the package. When you run
`roxygen2::roxygenise` with the `longtests_roclet`, it will
extract these long tests from your documentation and save them
in a separate directory. This allows you to run these long
tests separately from the rest of your tests, for example, on a
continuous integration server that is set up to run long tests.
biocViews: Software, Infrastructure
Author: Francesc Catala-Moll [aut, cre] (ORCID:
<https://orcid.org/0000-0002-2354-8648>)
Maintainer: Francesc Catala-Moll <fcatala@irsicaixa.es>
URL: https://github.com/xec-cm/biocroxytest
VignetteBuilder: knitr
BugReports: https://github.com/xec-cm/biocroxytest/issues
git_url: https://git.bioconductor.org/packages/biocroxytest
git_branch: devel
git_last_commit: d07605f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/biocroxytest_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/biocroxytest_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/biocroxytest_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/biocroxytest_1.3.0.tgz
vignettes: vignettes/biocroxytest/inst/doc/biocroxytest.html
vignetteTitles: Introduction to biocroxytest
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biocroxytest/inst/doc/biocroxytest.R
dependencyCount: 35
Package: BiocSet
Version: 1.21.0
Depends: R (>= 3.6), dplyr
Imports: methods, tibble, utils, rlang, plyr, S4Vectors, BiocIO,
AnnotationDbi, KEGGREST, ontologyIndex, tidyr
Suggests: GSEABase, airway, org.Hs.eg.db, DESeq2, limma, BiocFileCache,
GO.db, testthat, knitr, rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: 684c60b9923e24193278df574ae10c8d
NeedsCompilation: no
Title: Representing Different Biological Sets
Description: BiocSet displays different biological sets in a triple
tibble format. These three tibbles are `element`, `set`, and
`elementset`. The user has the abilty to activate one of these
three tibbles to perform common functions from the dplyr
package. Mapping functionality and accessing web references for
elements/sets are also available in BiocSet.
biocViews: GeneExpression, GO, KEGG, Software
Author: Kayla Morrell [aut, cre], Martin Morgan [aut], Kevin
Rue-Albrecht [ctb], LluÃs Revilla Sancho [ctb]
Maintainer: Kayla Morrell <kayla.morrell16@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BiocSet
git_branch: devel
git_last_commit: 263c319
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BiocSet_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BiocSet_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BiocSet_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BiocSet_1.21.0.tgz
vignettes: vignettes/BiocSet/inst/doc/BiocSet.html
vignetteTitles: BiocSet: Representing Element Sets in the Tidyverse
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BiocSet/inst/doc/BiocSet.R
dependsOnMe: RegEnrich
importsMe: sparrow
suggestsMe: dearseq
dependencyCount: 61
Package: BiocSingular
Version: 1.23.0
Imports: BiocGenerics, S4Vectors, Matrix, methods, utils, DelayedArray,
BiocParallel, ScaledMatrix, irlba, rsvd, Rcpp, beachmat (>=
2.21.1)
LinkingTo: Rcpp, beachmat, assorthead
Suggests: testthat, BiocStyle, knitr, rmarkdown, ResidualMatrix
License: GPL-3
MD5sum: 73c5f4c0bb232ab7d2de94bff775cde3
NeedsCompilation: yes
Title: Singular Value Decomposition for Bioconductor Packages
Description: Implements exact and approximate methods for singular
value decomposition and principal components analysis, in a
framework that allows them to be easily switched within
Bioconductor packages or workflows. Where possible,
parallelization is achieved using the BiocParallel framework.
biocViews: Software, DimensionReduction, PrincipalComponent
Author: Aaron Lun [aut, cre, cph]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://github.com/LTLA/BiocSingular
SystemRequirements: C++17
VignetteBuilder: knitr
BugReports: https://github.com/LTLA/BiocSingular/issues
git_url: https://git.bioconductor.org/packages/BiocSingular
git_branch: devel
git_last_commit: bd97d96
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BiocSingular_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BiocSingular_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BiocSingular_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BiocSingular_1.23.0.tgz
vignettes: vignettes/BiocSingular/inst/doc/decomposition.html,
vignettes/BiocSingular/inst/doc/representations.html
vignetteTitles: 1. SVD and PCA, 2. Matrix classes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BiocSingular/inst/doc/decomposition.R,
vignettes/BiocSingular/inst/doc/representations.R
dependsOnMe: OSCA.basic, OSCA.multisample, OSCA.workflows
importsMe: batchelor, BayesSpace, clusterExperiment, COTAN,
DelayedTensor, Dino, GSVA, miloR, MPAC, mumosa, NanoMethViz,
NewWave, PCAtools, ReactomeGSA, SCArray, SCArray.sat, scater,
scDblFinder, scMerge, scran, scry, StabMap, velociraptor
suggestsMe: alabaster.matrix, chihaya, ResidualMatrix, ScaledMatrix,
spatialHeatmap, splatter, Voyager, HCAData
dependencyCount: 38
Package: BiocSklearn
Version: 1.29.0
Depends: R (>= 4.0), reticulate, methods, SummarizedExperiment
Imports: basilisk
Suggests: testthat, HDF5Array, BiocStyle, rmarkdown, knitr
License: Artistic-2.0
Archs: x64
MD5sum: 9f3331d740d8444917b8d4068b164d24
NeedsCompilation: no
Title: interface to python sklearn via Rstudio reticulate
Description: This package provides interfaces to selected sklearn
elements, and demonstrates fault tolerant use of python modules
requiring extensive iteration.
biocViews: StatisticalMethod, DimensionReduction, Infrastructure
Author: Vince Carey [cre, aut]
Maintainer: Vince Carey <stvjc@channing.harvard.edu>
SystemRequirements: python (>= 2.7), sklearn, numpy, pandas, h5py
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BiocSklearn
git_branch: devel
git_last_commit: 8c69d92
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BiocSklearn_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BiocSklearn_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BiocSklearn_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BiocSklearn_1.29.0.tgz
vignettes: vignettes/BiocSklearn/inst/doc/BiocSklearn.html
vignetteTitles: BiocSklearn overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BiocSklearn/inst/doc/BiocSklearn.R
dependencyCount: 50
Package: BiocStyle
Version: 2.35.0
Imports: bookdown, knitr (>= 1.30), rmarkdown (>= 1.2), stats, utils,
yaml, BiocManager
Suggests: BiocGenerics, RUnit, htmltools
License: Artistic-2.0
MD5sum: 397df8a5405591881755daa79d907f7c
NeedsCompilation: no
Title: Standard styles for vignettes and other Bioconductor documents
Description: Provides standard formatting styles for Bioconductor PDF
and HTML documents. Package vignettes illustrate use and
functionality.
biocViews: Software
Author: Andrzej OleÅ› [aut] (ORCID:
<https://orcid.org/0000-0003-0285-2787>), Mike Smith [ctb]
(ORCID: <https://orcid.org/0000-0002-7800-3848>), Martin Morgan
[ctb], Wolfgang Huber [ctb], Bioconductor Package Maintainer
[cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://github.com/Bioconductor/BiocStyle
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/BiocStyle/issues
git_url: https://git.bioconductor.org/packages/BiocStyle
git_branch: devel
git_last_commit: 40286b4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BiocStyle_2.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BiocStyle_2.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BiocStyle_2.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BiocStyle_2.35.0.tgz
vignettes: vignettes/BiocStyle/inst/doc/LatexStyle2.pdf,
vignettes/BiocStyle/inst/doc/AuthoringRmdVignettes.html
vignetteTitles: Bioconductor LaTeX Style 2.0, Authoring R Markdown
vignettes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BiocStyle/inst/doc/AuthoringRmdVignettes.R,
vignettes/BiocStyle/inst/doc/LatexStyle2.R
dependsOnMe: ExpressionAtlas, iNETgrate, netresponse, Pigengene, RPA,
sangeranalyseR, org.Mxanthus.db, curatedBreastData,
cytofWorkflow, rnaseqGene, RnaSeqGeneEdgeRQL, OSCA, SingleRBook
importsMe: abseqR, ASpli, bandle, BiocWorkflowTools, BioGA, BPRMeth,
broadSeq, BubbleTree, chimeraviz, COMPASS, DiscoRhythm,
EasyCellType, gDRstyle, geneXtendeR, geyser, hdxmsqc, hypeR,
Melissa, meshr, methodical, MSnID, netboost, partCNV,
PathoStat, PhyloProfile, PLSDAbatch, rebook, RegEnrich,
RegionalST, regionReport, Rmmquant, Rqc, scMET, scTensor,
scTGIF, srnadiff, FieldEffectCrc, PhyloProfileData,
GeoMxWorkflows, simpleSingleCell, OHCA, EZtune
suggestsMe: ACE, ADAM, ADAMgui, ADImpute, AffiXcan, affycoretools,
aggregateBioVar, AHMassBank, alabaster, alabaster.base,
alabaster.bumpy, alabaster.files, alabaster.mae,
alabaster.matrix, alabaster.ranges, alabaster.sce,
alabaster.schemas, alabaster.se, alabaster.sfe,
alabaster.spatial, alabaster.string, alabaster.vcf, ALDEx2,
alevinQC, AllelicImbalance, AlpsNMR, AMARETTO, AMOUNTAIN,
amplican, AneuFinder, animalcules, annotate, AnnotationDbi,
AnnotationFilter, AnnotationForge, AnnotationHub,
AnnotationHubData, annotationTools, annotatr, AnVIL, AnVILAz,
AnVILBase, AnVILBilling, AnVILGCP, AnVILPublish, AnVILWorkflow,
APAlyzer, APL, arrayQualityMetrics, artMS, ASGSCA, ASICS,
AssessORF, ASSIGN, assorthead, ATACseqQC, ATACseqTFEA, atena,
atSNP, AUCell, autonomics, awst, BaalChIP, bacon, bamsignals,
BANDITS, Banksy, banocc, barcodetrackR, basecallQC, BASiCS,
basilisk, basilisk.utils, batchelor, BatchQC, bayNorm, baySeq,
beachmat, beachmat.hdf5, beachmat.tiledb, beadarray,
BeadDataPackR, BEclear, bedbaser, beer, benchdamic, BERT,
betaHMM, bettr, BG2, BgeeDB, bigmelon, BindingSiteFinder,
bioassayR, Biobase, biobtreeR, bioCancer, BioCartaImage,
BiocBaseUtils, BiocBook, BiocCheck, BiocFHIR, BiocFileCache,
BiocHail, BiocHubsShiny, BiocIO, biocmake, BiocNeighbors,
BioCor, BiocParallel, BiocPkgTools, biocroxytest, BiocSet,
BiocSingular, BiocSklearn, biocthis, biocViews, biodb,
biodbChebi, biodbHmdb, biodbNcbi, biodbNci, biodbUniprot,
biomaRt, biomformat, BioNAR, BioNERO, biosigner, Biostrings,
biotmle, biscuiteer, blacksheepr, blima, bluster, bnbc, bnem,
BOBaFIT, borealis, branchpointer, breakpointR, brendaDb,
BREW3R.r, BridgeDbR, BrowserViz, BSgenomeForge, bsseq,
bugsigdbr, BUMHMM, BumpyMatrix, BUScorrect, BUSpaRse, BUSseq,
CAFE, CAGEfightR, cageminer, CAGEr, CaMutQC, canceR, cardelino,
Cardinal, CardinalIO, CARDspa, CARNIVAL, CATALYST, CatsCradle,
cbaf, cBioPortalData, CBNplot, cbpManager, ccfindR, ccImpute,
CCPlotR, ccrepe, CDI, celda, CellBarcode, cellbaseR, CellBench,
CelliD, cellity, CellMapper, CellMixS, cellxgenedp, censcyt,
Cepo, CexoR, cfdnakit, cfDNAPro, cfTools, ChemmineOB,
ChemmineR, chevreulPlot, chevreulProcess, chevreulShiny,
Chicago, chihaya, chipenrich, ChIPexoQual, ChIPpeakAnno,
ChIPQC, chipseq, ChromSCape, CIMICE, CircSeqAlignTk, CiteFuse,
ClassifyR, cleanUpdTSeq, CleanUpRNAseq, cleaver, clevRvis,
clipper, cliProfiler, ClustAll, clusterExperiment,
ClusterFoldSimilarity, clusterSeq, ClusterSignificance,
clustifyr, ClustIRR, cmapR, CNEr, CNVfilteR, CNVMetrics,
CNVRanger, COCOA, CoGAPS, cogeqc, Cogito, comapr, coMethDMR,
compcodeR, CompoundDb, compSPOT, concordexR, CONFESS,
consensusOV, consensusSeekeR, consICA, CONSTANd, conumee,
CopyNumberPlots, coRdon, CoreGx, corral, coseq, CoSIA, cosmiq,
COTAN, covRNA, CPSM, cpvSNP, CRISPRball, crisprBase,
crisprBowtie, crisprBwa, crisprDesign, crisprScore, CRISPRseek,
crisprShiny, CrispRVariants, crisprVerse, crisprViz, csaw,
csdR, CSSQ, CTdata, CTDquerier, CTexploreR, ctsGE, CTSV,
customCMPdb, cydar, cypress, CytoGLMM, cytoKernel, cytomapper,
CytoMDS, CytoPipeline, CytoPipelineGUI, cytoviewer, dada2,
dagLogo, DAMEfinder, DaMiRseq, Damsel, dandelionR, DAPAR,
dcanr, DCATS, dce, ddPCRclust, debCAM, decompTumor2Sig,
decontam, decontX, deconvR, decoupleR, DeepTarget, DEFormats,
DegCre, DEGreport, DelayedArray, DelayedDataFrame,
DelayedMatrixStats, DelayedRandomArray, DelayedTensor, DELocal,
demuxmix, demuxSNP, densvis, DEP, DepecheR, DepInfeR,
derfinder, derfinderHelper, derfinderPlot, DEScan2, DESpace,
DEWSeq, DExMA, DEXSeq, DFplyr, DiffBind, diffcyt,
DifferentialRegulation, diffuStats, diffUTR, Dino, dir.expiry,
DirichletMultinomial, discordant, distinct, dittoSeq, DMCFB,
DMRScan, dmrseq, DNABarcodeCompatibility, DNABarcodes,
DNAcycP2, DNAfusion, doppelgangR, Doscheda, doseR,
doubletrouble, drawProteins, dreamlet, DRIMSeq, DropletUtils,
drugTargetInteractions, DSS, dStruct, DuplexDiscovereR,
dupRadar, easier, EasyCellType, easylift, easyreporting,
easyRNASeq, EBImage, EDASeq, edgeR, EGSEA, eiR, eisaR, ELMER,
EmpiricalBrownsMethod, ENmix, EnrichDO, EnrichmentBrowser,
enrichViewNet, ensembldb, EpiCompare, EpiDISH, epigraHMM,
EpiMix, epimutacions, epiNEM, epiregulon, epiregulon.extra,
epistack, epistasisGA, EpiTxDb, epivizr, epivizrChart,
epivizrData, epivizrServer, epivizrStandalone, erccdashboard,
erma, ERSSA, escape, escheR, evaluomeR, EventPointer, EWCE,
ExperimentHub, ExperimentHubData, ExperimentSubset,
ExploreModelMatrix, extraChIPs, faers, FamAgg, famat,
FastqCleaner, fastreeR, fastseg, fCCAC, fCI, fcScan, FEAST,
FeatSeekR, FELLA, fenr, fgga, FilterFFPE, findIPs, FindIT2,
FLAMES, flowAI, flowcatchR, flowGraph, FlowSOM, flowSpecs,
fmcsR, fobitools, FRASER, FuseSOM, G4SNVHunter, GA4GHclient,
GA4GHshiny, GARS, gatom, GBScleanR, gcapc, gDNAx, gDR, gDRcore,
gDRimport, gDRutils, GDSArray, GeDi, gemma.R,
GeneExpressionSignature, genefilter, genefu,
GeneNetworkBuilder, GeneOverlap, geneplast, geneplotter,
GENESIS, GeneStructureTools, GeneTonic, GENIE3,
GenomAutomorphism, GenomeInfoDb, GenomicAlignments,
GenomicDataCommons, GenomicDistributions, GenomicFeatures,
GenomicFiles, GenomicInteractionNodes, GenomicInteractions,
GenomicPlot, GenomicRanges, GenomicScores,
GenomicSuperSignature, GenomicTuples, GenVisR, geomeTriD,
gg4way, ggbio, ggkegg, ggmanh, ggmsa, GGPA, ggseqalign,
ggspavis, ggtreeSpace, gINTomics, GladiaTOX, Glimma, glmGamPoi,
glmSparseNet, GloScope, gmoviz, GMRP, GNOSIS, GOexpress,
GOfuncR, GOpro, goSorensen, goSTAG, GOstats, gpuMagic, GrafGen,
GRaNIE, granulator, graper, graph, graphite, GreyListChIP,
GRmetrics, GSAR, gscreend, GSEABase, GSEABenchmarkeR,
GSEAmining, GSgalgoR, GSVA, GUIDEseq, Gviz, GWAS.BAYES,
gwascat, GWASTools, gwasurvivr, GWENA, gypsum, h5mread, Harman,
hca, HDF5Array, HelloRanges, hermes, Herper, HGC, HiCBricks,
HiCDOC, HiCExperiment, HiContacts, HiCool, HiCParser,
hicVennDiagram, HiLDA, hipathia, HIREewas, HiTC, HoloFoodR,
hoodscanR, HPAanalyze, hpar, HPiP, HTSFilter, HuBMAPR,
hummingbird, HybridExpress, ideal, IFAA, iGC, IgGeneUsage,
igvR, igvShiny, IHW, illuminaio, ILoReg, IMAS, imcRtools,
immApex, immunoClust, immunogenViewer, immunotation, infercnv,
Informeasure, InPAS, INSPEcT, INTACT, InTAD, InteractionSet,
InterCellar, IONiseR, iPath, IRanges, ISAnalytics, iSEE,
iSEEde, iSEEfier, iSEEhex, iSEEhub, iSEEindex, iSEEpathways,
iSEEtree, iSEEu, ISLET, IsoBayes, IsoCorrectoR,
IsoCorrectoRGUI, isomiRs, IVAS, ivygapSE, jazzPanda,
karyoploteR, katdetectr, KEGGREST, kissDE, kmcut, koinar, LACE,
ldblock, lefser, lemur, levi, limma, limpa, limpca, LimROTS,
lineagespot, LinkHD, Linnorm, lipidr, lisaClust, loci2path,
LOLA, LoomExperiment, lpsymphony, LRBaseDbi, LRcell, lute,
m6Aboost, Macarron, MACSr, made4, magpie, magrene, MAI, MAPFX,
marr, maser, MassSpecWavelet, MAST, mastR, MatrixQCvis,
MatrixRider, matter, MBASED, MBECS, mbkmeans, mbQTL, MBttest,
MCbiclust, mCSEA, MEAL, MEAT, MEDIPS, megadepth, MEIGOR,
messina, metabCombiner, metabinR, MetaboAnnotation,
MetaboCoreUtils, MetaboDynamics, metabolomicsWorkbenchR,
MetaboSignal, metagene2, MetaPhOR, metapod, metaseqR2,
methimpute, methInheritSim, MethPed, MethReg, MethylAid,
methylCC, methylclock, methyLImp2, methylInheritance,
MethylMix, methylscaper, methylSig, MetMashR, MetNet, mfa,
MGnifyR, mia, miaSim, miaViz, microbiome, microSTASIS, MICSQTL,
miloR, mimager, minfi, miQC, MIRA, miRcomp, MIRit, miRSM,
miRspongeR, mirTarRnaSeq, missMethyl, missRows, mist, mistyR,
mixOmics, MLInterfaces, MMDiff2, MMUPHin, mnem, moanin,
mobileRNA, MODA, Modstrings, MOFA2, mogsa, MoleculeExperiment,
MOMA, monaLisa, Moonlight2R, MoonlightR, mosbi, MOSClip,
mosdef, MOSim, Motif2Site, motifbreakR, MotifDb, motifStack,
motifTestR, MouseFM, mpra, MSA2dist, MsBackendMassbank,
MsBackendMetaboLights, MsBackendMgf, MsBackendMsp,
MsBackendRawFileReader, MsBackendSql, MsCoreUtils, MsDataHub,
MsExperiment, MsFeatures, msImpute, mslp, MSnbase, mspms,
MSPrep, msPurity, msqrob2, MsQuality, MSstats, MSstatsBioNet,
MSstatsLiP, MSstatsLOBD, MSstatsTMT, MuData,
MultiAssayExperiment, MultiBaC, multicrispr, MultiDataSet,
multiGSEA, multiHiCcompare, multiMiR, MultimodalExperiment,
MultiRNAflow, multistateQTL, multiWGCNA, mumosa, MungeSumstats,
muscat, musicatk, MutationalPatterns, MWASTools, mygene,
myvariant, mzR, NADfinder, NanoMethViz, NanoStringDiff, ncGTW,
ncRNAtools, ndexr, Nebulosa, nempi, NetActivity, nethet,
NetPathMiner, netprioR, netSmooth, NewWave, ngsReports,
nipalsMCIA, nnSVG, NormalyzerDE, normr, NPARC, npGSEA,
nucleoSim, nucleR, oligo, omicade4, omicRexposome,
OmicsMLRepoR, omicsPrint, omicsViewer, Omixer, OmnipathR,
omXplore, oncoscanR, OncoScore, OncoSimulR, ontoProc,
optimalFlow, OPWeight, ORFhunteR, ORFik, Organism.dplyr,
OrganismDbi, orthogene, orthos, Oscope, OSTA.data, OUTRIDER,
OutSplice, OVESEG, PAA, packFinder, padma, PAIRADISE,
pairedGSEA, pairkat, PanomiR, parglms, parody, Path2PPI,
pathlinkR, pcaExplorer, PCAN, PDATK, PeacoQC, peakPantheR,
Pedixplorer, PepSetTest, PepsNMR, pfamAnalyzeR, pgxRpi,
phantasus, phantasusLite, PharmacoGx, phenomis, phenopath,
philr, PhIPData, PhosR, phyloseq, piano, PICB, pipeComp,
PIPETS, Pirat, PIUMA, planttfhunter, plasmut, plotGrouper,
plyinteractions, plyranges, pmp, PoDCall, poem, pogos,
PolySTest, POMA, powerTCR, POWSC, ppcseq, pqsfinder, pram,
preciseTAD, PrInCE, proDA, profileplyr, profileScoreDist,
progeny, projectR, pRoloc, pRolocGUI, PRONE, Prostar,
ProteoDisco, ProteoMM, PSMatch, ptairMS, PureCN, PWMEnrich,
qcmetrics, QDNAseq, QFeatures, qmtools, qpgraph, qsea, qsmooth,
QSutils, qsvaR, QTLExperiment, Qtlizer, quantiseqr, quantro,
QuasR, R3CPET, RadioGx, raer, RaggedExperiment, RAIDS, rain,
ramwas, randRotation, RAREsim, Rarr, rawDiag, rawrr, RBGL,
RBioFormats, Rbowtie, Rbwa, rcellminer, rCGH, RcisTarget,
Rcollectl, RCSL, Rcwl, RcwlPipelines, RCX, RCy3, RCyjs,
ReactomePA, recount, recount3, recountmethylation, recoup,
RedeR, RedisParam, ReducedExperiment, regionalpcs, regioneR,
regioneReloaded, regsplice, regutools, ResidualMatrix, RESOLVE,
retrofit, ReUseData, rexposome, rfaRm, Rfastp, rfPred, RgnTX,
rgoslin, RGraph2js, RGSEA, rhdf5, rhdf5client, rhdf5filters,
Rhdf5lib, rhinotypeR, Rhisat2, Rhtslib, RiboCrypt,
RiboProfiling, riboSeqR, ribosomeProfilingQC, rifi,
rifiComparative, Rigraphlib, RIVER, RJMCMCNucleosomes,
RMassBank, rmspc, RNAmodR, RNAmodR.AlkAnilineSeq, RNAmodR.ML,
RNAmodR.RiboMethSeq, rnaseqcomp, RNAseqCovarImpute,
RnaSeqSampleSize, Rnits, roastgsa, ROC, ROCpAI, rols, ropls,
rprimer, rpx, rqt, rrvgo, Rsamtools, rScudo, rsemmed, rSWeeP,
RTCGAToolbox, RTN, RTNduals, RTNsurvival, Rtpca, rTRM, RUVSeq,
Rvisdiff, RVS, rWikiPathways, S4Arrays, S4Vectors,
sampleClassifier, sangerseqR, satuRn, SC3, ScaledMatrix,
scanMiR, scanMiRApp, SCArray.sat, scater, scBubbletree, scCB2,
scClassify, sccomp, scDblFinder, scDD, scDDboost, scDesign3,
scDiagnostics, scDotPlot, scds, SCFA, scFeatureFilter,
scFeatures, scifer, scmap, scMerge, scMitoMut, SCnorm, scone,
scoreInvHap, scoup, scp, scPCA, scPipe, scQTLtools, scran,
scrapper, scReClassify, screenCounter, ScreenR, scRepertoire,
scRNAseqApp, scruff, scTHI, scTreeViz, scuttle, scviR,
seahtrue, sechm, segmentSeq, selectKSigs, seq.hotSPOT,
seqArchR, seqArchRplus, seqCAT, SeqGate, seqLogo, seqPattern,
seqsetvis, SeqSQC, SeqVarTools, sesame, SEtools, sevenC, SGCP,
SGSeq, SharedObject, shinyepico, shinyMethyl, ShortRead,
SIAMCAT, SigCheck, SigFuge, signatureSearch, signifinder,
SigsPack, SIMD, SimFFPE, similaRpeak, SIMLR, simPIC, simpleSeg,
sincell, SingleCellAlleleExperiment, SingleCellExperiment,
singleCellTK, SingleR, singscore, SiPSiC, sitadela, sitePath,
sketchR, slalom, slingshot, SMAD, smartid, smoothclust,
snapcount, snifter, SNPediaR, SNPhood, soGGi, SOMNiBUS, sosta,
SpaceMarkers, SpaNorm, sparrow, SparseArray, sparseMatrixStats,
sparsenetgls, SparseSignatures, spaSim, SpatialCPie, spatialDE,
SpatialExperiment, SpatialExperimentIO, spatialFDA,
SpatialFeatureExperiment, spatialHeatmap, speckle, specL,
Spectra, SpectralTAD, SpectraQL, SPIAT, spicyR, spillR,
splatter, SplineDV, SPLINTER, splots, spoon, SpotClean,
SPOTlight, SpotSweeper, spqn, SPsimSeq, SQLDataFrame, squallms,
sRACIPE, srnadiff, sSNAPPY, ssrch, StabMap, stageR, STATegRa,
Statial, statTarget, stJoincount, strandCheckR, struct,
Structstrings, structToolbox, SubCellBarCode, SUITOR,
SummarizedExperiment, SurfR, sva, svaRetro, SVMDO, swfdr,
switchde, synapsis, synapter, SynExtend, synlet, syntenet,
systemPipeR, systemPipeShiny, systemPipeTools, tadar,
TADCompare, tanggle, TAPseq, TargetDecoy, TargetSearch,
TBSignatureProfiler, TCGAbiolinks, TCGAutils, TDbasedUFE,
TDbasedUFEadv, TEKRABber, TENET, TENxIO, tenXplore, terapadog,
terraTCGAdata, TFARM, TFBSTools, TFHAZ, TFutils, tidybulk,
tidyCoverage, tidyFlowCore, tidysbml, tidySingleCellExperiment,
tidySpatialExperiment, tidySummarizedExperiment, tigre,
TileDBArray, timeOmics, TMixClust, TMSig, TOAST, tomoda,
tomoseqr, TOP, topconfects, topdownr, ToxicoGx, TPP, TPP2D,
tpSVG, tracktables, trackViewer, TrajectoryUtils,
transcriptogramer, transcriptR, transformGamPoi, transmogR,
transomics2cytoscape, TreeAndLeaf, treeclimbR, treekoR,
TreeSummarizedExperiment, TREG, Trendy, tricycle, TrIdent,
tripr, tRNA, tRNAdbImport, tRNAscanImport, TRONCO, TTMap,
TurboNorm, TVTB, twoddpcr, txcutr, txdbmaker, UCell,
UCSC.utils, Ularcirc, UMI4Cats, uncoverappLib, UniProt.ws,
updateObject, UPDhmm, variancePartition, VariantAnnotation,
VariantExperiment, VariantFiltering, VCFArray, VDJdive,
velociraptor, VERSO, vidger, ViSEAGO, VisiumIO, visiumStitched,
vissE, Voyager, vsclust, vsn, wateRmelon, wavClusteR, weitrix,
wpm, xCell2, xcms, xcore, XeniumIO, xenLite, Xeva, XNAString,
yamss, YAPSA, zellkonverter, zenith, zinbwave, zitools,
zlibbioc, ZygosityPredictor, AHEnsDbs, AHLRBaseDbs, AHMeSHDbs,
AHPathbankDbs, AHPubMedDbs, AHWikipathwaysDbs,
AlphaMissense.v2023.hg19, AlphaMissense.v2023.hg38,
ath1121501frmavecs, cadd.v1.6.hg19, cadd.v1.6.hg38, CTCF,
EpiTxDb.Hs.hg38, EpiTxDb.Mm.mm10, EpiTxDb.Sc.sacCer3, EuPathDB,
excluderanges, geneplast.data, GenomicState, hpAnnot,
JASPAR2022, JASPAR2024, phastCons35way.UCSC.mm39,
phyloP35way.UCSC.mm39, rat2302frmavecs, SomaScan.db,
synaptome.data, TENET.AnnotationHub, UCSCRepeatMasker,
ASICSdata, AssessORFData, benchmarkfdrData2019, BioImageDbs,
BioPlex, blimaTestingData, BloodCancerMultiOmics2017,
bodymapRat, brgedata, bugphyzz, CardinalWorkflows, celldex,
CellMapperData, cfToolsData, chipenrich.data,
ChIPexoQualExample, chipseqDBData, CLLmethylation,
clustifyrdatahub, CopyhelpeR, CoSIAdata, COSMIC.67,
crisprScoreData, curatedBladderData, curatedMetagenomicData,
curatedOvarianData, curatedPCaData, curatedTBData,
curatedTCGAData, CytoMethIC, DAPARdata, depmap, derfinderData,
DExMAdata, DNAZooData, dorothea, DropletTestFiles,
DuoClustering2018, easierData, ELMER.data, emtdata, eoPredData,
epimutacionsData, ewceData, fourDNData, furrowSeg,
gDNAinRNAseqData, gDRtestData, GenomicDistributionsData,
GeuvadisTranscriptExpr, GSE103322, GSE13015, GSE159526,
GSE62944, HarmanData, HCAData, HCATonsilData, HD2013SGI,
HDCytoData, healthyControlsPresenceChecker, HelloRangesData,
HiCDataHumanIMR90, HiContactsData, HighlyReplicatedRNASeq,
HMP16SData, HMP2Data, HumanAffyData, humanHippocampus2024,
IHWpaper, imcdatasets, JohnsonKinaseData, LegATo,
LRcellTypeMarkers, mCSEAdata, mcsurvdata, MerfishData,
MetaGxOvarian, MetaGxPancreas, MetaScope, MethylAidData,
methylclockData, MethylSeqData, MicrobiomeBenchmarkData,
microbiomeDataSets, minionSummaryData, MOFAdata,
MouseAgingData, MouseGastrulationData, MouseThymusAgeing,
msigdb, MSMB, msqc1, multiWGCNAdata, muscData, muSpaData,
nanotubes, NestLink, NetActivityData, OnassisJavaLibs,
optimalFlowData, orthosData, parathyroidSE, pasilla,
PasillaTranscriptExpr, PCHiCdata, PepsNMRData, preciseTADhub,
ProteinGymR, ptairData, raerdata, rcellminerData,
RforProteomics, RGMQLlib, RNAmodR.Data, RnaSeqSampleSizeData,
sampleClassifierData, scaeData, scanMiRData, scATAC.Explorer,
SCLCBam, scMultiome, scpdata, scRNAseq, seventyGeneData,
SFEData, SimBenchData, Single.mTEC.Transcriptomes,
SingleCellMultiModal, smokingMouse, SpatialDatasets,
spatialDmelxsim, spatialLIBD, STexampleData,
SubcellularSpatialData, systemPipeRdata, TabulaMurisData,
TabulaMurisSenisData, tartare, TCGAbiolinksGUI.data,
TCGAWorkflowData, TENET.ExperimentHub, TENxBrainData,
TENxBUSData, TENxPBMCData, TENxVisiumData, TENxXeniumData,
timecoursedata, TimerQuant, tissueTreg, TMExplorer,
TransOmicsData, tuberculosis, tweeDEseqCountData,
VariantToolsData, VectraPolarisData, WeberDivechaLCdata,
zebrafishRNASeq, arrays, CAGEWorkflow, chipseqDB,
csawUsersGuide, EGSEA123, ExpressionNormalizationWorkflow,
generegulation, highthroughputassays, liftOver, maEndToEnd,
recountWorkflow, RNAseq123, sequencing, SingscoreAMLMutations,
variants, aIc, asteRisk, BiocManager, corrmeta, cyjShiny,
DEHOGT, EHRtemporalVariability, genetic.algo.optimizeR,
ggBubbles, GSEMA, ipsRdbs, magmaR, MariNET, MarZIC,
multiclassPairs, MVN, net4pg, NutrienTrackeR, openSkies,
PlackettLuce, Rediscover, rjsoncons, rworkflows, SCIntRuler,
StepReg, TFactSR
dependencyCount: 33
Package: biocthis
Version: 1.17.4
Imports: BiocManager, fs, glue, rlang, styler, usethis (>= 2.0.1)
Suggests: BiocStyle, covr, devtools, knitr, pkgdown, RefManageR,
rmarkdown, sessioninfo, testthat, utils
License: Artistic-2.0
MD5sum: 30a1e974744920abdd05e0712e0e4174
NeedsCompilation: no
Title: Automate package and project setup for Bioconductor packages
Description: This package expands the usethis package with the goal of
helping automate the process of creating R packages for
Bioconductor or making them Bioconductor-friendly.
biocViews: Software, ReportWriting
Author: Leonardo Collado-Torres [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2140-308X>), Marcel Ramos [ctb]
(ORCID: <https://orcid.org/0000-0002-3242-0582>)
Maintainer: Leonardo Collado-Torres <lcolladotor@gmail.com>
URL: https://github.com/lcolladotor/biocthis
VignetteBuilder: knitr
BugReports: https://github.com/lcolladotor/biocthis/issues
git_url: https://git.bioconductor.org/packages/biocthis
git_branch: devel
git_last_commit: 0d833a1
git_last_commit_date: 2025-03-18
Date/Publication: 2025-03-19
source.ver: src/contrib/biocthis_1.17.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/biocthis_1.17.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/biocthis_1.17.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/biocthis_1.17.4.tgz
vignettes: vignettes/biocthis/inst/doc/biocthis_dev_notes.html,
vignettes/biocthis/inst/doc/biocthis.html
vignetteTitles: biocthis developer notes, Introduction to biocthis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biocthis/inst/doc/biocthis_dev_notes.R,
vignettes/biocthis/inst/doc/biocthis.R
importsMe: HubPub
suggestsMe: tripr
dependencyCount: 44
Package: BiocVersion
Version: 3.21.1
Depends: R (>= 4.5.0)
License: Artistic-2.0
MD5sum: 58ff0a6132b77db8a38aad3668ea3e11
NeedsCompilation: no
Title: Set the appropriate version of Bioconductor packages
Description: This package provides repository information for the
appropriate version of Bioconductor.
biocViews: Infrastructure
Author: Martin Morgan [aut], Marcel Ramos [ctb], Bioconductor Package
Maintainer [ctb, cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/BiocVersion
git_branch: devel
git_last_commit: 1e5e5af
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BiocVersion_3.21.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BiocVersion_3.21.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BiocVersion_3.21.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BiocVersion_3.21.1.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
importsMe: AnnotationHub, pkgndep
suggestsMe: BiocManager
dependencyCount: 0
Package: biocViews
Version: 1.75.0
Depends: R (>= 3.6.0)
Imports: Biobase, graph (>= 1.9.26), methods, RBGL (>= 1.13.5), tools,
utils, XML, RCurl, RUnit, BiocManager
Suggests: BiocGenerics, knitr, commonmark, BiocStyle
License: Artistic-2.0
MD5sum: 79a4d8013922b825f35d532d8f4be6df
NeedsCompilation: no
Title: Categorized views of R package repositories
Description: Infrastructure to support 'views' used to classify
Bioconductor packages. 'biocViews' are directed acyclic graphs
of terms from a controlled vocabulary. There are three major
classifications, corresponding to 'software', 'annotation', and
'experiment data' packages.
biocViews: Infrastructure
Author: VJ Carey <stvjc@channing.harvard.edu>, BJ Harshfield
<rebjh@channing.harvard.edu>, S Falcon <sfalcon@fhcrc.org> ,
Sonali Arora, Lori Shepherd <lori.shepherd@roswellpark.org>
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: http://bioconductor.org/packages/biocViews
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/biocViews/issues
git_url: https://git.bioconductor.org/packages/biocViews
git_branch: devel
git_last_commit: 8aa6daf
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/biocViews_1.75.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/biocViews_1.75.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/biocViews_1.75.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/biocViews_1.75.0.tgz
vignettes: vignettes/biocViews/inst/doc/createReposHtml.html,
vignettes/biocViews/inst/doc/HOWTO-BCV.html
vignetteTitles: biocViews-CreateRepositoryHTML, biocViews-HOWTO
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biocViews/inst/doc/createReposHtml.R,
vignettes/biocViews/inst/doc/HOWTO-BCV.R
importsMe: AnnotationHubData, BiocCheck, BiocPkgTools, BioGA, monocle,
sigFeature, RforProteomics, genetic.algo.optimizeR
suggestsMe: packFinder, plasmut, ReducedExperiment, rworkflows
dependencyCount: 17
Package: BiocWorkflowTools
Version: 1.33.0
Depends: R (>= 3.4)
Imports: BiocStyle, bookdown, git2r, httr, knitr, rmarkdown,
rstudioapi, stringr, tools, utils, usethis
License: MIT + file LICENSE
MD5sum: c33e3dda8d6e76bc6ae282057ccecd70
NeedsCompilation: no
Title: Tools to aid the development of Bioconductor Workflow packages
Description: Provides functions to ease the transition between
Rmarkdown and LaTeX documents when authoring a Bioconductor
Workflow.
biocViews: Software, ReportWriting
Author: Mike Smith [aut, cre], Andrzej OleÅ› [aut]
Maintainer: Mike Smith <grimbough@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BiocWorkflowTools
git_branch: devel
git_last_commit: f751dad
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BiocWorkflowTools_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BiocWorkflowTools_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BiocWorkflowTools_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BiocWorkflowTools_1.33.0.tgz
vignettes:
vignettes/BiocWorkflowTools/inst/doc/Generate_F1000_Latex.html
vignetteTitles: Converting Rmarkdown to F1000Research LaTeX Format
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BiocWorkflowTools/inst/doc/Generate_F1000_Latex.R
dependsOnMe: RNAseq123
suggestsMe: CAGEWorkflow, recountWorkflow, SingscoreAMLMutations
dependencyCount: 61
Package: biodb
Version: 1.15.0
Depends: R (>= 4.1.0)
Imports: BiocFileCache, R6, RCurl, RSQLite, Rcpp, XML, chk, git2r,
jsonlite, lgr, lifecycle, methods, openssl, plyr, progress,
rappdirs, stats, stringr, tools, withr, yaml
LinkingTo: Rcpp, testthat
Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr,
rmarkdown, covr, xml2
License: AGPL-3
MD5sum: 0bf7bc73882fd9b4126ef77971ac373c
NeedsCompilation: yes
Title: biodb, a library and a development framework for connecting to
chemical and biological databases
Description: The biodb package provides access to standard remote
chemical and biological databases (ChEBI, KEGG, HMDB, ...), as
well as to in-house local database files (CSV, SQLite), with
easy retrieval of entries, access to web services, search of
compounds by mass and/or name, and mass spectra matching for
LCMS and MSMS. Its architecture as a development framework
facilitates the development of new database connectors for
local projects or inside separate published packages.
biocViews: Software, Infrastructure, DataImport, KEGG
Author: Pierrick Roger [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8177-4873>), Alexis Delabrière
[ctb] (ORCID: <https://orcid.org/0000-0003-3308-4549>)
Maintainer: Pierrick Roger <pierrick.roger@cea.fr>
URL: https://github.com/pkrog/biodb
VignetteBuilder: knitr
BugReports: https://github.com/pkrog/biodb/issues
git_url: https://git.bioconductor.org/packages/biodb
git_branch: devel
git_last_commit: 9aceaac
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/biodb_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/biodb_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/biodb_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/biodb_1.15.0.tgz
vignettes: vignettes/biodb/inst/doc/biodb.html,
vignettes/biodb/inst/doc/details.html,
vignettes/biodb/inst/doc/entries.html,
vignettes/biodb/inst/doc/new_connector.html,
vignettes/biodb/inst/doc/new_entry_field.html
vignetteTitles: Introduction to the biodb package., Details on general
*biodb* usage and principles, Manipulating entry objects,
Creating a new connector class for accessing a database.,
Creating a new field for entries.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biodb/inst/doc/biodb.R,
vignettes/biodb/inst/doc/details.R,
vignettes/biodb/inst/doc/entries.R,
vignettes/biodb/inst/doc/new_connector.R,
vignettes/biodb/inst/doc/new_entry_field.R
importsMe: biodbChebi, biodbHmdb, biodbNcbi, biodbNci, biodbUniprot,
phenomis
dependencyCount: 75
Package: biodbChebi
Version: 1.13.0
Depends: R (>= 4.1)
Imports: R6, biodb (>= 1.1.5)
Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr,
rmarkdown, lgr
License: AGPL-3
Archs: x64
MD5sum: 7f9c2e3e15310e90c10fd5579c3e20c2
NeedsCompilation: no
Title: biodbChebi, a library for connecting to the ChEBI Database
Description: The biodbChebi library provides access to the ChEBI
Database, using biodb package framework. It allows to retrieve
entries by their accession number. Web services can be accessed
for searching the database by name, mass or other fields.
biocViews: Software, Infrastructure, DataImport
Author: Pierrick Roger [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8177-4873>)
Maintainer: Pierrick Roger <pierrick.roger@cea.fr>
URL: https://github.com/pkrog/biodbChebi
VignetteBuilder: knitr
BugReports: https://github.com/pkrog/biodbChebi/issues
git_url: https://git.bioconductor.org/packages/biodbChebi
git_branch: devel
git_last_commit: 686a27c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/biodbChebi_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/biodbChebi_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/biodbChebi_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/biodbChebi_1.13.0.tgz
vignettes: vignettes/biodbChebi/inst/doc/biodbChebi.html
vignetteTitles: Introduction to the biodbChebi package.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biodbChebi/inst/doc/biodbChebi.R
importsMe: phenomis
dependencyCount: 76
Package: biodbHmdb
Version: 1.13.0
Depends: R (>= 4.1)
Imports: R6, biodb (>= 1.3.2), Rcpp, zip
LinkingTo: Rcpp, testthat
Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr,
rmarkdown, covr, lgr
License: AGPL-3
MD5sum: 441674ec510115b14cecd2707f115882
NeedsCompilation: yes
Title: biodbHmdb, a library for connecting to the HMDB Database
Description: The biodbHmdb library is an extension of the biodb
framework package that provides access to the HMDB Metabolites
database. It allows to download the whole HMDB Metabolites
database locally, access entries and search for entries by name
or description. A future version of this package will also
include a search by mass and mass spectra annotation.
biocViews: Software, Infrastructure, DataImport
Author: Pierrick Roger [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8177-4873>)
Maintainer: Pierrick Roger <pierrick.roger@cea.fr>
URL: https://github.com/pkrog/biodbHmdb
VignetteBuilder: knitr
BugReports: https://github.com/pkrog/biodbHmdb/issues
git_url: https://git.bioconductor.org/packages/biodbHmdb
git_branch: devel
git_last_commit: d2b9830
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/biodbHmdb_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/biodbHmdb_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/biodbHmdb_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/biodbHmdb_1.13.0.tgz
vignettes: vignettes/biodbHmdb/inst/doc/biodbHmdb.html
vignetteTitles: Introduction to the biodbHmdb package.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biodbHmdb/inst/doc/biodbHmdb.R
dependencyCount: 77
Package: biodbNcbi
Version: 1.11.0
Depends: R (>= 4.1)
Imports: biodb (>= 1.3.2), R6, XML, chk
Suggests: roxygen2, BiocStyle, testthat (>= 2.0.0), devtools, knitr,
rmarkdown, covr, lgr
License: AGPL-3
Archs: x64
MD5sum: 8980bc87be701b854c81965cc860bc01
NeedsCompilation: no
Title: biodbNcbi, a library for connecting to NCBI Databases.
Description: The biodbNcbi library provides access to the NCBI
databases CCDS, Gene, Pubchem Comp and Pubchem Subst, using
biodb package framework. It allows to retrieve entries by their
accession number. Web services can be accessed for searching
the database by name or mass.
biocViews: Software, Infrastructure, DataImport
Author: Pierrick Roger [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8177-4873>)
Maintainer: Pierrick Roger <pierrick.roger@cea.fr>
URL: https://github.com/pkrog/biodbNcbi
VignetteBuilder: knitr
BugReports: https://github.com/pkrog/biodbNCbi/issues
git_url: https://git.bioconductor.org/packages/biodbNcbi
git_branch: devel
git_last_commit: 4a3f8f2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/biodbNcbi_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/biodbNcbi_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/biodbNcbi_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/biodbNcbi_1.11.0.tgz
vignettes: vignettes/biodbNcbi/inst/doc/biodbNcbi.html
vignetteTitles: Introduction to the biodbNcbi package.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biodbNcbi/inst/doc/biodbNcbi.R
dependencyCount: 76
Package: biodbNci
Version: 1.11.0
Depends: R (>= 4.1)
Imports: biodb (>= 1.3.1), R6, Rcpp, chk
LinkingTo: Rcpp, testthat
Suggests: roxygen2, BiocStyle, testthat (>= 2.0.0), devtools, knitr,
rmarkdown, covr, lgr
License: AGPL-3
MD5sum: 32ed12e5d8513dbb30c72d9e3902a66a
NeedsCompilation: yes
Title: biodbNci, a library for connecting to biodbNci, a library for
connecting to the National Cancer Institute (USA) CACTUS
Database
Description: The biodbNci library is an extension of the biodb
framework package. It provides access to biodbNci, a library
for connecting to the National Cancer Institute (USA) CACTUS
Database. It allows to retrieve entries by their accession
number, and run specific web services.
biocViews: Software, Infrastructure, DataImport
Author: Pierrick Roger [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8177-4873>)
Maintainer: Pierrick Roger <pierrick.roger@cea.fr>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/biodbNci
git_branch: devel
git_last_commit: 10e1a05
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/biodbNci_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/biodbNci_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/biodbNci_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/biodbNci_1.11.0.tgz
vignettes: vignettes/biodbNci/inst/doc/biodbNci.html
vignetteTitles: Introduction to the biodbNci package.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biodbNci/inst/doc/biodbNci.R
dependencyCount: 76
Package: biodbUniprot
Version: 1.13.0
Depends: R (>= 4.1.0)
Imports: R6, biodb (>= 1.4.2)
Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr,
rmarkdown, lgr, covr
License: AGPL-3
Archs: x64
MD5sum: 3315e1a472bd14a76ead171e22cf04cb
NeedsCompilation: no
Title: biodbUniprot, a library for connecting to the Uniprot Database
Description: The biodbUniprot library is an extension of the biodb
framework package. It provides access to the UniProt database.
It allows to retrieve entries by their accession number, and
run web service queries for searching for entries.
biocViews: Software, Infrastructure, DataImport
Author: Pierrick Roger [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8177-4873>)
Maintainer: Pierrick Roger <pierrick.roger@cea.fr>
URL: https://github.com/pkrog/biodbUniprot
VignetteBuilder: knitr
BugReports: https://github.com/pkrog/biodbUniprot/issues
git_url: https://git.bioconductor.org/packages/biodbUniprot
git_branch: devel
git_last_commit: 098a02a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/biodbUniprot_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/biodbUniprot_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/biodbUniprot_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/biodbUniprot_1.13.0.tgz
vignettes: vignettes/biodbUniprot/inst/doc/biodbUniprot.html
vignetteTitles: Introduction to the biodbUniprot package.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biodbUniprot/inst/doc/biodbUniprot.R
dependencyCount: 76
Package: bioDist
Version: 1.79.0
Depends: R (>= 2.0), methods, Biobase,KernSmooth
Suggests: locfit
License: Artistic-2.0
MD5sum: e822979703d707013640accf415f0a00
NeedsCompilation: no
Title: Different distance measures
Description: A collection of software tools for calculating distance
measures.
biocViews: Clustering, Classification
Author: B. Ding, R. Gentleman and Vincent Carey
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/bioDist
git_branch: devel
git_last_commit: a634128
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/bioDist_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/bioDist_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/bioDist_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/bioDist_1.79.0.tgz
vignettes: vignettes/bioDist/inst/doc/bioDist.pdf
vignetteTitles: bioDist Introduction
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bioDist/inst/doc/bioDist.R
importsMe: CHETAH, PhyloProfile
dependencyCount: 8
Package: BioGA
Version: 1.1.0
Depends: R (>= 4.4)
Imports: ggplot2, graphics, Rcpp, SummarizedExperiment, animation,
rlang, biocViews, sessioninfo, BiocStyle
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: 0f6b1f9143073e3cae00d4ed8fc9ca29
NeedsCompilation: yes
Title: Bioinformatics Genetic Algorithm (BioGA)
Description: Genetic algorithm are a class of optimization algorithms
inspired by the process of natural selection and genetics. This
package allows users to analyze and optimize high throughput
genomic data using genetic algorithms. The functions provided
are implemented in C++ for improved speed and efficiency, with
an easy-to-use interface for use within R.
biocViews: ExperimentalDesign, Technology
Author: Dany Mukesha [aut, cre] (ORCID:
<https://orcid.org/0009-0001-9514-751X>)
Maintainer: Dany Mukesha <danymukesha@gmail.com>
URL: https://danymukesha.github.io/BioGA/
VignetteBuilder: knitr
BugReports: https://github.com/danymukesha/BioGA/issues
git_url: https://git.bioconductor.org/packages/BioGA
git_branch: devel
git_last_commit: 7656c41
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BioGA_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BioGA_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BioGA_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BioGA_1.1.0.tgz
vignettes: vignettes/BioGA/inst/doc/Introduction.html
vignetteTitles: Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BioGA/inst/doc/Introduction.R
dependencyCount: 96
Package: biomaRt
Version: 2.63.3
Depends: R (>= 4.1.0), methods
Imports: utils, AnnotationDbi, progress, stringr, httr2, digest,
BiocFileCache, rappdirs, xml2, curl
Suggests: BiocStyle, knitr, mockery, rmarkdown, testthat, httptest2
License: Artistic-2.0
Archs: x64
MD5sum: faa68191620b933328c756ca40efe5f4
NeedsCompilation: no
Title: Interface to BioMart databases (i.e. Ensembl)
Description: In recent years a wealth of biological data has become
available in public data repositories. Easy access to these
valuable data resources and firm integration with data analysis
is needed for comprehensive bioinformatics data analysis.
biomaRt provides an interface to a growing collection of
databases implementing the BioMart software suite
(<http://www.biomart.org>). The package enables retrieval of
large amounts of data in a uniform way without the need to know
the underlying database schemas or write complex SQL queries.
The most prominent examples of BioMart databases are maintain
by Ensembl, which provides biomaRt users direct access to a
diverse set of data and enables a wide range of powerful online
queries from gene annotation to database mining.
biocViews: Annotation
Author: Steffen Durinck [aut], Wolfgang Huber [aut], Sean Davis [ctb],
Francois Pepin [ctb], Vince S Buffalo [ctb], Mike Smith [ctb,
cre] (ORCID: <https://orcid.org/0000-0002-7800-3848>)
Maintainer: Mike Smith <grimbough@gmail.com>
URL: https://github.com/Huber-group-EMBL/biomaRt
VignetteBuilder: knitr
BugReports: https://github.com/Huber-group-EMBL/biomaRt/issues
git_url: https://git.bioconductor.org/packages/biomaRt
git_branch: devel
git_last_commit: 6ea7d6f
git_last_commit_date: 2025-03-14
Date/Publication: 2025-03-20
source.ver: src/contrib/biomaRt_2.63.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/biomaRt_2.63.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/biomaRt_2.63.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/biomaRt_2.63.3.tgz
vignettes: vignettes/biomaRt/inst/doc/accessing_ensembl.html,
vignettes/biomaRt/inst/doc/accessing_other_marts.html
vignetteTitles: Accessing Ensembl annotation with biomaRt, Using a
BioMart other than Ensembl
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biomaRt/inst/doc/accessing_ensembl.R,
vignettes/biomaRt/inst/doc/accessing_other_marts.R
dependsOnMe: chromPlot, customProDB, DrugVsDisease, genefu,
GenomicOZone, MineICA, NetSAM, PPInfer, RepViz, VegaMC
importsMe: BadRegionFinder, BgeeCall, branchpointer, BUSpaRse,
ChIPpeakAnno, CHRONOS, CoSIA, dagLogo, DEXSeq, DMRcate,
DominoEffect, dominoSignal, easyRNASeq, EDASeq, ELMER, EpiMix,
epimutacions, FRASER, GDCRNATools, GenVisR, gINTomics,
glmSparseNet, GOexpress, goSTAG, GRaNIE, Gviz, hermes,
InterCellar, isobar, LACE, mCSEA, MEDIPS, MetaboSignal,
metaseqR2, MGFR, motifbreakR, MouseFM, OncoScore, oposSOM,
ORFik, pcaExplorer, phenoTest, pRoloc, ProteoMM,
R453Plus1Toolbox, ramwas, recoup, ReducedExperiment, rgsepd,
scPipe, scQTLtools, seq2pathway, SeqGSEA, sitadela, SPLINTER,
SPONGE, surfaltr, SurfR, SWATH2stats, TCGAbiolinks, TEKRABber,
terapadog, TFEA.ChIP, transcriptogramer, txdbmaker, ViSEAGO,
yarn, ExpHunterSuite, biomartr, BioVenn, convertid,
DiNAMIC.Duo, GOxploreR, GRIN2, scGOclust, scPipeline,
snplinkage, snplist
suggestsMe: AnnotationForge, bioassayR, celda, ClusterJudge,
crisprDesign, cTRAP, Damsel, DELocal, epistack, fedup, FELLA,
GeDi, h5vc, martini, massiR, MethReg, MineICA, MiRaGE, MIRit,
MutationalPatterns, netSmooth, oligo, OrganismDbi, pathlinkR,
piano, Pigengene, progeny, R3CPET, RnBeads, rTRM, scater,
ShortRead, SIM, sincell, tidysbml, trackViewer, wiggleplotr,
zinbwave, BioMartGOGeneSets, BloodCancerMultiOmics2017,
leeBamViews, RegParallel, RforProteomics, BED, BioInsight,
DGEobj, DGEobj.utils, dnapath, gaawr2, geneviewer, grandR,
kangar00, MoBPS, Patterns, Platypus, ProFAST, scDiffCom,
SNPassoc
dependencyCount: 67
Package: biomformat
Version: 1.35.0
Depends: R (>= 3.2), methods
Imports: plyr (>= 1.8), jsonlite (>= 0.9.16), Matrix (>= 1.2), rhdf5
Suggests: testthat (>= 0.10), knitr (>= 1.10), BiocStyle (>= 1.6),
rmarkdown (>= 0.7)
License: GPL-2
MD5sum: a95d2efd7d733cbccc96aec0e17b7aa4
NeedsCompilation: no
Title: An interface package for the BIOM file format
Description: This is an R package for interfacing with the BIOM format.
This package includes basic tools for reading biom-format
files, accessing and subsetting data tables from a biom object
(which is more complex than a single table), as well as limited
support for writing a biom-object back to a biom-format file.
The design of this API is intended to match the python API and
other tools included with the biom-format project, but with a
decidedly "R flavor" that should be familiar to R users. This
includes S4 classes and methods, as well as extensions of
common core functions/methods.
biocViews: ImmunoOncology, DataImport, Metagenomics, Microbiome
Author: Paul J. McMurdie <mcmurdie@alumni.stanford.edu> and Joseph N
Paulson <jpaulson@jimmy.harvard.edu>
Maintainer: Paul J. McMurdie <mcmurdie@alumni.stanford.edu>
URL: https://github.com/joey711/biomformat/, http://biom-format.org/
VignetteBuilder: knitr
BugReports: https://github.com/joey711/biomformat/issues
git_url: https://git.bioconductor.org/packages/biomformat
git_branch: devel
git_last_commit: a3d34c3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/biomformat_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/biomformat_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/biomformat_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/biomformat_1.35.0.tgz
vignettes: vignettes/biomformat/inst/doc/biomformat.html
vignetteTitles: The biomformat package Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biomformat/inst/doc/biomformat.R
importsMe: microbiomeExplorer, phyloseq
suggestsMe: animalcules, iSEEtree, metagenomeSeq, MGnifyR, mia,
MicrobiotaProcess, MetaScope, metacoder
dependencyCount: 14
Package: BioMVCClass
Version: 1.75.0
Depends: R (>= 2.1.0), methods, MVCClass, Biobase, graph, Rgraphviz
License: LGPL
MD5sum: d118422b5114c999e4b0728ec566e4ec
NeedsCompilation: no
Title: Model-View-Controller (MVC) Classes That Use Biobase
Description: Creates classes used in model-view-controller (MVC) design
biocViews: Visualization, Infrastructure, GraphAndNetwork
Author: Elizabeth Whalen
Maintainer: Elizabeth Whalen <ewhalen@hsph.harvard.edu>
git_url: https://git.bioconductor.org/packages/BioMVCClass
git_branch: devel
git_last_commit: 61ff376
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BioMVCClass_1.75.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BioMVCClass_1.75.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BioMVCClass_1.75.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BioMVCClass_1.75.0.tgz
vignettes: vignettes/BioMVCClass/inst/doc/BioMVCClass.pdf
vignetteTitles: BioMVCClass
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 13
Package: biomvRCNS
Version: 1.47.0
Depends: IRanges, GenomicRanges, Gviz
Imports: methods, mvtnorm
Suggests: cluster, parallel, GenomicFeatures, dynamicTreeCut,
Rsamtools, TxDb.Hsapiens.UCSC.hg19.knownGene
License: GPL (>= 2)
MD5sum: eae8b3e80a6262c3b6a80a9707a6fc86
NeedsCompilation: yes
Title: Copy Number study and Segmentation for multivariate biological
data
Description: In this package, a Hidden Semi Markov Model (HSMM) and one
homogeneous segmentation model are designed and implemented for
segmentation genomic data, with the aim of assisting in
transcripts detection using high throughput technology like
RNA-seq or tiling array, and copy number analysis using aCGH or
sequencing.
biocViews: aCGH, CopyNumberVariation, Microarray, Sequencing,
Visualization, Genetics
Author: Yang Du
Maintainer: Yang Du <tooyoung@gmail.com>
git_url: https://git.bioconductor.org/packages/biomvRCNS
git_branch: devel
git_last_commit: de5ca15
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/biomvRCNS_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/biomvRCNS_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/biomvRCNS_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/biomvRCNS_1.47.0.tgz
vignettes: vignettes/biomvRCNS/inst/doc/biomvRCNS.pdf
vignetteTitles: biomvRCNS package introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biomvRCNS/inst/doc/biomvRCNS.R
dependencyCount: 157
Package: BioNAR
Version: 1.9.5
Depends: R (>= 3.5.0), igraph (>= 2.0.1.1), poweRlaw, latex2exp,
RSpectra, Rdpack
Imports: stringr, viridis, fgsea, grid, methods, AnnotationDbi, dplyr,
GO.db, org.Hs.eg.db (>= 3.19.1), rSpectral, WGCNA, ggplot2,
ggrepel, minpack.lm, cowplot, data.table, scales, stats, Matrix
Suggests: knitr, BiocStyle, magick, rmarkdown, igraphdata, testthat (>=
3.0.0), vdiffr, devtools, pander, plotly, randomcoloR
License: Artistic-2.0
Archs: x64
MD5sum: 524329f7db0905fff403dbf0f58928e3
NeedsCompilation: no
Title: Biological Network Analysis in R
Description: the R package BioNAR, developed to step by step analysis
of PPI network. The aim is to quantify and rank each protein’s
simultaneous impact into multiple complexes based on network
topology and clustering. Package also enables estimating of
co-occurrence of diseases across the network and specific
clusters pointing towards shared/common mechanisms.
biocViews: Software, GraphAndNetwork, Network
Author: Colin Mclean [aut], Anatoly Sorokin [aut, cre], Oksana Sorokina
[aut], J. Douglas Armstrong [aut, fnd]
Maintainer: Anatoly Sorokin <lptolik@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/lptolik/BioNAR/issues/
git_url: https://git.bioconductor.org/packages/BioNAR
git_branch: devel
git_last_commit: b873ae2
git_last_commit_date: 2025-02-25
Date/Publication: 2025-02-26
source.ver: src/contrib/BioNAR_1.9.5.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BioNAR_1.9.5.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BioNAR_1.9.5.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BioNAR_1.9.5.tgz
vignettes: vignettes/BioNAR/inst/doc/BioNAR_overview.html
vignetteTitles: BioNAR: Biological Network Analysis in R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BioNAR/inst/doc/BioNAR_overview.R
dependencyCount: 140
Package: BioNERO
Version: 1.15.0
Depends: R (>= 4.1)
Imports: WGCNA, dynamicTreeCut, ggdendro, matrixStats, sva,
RColorBrewer, ComplexHeatmap, ggplot2, rlang, ggrepel,
patchwork, reshape2, igraph, ggnetwork, intergraph, NetRep,
stats, grDevices, utils, methods, BiocParallel, minet, GENIE3,
SummarizedExperiment
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle, DESeq2,
networkD3, covr
License: GPL-3
MD5sum: 87e1f16d89087129db755176edd6d2aa
NeedsCompilation: no
Title: Biological Network Reconstruction Omnibus
Description: BioNERO aims to integrate all aspects of biological
network inference in a single package, including data
preprocessing, exploratory analyses, network inference, and
analyses for biological interpretations. BioNERO can be used to
infer gene coexpression networks (GCNs) and gene regulatory
networks (GRNs) from gene expression data. Additionally, it can
be used to explore topological properties of protein-protein
interaction (PPI) networks. GCN inference relies on the popular
WGCNA algorithm. GRN inference is based on the "wisdom of the
crowds" principle, which consists in inferring GRNs with
multiple algorithms (here, CLR, GENIE3 and ARACNE) and
calculating the average rank for each interaction pair. As all
steps of network analyses are included in this package, BioNERO
makes users avoid having to learn the syntaxes of several
packages and how to communicate between them. Finally, users
can also identify consensus modules across independent
expression sets and calculate intra and interspecies module
preservation statistics between different networks.
biocViews: Software, GeneExpression, GeneRegulation, SystemsBiology,
GraphAndNetwork, Preprocessing, Network, NetworkInference
Author: Fabricio Almeida-Silva [cre, aut] (ORCID:
<https://orcid.org/0000-0002-5314-2964>), Thiago Venancio [aut]
(ORCID: <https://orcid.org/0000-0002-2215-8082>)
Maintainer: Fabricio Almeida-Silva <fabricio_almeidasilva@hotmail.com>
URL: https://github.com/almeidasilvaf/BioNERO
VignetteBuilder: knitr
BugReports: https://github.com/almeidasilvaf/BioNERO/issues
git_url: https://git.bioconductor.org/packages/BioNERO
git_branch: devel
git_last_commit: 20a3539
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BioNERO_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BioNERO_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BioNERO_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BioNERO_1.15.0.tgz
vignettes: vignettes/BioNERO/inst/doc/vignette_01_GCN_inference.html,
vignettes/BioNERO/inst/doc/vignette_02_GRN_inference.html,
vignettes/BioNERO/inst/doc/vignette_03_network_comparison.html
vignetteTitles: Gene coexpression network inference, Gene regulatory
network inference with BioNERO, Network comparison: consensus
modules and module preservation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BioNERO/inst/doc/vignette_01_GCN_inference.R,
vignettes/BioNERO/inst/doc/vignette_02_GRN_inference.R,
vignettes/BioNERO/inst/doc/vignette_03_network_comparison.R
importsMe: cageminer
dependencyCount: 164
Package: BioNet
Version: 1.67.0
Depends: R (>= 2.10.0), graph, RBGL
Imports: igraph (>= 1.0.1), AnnotationDbi, Biobase
Suggests: rgl, impute, DLBCL, genefilter, xtable, ALL, limma,
hgu95av2.db, XML
License: GPL (>= 2)
MD5sum: 24ed1488c9424d4359f29c4c4dd21a40
NeedsCompilation: no
Title: Routines for the functional analysis of biological networks
Description: This package provides functions for the integrated
analysis of protein-protein interaction networks and the
detection of functional modules. Different datasets can be
integrated into the network by assigning p-values of
statistical tests to the nodes of the network. E.g. p-values
obtained from the differential expression of the genes from an
Affymetrix array are assigned to the nodes of the network. By
fitting a beta-uniform mixture model and calculating scores
from the p-values, overall scores of network regions can be
calculated and an integer linear programming algorithm
identifies the maximum scoring subnetwork.
biocViews: Microarray, DataImport, GraphAndNetwork, Network,
NetworkEnrichment, GeneExpression, DifferentialExpression
Author: Marcus Dittrich and Daniela Beisser
Maintainer: Marcus Dittrich
<marcus.dittrich@biozentrum.uni-wuerzburg.de>
URL: http://bionet.bioapps.biozentrum.uni-wuerzburg.de/
git_url: https://git.bioconductor.org/packages/BioNet
git_branch: devel
git_last_commit: b06522b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BioNet_1.67.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BioNet_1.67.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BioNet_1.67.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BioNet_1.67.0.tgz
vignettes: vignettes/BioNet/inst/doc/Tutorial.pdf
vignetteTitles: BioNet Tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BioNet/inst/doc/Tutorial.R
importsMe: gatom, SMITE
suggestsMe: SANTA, mwcsr
dependencyCount: 53
Package: BioQC
Version: 1.35.0
Depends: R (>= 3.5.0), Biobase
Imports: edgeR, Rcpp, methods, stats, utils
LinkingTo: Rcpp
Suggests: testthat, knitr, rmarkdown, lattice, latticeExtra,
rbenchmark, gplots, gridExtra, org.Hs.eg.db, hgu133plus2.db,
ggplot2, reshape2, plyr, ineq, covr, limma, RColorBrewer
License: GPL (>=3) + file LICENSE
MD5sum: c5b8fb40c96c3dfce66cf5a3e582c629
NeedsCompilation: yes
Title: Detect tissue heterogeneity in expression profiles with gene
sets
Description: BioQC performs quality control of high-throughput
expression data based on tissue gene signatures. It can detect
tissue heterogeneity in gene expression data. The core
algorithm is a Wilcoxon-Mann-Whitney test that is optimised for
high performance.
biocViews: GeneExpression,QualityControl,StatisticalMethod,
GeneSetEnrichment
Author: Jitao David Zhang [cre, aut], Laura Badi [aut], Gregor Sturm
[aut], Roland Ambs [aut], Iakov Davydov [aut]
Maintainer: Jitao David Zhang <jitao_david.zhang@roche.com>
URL: https://accio.github.io/BioQC
VignetteBuilder: knitr
BugReports: https://accio.github.io/BioQC/issues
git_url: https://git.bioconductor.org/packages/BioQC
git_branch: devel
git_last_commit: 33cb26a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BioQC_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BioQC_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BioQC_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BioQC_1.35.0.tgz
vignettes: vignettes/BioQC/inst/doc/bioqc-efficiency.html,
vignettes/BioQC/inst/doc/bioqc-introduction.html,
vignettes/BioQC/inst/doc/bioqc-signedGenesets.html,
vignettes/BioQC/inst/doc/bioqc-simulation.html,
vignettes/BioQC/inst/doc/bioqc-wmw-test-performance.html,
vignettes/BioQC/inst/doc/BioQC.html
vignetteTitles: BioQC Algorithm: Speeding up the Wilcoxon-Mann-Whitney
Test, BioQC: Detect tissue heterogeneity in gene expression
data, Using BioQC with signed genesets, BioQC-benchmark:
Testing Efficiency,, Sensitivity and Specificity of BioQC on
simulated and real-world data, Comparing the
Wilcoxon-Mann-Whitney to alternative statistical tests,
BioQC-kidney: The kidney expression example
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BioQC/inst/doc/bioqc-efficiency.R,
vignettes/BioQC/inst/doc/bioqc-introduction.R,
vignettes/BioQC/inst/doc/bioqc-signedGenesets.R,
vignettes/BioQC/inst/doc/bioqc-simulation.R,
vignettes/BioQC/inst/doc/bioqc-wmw-test-performance.R,
vignettes/BioQC/inst/doc/BioQC.R
dependencyCount: 15
Package: biosigner
Version: 1.35.0
Imports: Biobase, methods, e1071, grDevices, graphics,
MultiAssayExperiment, MultiDataSet, randomForest, ropls, stats,
SummarizedExperiment, utils
Suggests: BioMark, BiocGenerics, BiocStyle, golubEsets, hu6800.db,
knitr, omicade4, rmarkdown, testthat
License: CeCILL
Archs: x64
MD5sum: 45ea92ddcfb689837fd6d9bde14a9e88
NeedsCompilation: no
Title: Signature discovery from omics data
Description: Feature selection is critical in omics data analysis to
extract restricted and meaningful molecular signatures from
complex and high-dimension data, and to build robust
classifiers. This package implements a new method to assess the
relevance of the variables for the prediction performances of
the classifier. The approach can be run in parallel with the
PLS-DA, Random Forest, and SVM binary classifiers. The
signatures and the corresponding 'restricted' models are
returned, enabling future predictions on new datasets. A Galaxy
implementation of the package is available within the
Workflow4metabolomics.org online infrastructure for
computational metabolomics.
biocViews: Classification, FeatureExtraction, Transcriptomics,
Proteomics, Metabolomics, Lipidomics, MassSpectrometry
Author: Philippe Rinaudo [aut], Etienne A. Thevenot [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1019-4577>)
Maintainer: Etienne A. Thevenot <etienne.thevenot@cea.fr>
URL: http://dx.doi.org/10.3389/fmolb.2016.00026
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/biosigner
git_branch: devel
git_last_commit: 8a970ea
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/biosigner_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/biosigner_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/biosigner_1.35.0.tgz
vignettes: vignettes/biosigner/inst/doc/biosigner-vignette.html
vignetteTitles: biosigner-vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biosigner/inst/doc/biosigner-vignette.R
suggestsMe: phenomis
dependencyCount: 109
Package: Biostrings
Version: 2.75.4
Depends: R (>= 4.1.0), BiocGenerics (>= 0.37.0), S4Vectors (>=
0.27.12), IRanges (>= 2.31.2), XVector (>= 0.37.1),
GenomeInfoDb
Imports: methods, utils, grDevices, stats, crayon
LinkingTo: S4Vectors, IRanges, XVector
Suggests: graphics, pwalign, BSgenome (>= 1.13.14),
BSgenome.Celegans.UCSC.ce2 (>= 1.3.11),
BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.11),
BSgenome.Hsapiens.UCSC.hg18, drosophila2probe, hgu95av2probe,
hgu133aprobe, GenomicFeatures (>= 1.3.14), hgu95av2cdf, affy
(>= 1.41.3), affydata (>= 1.11.5), RUnit, BiocStyle, knitr,
testthat (>= 3.0.0), covr
License: Artistic-2.0
MD5sum: 2b2d9f6fc84ecba9431b345fbabfee2a
NeedsCompilation: yes
Title: Efficient manipulation of biological strings
Description: Memory efficient string containers, string matching
algorithms, and other utilities, for fast manipulation of large
biological sequences or sets of sequences.
biocViews: SequenceMatching, Alignment, Sequencing, Genetics,
DataImport, DataRepresentation, Infrastructure
Author: Hervé Pagès [aut, cre], Patrick Aboyoun [aut], Robert Gentleman
[aut], Saikat DebRoy [aut], Vince Carey [ctb], Nicolas Delhomme
[ctb], Felix Ernst [ctb], Wolfgang Huber [ctb] ('matchprobes'
vignette), Beryl Kanali [ctb] (Converted 'MultipleAlignments'
vignette from Sweave to RMarkdown), Haleema Khan [ctb]
(Converted 'matchprobes' vignette from Sweave to RMarkdown),
Aidan Lakshman [ctb], Kieran O'Neill [ctb], Valerie Obenchain
[ctb], Marcel Ramos [ctb], Albert Vill [ctb], Jen Wokaty [ctb]
(Converted 'matchprobes' vignette from Sweave to RMarkdown),
Erik Wright [ctb]
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/Biostrings
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/Biostrings/issues
git_url: https://git.bioconductor.org/packages/Biostrings
git_branch: devel
git_last_commit: d6f21ed
git_last_commit_date: 2025-02-20
Date/Publication: 2025-02-21
source.ver: src/contrib/Biostrings_2.75.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Biostrings_2.75.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Biostrings_2.75.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Biostrings_2.75.4.tgz
vignettes: vignettes/Biostrings/inst/doc/Biostrings2Classes.pdf,
vignettes/Biostrings/inst/doc/BiostringsQuickOverview.pdf,
vignettes/Biostrings/inst/doc/PairwiseAlignments.pdf,
vignettes/Biostrings/inst/doc/matchprobes.html,
vignettes/Biostrings/inst/doc/MultipleAlignments.html
vignetteTitles: A short presentation of the basic classes defined in
Biostrings 2, Biostrings Quick Overview, Pairwise Sequence
Alignments, Handling probe sequence information, Multiple
Alignments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Biostrings/inst/doc/Biostrings2Classes.R,
vignettes/Biostrings/inst/doc/matchprobes.R,
vignettes/Biostrings/inst/doc/MultipleAlignments.R
dependsOnMe: alabaster.string, altcdfenvs, amplican, Basic4Cseq, BRAIN,
BSgenome, BSgenomeForge, chimeraviz, ChIPanalyser, ChIPsim,
cleaver, CODEX, CRISPRseek, DECIPHER, deepSNV, GeneRegionScan,
GenomicAlignments, GOTHiC, HelloRanges, hiReadsProcessor,
kebabs, MethTargetedNGS, minfi, Modstrings, MotifDb,
motifTestR, msa, muscle, oligo, ORFhunteR, periodicDNA,
pqsfinder, pwalign, PWMEnrich, QSutils, R453Plus1Toolbox,
R4RNA, rBLAST, REDseq, rGADEM, RiboProfiling, rRDP, Rsamtools,
RSVSim, rSWeeP, sangeranalyseR, sangerseqR, SCAN.UPC, SELEX,
ShortRead, SICtools, SimFFPE, ssviz, Structstrings, svaNUMT,
systemPipeR, topdownr, transmogR, TreeSummarizedExperiment,
triplex, VarCon, FDb.FANTOM4.promoters.hg19, pd.ag,
pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501,
pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine,
pd.bsubtilis, pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine,
pd.canine.2, pd.celegans, pd.chicken, pd.chigene.1.0.st,
pd.chigene.1.1.st, pd.chogene.2.0.st, pd.chogene.2.1.st,
pd.citrus, pd.clariom.d.human, pd.clariom.s.human,
pd.clariom.s.human.ht, pd.clariom.s.mouse,
pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht,
pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st,
pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array,
pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1,
pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2,
pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st,
pd.equgene.1.1.st, pd.felgene.1.0.st, pd.felgene.1.1.st,
pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5,
pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st,
pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a,
pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219,
pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d,
pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm,
pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800,
pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1,
pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize,
pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240,
pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st,
pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a,
pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c,
pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0,
pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1,
pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st,
pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0,
pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180,
pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st,
pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine,
pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st,
pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1,
pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st,
pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st,
pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c,
pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice,
pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0,
pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean,
pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato,
pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2,
pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98,
pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, harbChIP,
JASPAR2014, NestLink, generegulation, sequencing,
CleanBSequences, SubVis
importsMe: AllelicImbalance, AneuFinder, AnnotationHubData, appreci8R,
AssessORF, ATACseqQC, BBCAnalyzer, BCRANK, bcSeq, BEAT,
BgeeCall, biovizBase, branchpointer, bsseq, BUMHMM, BUSpaRse,
CAGEr, CellBarcode, ChIPpeakAnno, ChIPseqR, ChIPsim, chromVAR,
circRNAprofiler, CircSeqAlignTk, cleanUpdTSeq, CleanUpRNAseq,
cliProfiler, CNEr, CNVfilteR, cogeqc, compEpiTools,
consensusDE, coRdon, crisprBase, crisprBowtie, crisprDesign,
crisprScore, crisprShiny, CrispRVariants, crisprViz,
customProDB, dada2, dagLogo, DAMEfinder, Damsel,
decompTumor2Sig, diffHic, DNAshapeR, DominoEffect,
doubletrouble, DuplexDiscovereR, easyRNASeq, EDASeq,
enhancerHomologSearch, ensembldb, EpiTxDb, esATAC, eudysbiome,
EventPointer, factR, FastqCleaner, FLAMES, G4SNVHunter,
GA4GHclient, gcapc, gcrma, gDNAx, GeneRegionScan, genomation,
GenomAutomorphism, GenomicAlignments, GenomicDistributions,
GenomicFeatures, GenomicScores, GenVisR, ggbio, ggmsa, girafe,
gmapR, gmoviz, GRaNIE, GUIDEseq, Gviz, gwascat, h5vc, heatmaps,
HiLDA, HiTC, icetea, idpr, IntEREst, IONiseR, ipdDb,
IsoformSwitchAnalyzeR, KEGGREST, LinTInd, LymphoSeq, m6Aboost,
MADSEQ, MatrixRider, MDTS, MEDIPS, MEDME, memes, MesKit,
metaseqR2, methimpute, methodical, methylPipe, methylscaper,
mia, microbiome, MicrobiotaProcess, microRNA, MMDiff2,
mobileRNA, monaLisa, Motif2Site, motifbreakR, motifcounter,
motifmatchr, MotifPeeker, motifStack, MSA2dist, MSnID,
MSstatsLiP, MSstatsPTM, multicrispr, MungeSumstats, musicatk,
MutationalPatterns, NanoMethViz, NanoStringNCTools, ngsReports,
nucleR, oligoClasses, OmaDB, openPrimeR, ORFik, OTUbase,
packFinder, pdInfoBuilder, PhyloProfile, phyloseq, PICB,
pipeFrame, planttfhunter, podkat, primirTSS, proBAMr, procoil,
ProteoDisco, PureCN, Pviz, qPLEXanalyzer, qsea, QuasR, r3Cseq,
raer, ramwas, RCAS, Rcpi, recoup, regioneR, regutools, REMP,
Repitools, RESOLVE, rfaRm, rGADEM, rhinotypeR, RiboCrypt,
ribosomeProfilingQC, RNAmodR, rprimer, Rqc, rtracklayer, sarks,
scanMiR, scanMiRApp, scifer, scmeth, SCOPE, scoreInvHap, scoup,
scPipe, scruff, seqArchR, seqArchRplus, SeqArray, seqPattern,
SGSeq, signeR, SigsPack, SingleMoleculeFootprinting, sitadela,
SNPhood, soGGi, SomaticSignatures, SparseSignatures, spiky,
SpliceWiz, SPLINTER, sscu, StructuralVariantAnnotation,
supersigs, surfaltr, svaRetro, synapter, SynExtend, SynMut,
syntenet, TAPseq, TENET, TFBSTools, transite, tRNA,
tRNAdbImport, tRNAscanImport, TVTB, txcutr, tximeta, Ularcirc,
UMI4Cats, universalmotif, VariantAnnotation, VariantExperiment,
VariantFiltering, VariantTools, wavClusteR, XNAString, YAPSA,
EuPathDB, FDb.InfiniumMethylation.hg18,
FDb.InfiniumMethylation.hg19,
pd.081229.hg18.promoter.medip.hx1,
pd.2006.07.18.hg18.refseq.promoter,
pd.2006.07.18.mm8.refseq.promoter,
pd.2006.10.31.rn34.refseq.promoter, pd.charm.hg18.example,
pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.mirna.3.1,
MetaScope, microbiomeDataSets, pd.atdschip.tiling,
PhyloProfileData, systemPipeRdata, alakazam, AntibodyForests,
BASiNET, BASiNETEntropy, biomartr, copyseparator,
crispRdesignR, CSESA, cubar, deepredeff, DNAmotif, dowser,
EncDNA, ensembleTax, EpiSemble, GB5mcPred, genBaRcode,
geneHapR, GenomicSig, hoardeR, ICAMS, iimi, immuneSIM, kibior,
kmeRtone, longreadvqs, metaCluster, MIC, MitoHEAR, MixviR,
ogrdbstats, OpEnHiMR, PACVr, Platypus, PredCRG, refseqR,
revert, SeedMatchR, seqmagick, simMP, SMITIDstruct, vhcub
suggestsMe: alabaster.files, annotate, AnnotationForge, AnnotationHub,
autonomics, bambu, BANDITS, CSAR, DNAcycP2, eisaR,
GenomicFiles, GenomicRanges, GenomicTuples, ggseqalign,
GWASTools, HiContacts, HPiP, maftools, methrix, methylumi,
MiRaGE, mitoClone2, nuCpos, plyinteractions,
RNAmodR.AlkAnilineSeq, rpx, rTRM, screenCounter, spatzie,
splatter, systemPipeTools, treeio, tripr, XVector,
SNPlocs.Hsapiens.dbSNP144.GRCh37,
SNPlocs.Hsapiens.dbSNP144.GRCh38,
SNPlocs.Hsapiens.dbSNP149.GRCh38,
SNPlocs.Hsapiens.dbSNP150.GRCh38,
SNPlocs.Hsapiens.dbSNP155.GRCh37,
SNPlocs.Hsapiens.dbSNP155.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh37,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, BeadArrayUseCases, bbl,
bio3d, DDPNA, file2meco, geneviewer, gkmSVM, karyotapR, maGUI,
MARVEL, MiscMetabar, msaR, NameNeedle, orthGS, phangorn,
polyRAD, protr, seqtrie, sigminer, Signac, tidysq
linksToMe: DECIPHER, kebabs, MatrixRider, pwalign, Rsamtools,
ShortRead, triplex, VariantAnnotation, VariantFiltering
dependencyCount: 24
Package: BioTIP
Version: 1.21.0
Depends: R (>= 3.6)
Imports: igraph, cluster, psych, stringr, GenomicRanges, MASS, scran
Suggests: knitr, markdown, base, rmarkdown, ggplot2
License: GPL-2
Archs: x64
MD5sum: 67584ffd07854144fd66d2462f5f2a95
NeedsCompilation: no
Title: BioTIP: An R package for characterization of Biological
Tipping-Point
Description: Adopting tipping-point theory to transcriptome profiles to
unravel disease regulatory trajectory.
biocViews: Sequencing, RNASeq, GeneExpression, Transcription, Software
Author: Zhezhen Wang, Andrew Goldstein, Yuxi Sun, Biniam Feleke, Qier
An, Antonio Feliciano, Xinan Yang
Maintainer: Yuxi (Jennifer) Sun <ysun11@uchicago.edu>, Zhezhen Wang
<zhezhen@uchicago.edu>, and X Holly Yang <xyang2@uchicago.edu>
URL: https://github.com/xyang2uchicago/BioTIP
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BioTIP
git_branch: devel
git_last_commit: 3d67b56
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BioTIP_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BioTIP_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BioTIP_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BioTIP_1.21.0.tgz
vignettes: vignettes/BioTIP/inst/doc/BioTIP.html
vignetteTitles: BioTIP- an R package for characterization of Biological
Tipping-Point
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BioTIP/inst/doc/BioTIP.R
dependencyCount: 81
Package: biotmle
Version: 1.31.0
Depends: R (>= 4.0)
Imports: stats, methods, dplyr, tibble, ggplot2, ggsci, superheat,
assertthat, drtmle (>= 1.0.4), S4Vectors, BiocGenerics,
BiocParallel, SummarizedExperiment, limma
Suggests: testthat, knitr, rmarkdown, BiocStyle, arm, earth, ranger,
SuperLearner, Matrix, DBI, biotmleData (>= 1.1.1)
License: MIT + file LICENSE
MD5sum: d2556e05683a882534894366b6344a18
NeedsCompilation: no
Title: Targeted Learning with Moderated Statistics for Biomarker
Discovery
Description: Tools for differential expression biomarker discovery
based on microarray and next-generation sequencing data that
leverage efficient semiparametric estimators of the average
treatment effect for variable importance analysis. Estimation
and inference of the (marginal) average treatment effects of
potential biomarkers are computed by targeted minimum
loss-based estimation, with joint, stable inference constructed
across all biomarkers using a generalization of moderated
statistics for use with the estimated efficient influence
function. The procedure accommodates the use of ensemble
machine learning for the estimation of nuisance functions.
biocViews: Regression, GeneExpression, DifferentialExpression,
Sequencing, Microarray, RNASeq, ImmunoOncology
Author: Nima Hejazi [aut, cre, cph] (ORCID:
<https://orcid.org/0000-0002-7127-2789>), Alan Hubbard [aut,
ths] (ORCID: <https://orcid.org/0000-0002-3769-0127>), Mark van
der Laan [aut, ths] (ORCID:
<https://orcid.org/0000-0003-1432-5511>), Weixin Cai [ctb]
(ORCID: <https://orcid.org/0000-0003-2680-3066>), Philippe
Boileau [ctb] (ORCID: <https://orcid.org/0000-0002-4850-2507>)
Maintainer: Nima Hejazi <nh@nimahejazi.org>
URL: https://code.nimahejazi.org/biotmle
VignetteBuilder: knitr
BugReports: https://github.com/nhejazi/biotmle/issues
git_url: https://git.bioconductor.org/packages/biotmle
git_branch: devel
git_last_commit: 7b0119e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/biotmle_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/biotmle_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/biotmle_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/biotmle_1.31.0.tgz
vignettes: vignettes/biotmle/inst/doc/exposureBiomarkers.html
vignetteTitles: Identifying Biomarkers from an Exposure Variable
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/biotmle/inst/doc/exposureBiomarkers.R
dependencyCount: 110
Package: biovizBase
Version: 1.55.0
Depends: R (>= 3.5.0), methods
Imports: grDevices, stats, scales, Hmisc, RColorBrewer, dichromat,
BiocGenerics, S4Vectors (>= 0.23.19), IRanges (>= 1.99.28),
GenomeInfoDb (>= 1.5.14), GenomicRanges (>= 1.23.21),
SummarizedExperiment, Biostrings (>= 2.33.11), Rsamtools (>=
1.17.28), GenomicAlignments (>= 1.1.16), GenomicFeatures (>=
1.21.19), AnnotationDbi, VariantAnnotation (>= 1.11.4),
ensembldb (>= 1.99.13), AnnotationFilter (>= 0.99.8), rlang
Suggests: BSgenome.Hsapiens.UCSC.hg19,
TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome, rtracklayer,
EnsDb.Hsapiens.v75, RUnit
License: Artistic-2.0
MD5sum: d8873e007c9c6a7066a319acdf723e82
NeedsCompilation: yes
Title: Basic graphic utilities for visualization of genomic data.
Description: The biovizBase package is designed to provide a set of
utilities, color schemes and conventions for genomic data. It
serves as the base for various high-level packages for
biological data visualization. This saves development effort
and encourages consistency.
biocViews: Infrastructure, Visualization, Preprocessing
Author: Tengfei Yin [aut], Michael Lawrence [aut, ths, cre], Dianne
Cook [aut, ths], Johannes Rainer [ctb]
Maintainer: Michael Lawrence <michafla@gene.com>
git_url: https://git.bioconductor.org/packages/biovizBase
git_branch: devel
git_last_commit: 85d7c94
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/biovizBase_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/biovizBase_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/biovizBase_1.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/biovizBase_1.55.0.tgz
vignettes: vignettes/biovizBase/inst/doc/intro.pdf
vignetteTitles: An Introduction to biovizBase
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biovizBase/inst/doc/intro.R
dependsOnMe: CAFE
importsMe: BubbleTree, ChIPexoQual, ggbio, Gviz, karyoploteR, Pviz, Rqc
suggestsMe: CINdex, Damsel, derfinderPlot, FRASER, NanoStringNCTools,
OUTRIDER, R3CPET, regionReport, shiny.gosling,
StructuralVariantAnnotation, Signac
dependencyCount: 135
Package: BiRewire
Version: 3.39.0
Depends: igraph, slam, Rtsne, Matrix
Suggests: RUnit, BiocGenerics
License: GPL-3
Archs: x64
MD5sum: 0fd2539da644b312a3ba3b5dddb6275e
NeedsCompilation: yes
Title: High-performing routines for the randomization of a bipartite
graph (or a binary event matrix), undirected and directed
signed graph preserving degree distribution (or marginal
totals)
Description: Fast functions for bipartite network rewiring through N
consecutive switching steps (See References) and for the
computation of the minimal number of switching steps to be
performed in order to maximise the dissimilarity with respect
to the original network. Includes functions for the analysis of
the introduced randomness across the switching steps and
several other routines to analyse the resulting networks and
their natural projections. Extension to undirected networks and
directed signed networks is also provided. Starting from
version 1.9.7 a more precise bound (especially for small
network) has been implemented. Starting from version 2.2.0 the
analysis routine is more complete and a visual montioring of
the underlying Markov Chain has been implemented. Starting from
3.6.0 the library can handle also matrices with NA (not for the
directed signed graphs). Since version 3.27.1 it is possible to
add a constraint for dsg generation: usually positive and
negative arc between two nodes could be not accepted.
biocViews: Network
Author: Andrea Gobbi [aut], Francesco Iorio [aut], Giuseppe Jurman
[cbt], Davide Albanese [cbt], Julio Saez-Rodriguez [cbt].
Maintainer: Andrea Gobbi <gobbi.andrea@mail.com>
URL: http://www.ebi.ac.uk/~iorio/BiRewire
git_url: https://git.bioconductor.org/packages/BiRewire
git_branch: devel
git_last_commit: 8cf10d9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BiRewire_3.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BiRewire_3.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BiRewire_3.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BiRewire_3.39.0.tgz
vignettes: vignettes/BiRewire/inst/doc/BiRewire.pdf
vignetteTitles: BiRewire
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BiRewire/inst/doc/BiRewire.R
dependencyCount: 20
Package: biscuiteer
Version: 1.21.0
Depends: R (>= 4.1.0), biscuiteerData, bsseq
Imports: readr, qualV, Matrix, impute, HDF5Array, S4Vectors, Rsamtools,
data.table, Biobase, GenomicRanges, IRanges, BiocGenerics,
VariantAnnotation, DelayedMatrixStats, SummarizedExperiment,
GenomeInfoDb, Mus.musculus, Homo.sapiens, matrixStats,
rtracklayer, QDNAseq, dmrseq, methods, utils, R.utils, gtools,
BiocParallel
Suggests: DSS, covr, knitr, rmarkdown, markdown, rlang, scmeth,
pkgdown, roxygen2, testthat, QDNAseq.hg19, QDNAseq.mm10,
BiocStyle
License: GPL-3
Archs: x64
MD5sum: 1fbd4eaa98633c7ce8008eb447a3e7dc
NeedsCompilation: no
Title: Convenience Functions for Biscuit
Description: A test harness for bsseq loading of Biscuit output,
summarization of WGBS data over defined regions and in mappable
samples, with or without imputation, dropping of mostly-NA
rows, age estimates, etc.
biocViews: DataImport, MethylSeq, DNAMethylation
Author: Tim Triche [aut], Wanding Zhou [aut], Benjamin Johnson [aut],
Jacob Morrison [aut, cre], Lyong Heo [aut], James Eapen [aut]
Maintainer: Jacob Morrison <jacob.morrison@vai.org>
URL: https://github.com/trichelab/biscuiteer
VignetteBuilder: knitr
BugReports: https://github.com/trichelab/biscuiteer/issues
git_url: https://git.bioconductor.org/packages/biscuiteer
git_branch: devel
git_last_commit: 7fd1d20
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/biscuiteer_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/biscuiteer_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/biscuiteer_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/biscuiteer_1.21.0.tgz
vignettes: vignettes/biscuiteer/inst/doc/biscuiteer.html
vignetteTitles: Biscuiteer User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/biscuiteer/inst/doc/biscuiteer.R
dependencyCount: 181
Package: BiSeq
Version: 1.47.0
Depends: R (>= 3.5.0), methods, S4Vectors, IRanges (>= 1.17.24),
GenomicRanges, SummarizedExperiment (>= 0.2.0), Formula
Imports: methods, BiocGenerics, Biobase, S4Vectors, IRanges,
GenomeInfoDb, GenomicRanges, SummarizedExperiment, rtracklayer,
parallel, betareg, lokern, Formula, globaltest
License: LGPL-3
MD5sum: e822404b65d3bc4b042a51b7f8f92b0f
NeedsCompilation: no
Title: Processing and analyzing bisulfite sequencing data
Description: The BiSeq package provides useful classes and functions to
handle and analyze targeted bisulfite sequencing (BS) data such
as reduced-representation bisulfite sequencing (RRBS) data. In
particular, it implements an algorithm to detect differentially
methylated regions (DMRs). The package takes already aligned BS
data from one or multiple samples.
biocViews: Genetics, Sequencing, MethylSeq, DNAMethylation
Author: Katja Hebestreit, Hans-Ulrich Klein
Maintainer: Katja Hebestreit <katja.hebestreit@gmail.com>
git_url: https://git.bioconductor.org/packages/BiSeq
git_branch: devel
git_last_commit: 225e029
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BiSeq_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BiSeq_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BiSeq_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BiSeq_1.47.0.tgz
vignettes: vignettes/BiSeq/inst/doc/BiSeq.pdf
vignetteTitles: An Introduction to BiSeq
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BiSeq/inst/doc/BiSeq.R
dependsOnMe: RRBSdata
suggestsMe: updateObject
dependencyCount: 91
Package: blacksheepr
Version: 1.21.0
Depends: R (>= 3.6)
Imports: grid, stats, grDevices, utils, circlize, viridis,
RColorBrewer, ComplexHeatmap, SummarizedExperiment, pasilla
Suggests: testthat (>= 2.1.0), knitr, BiocStyle, rmarkdown, curl
License: MIT + file LICENSE
MD5sum: cc3fb1d5635e50c4b2841d4e3f50466b
NeedsCompilation: no
Title: Outlier Analysis for pairwise differential comparison
Description: Blacksheep is a tool designed for outlier analysis in the
context of pairwise comparisons in an effort to find
distinguishing characteristics from two groups. This tool was
designed to be applied for biological applications such as
phosphoproteomics or transcriptomics, but it can be used for
any data that can be represented by a 2D table, and has two sub
populations within the table to compare.
biocViews: Sequencing, RNASeq, GeneExpression, Transcription,
DifferentialExpression, Transcriptomics
Author: MacIntosh Cornwell [aut], RugglesLab [cre]
Maintainer: RugglesLab <ruggleslab@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/ruggleslab/blacksheepr/issues
git_url: https://git.bioconductor.org/packages/blacksheepr
git_branch: devel
git_last_commit: 4a71690
git_last_commit_date: 2024-10-29
Date/Publication: 2025-03-11
source.ver: src/contrib/blacksheepr_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/blacksheepr_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/blacksheepr_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/blacksheepr_1.21.0.tgz
vignettes: vignettes/blacksheepr/inst/doc/blacksheepr_vignette.html
vignetteTitles: Outlier Analysis using blacksheepr - Phosphoprotein
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/blacksheepr/inst/doc/blacksheepr_vignette.R
dependencyCount: 132
Package: blima
Version: 1.41.0
Depends: R(>= 3.3)
Imports: beadarray(>= 2.0.0), Biobase(>= 2.0.0), Rcpp (>= 0.12.8),
BiocGenerics, grDevices, stats, graphics
LinkingTo: Rcpp
Suggests: xtable, blimaTestingData, BiocStyle, illuminaHumanv4.db,
lumi, knitr
License: GPL-3
MD5sum: f855c2e6d71b12431f2dadd6717b556c
NeedsCompilation: yes
Title: Tools for the preprocessing and analysis of the Illumina
microarrays on the detector (bead) level
Description: Package blima includes several algorithms for the
preprocessing of Illumina microarray data. It focuses to the
bead level analysis and provides novel approach to the quantile
normalization of the vectors of unequal lengths. It provides
variety of the methods for background correction including
background subtraction, RMA like convolution and background
outlier removal. It also implements variance stabilizing
transformation on the bead level. There are also implemented
methods for data summarization. It also provides the methods
for performing T-tests on the detector (bead) level and on the
probe level for differential expression testing.
biocViews: Microarray, Preprocessing, Normalization,
DifferentialExpression, GeneRegulation, GeneExpression
Author: Vojtěch Kulvait
Maintainer: Vojtěch Kulvait <kulvait@gmail.com>
URL: https://bitbucket.org/kulvait/blima
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/blima
git_branch: devel
git_last_commit: c59eaff
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/blima_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/blima_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/blima_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/blima_1.41.0.tgz
vignettes: vignettes/blima/inst/doc/blima.pdf
vignetteTitles: blima.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/blima/inst/doc/blima.R
suggestsMe: blimaTestingData
dependencyCount: 81
Package: BLMA
Version: 1.31.0
Depends: ROntoTools, GSA, PADOG, limma, graph, stats, utils, parallel,
Biobase, metafor, methods
Suggests: RUnit, BiocGenerics
License: GPL (>=2)
Archs: x64
MD5sum: 2ac66c3cc15d2f2cc789ac408584de21
NeedsCompilation: no
Title: BLMA: A package for bi-level meta-analysis
Description: Suit of tools for bi-level meta-analysis. The package can
be used in a wide range of applications, including general
hypothesis testings, differential expression analysis,
functional analysis, and pathway analysis.
biocViews: GeneSetEnrichment, Pathways, DifferentialExpression,
Microarray
Author: Tin Nguyen <tinn@auburn.edu>, Hung Nguyen
<pzb00047@auburn.edu>, and Sorin Draghici <sorin@wayne.edu>
Maintainer: Van-Dung Pham <dvp0001@auburn.edu>
git_url: https://git.bioconductor.org/packages/BLMA
git_branch: devel
git_last_commit: 3ad7545
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BLMA_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BLMA_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BLMA_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BLMA_1.31.0.tgz
vignettes: vignettes/BLMA/inst/doc/BLMA.pdf
vignetteTitles: BLMA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BLMA/inst/doc/BLMA.R
dependencyCount: 77
Package: BloodGen3Module
Version: 1.15.0
Depends: R (>= 4.1)
Imports: SummarizedExperiment, ExperimentHub, methods, grid, graphics,
stats, grDevices, circlize, testthat, ComplexHeatmap(>=
1.99.8), ggplot2, matrixStats, gtools, reshape2,
preprocessCore, randomcoloR, V8, limma
Suggests: RUnit, devtools, BiocGenerics, knitr, rmarkdown
License: GPL-2
MD5sum: 664258ed1f8ec1dd4f826257d04bce9e
NeedsCompilation: no
Title: This R package for performing module repertoire analyses and
generating fingerprint representations
Description: The BloodGen3Module package provides functions for R user
performing module repertoire analyses and generating
fingerprint representations. Functions can perform group
comparison or individual sample analysis and visualization by
fingerprint grid plot or fingerprint heatmap. Module repertoire
analyses typically involve determining the percentage of the
constitutive genes for each module that are significantly
increased or decreased. As we describe in
details;https://www.biorxiv.org/content/10.1101/525709v2 and
https://pubmed.ncbi.nlm.nih.gov/33624743/, the results of
module repertoire analyses can be represented in a fingerprint
format, where red and blue spots indicate increases or
decreases in module activity. These spots are subsequently
represented either on a grid, with each position being assigned
to a given module, or in a heatmap where the samples are
arranged in columns and the modules in rows.
biocViews: Software, Visualization, GeneExpression
Author: Darawan Rinchai [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8851-7730>)
Maintainer: Darawan Rinchai <drinchai@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BloodGen3Module
git_branch: devel
git_last_commit: 1a37edb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BloodGen3Module_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BloodGen3Module_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BloodGen3Module_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BloodGen3Module_1.15.0.tgz
vignettes: vignettes/BloodGen3Module/inst/doc/BloodGen3Module.html
vignetteTitles: BloodGen3Module: Modular Repertoire Analysis and
Visualization
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BloodGen3Module/inst/doc/BloodGen3Module.R
dependencyCount: 129
Package: bluster
Version: 1.17.0
Imports: stats, methods, utils, cluster, Matrix, Rcpp, igraph,
S4Vectors, BiocParallel, BiocNeighbors
LinkingTo: Rcpp, assorthead
Suggests: knitr, rmarkdown, testthat, BiocStyle, dynamicTreeCut,
scRNAseq, scuttle, scater, scran, pheatmap, viridis, mbkmeans,
kohonen, apcluster, DirichletMultinomial, vegan, fastcluster
License: GPL-3
MD5sum: 76202e3d1ed5b0e0b51b5db7570cd3e3
NeedsCompilation: yes
Title: Clustering Algorithms for Bioconductor
Description: Wraps common clustering algorithms in an easily extended
S4 framework. Backends are implemented for hierarchical,
k-means and graph-based clustering. Several utilities are also
provided to compare and evaluate clustering results.
biocViews: ImmunoOncology, Software, GeneExpression, Transcriptomics,
SingleCell, Clustering
Author: Aaron Lun [aut, cre], Stephanie Hicks [ctb], Basil Courbayre
[ctb], Tuomas Borman [ctb], Leo Lahti [ctb]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
SystemRequirements: C++17
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/bluster
git_branch: devel
git_last_commit: 510e7a1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/bluster_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/bluster_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/bluster_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/bluster_1.17.0.tgz
vignettes: vignettes/bluster/inst/doc/clusterRows.html,
vignettes/bluster/inst/doc/diagnostics.html
vignetteTitles: 1. Clustering algorithms, 2. Clustering diagnostics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bluster/inst/doc/clusterRows.R,
vignettes/bluster/inst/doc/diagnostics.R
dependsOnMe: OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows,
SingleRBook
importsMe: chevreulProcess, concordexR, dandelionR, epiregulon, mia,
MPAC, poem, scDblFinder, scDiagnostics, scran, Voyager, Canek
suggestsMe: batchelor, ChromSCape, dittoSeq, mbkmeans, miaViz, MOSim,
mumosa, SpatialDDLS, SuperCell
dependencyCount: 34
Package: bnbc
Version: 1.29.0
Depends: R (>= 3.5.0), methods, BiocGenerics, SummarizedExperiment,
GenomicRanges
Imports: Rcpp (>= 0.12.12), IRanges, rhdf5, data.table, GenomeInfoDb,
S4Vectors, matrixStats, preprocessCore, sva, parallel, EBImage,
utils, HiCBricks
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, rmarkdown, RUnit,
BSgenome.Hsapiens.UCSC.hg19
License: Artistic-2.0
Archs: x64
MD5sum: d9537db99ad482734e1d8caf242a61f5
NeedsCompilation: yes
Title: Bandwise normalization and batch correction of Hi-C data
Description: Tools to normalize (several) Hi-C data from replicates.
biocViews: HiC, Preprocessing, Normalization, Software
Author: Kipper Fletez-Brant [cre, aut], Kasper Daniel Hansen [aut]
Maintainer: Kipper Fletez-Brant <cafletezbrant@gmail.com>
URL: https://github.com/hansenlab/bnbc
VignetteBuilder: knitr
BugReports: https://github.com/hansenlab/bnbc/issues
git_url: https://git.bioconductor.org/packages/bnbc
git_branch: devel
git_last_commit: c54d071
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/bnbc_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/bnbc_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/bnbc_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/bnbc_1.29.0.tgz
vignettes: vignettes/bnbc/inst/doc/bnbc.html
vignetteTitles: bnbc User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bnbc/inst/doc/bnbc.R
dependencyCount: 142
Package: bnem
Version: 1.15.0
Depends: R (>= 4.1)
Imports: CellNOptR, matrixStats, snowfall, Rgraphviz, cluster,
flexclust, stats, RColorBrewer, epiNEM, mnem, Biobase, methods,
utils, graphics, graph, affy, binom, limma, sva, vsn, rmarkdown
Suggests: knitr, BiocGenerics, MatrixGenerics, BiocStyle, RUnit
License: GPL-3
MD5sum: da0e2f4a7c24868dae9b147bf4f83f72
NeedsCompilation: no
Title: Training of logical models from indirect measurements of
perturbation experiments
Description: bnem combines the use of indirect measurements of Nested
Effects Models (package mnem) with the Boolean networks of
CellNOptR. Perturbation experiments of signalling nodes in
cells are analysed for their effect on the global gene
expression profile. Those profiles give evidence for the
Boolean regulation of down-stream nodes in the network, e.g.,
whether two parents activate their child independently
(OR-gate) or jointly (AND-gate).
biocViews: Pathways, SystemsBiology, NetworkInference, Network,
GeneExpression, GeneRegulation, Preprocessing
Author: Martin Pirkl [aut, cre]
Maintainer: Martin Pirkl <martinpirkl@yahoo.de>
URL: https://github.com/MartinFXP/bnem/
VignetteBuilder: knitr
BugReports: https://github.com/MartinFXP/bnem/issues
git_url: https://git.bioconductor.org/packages/bnem
git_branch: devel
git_last_commit: ea50de2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/bnem_1.15.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/bnem_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/bnem_1.15.0.tgz
vignettes: vignettes/bnem/inst/doc/bnem.html
vignetteTitles: bnem.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bnem/inst/doc/bnem.R
dependencyCount: 179
Package: BOBaFIT
Version: 1.11.0
Depends: R (>= 2.10)
Imports: dplyr, NbClust, ggplot2, ggbio, grDevices, stats, tidyr,
GenomicRanges, ggforce, stringr, plyranges, methods, utils,
magrittr
Suggests: rmarkdown, markdown, BiocStyle, knitr, testthat (>= 3.0.0),
utils, testthat
License: GPL (>= 3)
Archs: x64
MD5sum: d48a94d844122e83472cc50ccbffe68f
NeedsCompilation: no
Title: Refitting diploid region profiles using a clustering procedure
Description: This package provides a method to refit and correct the
diploid region in copy number profiles. It uses a clustering
algorithm to identify pathology-specific normal (diploid)
chromosomes and then use their copy number signal to refit the
whole profile. The package is composed by three functions:
DRrefit (the main function), ComputeNormalChromosome and
PlotCluster.
biocViews: CopyNumberVariation, Clustering, Visualization,
Normalization, Software
Author: Andrea Poletti [aut], Gaia Mazzocchetti [aut, cre], Vincenza
Solli [aut]
Maintainer: Gaia Mazzocchetti <bioinformatic.seragnoli@gmail.com>
URL: https://github.com/andrea-poletti-unibo/BOBaFIT
VignetteBuilder: knitr
BugReports: https://github.com/andrea-poletti-unibo/BOBaFIT/issues
git_url: https://git.bioconductor.org/packages/BOBaFIT
git_branch: devel
git_last_commit: eb24815
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BOBaFIT_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BOBaFIT_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BOBaFIT_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BOBaFIT_1.11.0.tgz
vignettes: vignettes/BOBaFIT/inst/doc/BOBaFIT.html,
vignettes/BOBaFIT/inst/doc/Data-Preparation.html
vignetteTitles: BOBaFIT.Rmd, Data preparation using TCGA-BRCA
database.Rmd
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BOBaFIT/inst/doc/BOBaFIT.R,
vignettes/BOBaFIT/inst/doc/Data-Preparation.R
dependencyCount: 169
Package: borealis
Version: 1.11.0
Depends: R (>= 4.2.0), Biobase
Imports: doParallel, snow, purrr, plyr, foreach, gamlss, gamlss.dist,
bsseq, methods, DSS, R.utils, utils, stats, ggplot2, cowplot,
dplyr, rlang, GenomicRanges
Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics, annotatr,
tidyr, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db
License: GPL-3
MD5sum: 4525256e8268c65c9f29050ba3140831
NeedsCompilation: no
Title: Bisulfite-seq OutlieR mEthylation At singLe-sIte reSolution
Description: Borealis is an R library performing outlier analysis for
count-based bisulfite sequencing data. It detectes outlier
methylated CpG sites from bisulfite sequencing (BS-seq). The
core of Borealis is modeling Beta-Binomial distributions. This
can be useful for rare disease diagnoses.
biocViews: Sequencing, Coverage, DNAMethylation,
DifferentialMethylation
Author: Garrett Jenkinson [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2548-098X>)
Maintainer: Garrett Jenkinson <gargar934@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/borealis
git_branch: devel
git_last_commit: 9ec3fee
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/borealis_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/borealis_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/borealis_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/borealis_1.11.0.tgz
vignettes: vignettes/borealis/inst/doc/borealis.html
vignetteTitles: Borealis outlier methylation detection
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/borealis/inst/doc/borealis.R
dependencyCount: 118
Package: BPRMeth
Version: 1.33.0
Depends: R (>= 3.5.0), GenomicRanges
Imports: assertthat, methods, MASS, doParallel, parallel, e1071, earth,
foreach, randomForest, stats, IRanges, S4Vectors, data.table,
graphics, truncnorm, mvtnorm, Rcpp (>= 0.12.14), matrixcalc,
magrittr, kernlab, ggplot2, cowplot, BiocStyle
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, knitr, rmarkdown
License: GPL-3 | file LICENSE
MD5sum: 84c0b179902afccd9acf30d4d52d9b28
NeedsCompilation: yes
Title: Model higher-order methylation profiles
Description: The BPRMeth package is a probabilistic method to quantify
explicit features of methylation profiles, in a way that would
make it easier to formally use such profiles in downstream
modelling efforts, such as predicting gene expression levels or
clustering genomic regions or cells according to their
methylation profiles.
biocViews: ImmunoOncology, DNAMethylation, GeneExpression,
GeneRegulation, Epigenetics, Genetics, Clustering,
FeatureExtraction, Regression, RNASeq, Bayesian, KEGG,
Sequencing, Coverage, SingleCell
Author: Chantriolnt-Andreas Kapourani [aut, cre]
Maintainer: Chantriolnt-Andreas Kapourani
<kapouranis.andreas@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BPRMeth
git_branch: devel
git_last_commit: 5371378
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BPRMeth_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BPRMeth_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BPRMeth_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BPRMeth_1.33.0.tgz
vignettes: vignettes/BPRMeth/inst/doc/BPRMeth_vignette.html
vignetteTitles: BPRMeth: Model higher-order methylation profiles
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BPRMeth/inst/doc/BPRMeth_vignette.R
dependsOnMe: Melissa
dependencyCount: 97
Package: BRAIN
Version: 1.53.0
Depends: R (>= 2.8.1), PolynomF, Biostrings, lattice
License: GPL-2
Archs: x64
MD5sum: 0972cf7fdcdf11caff0f71640ea4bd2e
NeedsCompilation: no
Title: Baffling Recursive Algorithm for Isotope distributioN
calculations
Description: Package for calculating aggregated isotopic distribution
and exact center-masses for chemical substances (in this
version composed of C, H, N, O and S). This is an
implementation of the BRAIN algorithm described in the paper by
J. Claesen, P. Dittwald, T. Burzykowski and D. Valkenborg.
biocViews: ImmunoOncology, MassSpectrometry, Proteomics
Author: Piotr Dittwald, with contributions of Dirk Valkenborg and
Jurgen Claesen
Maintainer: Piotr Dittwald <piotr.dittwald@mimuw.edu.pl>
git_url: https://git.bioconductor.org/packages/BRAIN
git_branch: devel
git_last_commit: e298262
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BRAIN_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BRAIN_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BRAIN_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BRAIN_1.53.0.tgz
vignettes: vignettes/BRAIN/inst/doc/BRAIN-vignette.pdf
vignetteTitles: BRAIN Usage
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BRAIN/inst/doc/BRAIN-vignette.R
suggestsMe: cleaver, synapter, RforProteomics
dependencyCount: 29
Package: branchpointer
Version: 1.33.0
Depends: caret, R(>= 3.4)
Imports: plyr, kernlab, gbm, stringr, cowplot, ggplot2, biomaRt,
Biostrings, parallel, utils, stats,
BSgenome.Hsapiens.UCSC.hg38, rtracklayer, GenomicRanges,
GenomeInfoDb, IRanges, S4Vectors, data.table
Suggests: knitr, BiocStyle
License: BSD_3_clause + file LICENSE
MD5sum: 68736754ab3004999df0990ba4acb7fa
NeedsCompilation: no
Title: Prediction of intronic splicing branchpoints
Description: Predicts branchpoint probability for sites in intronic
branchpoint windows. Queries can be supplied as intronic
regions; or to evaluate the effects of mutations, SNPs.
biocViews: Software, GenomeAnnotation, GenomicVariation,
MotifAnnotation
Author: Beth Signal
Maintainer: Beth Signal <b.signal@garvan.org.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/branchpointer
git_branch: devel
git_last_commit: 7492c70
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/branchpointer_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/branchpointer_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/branchpointer_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/branchpointer_1.33.0.tgz
vignettes: vignettes/branchpointer/inst/doc/branchpointer.pdf
vignetteTitles: Using Branchpointer for annotation of intronic human
splicing branchpoints
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/branchpointer/inst/doc/branchpointer.R
dependencyCount: 156
Package: breakpointR
Version: 1.25.0
Depends: R (>= 3.5), GenomicRanges, cowplot, breakpointRdata
Imports: methods, utils, grDevices, stats, S4Vectors, GenomeInfoDb (>=
1.12.3), IRanges, Rsamtools, GenomicAlignments, ggplot2,
BiocGenerics, gtools, doParallel, foreach
Suggests: knitr, BiocStyle, testthat
License: file LICENSE
MD5sum: 45d40475f8b3c711590207c96854c974
NeedsCompilation: no
Title: Find breakpoints in Strand-seq data
Description: This package implements functions for finding breakpoints,
plotting and export of Strand-seq data.
biocViews: Software, Sequencing, DNASeq, SingleCell, Coverage
Author: David Porubsky, Ashley Sanders, Aaron Taudt
Maintainer: David Porubsky <david.porubsky@gmail.com>
URL: https://github.com/daewoooo/BreakPointR
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/breakpointR
git_branch: devel
git_last_commit: da582c9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/breakpointR_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/breakpointR_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/breakpointR_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/breakpointR_1.25.0.tgz
vignettes: vignettes/breakpointR/inst/doc/breakpointR.pdf
vignetteTitles: How to use breakpointR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/breakpointR/inst/doc/breakpointR.R
dependencyCount: 83
Package: brendaDb
Version: 1.21.0
Imports: dplyr, Rcpp, tibble, stringr, magrittr, purrr, BiocParallel,
crayon, utils, tidyr, grDevices, rlang, BiocFileCache, rappdirs
LinkingTo: Rcpp
Suggests: testthat, BiocStyle, knitr, rmarkdown, devtools
License: MIT + file LICENSE
MD5sum: c5a5fed736ff4c22dd5990fb1173a7c7
NeedsCompilation: yes
Title: The BRENDA Enzyme Database
Description: R interface for importing and analyzing enzyme information
from the BRENDA database.
biocViews: ThirdPartyClient, Annotation, DataImport
Author: Yi Zhou [aut, cre] (ORCID:
<https://orcid.org/0000-0003-0969-3993>)
Maintainer: Yi Zhou <yi.zhou@uga.edu>
URL: https://github.com/y1zhou/brendaDb
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/y1zhou/brendaDb/issues
git_url: https://git.bioconductor.org/packages/brendaDb
git_branch: devel
git_last_commit: 513401a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/brendaDb_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/brendaDb_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/brendaDb_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/brendaDb_1.21.0.tgz
vignettes: vignettes/brendaDb/inst/doc/brendaDb.html
vignetteTitles: brendaDb
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/brendaDb/inst/doc/brendaDb.R
dependencyCount: 58
Package: BREW3R.r
Version: 1.3.0
Imports: GenomicRanges, methods, rlang, S4Vectors, utils
Suggests: testthat (>= 3.0.0), IRanges, knitr, rmarkdown, BiocStyle,
rtracklayer
License: GPL-3
Archs: x64
MD5sum: 0984a60c2e7f466025e5ccfc45623f02
NeedsCompilation: no
Title: R package associated to BREW3R
Description: This R package provide functions that are used in the
BREW3R workflow. This mainly contains a function that extend a
gtf as GRanges using information from another gtf (also as
GRanges). The process allows to extend gene annotation without
increasing the overlap between gene ids.
biocViews: GenomeAnnotation
Author: Lucille Lopez-Delisle [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1964-4960>)
Maintainer: Lucille Lopez-Delisle <lucille.delisle@epfl.ch>
URL: https://github.com/lldelisle/BREW3R.r
VignetteBuilder: knitr
BugReports: https://github.com/lldelisle/BREW3R.r/issues/
git_url: https://git.bioconductor.org/packages/BREW3R.r
git_branch: devel
git_last_commit: 1dca2c7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BREW3R.r_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BREW3R.r_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BREW3R.r_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BREW3R.r_1.3.0.tgz
vignettes: vignettes/BREW3R.r/inst/doc/BREW3R.r.html
vignetteTitles: BREW3R.r
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BREW3R.r/inst/doc/BREW3R.r.R
dependencyCount: 24
Package: BridgeDbR
Version: 2.17.1
Depends: R (>= 3.3.0), rJava
Imports: curl
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: AGPL-3
MD5sum: fde2bac38f709d8274f6b0e962d10a7e
NeedsCompilation: no
Title: Code for using BridgeDb identifier mapping framework from within
R
Description: Use BridgeDb functions and load identifier mapping
databases in R. It uses GitHub, Zenodo, and Figshare if you use
this package to download identifier mappings files.
biocViews: Software, Annotation, Metabolomics, Cheminformatics
Author: Christ Leemans <christleemans@gmail.com>, Egon Willighagen
<egon.willighagen@gmail.com>, Denise Slenter, Anwesha Bohler
<anweshabohler@gmail.com>, Lars Eijssen
<l.eijssen@maastrichtuniversity.nl>, Tooba Abbassi-Daloii
Maintainer: Egon Willighagen <egon.willighagen@gmail.com>
URL: https://github.com/bridgedb/BridgeDbR
VignetteBuilder: knitr
BugReports: https://github.com/bridgedb/BridgeDbR/issues
git_url: https://git.bioconductor.org/packages/BridgeDbR
git_branch: devel
git_last_commit: ec6e592
git_last_commit_date: 2024-11-06
Date/Publication: 2024-11-06
source.ver: src/contrib/BridgeDbR_2.17.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BridgeDbR_2.17.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BridgeDbR_2.17.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BridgeDbR_2.17.1.tgz
vignettes: vignettes/BridgeDbR/inst/doc/tutorial.html
vignetteTitles: Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BridgeDbR/inst/doc/tutorial.R
dependencyCount: 3
Package: broadSeq
Version: 1.1.0
Depends: dplyr, ggpubr, SummarizedExperiment
Imports: BiocStyle, DELocal, EBSeq (>= 1.38.0), DESeq2 (>= 1.38.2),
NOISeq, forcats (>= 1.0.0), genefilter, ggplot2, ggplotify,
plyr, clusterProfiler (>= 4.8.2), pheatmap, sechm (>= 1.6.0),
stringr, purrr (>= 0.3.5), edgeR (>= 3.40.1)
Suggests: knitr, limma (>= 3.54.0), rmarkdown, stats (>= 4.2.2), samr
License: MIT + file LICENSE
MD5sum: b5f1ff8623c91377dccf3d99847ee9c7
NeedsCompilation: no
Title: broadSeq : for streamlined exploration of RNA-seq data
Description: This package helps user to do easily RNA-seq data analysis
with multiple methods (usually which needs many different input
formats). Here the user will provid the expression data as a
SummarizedExperiment object and will get results from different
methods. It will help user to quickly evaluate different
methods.
biocViews: GeneExpression, DifferentialExpression, RNASeq,
Transcriptomics, Sequencing, Coverage, GeneSetEnrichment, GO
Author: Rishi Das Roy [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3276-7279>)
Maintainer: Rishi Das Roy <rishi.dasroy@gmail.com>
URL: https://github.com/dasroy/broadSeq
VignetteBuilder: knitr
BugReports: https://github.com/dasroy/broadSeq/issues
git_url: https://git.bioconductor.org/packages/broadSeq
git_branch: devel
git_last_commit: 78af4d8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/broadSeq_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/broadSeq_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/broadSeq_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/broadSeq_1.1.0.tgz
vignettes: vignettes/broadSeq/inst/doc/broadSeq.html
vignetteTitles: Using broadSeq to analyze RNA-seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/broadSeq/inst/doc/broadSeq.R
dependencyCount: 234
Package: BrowserViz
Version: 2.29.0
Depends: R (>= 3.5.0), jsonlite (>= 1.5), httpuv(>= 1.5.0)
Imports: methods, BiocGenerics
Suggests: RUnit, BiocStyle, knitr, rmarkdown
License: GPL-2
MD5sum: 8dbfedeed23f178c6468fb2a622f46fa
NeedsCompilation: no
Title: BrowserViz: interactive R/browser graphics using websockets and
JSON
Description: Interactvive graphics in a web browser from R, using
websockets and JSON.
biocViews: Visualization, ThirdPartyClient
Author: Paul Shannon
Maintainer: Arkadiusz Gladki <gladki.arkadiusz@gmail.com>
URL: https://gladkia.github.io/BrowserViz/
VignetteBuilder: knitr
BugReports: https://github.com/gladkia/BrowserViz/issues
git_url: https://git.bioconductor.org/packages/BrowserViz
git_branch: devel
git_last_commit: 0acee92
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BrowserViz_2.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BrowserViz_2.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BrowserViz_2.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BrowserViz_2.29.0.tgz
vignettes: vignettes/BrowserViz/inst/doc/BrowserViz.html
vignetteTitles: "BrowserViz: support programmatic access to javascript
apps running in your web browser"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BrowserViz/inst/doc/BrowserViz.R
dependsOnMe: igvR, RCyjs
dependencyCount: 15
Package: BSgenome
Version: 1.75.1
Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.13.8), S4Vectors (>=
0.17.28), IRanges (>= 2.13.16), GenomeInfoDb (>= 1.25.6),
GenomicRanges (>= 1.31.10), Biostrings (>= 2.47.6), BiocIO,
rtracklayer
Imports: utils, stats, matrixStats, XVector, Rsamtools
Suggests: BiocManager, BSgenome.Celegans.UCSC.ce2,
BSgenome.Hsapiens.UCSC.hg38,
BSgenome.Hsapiens.UCSC.hg38.masked,
BSgenome.Mmusculus.UCSC.mm10, BSgenome.Rnorvegicus.UCSC.rn5,
BSgenome.Scerevisiae.UCSC.sacCer1,
BSgenome.Hsapiens.NCBI.GRCh38,
TxDb.Hsapiens.UCSC.hg38.knownGene,
TxDb.Mmusculus.UCSC.mm10.knownGene,
SNPlocs.Hsapiens.dbSNP144.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, hgu95av2probe, RUnit,
BSgenomeForge
License: Artistic-2.0
MD5sum: 881357649c83c17f0634a0d6126f770f
NeedsCompilation: no
Title: Software infrastructure for efficient representation of full
genomes and their SNPs
Description: Infrastructure shared by all the Biostrings-based genome
data packages.
biocViews: Genetics, Infrastructure, DataRepresentation,
SequenceMatching, Annotation, SNP
Author: Hervé Pagès [aut, cre]
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/BSgenome
BugReports: https://github.com/Bioconductor/BSgenome/issues
git_url: https://git.bioconductor.org/packages/BSgenome
git_branch: devel
git_last_commit: 2a208e2
git_last_commit_date: 2025-01-21
Date/Publication: 2025-01-22
source.ver: src/contrib/BSgenome_1.75.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BSgenome_1.75.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BSgenome_1.75.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BSgenome_1.75.1.tgz
vignettes: vignettes/BSgenome/inst/doc/BSgenomeForge.pdf,
vignettes/BSgenome/inst/doc/GenomeSearching.pdf
vignetteTitles: How to forge a BSgenome data package, Efficient genome
searching with Biostrings and the BSgenome data packages
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BSgenome/inst/doc/GenomeSearching.R
dependsOnMe: bambu, BSgenomeForge, ChIPanalyser, GOTHiC, HelloRanges,
MEDIPS, periodicDNA, REDseq, rGADEM, VarCon,
BSgenome.Alyrata.JGI.v1, BSgenome.Amellifera.BeeBase.assembly4,
BSgenome.Amellifera.NCBI.AmelHAv3.1,
BSgenome.Amellifera.UCSC.apiMel2,
BSgenome.Amellifera.UCSC.apiMel2.masked,
BSgenome.Aofficinalis.NCBI.V1,
BSgenome.Athaliana.TAIR.04232008,
BSgenome.Athaliana.TAIR.TAIR9, BSgenome.Btaurus.UCSC.bosTau3,
BSgenome.Btaurus.UCSC.bosTau3.masked,
BSgenome.Btaurus.UCSC.bosTau4,
BSgenome.Btaurus.UCSC.bosTau4.masked,
BSgenome.Btaurus.UCSC.bosTau6,
BSgenome.Btaurus.UCSC.bosTau6.masked,
BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9,
BSgenome.Btaurus.UCSC.bosTau9.masked,
BSgenome.Carietinum.NCBI.v1, BSgenome.Celegans.UCSC.ce10,
BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2,
BSgenome.Celegans.UCSC.ce6, BSgenome.Cfamiliaris.UCSC.canFam2,
BSgenome.Cfamiliaris.UCSC.canFam2.masked,
BSgenome.Cfamiliaris.UCSC.canFam3,
BSgenome.Cfamiliaris.UCSC.canFam3.masked,
BSgenome.Cjacchus.UCSC.calJac3, BSgenome.Cjacchus.UCSC.calJac4,
BSgenome.CneoformansVarGrubiiKN99.NCBI.ASM221672v1,
BSgenome.Creinhardtii.JGI.v5.6,
BSgenome.Dmelanogaster.UCSC.dm2,
BSgenome.Dmelanogaster.UCSC.dm2.masked,
BSgenome.Dmelanogaster.UCSC.dm3,
BSgenome.Dmelanogaster.UCSC.dm3.masked,
BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10,
BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5,
BSgenome.Drerio.UCSC.danRer5.masked,
BSgenome.Drerio.UCSC.danRer6,
BSgenome.Drerio.UCSC.danRer6.masked,
BSgenome.Drerio.UCSC.danRer7,
BSgenome.Drerio.UCSC.danRer7.masked,
BSgenome.Dvirilis.Ensembl.dvircaf1,
BSgenome.Ecoli.NCBI.20080805, BSgenome.Gaculeatus.UCSC.gasAcu1,
BSgenome.Gaculeatus.UCSC.gasAcu1.masked,
BSgenome.Ggallus.UCSC.galGal3,
BSgenome.Ggallus.UCSC.galGal3.masked,
BSgenome.Ggallus.UCSC.galGal4,
BSgenome.Ggallus.UCSC.galGal4.masked,
BSgenome.Ggallus.UCSC.galGal5, BSgenome.Ggallus.UCSC.galGal6,
BSgenome.Gmax.NCBI.Gmv40, BSgenome.Hsapiens.1000genomes.hs37d5,
BSgenome.Hsapiens.NCBI.GRCh38,
BSgenome.Hsapiens.NCBI.T2T.CHM13v2.0,
BSgenome.Hsapiens.UCSC.hg17,
BSgenome.Hsapiens.UCSC.hg17.masked,
BSgenome.Hsapiens.UCSC.hg18,
BSgenome.Hsapiens.UCSC.hg18.masked,
BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Hsapiens.UCSC.hg19.masked,
BSgenome.Hsapiens.UCSC.hg38,
BSgenome.Hsapiens.UCSC.hg38.dbSNP151.major,
BSgenome.Hsapiens.UCSC.hg38.dbSNP151.minor,
BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Hsapiens.UCSC.hs1,
BSgenome.Mdomestica.UCSC.monDom5,
BSgenome.Mfascicularis.NCBI.5.0,
BSgenome.Mfascicularis.NCBI.6.0, BSgenome.Mfuro.UCSC.musFur1,
BSgenome.Mmulatta.UCSC.rheMac10,
BSgenome.Mmulatta.UCSC.rheMac2,
BSgenome.Mmulatta.UCSC.rheMac2.masked,
BSgenome.Mmulatta.UCSC.rheMac3,
BSgenome.Mmulatta.UCSC.rheMac3.masked,
BSgenome.Mmulatta.UCSC.rheMac8, BSgenome.Mmusculus.UCSC.mm10,
BSgenome.Mmusculus.UCSC.mm10.masked,
BSgenome.Mmusculus.UCSC.mm39, BSgenome.Mmusculus.UCSC.mm8,
BSgenome.Mmusculus.UCSC.mm8.masked,
BSgenome.Mmusculus.UCSC.mm9,
BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Osativa.MSU.MSU7,
BSgenome.Ppaniscus.UCSC.panPan1,
BSgenome.Ppaniscus.UCSC.panPan2,
BSgenome.Ptroglodytes.UCSC.panTro2,
BSgenome.Ptroglodytes.UCSC.panTro2.masked,
BSgenome.Ptroglodytes.UCSC.panTro3,
BSgenome.Ptroglodytes.UCSC.panTro3.masked,
BSgenome.Ptroglodytes.UCSC.panTro5,
BSgenome.Ptroglodytes.UCSC.panTro6,
BSgenome.Rnorvegicus.UCSC.rn4,
BSgenome.Rnorvegicus.UCSC.rn4.masked,
BSgenome.Rnorvegicus.UCSC.rn5,
BSgenome.Rnorvegicus.UCSC.rn5.masked,
BSgenome.Rnorvegicus.UCSC.rn6, BSgenome.Rnorvegicus.UCSC.rn7,
BSgenome.Scerevisiae.UCSC.sacCer1,
BSgenome.Scerevisiae.UCSC.sacCer2,
BSgenome.Scerevisiae.UCSC.sacCer3,
BSgenome.Sscrofa.UCSC.susScr11, BSgenome.Sscrofa.UCSC.susScr3,
BSgenome.Sscrofa.UCSC.susScr3.masked,
BSgenome.Tgondii.ToxoDB.7.0, BSgenome.Tguttata.UCSC.taeGut1,
BSgenome.Tguttata.UCSC.taeGut1.masked,
BSgenome.Tguttata.UCSC.taeGut2,
BSgenome.Vvinifera.URGI.IGGP12Xv0,
BSgenome.Vvinifera.URGI.IGGP12Xv2,
BSgenome.Vvinifera.URGI.IGGP8X,
SNPlocs.Hsapiens.dbSNP144.GRCh37,
SNPlocs.Hsapiens.dbSNP144.GRCh38,
SNPlocs.Hsapiens.dbSNP149.GRCh38,
SNPlocs.Hsapiens.dbSNP150.GRCh38,
SNPlocs.Hsapiens.dbSNP155.GRCh37,
SNPlocs.Hsapiens.dbSNP155.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh37,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, leeBamViews
importsMe: AllelicImbalance, appreci8R, ATACseqQC, atSNP, BEAT, bsseq,
BUSpaRse, CAGEr, chromVAR, cleanUpdTSeq, CleanUpRNAseq,
cliProfiler, crisprBowtie, crisprBwa, crisprDesign, CRISPRseek,
crisprShiny, crisprViz, diffHic, enhancerHomologSearch, esATAC,
EventPointer, FRASER, gcapc, genomation, GenVisR, ggbio, gmapR,
GreyListChIP, GUIDEseq, Gviz, hiAnnotator,
IsoformSwitchAnalyzeR, katdetectr, m6Aboost, MADSEQ,
methodical, methrix, MethylSeekR, MMDiff2, monaLisa,
Motif2Site, motifbreakR, motifmatchr, MotifPeeker, msgbsR,
multicrispr, MungeSumstats, musicatk, MutationalPatterns,
ORFik, PING, pipeFrame, podkat, qsea, QuasR, R453Plus1Toolbox,
raer, RAIDS, RareVariantVis, RCAS, regioneR, REMP, Repitools,
RESOLVE, ribosomeProfilingQC, RNAmodR, scmeth, SCOPE,
seqArchRplus, signeR, SigsPack, SingleMoleculeFootprinting,
SparseSignatures, spatzie, spiky, SpliceWiz, TAPseq, TFBSTools,
transmogR, tRNAscanImport, Ularcirc, UMI4Cats,
VariantAnnotation, VariantFiltering, VariantTools, XNAString,
BSgenome.Alyrata.JGI.v1, BSgenome.Amellifera.BeeBase.assembly4,
BSgenome.Amellifera.NCBI.AmelHAv3.1,
BSgenome.Amellifera.UCSC.apiMel2,
BSgenome.Amellifera.UCSC.apiMel2.masked,
BSgenome.Aofficinalis.NCBI.V1,
BSgenome.Athaliana.TAIR.04232008,
BSgenome.Athaliana.TAIR.TAIR9, BSgenome.Btaurus.UCSC.bosTau3,
BSgenome.Btaurus.UCSC.bosTau3.masked,
BSgenome.Btaurus.UCSC.bosTau4,
BSgenome.Btaurus.UCSC.bosTau4.masked,
BSgenome.Btaurus.UCSC.bosTau6,
BSgenome.Btaurus.UCSC.bosTau6.masked,
BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9,
BSgenome.Btaurus.UCSC.bosTau9.masked,
BSgenome.Carietinum.NCBI.v1, BSgenome.Celegans.UCSC.ce10,
BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2,
BSgenome.Celegans.UCSC.ce6, BSgenome.Cfamiliaris.UCSC.canFam2,
BSgenome.Cfamiliaris.UCSC.canFam2.masked,
BSgenome.Cfamiliaris.UCSC.canFam3,
BSgenome.Cfamiliaris.UCSC.canFam3.masked,
BSgenome.Cjacchus.UCSC.calJac3, BSgenome.Cjacchus.UCSC.calJac4,
BSgenome.CneoformansVarGrubiiKN99.NCBI.ASM221672v1,
BSgenome.Creinhardtii.JGI.v5.6,
BSgenome.Dmelanogaster.UCSC.dm2,
BSgenome.Dmelanogaster.UCSC.dm2.masked,
BSgenome.Dmelanogaster.UCSC.dm3,
BSgenome.Dmelanogaster.UCSC.dm3.masked,
BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10,
BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5,
BSgenome.Drerio.UCSC.danRer5.masked,
BSgenome.Drerio.UCSC.danRer6,
BSgenome.Drerio.UCSC.danRer6.masked,
BSgenome.Drerio.UCSC.danRer7,
BSgenome.Drerio.UCSC.danRer7.masked,
BSgenome.Dvirilis.Ensembl.dvircaf1,
BSgenome.Ecoli.NCBI.20080805, BSgenome.Gaculeatus.UCSC.gasAcu1,
BSgenome.Gaculeatus.UCSC.gasAcu1.masked,
BSgenome.Ggallus.UCSC.galGal3,
BSgenome.Ggallus.UCSC.galGal3.masked,
BSgenome.Ggallus.UCSC.galGal4,
BSgenome.Ggallus.UCSC.galGal4.masked,
BSgenome.Ggallus.UCSC.galGal5, BSgenome.Ggallus.UCSC.galGal6,
BSgenome.Gmax.NCBI.Gmv40, BSgenome.Hsapiens.NCBI.GRCh38,
BSgenome.Hsapiens.NCBI.T2T.CHM13v2.0,
BSgenome.Hsapiens.UCSC.hg17,
BSgenome.Hsapiens.UCSC.hg17.masked,
BSgenome.Hsapiens.UCSC.hg18,
BSgenome.Hsapiens.UCSC.hg18.masked,
BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Hsapiens.UCSC.hs1,
BSgenome.Mdomestica.UCSC.monDom5,
BSgenome.Mfascicularis.NCBI.5.0,
BSgenome.Mfascicularis.NCBI.6.0, BSgenome.Mfuro.UCSC.musFur1,
BSgenome.Mmulatta.UCSC.rheMac10,
BSgenome.Mmulatta.UCSC.rheMac2,
BSgenome.Mmulatta.UCSC.rheMac2.masked,
BSgenome.Mmulatta.UCSC.rheMac3,
BSgenome.Mmulatta.UCSC.rheMac3.masked,
BSgenome.Mmulatta.UCSC.rheMac8, BSgenome.Mmusculus.UCSC.mm10,
BSgenome.Mmusculus.UCSC.mm10.masked,
BSgenome.Mmusculus.UCSC.mm39, BSgenome.Mmusculus.UCSC.mm8,
BSgenome.Mmusculus.UCSC.mm8.masked,
BSgenome.Mmusculus.UCSC.mm9,
BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Osativa.MSU.MSU7,
BSgenome.Ppaniscus.UCSC.panPan1,
BSgenome.Ppaniscus.UCSC.panPan2,
BSgenome.Ptroglodytes.UCSC.panTro2,
BSgenome.Ptroglodytes.UCSC.panTro2.masked,
BSgenome.Ptroglodytes.UCSC.panTro3,
BSgenome.Ptroglodytes.UCSC.panTro3.masked,
BSgenome.Ptroglodytes.UCSC.panTro5,
BSgenome.Ptroglodytes.UCSC.panTro6,
BSgenome.Rnorvegicus.UCSC.rn4,
BSgenome.Rnorvegicus.UCSC.rn4.masked,
BSgenome.Rnorvegicus.UCSC.rn5,
BSgenome.Rnorvegicus.UCSC.rn5.masked,
BSgenome.Rnorvegicus.UCSC.rn6, BSgenome.Rnorvegicus.UCSC.rn7,
BSgenome.Scerevisiae.UCSC.sacCer1,
BSgenome.Scerevisiae.UCSC.sacCer2,
BSgenome.Scerevisiae.UCSC.sacCer3,
BSgenome.Sscrofa.UCSC.susScr11, BSgenome.Sscrofa.UCSC.susScr3,
BSgenome.Sscrofa.UCSC.susScr3.masked,
BSgenome.Tgondii.ToxoDB.7.0, BSgenome.Tguttata.UCSC.taeGut1,
BSgenome.Tguttata.UCSC.taeGut1.masked,
BSgenome.Tguttata.UCSC.taeGut2,
BSgenome.Vvinifera.URGI.IGGP12Xv0,
BSgenome.Vvinifera.URGI.IGGP12Xv2,
BSgenome.Vvinifera.URGI.IGGP8X, fitCons.UCSC.hg19,
MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5,
MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5,
MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5,
MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5,
MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5,
MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5,
MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38,
MafH5.gnomAD.v4.0.GRCh38, phastCons100way.UCSC.hg19,
phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38,
SNPlocs.Hsapiens.dbSNP144.GRCh37,
SNPlocs.Hsapiens.dbSNP144.GRCh38,
SNPlocs.Hsapiens.dbSNP149.GRCh38,
SNPlocs.Hsapiens.dbSNP150.GRCh38,
SNPlocs.Hsapiens.dbSNP155.GRCh37,
SNPlocs.Hsapiens.dbSNP155.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh37,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, GenomicDistributionsData,
ICAMS, revert, simMP
suggestsMe: Biostrings, biovizBase, ChIPpeakAnno, chipseq, DegCre,
DiffBind, easyRNASeq, eisaR, factR, GeneRegionScan,
GenomeInfoDb, GenomicAlignments, GenomicFeatures,
GenomicRanges, maftools, metaseqR2, MiRaGE, PICB, plotgardener,
ProteoDisco, PWMEnrich, QDNAseq, recoup, RiboCrypt,
rtracklayer, sitadela, gkmSVM, MARVEL, sigminer, Signac
dependencyCount: 58
Package: BSgenomeForge
Version: 1.7.0
Depends: R (>= 4.3.0), methods, BiocGenerics, IRanges, GenomeInfoDb (>=
1.33.17), Biostrings, BSgenome
Imports: utils, stats, Biobase, S4Vectors, GenomicRanges, BiocIO,
rtracklayer
Suggests: GenomicFeatures, Rsamtools, testthat, knitr, rmarkdown,
BiocStyle, devtools, BSgenome.Celegans.UCSC.ce2
License: Artistic-2.0
MD5sum: e93b7e14d76e1849508c026992096e89
NeedsCompilation: no
Title: Forge your own BSgenome data package
Description: A set of tools to forge BSgenome data packages. Supersedes
the old seed-based tools from the BSgenome software package.
This package allows the user to create a BSgenome data package
in one function call, simplifying the old seed-based process.
biocViews: Infrastructure, DataRepresentation, GenomeAssembly,
Annotation, GenomeAnnotation, Sequencing, Alignment,
DataImport, SequenceMatching
Author: Hervé Pagès [aut, cre], Atuhurira Kirabo Kakopo [aut], Emmanuel
Chigozie Elendu [ctb], Prisca Chidimma Maduka [ctb]
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/BSgenomeForge
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/BSgenomeForge/issues
git_url: https://git.bioconductor.org/packages/BSgenomeForge
git_branch: devel
git_last_commit: b850d52
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BSgenomeForge_1.7.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BSgenomeForge_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BSgenomeForge_1.7.0.tgz
vignettes: vignettes/BSgenomeForge/inst/doc/AdvancedBSgenomeForge.pdf,
vignettes/BSgenomeForge/inst/doc/QuickBSgenomeForge.html
vignetteTitles: Advanced BSgenomeForge usage, A quick introduction to
the BSgenomeForge package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BSgenomeForge/inst/doc/AdvancedBSgenomeForge.R,
vignettes/BSgenomeForge/inst/doc/QuickBSgenomeForge.R
suggestsMe: BSgenome
dependencyCount: 59
Package: bsseq
Version: 1.43.1
Depends: R (>= 4.0), methods, BiocGenerics, GenomicRanges (>= 1.41.5),
SummarizedExperiment (>= 1.19.5)
Imports: IRanges (>= 2.23.9), GenomeInfoDb, scales, stats, parallel,
tools, graphics, Biobase, locfit, gtools, data.table (>=
1.11.8), S4Vectors (>= 0.27.12), R.utils (>= 2.0.0),
DelayedMatrixStats (>= 1.5.2), permute, limma, DelayedArray (>=
0.15.16), Rcpp, BiocParallel, BSgenome, Biostrings, utils,
HDF5Array (>= 1.19.11), rhdf5, beachmat (>= 2.23.2)
LinkingTo: Rcpp, beachmat, assorthead (>= 1.1.4)
Suggests: testthat, bsseqData, BiocStyle, rmarkdown, knitr, Matrix,
doParallel, rtracklayer, BSgenome.Hsapiens.UCSC.hg38,
batchtools
License: Artistic-2.0
MD5sum: fe099c142e417c5335b6aa13ec9c25fe
NeedsCompilation: yes
Title: Analyze, manage and store whole-genome methylation data
Description: A collection of tools for analyzing and visualizing
whole-genome methylation data from sequencing. This includes
whole-genome bisulfite sequencing and Oxford nanopore data.
biocViews: DNAMethylation
Author: Kasper Daniel Hansen [aut, cre] (ORCID:
<https://orcid.org/0000-0003-0086-0687>), Peter Hickey [aut]
(ORCID: <https://orcid.org/0000-0002-8153-6258>), Hervé Pagès
[ctb], Aaron Lun [ctb]
Maintainer: Kasper Daniel Hansen <kasperdanielhansen@gmail.com>
URL: https://github.com/kasperdanielhansen/bsseq
SystemRequirements: C++17
VignetteBuilder: knitr
BugReports: https://github.com/kasperdanielhansen/bsseq/issues
git_url: https://git.bioconductor.org/packages/bsseq
git_branch: devel
git_last_commit: 1cce9a0
git_last_commit_date: 2024-12-15
Date/Publication: 2024-12-16
source.ver: src/contrib/bsseq_1.43.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/bsseq_1.43.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/bsseq_1.43.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/bsseq_1.43.1.tgz
vignettes: vignettes/bsseq/inst/doc/bsseq_analysis.html,
vignettes/bsseq/inst/doc/bsseq.html
vignetteTitles: Analyzing WGBS data with bsseq, bsseq User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bsseq/inst/doc/bsseq_analysis.R,
vignettes/bsseq/inst/doc/bsseq.R
dependsOnMe: biscuiteer, dmrseq, DSS, bsseqData
importsMe: borealis, DMRcate, methylCC, methylSig, MIRA, NanoMethViz,
scmeth, SOMNiBUS
suggestsMe: methrix, tissueTreg
dependencyCount: 89
Package: BubbleTree
Version: 2.37.0
Depends: R (>= 3.5), IRanges, GenomicRanges, plyr, dplyr, magrittr
Imports: BiocGenerics (>= 0.31.6), BiocStyle, Biobase, ggplot2,
WriteXLS, gtools, RColorBrewer, limma, grid, gtable, gridExtra,
biovizBase, e1071, methods, grDevices, stats, utils
Suggests: knitr, rmarkdown
License: LGPL (>= 3)
Archs: x64
MD5sum: 297d6761d3befaafb608706b95ba8f9e
NeedsCompilation: no
Title: BubbleTree: an intuitive visualization to elucidate tumoral
aneuploidy and clonality in somatic mosaicism using next
generation sequencing data
Description: CNV analysis in groups of tumor samples.
biocViews: CopyNumberVariation, Software, Sequencing, Coverage
Author: Wei Zhu <zhuw@medimmune.com>, Michael Kuziora
<kuzioram@medimmune.com>, Todd Creasy <creasyt@medimmune.com>,
Brandon Higgs <higgsb@medimmune.com>
Maintainer: Todd Creasy <creasyt@medimmune.com>, Wei Zhu
<weizhu365@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BubbleTree
git_branch: devel
git_last_commit: 19789a5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BubbleTree_2.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BubbleTree_2.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BubbleTree_2.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BubbleTree_2.37.0.tgz
vignettes: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.html
vignetteTitles: BubbleTree Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.R
dependencyCount: 150
Package: BufferedMatrix
Version: 1.71.1
Depends: R (>= 2.6.0), methods
License: LGPL (>= 2)
MD5sum: 0e70e72d5a77338d779dde96183a9e6b
NeedsCompilation: yes
Title: A matrix data storage object held in temporary files
Description: A tabular style data object where most data is stored
outside main memory. A buffer is used to speed up access to
data.
biocViews: Infrastructure
Author: Ben Bolstad <bmb@bmbolstad.com>
Maintainer: Ben Bolstad <bmb@bmbolstad.com>
URL: https://github.com/bmbolstad/BufferedMatrix
git_url: https://git.bioconductor.org/packages/BufferedMatrix
git_branch: devel
git_last_commit: 824836d
git_last_commit_date: 2024-12-14
Date/Publication: 2024-12-15
source.ver: src/contrib/BufferedMatrix_1.71.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BufferedMatrix_1.71.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BufferedMatrix_1.71.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BufferedMatrix_1.71.1.tgz
vignettes: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.pdf
vignetteTitles: BufferedMatrix: Introduction
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.R
dependsOnMe: BufferedMatrixMethods
linksToMe: BufferedMatrixMethods
dependencyCount: 1
Package: BufferedMatrixMethods
Version: 1.71.1
Depends: R (>= 2.6.0), BufferedMatrix (>= 1.3.0), methods
LinkingTo: BufferedMatrix
Suggests: affyio, affy
License: GPL (>= 2)
Archs: x64
MD5sum: 38b948ecb73b4809cdf201fb371ec37d
NeedsCompilation: yes
Title: Microarray Data related methods that utlize BufferedMatrix
objects
Description: Microarray analysis methods that use BufferedMatrix
objects
biocViews: Infrastructure
Author: Ben Bolstad <bmb@bmbolstad.com>
Maintainer: Ben Bolstad <bmb@bmbolstad.com>
URL: https://github.bom/bmbolstad/BufferedMatrixMethods
git_url: https://git.bioconductor.org/packages/BufferedMatrixMethods
git_branch: devel
git_last_commit: 15eb988
git_last_commit_date: 2024-12-14
Date/Publication: 2024-12-15
source.ver: src/contrib/BufferedMatrixMethods_1.71.1.tar.gz
win.binary.ver:
bin/windows/contrib/4.5/BufferedMatrixMethods_1.71.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BufferedMatrixMethods_1.71.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BufferedMatrixMethods_1.71.1.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 2
Package: bugsigdbr
Version: 1.13.5
Depends: R (>= 4.1)
Imports: BiocFileCache, methods, vroom, utils
Suggests: BiocStyle, knitr, ontologyIndex, rmarkdown, testthat (>=
3.0.0)
License: GPL-3
Archs: x64
MD5sum: 6bc6f861c35befd992bf4a80e5e37a28
NeedsCompilation: no
Title: R-side access to published microbial signatures from BugSigDB
Description: The bugsigdbr package implements convenient access to
bugsigdb.org from within R/Bioconductor. The goal of the
package is to facilitate import of BugSigDB data into
R/Bioconductor, provide utilities for extracting microbe
signatures, and enable export of the extracted signatures to
plain text files in standard file formats such as GMT.
biocViews: DataImport, GeneSetEnrichment, Metagenomics, Microbiome
Author: Ludwig Geistlinger [aut, cre], Jennifer Wokaty [aut], Levi
Waldron [aut]
Maintainer: Ludwig Geistlinger <ludwig.geistlinger@gmail.com>
URL: https://github.com/waldronlab/bugsigdbr
VignetteBuilder: knitr
BugReports: https://github.com/waldronlab/bugsigdbr/issues
git_url: https://git.bioconductor.org/packages/bugsigdbr
git_branch: devel
git_last_commit: 65b0cfe
git_last_commit_date: 2025-03-07
Date/Publication: 2025-03-09
source.ver: src/contrib/bugsigdbr_1.13.5.tar.gz
win.binary.ver: bin/windows/contrib/4.5/bugsigdbr_1.13.5.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/bugsigdbr_1.13.5.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/bugsigdbr_1.13.5.tgz
vignettes: vignettes/bugsigdbr/inst/doc/bugsigdbr.html
vignetteTitles: R-side access to BugSigDB
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bugsigdbr/inst/doc/bugsigdbr.R
dependencyCount: 52
Package: BulkSignalR
Version: 0.99.22
Depends: R (>= 4.5)
Imports: BiocFileCache, httr, DBI, RSQLite, cli, curl, dplyr, rlang,
jsonlite, matrixStats, methods, doParallel, glmnet, ggalluvial,
ggplot2, gridExtra, grid, Rtsne, ggrepel, foreach, multtest,
igraph, orthogene, stabledist, circlize (>= 0.4.14),
ComplexHeatmap (>= 2.0.0), stats, scales, RANN,
SpatialExperiment, SummarizedExperiment, tools
Suggests: knitr, markdown, rmarkdown, STexampleData, testthat (>=
3.0.0), codetools, Matrix, lattice, cluster, survival, MASS,
nlme
License: CeCILL | file LICENSE
MD5sum: d6d83e51092e49b00d22dee4d2d198f7
NeedsCompilation: no
Title: Infer Ligand-Receptor Interactions from bulk expression
(transcriptomics/proteomics) data, or spatial transcriptomics
Description: Inference of ligand-receptor (LR) interactions from bulk
expression (transcriptomics/proteomics) data, or spatial
transcriptomics. BulkSignalR bases its inferences on the LRdb
database included in our other package, SingleCellSignalR
available from Bioconductor. It relies on a statistical model
that is specific to bulk data sets. Different visualization and
data summary functions are proposed to help navigating
prediction results.
biocViews: Network, RNASeq, Software, Proteomics, Transcriptomics,
NetworkInference, Spatial
Author: Jacques Colinge [aut] (ORCID:
<https://orcid.org/0000-0003-2466-4824>), Jean-Philippe
Villemin [cre] (ORCID: <https://orcid.org/0000-0002-1838-5880>)
Maintainer: Jean-Philippe Villemin <jpvillemin@gmail.com>
URL: https://github.com/ZheFrench/BulkSignalR
VignetteBuilder: knitr
BugReports: https://github.com/ZheFrench/BulkSignalR/issues
git_url: https://git.bioconductor.org/packages/BulkSignalR
git_branch: devel
git_last_commit: a707d8a
git_last_commit_date: 2024-12-20
Date/Publication: 2025-01-08
source.ver: src/contrib/BulkSignalR_0.99.22.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BulkSignalR_0.99.22.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BulkSignalR_0.99.22.tgz
vignettes:
vignettes/BulkSignalR/inst/doc/BulkSignalR-Differential.html,
vignettes/BulkSignalR/inst/doc/BulkSignalR-Main.html
vignetteTitles: BulkSignalR-Differential, BulkSignalR-Main
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BulkSignalR/inst/doc/BulkSignalR-Differential.R,
vignettes/BulkSignalR/inst/doc/BulkSignalR-Main.R
dependencyCount: 192
Package: BUMHMM
Version: 1.31.0
Depends: R (>= 3.5.0)
Imports: devtools, stringi, gtools, stats, utils, SummarizedExperiment,
Biostrings, IRanges
Suggests: testthat, knitr, BiocStyle
License: GPL-3
MD5sum: 89a9a3c50b1209fa3b04d9f30af0cd22
NeedsCompilation: no
Title: Computational pipeline for computing probability of modification
from structure probing experiment data
Description: This is a probabilistic modelling pipeline for computing
per- nucleotide posterior probabilities of modification from
the data collected in structure probing experiments. The model
supports multiple experimental replicates and empirically
corrects coverage- and sequence-dependent biases. The model
utilises the measure of a "drop-off rate" for each nucleotide,
which is compared between replicates through a log-ratio (LDR).
The LDRs between control replicates define a null distribution
of variability in drop-off rate observed by chance and LDRs
between treatment and control replicates gets compared to this
distribution. Resulting empirical p-values (probability of
being "drawn" from the null distribution) are used as
observations in a Hidden Markov Model with a Beta-Uniform
Mixture model used as an emission model. The resulting
posterior probabilities indicate the probability of a
nucleotide of having being modified in a structure probing
experiment.
biocViews: ImmunoOncology, GeneticVariability, Transcription,
GeneExpression, GeneRegulation, Coverage, Genetics,
StructuralPrediction, Transcriptomics, Bayesian,
Classification, FeatureExtraction, HiddenMarkovModel,
Regression, RNASeq, Sequencing
Author: Alina Selega (alina.selega@gmail.com), Sander Granneman, Guido
Sanguinetti
Maintainer: Alina Selega <alina.selega@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BUMHMM
git_branch: devel
git_last_commit: 61b2956
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BUMHMM_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BUMHMM_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BUMHMM_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BUMHMM_1.31.0.tgz
vignettes: vignettes/BUMHMM/inst/doc/BUMHMM.pdf
vignetteTitles: An Introduction to the BUMHMM pipeline
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BUMHMM/inst/doc/BUMHMM.R
dependencyCount: 124
Package: bumphunter
Version: 1.49.0
Depends: R (>= 3.5), S4Vectors (>= 0.9.25), IRanges (>= 2.3.23),
GenomeInfoDb, GenomicRanges, foreach, iterators, methods,
parallel, locfit
Imports: matrixStats, limma, doRNG, BiocGenerics, utils,
GenomicFeatures, AnnotationDbi, stats
Suggests: testthat, RUnit, doParallel, txdbmaker, org.Hs.eg.db,
TxDb.Hsapiens.UCSC.hg19.knownGene
License: Artistic-2.0
MD5sum: 2b7e58aba276896434435ee20d929eac
NeedsCompilation: no
Title: Bump Hunter
Description: Tools for finding bumps in genomic data
biocViews: DNAMethylation, Epigenetics, Infrastructure,
MultipleComparison, ImmunoOncology
Author: Rafael A. Irizarry [aut], Martin Aryee [aut], Kasper Daniel
Hansen [aut], Hector Corrada Bravo [aut], Shan Andrews [ctb],
Andrew E. Jaffe [ctb], Harris Jaffee [ctb], Leonardo
Collado-Torres [ctb], Tamilselvi Guharaj [cre]
Maintainer: Tamilselvi Guharaj <selvi@ds.dfci.harvard.edu>
URL: https://github.com/rafalab/bumphunter
git_url: https://git.bioconductor.org/packages/bumphunter
git_branch: devel
git_last_commit: 28669ca
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/bumphunter_1.49.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/bumphunter_1.49.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/bumphunter_1.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/bumphunter_1.49.0.tgz
vignettes: vignettes/bumphunter/inst/doc/bumphunter.pdf
vignetteTitles: The bumphunter user's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/bumphunter/inst/doc/bumphunter.R
dependsOnMe: minfi
importsMe: coMethDMR, DAMEfinder, derfinder, dmrseq, epimutacions,
epivizr, methylCC, rnaEditr, GenomicState, recountWorkflow
suggestsMe: bigmelon, derfinderPlot, epivizrData, regionReport
dependencyCount: 85
Package: BumpyMatrix
Version: 1.15.0
Imports: utils, methods, Matrix, S4Vectors, IRanges
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: MIT + file LICENSE
MD5sum: c67043ff427cc53eecf4388465a7539b
NeedsCompilation: no
Title: Bumpy Matrix of Non-Scalar Objects
Description: Implements the BumpyMatrix class and several subclasses
for holding non-scalar objects in each entry of the matrix.
This is akin to a ragged array but the raggedness is in the
third dimension, much like a bumpy surface - hence the name. Of
particular interest is the BumpyDataFrameMatrix, where each
entry is a Bioconductor data frame. This allows us to naturally
represent multivariate data in a format that is compatible with
two-dimensional containers like the SummarizedExperiment and
MultiAssayExperiment objects.
biocViews: Software, Infrastructure, DataRepresentation
Author: Aaron Lun [aut, cre], Genentech, Inc. [cph]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://bioconductor.org/packages/BumpyMatrix
VignetteBuilder: knitr
BugReports: https://github.com/LTLA/BumpyMatrix/issues
git_url: https://git.bioconductor.org/packages/BumpyMatrix
git_branch: devel
git_last_commit: e7f4260
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BumpyMatrix_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BumpyMatrix_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BumpyMatrix_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BumpyMatrix_1.15.0.tgz
vignettes: vignettes/BumpyMatrix/inst/doc/BumpyMatrix.html
vignetteTitles: The BumpyMatrix class
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BumpyMatrix/inst/doc/BumpyMatrix.R
dependsOnMe: alabaster.bumpy
importsMe: CoreGx, gDRcore, gDRimport, gDRutils, MerfishData,
MouseGastrulationData, TENxXeniumData
suggestsMe: escheR, gDR, ggspavis, SpatialExperiment, tpSVG,
STexampleData
dependencyCount: 13
Package: BUS
Version: 1.63.0
Depends: R (>= 2.3.0), minet
Imports: stats, infotheo
License: GPL-3
MD5sum: 4dbbe1d0a01fcaa01290b052bbea0c97
NeedsCompilation: yes
Title: Gene network reconstruction
Description: This package can be used to compute associations among
genes (gene-networks) or between genes and some external traits
(i.e. clinical).
biocViews: Preprocessing
Author: Yin Jin, Hesen Peng, Lei Wang, Raffaele Fronza, Yuanhua Liu and
Christine Nardini
Maintainer: Yuanhua Liu <liuyuanhua@picb.ac.cn>
git_url: https://git.bioconductor.org/packages/BUS
git_branch: devel
git_last_commit: 17fe722
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BUS_1.63.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BUS_1.63.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BUS_1.63.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BUS_1.63.0.tgz
vignettes: vignettes/BUS/inst/doc/bus.pdf
vignetteTitles: bus.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BUS/inst/doc/bus.R
dependencyCount: 3
Package: BUScorrect
Version: 1.25.0
Depends: R (>= 3.5.0)
Imports: gplots, methods, grDevices, stats, SummarizedExperiment
Suggests: BiocStyle, knitr, RUnit, BiocGenerics
License: GPL (>= 2)
Archs: x64
MD5sum: c4e6e6b1243c792c85ba6800cc5fc57c
NeedsCompilation: yes
Title: Batch Effects Correction with Unknown Subtypes
Description: High-throughput experimental data are accumulating
exponentially in public databases. However, mining valid
scientific discoveries from these abundant resources is
hampered by technical artifacts and inherent biological
heterogeneity. The former are usually termed "batch effects,"
and the latter is often modelled by "subtypes." The R package
BUScorrect fits a Bayesian hierarchical model, the
Batch-effects-correction-with-Unknown-Subtypes model (BUS), to
correct batch effects in the presence of unknown subtypes. BUS
is capable of (a) correcting batch effects explicitly, (b)
grouping samples that share similar characteristics into
subtypes, (c) identifying features that distinguish subtypes,
and (d) enjoying a linear-order computation complexity.
biocViews: GeneExpression, StatisticalMethod, Bayesian, Clustering,
FeatureExtraction, BatchEffect
Author: Xiangyu Luo <xyluo1991@gmail.com>, Yingying Wei
<yweicuhk@gmail.com>
Maintainer: Xiangyu Luo <xyluo1991@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/BUScorrect
git_branch: devel
git_last_commit: 0194875
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BUScorrect_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BUScorrect_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BUScorrect_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BUScorrect_1.25.0.tgz
vignettes: vignettes/BUScorrect/inst/doc/BUScorrect_user_guide.pdf
vignetteTitles: BUScorrect_user_guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BUScorrect/inst/doc/BUScorrect_user_guide.R
dependencyCount: 41
Package: BUSpaRse
Version: 1.21.0
Depends: R (>= 3.6)
Imports: AnnotationDbi, AnnotationFilter, biomaRt, BiocGenerics,
Biostrings, BSgenome, dplyr, ensembldb, GenomeInfoDb,
GenomicFeatures, GenomicRanges, ggplot2, IRanges, magrittr,
Matrix, methods, plyranges, Rcpp, S4Vectors, stats, stringr,
tibble, tidyr, utils, zeallot
LinkingTo: Rcpp, RcppArmadillo, RcppProgress, BH
Suggests: knitr, rmarkdown, testthat, BiocStyle, txdbmaker,
TENxBUSData, TxDb.Hsapiens.UCSC.hg38.knownGene, txdbmaker,
BSgenome.Hsapiens.UCSC.hg38, EnsDb.Hsapiens.v86
License: BSD_2_clause + file LICENSE
Archs: x64
MD5sum: b1f4571234413c981dd2e10c8200cc70
NeedsCompilation: yes
Title: kallisto | bustools R utilities
Description: The kallisto | bustools pipeline is a fast and modular set
of tools to convert single cell RNA-seq reads in fastq files
into gene count or transcript compatibility counts (TCC)
matrices for downstream analysis. Central to this pipeline is
the barcode, UMI, and set (BUS) file format. This package
serves the following purposes: First, this package allows users
to manipulate BUS format files as data frames in R and then
convert them into gene count or TCC matrices. Furthermore,
since R and Rcpp code is easier to handle than pure C++ code,
users are encouraged to tweak the source code of this package
to experiment with new uses of BUS format and different ways to
convert the BUS file into gene count matrix. Second, this
package can conveniently generate files required to generate
gene count matrices for spliced and unspliced transcripts for
RNA velocity. Here biotypes can be filtered and scaffolds and
haplotypes can be removed, and the filtered transcriptome can
be extracted and written to disk. Third, this package
implements utility functions to get transcripts and associated
genes required to convert BUS files to gene count matrices, to
write the transcript to gene information in the format required
by bustools, and to read output of bustools into R as sparses
matrices.
biocViews: SingleCell, RNASeq, WorkflowStep
Author: Lambda Moses [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7092-9427>), Lior Pachter [aut,
ths] (ORCID: <https://orcid.org/0000-0002-9164-6231>)
Maintainer: Lambda Moses <dlu2@caltech.edu>
URL: https://github.com/BUStools/BUSpaRse
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/BUStools/BUSpaRse/issues
git_url: https://git.bioconductor.org/packages/BUSpaRse
git_branch: devel
git_last_commit: 199b6ed
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BUSpaRse_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BUSpaRse_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BUSpaRse_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BUSpaRse_1.21.0.tgz
vignettes: vignettes/BUSpaRse/inst/doc/sparse-matrix.html,
vignettes/BUSpaRse/inst/doc/tr2g.html
vignetteTitles: Converting BUS format into sparse matrix, Transcript to
gene
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/BUSpaRse/inst/doc/sparse-matrix.R,
vignettes/BUSpaRse/inst/doc/tr2g.R
dependencyCount: 124
Package: BUSseq
Version: 1.13.0
Depends: R (>= 3.6)
Imports: SingleCellExperiment, SummarizedExperiment, S4Vectors, gplots,
grDevices, methods, stats, utils
Suggests: BiocStyle, knitr, BiocGenerics
License: Artistic-2.0
Archs: x64
MD5sum: ac230a447b9df708273967590baad515
NeedsCompilation: yes
Title: Batch Effect Correction with Unknow Subtypes for scRNA-seq data
Description: BUSseq R package fits an interpretable Bayesian
hierarchical model---the Batch Effects Correction with Unknown
Subtypes for scRNA seq Data (BUSseq)---to correct batch effects
in the presence of unknown cell types. BUSseq is able to
simultaneously correct batch effects, clusters cell types, and
takes care of the count data nature, the overdispersion, the
dropout events, and the cell-specific sequencing depth of
scRNA-seq data. After correcting the batch effects with BUSseq,
the corrected value can be used for downstream analysis as if
all cells were sequenced in a single batch. BUSseq can
integrate read count matrices obtained from different scRNA-seq
platforms and allow cell types to be measured in some but not
all of the batches as long as the experimental design fulfills
the conditions listed in our manuscript.
biocViews: ExperimentalDesign, GeneExpression, StatisticalMethod,
Bayesian, Clustering, FeatureExtraction, BatchEffect,
SingleCell, Sequencing
Author: Fangda Song [aut, cre] (ORCID:
<https://orcid.org/0000-0001-6007-3517>), Ga Ming Chan [aut],
Yingying Wei [aut] (ORCID:
<https://orcid.org/0000-0003-3826-336X>)
Maintainer: Fangda Song <sfd1994895@gmail.com>
URL: https://github.com/songfd2018/BUSseq
VignetteBuilder: knitr
BugReports: https://github.com/songfd2018/BUSseq/issues
git_url: https://git.bioconductor.org/packages/BUSseq
git_branch: devel
git_last_commit: 4ec2844
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/BUSseq_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/BUSseq_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/BUSseq_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/BUSseq_1.13.0.tgz
vignettes: vignettes/BUSseq/inst/doc/BUSseq_user_guide.pdf
vignetteTitles: BUScorrect_user_guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/BUSseq/inst/doc/BUSseq_user_guide.R
dependencyCount: 42
Package: CaDrA
Version: 1.5.0
Depends: R (>= 4.4.0)
Imports: doParallel, ggplot2, gplots, graphics, grid, gtable, knnmi,
MASS, methods, misc3d, plyr, ppcor, R.cache, reshape2, stats,
SummarizedExperiment
Suggests: BiocManager, devtools, knitr, pheatmap, rmarkdown, testthat
(>= 3.1.6)
License: GPL-3 + file LICENSE
Archs: x64
MD5sum: ece8ce52575aeaa3f6ade6490b47de32
NeedsCompilation: yes
Title: Candidate Driver Analysis
Description: Performs both stepwise and backward heuristic search for
candidate (epi)genetic drivers based on a binary multi-omics
dataset. CaDrA's main objective is to identify features which,
together, are significantly skewed or enriched pertaining to a
given vector of continuous scores (e.g. sample-specific scores
representing a phenotypic readout of interest, such as protein
expression, pathway activity, etc.), based on the union
occurence (i.e. logical OR) of the events.
biocViews: Microarray, RNASeq, GeneExpression, Software,
FeatureExtraction
Author: Reina Chau [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3012-1404>), Katia Bulekova [aut]
(ORCID: <https://orcid.org/0000-0003-1560-2146>), Vinay Kartha
[aut], Stefano Monti [aut] (ORCID:
<https://orcid.org/0000-0002-9376-0660>)
Maintainer: Reina Chau <rchau88@bu.edu>
URL: https://github.com/montilab/CaDrA/
VignetteBuilder: knitr
BugReports: https://github.com/montilab/CaDrA/issues
git_url: https://git.bioconductor.org/packages/CaDrA
git_branch: devel
git_last_commit: 3f6af6c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CaDrA_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CaDrA_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CaDrA_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CaDrA_1.5.0.tgz
vignettes: vignettes/CaDrA/inst/doc/docker.html,
vignettes/CaDrA/inst/doc/permutation_based_testing.html,
vignettes/CaDrA/inst/doc/scoring_functions.html
vignetteTitles: How to run CaDrA within a Docker Environment,
Permutation-Based Testing, Scoring Functions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CaDrA/inst/doc/permutation_based_testing.R,
vignettes/CaDrA/inst/doc/scoring_functions.R
dependencyCount: 86
Package: CAEN
Version: 1.15.0
Depends: R (>= 4.1)
Imports: stats,PoiClaClu,SummarizedExperiment,methods
Suggests: knitr,rmarkdown,BiocManager,SummarizedExperiment,BiocStyle
License: GPL-2
Archs: x64
MD5sum: 1efc21f5728b88ef1b31a4c684b1394b
NeedsCompilation: no
Title: Category encoding method for selecting feature genes for the
classification of single-cell RNA-seq
Description: With the development of high-throughput techniques, more
and more gene expression analysis tend to replace
hybridization-based microarrays with the revolutionary
technology.The novel method encodes the category again by
employing the rank of samples for each gene in each class. We
then consider the correlation coefficient of gene and class
with rank of sample and new rank of category. The highest
correlation coefficient genes are considered as the feature
genes which are most effective to classify the samples.
biocViews: DifferentialExpression, Sequencing, Classification, RNASeq,
ATACSeq, SingleCell, GeneExpression, RIPSeq
Author: Zhou Yan [aut, cre]
Maintainer: Zhou Yan <2160090406@email.szu.edu.cn>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CAEN
git_branch: devel
git_last_commit: ad0515f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CAEN_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CAEN_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CAEN_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CAEN_1.15.0.tgz
vignettes: vignettes/CAEN/inst/doc/CAEN.html
vignetteTitles: CAEN Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CAEN/inst/doc/CAEN.R
dependencyCount: 37
Package: CAFE
Version: 1.43.0
Depends: R (>= 2.10), biovizBase, GenomicRanges, IRanges, ggbio
Imports: affy, ggplot2, annotate, grid, gridExtra, tcltk, Biobase
Suggests: RUnit, BiocGenerics, BiocStyle
License: GPL-3
MD5sum: cc62358039c41de986c0bd63b6e20404
NeedsCompilation: no
Title: Chromosmal Aberrations Finder in Expression data
Description: Detection and visualizations of gross chromosomal
aberrations using Affymetrix expression microarrays as input
biocViews: GeneExpression, Microarray, OneChannel, GeneSetEnrichment
Author: Sander Bollen
Maintainer: Sander Bollen <sander.h.bollen@gmail.com>
git_url: https://git.bioconductor.org/packages/CAFE
git_branch: devel
git_last_commit: da805c7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CAFE_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CAFE_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CAFE_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CAFE_1.43.0.tgz
vignettes: vignettes/CAFE/inst/doc/CAFE-manual.pdf
vignetteTitles: Manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CAFE/inst/doc/CAFE-manual.R
dependencyCount: 168
Package: CAGEfightR
Version: 1.27.0
Depends: R (>= 3.5), GenomicRanges (>= 1.30.1), rtracklayer (>=
1.38.2), SummarizedExperiment (>= 1.8.1)
Imports: pryr(>= 0.1.3), assertthat(>= 0.2.0), methods(>= 3.6.3),
Matrix(>= 1.2-12), BiocGenerics(>= 0.24.0), S4Vectors(>=
0.16.0), IRanges(>= 2.12.0), GenomeInfoDb(>= 1.14.0),
GenomicFeatures(>= 1.29.11), GenomicAlignments(>= 1.22.1),
BiocParallel(>= 1.12.0), GenomicFiles(>= 1.14.0), Gviz(>=
1.22.2), InteractionSet(>= 1.9.4), GenomicInteractions(>=
1.15.1)
Suggests: knitr, rmarkdown, BiocStyle, org.Mm.eg.db,
TxDb.Mmusculus.UCSC.mm9.knownGene
License: GPL-3 + file LICENSE
MD5sum: 970a7e0c9e0820b2285805b26083eae6
NeedsCompilation: no
Title: Analysis of Cap Analysis of Gene Expression (CAGE) data using
Bioconductor
Description: CAGE is a widely used high throughput assay for measuring
transcription start site (TSS) activity. CAGEfightR is an
R/Bioconductor package for performing a wide range of common
data analysis tasks for CAGE and 5'-end data in general. Core
functionality includes: import of CAGE TSSs (CTSSs), tag (or
unidirectional) clustering for TSS identification,
bidirectional clustering for enhancer identification,
annotation with transcript and gene models, correlation of TSS
and enhancer expression, calculation of TSS shapes,
quantification of CAGE expression as expression matrices and
genome brower visualization.
biocViews: Software, Transcription, Coverage, GeneExpression,
GeneRegulation, PeakDetection, DataImport, DataRepresentation,
Transcriptomics, Sequencing, Annotation, GenomeBrowsers,
Normalization, Preprocessing, Visualization
Author: Malte Thodberg
Maintainer: Malte Thodberg <maltethodberg@gmail.com>
URL: https://github.com/MalteThodberg/CAGEfightR
VignetteBuilder: knitr
BugReports: https://github.com/MalteThodberg/CAGEfightR/issues
git_url: https://git.bioconductor.org/packages/CAGEfightR
git_branch: devel
git_last_commit: 5e8f35b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CAGEfightR_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CAGEfightR_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CAGEfightR_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CAGEfightR_1.27.0.tgz
vignettes:
vignettes/CAGEfightR/inst/doc/Introduction_to_CAGEfightR.html
vignetteTitles: Introduction to CAGEfightR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CAGEfightR/inst/doc/Introduction_to_CAGEfightR.R
dependsOnMe: CAGEWorkflow
importsMe: CAGEr
suggestsMe: nanotubes
dependencyCount: 163
Package: cageminer
Version: 1.13.0
Depends: R (>= 4.1)
Imports: ggplot2, rlang, ggbio, ggtext, GenomeInfoDb, GenomicRanges,
IRanges, reshape2, methods, BioNERO
Suggests: testthat (>= 3.0.0), SummarizedExperiment, knitr, BiocStyle,
rmarkdown, covr, sessioninfo
License: GPL-3
MD5sum: 42fd5350c01354a81a2439cc1f528f87
NeedsCompilation: no
Title: Candidate Gene Miner
Description: This package aims to integrate GWAS-derived SNPs and
coexpression networks to mine candidate genes associated with a
particular phenotype. For that, users must define a set of
guide genes, which are known genes involved in the studied
phenotype. Additionally, the mined candidates can be given a
score that favor candidates that are hubs and/or transcription
factors. The scores can then be used to rank and select the top
n most promising genes for downstream experiments.
biocViews: Software, SNP, FunctionalPrediction, GenomeWideAssociation,
GeneExpression, NetworkEnrichment, VariantAnnotation,
FunctionalGenomics, Network
Author: FabrÃcio Almeida-Silva [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5314-2964>), Thiago Venancio [aut]
(ORCID: <https://orcid.org/0000-0002-2215-8082>)
Maintainer: FabrÃcio Almeida-Silva <fabricio_almeidasilva@hotmail.com>
URL: https://github.com/almeidasilvaf/cageminer
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/cageminer
git_url: https://git.bioconductor.org/packages/cageminer
git_branch: devel
git_last_commit: ce80090
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cageminer_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cageminer_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cageminer_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cageminer_1.13.0.tgz
vignettes: vignettes/cageminer/inst/doc/cageminer.html
vignetteTitles: Mining high-confidence candidate genes with cageminer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cageminer/inst/doc/cageminer.R
dependencyCount: 208
Package: CAGEr
Version: 2.13.0
Depends: methods, MultiAssayExperiment, R (>= 4.1.0)
Imports: BiocGenerics, BiocParallel, Biostrings, BSgenome, CAGEfightR,
data.table, formula.tools, GenomeInfoDb, GenomicAlignments,
GenomicFeatures, GenomicRanges (>= 1.37.16), ggplot2 (>=
2.2.0), gtools, IRanges (>= 2.18.0), KernSmooth, memoise, plyr,
rlang, Rsamtools, reshape2, rtracklayer, S4Vectors (>= 0.27.5),
scales, som, stringdist, stringi, SummarizedExperiment, utils,
vegan, VGAM
Suggests: BSgenome.Dmelanogaster.UCSC.dm3,
BSgenome.Drerio.UCSC.danRer7, BSgenome.Hsapiens.UCSC.hg18,
BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm9,
DESeq2, FANTOM3and4CAGE, ggseqlogo, BiocStyle, knitr, rmarkdown
License: GPL-3
MD5sum: 023128492841632e777bbb7223cfc272
NeedsCompilation: no
Title: Analysis of CAGE (Cap Analysis of Gene Expression) sequencing
data for precise mapping of transcription start sites and
promoterome mining
Description: The _CAGEr_ package identifies transcription start sites
(TSS) and their usage frequency from CAGE (Cap Analysis Gene
Expression) sequencing data. It normalises raw CAGE tag count,
clusters TSSs into tag clusters (TC) and aggregates them across
multiple CAGE experiments to construct consensus clusters (CC)
representing the promoterome. CAGEr provides functions to
profile expression levels of these clusters by cumulative
expression and rarefaction analysis, and outputs the plots in
ggplot2 format for further facetting and customisation. After
clustering, CAGEr performs analyses of promoter width and
detects differential usage of TSSs (promoter shifting) between
samples. CAGEr also exports its data as genome browser tracks,
and as R objects for downsteam expression analysis by other
Bioconductor packages such as DESeq2, CAGEfightR, or seqArchR.
biocViews: Preprocessing, Sequencing, Normalization,
FunctionalGenomics, Transcription, GeneExpression, Clustering,
Visualization
Author: Vanja Haberle [aut], Charles Plessy [cre], Damir Baranasic
[ctb], Sarvesh Nikumbh [ctb]
Maintainer: Charles Plessy <charles.plessy@oist.jp>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CAGEr
git_branch: devel
git_last_commit: cdfe2b1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CAGEr_2.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CAGEr_2.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CAGEr_2.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CAGEr_2.13.0.tgz
vignettes: vignettes/CAGEr/inst/doc/CAGE_Resources.html,
vignettes/CAGEr/inst/doc/CAGEexp.html
vignetteTitles: Use of CAGE resources with CAGEr, CAGEr: an R package
for CAGE data analysis and promoterome mining
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CAGEr/inst/doc/CAGE_Resources.R,
vignettes/CAGEr/inst/doc/CAGEexp.R
suggestsMe: seqArchRplus, seqPattern
dependencyCount: 177
Package: calm
Version: 1.21.0
Imports: mgcv, stats, graphics
Suggests: knitr, rmarkdown
License: GPL (>=2)
MD5sum: f087b619ff82097bdf60afb609ed6efe
NeedsCompilation: no
Title: Covariate Assisted Large-scale Multiple testing
Description: Statistical methods for multiple testing with covariate
information. Traditional multiple testing methods only consider
a list of test statistics, such as p-values. Our methods
incorporate the auxiliary information, such as the lengths of
gene coding regions or the minor allele frequencies of SNPs, to
improve power.
biocViews: Bayesian, DifferentialExpression, GeneExpression,
Regression, Microarray, Sequencing, RNASeq, MultipleComparison,
Genetics, ImmunoOncology, Metabolomics, Proteomics,
Transcriptomics
Author: Kun Liang [aut, cre]
Maintainer: Kun Liang <kun.liang@uwaterloo.ca>
VignetteBuilder: knitr
BugReports: https://github.com/k22liang/calm/issues
git_url: https://git.bioconductor.org/packages/calm
git_branch: devel
git_last_commit: 2e49d35
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/calm_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/calm_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/calm_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/calm_1.21.0.tgz
vignettes: vignettes/calm/inst/doc/calm_intro.html
vignetteTitles: Userguide for calm package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/calm/inst/doc/calm_intro.R
dependencyCount: 11
Package: CAMERA
Version: 1.63.0
Depends: R (>= 3.5.0), methods, Biobase, xcms (>= 1.13.5)
Imports: methods, xcms, RBGL, graph, graphics, grDevices, stats, utils,
Hmisc, igraph
Suggests: faahKO, RUnit, BiocGenerics, multtest
Enhances: Rmpi, snow
License: GPL (>= 2)
MD5sum: e65dc944e1fd00a1801fcf24a5ea96c3
NeedsCompilation: yes
Title: Collection of annotation related methods for mass spectrometry
data
Description: Annotation of peaklists generated by xcms, rule based
annotation of isotopes and adducts, isotope validation, EIC
correlation based tagging of unknown adducts and fragments
biocViews: ImmunoOncology, MassSpectrometry, Metabolomics
Author: Carsten Kuhl, Ralf Tautenhahn, Hendrik Treutler, Steffen
Neumann {ckuhl|htreutle|sneumann}@ipb-halle.de,
rtautenh@scripps.edu
Maintainer: Steffen Neumann <sneumann@ipb-halle.de>
URL: http://msbi.ipb-halle.de/msbi/CAMERA/
BugReports: https://github.com/sneumann/CAMERA/issues/new
git_url: https://git.bioconductor.org/packages/CAMERA
git_branch: devel
git_last_commit: 1d980b7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CAMERA_1.63.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CAMERA_1.63.0.zip
vignettes: vignettes/CAMERA/inst/doc/CAMERA.pdf,
vignettes/CAMERA/inst/doc/compoundQuantilesVignette.pdf,
vignettes/CAMERA/inst/doc/IsotopeDetectionVignette.pdf
vignetteTitles: Molecule Identification with CAMERA, Atom count
expectations with compoundQuantiles, Isotope pattern validation
with CAMERA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CAMERA/inst/doc/CAMERA.R
dependsOnMe: flagme, IPO, LOBSTAHS, MAIT, metaMS, PtH2O2lipids
suggestsMe: cliqueMS, msPurity, RMassBank, mtbls2
dependencyCount: 159
Package: CaMutQC
Version: 1.3.0
Depends: R (>= 4.0.0)
Imports: ggplot2, dplyr, org.Hs.eg.db, vcfR, clusterProfiler, stringr,
DT, MesKit, maftools, data.table, utils, stats, methods, tidyr
Suggests: knitr, rmarkdown, BiocStyle
License: GPL-3
Archs: x64
MD5sum: 31bc0a2ace40b7f805c9e91516306752
NeedsCompilation: no
Title: An R Package for Comprehensive Filtration and Selection of
Cancer Somatic Mutations
Description: CaMutQC is able to filter false positive mutations
generated due to technical issues, as well as to select
candidate cancer mutations through a series of well-structured
functions by labeling mutations with various flags. And a
detailed and vivid filter report will be offered after
completing a whole filtration or selection section. Also,
CaMutQC integrates serveral methods and gene panels for Tumor
Mutational Burden (TMB) estimation.
biocViews: Software, QualityControl, GeneTarget
Author: Xin Wang [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6072-599X>)
Maintainer: Xin Wang <sylviawang555@gmail.com>
URL: https://github.com/likelet/CaMutQC
VignetteBuilder: knitr
BugReports: https://github.com/likelet/CaMutQC/issues
git_url: https://git.bioconductor.org/packages/CaMutQC
git_branch: devel
git_last_commit: c466d6a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CaMutQC_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CaMutQC_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CaMutQC_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CaMutQC_1.3.0.tgz
vignettes: vignettes/CaMutQC/inst/doc/CaMutQC-manual.html
vignetteTitles: Manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CaMutQC/inst/doc/CaMutQC-manual.R
dependencyCount: 173
Package: canceR
Version: 1.41.0
Depends: R (>= 4.3), tcltk, cBioPortalData
Imports: GSEABase, tkrplot, geNetClassifier, RUnit, Formula, rpart,
survival, Biobase, phenoTest, circlize, plyr, tidyr, dplyr,
graphics, stats, utils, grDevices, R.oo, R.methodsS3
Suggests: testthat (>= 3.1), knitr, rmarkdown, BiocStyle
License: GPL-2
MD5sum: 7194bb476457a01b444c7ba9370b6733
NeedsCompilation: no
Title: A Graphical User Interface for accessing and modeling the Cancer
Genomics Data of MSKCC
Description: The package is user friendly interface based on the cgdsr
and other modeling packages to explore, compare, and analyse
all available Cancer Data (Clinical data, Gene Mutation, Gene
Methylation, Gene Expression, Protein Phosphorylation, Copy
Number Alteration) hosted by the Computational Biology Center
at Memorial-Sloan-Kettering Cancer Center (MSKCC).
biocViews: GUI, GeneExpression, Clustering, GO, GeneSetEnrichment,
KEGG, MultipleComparison
Author: Karim Mezhoud. Nuclear Safety & Security Department. Nuclear
Science Center of Tunisia.
Maintainer: Karim Mezhoud <kmezhoud@gmail.com>
SystemRequirements: Tktable, BWidget
VignetteBuilder: knitr
BugReports: https://github.com/kmezhoud/canceR/issues
git_url: https://git.bioconductor.org/packages/canceR
git_branch: devel
git_last_commit: 89371a8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/canceR_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/canceR_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/canceR_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/canceR_1.41.0.tgz
vignettes: vignettes/canceR/inst/doc/canceR.html
vignetteTitles: canceR: A Graphical User Interface for accessing and
modeling the Cancer Genomics Data of MSKCC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/canceR/inst/doc/canceR.R
dependencyCount: 213
Package: cancerclass
Version: 1.51.0
Depends: R (>= 2.14.0), Biobase, binom, methods, stats
Suggests: cancerdata
License: GPL 3
MD5sum: 4bc0b2a19abfa70beb2c5802db1e0abd
NeedsCompilation: yes
Title: Development and validation of diagnostic tests from
high-dimensional molecular data
Description: The classification protocol starts with a feature
selection step and continues with nearest-centroid
classification. The accurarcy of the predictor can be evaluated
using training and test set validation, leave-one-out
cross-validation or in a multiple random validation protocol.
Methods for calculation and visualization of continuous
prediction scores allow to balance sensitivity and specificity
and define a cutoff value according to clinical requirements.
biocViews: Cancer, Microarray, Classification, Visualization
Author: Jan Budczies, Daniel Kosztyla
Maintainer: Daniel Kosztyla <danielkossi@hotmail.com>
git_url: https://git.bioconductor.org/packages/cancerclass
git_branch: devel
git_last_commit: 7222a6b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cancerclass_1.51.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cancerclass_1.51.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cancerclass_1.51.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cancerclass_1.51.0.tgz
vignettes: vignettes/cancerclass/inst/doc/vignette_cancerclass.pdf
vignetteTitles: Cancerclass: An R package for development and
validation of diagnostic tests from high-dimensional molecular
data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cancerclass/inst/doc/vignette_cancerclass.R
dependencyCount: 8
Package: cardelino
Version: 1.9.0
Depends: R (>= 4.2), stats
Imports: combinat, GenomeInfoDb, GenomicRanges, ggplot2, ggtree,
Matrix, matrixStats, methods, pheatmap, snpStats, S4Vectors,
utils, VariantAnnotation, vcfR
Suggests: BiocStyle, foreach, knitr, pcaMethods, rmarkdown, testthat,
VGAM
Enhances: doMC
License: GPL-3
Archs: x64
MD5sum: 8185c2a2a54c7337598cb46df5af2560
NeedsCompilation: yes
Title: Clone Identification from Single Cell Data
Description: Methods to infer clonal tree configuration for a
population of cells using single-cell RNA-seq data (scRNA-seq),
and possibly other data modalities. Methods are also provided
to assign cells to inferred clones and explore differences in
gene expression between clones. These methods can flexibly
integrate information from imperfect clonal trees inferred
based on bulk exome-seq data, and sparse variant alleles
expressed in scRNA-seq data. A flexible beta-binomial error
model that accounts for stochastic dropout events as well as
systematic allelic imbalance is used.
biocViews: SingleCell, RNASeq, Visualization, Transcriptomics,
GeneExpression, Sequencing, Software, ExomeSeq
Author: Jeffrey Pullin [aut], Yuanhua Huang [aut], Davis McCarthy [aut,
cre]
Maintainer: Davis McCarthy <dmccarthy@svi.edu.au>
URL: https://github.com/single-cell-genetics/cardelino
VignetteBuilder: knitr
BugReports: https://github.com/single-cell-genetics/cardelino/issues
git_url: https://git.bioconductor.org/packages/cardelino
git_branch: devel
git_last_commit: a8b665e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cardelino_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cardelino_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cardelino_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cardelino_1.9.0.tgz
vignettes: vignettes/cardelino/inst/doc/vignette-cloneid.html
vignetteTitles: Clone ID with cardelino
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cardelino/inst/doc/vignette-cloneid.R
dependencyCount: 130
Package: Cardinal
Version: 3.9.0
Depends: R (>= 4.4), BiocParallel, BiocGenerics, ProtGenerics,
S4Vectors, methods, stats, stats4
Imports: CardinalIO, Biobase, EBImage, graphics, grDevices, irlba,
Matrix, matter (>= 2.7.10), nlme, parallel, utils
Suggests: BiocStyle, testthat, knitr, rmarkdown
License: Artistic-2.0 | file LICENSE
MD5sum: b81231f3abf7fed1186511c77fb6e0af
NeedsCompilation: no
Title: A mass spectrometry imaging toolbox for statistical analysis
Description: Implements statistical & computational tools for analyzing
mass spectrometry imaging datasets, including methods for
efficient pre-processing, spatial segmentation, and
classification.
biocViews: Software, Infrastructure, Proteomics, Lipidomics,
MassSpectrometry, ImagingMassSpectrometry, ImmunoOncology,
Normalization, Clustering, Classification, Regression
Author: Kylie Ariel Bemis [aut, cre]
Maintainer: Kylie Ariel Bemis <k.bemis@northeastern.edu>
URL: http://www.cardinalmsi.org
VignetteBuilder: knitr
BugReports: https://github.com/kuwisdelu/Cardinal/issues
git_url: https://git.bioconductor.org/packages/Cardinal
git_branch: devel
git_last_commit: 8b2a58c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Cardinal_3.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Cardinal_3.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Cardinal_3.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Cardinal_3.9.0.tgz
vignettes: vignettes/Cardinal/inst/doc/Cardinal3-guide.html,
vignettes/Cardinal/inst/doc/Cardinal3-stats.html
vignetteTitles: 1. Cardinal 3: User guide for mass spectrometry imaging
analysis, 2. Cardinal 3: Statistical methods for mass
spectrometry imaging
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Cardinal/inst/doc/Cardinal3-guide.R,
vignettes/Cardinal/inst/doc/Cardinal3-stats.R
dependsOnMe: CardinalWorkflows
dependencyCount: 66
Package: CardinalIO
Version: 1.5.0
Depends: R (>= 4.4), BiocParallel, matter, ontologyIndex
Imports: methods, S4Vectors, stats, utils, tools
Suggests: BiocStyle, testthat, knitr, rmarkdown
License: Artistic-2.0 | file LICENSE
MD5sum: 24d27af8711f95c79bb491a4d0c1c463
NeedsCompilation: yes
Title: Read and write mass spectrometry imaging files
Description: Fast and efficient reading and writing of mass
spectrometry imaging data files. Supports imzML and Analyze 7.5
formats. Provides ontologies for mass spectrometry imaging.
biocViews: Software, Infrastructure, DataImport, MassSpectrometry,
ImagingMassSpectrometry
Author: Kylie Ariel Bemis [aut, cre]
Maintainer: Kylie Ariel Bemis <k.bemis@northeastern.edu>
URL: http://www.cardinalmsi.org
VignetteBuilder: knitr
BugReports: https://github.com/kuwisdelu/CardinalIO/issues
git_url: https://git.bioconductor.org/packages/CardinalIO
git_branch: devel
git_last_commit: 60442d0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CardinalIO_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CardinalIO_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CardinalIO_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CardinalIO_1.5.0.tgz
vignettes: vignettes/CardinalIO/inst/doc/CardinalIO-guide.html
vignetteTitles: Parsing and writing imzML files
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CardinalIO/inst/doc/CardinalIO-guide.R
importsMe: Cardinal
dependencyCount: 28
Package: CARDspa
Version: 0.99.8
Depends: R (>= 4.3.0)
Imports: Rcpp (>= 1.0.7),RcppArmadillo, SummarizedExperiment, methods,
MCMCpack, fields, wrMisc, concaveman, sp, dplyr, sf, Matrix,
RANN, ggplot2, reshape2, RColorBrewer, S4Vectors, scatterpie,
grDevices,ggcorrplot, stats, nnls, BiocParallel, RcppML, NMF,
spatstat.random, gtools, SingleCellExperiment,
SpatialExperiment
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, testthat, BiocStyle
License: GPL-3 + file LICENSE
Archs: x64
MD5sum: 5d9e09b2d1edc8e71134c0987c0cb8d6
NeedsCompilation: yes
Title: Spatially Informed Cell Type Deconvolution for Spatial
Transcriptomics
Description: CARD is a reference-based deconvolution method that
estimates cell type composition in spatial transcriptomics
based on cell type specific expression information obtained
from a reference scRNA-seq data. A key feature of CARD is its
ability to accommodate spatial correlation in the cell type
composition across tissue locations, enabling accurate and
spatially informed cell type deconvolution as well as refined
spatial map construction. CARD relies on an efficient
optimization algorithm for constrained maximum likelihood
estimation and is scalable to spatial transcriptomics with tens
of thousands of spatial locations and tens of thousands of
genes.
biocViews: Spatial, SingleCell, Transcriptomics, Visualization
Author: Ying Ma [aut], Jing Fu [cre]
Maintainer: Jing Fu <jing_fu@brown.edu>
URL: https://github.com/YMa-lab/CARDspa
VignetteBuilder: knitr
BugReports: https://github.com/YMa-lab/CARDspa/issues
git_url: https://git.bioconductor.org/packages/CARDspa
git_branch: devel
git_last_commit: 97e9e57
git_last_commit_date: 2025-03-27
Date/Publication: 2025-03-27
source.ver: src/contrib/CARDspa_0.99.8.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CARDspa_0.99.8.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CARDspa_0.99.8.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CARDspa_0.99.8.tgz
vignettes: vignettes/CARDspa/inst/doc/Example_Analysis.html
vignetteTitles: Example_Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CARDspa/inst/doc/Example_Analysis.R
dependencyCount: 152
Package: CARNIVAL
Version: 2.17.0
Depends: R (>= 4.0)
Imports: readr, stringr, lpSolve, igraph, dplyr, tibble, tidyr, rjson,
rmarkdown
Suggests: RefManageR, BiocStyle, covr, knitr, testthat (>= 3.0.0),
sessioninfo
License: GPL-3
Archs: x64
MD5sum: 2ec647013758017a8d0417676a8cf481
NeedsCompilation: no
Title: A CAusal Reasoning tool for Network Identification (from gene
expression data) using Integer VALue programming
Description: An upgraded causal reasoning tool from Melas et al in R
with updated assignments of TFs' weights from PROGENy scores.
Optimization parameters can be freely adjusted and multiple
solutions can be obtained and aggregated.
biocViews: Transcriptomics, GeneExpression, Network
Author: Enio Gjerga [aut] (ORCID:
<https://orcid.org/0000-0002-3060-5786>), Panuwat Trairatphisan
[aut], Anika Liu [ctb], Alberto Valdeolivas [ctb], Nikolas
Peschke [ctb], Aurelien Dugourd [ctb], Attila Gabor [cre], Olga
Ivanova [aut]
Maintainer: Attila Gabor <attila.gabor@uni-heidelberg.de>
URL: https://github.com/saezlab/CARNIVAL
VignetteBuilder: knitr
BugReports: https://github.com/saezlab/CARNIVAL/issues
git_url: https://git.bioconductor.org/packages/CARNIVAL
git_branch: devel
git_last_commit: 153557f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CARNIVAL_2.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CARNIVAL_2.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CARNIVAL_2.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CARNIVAL_2.17.0.tgz
vignettes: vignettes/CARNIVAL/inst/doc/CARNIVAL.html
vignetteTitles: Contextualizing large scale signalling networks from
expression footprints with CARNIVAL
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CARNIVAL/inst/doc/CARNIVAL.R
importsMe: cosmosR
suggestsMe: dce
dependencyCount: 64
Package: casper
Version: 2.41.0
Depends: R (>= 3.6.0), Biobase, IRanges, methods, GenomicRanges
Imports: BiocGenerics (>= 0.31.6), coda, EBarrays, gaga, gtools,
GenomeInfoDb, GenomicFeatures, limma, mgcv, Rsamtools,
rtracklayer, S4Vectors (>= 0.9.25), sqldf, survival, VGAM
Enhances: parallel
License: GPL (>=2)
MD5sum: ea9ea0ebb467878ab14d59cf70152ba4
NeedsCompilation: yes
Title: Characterization of Alternative Splicing based on Paired-End
Reads
Description: Infer alternative splicing from paired-end RNA-seq data.
The model is based on counting paths across exons, rather than
pairwise exon connections, and estimates the fragment size and
start distributions non-parametrically, which improves
estimation precision.
biocViews: ImmunoOncology, GeneExpression, DifferentialExpression,
Transcription, RNASeq, Sequencing
Author: David Rossell, Camille Stephan-Otto, Manuel Kroiss, Miranda
Stobbe, Victor Pena
Maintainer: David Rossell <rosselldavid@gmail.com>
git_url: https://git.bioconductor.org/packages/casper
git_branch: devel
git_last_commit: d9348d1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/casper_2.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/casper_2.41.0.zip
vignettes: vignettes/casper/inst/doc/casper.pdf,
vignettes/casper/inst/doc/DesignRNASeq.pdf
vignetteTitles: Manual for the casper library, DesignRNASeq.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/casper/inst/doc/casper.R
dependencyCount: 93
Package: CATALYST
Version: 1.31.2
Depends: R (>= 4.4), SingleCellExperiment
Imports: circlize, ComplexHeatmap, ConsensusClusterPlus, cowplot,
data.table, dplyr, drc, flowCore, FlowSOM, ggplot2, ggrepel,
ggridges, graphics, grDevices, grid, gridExtra, Matrix,
matrixStats, methods, nnls, purrr, RColorBrewer, reshape2,
Rtsne, SummarizedExperiment, S4Vectors, scales, scater, stats
Suggests: BiocStyle, diffcyt, flowWorkspace, ggcyto, knitr, openCyto,
rmarkdown, testthat, uwot
License: GPL (>=2)
Archs: x64
MD5sum: 593f65337dc9985eb2cf90cac8f6ae0c
NeedsCompilation: no
Title: Cytometry dATa anALYSis Tools
Description: CATALYST provides tools for preprocessing of and
differential discovery in cytometry data such as FACS, CyTOF,
and IMC. Preprocessing includes i) normalization using bead
standards, ii) single-cell deconvolution, and iii) bead-based
compensation. For differential discovery, the package provides
a number of convenient functions for data processing (e.g.,
clustering, dimension reduction), as well as a suite of
visualizations for exploratory data analysis and exploration of
results from differential abundance (DA) and state (DS)
analysis in order to identify differences in composition and
expression profiles at the subpopulation-level, respectively.
biocViews: Clustering, DataImport, DifferentialExpression,
ExperimentalDesign, FlowCytometry, ImmunoOncology,
MassSpectrometry,Normalization, Preprocessing, SingleCell,
Software, StatisticalMethod, Visualization
Author: Helena L. Crowell [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4801-1767>), Vito R.T. Zanotelli
[aut] (ORCID: <https://orcid.org/0000-0001-7268-311X>),
Stéphane Chevrier [aut, dtc] (ORCID:
<https://orcid.org/0000-0002-9216-7910>), Mark D. Robinson
[aut, fnd] (ORCID: <https://orcid.org/0000-0002-3048-5518>),
Bernd Bodenmiller [fnd] (ORCID:
<https://orcid.org/0000-0002-6325-7861>)
Maintainer: Helena L. Crowell <helena@crowell.eu>
URL: https://github.com/HelenaLC/CATALYST
VignetteBuilder: knitr
BugReports: https://github.com/HelenaLC/CATALYST/issues
git_url: https://git.bioconductor.org/packages/CATALYST
git_branch: devel
git_last_commit: ada7c3c
git_last_commit_date: 2024-11-28
Date/Publication: 2024-11-29
source.ver: src/contrib/CATALYST_1.31.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CATALYST_1.31.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CATALYST_1.31.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CATALYST_1.31.2.tgz
vignettes: vignettes/CATALYST/inst/doc/differential.html,
vignettes/CATALYST/inst/doc/preprocessing.html
vignetteTitles: "2. Differential discovery", "1. Preprocessing"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CATALYST/inst/doc/differential.R,
vignettes/CATALYST/inst/doc/preprocessing.R
dependsOnMe: spillR, cytofWorkflow
importsMe: cytofQC
suggestsMe: diffcyt, imcRtools, treekoR
dependencyCount: 181
Package: Category
Version: 2.73.0
Depends: methods, stats4, BiocGenerics, AnnotationDbi, Biobase, Matrix
Imports: utils, stats, graph, RBGL, GSEABase, genefilter, annotate, DBI
Suggests: EBarrays, ALL, Rgraphviz, RColorBrewer, xtable (>= 1.4-6),
hgu95av2.db, KEGGREST, karyoploteR, geneplotter, limma,
lattice, RUnit, org.Sc.sgd.db, GOstats, GO.db
License: Artistic-2.0
MD5sum: 0c16729015a364a2bc797c3fded93722
NeedsCompilation: no
Title: Category Analysis
Description: A collection of tools for performing category (gene set
enrichment) analysis.
biocViews: Annotation, GO, Pathways, GeneSetEnrichment
Author: Robert Gentleman [aut], Seth Falcon [ctb], Deepayan Sarkar
[ctb], Robert Castelo [ctb], Bioconductor Package Maintainer
[cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/Category
git_branch: devel
git_last_commit: 1ae31f4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Category_2.73.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Category_2.73.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Category_2.73.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Category_2.73.0.tgz
vignettes: vignettes/Category/inst/doc/Category.pdf,
vignettes/Category/inst/doc/ChromBand.pdf
vignetteTitles: Using Categories to Analyze Microarray Data, Using
Chromosome Bands as Categories
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Category/inst/doc/Category.R,
vignettes/Category/inst/doc/ChromBand.R
dependsOnMe: GOstats
importsMe: categoryCompare, GmicR, interactiveDisplay, meshr, miRLAB,
phenoTest, scTensor
suggestsMe: qpgraph, RnBeads, maGUI
dependencyCount: 60
Package: categoryCompare
Version: 1.51.0
Depends: R (>= 2.10), Biobase, BiocGenerics (>= 0.13.8),
Imports: AnnotationDbi, hwriter, GSEABase, Category (>= 2.33.1),
GOstats, annotate, colorspace, graph, RCy3 (>= 1.99.29),
methods, grDevices, utils
Suggests: knitr, GO.db, KEGGREST, estrogen, org.Hs.eg.db, hgu95av2.db,
limma, affy, genefilter, rmarkdown
License: GPL-2
MD5sum: 72c8c2eddca7f53c761eef32aa62692a
NeedsCompilation: no
Title: Meta-analysis of high-throughput experiments using feature
annotations
Description: Calculates significant annotations (categories) in each of
two (or more) feature (i.e. gene) lists, determines the overlap
between the annotations, and returns graphical and tabular data
about the significant annotations and which combinations of
feature lists the annotations were found to be significant.
Interactive exploration is facilitated through the use of
RCytoscape (heavily suggested).
biocViews: Annotation, GO, MultipleComparison, Pathways, GeneExpression
Author: Robert M. Flight <rflight79@gmail.com>
Maintainer: Robert M. Flight <rflight79@gmail.com>
URL: https://github.com/rmflight/categoryCompare
SystemRequirements: Cytoscape (>= 3.6.1) (if used for visualization of
results, heavily suggested)
VignetteBuilder: knitr
BugReports: https://github.com/rmflight/categoryCompare/issues
git_url: https://git.bioconductor.org/packages/categoryCompare
git_branch: devel
git_last_commit: 570b4b8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/categoryCompare_1.51.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/categoryCompare_1.51.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/categoryCompare_1.51.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/categoryCompare_1.51.0.tgz
vignettes:
vignettes/categoryCompare/inst/doc/categoryCompare_vignette.html
vignetteTitles: categoryCompare: High-throughput data meta-analysis
using gene annotations
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.R
dependencyCount: 91
Package: CatsCradle
Version: 1.1.2
Depends: R (>= 4.4.0)
Imports: Seurat (>= 5.0.1), ggplot2, networkD3, stringr, pracma,
reshape2, rdist, igraph, geometry, Rfast, data.table, abind,
pheatmap, EBImage, S4Vectors, SeuratObject,
SingleCellExperiment, SpatialExperiment, Matrix, methods,
SummarizedExperiment, msigdbr
Suggests: fossil, interp, knitr, BiocStyle, tictoc
License: MIT + file LICENSE
MD5sum: 95e86f5348726b171e1faa265ec0195c
NeedsCompilation: no
Title: This package provides methods for analysing spatial
transcriptomics data and for discovering gene clusters
Description: This package addresses two broad areas. It allows for
in-depth analysis of spatial transcriptomic data by identifying
tissue neighbourhoods. These are contiguous regions of tissue
surrounding individual cells. 'CatsCradle' allows for the
categorisation of neighbourhoods by the cell types contained in
them and the genes expressed in them. In particular, it
produces Seurat objects whose individual elements are
neighbourhoods rather than cells. In addition, it enables the
categorisation and annotation of genes by producing Seurat
objects whose elements are genes.
biocViews: BiologicalQuestion, StatisticalMethod, GeneExpression,
SingleCell, Transcriptomics, Spatial
Author: Anna Laddach [aut] (ORCID:
<https://orcid.org/0000-0001-5552-6534>), Michael Shapiro [aut,
cre] (ORCID: <https://orcid.org/0000-0002-2769-9320>)
Maintainer: Michael Shapiro <michael.shapiro@crick.ac.uk>
URL: https://github.com/AnnaLaddach/CatsCradle
VignetteBuilder: knitr
BugReports: https://github.com/AnnaLaddach/CatsCradle/issues
git_url: https://git.bioconductor.org/packages/CatsCradle
git_branch: devel
git_last_commit: 799abf2
git_last_commit_date: 2025-03-17
Date/Publication: 2025-03-17
source.ver: src/contrib/CatsCradle_1.1.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CatsCradle_1.1.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CatsCradle_1.1.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CatsCradle_1.1.2.tgz
vignettes: vignettes/CatsCradle/inst/doc/CatsCradle.html,
vignettes/CatsCradle/inst/doc/CatsCradleExampleData.html,
vignettes/CatsCradle/inst/doc/CatsCradleQuickStart.html,
vignettes/CatsCradle/inst/doc/CatsCradleSingleCellExperimentQuickStart.html,
vignettes/CatsCradle/inst/doc/CatsCradleSpatial.html
vignetteTitles: CatsCradle, CatsCradle Example Data, CatsCradle Quick
Start, CatsCradle SingleCellExperiment Quick Start, CatsCradle
Spatial Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CatsCradle/inst/doc/CatsCradle.R,
vignettes/CatsCradle/inst/doc/CatsCradleExampleData.R,
vignettes/CatsCradle/inst/doc/CatsCradleQuickStart.R,
vignettes/CatsCradle/inst/doc/CatsCradleSingleCellExperimentQuickStart.R,
vignettes/CatsCradle/inst/doc/CatsCradleSpatial.R
dependencyCount: 203
Package: CausalR
Version: 1.39.0
Depends: R (>= 3.2.0)
Imports: igraph
Suggests: knitr, RUnit, BiocGenerics
License: GPL (>= 2)
MD5sum: bf244fa8787cc9dececec2abb351c8ba
NeedsCompilation: no
Title: Causal network analysis methods
Description: Causal network analysis methods for regulator prediction
and network reconstruction from genome scale data.
biocViews: ImmunoOncology, SystemsBiology, Network, GraphAndNetwork,
Network Inference, Transcriptomics, Proteomics,
DifferentialExpression, RNASeq, Microarray
Author: Glyn Bradley, Steven Barrett, Chirag Mistry, Mark Pipe, David
Wille, David Riley, Bhushan Bonde, Peter Woollard
Maintainer: Glyn Bradley <glyn.x.bradley@gsk.com>, Steven Barrett
<steven.j.barrett@gsk.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CausalR
git_branch: devel
git_last_commit: f417628
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CausalR_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CausalR_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CausalR_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CausalR_1.39.0.tgz
vignettes: vignettes/CausalR/inst/doc/CausalR.pdf
vignetteTitles: CausalR.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CausalR/inst/doc/CausalR.R
dependencyCount: 17
Package: cbaf
Version: 1.29.0
Depends: R (>= 4.1)
Imports: BiocFileCache, RColorBrewer, cBioPortalData, genefilter,
gplots, grDevices, stats, utils, openxlsx, zip
Suggests: knitr, rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: 1d5fd634d496cc654aed208ec315f350
NeedsCompilation: no
Title: Automated functions for comparing various omic data from
cbioportal.org
Description: This package contains functions that allow analysing and
comparing omic data across various cancers/cancer subgroups
easily. So far, it is compatible with RNA-seq, microRNA-seq,
microarray and methylation datasets that are stored on
cbioportal.org.
biocViews: Software, AssayDomain, DNAMethylation, GeneExpression,
Transcription, Microarray,ResearchField, BiomedicalInformatics,
ComparativeGenomics, Epigenetics, Genetics, Transcriptomics
Author: Arman Shahrisa [aut, cre, cph], Maryam Tahmasebi Birgani [aut]
Maintainer: Arman Shahrisa <shahrisa.arman@hotmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/cbaf
git_branch: devel
git_last_commit: 8845104
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cbaf_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cbaf_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cbaf_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cbaf_1.29.0.tgz
vignettes: vignettes/cbaf/inst/doc/cbaf.html
vignetteTitles: cbaf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cbaf/inst/doc/cbaf.R
dependencyCount: 154
Package: cBioPortalData
Version: 2.19.15
Depends: R (>= 4.5.0), AnVIL (>= 1.19.5), MultiAssayExperiment
Imports: BiocBaseUtils, BiocFileCache (>= 1.5.3), digest, dplyr,
GenomeInfoDb, GenomicRanges, httr, IRanges, methods, readr,
RaggedExperiment, RTCGAToolbox (>= 2.19.7), S4Vectors,
SummarizedExperiment, stats, tibble, tidyr, TCGAutils (>=
1.9.4), utils
Suggests: BiocStyle, jsonlite, knitr, survival, survminer, rmarkdown,
testthat
License: AGPL-3
MD5sum: 6177e89de153cdd2c4735b1d07b53422
NeedsCompilation: no
Title: Exposes and Makes Available Data from the cBioPortal Web
Resources
Description: The cBioPortalData R package accesses study datasets from
the cBio Cancer Genomics Portal. It accesses the data either
from the pre-packaged zip / tar files or from the API interface
that was recently implemented by the cBioPortal Data Team. The
package can provide data in either tabular format or with
MultiAssayExperiment object that uses familiar Bioconductor
data representations.
biocViews: Software, Infrastructure, ThirdPartyClient
Author: Levi Waldron [aut], Marcel Ramos [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3242-0582>), Karim Mezhoud [ctb]
Maintainer: Marcel Ramos <marcel.ramos@sph.cuny.edu>
URL: https://github.com/waldronlab/cBioPortalData
VignetteBuilder: knitr
BugReports: https://github.com/waldronlab/cBioPortalData/issues
git_url: https://git.bioconductor.org/packages/cBioPortalData
git_branch: devel
git_last_commit: 46596aa
git_last_commit_date: 2025-03-21
Date/Publication: 2025-03-23
source.ver: src/contrib/cBioPortalData_2.19.15.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cBioPortalData_2.19.15.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cBioPortalData_2.19.15.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cBioPortalData_2.19.15.tgz
vignettes: vignettes/cBioPortalData/inst/doc/cBioPortalData.html,
vignettes/cBioPortalData/inst/doc/cBioPortalDataErrors.html,
vignettes/cBioPortalData/inst/doc/cBioPortalRClient.html,
vignettes/cBioPortalData/inst/doc/cgdsrMigration.html
vignetteTitles: cBioPortalData User Guide, cBioPortal Data Build
Errors, cBioPortal Developer Guide, cgdsr to cBioPortalData
Migration
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cBioPortalData/inst/doc/cBioPortalData.R,
vignettes/cBioPortalData/inst/doc/cBioPortalDataErrors.R,
vignettes/cBioPortalData/inst/doc/cBioPortalRClient.R,
vignettes/cBioPortalData/inst/doc/cgdsrMigration.R
dependsOnMe: bioCancer, canceR
importsMe: cbaf, GNOSIS
suggestsMe: OmicsMLRepoR
dependencyCount: 142
Package: CBNplot
Version: 1.7.0
Depends: R (>= 4.3.0)
Imports: ggplot2, magrittr, graphite, ggraph, igraph, bnlearn (>= 4.7),
patchwork, org.Hs.eg.db, clusterProfiler, utils, enrichplot,
reshape2, ggforce, dplyr, tidyr, stringr, depmap,
ExperimentHub, Rmpfr, graphlayouts, BiocFileCache, ggdist,
purrr, pvclust, stats, rlang
Suggests: knitr, arules, concaveman, ReactomePA, bnviewer, rmarkdown,
withr, BiocStyle, testthat (>= 3.0.0)
License: Artistic-2.0
MD5sum: 32889a2dd375ebbefbbb69323024fa5e
NeedsCompilation: no
Title: plot bayesian network inferred from gene expression data based
on enrichment analysis results
Description: This package provides the visualization of bayesian
network inferred from gene expression data. The networks are
based on enrichment analysis results inferred from packages
including clusterProfiler and ReactomePA. The networks between
pathways and genes inside the pathways can be inferred and
visualized.
biocViews: Visualization, Bayesian, GeneExpression, NetworkInference,
Pathways, Reactome, Network, NetworkEnrichment,
GeneSetEnrichment
Author: Noriaki Sato [cre, aut]
Maintainer: Noriaki Sato <nori@hgc.jp>
URL: https://github.com/noriakis/CBNplot
VignetteBuilder: knitr
BugReports: https://github.com/noriakis/CBNplot/issues
git_url: https://git.bioconductor.org/packages/CBNplot
git_branch: devel
git_last_commit: 5dcddfc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CBNplot_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CBNplot_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CBNplot_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CBNplot_1.7.0.tgz
vignettes: vignettes/CBNplot/inst/doc/CBNplot_basic_usage.html
vignetteTitles: CBNplot
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CBNplot/inst/doc/CBNplot_basic_usage.R
dependencyCount: 151
Package: cbpManager
Version: 1.15.0
Depends: shiny, shinydashboard
Imports: utils, DT, htmltools, vroom, plyr, dplyr, magrittr, jsonlite,
rapportools, basilisk, reticulate, shinyBS, shinycssloaders,
rintrojs, rlang, markdown
Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0)
License: AGPL-3 + file LICENSE
MD5sum: 6580ed7b3e0bfe90a16f4ae66b2e49f4
NeedsCompilation: no
Title: Generate, manage, and edit data and metadata files suitable for
the import in cBioPortal for Cancer Genomics
Description: This R package provides an R Shiny application that
enables the user to generate, manage, and edit data and
metadata files suitable for the import in cBioPortal for Cancer
Genomics. Create cancer studies and edit its metadata. Upload
mutation data of a patient that will be concatenated to the
data_mutation_extended.txt file of the study. Create and edit
clinical patient data, sample data, and timeline data. Create
custom timeline tracks for patients.
biocViews: ImmunoOncology, DataImport, DataRepresentation, GUI,
ThirdPartyClient, Preprocessing, Visualization
Author: Arsenij Ustjanzew [aut, cre, cph] (ORCID:
<https://orcid.org/0000-0002-1014-4521>), Federico Marini [aut]
(ORCID: <https://orcid.org/0000-0003-3252-7758>)
Maintainer: Arsenij Ustjanzew <arsenij.ustjanzew@gmail.com>
URL: https://arsenij-ust.github.io/cbpManager/index.html
VignetteBuilder: knitr
BugReports: https://github.com/arsenij-ust/cbpManager/issues
git_url: https://git.bioconductor.org/packages/cbpManager
git_branch: devel
git_last_commit: fd4e30c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cbpManager_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cbpManager_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cbpManager_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cbpManager_1.15.0.tgz
vignettes: vignettes/cbpManager/inst/doc/intro.html
vignetteTitles: intro.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/cbpManager/inst/doc/intro.R
dependencyCount: 90
Package: ccfindR
Version: 1.27.0
Depends: R (>= 3.6.0)
Imports: stats, S4Vectors, utils, methods, Matrix,
SummarizedExperiment, SingleCellExperiment, Rtsne, graphics,
grDevices, gtools, RColorBrewer, ape, Rmpi, irlba, Rcpp, Rdpack
(>= 0.7)
LinkingTo: Rcpp, RcppEigen
Suggests: BiocStyle, knitr, rmarkdown
License: GPL (>= 2)
MD5sum: 976fbc36a6f78b10a00296780d68b2f0
NeedsCompilation: yes
Title: Cancer Clone Finder
Description: A collection of tools for cancer genomic data clustering
analyses, including those for single cell RNA-seq. Cell
clustering and feature gene selection analysis employ Bayesian
(and maximum likelihood) non-negative matrix factorization
(NMF) algorithm. Input data set consists of RNA count matrix,
gene, and cell bar code annotations. Analysis outputs are
factor matrices for multiple ranks and marginal likelihood
values for each rank. The package includes utilities for
downstream analyses, including meta-gene identification,
visualization, and construction of rank-based trees for
clusters.
biocViews: Transcriptomics, SingleCell, ImmunoOncology, Bayesian,
Clustering
Author: Jun Woo [aut, cre], Jinhua Wang [aut]
Maintainer: Jun Woo <jwoo@umn.edu>
URL: http://dx.doi.org/10.26508/lsa.201900443
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ccfindR
git_branch: devel
git_last_commit: 8f11dda
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ccfindR_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ccfindR_1.27.0.zip
vignettes: vignettes/ccfindR/inst/doc/ccfindR.html
vignetteTitles: ccfindR: single-cell RNA-seq analysis using Bayesian
non-negative matrix factorization
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ccfindR/inst/doc/ccfindR.R
suggestsMe: MutationalPatterns
dependencyCount: 50
Package: ccImpute
Version: 1.9.0
Imports: Rcpp, sparseMatrixStats, stats, BiocParallel, irlba,
SingleCellExperiment, Matrix, SummarizedExperiment
LinkingTo: Rcpp, RcppEigen
Suggests: knitr, rmarkdown, BiocStyle, sessioninfo, scRNAseq, scater,
mclust, testthat (>= 3.0.0), splatter
License: GPL-3
Archs: x64
MD5sum: e9dbeeb9bab1f5880eba31680b89b879
NeedsCompilation: yes
Title: ccImpute: an accurate and scalable consensus clustering based
approach to impute dropout events in the single-cell RNA-seq
data (https://doi.org/10.1186/s12859-022-04814-8)
Description: Dropout events make the lowly expressed genes
indistinguishable from true zero expression and different than
the low expression present in cells of the same type. This
issue makes any subsequent downstream analysis difficult.
ccImpute is an imputation algorithm that uses cell similarity
established by consensus clustering to impute the most probable
dropout events in the scRNA-seq datasets. ccImpute demonstrated
performance which exceeds the performance of existing
imputation approaches while introducing the least amount of new
noise as measured by clustering performance characteristics on
datasets with known cell identities.
biocViews: SingleCell, Sequencing, PrincipalComponent,
DimensionReduction, Clustering, RNASeq, Transcriptomics
Author: Marcin Malec [cre, aut] (ORCID:
<https://orcid.org/0000-0002-2354-513X>), Parichit Sharma [aut]
(ORCID: <https://orcid.org/0000-0003-0822-1089>), Hasan Kurban
[aut] (ORCID: <https://orcid.org/0000-0003-3142-2866>), Mehmet
Dalkilic [aut]
Maintainer: Marcin Malec <mamalec@iu.edu>
URL: https://github.com/khazum/ccImpute/
VignetteBuilder: knitr
BugReports: https://github.com/khazum/ccImpute/issues
git_url: https://git.bioconductor.org/packages/ccImpute
git_branch: devel
git_last_commit: 11f4990
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ccImpute_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ccImpute_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ccImpute_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ccImpute_1.9.0.tgz
vignettes: vignettes/ccImpute/inst/doc/ccImpute.html
vignetteTitles: ccImpute package manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ccImpute/inst/doc/ccImpute.R
dependencyCount: 51
Package: ccmap
Version: 1.33.0
Imports: AnnotationDbi (>= 1.36.2), BiocManager (>= 1.30.4), ccdata (>=
1.1.2), doParallel (>= 1.0.10), data.table (>= 1.10.4), foreach
(>= 1.4.3), parallel (>= 3.3.3), xgboost (>= 0.6.4), lsa (>=
0.73.1)
Suggests: crossmeta, knitr, rmarkdown, testthat, lydata
License: MIT + file LICENSE
Archs: x64
MD5sum: 282fad9f0e9ea248e78b7a64c78d4ca1
NeedsCompilation: no
Title: Combination Connectivity Mapping
Description: Finds drugs and drug combinations that are predicted to
reverse or mimic gene expression signatures. These drugs might
reverse diseases or mimic healthy lifestyles.
biocViews: GeneExpression, Transcription, Microarray,
DifferentialExpression
Author: Alex Pickering
Maintainer: Alex Pickering <alexvpickering@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ccmap
git_branch: devel
git_last_commit: 5eec3ab
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ccmap_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ccmap_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ccmap_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ccmap_1.33.0.tgz
vignettes: vignettes/ccmap/inst/doc/ccmap-vignette.html
vignetteTitles: ccmap vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ccmap/inst/doc/ccmap-vignette.R
dependencyCount: 59
Package: CCPlotR
Version: 1.5.0
Imports: plyr, tidyr, dplyr, ggplot2, forcats, ggraph, igraph,
scatterpie, circlize, ComplexHeatmap, tibble, grid, ggbump,
stringr, ggtext, ggh4x, patchwork, RColorBrewer, scales,
viridis, grDevices, graphics, stats, methods
Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: c60aa852df6f1e4bec5b2ef92f860da4
NeedsCompilation: no
Title: Plots For Visualising Cell-Cell Interactions
Description: CCPlotR is an R package for visualising results from tools
that predict cell-cell interactions from single-cell RNA-seq
data. These plots are generic and can be used to visualise
results from multiple tools such as Liana, CellPhoneDB, NATMI
etc.
biocViews: SingleCell, Network, Visualization, CellBiology,
SystemsBiology
Author: Sarah Ennis [aut, cre] (ORCID:
<https://orcid.org/0000-0001-6100-8573>), Pilib Ó Broin [aut],
Eva Szegezdi [aut]
Maintainer: Sarah Ennis <ennissarah94@gmail.com>
URL: https://github.com/Sarah145/CCPlotR
VignetteBuilder: knitr
BugReports: https://github.com/Sarah145/CCPlotR/issues
git_url: https://git.bioconductor.org/packages/CCPlotR
git_branch: devel
git_last_commit: c2f2f50
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CCPlotR_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CCPlotR_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CCPlotR_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CCPlotR_1.5.0.tgz
vignettes: vignettes/CCPlotR/inst/doc/CCPlotR_visualisations.html
vignetteTitles: User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CCPlotR/inst/doc/CCPlotR_visualisations.R
dependencyCount: 101
Package: CCPROMISE
Version: 1.33.0
Depends: R (>= 3.3.0), stats, methods, CCP, PROMISE, Biobase, GSEABase,
utils
License: GPL (>= 2)
MD5sum: 5648a2e22e45edc3ecc8dee5d7a94f54
NeedsCompilation: no
Title: PROMISE analysis with Canonical Correlation for Two Forms of
High Dimensional Genetic Data
Description: Perform Canonical correlation between two forms of high
demensional genetic data, and associate the first compoent of
each form of data with a specific biologically interesting
pattern of associations with multiple endpoints. A probe level
analysis is also implemented.
biocViews: Microarray, GeneExpression
Author: Xueyuan Cao <xueyuan.cao@stjude.org> and Stanley.pounds
<stanley.pounds@stjude.org>
Maintainer: Xueyuan Cao <xueyuan.cao@stjude.org>
git_url: https://git.bioconductor.org/packages/CCPROMISE
git_branch: devel
git_last_commit: 941008a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CCPROMISE_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CCPROMISE_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CCPROMISE_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CCPROMISE_1.33.0.tgz
vignettes: vignettes/CCPROMISE/inst/doc/CCPROMISE.pdf
vignetteTitles: An introduction to CCPROMISE
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CCPROMISE/inst/doc/CCPROMISE.R
dependencyCount: 52
Package: ccrepe
Version: 1.43.0
Imports: infotheo (>= 1.1)
Suggests: knitr, BiocStyle, BiocGenerics, testthat, RUnit
License: MIT + file LICENSE
MD5sum: d0b6bd3c7bc4bde3a9c4283e0e0da0d9
NeedsCompilation: no
Title: ccrepe_and_nc.score
Description: The CCREPE (Compositionality Corrected by REnormalizaion
and PErmutation) package is designed to assess the significance
of general similarity measures in compositional datasets. In
microbial abundance data, for example, the total abundances of
all microbes sum to one; CCREPE is designed to take this
constraint into account when assigning p-values to similarity
measures between the microbes. The package has two functions:
ccrepe: Calculates similarity measures, p-values and q-values
for relative abundances of bugs in one or two body sites using
bootstrap and permutation matrices of the data. nc.score:
Calculates species-level co-variation and co-exclusion patterns
based on an extension of the checkerboard score to ordinal
data.
biocViews: ImmunoOncology, Statistics, Metagenomics, Bioinformatics,
Software
Author: Emma Schwager <emh146@mail.harvard.edu>,Craig
Bielski<craig.bielski@gmail.com>, George
Weingart<george.weingart@gmail.com>
Maintainer: Emma Schwager <emma.schwager@gmail.com>,Craig
Bielski<craig.bielski@gmail.com>, George
Weingart<george.weingart@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ccrepe
git_branch: devel
git_last_commit: defe8c1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ccrepe_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ccrepe_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ccrepe_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ccrepe_1.43.0.tgz
vignettes: vignettes/ccrepe/inst/doc/ccrepe.pdf
vignetteTitles: ccrepe
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ccrepe/inst/doc/ccrepe.R
dependencyCount: 1
Package: CDI
Version: 1.5.0
Depends: R(>= 3.6)
Imports: matrixStats, Seurat, SeuratObject, stats, BiocParallel,
ggplot2, reshape2, grDevices, ggsci, SingleCellExperiment,
SummarizedExperiment, methods
Suggests: knitr, rmarkdown, RUnit, BiocGenerics, magick, BiocStyle
License: GPL-3 + file LICENSE
MD5sum: 74a2766784769e03adbc1a581b0bbd7a
NeedsCompilation: no
Title: Clustering Deviation Index (CDI)
Description: Single-cell RNA-sequencing (scRNA-seq) is widely used to
explore cellular variation. The analysis of scRNA-seq data
often starts from clustering cells into subpopulations. This
initial step has a high impact on downstream analyses, and
hence it is important to be accurate. However, there have not
been unsupervised metric designed for scRNA-seq to evaluate
clustering performance. Hence, we propose clustering deviation
index (CDI), an unsupervised metric based on the modeling of
scRNA-seq UMI counts to evaluate clustering of cells.
biocViews: SingleCell, Software, Clustering, Visualization, Sequencing,
RNASeq, CellBasedAssays
Author: Jiyuan Fang [cre, aut] (ORCID:
<https://orcid.org/0000-0002-5004-4138>), Jichun Xie [ctb],
Cliburn Chan [ctb], Kouros Owzar [ctb], Liuyang Wang [ctb],
Diyuan Qin [ctb], Qi-Jing Li [ctb], Jichun Xie [ctb]
Maintainer: Jiyuan Fang <jfanglovestats@gmail.com>
URL: https://github.com/jichunxie/CDI
VignetteBuilder: knitr
BugReports: https://github.com/jichunxie/CDI/issues
git_url: https://git.bioconductor.org/packages/CDI
git_branch: devel
git_last_commit: 82b7486
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CDI_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CDI_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CDI_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CDI_1.5.0.tgz
vignettes: vignettes/CDI/inst/doc/CDI.html
vignetteTitles: Clustering Deviation Index (CDI) Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CDI/inst/doc/CDI.R
dependencyCount: 178
Package: celaref
Version: 1.25.0
Depends: R (>= 3.5.0), SummarizedExperiment
Imports: MAST, ggplot2, Matrix, dplyr, magrittr, stats, utils, rlang,
BiocGenerics, S4Vectors, readr, tibble, DelayedArray
Suggests: limma, parallel, knitr, rmarkdown, ExperimentHub, testthat
License: GPL-3
MD5sum: 694bb2a78f54ff36d76fd76f7ff7c627
NeedsCompilation: no
Title: Single-cell RNAseq cell cluster labelling by reference
Description: After the clustering step of a single-cell RNAseq
experiment, this package aims to suggest labels/cell types for
the clusters, on the basis of similarity to a reference
dataset. It requires a table of read counts per cell per gene,
and a list of the cells belonging to each of the clusters, (for
both test and reference data).
biocViews: SingleCell
Author: Sarah Williams [aut, cre]
Maintainer: Sarah Williams <sarah.williams1@monash.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/celaref
git_branch: devel
git_last_commit: 34ca877
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/celaref_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/celaref_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/celaref_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/celaref_1.25.0.tgz
vignettes: vignettes/celaref/inst/doc/celaref_doco.html
vignetteTitles: Manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/celaref/inst/doc/celaref_doco.R
dependencyCount: 83
Package: celda
Version: 1.23.1
Depends: R (>= 4.0), SingleCellExperiment, Matrix
Imports: plyr, foreach, ggplot2, RColorBrewer, grid, scales, gtable,
grDevices, graphics, matrixStats, doParallel, digest, methods,
reshape2, S4Vectors, data.table, Rcpp, RcppEigen, uwot,
enrichR, SummarizedExperiment, MCMCprecision, ggrepel, Rtsne,
withr, scater (>= 1.14.4), scran, dbscan, DelayedArray,
stringr, ComplexHeatmap, gridExtra, circlize, dendextend,
ggdendro, pROC
LinkingTo: Rcpp, RcppEigen
Suggests: testthat, knitr, roxygen2, rmarkdown, biomaRt, covr,
BiocManager, BiocStyle, TENxPBMCData, singleCellTK,
M3DExampleData
License: MIT + file LICENSE
MD5sum: 9ca87832b169b7316982964bfb04108f
NeedsCompilation: yes
Title: CEllular Latent Dirichlet Allocation
Description: Celda is a suite of Bayesian hierarchical models for
clustering single-cell RNA-sequencing (scRNA-seq) data. It is
able to perform "bi-clustering" and simultaneously cluster
genes into gene modules and cells into cell subpopulations. It
also contains DecontX, a novel Bayesian method to
computationally estimate and remove RNA contamination in
individual cells without empty droplet information. A variety
of scRNA-seq data visualization functions is also included.
biocViews: SingleCell, GeneExpression, Clustering, Sequencing,
Bayesian, ImmunoOncology, DataImport
Author: Joshua Campbell [aut, cre], Shiyi Yang [aut], Zhe Wang [aut],
Sean Corbett [aut], Yusuke Koga [aut]
Maintainer: Joshua Campbell <camp@bu.edu>
VignetteBuilder: knitr
BugReports: https://github.com/campbio/celda/issues
git_url: https://git.bioconductor.org/packages/celda
git_branch: devel
git_last_commit: 528b6a9d
git_last_commit_date: 2025-02-16
Date/Publication: 2025-02-17
source.ver: src/contrib/celda_1.23.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/celda_1.23.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/celda_1.23.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/celda_1.23.1.tgz
vignettes: vignettes/celda/inst/doc/celda.html,
vignettes/celda/inst/doc/decontX.html
vignetteTitles: Analysis of single-cell genomic data with celda,
Estimate and remove cross-contamination from ambient RNA in
single-cell data with DecontX
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/celda/inst/doc/celda.R,
vignettes/celda/inst/doc/decontX.R
importsMe: decontX, singleCellTK
dependencyCount: 141
Package: CellBarcode
Version: 1.13.2
Depends: R (>= 4.1.0)
Imports: methods, stats, data.table (>= 1.12.6), plyr, ggplot2,
stringr, magrittr, ShortRead (>= 1.48.0), Biostrings (>=
2.58.0), egg, Ckmeans.1d.dp, utils, S4Vectors, seqinr,
Rsamtools
LinkingTo: BH
Suggests: BiocStyle, testthat (>= 3.0.0), knitr, rmarkdown
License: Artistic-2.0
Archs: x64
MD5sum: 6621aa4e0646f7bf5966913e7340ab5e
NeedsCompilation: yes
Title: Cellular DNA Barcode Analysis toolkit
Description: The package CellBarcode performs Cellular DNA Barcode
analysis. It can handle all kinds of DNA barcodes, as long as
the barcode is within a single sequencing read and has a
pattern that can be matched by a regular expression.
\code{CellBarcode} can handle barcodes with flexible lengths,
with or without UMI (unique molecular identifier). This tool
also can be used for pre-processing some amplicon data such as
CRISPR gRNA screening, immune repertoire sequencing, and
metagenome data.
biocViews: Preprocessing, QualityControl, Sequencing, CRISPR
Author: Wenjie Sun [cre, aut] (ORCID:
<https://orcid.org/0000-0002-3100-2346>), Anne-Marie Lyne
[aut], Leila Perie [aut]
Maintainer: Wenjie Sun <sunwjie@gmail.com>
URL: https://wenjie1991.github.io/CellBarcode/
SystemRequirements: Cargo (Rust's package manager), rustc
VignetteBuilder: knitr
BugReports: https://github.com/wenjie1991/CellBarcode/issues
git_url: https://git.bioconductor.org/packages/CellBarcode
git_branch: devel
git_last_commit: 9c54c56
git_last_commit_date: 2025-03-04
Date/Publication: 2025-03-05
source.ver: src/contrib/CellBarcode_1.13.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CellBarcode_1.13.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CellBarcode_1.13.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CellBarcode_1.13.2.tgz
vignettes: vignettes/CellBarcode/inst/doc/Barcode_in_10X_scRNASeq.html,
vignettes/CellBarcode/inst/doc/UMI_VDJ_Barcode.html
vignetteTitles: 10X_Barcode, UMI_Barcode
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CellBarcode/inst/doc/Barcode_in_10X_scRNASeq.R,
vignettes/CellBarcode/inst/doc/UMI_VDJ_Barcode.R
dependencyCount: 102
Package: cellbaseR
Version: 1.31.0
Depends: R(>= 3.4)
Imports: methods, jsonlite, httr, data.table, pbapply, tidyr, R.utils,
Rsamtools, BiocParallel, foreach, utils, parallel, doParallel
Suggests: BiocStyle, knitr, rmarkdown, Gviz, VariantAnnotation
License: Apache License (== 2.0)
Archs: x64
MD5sum: f815692fd7071cffe16bdd4e672e1a89
NeedsCompilation: no
Title: Querying annotation data from the high performance Cellbase web
Description: This R package makes use of the exhaustive RESTful Web
service API that has been implemented for the Cellabase
database. It enable researchers to query and obtain a wealth of
biological information from a single database saving a lot of
time. Another benefit is that researchers can easily make
queries about different biological topics and link all this
information together as all information is integrated.
biocViews: Annotation, VariantAnnotation
Author: Mohammed OE Abdallah
Maintainer: Mohammed OE Abdallah <melsiddieg@gmail.com>
URL: https://github.com/melsiddieg/cellbaseR
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/cellbaseR
git_branch: devel
git_last_commit: 76824da
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cellbaseR_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cellbaseR_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cellbaseR_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cellbaseR_1.31.0.tgz
vignettes: vignettes/cellbaseR/inst/doc/cellbaseR.html
vignetteTitles: "Simplifying Genomic Annotations in R"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cellbaseR/inst/doc/cellbaseR.R
dependencyCount: 65
Package: CellBench
Version: 1.23.0
Depends: R (>= 3.6), SingleCellExperiment, magrittr, methods, stats,
tibble, utils
Imports: assertthat, BiocGenerics, BiocFileCache, BiocParallel, dplyr,
rlang, glue, memoise, purrr (>= 0.3.0), rappdirs, tidyr,
tidyselect, lubridate
Suggests: BiocStyle, covr, knitr, rmarkdown, testthat, limma, ggplot2
License: GPL-3
MD5sum: f307b90e08b6a67eece0e6acbcf2b3b2
NeedsCompilation: no
Title: Construct Benchmarks for Single Cell Analysis Methods
Description: This package contains infrastructure for benchmarking
analysis methods and access to single cell mixture benchmarking
data. It provides a framework for organising analysis methods
and testing combinations of methods in a pipeline without
explicitly laying out each combination. It also provides
utilities for sampling and filtering SingleCellExperiment
objects, constructing lists of functions with varying
parameters, and multithreaded evaluation of analysis methods.
biocViews: Software, Infrastructure, SingleCell
Author: Shian Su [cre, aut], Saskia Freytag [aut], Luyi Tian [aut],
Xueyi Dong [aut], Matthew Ritchie [aut], Peter Hickey [ctb],
Stuart Lee [ctb]
Maintainer: Shian Su <su.s@wehi.edu.au>
URL: https://github.com/shians/cellbench
VignetteBuilder: knitr
BugReports: https://github.com/Shians/CellBench/issues
git_url: https://git.bioconductor.org/packages/CellBench
git_branch: devel
git_last_commit: 65e4041
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CellBench_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CellBench_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CellBench_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CellBench_1.23.0.tgz
vignettes: vignettes/CellBench/inst/doc/DataManipulation.html,
vignettes/CellBench/inst/doc/Introduction.html,
vignettes/CellBench/inst/doc/TidyversePatterns.html,
vignettes/CellBench/inst/doc/Timing.html,
vignettes/CellBench/inst/doc/WritingWrappers.html
vignetteTitles: Data Manipulation, Introduction, Tidyverse Patterns,
Timing, Writing Wrappers
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CellBench/inst/doc/DataManipulation.R,
vignettes/CellBench/inst/doc/Introduction.R,
vignettes/CellBench/inst/doc/TidyversePatterns.R,
vignettes/CellBench/inst/doc/Timing.R,
vignettes/CellBench/inst/doc/WritingWrappers.R
suggestsMe: corral, speckle
dependencyCount: 81
Package: CelliD
Version: 1.15.0
Depends: R (>= 4.1), Seurat (>= 4.0.1), SingleCellExperiment
Imports: Rcpp, RcppArmadillo, stats, utils, Matrix, tictoc, scater,
stringr, irlba, data.table, glue, pbapply, umap, Rtsne,
reticulate, fastmatch, matrixStats, ggplot2, BiocParallel,
SummarizedExperiment, fgsea
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, BiocStyle, testthat, tidyverse, ggpubr,
destiny, ggrepel
License: GPL-3 + file LICENSE
MD5sum: 8f30993883fc40954f998e189f5e31a2
NeedsCompilation: yes
Title: Unbiased Extraction of Single Cell gene signatures using
Multiple Correspondence Analysis
Description: CelliD is a clustering-free multivariate statistical
method for the robust extraction of per-cell gene signatures
from single-cell RNA-seq. CelliD allows unbiased cell identity
recognition across different donors, tissues-of-origin, model
organisms and single-cell omics protocols. The package can also
be used to explore functional pathways enrichment in single
cell data.
biocViews: RNASeq, SingleCell, DimensionReduction, Clustering,
GeneSetEnrichment, GeneExpression, ATACSeq
Author: Akira Cortal [aut, cre], Antonio Rausell [aut, ctb]
Maintainer: Akira Cortal <akira.cortal@institutimagine.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CelliD
git_branch: devel
git_last_commit: af4d3e3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CelliD_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CelliD_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CelliD_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CelliD_1.15.0.tgz
vignettes: vignettes/CelliD/inst/doc/BioconductorVignette.html
vignetteTitles: CelliD Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CelliD/inst/doc/BioconductorVignette.R
dependencyCount: 200
Package: cellity
Version: 1.35.0
Depends: R (>= 3.3)
Imports: AnnotationDbi, e1071, ggplot2, graphics, grDevices, grid,
mvoutlier, org.Hs.eg.db, org.Mm.eg.db, robustbase, stats,
topGO, utils
Suggests: BiocStyle, caret, knitr, testthat, rmarkdown
License: GPL (>= 2)
Archs: x64
MD5sum: bef43724dd0edf1636ca6ad6f1cea0b6
NeedsCompilation: no
Title: Quality Control for Single-Cell RNA-seq Data
Description: A support vector machine approach to identifying and
filtering low quality cells from single-cell RNA-seq datasets.
biocViews: ImmunoOncology, RNASeq, QualityControl, Preprocessing,
Normalization, Visualization, DimensionReduction,
Transcriptomics, GeneExpression, Sequencing, Software,
SupportVectorMachine
Author: Tomislav Illicic, Davis McCarthy
Maintainer: Tomislav Ilicic <ti243@cam.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/cellity
git_branch: devel
git_last_commit: 614b66c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cellity_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cellity_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cellity_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cellity_1.35.0.tgz
vignettes: vignettes/cellity/inst/doc/cellity_vignette.html
vignetteTitles: An introduction to the cellity package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cellity/inst/doc/cellity_vignette.R
dependencyCount: 82
Package: CellMapper
Version: 1.33.0
Depends: S4Vectors, methods
Imports: stats, utils
Suggests: CellMapperData, Biobase, HumanAffyData, ALL, BiocStyle,
ExperimentHub
License: Artistic-2.0
MD5sum: fe5dab5461e3585f50d53bbb6470a492
NeedsCompilation: no
Title: Predict genes expressed selectively in specific cell types
Description: Infers cell type-specific expression based on
co-expression similarity with known cell type marker genes. Can
make accurate predictions using publicly available expression
data, even when a cell type has not been isolated before.
biocViews: Microarray, Software, GeneExpression
Author: Brad Nelms
Maintainer: Brad Nelms <bnelms.research@gmail.com>
git_url: https://git.bioconductor.org/packages/CellMapper
git_branch: devel
git_last_commit: 5a2c66b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CellMapper_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CellMapper_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CellMapper_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CellMapper_1.33.0.tgz
vignettes: vignettes/CellMapper/inst/doc/CellMapper.pdf
vignetteTitles: CellMapper Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CellMapper/inst/doc/CellMapper.R
dependsOnMe: CellMapperData
dependencyCount: 8
Package: cellmigRation
Version: 1.15.0
Depends: R (>= 4.1), methods, foreach
Imports: tiff, graphics, stats, utils, reshape2, parallel, doParallel,
grDevices, matrixStats, FME, SpatialTools, sp, vioplot,
FactoMineR, Hmisc
Suggests: knitr, rmarkdown, dplyr, ggplot2, RUnit, BiocGenerics,
BiocManager, kableExtra, rgl
License: GPL-2
MD5sum: 871eaa3d8450f20a1901eb12c9a7f6b5
NeedsCompilation: no
Title: Track Cells, Analyze Cell Trajectories and Compute Migration
Statistics
Description: Import TIFF images of fluorescently labeled cells, and
track cell movements over time. Parallelization is supported
for image processing and for fast computation of cell
trajectories. In-depth analysis of cell trajectories is enabled
by 15 trajectory analysis functions.
biocViews: CellBiology, DataRepresentation, DataImport
Author: Salim Ghannoum [aut, cph], Damiano Fantini [aut, cph], Waldir
Leoncio [cre, aut], Øystein Sørensen [aut]
Maintainer: Waldir Leoncio <w.l.netto@medisin.uio.no>
URL: https://github.com/ocbe-uio/cellmigRation/
VignetteBuilder: knitr
BugReports: https://github.com/ocbe-uio/cellmigRation/issues
git_url: https://git.bioconductor.org/packages/cellmigRation
git_branch: devel
git_last_commit: 16757c8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cellmigRation_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cellmigRation_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cellmigRation_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cellmigRation_1.15.0.tgz
vignettes: vignettes/cellmigRation/inst/doc/cellmigRation.html
vignetteTitles: cellmigRation
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cellmigRation/inst/doc/cellmigRation.R
dependencyCount: 141
Package: CellMixS
Version: 1.23.0
Depends: kSamples, R (>= 4.0)
Imports: BiocNeighbors, ggplot2, scater, viridis, cowplot,
SummarizedExperiment, SingleCellExperiment, tidyr, magrittr,
dplyr, ggridges, stats, purrr, methods, BiocParallel,
BiocGenerics
Suggests: BiocStyle, knitr, rmarkdown, testthat, limma, Rtsne
License: GPL (>=2)
MD5sum: 6afeca36732eaefa9ff7dc97c945651a
NeedsCompilation: no
Title: Evaluate Cellspecific Mixing
Description: CellMixS provides metrics and functions to evaluate batch
effects, data integration and batch effect correction in single
cell trancriptome data with single cell resolution. Results can
be visualized and summarised on different levels, e.g. on cell,
celltype or dataset level.
biocViews: SingleCell, Transcriptomics, GeneExpression, BatchEffect
Author: Almut Lütge [aut, cre]
Maintainer: Almut Lütge <almut.lue@gmail.com>
URL: https://github.com/almutlue/CellMixS
VignetteBuilder: knitr
BugReports: https://github.com/almutlue/CellMixS/issues
git_url: https://git.bioconductor.org/packages/CellMixS
git_branch: devel
git_last_commit: 0d878da
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CellMixS_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CellMixS_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CellMixS_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CellMixS_1.23.0.tgz
vignettes: vignettes/CellMixS/inst/doc/CellMixS.html
vignetteTitles: Explore data integration and batch effects
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CellMixS/inst/doc/CellMixS.R
dependencyCount: 116
Package: CellNOptR
Version: 1.53.2
Depends: R (>= 4.0.0), RBGL, graph, methods, RCurl, Rgraphviz, XML,
ggplot2, rmarkdown
Imports: igraph, stringi, stringr
Suggests: data.table, dplyr, tidyr, readr, knitr, RUnit, BiocGenerics,
Enhances: doParallel, foreach
License: GPL-3
MD5sum: 2b8c4b8e599ebf3d90e8714d883ea493
NeedsCompilation: yes
Title: Training of boolean logic models of signalling networks using
prior knowledge networks and perturbation data
Description: This package does optimisation of boolean logic networks
of signalling pathways based on a previous knowledge network
and a set of data upon perturbation of the nodes in the
network.
biocViews: CellBasedAssays, CellBiology, Proteomics, Pathways, Network,
TimeCourse, ImmunoOncology
Author: Thomas Cokelaer [aut], Federica Eduati [aut], Aidan MacNamara
[aut], S Schrier [ctb], Camille Terfve [aut], Enio Gjerga
[ctb], Attila Gabor [cre]
Maintainer: Attila Gabor <attila.gabor@uni-heidelberg.de>
SystemRequirements: Graphviz version >= 2.2
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CellNOptR
git_branch: devel
git_last_commit: 6deca70
git_last_commit_date: 2025-03-24
Date/Publication: 2025-03-24
source.ver: src/contrib/CellNOptR_1.53.2.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CellNOptR_1.53.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CellNOptR_1.53.2.tgz
vignettes: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.html
vignetteTitles: Training of boolean logic models of signalling networks
using prior knowledge networks and perturbation data with
CellNOptR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.R
dependsOnMe: CNORdt, CNORfuzzy, CNORode
importsMe: bnem, CNORfeeder
suggestsMe: MEIGOR
dependencyCount: 71
Package: cellscape
Version: 1.31.0
Depends: R (>= 3.3)
Imports: dplyr (>= 0.4.3), gtools (>= 3.5.0), htmlwidgets (>= 0.5),
jsonlite (>= 0.9.19), reshape2 (>= 1.4.1), stringr (>= 1.0.0)
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: e51470c789844af8f45a1ea0d155a384
NeedsCompilation: no
Title: Explores single cell copy number profiles in the context of a
single cell tree
Description: CellScape facilitates interactive browsing of single cell
clonal evolution datasets. The tool requires two main inputs:
(i) the genomic content of each single cell in the form of
either copy number segments or targeted mutation values, and
(ii) a single cell phylogeny. Phylogenetic formats can vary
from dendrogram-like phylogenies with leaf nodes to
evolutionary model-derived phylogenies with observed or latent
internal nodes. The CellScape phylogeny is flexibly input as a
table of source-target edges to support arbitrary
representations, where each node may or may not have associated
genomic data. The output of CellScape is an interactive
interface displaying a single cell phylogeny and a
cell-by-locus genomic heatmap representing the mutation status
in each cell for each locus.
biocViews: Visualization
Author: Shixiang Wang [aut, cre] (ORCID:
<https://orcid.org/0000-0001-9855-7357>), Maia Smith [aut]
Maintainer: Shixiang Wang <w_shixiang@163.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/cellscape
git_branch: devel
git_last_commit: 1a8351b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cellscape_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cellscape_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cellscape_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cellscape_1.31.0.tgz
vignettes: vignettes/cellscape/inst/doc/cellscape_vignette.html
vignetteTitles: CellScape vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cellscape/inst/doc/cellscape_vignette.R
dependencyCount: 50
Package: CellScore
Version: 1.27.0
Depends: R (>= 4.3.0)
Imports: Biobase (>= 2.39.1), graphics (>= 3.5.0), grDevices (>=
3.5.0), gplots (>= 3.0.1), lsa (>= 0.73.1), methods (>= 3.5.0),
RColorBrewer(>= 1.1-2), squash (>= 1.0.8), stats (>= 3.5.0),
utils(>= 3.5.0), SummarizedExperiment
Suggests: hgu133plus2CellScore, knitr, testthat (>= 3.0.0)
License: GPL-3
MD5sum: 4464713764559ce74fe042e27133ba3a
NeedsCompilation: no
Title: Tool for Evaluation of Cell Identity from Transcription Profiles
Description: The CellScore package contains functions to evaluate the
cell identity of a test sample, given a cell transition defined
with a starting (donor) cell type and a desired target cell
type. The evaluation is based upon a scoring system, which uses
a set of standard samples of known cell types, as the reference
set. The functions have been carried out on a large set of
microarray data from one platform (Affymetrix Human Genome U133
Plus 2.0). In principle, the method could be applied to any
expression dataset, provided that there are a sufficient number
of standard samples and that the data are normalized.
biocViews: GeneExpression, Transcription, Microarray,
MultipleComparison, ReportWriting, DataImport, Visualization
Author: Nancy Mah [aut, cre], Katerina Taskova [aut], Justin Marsh
[aut]
Maintainer: Nancy Mah <nancy.l.mah@googlemail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CellScore
git_branch: devel
git_last_commit: 9e8a1d2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CellScore_1.27.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CellScore_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CellScore_1.27.0.tgz
vignettes: vignettes/CellScore/inst/doc/CellScoreVignette.pdf
vignetteTitles: R packages: CellScore
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CellScore/inst/doc/CellScoreVignette.R
suggestsMe: homosapienDEE2CellScore
dependencyCount: 45
Package: CellTrails
Version: 1.25.0
Depends: R (>= 3.5), SingleCellExperiment
Imports: BiocGenerics, Biobase, cba, dendextend, dtw, EnvStats,
ggplot2, ggrepel, grDevices, igraph, maptree, methods, mgcv,
reshape2, Rtsne, stats, splines, SummarizedExperiment, utils
Suggests: AnnotationDbi, destiny, RUnit, scater, scran, knitr,
org.Mm.eg.db, rmarkdown
License: Artistic-2.0
MD5sum: 4779e915b71aef01628b8d5e579ad7d7
NeedsCompilation: no
Title: Reconstruction, visualization and analysis of branching
trajectories
Description: CellTrails is an unsupervised algorithm for the de novo
chronological ordering, visualization and analysis of
single-cell expression data. CellTrails makes use of a
geometrically motivated concept of lower-dimensional manifold
learning, which exhibits a multitude of virtues that counteract
intrinsic noise of single cell data caused by drop-outs,
technical variance, and redundancy of predictive variables.
CellTrails enables the reconstruction of branching trajectories
and provides an intuitive graphical representation of
expression patterns along all branches simultaneously. It
allows the user to define and infer the expression dynamics of
individual and multiple pathways towards distinct phenotypes.
biocViews: ImmunoOncology, Clustering, DataRepresentation,
DifferentialExpression, DimensionReduction, GeneExpression,
Sequencing, SingleCell, Software, TimeCourse
Author: Daniel Ellwanger [aut, cre, cph]
Maintainer: Daniel Ellwanger <dc.ellwanger.dev@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CellTrails
git_branch: devel
git_last_commit: b064f50
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CellTrails_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CellTrails_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CellTrails_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CellTrails_1.25.0.tgz
vignettes: vignettes/CellTrails/inst/doc/vignette.pdf
vignetteTitles: CellTrails: Reconstruction,, visualization,, and
analysis of branching trajectories from single-cell expression
data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CellTrails/inst/doc/vignette.R
dependencyCount: 84
Package: cellxgenedp
Version: 1.11.0
Depends: dplyr
Imports: httr, curl, utils, tools, cli, shiny, DT, rjsoncons
Suggests: zellkonverter, SingleCellExperiment, HDF5Array, tidyr,
BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), mockery
License: Artistic-2.0
MD5sum: 1161a0d753031ead9908340f785b93c5
NeedsCompilation: no
Title: Discover and Access Single Cell Data Sets in the CELLxGENE Data
Portal
Description: The cellxgene data portal
(https://cellxgene.cziscience.com/) provides a graphical user
interface to collections of single-cell sequence data processed
in standard ways to 'count matrix' summaries. The cellxgenedp
package provides an alternative, R-based inteface, allowind
data discovery, viewing, and downloading.
biocViews: SingleCell, DataImport, ThirdPartyClient
Author: Martin Morgan [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5874-8148>), Kayla Interdonato
[aut]
Maintainer: Martin Morgan <mtmorgan.bioc@gmail.com>
URL: https://mtmorgan.github.io/cellxgenedp/,
https://github.com/mtmorgan/cellxgenedp
VignetteBuilder: knitr
BugReports: https://github.com/mtmorgan/cellxgenedp/issues
git_url: https://git.bioconductor.org/packages/cellxgenedp
git_branch: devel
git_last_commit: c17b43e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cellxgenedp_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cellxgenedp_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cellxgenedp_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cellxgenedp_1.11.0.tgz
vignettes: vignettes/cellxgenedp/inst/doc/a_using_cellxgenedp.html,
vignettes/cellxgenedp/inst/doc/b_case_studies.html
vignetteTitles: Discovery and retrieval, Case studies
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cellxgenedp/inst/doc/a_using_cellxgenedp.R,
vignettes/cellxgenedp/inst/doc/b_case_studies.R
dependencyCount: 63
Package: CEMiTool
Version: 1.31.0
Depends: R (>= 4.0)
Imports: methods, scales, dplyr, data.table (>= 1.9.4), WGCNA, grid,
ggplot2, ggpmisc, ggthemes, ggrepel, sna, clusterProfiler,
fgsea, stringr, knitr, rmarkdown, igraph, DT, htmltools,
pracma, intergraph, grDevices, utils, network, matrixStats,
ggdendro, gridExtra, gtable, fastcluster
Suggests: testthat, BiocManager
License: GPL-3
MD5sum: 44ee1f61bab5626fc0f0a6983218540f
NeedsCompilation: no
Title: Co-expression Modules identification Tool
Description: The CEMiTool package unifies the discovery and the
analysis of coexpression gene modules in a fully automatic
manner, while providing a user-friendly html report with high
quality graphs. Our tool evaluates if modules contain genes
that are over-represented by specific pathways or that are
altered in a specific sample group. Additionally, CEMiTool is
able to integrate transcriptomic data with interactome
information, identifying the potential hubs on each network.
biocViews: GeneExpression, Transcriptomics, GraphAndNetwork,
mRNAMicroarray, RNASeq, Network, NetworkEnrichment, Pathways,
ImmunoOncology
Author: Pedro Russo [aut], Gustavo Ferreira [aut], Matheus Bürger
[aut], Lucas Cardozo [aut], Diogenes Lima [aut], Thiago Hirata
[aut], Melissa Lever [aut], Helder Nakaya [aut, cre]
Maintainer: Helder Nakaya <hnakaya@usp.br>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CEMiTool
git_branch: devel
git_last_commit: 3dd9735
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/CEMiTool_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CEMiTool_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CEMiTool_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CEMiTool_1.31.0.tgz
vignettes: vignettes/CEMiTool/inst/doc/CEMiTool.html
vignetteTitles: CEMiTool: Co-expression Modules Identification Tool
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CEMiTool/inst/doc/CEMiTool.R
dependencyCount: 192
Package: censcyt
Version: 1.15.0
Depends: R (>= 4.0), diffcyt
Imports: BiocParallel, broom.mixed, dirmult, dplyr, edgeR,
fitdistrplus, lme4, magrittr, MASS, methods, mice, multcomp,
purrr, rlang, S4Vectors, stats, stringr, SummarizedExperiment,
survival, tibble, tidyr, utils
Suggests: BiocStyle, knitr, rmarkdown, testthat, ggplot2
License: MIT + file LICENSE
MD5sum: e5461b6d2f9a4598ea9bde932891c747
NeedsCompilation: no
Title: Differential abundance analysis with a right censored covariate
in high-dimensional cytometry
Description: Methods for differential abundance analysis in
high-dimensional cytometry data when a covariate is subject to
right censoring (e.g. survival time) based on multiple
imputation and generalized linear mixed models.
biocViews: ImmunoOncology, FlowCytometry, Proteomics, SingleCell,
CellBasedAssays, CellBiology, Clustering, FeatureExtraction,
Software, Survival
Author: Reto Gerber [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5414-8906>)
Maintainer: Reto Gerber <gerberreto@pm.me>
URL: https://github.com/retogerber/censcyt
VignetteBuilder: knitr
BugReports: https://github.com/retogerber/censcyt/issues
git_url: https://git.bioconductor.org/packages/censcyt
git_branch: devel
git_last_commit: 22cee1d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/censcyt_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/censcyt_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/censcyt_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/censcyt_1.15.0.tgz
vignettes: vignettes/censcyt/inst/doc/censored_covariate.html
vignetteTitles: Censored covariate
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/censcyt/inst/doc/censored_covariate.R
dependencyCount: 181
Package: Cepo
Version: 1.13.0
Depends: GSEABase, R (>= 4.1)
Imports: DelayedMatrixStats, DelayedArray, HDF5Array, S4Vectors,
methods, SingleCellExperiment, SummarizedExperiment, ggplot2,
rlang, grDevices, patchwork, reshape2, BiocParallel, stats,
dplyr, purrr
Suggests: knitr, rmarkdown, BiocStyle, testthat, covr, UpSetR, scater,
scMerge, fgsea, escape, pheatmap
License: MIT + file LICENSE
MD5sum: 9d149c1c4b3d3d15fe020c4c6f9f350a
NeedsCompilation: no
Title: Cepo for the identification of differentially stable genes
Description: Defining the identity of a cell is fundamental to
understand the heterogeneity of cells to various environmental
signals and perturbations. We present Cepo, a new method to
explore cell identities from single-cell RNA-sequencing data
using differential stability as a new metric to define cell
identity genes. Cepo computes cell-type specific gene
statistics pertaining to differential stable gene expression.
biocViews: Classification, GeneExpression, SingleCell, Software,
Sequencing, DifferentialExpression
Author: Hani Jieun Kim [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1844-3275>), Kevin Wang [aut]
(ORCID: <https://orcid.org/0000-0003-2615-6102>)
Maintainer: Hani Jieun Kim <hani.kim127@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Cepo
git_branch: devel
git_last_commit: 5cb935f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Cepo_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Cepo_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Cepo_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Cepo_1.13.0.tgz
vignettes: vignettes/Cepo/inst/doc/cepo.html
vignetteTitles: Cepo method for differential stability analysis of
scRNA-seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Cepo/inst/doc/cepo.R
importsMe: scClassify
dependencyCount: 107
Package: ceRNAnetsim
Version: 1.19.0
Depends: R (>= 4.0.0), dplyr, tidygraph
Imports: furrr, rlang, tibble, ggplot2, ggraph, igraph, purrr, tidyr,
future, stats
Suggests: knitr, png, rmarkdown, testthat, covr
License: GPL (>= 3.0)
MD5sum: 56b8a4b953f86d03c4cc8abf8b99d37e
NeedsCompilation: no
Title: Regulation Simulator of Interaction between miRNA and Competing
RNAs (ceRNA)
Description: This package simulates regulations of ceRNA (Competing
Endogenous) expression levels after a expression level change
in one or more miRNA/mRNAs. The methodolgy adopted by the
package has potential to incorparate any ceRNA (circRNA,
lincRNA, etc.) into miRNA:target interaction network. The
package basically distributes miRNA expression over available
ceRNAs where each ceRNA attracks miRNAs proportional to its
amount. But, the package can utilize multiple parameters that
modify miRNA effect on its target (seed type, binding energy,
binding location, etc.). The functions handle the given
dataset as graph object and the processes progress via edge and
node variables.
biocViews: NetworkInference, SystemsBiology, Network, GraphAndNetwork,
Transcriptomics
Author: Selcen Ari Yuka [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0028-2453>), Alper Yilmaz [aut]
(ORCID: <https://orcid.org/0000-0002-8827-4887>)
Maintainer: Selcen Ari Yuka <selcenarii@gmail.com>
URL: https://github.com/selcenari/ceRNAnetsim
VignetteBuilder: knitr
BugReports: https://github.com/selcenari/ceRNAnetsim/issues
git_url: https://git.bioconductor.org/packages/ceRNAnetsim
git_branch: devel
git_last_commit: 1046dbf
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ceRNAnetsim_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ceRNAnetsim_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ceRNAnetsim_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ceRNAnetsim_1.19.0.tgz
vignettes: vignettes/ceRNAnetsim/inst/doc/auxiliary_commands.html,
vignettes/ceRNAnetsim/inst/doc/basic_usage.html,
vignettes/ceRNAnetsim/inst/doc/convenient_iteration.html,
vignettes/ceRNAnetsim/inst/doc/mirtarbase_example.html
vignetteTitles: auxiliary_commands, basic_usage, A Suggestion: How to
Find the Appropriate Iteration for Simulation, An TCGA dataset
application
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ceRNAnetsim/inst/doc/auxiliary_commands.R,
vignettes/ceRNAnetsim/inst/doc/basic_usage.R,
vignettes/ceRNAnetsim/inst/doc/convenient_iteration.R,
vignettes/ceRNAnetsim/inst/doc/mirtarbase_example.R
dependencyCount: 70
Package: CeTF
Version: 1.19.0
Depends: R (>= 4.0)
Imports: circlize, ComplexHeatmap, clusterProfiler, DESeq2, dplyr,
GenomicTools.fileHandler, GGally, ggnetwork, ggplot2, ggpubr,
ggrepel, graphics, grid, igraph, Matrix, network, Rcpp, RCy3,
stats, SummarizedExperiment, S4Vectors, utils, methods
LinkingTo: Rcpp, RcppArmadillo
Suggests: airway, kableExtra, knitr, org.Hs.eg.db, rmarkdown, testthat
License: GPL-3
MD5sum: 51c52b25f8d3799f875c97d8490a5eea
NeedsCompilation: yes
Title: Coexpression for Transcription Factors using Regulatory Impact
Factors and Partial Correlation and Information Theory analysis
Description: This package provides the necessary functions for
performing the Partial Correlation coefficient with Information
Theory (PCIT) (Reverter and Chan 2008) and Regulatory Impact
Factors (RIF) (Reverter et al. 2010) algorithm. The PCIT
algorithm identifies meaningful correlations to define edges in
a weighted network and can be applied to any correlation-based
network including but not limited to gene co-expression
networks, while the RIF algorithm identify critical
Transcription Factors (TF) from gene expression data. These two
algorithms when combined provide a very relevant layer of
information for gene expression studies (Microarray, RNA-seq
and single-cell RNA-seq data).
biocViews: Sequencing, RNASeq, Microarray, GeneExpression,
Transcription, Normalization, DifferentialExpression,
SingleCell, Network, Regression, ChIPSeq, ImmunoOncology,
Coverage
Author: Carlos Alberto Oliveira de Biagi Junior [aut, cre], Ricardo
Perecin Nociti [aut], Breno Osvaldo Funicheli [aut], João Paulo
Bianchi Ximenez [ctb], PatrÃcia de Cássia Ruy [ctb], Marcelo
Gomes de Paula [ctb], Rafael dos Santos Bezerra [ctb], Wilson
Araújo da Silva Junior [aut, ths]
Maintainer: Carlos Alberto Oliveira de Biagi Junior
<cbiagijr@gmail.com>
SystemRequirements: libcurl4-openssl-dev, libxml2-dev, libssl-dev,
gfortran, build-essential, libz-dev, zlib1g-dev
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CeTF
git_branch: devel
git_last_commit: 759b9e0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CeTF_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CeTF_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CeTF_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CeTF_1.19.0.tgz
vignettes: vignettes/CeTF/inst/doc/CeTF.html
vignetteTitles: Analyzing Regulatory Impact Factors and Partial
Correlation and Information Theory
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CeTF/inst/doc/CeTF.R
dependencyCount: 208
Package: CexoR
Version: 1.45.0
Depends: R (>= 4.2.0), S4Vectors, IRanges
Imports: Rsamtools, GenomeInfoDb, GenomicRanges, rtracklayer, idr,
RColorBrewer, genomation
Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown
License: Artistic-2.0 | GPL-2 + file LICENSE
Archs: x64
MD5sum: 00e2267743777706e78422dc6b12ab36
NeedsCompilation: no
Title: An R package to uncover high-resolution protein-DNA interactions
in ChIP-exo replicates
Description: Strand specific peak-pair calling in ChIP-exo replicates.
The cumulative Skellam distribution function is used to detect
significant normalised count differences of opposed sign at
each DNA strand (peak-pairs). Then, irreproducible discovery
rate for overlapping peak-pairs across biological replicates is
computed.
biocViews: FunctionalGenomics, Sequencing, Coverage, ChIPSeq,
PeakDetection
Author: Pedro Madrigal [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1959-8199>)
Maintainer: Pedro Madrigal <pmadrigal@ebi.ac.uk>
git_url: https://git.bioconductor.org/packages/CexoR
git_branch: devel
git_last_commit: bc1c514
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CexoR_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CexoR_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CexoR_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CexoR_1.45.0.tgz
vignettes: vignettes/CexoR/inst/doc/CexoR.pdf
vignetteTitles: CexoR Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CexoR/inst/doc/CexoR.R
dependencyCount: 108
Package: CFAssay
Version: 1.41.0
Depends: R (>= 2.10.0)
License: LGPL
MD5sum: 2563526d2bc93f18eadda8a99991a572
NeedsCompilation: no
Title: Statistical analysis for the Colony Formation Assay
Description: The package provides functions for calculation of
linear-quadratic cell survival curves and for ANOVA of
experimental 2-way designs along with the colony formation
assay.
biocViews: CellBasedAssays, CellBiology, ImmunoOncology, Regression,
Survival
Author: Herbert Braselmann
Maintainer: Herbert Braselmann <hbraselmann@online.de>
git_url: https://git.bioconductor.org/packages/CFAssay
git_branch: devel
git_last_commit: c9b6501
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CFAssay_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CFAssay_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CFAssay_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CFAssay_1.41.0.tgz
vignettes: vignettes/CFAssay/inst/doc/cfassay.pdf
vignetteTitles: CFAssay
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CFAssay/inst/doc/cfassay.R
dependencyCount: 0
Package: cfdnakit
Version: 1.5.0
Depends: R (>= 4.3)
Imports: Biobase, dplyr, GenomicRanges, GenomeInfoDb, ggplot2, IRanges,
magrittr, PSCBS, QDNAseq, Rsamtools, utils, S4Vectors, stats,
rlang
Suggests: rmarkdown, knitr, roxygen2, BiocStyle
License: GPL-3
MD5sum: 4c99e84a52eb8c2e6c4fa21882f2d009
NeedsCompilation: no
Title: Fragmen-length analysis package from high-throughput sequencing
of cell-free DNA (cfDNA)
Description: This package provides basic functions for analyzing
shallow whole-genome sequencing (~0.3X or more) of cell-free
DNA (cfDNA). The package basically extracts the length of cfDNA
fragments and aids the vistualization of fragment-length
information. The package also extract fragment-length
information per non-overlapping fixed-sized bins and used it
for calculating ctDNA estimation score (CES).
biocViews: CopyNumberVariation, Sequencing, WholeGenome
Author: Pitithat Puranachot [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6786-9240>)
Maintainer: Pitithat Puranachot <pitithat@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/Pitithat-pu/cfdnakit/issues
git_url: https://git.bioconductor.org/packages/cfdnakit
git_branch: devel
git_last_commit: 889853b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cfdnakit_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cfdnakit_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cfdnakit_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cfdnakit_1.5.0.tgz
vignettes: vignettes/cfdnakit/inst/doc/cfdnakit-vignette.html
vignetteTitles: cfdnakit vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cfdnakit/inst/doc/cfdnakit-vignette.R
dependencyCount: 93
Package: cfDNAPro
Version: 1.13.0
Depends: R (>= 4.1.0), magrittr (>= 1.5.0)
Imports: tibble, GenomicAlignments, IRanges, plyranges, GenomeInfoDb,
GenomicRanges, BiocGenerics, stats, utils, dplyr (>= 0.8.3),
stringr (>= 1.4.0), quantmod (>= 0.4), ggplot2 (>= 3.2.1),
Rsamtools (>= 2.4.0), rlang (>= 0.4.0),
BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Hsapiens.NCBI.GRCh38
Suggests: scales, ggpubr, knitr (>= 1.23), rmarkdown (>= 1.14),
devtools (>= 2.3.0), BiocStyle, testthat
License: GPL-3
MD5sum: 91fabf11b956cfedf9eae31c9ad8e209
NeedsCompilation: no
Title: cfDNAPro extracts and Visualises biological features from whole
genome sequencing data of cell-free DNA
Description: cfDNA fragments carry important features for building
cancer sample classification ML models, such as fragment size,
and fragment end motif etc. Analyzing and visualizing fragment
size metrics, as well as other biological features in a
curated, standardized, scalable, well-documented, and
reproducible way might be time intensive. This package intends
to resolve these problems and simplify the process. It offers
two sets of functions for cfDNA feature characterization and
visualization.
biocViews: Visualization, Sequencing, WholeGenome
Author: Haichao Wang [aut, cre], Hui Zhao [ctb], Elkie Chan [ctb],
Christopher Smith [ctb], Tomer Kaplan [ctb], Florian Markowetz
[ctb], Nitzan Rosenfeld [ctb]
Maintainer: Haichao Wang <hw538@cam.ac.uk>
URL: https://github.com/hw538/cfDNAPro
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/cfDNAPro
git_branch: devel
git_last_commit: 384ebef
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cfDNAPro_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cfDNAPro_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cfDNAPro_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cfDNAPro_1.13.0.tgz
vignettes: vignettes/cfDNAPro/inst/doc/cfDNAPro.html
vignetteTitles: cfDNAPro Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cfDNAPro/inst/doc/cfDNAPro.R
dependencyCount: 97
Package: cfTools
Version: 1.7.0
Imports: Rcpp, utils, GenomicRanges, basilisk, R.utils, stats,
cfToolsData
LinkingTo: Rcpp, BH
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0)
License: file LICENSE
MD5sum: 6070453de266f0287ee98d983ef5e7fb
NeedsCompilation: yes
Title: Informatics Tools for Cell-Free DNA Study
Description: The cfTools R package provides methods for cell-free DNA
(cfDNA) methylation data analysis to facilitate cfDNA-based
studies. Given the methylation sequencing data of a cfDNA
sample, for each cancer marker or tissue marker, we deconvolve
the tumor-derived or tissue-specific reads from all reads
falling in the marker region. Our read-based deconvolution
algorithm exploits the pervasiveness of DNA methylation for
signal enhancement, therefore can sensitively identify a trace
amount of tumor-specific or tissue-specific cfDNA in plasma.
cfTools provides functions for (1) cancer detection:
sensitively detect tumor-derived cfDNA and estimate the
tumor-derived cfDNA fraction (tumor burden); (2) tissue
deconvolution: infer the tissue type composition and the cfDNA
fraction of multiple tissue types for a plasma cfDNA sample.
These functions can serve as foundations for more advanced
cfDNA-based studies, including cancer diagnosis and disease
monitoring.
biocViews: Software, BiomedicalInformatics, Epigenetics, Sequencing,
MethylSeq, DNAMethylation, DifferentialMethylation
Author: Ran Hu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0563-8957>), Mary Louisa Stackpole
[aut] (ORCID: <https://orcid.org/0000-0003-4432-6718>), Shuo Li
[aut] (ORCID: <https://orcid.org/0000-0002-1960-6016>),
Xianghong Jasmine Zhou [aut] (ORCID:
<https://orcid.org/0000-0002-4522-7490>), Wenyuan Li [aut]
(ORCID: <https://orcid.org/0000-0002-5029-8525>)
Maintainer: Ran Hu <huran@ucla.edu>
URL: https://github.com/jasminezhoulab/cfTools
VignetteBuilder: knitr
BugReports: https://github.com/jasminezhoulab/cfTools/issues
git_url: https://git.bioconductor.org/packages/cfTools
git_branch: devel
git_last_commit: f90509a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cfTools_1.7.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cfTools_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cfTools_1.7.0.tgz
vignettes: vignettes/cfTools/inst/doc/cfTools-vignette.html
vignetteTitles: cfTools-vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/cfTools/inst/doc/cfTools-vignette.R
dependencyCount: 84
Package: CGEN
Version: 3.43.0
Depends: R (>= 4.0), survival, mvtnorm
Imports: stats, graphics, utils, grDevices
Suggests: cluster
License: GPL-2 + file LICENSE
MD5sum: 1f63591c22936fbfbdbf7e798d17e6b1
NeedsCompilation: yes
Title: An R package for analysis of case-control studies in genetic
epidemiology
Description: This is a package for analysis of case-control data in
genetic epidemiology. It provides a set of statistical methods
for evaluating gene-environment (or gene-genes) interactions
under multiplicative and additive risk models, with or without
assuming gene-environment (or gene-gene) independence in the
underlying population.
biocViews: SNP, MultipleComparison, Clustering
Author: Samsiddhi Bhattacharjee [aut], Nilanjan Chatterjee [aut],
Summer Han [aut], Minsun Song [aut], William Wheeler [aut],
Matthieu de Rochemonteix [aut], Nilotpal Sanyal [aut], Justin
Lee [cre]
Maintainer: Justin Lee <jhylee@stanford.edu>
git_url: https://git.bioconductor.org/packages/CGEN
git_branch: devel
git_last_commit: a8ad56a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CGEN_3.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CGEN_3.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CGEN_3.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CGEN_3.43.0.tgz
vignettes: vignettes/CGEN/inst/doc/vignette_GxE.pdf,
vignettes/CGEN/inst/doc/vignette.pdf
vignetteTitles: CGEN Scan Vignette, CGEN Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CGEN/inst/doc/vignette_GxE.R,
vignettes/CGEN/inst/doc/vignette.R
dependencyCount: 11
Package: CGHbase
Version: 1.67.0
Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), marray
License: GPL
Archs: x64
MD5sum: b2643d0f0cf99876157e7d37430d055f
NeedsCompilation: no
Title: CGHbase: Base functions and classes for arrayCGH data analysis.
Description: Contains functions and classes that are needed by arrayCGH
packages.
biocViews: Infrastructure, Microarray, CopyNumberVariation
Author: Sjoerd Vosse, Mark van de Wiel
Maintainer: Mark van de Wiel <mark.vdwiel@vumc.nl>
URL: https://github.com/tgac-vumc/CGHbase
BugReports: https://github.com/tgac-vumc/CGHbase/issues
git_url: https://git.bioconductor.org/packages/CGHbase
git_branch: devel
git_last_commit: 2905ae7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CGHbase_1.67.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CGHbase_1.67.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CGHbase_1.67.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CGHbase_1.67.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: CGHcall, CGHnormaliter, CGHregions, GeneBreak
importsMe: CGHnormaliter, QDNAseq
dependencyCount: 11
Package: CGHcall
Version: 2.69.0
Depends: R (>= 2.0.0), impute(>= 1.8.0), DNAcopy (>= 1.6.0), methods,
Biobase, CGHbase (>= 1.15.1), snowfall
License: GPL (http://www.gnu.org/copyleft/gpl.html)
MD5sum: e5d716b07e22c726e0e1f9347358daf6
NeedsCompilation: no
Title: Calling aberrations for array CGH tumor profiles.
Description: Calls aberrations for array CGH data using a six state
mixture model as well as several biological concepts that are
ignored by existing algorithms. Visualization of profiles is
also provided.
biocViews: Microarray,Preprocessing,Visualization
Author: Mark van de Wiel, Sjoerd Vosse
Maintainer: Mark van de Wiel <mark.vdwiel@vumc.nl>
git_url: https://git.bioconductor.org/packages/CGHcall
git_branch: devel
git_last_commit: 3089e44
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CGHcall_2.69.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CGHcall_2.69.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CGHcall_2.69.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CGHcall_2.69.0.tgz
vignettes: vignettes/CGHcall/inst/doc/CGHcall.pdf
vignetteTitles: CGHcall
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CGHcall/inst/doc/CGHcall.R
dependsOnMe: CGHnormaliter, GeneBreak
importsMe: CGHnormaliter, QDNAseq
dependencyCount: 16
Package: cghMCR
Version: 1.65.0
Depends: methods, DNAcopy, CNTools, limma
Imports: BiocGenerics (>= 0.1.6), stats4
License: LGPL
MD5sum: 1f74e0e05ef75f8825996c329c3d3e51
NeedsCompilation: no
Title: Find chromosome regions showing common gains/losses
Description: This package provides functions to identify genomic
regions of interests based on segmented copy number data from
multiple samples.
biocViews: Microarray, CopyNumberVariation
Author: J. Zhang and B. Feng
Maintainer: J. Zhang <jzhang@jimmy.harvard.edu>
git_url: https://git.bioconductor.org/packages/cghMCR
git_branch: devel
git_last_commit: 7280ca7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cghMCR_1.65.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cghMCR_1.65.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cghMCR_1.65.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cghMCR_1.65.0.tgz
vignettes: vignettes/cghMCR/inst/doc/findMCR.pdf
vignetteTitles: cghMCR findMCR
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cghMCR/inst/doc/findMCR.R
dependencyCount: 60
Package: CGHnormaliter
Version: 1.61.0
Depends: CGHcall (>= 2.17.0), CGHbase (>= 1.15.0)
Imports: Biobase, CGHbase, CGHcall, methods, stats, utils
License: GPL (>= 3)
MD5sum: de5c2c8249ada1c4efc18e6e7640369f
NeedsCompilation: no
Title: Normalization of array CGH data with imbalanced aberrations.
Description: Normalization and centralization of array comparative
genomic hybridization (aCGH) data. The algorithm uses an
iterative procedure that effectively eliminates the influence
of imbalanced copy numbers. This leads to a more reliable
assessment of copy number alterations (CNAs).
biocViews: Microarray, Preprocessing
Author: Bart P.P. van Houte, Thomas W. Binsl, Hannes Hettling
Maintainer: Bart P.P. van Houte <bvhoute@few.vu.nl>
git_url: https://git.bioconductor.org/packages/CGHnormaliter
git_branch: devel
git_last_commit: bb90e82
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CGHnormaliter_1.61.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CGHnormaliter_1.61.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CGHnormaliter_1.61.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CGHnormaliter_1.61.0.tgz
vignettes: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.pdf
vignetteTitles: CGHnormaliter
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.R
dependencyCount: 17
Package: CGHregions
Version: 1.65.0
Depends: R (>= 2.0.0), methods, Biobase, CGHbase
License: GPL (http://www.gnu.org/copyleft/gpl.html)
MD5sum: 4e2a33ec6e572cf9f80f4a38008a52e1
NeedsCompilation: no
Title: Dimension Reduction for Array CGH Data with Minimal Information
Loss.
Description: Dimension Reduction for Array CGH Data with Minimal
Information Loss
biocViews: Microarray, CopyNumberVariation, Visualization
Author: Sjoerd Vosse & Mark van de Wiel
Maintainer: Sjoerd Vosse <info@vossewebdevelopment.nl>
git_url: https://git.bioconductor.org/packages/CGHregions
git_branch: devel
git_last_commit: 39db6ae
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CGHregions_1.65.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CGHregions_1.65.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CGHregions_1.65.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CGHregions_1.65.0.tgz
vignettes: vignettes/CGHregions/inst/doc/CGHregions.pdf
vignetteTitles: CGHcall
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CGHregions/inst/doc/CGHregions.R
suggestsMe: ADaCGH2
dependencyCount: 12
Package: ChAMP
Version: 2.37.0
Depends: R (>= 3.3), minfi, ChAMPdata (>= 2.6.0),DMRcate,
Illumina450ProbeVariants.db,IlluminaHumanMethylationEPICmanifest,
DT, RPMM
Imports:
prettydoc,Hmisc,globaltest,sva,illuminaio,rmarkdown,IlluminaHumanMethylation450kmanifest,IlluminaHumanMethylationEPICanno.ilm10b4.hg19,
limma, DNAcopy, preprocessCore,impute, marray, wateRmelon,
plyr,goseq,missMethyl,kpmt,ggplot2,
GenomicRanges,qvalue,isva,doParallel,bumphunter,quadprog,shiny,shinythemes,plotly
(>= 4.5.6),RColorBrewer,dendextend, matrixStats,combinat
Suggests: knitr,rmarkdown
License: GPL-3
MD5sum: c8f8d3363cccfce3befdbc7fc7e3ae9f
NeedsCompilation: no
Title: Chip Analysis Methylation Pipeline for Illumina
HumanMethylation450 and EPIC
Description: The package includes quality control metrics, a selection
of normalization methods and novel methods to identify
differentially methylated regions and to highlight copy number
alterations.
biocViews: Microarray, MethylationArray, Normalization, TwoChannel,
CopyNumber, DNAMethylation
Author: Yuan Tian [cre,aut], Tiffany Morris [ctb], Lee Stirling [ctb],
Andrew Feber [ctb], Andrew Teschendorff [ctb], Ankur
Chakravarthy [ctb]
Maintainer: Yuan Tian <champ450k@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ChAMP
git_branch: devel
git_last_commit: bebfd7d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ChAMP_2.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ChAMP_2.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ChAMP_2.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ChAMP_2.37.0.tgz
vignettes: vignettes/ChAMP/inst/doc/ChAMP.html
vignetteTitles: ChAMP: The Chip Analysis Methylation Pipeline
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChAMP/inst/doc/ChAMP.R
suggestsMe: GeoTcgaData
dependencyCount: 264
Package: ChemmineOB
Version: 1.45.0
Depends: R (>= 2.15.1), methods
Imports: BiocGenerics, zlibbioc, Rcpp (>= 0.11.0)
LinkingTo: BH, Rcpp, zlibbioc
Suggests: ChemmineR, BiocStyle, knitr, knitrBootstrap, BiocManager,
rmarkdown,RUnit,codetools
Enhances: ChemmineR (>= 2.13.0)
License: Artistic-2.0
MD5sum: e97b4cd1d3b03fdeef965200bf5b1671
NeedsCompilation: yes
Title: R interface to a subset of OpenBabel functionalities
Description: ChemmineOB provides an R interface to a subset of
cheminformatics functionalities implemented by the OpelBabel
C++ project. OpenBabel is an open source cheminformatics
toolbox that includes utilities for structure format
interconversions, descriptor calculations, compound similarity
searching and more. ChemineOB aims to make a subset of these
utilities available from within R. For non-developers,
ChemineOB is primarily intended to be used from ChemmineR as an
add-on package rather than used directly.
biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics,
Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays,
Visualization, Infrastructure, DataImport, Clustering,
Proteomics, Metabolomics
Author: Kevin Horan, Thomas Girke
Maintainer: Thomas Girke <thomas.girke@ucr.edu>
URL: https://github.com/girke-lab/ChemmineOB
SystemRequirements: OpenBabel (>= 3.0.0) with headers
(http://openbabel.org). Eigen3 with headers.
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ChemmineOB
git_branch: devel
git_last_commit: e269adf
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ChemmineOB_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ChemmineOB_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ChemmineOB_1.45.0.tgz
vignettes: vignettes/ChemmineOB/inst/doc/ChemmineOB.html
vignetteTitles: ChemmineOB
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: TRUE
Rfiles: vignettes/ChemmineOB/inst/doc/ChemmineOB.R
suggestsMe: MetMashR
dependencyCount: 9
Package: ChemmineR
Version: 3.59.0
Depends: R (>= 2.10.0), methods
Imports: rjson, graphics, stats, RCurl, DBI, digest, BiocGenerics, Rcpp
(>= 0.11.0), ggplot2,grid,gridExtra,
png,base64enc,DT,rsvg,jsonlite,stringi
LinkingTo: Rcpp, BH
Suggests: RSQLite, scatterplot3d, gplots, fmcsR, snow, RPostgreSQL,
BiocStyle, knitr, knitcitations, knitrBootstrap, ChemmineDrugs,
png,rmarkdown, BiocManager,bibtex,codetools
Enhances: ChemmineOB
License: Artistic-2.0
Archs: x64
MD5sum: 3133086ed179214c122bd7385810c172
NeedsCompilation: yes
Title: Cheminformatics Toolkit for R
Description: ChemmineR is a cheminformatics package for analyzing
drug-like small molecule data in R. Its latest version contains
functions for efficient processing of large numbers of
molecules, physicochemical/structural property predictions,
structural similarity searching, classification and clustering
of compound libraries with a wide spectrum of algorithms. In
addition, it offers visualization functions for compound
clustering results and chemical structures.
biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics,
Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays,
Visualization, Infrastructure, DataImport, Clustering,
Proteomics,Metabolomics
Author: Y. Eddie Cao, Kevin Horan, Tyler Backman, Thomas Girke
Maintainer: Thomas Girke <thomas.girke@ucr.edu>
URL: https://github.com/girke-lab/ChemmineR
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ChemmineR
git_branch: devel
git_last_commit: ee3bf9e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ChemmineR_3.59.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ChemmineR_3.59.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ChemmineR_3.59.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ChemmineR_3.59.0.tgz
vignettes: vignettes/ChemmineR/inst/doc/ChemmineR.html
vignetteTitles: ChemmineR
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChemmineR/inst/doc/ChemmineR.R
dependsOnMe: eiR, fmcsR, ChemmineDrugs
importsMe: bioassayR, CompoundDb, customCMPdb, eiR, fmcsR, MetID,
RMassBank, chemodiv, DrugSim2DR
suggestsMe: ChemmineOB, xnet
dependencyCount: 76
Package: CHETAH
Version: 1.23.0
Depends: R (>= 4.2), ggplot2, SingleCellExperiment
Imports: shiny, plotly, pheatmap, bioDist, dendextend, cowplot,
corrplot, grDevices, stats, graphics, reshape2, S4Vectors,
SummarizedExperiment
Suggests: knitr, rmarkdown, Matrix, testthat, vdiffr
License: file LICENSE
Archs: x64
MD5sum: f3db529da2e143cb8ea256bccca9345b
NeedsCompilation: no
Title: Fast and accurate scRNA-seq cell type identification
Description: CHETAH (CHaracterization of cEll Types Aided by
Hierarchical classification) is an accurate, selective and fast
scRNA-seq classifier. Classification is guided by a reference
dataset, preferentially also a scRNA-seq dataset. By
hierarchical clustering of the reference data, CHETAH creates a
classification tree that enables a step-wise, top-to-bottom
classification. Using a novel stopping rule, CHETAH classifies
the input cells to the cell types of the references and to
"intermediate types": more general classifications that ended
in an intermediate node of the tree.
biocViews: Classification, RNASeq, SingleCell, Clustering,
GeneExpression, ImmunoOncology
Author: Jurrian de Kanter [aut, cre], Philip Lijnzaad [aut]
Maintainer: Jurrian de Kanter <jurriandekanter@gmail.com>
URL: https://github.com/jdekanter/CHETAH
VignetteBuilder: knitr
BugReports: https://github.com/jdekanter/CHETAH
git_url: https://git.bioconductor.org/packages/CHETAH
git_branch: devel
git_last_commit: 2fa43fe
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CHETAH_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CHETAH_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CHETAH_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CHETAH_1.23.0.tgz
vignettes: vignettes/CHETAH/inst/doc/CHETAH_introduction.html
vignetteTitles: Introduction to the CHETAH package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CHETAH/inst/doc/CHETAH_introduction.R
suggestsMe: adverSCarial
dependencyCount: 113
Package: chevreulPlot
Version: 0.99.34
Depends: R (>= 4.5.0), SingleCellExperiment, chevreulProcess
Imports: base, cluster, clustree, ComplexHeatmap (>= 2.5.4), circlize,
dplyr, EnsDb.Hsapiens.v86, forcats, fs, ggplot2, grid, plotly,
purrr, S4Vectors, scales, scater, scran, scuttle, stats,
stringr, tibble, tidyr, utils, wiggleplotr (>= 1.13.1),
tidyselect, patchwork
Suggests: BiocStyle, knitr, RefManageR, rmarkdown, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: 196f83f8b6662d13cc274f08bfb0417e
NeedsCompilation: no
Title: Plots used in the chevreulPlot package
Description: Tools for plotting SingleCellExperiment objects in the
chevreulPlot package. Includes functions for analysis and
visualization of single-cell data. Supported by NIH grants
R01CA137124 and R01EY026661 to David Cobrinik.
biocViews: Coverage, RNASeq, Sequencing, Visualization, GeneExpression,
Transcription, SingleCell, Transcriptomics, Normalization,
Preprocessing, QualityControl, DimensionReduction, DataImport
Author: Kevin Stachelek [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2085-695X>), Bhavana Bhat [aut]
Maintainer: Kevin Stachelek <kevin.stachelek@gmail.com>
URL: https://github.com/whtns/chevreulPlot,
https://whtns.github.io/chevreulPlot/
VignetteBuilder: knitr
BugReports: https://github.com/cobriniklab/chevreulPlot/issues
git_url: https://git.bioconductor.org/packages/chevreulPlot
git_branch: devel
git_last_commit: 5e39a4d
git_last_commit_date: 2025-02-27
Date/Publication: 2025-02-27
source.ver: src/contrib/chevreulPlot_0.99.34.tar.gz
win.binary.ver: bin/windows/contrib/4.5/chevreulPlot_0.99.34.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/chevreulPlot_0.99.34.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/chevreulPlot_0.99.34.tgz
vignettes: vignettes/chevreulPlot/inst/doc/chevreulPlot.html,
vignettes/chevreulPlot/inst/doc/visualization.html
vignetteTitles: Preprocessing, Visualization
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/chevreulPlot/inst/doc/chevreulPlot.R,
vignettes/chevreulPlot/inst/doc/visualization.R
dependsOnMe: chevreulShiny
dependencyCount: 235
Package: chevreulProcess
Version: 0.99.27
Depends: R (>= 4.5.0), SingleCellExperiment, scater
Imports: batchelor, bluster, circlize, cluster, DBI, dplyr,
EnsDb.Hsapiens.v86, ensembldb, fs, GenomicFeatures, glue,
megadepth, methods, purrr, RSQLite, S4Vectors, scran, scuttle,
stringr, tibble, tidyr, tidyselect, utils
Suggests: BiocStyle, knitr, RefManageR, rmarkdown, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: 04a38fc560891ed8a007e5fa6a238624
NeedsCompilation: no
Title: Tools for managing SingleCellExperiment objects as projects
Description: Tools analyzing SingleCellExperiment objects as projects.
for input into the Chevreul app downstream. Includes functions
for analysis of single cell RNA sequencing data. Supported by
NIH grants R01CA137124 and R01EY026661 to David Cobrinik.
biocViews: Coverage, RNASeq, Sequencing, Visualization, GeneExpression,
Transcription, SingleCell, Transcriptomics, Normalization,
Preprocessing, QualityControl, DimensionReduction, DataImport
Author: Kevin Stachelek [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2085-695X>), Bhavana Bhat [aut]
Maintainer: Kevin Stachelek <kevin.stachelek@gmail.com>
URL: https://github.com/whtns/chevreulProcess,
https://whtns.github.io/chevreulProcess/
VignetteBuilder: knitr
BugReports: https://github.com/cobriniklab/chevreulProcess/issues
git_url: https://git.bioconductor.org/packages/chevreulProcess
git_branch: devel
git_last_commit: 0bcf855
git_last_commit_date: 2025-02-11
Date/Publication: 2025-02-12
source.ver: src/contrib/chevreulProcess_0.99.27.tar.gz
win.binary.ver: bin/windows/contrib/4.5/chevreulProcess_0.99.27.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/chevreulProcess_0.99.27.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/chevreulProcess_0.99.27.tgz
vignettes: vignettes/chevreulProcess/inst/doc/chevreulProcess.html
vignetteTitles: Preprocessing
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/chevreulProcess/inst/doc/chevreulProcess.R
dependsOnMe: chevreulPlot, chevreulShiny
dependencyCount: 196
Package: chevreulShiny
Version: 0.99.29
Depends: R (>= 4.5.0), SingleCellExperiment, shiny (>= 1.6.0),
shinydashboard, chevreulProcess, chevreulPlot
Imports: alabaster.base, clustree, ComplexHeatmap, DataEditR (>=
0.0.9), DBI, dplyr, DT, EnhancedVolcano, fs, future, ggplot2,
ggplotify, grDevices, methods, patchwork, plotly, purrr,
rappdirs, readr, RSQLite, S4Vectors, scales, shinyFiles,
shinyhelper, shinyjs, shinyWidgets, stats, stringr, tibble,
tidyr, tidyselect, utils, waiter, wiggleplotr
Suggests: BiocStyle, knitr, RefManageR, rmarkdown, testthat (>= 3.0.0),
EnsDb.Mmusculus.v79, EnsDb.Hsapiens.v86
License: MIT + file LICENSE
MD5sum: 47e9bb04f17a5db4d6df24729d8ea0e0
NeedsCompilation: no
Title: Tools for managing SingleCellExperiment objects as projects
Description: Tools for managing SingleCellExperiment objects as
projects. Includes functions for analysis and visualization of
single-cell data. Also included is a shiny app for
visualization of pre-processed scRNA data. Supported by NIH
grants R01CA137124 and R01EY026661 to David Cobrinik.
biocViews: Coverage, RNASeq, Sequencing, Visualization, GeneExpression,
Transcription, SingleCell, Transcriptomics, Normalization,
Preprocessing, QualityControl, DimensionReduction, DataImport
Author: Kevin Stachelek [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2085-695X>), Bhavana Bhat [aut]
Maintainer: Kevin Stachelek <kevin.stachelek@gmail.com>
URL: https://github.com/whtns/chevreulShiny,
https://whtns.github.io/chevreulShiny/
VignetteBuilder: knitr
BugReports: https://github.com/cobriniklab/chevreulShiny/issues
git_url: https://git.bioconductor.org/packages/chevreulShiny
git_branch: devel
git_last_commit: 1e6f9d7
git_last_commit_date: 2025-03-03
Date/Publication: 2025-03-04
source.ver: src/contrib/chevreulShiny_0.99.29.tar.gz
win.binary.ver: bin/windows/contrib/4.5/chevreulShiny_0.99.29.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/chevreulShiny_0.99.29.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/chevreulShiny_0.99.29.tgz
vignettes: vignettes/chevreulShiny/inst/doc/chevreulShiny.html,
vignettes/chevreulShiny/inst/doc/shiny_app.html
vignetteTitles: Preprocessing, Shiny App
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/chevreulShiny/inst/doc/chevreulShiny.R,
vignettes/chevreulShiny/inst/doc/shiny_app.R
dependencyCount: 269
Package: Chicago
Version: 1.35.0
Depends: R (>= 3.3.1), data.table
Imports: matrixStats, MASS, Hmisc, Delaporte, methods, grDevices,
graphics, stats, utils
Suggests: argparser, BiocStyle, knitr, rmarkdown, PCHiCdata, testthat,
Rsamtools, GenomicInteractions, GenomicRanges, IRanges,
AnnotationHub
License: Artistic-2.0
MD5sum: bfdeab4d702443640822233982b39ede
NeedsCompilation: no
Title: CHiCAGO: Capture Hi-C Analysis of Genomic Organization
Description: A pipeline for analysing Capture Hi-C data.
biocViews: Epigenetics, HiC, Sequencing, Software
Author: Jonathan Cairns, Paula Freire Pritchett, Steven Wingett,
Mikhail Spivakov
Maintainer: Mikhail Spivakov <mikhail.spivakov@lms.mrc.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Chicago
git_branch: devel
git_last_commit: eb0001c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Chicago_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Chicago_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Chicago_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Chicago_1.35.0.tgz
vignettes: vignettes/Chicago/inst/doc/Chicago.html
vignetteTitles: CHiCAGO Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Chicago/inst/doc/Chicago.R
dependsOnMe: PCHiCdata
dependencyCount: 76
Package: chihaya
Version: 1.7.0
Depends: DelayedArray
Imports: methods, Matrix, rhdf5, Rcpp, HDF5Array
LinkingTo: Rcpp, Rhdf5lib
Suggests: BiocGenerics, S4Vectors, BiocSingular, ResidualMatrix,
BiocStyle, testthat, rmarkdown, knitr
License: GPL-3
MD5sum: 159875e93617c25b9e318d0f057074f9
NeedsCompilation: yes
Title: Save Delayed Operations to a HDF5 File
Description: Saves the delayed operations of a DelayedArray to a HDF5
file. This enables efficient recovery of the DelayedArray's
contents in other languages and analysis frameworks.
biocViews: DataImport, DataRepresentation
Author: Aaron Lun [cre, aut]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://github.com/ArtifactDB/chihaya-R
SystemRequirements: C++17, GNU make
VignetteBuilder: knitr
BugReports: https://github.com/ArtifactDB/chihaya-R/issues
git_url: https://git.bioconductor.org/packages/chihaya
git_branch: devel
git_last_commit: e02b134
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/chihaya_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/chihaya_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/chihaya_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/chihaya_1.7.0.tgz
vignettes: vignettes/chihaya/inst/doc/userguide.html
vignetteTitles: User guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/chihaya/inst/doc/userguide.R
suggestsMe: alabaster.matrix
dependencyCount: 28
Package: chimeraviz
Version: 1.33.0
Depends: Biostrings, GenomicRanges, IRanges, Gviz, S4Vectors,
ensembldb, AnnotationFilter, data.table
Imports: methods, grid, Rsamtools, GenomeInfoDb, GenomicAlignments,
RColorBrewer, graphics, AnnotationDbi, RCircos, org.Hs.eg.db,
org.Mm.eg.db, rmarkdown, graph, Rgraphviz, DT, plyr, dplyr,
BiocStyle, checkmate, gtools, magick
Suggests: testthat, roxygen2, devtools, knitr, lintr
License: Artistic-2.0
Archs: x64
MD5sum: 30764fe009171508fd159bddae61af08
NeedsCompilation: no
Title: Visualization tools for gene fusions
Description: chimeraviz manages data from fusion gene finders and
provides useful visualization tools.
biocViews: Infrastructure, Alignment
Author: Stian LÃ¥gstad [aut, cre], Sen Zhao [ctb], Andreas M. Hoff
[ctb], Bjarne Johannessen [ctb], Ole Christian Lingjærde [ctb],
Rolf Skotheim [ctb]
Maintainer: Stian LÃ¥gstad <stianlagstad@gmail.com>
URL: https://github.com/stianlagstad/chimeraviz
SystemRequirements: bowtie, samtools, and egrep are required for some
functionalities
VignetteBuilder: knitr
BugReports: https://github.com/stianlagstad/chimeraviz/issues
git_url: https://git.bioconductor.org/packages/chimeraviz
git_branch: devel
git_last_commit: 3819c30
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/chimeraviz_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/chimeraviz_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/chimeraviz_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/chimeraviz_1.33.0.tgz
vignettes: vignettes/chimeraviz/inst/doc/chimeraviz-vignette.html
vignetteTitles: chimeraviz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/chimeraviz/inst/doc/chimeraviz-vignette.R
dependencyCount: 172
Package: ChIPanalyser
Version: 1.29.0
Depends: R (>= 3.5.0),GenomicRanges, Biostrings, BSgenome, RcppRoll,
parallel
Imports: methods, IRanges,
S4Vectors,grDevices,graphics,stats,utils,rtracklayer,ROCR,
BiocManager,GenomeInfoDb,RColorBrewer
Suggests: BSgenome.Dmelanogaster.UCSC.dm6,knitr, RUnit, BiocGenerics
License: GPL-3
MD5sum: e61b2fa82d18772668b0749e87e56fd2
NeedsCompilation: no
Title: ChIPanalyser: Predicting Transcription Factor Binding Sites
Description: ChIPanalyser is a package to predict and understand TF
binding by utilizing a statistical thermodynamic model. The
model incorporates 4 main factors thought to drive TF binding:
Chromatin State, Binding energy, Number of bound molecules and
a scaling factor modulating TF binding affinity. Taken
together, ChIPanalyser produces ChIP-like profiles that closely
mimic the patterns seens in real ChIP-seq data.
biocViews: Software, BiologicalQuestion, WorkflowStep, Transcription,
Sequencing, ChipOnChip, Coverage, Alignment, ChIPSeq,
SequenceMatching, DataImport ,PeakDetection
Author: Patrick C.N.Martin & Nicolae Radu Zabet
Maintainer: Patrick C.N. Martin <pcnmartin@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ChIPanalyser
git_branch: devel
git_last_commit: 0c32095
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ChIPanalyser_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ChIPanalyser_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ChIPanalyser_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ChIPanalyser_1.29.0.tgz
vignettes: vignettes/ChIPanalyser/inst/doc/ChIPanalyser.pdf,
vignettes/ChIPanalyser/inst/doc/GA_ChIPanalyser.pdf
vignetteTitles: ChIPanalyser User's Guide, ChIPanalyser User's Guide
for Genetic Algorithms
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChIPanalyser/inst/doc/ChIPanalyser.R,
vignettes/ChIPanalyser/inst/doc/GA_ChIPanalyser.R
dependencyCount: 68
Package: ChIPComp
Version: 1.37.0
Depends: R (>=
3.2.0),GenomicRanges,IRanges,rtracklayer,GenomeInfoDb,S4Vectors
Imports: Rsamtools,limma,BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Mmusculus.UCSC.mm9,BiocGenerics
Suggests: BiocStyle,RUnit
License: GPL
MD5sum: 793992737316c9e39138fab2afe340da
NeedsCompilation: yes
Title: Quantitative comparison of multiple ChIP-seq datasets
Description: ChIPComp detects differentially bound sharp binding sites
across multiple conditions considering matching control.
biocViews: ChIPSeq, Sequencing, Transcription, Genetics,Coverage,
MultipleComparison, DataImport
Author: Hao Wu, Li Chen, Zhaohui S.Qin, Chi Wang
Maintainer: Li Chen <li.chen@emory.edu>
git_url: https://git.bioconductor.org/packages/ChIPComp
git_branch: devel
git_last_commit: 3fbdb06
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ChIPComp_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ChIPComp_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ChIPComp_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ChIPComp_1.37.0.tgz
vignettes: vignettes/ChIPComp/inst/doc/ChIPComp.pdf
vignetteTitles: ChIPComp
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChIPComp/inst/doc/ChIPComp.R
dependencyCount: 63
Package: chipenrich
Version: 2.31.0
Depends: R (>= 3.4.0)
Imports: AnnotationDbi, BiocGenerics, chipenrich.data, GenomeInfoDb,
GenomicRanges, grDevices, grid, IRanges, lattice, latticeExtra,
MASS, methods, mgcv, org.Dm.eg.db, org.Dr.eg.db, org.Hs.eg.db,
org.Mm.eg.db, org.Rn.eg.db, parallel, plyr, rms, rtracklayer,
S4Vectors (>= 0.23.10), stats, stringr, utils
Suggests: BiocStyle, devtools, knitr, rmarkdown, roxygen2, testthat
License: GPL-3
Archs: x64
MD5sum: f794ecb39f9255bd06d657f5fb6750d1
NeedsCompilation: no
Title: Gene Set Enrichment For ChIP-seq Peak Data
Description: ChIP-Enrich and Poly-Enrich perform gene set enrichment
testing using peaks called from a ChIP-seq experiment. The
method empirically corrects for confounding factors such as the
length of genes, and the mappability of the sequence
surrounding genes.
biocViews: ImmunoOncology, ChIPSeq, Epigenetics, FunctionalGenomics,
GeneSetEnrichment, HistoneModification, Regression
Author: Ryan P. Welch [aut, cph], Chee Lee [aut], Raymond G. Cavalcante
[aut], Kai Wang [cre], Chris Lee [aut], Laura J. Scott [ths],
Maureen A. Sartor [ths]
Maintainer: Kai Wang <wangdaha@umich.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/chipenrich
git_branch: devel
git_last_commit: ae52d55
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/chipenrich_2.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/chipenrich_2.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/chipenrich_2.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/chipenrich_2.31.0.tgz
vignettes: vignettes/chipenrich/inst/doc/chipenrich-vignette.html
vignetteTitles: chipenrich_vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/chipenrich/inst/doc/chipenrich-vignette.R
dependencyCount: 159
Package: ChIPexoQual
Version: 1.31.0
Depends: R (>= 3.5.0), GenomicAlignments (>= 1.0.1)
Imports: methods, utils, GenomeInfoDb, stats, BiocParallel,
GenomicRanges (>= 1.14.4), ggplot2 (>= 1.0), data.table (>=
1.9.6), Rsamtools (>= 1.16.1), IRanges (>= 1.6), S4Vectors (>=
0.8), biovizBase (>= 1.18), broom (>= 0.4), RColorBrewer (>=
1.1), dplyr (>= 0.5), scales (>= 0.4.0), viridis (>= 0.3),
hexbin (>= 1.27), rmarkdown
Suggests: ChIPexoQualExample (>= 0.99.1), knitr (>= 1.10), BiocStyle,
gridExtra (>= 2.2), testthat
License: GPL (>=2)
MD5sum: 9c367d7079918d30c0005d89167c99a8
NeedsCompilation: no
Title: ChIPexoQual
Description: Package with a quality control pipeline for ChIP-exo/nexus
data.
biocViews: ChIPSeq, Sequencing, Transcription, Visualization,
QualityControl, Coverage, Alignment
Author: Rene Welch, Dongjun Chung, Sunduz Keles
Maintainer: Rene Welch <welch@stat.wisc.edu>
URL: https:github.com/keleslab/ChIPexoQual
VignetteBuilder: knitr
BugReports: https://github.com/welch16/ChIPexoQual/issues
git_url: https://git.bioconductor.org/packages/ChIPexoQual
git_branch: devel
git_last_commit: 7b88583
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ChIPexoQual_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ChIPexoQual_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ChIPexoQual_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ChIPexoQual_1.31.0.tgz
vignettes: vignettes/ChIPexoQual/inst/doc/vignette.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChIPexoQual/inst/doc/vignette.R
dependencyCount: 142
Package: ChIPpeakAnno
Version: 3.41.1
Depends: R (>= 3.5), methods, IRanges (>= 2.13.12), GenomicRanges (>=
1.31.8), S4Vectors (>= 0.17.25)
Imports: AnnotationDbi, BiocGenerics (>= 0.1.0), Biostrings (>=
2.47.6), pwalign, DBI, dplyr, GenomeInfoDb, GenomicAlignments,
GenomicFeatures, RBGL, Rsamtools, SummarizedExperiment,
VennDiagram, biomaRt, ggplot2, grDevices, graph, graphics,
grid, InteractionSet, KEGGREST, matrixStats, multtest,
regioneR, rtracklayer, stats, utils, universalmotif, stringr,
tibble, tidyr, data.table, scales, ensembldb
Suggests: AnnotationHub, BSgenome, limma, reactome.db, BiocManager,
BiocStyle, BSgenome.Ecoli.NCBI.20080805,
BSgenome.Hsapiens.UCSC.hg19, org.Ce.eg.db, org.Hs.eg.db,
BSgenome.Celegans.UCSC.ce10, BSgenome.Drerio.UCSC.danRer7,
BSgenome.Hsapiens.UCSC.hg38, DelayedArray, idr, seqinr,
EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v79, EnsDb.Hsapiens.v86,
TxDb.Hsapiens.UCSC.hg18.knownGene,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Hsapiens.UCSC.hg38.knownGene, GO.db, gplots, UpSetR,
knitr, rmarkdown, reshape2, testthat, trackViewer, motifStack,
OrganismDbi, BiocFileCache
License: GPL (>= 2)
MD5sum: 63cd7e2a3b1b6ae03eed95060da418f9
NeedsCompilation: no
Title: Batch annotation of the peaks identified from either ChIP-seq,
ChIP-chip experiments, or any experiments that result in large
number of genomic interval data
Description: The package encompasses a range of functions for
identifying the closest gene, exon, miRNA, or custom
features—such as highly conserved elements and user-supplied
transcription factor binding sites. Additionally, users can
retrieve sequences around the peaks and obtain enriched Gene
Ontology (GO) or Pathway terms. In version 2.0.5 and beyond,
new functionalities have been introduced. These include
features for identifying peaks associated with bi-directional
promoters along with summary statistics (peaksNearBDP),
summarizing motif occurrences in peaks
(summarizePatternInPeaks), and associating additional
identifiers with annotated peaks or enrichedGO (addGeneIDs).
The package integrates with various other packages such as
biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest, and
stat to enhance its analytical capabilities.
biocViews: Annotation, ChIPSeq, ChIPchip
Author: Lihua Julie Zhu, Jianhong Ou, Jun Yu, Kai Hu, Haibo Liu, Junhui
Li, Hervé Pagès, Claude Gazin, Nathan Lawson, Ryan Thompson,
Simon Lin, David Lapointe, Michael Green
Maintainer: Jianhong Ou <jou@morgridge.org>, Lihua Julie Zhu
<julie.zhu@umassmed.edu>, Kai Hu <kai.hu@umassmed.edu>, Junhui
Li <junhui.li@umassmed.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ChIPpeakAnno
git_branch: devel
git_last_commit: cfbf335
git_last_commit_date: 2025-01-03
Date/Publication: 2025-01-03
source.ver: src/contrib/ChIPpeakAnno_3.41.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ChIPpeakAnno_3.41.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ChIPpeakAnno_3.41.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ChIPpeakAnno_3.41.1.tgz
vignettes: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.html
vignetteTitles: ChIPpeakAnno: annotate,, visualize,, and compare peak
data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.R
dependsOnMe: REDseq, csawBook
importsMe: ATACseqQC, DEScan2, GUIDEseq
suggestsMe: hicVennDiagram, R3CPET, seqsetvis, chipseqDB
dependencyCount: 131
Package: ChIPQC
Version: 1.43.0
Depends: R (>= 3.5.0), ggplot2, DiffBind, GenomicRanges (>= 1.17.19),
BiocParallel
Imports: BiocGenerics (>= 0.11.3), S4Vectors (>= 0.1.0), IRanges (>=
1.99.17), Rsamtools (>= 1.17.28), GenomicAlignments (>=
1.1.16), chipseq (>= 1.12.0), gtools, methods, reshape2,
Nozzle.R1, Biobase, grDevices, stats, utils, GenomicFeatures,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Hsapiens.UCSC.hg18.knownGene,
TxDb.Mmusculus.UCSC.mm10.knownGene,
TxDb.Mmusculus.UCSC.mm9.knownGene,
TxDb.Rnorvegicus.UCSC.rn4.ensGene,
TxDb.Celegans.UCSC.ce6.ensGene,
TxDb.Dmelanogaster.UCSC.dm3.ensGene
Suggests: BiocStyle
License: GPL (>= 3)
MD5sum: e4b789f16f8ef5bc1eab4a88f48ed0ea
NeedsCompilation: no
Title: Quality metrics for ChIPseq data
Description: Quality metrics for ChIPseq data.
biocViews: Sequencing, ChIPSeq, QualityControl, ReportWriting
Author: Tom Carroll, Wei Liu, Ines de Santiago, Rory Stark
Maintainer: Tom Carroll <tc.infomatics@gmail.com>, Rory Stark
<bioconductor@starkhome.com>
git_url: https://git.bioconductor.org/packages/ChIPQC
git_branch: devel
git_last_commit: a673e8a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ChIPQC_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ChIPQC_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ChIPQC_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ChIPQC_1.43.0.tgz
vignettes: vignettes/ChIPQC/inst/doc/ChIPQC.pdf,
vignettes/ChIPQC/inst/doc/ChIPQCSampleReport.pdf
vignetteTitles: Assessing ChIP-seq sample quality with ChIPQC,
ChIPQCSampleReport.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChIPQC/inst/doc/ChIPQC.R
dependencyCount: 167
Package: ChIPseeker
Version: 1.43.0
Depends: R (>= 3.5.0)
Imports: AnnotationDbi, aplot, BiocGenerics, boot, dplyr, enrichplot,
IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, ggplot2,
gplots, graphics, grDevices, gtools, magrittr, methods,
plotrix, parallel, RColorBrewer, rlang, rtracklayer, S4Vectors,
scales, stats, tibble, TxDb.Hsapiens.UCSC.hg19.knownGene,
utils, yulab.utils (>= 0.1.5)
Suggests: clusterProfiler, ggimage, ggplotify, ggupset, ggVennDiagram,
knitr, org.Hs.eg.db, prettydoc, ReactomePA, rmarkdown, testthat
License: Artistic-2.0
MD5sum: f097ad1561aeb255ae9c69c8d723337e
NeedsCompilation: no
Title: ChIPseeker for ChIP peak Annotation, Comparison, and
Visualization
Description: This package implements functions to retrieve the nearest
genes around the peak, annotate genomic region of the peak,
statstical methods for estimate the significance of overlap
among ChIP peak data sets, and incorporate GEO database for
user to compare the own dataset with those deposited in
database. The comparison can be used to infer cooperative
regulation and thus can be used to generate hypotheses. Several
visualization functions are implemented to summarize the
coverage of the peak experiment, average profile and heatmap of
peaks binding to TSS regions, genomic annotation, distance to
TSS, and overlap of peaks or genes.
biocViews: Annotation, ChIPSeq, Software, Visualization,
MultipleComparison
Author: Guangchuang Yu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6485-8781>), Ming Li [ctb],
Qianwen Wang [ctb], Yun Yan [ctb], Hervé Pagès [ctb], Michael
Kluge [ctb], Thomas Schwarzl [ctb], Zhougeng Xu [ctb], Chun-Hui
Gao [ctb] (ORCID: <https://orcid.org/0000-0002-1445-7939>)
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
URL: https://yulab-smu.top/contribution-knowledge-mining/
VignetteBuilder: knitr
BugReports: https://github.com/YuLab-SMU/ChIPseeker/issues
git_url: https://git.bioconductor.org/packages/ChIPseeker
git_branch: devel
git_last_commit: 9e54d40
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ChIPseeker_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ChIPseeker_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ChIPseeker_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ChIPseeker_1.43.0.tgz
vignettes: vignettes/ChIPseeker/inst/doc/ChIPseeker.html
vignetteTitles: ChIPseeker: an R package for ChIP peak Annotation,,
Comparison and Visualization
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChIPseeker/inst/doc/ChIPseeker.R
importsMe: EpiCompare, esATAC, segmenter, seqArchRplus, cinaR
suggestsMe: GRaNIE, curatedAdipoChIP
dependencyCount: 148
Package: chipseq
Version: 1.57.0
Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.1.0), S4Vectors (>=
0.17.25), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8),
ShortRead
Imports: methods, stats, lattice, BiocGenerics, IRanges, GenomicRanges,
ShortRead
Suggests: BSgenome, GenomicFeatures, TxDb.Mmusculus.UCSC.mm9.knownGene,
BSgenome.Mmusculus.UCSC.mm9, BiocStyle, knitr
License: Artistic-2.0
MD5sum: 402e5d5b322250f32af792ee6ee59610
NeedsCompilation: yes
Title: chipseq: A package for analyzing chipseq data
Description: Tools for helping process short read data for chipseq
experiments.
biocViews: ChIPSeq, Sequencing, Coverage, QualityControl, DataImport
Author: Deepayan Sarkar [aut], Robert Gentleman [aut], Michael Lawrence
[aut], Zizhen Yao [aut], Oluwabukola Bamigbade [ctb] (Converted
vignette from Sweave to R Markdown / HTML.), Bioconductor
Package Maintainer [cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/chipseq
git_branch: devel
git_last_commit: 205b179
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/chipseq_1.57.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/chipseq_1.57.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/chipseq_1.57.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/chipseq_1.57.0.tgz
vignettes: vignettes/chipseq/inst/doc/Workflow.html
vignetteTitles: Some Basic Analysis of ChIP-Seq Data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/chipseq/inst/doc/Workflow.R
importsMe: ChIPQC, soGGi, transcriptR
dependencyCount: 63
Package: ChIPseqR
Version: 1.61.0
Depends: R (>= 2.10.0), methods, BiocGenerics, S4Vectors (>= 0.9.25)
Imports: Biostrings, fBasics, GenomicRanges, IRanges (>= 2.5.14),
graphics, grDevices, HilbertVis, ShortRead, stats, timsac,
utils
License: GPL (>= 2)
Archs: x64
MD5sum: 868c03512c0bb7ec22cba01829e214cb
NeedsCompilation: yes
Title: Identifying Protein Binding Sites in High-Throughput Sequencing
Data
Description: ChIPseqR identifies protein binding sites from ChIP-seq
and nucleosome positioning experiments. The model used to
describe binding events was developed to locate nucleosomes but
should flexible enough to handle other types of experiments as
well.
biocViews: ChIPSeq, Infrastructure
Author: Peter Humburg
Maintainer: Peter Humburg <peter.humburg@gmail.com>
git_url: https://git.bioconductor.org/packages/ChIPseqR
git_branch: devel
git_last_commit: 87a6195
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ChIPseqR_1.61.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ChIPseqR_1.61.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ChIPseqR_1.61.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ChIPseqR_1.61.0.tgz
vignettes: vignettes/ChIPseqR/inst/doc/Introduction.pdf
vignetteTitles: Introduction to ChIPseqR
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChIPseqR/inst/doc/Introduction.R
dependencyCount: 71
Package: ChIPsim
Version: 1.61.0
Depends: Biostrings (>= 2.29.2)
Imports: IRanges, XVector, Biostrings, ShortRead, graphics, methods,
stats, utils
Suggests: actuar, zoo
License: GPL (>= 2)
Archs: x64
MD5sum: 0d9c7db033dd65845d01914a8a18eb4d
NeedsCompilation: no
Title: Simulation of ChIP-seq experiments
Description: A general framework for the simulation of ChIP-seq data.
Although currently focused on nucleosome positioning the
package is designed to support different types of experiments.
biocViews: Infrastructure, ChIPSeq
Author: Peter Humburg
Maintainer: Peter Humburg <Peter.Humburg@gmail.com>
git_url: https://git.bioconductor.org/packages/ChIPsim
git_branch: devel
git_last_commit: 267ec77
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ChIPsim_1.61.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ChIPsim_1.61.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ChIPsim_1.61.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ChIPsim_1.61.0.tgz
vignettes: vignettes/ChIPsim/inst/doc/ChIPsimIntro.pdf
vignetteTitles: Simulating ChIP-seq experiments
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChIPsim/inst/doc/ChIPsimIntro.R
dependencyCount: 63
Package: ChIPXpress
Version: 1.51.0
Depends: R (>= 2.10), ChIPXpressData
Imports: Biobase, GEOquery, frma, affy, bigmemory, biganalytics
Suggests: mouse4302frmavecs, mouse4302.db, mouse4302cdf, RUnit,
BiocGenerics
License: GPL(>=2)
MD5sum: 8ae3ba698c40243f61d077e750666606
NeedsCompilation: no
Title: ChIPXpress: enhanced transcription factor target gene
identification from ChIP-seq and ChIP-chip data using publicly
available gene expression profiles
Description: ChIPXpress takes as input predicted TF bound genes from
ChIPx data and uses a corresponding database of gene expression
profiles downloaded from NCBI GEO to rank the TF bound targets
in order of which gene is most likely to be functional TF
target.
biocViews: ChIPchip, ChIPSeq
Author: George Wu
Maintainer: George Wu <georgetwu@gmail.com>
git_url: https://git.bioconductor.org/packages/ChIPXpress
git_branch: devel
git_last_commit: 498fd5f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ChIPXpress_1.51.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ChIPXpress_1.51.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ChIPXpress_1.51.0.tgz
vignettes: vignettes/ChIPXpress/inst/doc/ChIPXpress.pdf
vignetteTitles: ChIPXpress
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChIPXpress/inst/doc/ChIPXpress.R
dependencyCount: 108
Package: chopsticks
Version: 1.73.0
Imports: graphics, stats, utils, methods, survival
Suggests: hexbin
License: GPL-3
MD5sum: 82f057035f1f0859ae28234b717822fd
NeedsCompilation: yes
Title: The 'snp.matrix' and 'X.snp.matrix' Classes
Description: Implements classes and methods for large-scale SNP
association studies
biocViews: Microarray, SNPsAndGeneticVariability, SNP,
GeneticVariability
Author: Hin-Tak Leung <htl10@users.sourceforge.net>
Maintainer: Hin-Tak Leung <htl10@users.sourceforge.net>
URL: http://outmodedbonsai.sourceforge.net/
git_url: https://git.bioconductor.org/packages/chopsticks
git_branch: devel
git_last_commit: a2c9896
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/chopsticks_1.73.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/chopsticks_1.73.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/chopsticks_1.73.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/chopsticks_1.73.0.tgz
vignettes: vignettes/chopsticks/inst/doc/chopsticks-vignette.pdf
vignetteTitles: snpMatrix
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/chopsticks/inst/doc/chopsticks-vignette.R
dependencyCount: 10
Package: chromDraw
Version: 2.37.0
Depends: R (>= 3.0.0)
Imports: Rcpp (>= 0.11.1), GenomicRanges (>= 1.17.46)
LinkingTo: Rcpp
License: GPL-3
MD5sum: 5f93c5a09f5cd2abfb7d219e60b90bc9
NeedsCompilation: yes
Title: chromDraw is a R package for drawing the schemes of karyotypes
in the linear and circular fashion.
Description: ChromDraw is a R package for drawing the schemes of
karyotype(s) in the linear and circular fashion. It is possible
to visualized cytogenetic marsk on the chromosomes. This tool
has own input data format. Input data can be imported from the
GenomicRanges data structure. This package can visualized the
data in the BED file format. Here is requirement on to the
first nine fields of the BED format. Output files format are
*.eps and *.svg.
biocViews: Software
Author: Jan Janecka, Ing., Mgr. CEITEC Masaryk University
Maintainer: Jan Janecka <jan.janecka@ceitec.muni.cz>
URL: www.plantcytogenomics.org/chromDraw
SystemRequirements: Rtools (>= 3.1)
git_url: https://git.bioconductor.org/packages/chromDraw
git_branch: devel
git_last_commit: c40d500
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/chromDraw_2.37.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/chromDraw_2.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/chromDraw_2.37.0.tgz
vignettes: vignettes/chromDraw/inst/doc/chromDraw.pdf
vignetteTitles: chromDraw
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/chromDraw/inst/doc/chromDraw.R
dependencyCount: 24
Package: ChromHeatMap
Version: 1.61.0
Depends: R (>= 2.9.0), BiocGenerics (>= 0.3.2), annotate (>= 1.20.0),
AnnotationDbi (>= 1.4.0)
Imports: Biobase (>= 2.17.8), graphics, grDevices, methods, stats,
IRanges, rtracklayer, GenomicRanges
Suggests: ALL, hgu95av2.db
License: Artistic-2.0
Archs: x64
MD5sum: e312d4f15f5d76fe08b57ce786ebb15b
NeedsCompilation: no
Title: Heat map plotting by genome coordinate
Description: The ChromHeatMap package can be used to plot genome-wide
data (e.g. expression, CGH, SNP) along each strand of a given
chromosome as a heat map. The generated heat map can be used to
interactively identify probes and genes of interest.
biocViews: Visualization
Author: Tim F. Rayner
Maintainer: Tim F. Rayner <tfrayner@gmail.com>
git_url: https://git.bioconductor.org/packages/ChromHeatMap
git_branch: devel
git_last_commit: 31693e7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ChromHeatMap_1.61.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ChromHeatMap_1.61.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ChromHeatMap_1.61.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ChromHeatMap_1.61.0.tgz
vignettes: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.pdf
vignetteTitles: Plotting expression data with ChromHeatMap
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.R
dependencyCount: 78
Package: chromPlot
Version: 1.35.0
Depends: stats, utils, graphics, grDevices, datasets, base, biomaRt,
GenomicRanges, R (>= 3.1.0)
Suggests: qtl, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene
License: GPL (>= 2)
MD5sum: f26e33a1db8bda1836f0546bf2c16afa
NeedsCompilation: no
Title: Global visualization tool of genomic data
Description: Package designed to visualize genomic data along the
chromosomes, where the vertical chromosomes are sorted by
number, with sex chromosomes at the end.
biocViews: DataRepresentation, FunctionalGenomics, Genetics,
Sequencing, Annotation, Visualization
Author: Ricardo A. Verdugo and Karen Y. Orostica
Maintainer: Karen Y. Orostica <korostica09@gmail.com>
git_url: https://git.bioconductor.org/packages/chromPlot
git_branch: devel
git_last_commit: df675e7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/chromPlot_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/chromPlot_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/chromPlot_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/chromPlot_1.35.0.tgz
vignettes: vignettes/chromPlot/inst/doc/chromPlot.pdf
vignetteTitles: General Manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/chromPlot/inst/doc/chromPlot.R
dependencyCount: 71
Package: ChromSCape
Version: 1.17.0
Depends: R (>= 4.1)
Imports: shiny, colourpicker, shinyjs, rtracklayer, shinyFiles,
shinyhelper, shinyWidgets, shinydashboardPlus, shinycssloaders,
Matrix, plotly, shinydashboard, colorRamps, kableExtra,
viridis, batchelor, BiocParallel, parallel, Rsamtools, ggplot2,
ggrepel, gggenes, gridExtra, qualV, stringdist, stringr, fs,
qs, DT, scran, scater, ConsensusClusterPlus, Rtsne, dplyr,
tidyr, GenomicRanges, IRanges, irlba, rlist, umap, tibble,
methods, jsonlite, edgeR, stats, graphics, grDevices, utils,
S4Vectors, SingleCellExperiment, SummarizedExperiment, msigdbr,
forcats, Rcpp, coop, matrixTests, DelayedArray
LinkingTo: Rcpp
Suggests: testthat, knitr, markdown, rmarkdown, BiocStyle, Signac,
future, igraph, bluster, httr
License: GPL-3
MD5sum: 2109a7c4024b47af14005ee54029d091
NeedsCompilation: yes
Title: Analysis of single-cell epigenomics datasets with a Shiny App
Description: ChromSCape - Chromatin landscape profiling for Single
Cells - is a ready-to-launch user-friendly Shiny Application
for the analysis of single-cell epigenomics datasets
(scChIP-seq, scATAC-seq, scCUT&Tag, ...) from aligned data to
differential analysis & gene set enrichment analysis. It is
highly interactive, enables users to save their analysis and
covers a wide range of analytical steps: QC, preprocessing,
filtering, batch correction, dimensionality reduction,
vizualisation, clustering, differential analysis and gene set
analysis.
biocViews: ShinyApps, Software, SingleCell, ChIPSeq, ATACSeq,
MethylSeq, Classification, Clustering, Epigenetics,
PrincipalComponent, SingleCell, ATACSeq, ChIPSeq, Annotation,
BatchEffect, MultipleComparison, Normalization, Pathways,
Preprocessing, QualityControl, ReportWriting, Visualization,
GeneSetEnrichment, DifferentialPeakCalling
Author: Pacome Prompsy [aut, cre] (ORCID:
<https://orcid.org/0000-0003-4375-7583>), Celine Vallot [aut]
(ORCID: <https://orcid.org/0000-0003-1601-2359>)
Maintainer: Pacome Prompsy <pacome.prompsy@curie.fr>
URL: https://github.com/vallotlab/ChromSCape
VignetteBuilder: knitr
BugReports: https://github.com/vallotlab/ChromSCape/issues
git_url: https://git.bioconductor.org/packages/ChromSCape
git_branch: devel
git_last_commit: 237c895
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ChromSCape_1.17.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ChromSCape_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ChromSCape_1.17.0.tgz
vignettes: vignettes/ChromSCape/inst/doc/vignette.html
vignetteTitles: ChromSCape
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ChromSCape/inst/doc/vignette.R
dependencyCount: 212
Package: chromVAR
Version: 1.29.0
Depends: R (>= 3.5.0)
Imports: IRanges, GenomeInfoDb, GenomicRanges, ggplot2, nabor,
BiocParallel, BiocGenerics, Biostrings, TFBSTools, Rsamtools,
S4Vectors, methods, Rcpp, grid, plotly, shiny, miniUI, stats,
utils, graphics, DT, Rtsne, Matrix, SummarizedExperiment,
RColorBrewer, BSgenome
LinkingTo: Rcpp, RcppArmadillo
Suggests: JASPAR2016, BSgenome.Hsapiens.UCSC.hg19, readr, testthat,
knitr, rmarkdown, pheatmap, motifmatchr
License: MIT + file LICENSE
MD5sum: 5fd9aa5a2ca3308e476c46ce6f3e8127
NeedsCompilation: yes
Title: Chromatin Variation Across Regions
Description: Determine variation in chromatin accessibility across sets
of annotations or peaks. Designed primarily for single-cell or
sparse chromatin accessibility data, e.g. from scATAC-seq or
sparse bulk ATAC or DNAse-seq experiments.
biocViews: SingleCell, Sequencing, GeneRegulation, ImmunoOncology
Author: Alicia Schep [aut, cre], Jason Buenrostro [ctb], Caleb Lareau
[ctb], William Greenleaf [ths], Stanford University [cph]
Maintainer: Alicia Schep <aschep@gmail.com>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/chromVAR
git_branch: devel
git_last_commit: 261e3b9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/chromVAR_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/chromVAR_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/chromVAR_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/chromVAR_1.29.0.tgz
vignettes: vignettes/chromVAR/inst/doc/Introduction.html
vignetteTitles: Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/chromVAR/inst/doc/Introduction.R
suggestsMe: Signac
dependencyCount: 141
Package: CHRONOS
Version: 1.35.0
Depends: R (>= 3.5)
Imports: XML, RCurl, RBGL, parallel, foreach, doParallel, openxlsx,
igraph, circlize, graph, stats, utils, grDevices, graphics,
methods, biomaRt, rJava
Suggests: RUnit, BiocGenerics, knitr, rmarkdown
License: GPL-2
Archs: x64
MD5sum: 09dce8670ffea5e682a23cafa15e6b23
NeedsCompilation: no
Title: CHRONOS: A time-varying method for microRNA-mediated sub-pathway
enrichment analysis
Description: A package used for efficient unraveling of the inherent
dynamic properties of pathways. MicroRNA-mediated subpathway
topologies are extracted and evaluated by exploiting the
temporal transition and the fold change activity of the linked
genes/microRNAs.
biocViews: SystemsBiology, GraphAndNetwork, Pathways, KEGG
Author: Aristidis G. Vrahatis, Konstantina Dimitrakopoulou, Panos
Balomenos
Maintainer: Panos Balomenos <balomenos@upatras.gr>
SystemRequirements: Java version >= 1.7, Pandoc
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CHRONOS
git_branch: devel
git_last_commit: 630e48d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CHRONOS_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CHRONOS_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CHRONOS_1.35.0.tgz
vignettes: vignettes/CHRONOS/inst/doc/CHRONOS.pdf
vignetteTitles: CHRONOS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CHRONOS/inst/doc/CHRONOS.R
dependencyCount: 91
Package: cicero
Version: 1.25.0
Depends: R (>= 3.5.0), monocle, Gviz (>= 1.22.3)
Imports: assertthat (>= 0.2.0), Biobase (>= 2.37.2), BiocGenerics (>=
0.23.0), data.table (>= 1.10.4), dplyr (>= 0.7.4), FNN (>=
1.1), GenomicRanges (>= 1.30.3), ggplot2 (>= 2.2.1), glasso (>=
1.8), grDevices, igraph (>= 1.1.0), IRanges (>= 2.10.5), Matrix
(>= 1.2-12), methods, parallel, plyr (>= 1.8.4), reshape2 (>=
1.4.3), S4Vectors (>= 0.14.7), stats, stringi, stringr (>=
1.2.0), tibble (>= 1.4.2), tidyr, VGAM (>= 1.0-5), utils
Suggests: AnnotationDbi (>= 1.38.2), knitr, markdown, rmarkdown,
rtracklayer (>= 1.36.6), testthat, vdiffr (>= 0.2.3), covr
License: MIT + file LICENSE
MD5sum: 94a8f62d6981454412a47d08ff7b62de
NeedsCompilation: no
Title: Predict cis-co-accessibility from single-cell chromatin
accessibility data
Description: Cicero computes putative cis-regulatory maps from
single-cell chromatin accessibility data. It also extends
monocle 2 for use in chromatin accessibility data.
biocViews: Sequencing, Clustering, CellBasedAssays, ImmunoOncology,
GeneRegulation, GeneTarget, Epigenetics, ATACSeq, SingleCell
Author: Hannah Pliner [aut, cre], Cole Trapnell [aut]
Maintainer: Hannah Pliner <hpliner@uw.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/cicero
git_branch: devel
git_last_commit: ff77b2c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cicero_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cicero_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cicero_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cicero_1.25.0.tgz
vignettes: vignettes/cicero/inst/doc/website.html
vignetteTitles: Vignette from Cicero Website
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/cicero/inst/doc/website.R
dependencyCount: 182
Package: CIMICE
Version: 1.15.0
Imports: dplyr, ggplot2, glue, tidyr, igraph, networkD3, visNetwork,
ggcorrplot, purrr, ggraph, stats, utils, maftools, assertthat,
tidygraph, expm, Matrix
Suggests: BiocStyle, knitr, rmarkdown, testthat, webshot
License: Artistic-2.0
MD5sum: ae5498d2cf9b8186a417e1ba0260f1a9
NeedsCompilation: no
Title: CIMICE-R: (Markov) Chain Method to Inferr Cancer Evolution
Description: CIMICE is a tool in the field of tumor phylogenetics and
its goal is to build a Markov Chain (called Cancer Progression
Markov Chain, CPMC) in order to model tumor subtypes evolution.
The input of CIMICE is a Mutational Matrix, so a boolean matrix
representing altered genes in a collection of samples. These
samples are assumed to be obtained with single-cell DNA
analysis techniques and the tool is specifically written to use
the peculiarities of this data for the CMPC construction.
biocViews: Software, BiologicalQuestion, NetworkInference,
ResearchField, Phylogenetics, StatisticalMethod,
GraphAndNetwork, Technology, SingleCell
Author: Nicolò Rossi [aut, cre] (Lab. of Computational Biology and
Bioinformatics, Department of Mathematics, Computer Science and
Physics, University of Udine, ORCID:
<https://orcid.org/0000-0002-6353-7396>)
Maintainer: Nicolò Rossi <olocin.issor@gmail.com>
URL: https://github.com/redsnic/CIMICE
VignetteBuilder: knitr
BugReports: https://github.com/redsnic/CIMICE/issues
git_url: https://git.bioconductor.org/packages/CIMICE
git_branch: devel
git_last_commit: 93f6739
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CIMICE_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CIMICE_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CIMICE_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CIMICE_1.15.0.tgz
vignettes: vignettes/CIMICE/inst/doc/CIMICE_SHORT.html,
vignettes/CIMICE/inst/doc/CIMICER.html
vignetteTitles: Quick guide, CIMICE-R: (Markov) Chain Method to Infer
Cancer Evolution
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CIMICE/inst/doc/CIMICE_SHORT.R,
vignettes/CIMICE/inst/doc/CIMICER.R
dependencyCount: 94
Package: CINdex
Version: 1.35.0
Depends: R (>= 3.3), GenomicRanges
Imports: bitops,gplots,grDevices,som,
dplyr,gridExtra,png,stringr,S4Vectors, IRanges,
GenomeInfoDb,graphics, stats, utils
Suggests: knitr, testthat, ReactomePA, RUnit, BiocGenerics,
AnnotationHub, rtracklayer, pd.genomewidesnp.6, org.Hs.eg.db,
biovizBase, TxDb.Hsapiens.UCSC.hg18.knownGene, methods,
Biostrings,Homo.sapiens, R.utils
License: GPL (>= 2)
MD5sum: e5325bc0639413880ef4de1bbae3ee7a
NeedsCompilation: no
Title: Chromosome Instability Index
Description: The CINdex package addresses important area of
high-throughput genomic analysis. It allows the automated
processing and analysis of the experimental DNA copy number
data generated by Affymetrix SNP 6.0 arrays or similar high
throughput technologies. It calculates the chromosome
instability (CIN) index that allows to quantitatively
characterize genome-wide DNA copy number alterations as a
measure of chromosomal instability. This package calculates not
only overall genomic instability, but also instability in terms
of copy number gains and losses separately at the chromosome
and cytoband level.
biocViews: Software, CopyNumberVariation, GenomicVariation, aCGH,
Microarray, Genetics, Sequencing
Author: Lei Song [aut] (Innovation Center for Biomedical Informatics,
Georgetown University Medical Center), Krithika Bhuvaneshwar
[aut] (Innovation Center for Biomedical Informatics, Georgetown
University Medical Center), Yue Wang [aut, ths] (Virginia
Polytechnic Institute and State University), Yuanjian Feng
[aut] (Virginia Polytechnic Institute and State University),
Ie-Ming Shih [aut] (Johns Hopkins University School of
Medicine), Subha Madhavan [aut] (Innovation Center for
Biomedical Informatics, Georgetown University Medical Center),
Yuriy Gusev [aut, cre] (Innovation Center for Biomedical
Informatics, Georgetown University Medical Center)
Maintainer: Yuriy Gusev <yg63@georgetown.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CINdex
git_branch: devel
git_last_commit: 5f3d72a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CINdex_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CINdex_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CINdex_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CINdex_1.35.0.tgz
vignettes: vignettes/CINdex/inst/doc/CINdex.pdf,
vignettes/CINdex/inst/doc/HowToDownloadCytobandInfo.pdf,
vignettes/CINdex/inst/doc/PrepareInputData.pdf
vignetteTitles: CINdex Tutorial, How to obtain Cytoband and Stain
Information, Prepare input data for CINdex
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CINdex/inst/doc/CINdex.R,
vignettes/CINdex/inst/doc/HowToDownloadCytobandInfo.R,
vignettes/CINdex/inst/doc/PrepareInputData.R
dependencyCount: 50
Package: circRNAprofiler
Version: 1.21.0
Depends: R(>= 4.3.0)
Imports: dplyr, magrittr, readr, rtracklayer, stringr, stringi, DESeq2,
edgeR, GenomicRanges, IRanges, seqinr, R.utils, reshape2,
ggplot2, utils, rlang, S4Vectors, stats, GenomeInfoDb,
universalmotif, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19,
Biostrings, gwascat, BSgenome,
Suggests: testthat, knitr, roxygen2, rmarkdown, devtools, gridExtra,
ggpubr, VennDiagram, BSgenome.Mmusculus.UCSC.mm9,
BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10,
BiocManager,
License: GPL-3
MD5sum: 9492d376fa03e7d5f20d7063592ddf81
NeedsCompilation: no
Title: circRNAprofiler: An R-Based Computational Framework for the
Downstream Analysis of Circular RNAs
Description: R-based computational framework for a comprehensive in
silico analysis of circRNAs. This computational framework
allows to combine and analyze circRNAs previously detected by
multiple publicly available annotation-based circRNA detection
tools. It covers different aspects of circRNAs analysis from
differential expression analysis, evolutionary conservation,
biogenesis to functional analysis.
biocViews: Annotation, StructuralPrediction, FunctionalPrediction,
GenePrediction, GenomeAssembly, DifferentialExpression
Author: Simona Aufiero
Maintainer: Simona Aufiero <simo.aufiero@gmail.com>
URL: https://github.com/Aufiero/circRNAprofiler
VignetteBuilder: knitr
BugReports: https://github.com/Aufiero/circRNAprofiler/issues
git_url: https://git.bioconductor.org/packages/circRNAprofiler
git_branch: devel
git_last_commit: 1018c6c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/circRNAprofiler_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/circRNAprofiler_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/circRNAprofiler_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/circRNAprofiler_1.21.0.tgz
vignettes: vignettes/circRNAprofiler/inst/doc/circRNAprofiler.html
vignetteTitles: circRNAprofiler
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/circRNAprofiler/inst/doc/circRNAprofiler.R
dependencyCount: 144
Package: CircSeqAlignTk
Version: 1.9.0
Depends: R (>= 4.2)
Imports: stats, tools, utils, R.utils, methods, S4Vectors, rlang,
magrittr, dplyr, tidyr, ggplot2, BiocGenerics, Biostrings,
IRanges, ShortRead, Rsamtools, Rbowtie2, Rhisat2, shiny,
shinyFiles, shinyjs, plotly, parallel, htmltools
Suggests: knitr, rmarkdown, testthat, BiocStyle
License: MIT + file LICENSE
MD5sum: 4ff40c96a2e5525f1a7df9e4435d8133
NeedsCompilation: no
Title: A toolkit for end-to-end analysis of RNA-seq data for circular
genomes
Description: CircSeqAlignTk is designed for end-to-end RNA-Seq data
analysis of circular genome sequences, from alignment to
visualization. It mainly targets viroids which are composed of
246-401 nt circular RNAs. In addition, CircSeqAlignTk
implements a tidy interface to generate synthetic sequencing
data that mimic real RNA-Seq data, allowing developers to
evaluate the performance of alignment tools and workflows.
biocViews: Sequencing, SmallRNA, Alignment, Software
Author: Jianqiang Sun [cre, aut] (ORCID:
<https://orcid.org/0000-0002-3438-3199>), Xi Fu [ctb], Wei Cao
[ctb]
Maintainer: Jianqiang Sun <sun@biunit.dev>
URL: https://github.com/jsun/CircSeqAlignTk
VignetteBuilder: knitr
BugReports: https://github.com/jsun/CircSeqAlignTk/issues
git_url: https://git.bioconductor.org/packages/CircSeqAlignTk
git_branch: devel
git_last_commit: c5921ae
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CircSeqAlignTk_1.9.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CircSeqAlignTk_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CircSeqAlignTk_1.9.0.tgz
vignettes: vignettes/CircSeqAlignTk/inst/doc/CircSeqAlignTk.html
vignetteTitles: CircSeqAlignTk
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CircSeqAlignTk/inst/doc/CircSeqAlignTk.R
dependencyCount: 159
Package: cisPath
Version: 1.47.0
Depends: R (>= 2.10.0)
Imports: methods, utils
License: GPL (>= 3)
MD5sum: b629b7b1e2616a5f711e5e8f9432a791
NeedsCompilation: yes
Title: Visualization and management of the protein-protein interaction
networks.
Description: cisPath is an R package that uses web browsers to
visualize and manage protein-protein interaction networks.
biocViews: Proteomics
Author: Likun Wang <wanglk@hsc.pku.edu.cn>
Maintainer: Likun Wang <wanglk@hsc.pku.edu.cn>
git_url: https://git.bioconductor.org/packages/cisPath
git_branch: devel
git_last_commit: 7d721c8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cisPath_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cisPath_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cisPath_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cisPath_1.47.0.tgz
vignettes: vignettes/cisPath/inst/doc/cisPath.pdf
vignetteTitles: cisPath
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cisPath/inst/doc/cisPath.R
dependencyCount: 2
Package: CiteFuse
Version: 1.19.0
Depends: R (>= 4.0)
Imports: SingleCellExperiment (>= 1.8.0), SummarizedExperiment (>=
1.16.0), Matrix, mixtools, cowplot, ggplot2, gridExtra, grid,
dbscan, uwot, Rtsne, S4Vectors (>= 0.24.0), igraph, scales,
scran (>= 1.14.6), graphics, methods, stats, utils, reshape2,
ggridges, randomForest, pheatmap, ggraph, grDevices, rhdf5,
rlang, Rcpp, compositions
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, DT, mclust, scater, ExPosition, BiocStyle,
pkgdown
License: GPL-3
Archs: x64
MD5sum: 5b22f8d54bdca84382fa5ed9c93e3ebc
NeedsCompilation: yes
Title: CiteFuse: multi-modal analysis of CITE-seq data
Description: CiteFuse pacakage implements a suite of methods and tools
for CITE-seq data from pre-processing to integrative analytics,
including doublet detection, network-based modality
integration, cell type clustering, differential RNA and protein
expression analysis, ADT evaluation, ligand-receptor
interaction analysis, and interactive web-based visualisation
of the analyses.
biocViews: SingleCell, GeneExpression
Author: Yingxin Lin [aut, cre], Hani Kim [aut]
Maintainer: Yingxin Lin <yingxin.lin@sydney.edu.au>
VignetteBuilder: knitr
BugReports: https://github.com/SydneyBioX/CiteFuse/issues
git_url: https://git.bioconductor.org/packages/CiteFuse
git_branch: devel
git_last_commit: 699f98c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CiteFuse_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CiteFuse_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CiteFuse_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CiteFuse_1.19.0.tgz
vignettes: vignettes/CiteFuse/inst/doc/CiteFuse.html
vignetteTitles: CiteFuse
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CiteFuse/inst/doc/CiteFuse.R
suggestsMe: MuData
dependencyCount: 162
Package: ClassifyR
Version: 3.11.11
Depends: R (>= 4.1.0), generics, methods, S4Vectors,
MultiAssayExperiment, BiocParallel, survival
Imports: grid, genefilter, utils, dplyr, tidyr, rlang, ranger, ggplot2
(>= 3.0.0), ggpubr, reshape2, ggupset, broom, dcanr
Suggests: limma, edgeR, car, Rmixmod, gridExtra (>= 2.0.0), cowplot,
BiocStyle, pamr, PoiClaClu, knitr, htmltools, gtable, scales,
e1071, rmarkdown, IRanges, robustbase, glmnet, class,
randomForestSRC, MatrixModels, xgboost, data.tree, ggnewscale,
TOP, BiocNeighbors
License: GPL-3
Archs: x64
MD5sum: ed0471bea487867043219c1ac2d7adcc
NeedsCompilation: yes
Title: A framework for cross-validated classification problems, with
applications to differential variability and differential
distribution testing
Description: The software formalises a framework for classification and
survival model evaluation in R. There are four stages; Data
transformation, feature selection, model training, and
prediction. The requirements of variable types and variable
order are fixed, but specialised variables for functions can
also be provided. The framework is wrapped in a driver loop
that reproducibly carries out a number of cross-validation
schemes. Functions for differential mean, differential
variability, and differential distribution are included.
Additional functions may be developed by the user, by creating
an interface to the framework.
biocViews: Classification, Survival
Author: Dario Strbenac [aut, cre], Ellis Patrick [aut], Sourish Iyengar
[aut], Harry Robertson [aut], Andy Tran [aut], John Ormerod
[aut], Graham Mann [aut], Jean Yang [aut]
Maintainer: Dario Strbenac <dario.strbenac@sydney.edu.au>
URL: https://sydneybiox.github.io/ClassifyR/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ClassifyR
git_branch: devel
git_last_commit: 3dbcf3b
git_last_commit_date: 2025-03-24
Date/Publication: 2025-03-25
source.ver: src/contrib/ClassifyR_3.11.11.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ClassifyR_3.11.11.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ClassifyR_3.11.11.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ClassifyR_3.11.11.tgz
vignettes: vignettes/ClassifyR/inst/doc/ClassifyR.html,
vignettes/ClassifyR/inst/doc/DevelopersGuide.html
vignetteTitles: An Introduction to the ClassifyR Package, Developer's
Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ClassifyR/inst/doc/ClassifyR.R,
vignettes/ClassifyR/inst/doc/DevelopersGuide.R
importsMe: spicyR, TOP
suggestsMe: scFeatures, Statial
dependencyCount: 145
Package: cleanUpdTSeq
Version: 1.45.1
Depends: R (>= 3.5.0), BSgenome.Drerio.UCSC.danRer7, methods
Imports: BSgenome, GenomicRanges, seqinr, e1071, Biostrings,
GenomeInfoDb, IRanges, utils, stringr, stats, S4Vectors
Suggests: BiocStyle, rmarkdown, knitr, RUnit, BiocGenerics (>= 0.1.0)
License: GPL-2
Archs: x64
MD5sum: 90e339d2ea59fa50703079b8c4a62c0f
NeedsCompilation: no
Title: cleanUpdTSeq cleans up artifacts from polyadenylation sites from
oligo(dT)-mediated 3' end RNA sequending data
Description: This package implements a Naive Bayes classifier for
accurately differentiating true polyadenylation sites (pA
sites) from oligo(dT)-mediated 3' end sequencing such as
PAS-Seq, PolyA-Seq and RNA-Seq by filtering out false
polyadenylation sites, mainly due to oligo(dT)-mediated
internal priming during reverse transcription. The classifer is
highly accurate and outperforms other heuristic methods.
biocViews: Sequencing, 3' end sequencing, polyadenylation site,
internal priming
Author: Sarah Sheppard, Haibo Liu, Jianhong Ou, Nathan Lawson, Lihua
Julie Zhu
Maintainer: Jianhong Ou <jou@morgridge.org>; Lihua Julie Zhu
<Julie.Zhu@umassmed.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/cleanUpdTSeq
git_branch: devel
git_last_commit: 0fa892c
git_last_commit_date: 2025-01-03
Date/Publication: 2025-01-03
source.ver: src/contrib/cleanUpdTSeq_1.45.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cleanUpdTSeq_1.45.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cleanUpdTSeq_1.45.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cleanUpdTSeq_1.45.1.tgz
vignettes: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.html
vignetteTitles: cleanUpdTSeq Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.R
dependencyCount: 80
Package: CleanUpRNAseq
Version: 1.1.1
Depends: R (>= 4.4.0)
Imports: AnnotationFilter, BiocGenerics, Biostrings, BSgenome, DESeq2,
edgeR, ensembldb, GenomeInfoDb, GenomicRanges, ggplot2,
ggrepel, graphics, grDevices, KernSmooth, limma, methods,
pheatmap, qsmooth, R6, RColorBrewer, Rsamtools, Rsubread,
reshape2, SummarizedExperiment, stats, tximport, utils
Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg38, EnsDb.Hsapiens.v86,
ggplotify, knitr, patchwork, R.utils, rmarkdown, testthat (>=
3.0.0)
License: GPL-3
MD5sum: bb6113a1c741d48f2a089a1cf18b0fec
NeedsCompilation: no
Title: Detect and Correct Genomic DNA Contamination in RNA-seq Data
Description: RNA-seq data generated by some library preparation
methods, such as rRNA-depletion-based method and the SMART-seq
method, might be contaminated by genomic DNA (gDNA), if DNase I
disgestion is not performed properly during RNA preparation.
CleanUpRNAseq is developed to check if RNA-seq data is suffered
from gDNA contamination. If so, it can perform correction for
gDNA contamination and reduce false discovery rate of
differentially expressed genes.
biocViews: QualityControl, Sequencing, GeneExpression
Author: Haibo Liu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4213-2883>), Kevin O'Connor [ctb],
Michelle Kelliher [ctb], Lihua Julie Zhu [aut], Kai Hu [aut]
Maintainer: Haibo Liu <haibo.liu@umassmed.edu>
VignetteBuilder: knitr
BugReports: https://github.com/haibol2016/CleanUpRNAseq/issues
git_url: https://git.bioconductor.org/packages/CleanUpRNAseq
git_branch: devel
git_last_commit: dac4dc8
git_last_commit_date: 2024-11-19
Date/Publication: 2024-11-19
source.ver: src/contrib/CleanUpRNAseq_1.1.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CleanUpRNAseq_1.1.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CleanUpRNAseq_1.1.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CleanUpRNAseq_1.1.1.tgz
vignettes: vignettes/CleanUpRNAseq/inst/doc/CleanUpRNAseq.html
vignetteTitles: CleanUpRNAseq: detecting and correcting for DNA
contamination\nin RNA-seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CleanUpRNAseq/inst/doc/CleanUpRNAseq.R
dependencyCount: 153
Package: cleaver
Version: 1.45.0
Depends: R (>= 3.0.0), methods, Biostrings (>= 1.29.8)
Imports: S4Vectors, IRanges
Suggests: testthat (>= 0.8), knitr, BiocStyle (>= 0.0.14), rmarkdown,
BRAIN, UniProt.ws (>= 2.36.5)
License: GPL (>= 3)
MD5sum: 86f1c339416015f82933260af9ea8cd7
NeedsCompilation: no
Title: Cleavage of Polypeptide Sequences
Description: In-silico cleavage of polypeptide sequences. The cleavage
rules are taken from:
http://web.expasy.org/peptide_cutter/peptidecutter_enzymes.html
biocViews: Proteomics
Author: Sebastian Gibb [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7406-4443>)
Maintainer: Sebastian Gibb <mail@sebastiangibb.de>
URL: https://github.com/sgibb/cleaver/
VignetteBuilder: knitr
BugReports: https://github.com/sgibb/cleaver/issues/
git_url: https://git.bioconductor.org/packages/cleaver
git_branch: devel
git_last_commit: 739ea76
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cleaver_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cleaver_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cleaver_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cleaver_1.45.0.tgz
vignettes: vignettes/cleaver/inst/doc/cleaver.html
vignetteTitles: In-silico cleavage of polypeptides
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cleaver/inst/doc/cleaver.R
importsMe: ProteoDisco, synapter
suggestsMe: RforProteomics
dependencyCount: 25
Package: clevRvis
Version: 1.7.0
Imports: shiny, ggraph, igraph, ggiraph, cowplot, htmlwidgets, readxl,
dplyr, readr, purrr, tibble, patchwork, R.utils, shinyWidgets,
colorspace, shinyhelper, shinycssloaders, ggnewscale,
shinydashboard, DT, colourpicker, grDevices, methods, utils,
stats, ggplot2, magrittr, tools
Suggests: knitr, rmarkdown, BiocStyle
License: LGPL-3
MD5sum: e53a540cbd6fff8f9c3cadf60d0d9de8
NeedsCompilation: no
Title: Visualization Techniques for Clonal Evolution
Description: clevRvis provides a set of visualization techniques for
clonal evolution. These include shark plots, dolphin plots and
plaice plots. Algorithms for time point interpolation as well
as therapy effect estimation are provided. Phylogeny-aware
color coding is implemented. A shiny-app for generating plots
interactively is additionally provided.
biocViews: Software, ShinyApps, Visualization
Author: Sarah Sandmann [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5011-0641>)
Maintainer: Sarah Sandmann <sarah.sandmann@uni-muenster.de>
URL: https://github.com/sandmanns/clevRvis
VignetteBuilder: knitr
BugReports: https://github.com/sandmanns/clevRvis/issues
git_url: https://git.bioconductor.org/packages/clevRvis
git_branch: devel
git_last_commit: 6e1b698
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/clevRvis_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/clevRvis_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/clevRvis_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/clevRvis_1.7.0.tgz
vignettes: vignettes/clevRvis/inst/doc/clevRvis.html
vignetteTitles: ClEvR Viz vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/clevRvis/inst/doc/clevRvis.R
dependencyCount: 120
Package: clippda
Version: 1.57.0
Depends: R (>= 2.13.1),limma, statmod, rgl, lattice, scatterplot3d,
graphics, grDevices, stats, utils, Biobase, tools, methods
License: GPL (>=2)
MD5sum: 15f983522219f6ebe2ac78f8e3c9050c
NeedsCompilation: no
Title: A package for the clinical proteomic profiling data analysis
Description: Methods for the nalysis of data from clinical proteomic
profiling studies. The focus is on the studies of human
subjects, which are often observational case-control by design
and have technical replicates. A method for sample size
determination for planning these studies is proposed. It
incorporates routines for adjusting for the expected
heterogeneities and imbalances in the data and the
within-sample replicate correlations.
biocViews: Proteomics, OneChannel, Preprocessing,
DifferentialExpression, MultipleComparison
Author: Stephen Nyangoma
Maintainer: Stephen Nyangoma <s.o.nyangoma@bham.ac.uk>
URL: http://www.cancerstudies.bham.ac.uk/crctu/CLIPPDA.shtml
git_url: https://git.bioconductor.org/packages/clippda
git_branch: devel
git_last_commit: d7fcfbb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/clippda_1.57.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/clippda_1.57.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/clippda_1.57.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/clippda_1.57.0.tgz
vignettes: vignettes/clippda/inst/doc/clippda.pdf
vignetteTitles: Sample Size Calculation
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/clippda/inst/doc/clippda.R
dependencyCount: 43
Package: clipper
Version: 1.47.0
Depends: R (>= 2.15.0), Matrix, graph
Imports: methods, Biobase, Rcpp, igraph, gRbase (>= 1.6.6), qpgraph,
KEGGgraph, corpcor
Suggests: RUnit, BiocGenerics, graphite, ALL, hgu95av2.db, MASS,
BiocStyle
Enhances: RCy3
License: AGPL-3
MD5sum: f07dfbf2bc13bf336e50520244f65faa
NeedsCompilation: no
Title: Gene Set Analysis Exploiting Pathway Topology
Description: Implements topological gene set analysis using a two-step
empirical approach. It exploits graph decomposition theory to
create a junction tree and reconstruct the most relevant signal
path. In the first step clipper selects significant pathways
according to statistical tests on the means and the
concentration matrices of the graphs derived from pathway
topologies. Then, it "clips" the whole pathway identifying the
signal paths having the greatest association with a specific
phenotype.
Author: Paolo Martini <paolo.cavei@gmail.com>, Gabriele Sales
<gabriele.sales@unipd.it>, Chiara Romualdi
<chiara.romualdi@unipd.it>
Maintainer: Paolo Martini <paolo.cavei@gmail.com>
git_url: https://git.bioconductor.org/packages/clipper
git_branch: devel
git_last_commit: 623d9a9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/clipper_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/clipper_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/clipper_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/clipper_1.47.0.tgz
vignettes: vignettes/clipper/inst/doc/clipper.pdf
vignetteTitles: clipper
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/clipper/inst/doc/clipper.R
dependencyCount: 92
Package: cliProfiler
Version: 1.13.0
Depends: S4Vectors, methods, R (>= 4.1)
Imports: dplyr, rtracklayer, GenomicRanges, ggplot2, BSgenome,
Biostrings, utils
Suggests: knitr, rmarkdown, bookdown, testthat, BiocStyle,
BSgenome.Mmusculus.UCSC.mm10
License: Artistic-2.0
MD5sum: aa7249aefa547b15680a24574a8c16eb
NeedsCompilation: no
Title: A package for the CLIP data visualization
Description: An easy and fast way to visualize and profile the
high-throughput IP data. This package generates the meta gene
profile and other profiles. These profiles could provide
valuable information for understanding the IP experiment
results.
biocViews: Sequencing, ChIPSeq, Visualization, Epigenetics, Genetics
Author: You Zhou [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1755-9073>), Kathi Zarnack [aut]
(ORCID: <https://orcid.org/0000-0003-3527-3378>)
Maintainer: You Zhou <youzhoulearning@gmail.com>
URL: https://github.com/Codezy99/cliProfiler
VignetteBuilder: knitr
BugReports: https://github.com/Codezy99/cliProfiler/issues
git_url: https://git.bioconductor.org/packages/cliProfiler
git_branch: devel
git_last_commit: bbef029
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cliProfiler_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cliProfiler_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cliProfiler_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cliProfiler_1.13.0.tgz
vignettes: vignettes/cliProfiler/inst/doc/cliProfilerIntroduction.html
vignetteTitles: cliProfiler Vignettes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cliProfiler/inst/doc/cliProfilerIntroduction.R
dependencyCount: 87
Package: cliqueMS
Version: 1.21.0
Depends: R (>= 4.3.0)
Imports: Rcpp (>= 0.12.15), xcms(>= 3.0.0), MSnbase, igraph, coop,
slam, matrixStats, methods
LinkingTo: Rcpp, BH, RcppArmadillo
Suggests: BiocParallel, knitr, rmarkdown, testthat, CAMERA
License: GPL (>= 2)
MD5sum: e9f8a1bad3fc05ff19669d3edcdf075b
NeedsCompilation: yes
Title: Annotation of Isotopes, Adducts and Fragmentation Adducts for
in-Source LC/MS Metabolomics Data
Description: Annotates data from liquid chromatography coupled to mass
spectrometry (LC/MS) metabolomics experiments. Based on a
network algorithm (O.Senan, A. Aguilar- Mogas, M. Navarro, O.
Yanes, R.Guimerà and M. Sales-Pardo, Bioinformatics, 35(20),
2019), 'CliqueMS' builds a weighted similarity network where
nodes are features and edges are weighted according to the
similarity of this features. Then it searches for the most
plausible division of the similarity network into cliques
(fully connected components). Finally it annotates metabolites
within each clique, obtaining for each annotated metabolite the
neutral mass and their features, corresponding to isotopes,
ionization adducts and fragmentation adducts of that
metabolite.
biocViews: Metabolomics, MassSpectrometry, Network, NetworkInference
Author: Oriol Senan Campos [aut, cre], Antoni Aguilar-Mogas [aut],
Jordi Capellades [aut], Miriam Navarro [aut], Oscar Yanes
[aut], Roger Guimera [aut], Marta Sales-Pardo [aut]
Maintainer: Oriol Senan Campos <oriol.senan@praenoscere.com>
URL: http://cliquems.seeslab.net
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/osenan/cliqueMS/issues
git_url: https://git.bioconductor.org/packages/cliqueMS
git_branch: devel
git_last_commit: 6b0f4ec
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cliqueMS_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cliqueMS_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cliqueMS_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cliqueMS_1.21.0.tgz
vignettes: vignettes/cliqueMS/inst/doc/annotate_features.html
vignetteTitles: Annotating LC/MS data with cliqueMS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cliqueMS/inst/doc/annotate_features.R
dependencyCount: 149
Package: Clomial
Version: 1.43.0
Depends: R (>= 2.10), matrixStats
Imports: methods, permute
License: GPL (>= 2)
Archs: x64
MD5sum: cdf58efe17351d749ff14391293cf914
NeedsCompilation: no
Title: Infers clonal composition of a tumor
Description: Clomial fits binomial distributions to counts obtained
from Next Gen Sequencing data of multiple samples of the same
tumor. The trained parameters can be interpreted to infer the
clonal structure of the tumor.
biocViews: Genetics, GeneticVariability, Sequencing, Clustering,
MultipleComparison, Bayesian, DNASeq, ExomeSeq,
TargetedResequencing, ImmunoOncology
Author: Habil Zare and Alex Hu
Maintainer: Habil Zare <zare@u.washington.edu>
git_url: https://git.bioconductor.org/packages/Clomial
git_branch: devel
git_last_commit: ed32b0f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Clomial_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Clomial_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Clomial_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Clomial_1.43.0.tgz
vignettes:
vignettes/Clomial/inst/doc/Clonal_decomposition_by_Clomial.pdf
vignetteTitles: A likelihood maximization approach to infer the clonal
structure of a cancer using multiple tumor samples
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Clomial/inst/doc/Clonal_decomposition_by_Clomial.R
dependencyCount: 4
Package: clst
Version: 1.55.0
Depends: R (>= 2.10)
Imports: ROC, lattice
Suggests: RUnit
License: GPL-3
MD5sum: 365b3194463e2845e6930fc6b47e4cb5
NeedsCompilation: no
Title: Classification by local similarity threshold
Description: Package for modified nearest-neighbor classification based
on calculation of a similarity threshold distinguishing
within-group from between-group comparisons.
biocViews: Classification
Author: Noah Hoffman
Maintainer: Noah Hoffman <ngh2@uw.edu>
git_url: https://git.bioconductor.org/packages/clst
git_branch: devel
git_last_commit: b7497fc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/clst_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/clst_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/clst_1.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/clst_1.55.0.tgz
vignettes: vignettes/clst/inst/doc/clstDemo.pdf
vignetteTitles: clst
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/clst/inst/doc/clstDemo.R
dependsOnMe: clstutils
dependencyCount: 14
Package: clstutils
Version: 1.55.0
Depends: R (>= 2.10), clst, rjson, ape
Imports: lattice, RSQLite
Suggests: RUnit
License: GPL-3
MD5sum: 6cc7be1c9c0ac6bcad01cf5f1d240afc
NeedsCompilation: no
Title: Tools for performing taxonomic assignment
Description: Tools for performing taxonomic assignment based on
phylogeny using pplacer and clst.
biocViews: Sequencing, Classification, Visualization, QualityControl
Author: Noah Hoffman
Maintainer: Noah Hoffman <ngh2@uw.edu>
git_url: https://git.bioconductor.org/packages/clstutils
git_branch: devel
git_last_commit: 467cc63
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/clstutils_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/clstutils_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/clstutils_1.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/clstutils_1.55.0.tgz
vignettes: vignettes/clstutils/inst/doc/pplacerDemo.pdf,
vignettes/clstutils/inst/doc/refSet.pdf
vignetteTitles: clst, clstutils
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/clstutils/inst/doc/pplacerDemo.R,
vignettes/clstutils/inst/doc/refSet.R
dependencyCount: 37
Package: CluMSID
Version: 1.23.0
Depends: R (>= 3.6)
Imports: mzR, S4Vectors, dbscan, RColorBrewer, ape, network, GGally,
ggplot2, plotly, methods, utils, stats, sna, grDevices,
graphics, Biobase, gplots, MSnbase
Suggests: knitr, rmarkdown, testthat, dplyr, readr, stringr, magrittr,
CluMSIDdata, metaMS, metaMSdata, xcms
License: MIT + file LICENSE
MD5sum: ccb8461d20794b8776d4fe25aa58cdbf
NeedsCompilation: no
Title: Clustering of MS2 Spectra for Metabolite Identification
Description: CluMSID is a tool that aids the identification of features
in untargeted LC-MS/MS analysis by the use of MS2 spectra
similarity and unsupervised statistical methods. It offers
functions for a complete and customisable workflow from raw
data to visualisations and is interfaceable with the xmcs
family of preprocessing packages.
biocViews: Metabolomics, Preprocessing, Clustering
Author: Tobias Depke [aut, cre], Raimo Franke [ctb], Mark Broenstrup
[ths]
Maintainer: Tobias Depke <depke@mailbox.org>
URL: https://github.com/tdepke/CluMSID
VignetteBuilder: knitr
BugReports: https://github.com/tdepke/CluMSID/issues
git_url: https://git.bioconductor.org/packages/CluMSID
git_branch: devel
git_last_commit: e5261c4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CluMSID_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CluMSID_1.23.0.zip
vignettes: vignettes/CluMSID/inst/doc/CluMSID_DI-MSMS.html,
vignettes/CluMSID/inst/doc/CluMSID_GC-EI-MS.html,
vignettes/CluMSID/inst/doc/CluMSID_lowres-LC-MSMS.html,
vignettes/CluMSID/inst/doc/CluMSID_MTBLS.html,
vignettes/CluMSID/inst/doc/CluMSID_tutorial.html
vignetteTitles: CluMSID DI-MS/MS Tutorial, CluMSID GC-EI-MS Tutorial,
CluMSID LowRes Tutorial, CluMSID MTBLS Tutorial, CluMSID
Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CluMSID/inst/doc/CluMSID_DI-MSMS.R,
vignettes/CluMSID/inst/doc/CluMSID_GC-EI-MS.R,
vignettes/CluMSID/inst/doc/CluMSID_lowres-LC-MSMS.R,
vignettes/CluMSID/inst/doc/CluMSID_MTBLS.R,
vignettes/CluMSID/inst/doc/CluMSID_tutorial.R
dependencyCount: 154
Package: ClustAll
Version: 1.3.0
Depends: R (>= 4.2.0)
Imports: FactoMineR, bigstatsr, clValid, doSNOW, parallel, foreach,
dplyr, fpc, mice, modeest, flock, networkD3, methods,
ComplexHeatmap, cluster, RColorBrewer, circlize, grDevices,
ggplot2, grid, stats, utils, pbapply
Suggests: RUnit, knitr, BiocGenerics, rmarkdown, BiocStyle, roxygen2
License: GPL-2
MD5sum: 6e017319dae1668f1e65b807e1d54d49
NeedsCompilation: no
Title: ClustAll: Data driven strategy to robustly identify
stratification of patients within complex diseases
Description: Data driven strategy to find hidden groups of patients
with complex diseases using clinical data. ClustAll facilitates
the unsupervised identification of multiple robust
stratifications. ClustAll, is able to overcome the most common
limitations found when dealing with clinical data (missing
values, correlated data, mixed data types).
biocViews: Software, StatisticalMethod, Clustering, DimensionReduction,
PrincipalComponent
Author: Asier Ortega-Legarreta [aut, cre] (ORCID:
<https://orcid.org/0009-0000-3563-5362>), Sara
Palomino-Echeverria [aut]
Maintainer: Asier Ortega-Legarreta <aortegal@navarra.es>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ClustAll
git_branch: devel
git_last_commit: 3058c84
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ClustAll_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ClustAll_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ClustAll_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ClustAll_1.3.0.tgz
vignettes: vignettes/ClustAll/inst/doc/Vignette_Clustall.html
vignetteTitles: ClustALL User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ClustAll/inst/doc/Vignette_Clustall.R
dependencyCount: 185
Package: clustComp
Version: 1.35.0
Depends: R (>= 3.3)
Imports: sm, stats, graphics, grDevices
Suggests: Biobase, colonCA, RUnit, BiocGenerics
License: GPL (>= 2)
MD5sum: fbb81ed42b70cb0f4bd1fed7f2f0d11f
NeedsCompilation: no
Title: Clustering Comparison Package
Description: clustComp is a package that implements several techniques
for the comparison and visualisation of relationships between
different clustering results, either flat versus flat or
hierarchical versus flat. These relationships among clusters
are displayed using a weighted bi-graph, in which the nodes
represent the clusters and the edges connect pairs of nodes
with non-empty intersection; the weight of each edge is the
number of elements in that intersection and is displayed
through the edge thickness. The best layout of the bi-graph is
provided by the barycentre algorithm, which minimises the
weighted number of crossings. In the case of comparing a
hierarchical and a non-hierarchical clustering, the dendrogram
is pruned at different heights, selected by exploring the tree
by depth-first search, starting at the root. Branches are
decided to be split according to the value of a scoring
function, that can be based either on the aesthetics of the
bi-graph or on the mutual information between the hierarchical
and the flat clusterings. A mapping between groups of clusters
from each side is constructed with a greedy algorithm, and can
be additionally visualised.
biocViews: GeneExpression, Clustering, Visualization
Author: Aurora Torrente and Alvis Brazma.
Maintainer: Aurora Torrente <aurora@ebi.ac.uk>
git_url: https://git.bioconductor.org/packages/clustComp
git_branch: devel
git_last_commit: b968259
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/clustComp_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/clustComp_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/clustComp_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/clustComp_1.35.0.tgz
vignettes: vignettes/clustComp/inst/doc/clustComp.pdf
vignetteTitles: The clustComp Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/clustComp/inst/doc/clustComp.R
dependencyCount: 4
Package: clusterExperiment
Version: 2.27.0
Depends: R (>= 3.6.0), SingleCellExperiment, SummarizedExperiment (>=
1.15.4), BiocGenerics
Imports: methods, NMF, RColorBrewer, ape (>= 5.0), cluster, stats,
limma, locfdr, matrixStats, graphics, parallel, BiocSingular,
kernlab, stringr, S4Vectors, grDevices, DelayedArray (>=
0.7.48), HDF5Array (>= 1.7.10), Matrix, Rcpp, edgeR, scales,
zinbwave, phylobase, pracma, mbkmeans
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, testthat, MAST, Rtsne, scran, igraph,
rmarkdown
License: Artistic-2.0
MD5sum: d462c13a3acc5ea801a70ed8f72943d5
NeedsCompilation: yes
Title: Compare Clusterings for Single-Cell Sequencing
Description: Provides functionality for running and comparing many
different clusterings of single-cell sequencing data or other
large mRNA Expression data sets.
biocViews: Clustering, RNASeq, Sequencing, Software, SingleCell
Author: Elizabeth Purdom [aut, cre, cph], Davide Risso [aut]
Maintainer: Elizabeth Purdom <epurdom@stat.berkeley.edu>
VignetteBuilder: knitr
BugReports: https://github.com/epurdom/clusterExperiment/issues
git_url: https://git.bioconductor.org/packages/clusterExperiment
git_branch: devel
git_last_commit: 3bb82fb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/clusterExperiment_2.27.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/clusterExperiment_2.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/clusterExperiment_2.27.0.tgz
vignettes:
vignettes/clusterExperiment/inst/doc/clusterExperimentTutorial.html,
vignettes/clusterExperiment/inst/doc/largeDataSets.html
vignetteTitles: clusterExperiment Vignette, Working with Large Datasets
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/clusterExperiment/inst/doc/clusterExperimentTutorial.R,
vignettes/clusterExperiment/inst/doc/largeDataSets.R
dependsOnMe: netSmooth
suggestsMe: slingshot, tradeSeq
dependencyCount: 149
Package: ClusterFoldSimilarity
Version: 1.3.0
Imports: methods, igraph, ggplot2, scales, BiocParallel, graphics,
stats, utils, Matrix, cowplot, dplyr, reshape2, Seurat,
SeuratObject, SingleCellExperiment, ggdendro
Suggests: knitr, rmarkdown, kableExtra, scRNAseq, BiocStyle
License: Artistic-2.0
MD5sum: 8c6d5e4cbca500be9db5b3f4d34f9673
NeedsCompilation: no
Title: Calculate similarity of clusters from different single cell
samples using foldchanges
Description: This package calculates a similarity coefficient using the
fold changes of shared features (e.g. genes) among clusters of
different samples/batches/datasets. The similarity coefficient
is calculated using the dot-product (Hadamard product) of every
pairwise combination of Fold Changes between a source cluster i
of sample/dataset n and all the target clusters j in
sample/dataset m
biocViews: SingleCell, Clustering, FeatureExtraction, GraphAndNetwork,
GeneTarget, RNASeq
Author: Oscar Gonzalez-Velasco [cre, aut] (ORCID:
<https://orcid.org/0000-0002-5054-8635>)
Maintainer: Oscar Gonzalez-Velasco <oscargvelasco@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ClusterFoldSimilarity
git_branch: devel
git_last_commit: 60167ea
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ClusterFoldSimilarity_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ClusterFoldSimilarity_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ClusterFoldSimilarity_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ClusterFoldSimilarity_1.3.0.tgz
vignettes:
vignettes/ClusterFoldSimilarity/inst/doc/ClusterFoldSimilarity.html
vignetteTitles: ClusterFoldSimilarity:
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles:
vignettes/ClusterFoldSimilarity/inst/doc/ClusterFoldSimilarity.R
dependencyCount: 178
Package: ClusterJudge
Version: 1.29.1
Depends: R (>= 3.6), stats, utils, graphics, infotheo, lattice,
latticeExtra, httr, jsonlite
Suggests: yeastExpData, knitr, rmarkdown, devtools, testthat, biomaRt
License: Artistic-2.0
MD5sum: ab2238db915ce447972e66924a547e35
NeedsCompilation: no
Title: Judging Quality of Clustering Methods using Mutual Information
Description: ClusterJudge implements the functions, examples and other
software published as an algorithm by Gibbons, FD and Roth FP.
The article is called "Judging the Quality of Gene
Expression-Based Clustering Methods Using Gene Annotation" and
it appeared in Genome Research, vol. 12, pp1574-1581 (2002).
See package?ClusterJudge for an overview.
biocViews: Software, StatisticalMethod, Clustering, GeneExpression, GO
Author: Adrian Pasculescu
Maintainer: Adrian Pasculescu <a.pasculescu@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ClusterJudge
git_branch: devel
git_last_commit: 4c56470
git_last_commit_date: 2025-01-21
Date/Publication: 2025-01-22
source.ver: src/contrib/ClusterJudge_1.29.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ClusterJudge_1.29.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ClusterJudge_1.29.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ClusterJudge_1.29.1.tgz
vignettes: vignettes/ClusterJudge/inst/doc/ClusterJudge-intro.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ClusterJudge/inst/doc/ClusterJudge-intro.R
dependencyCount: 26
Package: clusterProfiler
Version: 4.15.1
Depends: R (>= 3.5.0)
Imports: AnnotationDbi, DOSE (>= 3.23.2), dplyr, enrichplot (>= 1.9.3),
GO.db, GOSemSim (>= 2.27.2), gson (>= 0.0.7), httr, igraph,
magrittr, methods, plyr, qvalue, rlang, stats, tidyr, utils,
yulab.utils (>= 0.1.6)
Suggests: AnnotationHub, knitr, jsonlite, readr, rmarkdown,
org.Hs.eg.db, prettydoc, BiocManager, testthat
License: Artistic-2.0
MD5sum: 201bcb9bd63b9b5280cb806eb98e89ca
NeedsCompilation: no
Title: A universal enrichment tool for interpreting omics data
Description: This package supports functional characteristics of both
coding and non-coding genomics data for thousands of species
with up-to-date gene annotation. It provides a univeral
interface for gene functional annotation from a variety of
sources and thus can be applied in diverse scenarios. It
provides a tidy interface to access, manipulate, and visualize
enrichment results to help users achieve efficient data
interpretation. Datasets obtained from multiple treatments and
time points can be analyzed and compared in a single run,
easily revealing functional consensus and differences among
distinct conditions.
biocViews: Annotation, Clustering, GeneSetEnrichment, GO, KEGG,
MultipleComparison, Pathways, Reactome, Visualization
Author: Guangchuang Yu [aut, cre, cph] (ORCID:
<https://orcid.org/0000-0002-6485-8781>), Li-Gen Wang [ctb],
Xiao Luo [ctb], Meijun Chen [ctb], Giovanni Dall'Olio [ctb],
Wanqian Wei [ctb], Chun-Hui Gao [ctb] (ORCID:
<https://orcid.org/0000-0002-1445-7939>)
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
URL: https://yulab-smu.top/contribution-knowledge-mining/
VignetteBuilder: knitr
BugReports: https://github.com/YuLab-SMU/clusterProfiler/issues
git_url: https://git.bioconductor.org/packages/clusterProfiler
git_branch: devel
git_last_commit: 1aedd90
git_last_commit_date: 2024-11-29
Date/Publication: 2024-11-29
source.ver: src/contrib/clusterProfiler_4.15.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/clusterProfiler_4.15.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/clusterProfiler_4.15.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/clusterProfiler_4.15.1.tgz
vignettes: vignettes/clusterProfiler/inst/doc/clusterProfiler.html
vignetteTitles: Statistical analysis and visualization of functional
profiles for genes and gene clusters
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/clusterProfiler/inst/doc/clusterProfiler.R
dependsOnMe: maEndToEnd
importsMe: bioCancer, broadSeq, CaMutQC, CBNplot, CEMiTool, CeTF,
debrowser, EasyCellType, EnrichDO, epiregulon.extra, esATAC,
famat, GDCRNATools, gINTomics, goSorensen, MetaPhOR, methylGSA,
MicrobiomeProfiler, miRSM, miRspongeR, Moonlight2R, MoonlightR,
mosdef, PanomiR, pathlinkR, Pigengene, ReducedExperiment,
seqArchRplus, signatureSearch, ExpHunterSuite, recountWorkflow,
DRviaSPCN, genekitr, Grouphmap, immcp, pathwayTMB, PMAPscore,
RVA, ssdGSA, tinyarray
suggestsMe: ChIPseeker, cola, DAPAR, DOSE, enrichplot, EpiCompare,
EpiMix, GeDi, GeneTonic, GenomicSuperSignature, GeoTcgaData,
ggkegg, GOSemSim, GRaNIE, GSEAmining, mastR, MesKit,
ReactomePA, rrvgo, scFeatures, scGPS, TCGAbiolinks, tidybulk,
vsclust, org.Mxanthus.db, GeneSelectR, grandR, MARVEL,
OlinkAnalyze, ReporterScore, SCpubr
dependencyCount: 122
Package: clusterSeq
Version: 1.31.0
Depends: R (>= 3.0.0), methods, BiocParallel, baySeq, graphics, stats,
utils
Imports: BiocGenerics
Suggests: BiocStyle
License: GPL-3
MD5sum: 9606fb8e65f7f4295efa130ad7991888
NeedsCompilation: no
Title: Clustering of high-throughput sequencing data by identifying
co-expression patterns
Description: Identification of clusters of co-expressed genes based on
their expression across multiple (replicated) biological
samples.
biocViews: Sequencing, DifferentialExpression, MultipleComparison,
Clustering, GeneExpression
Author: Thomas J. Hardcastle [aut], Irene Papatheodorou [aut], Samuel
Granjeaud [cre] (ORCID:
<https://orcid.org/0000-0001-9245-1535>)
Maintainer: Samuel Granjeaud <samuel.granjeaud@inserm.fr>
URL: https://github.com/samgg/clusterSeq
BugReports: https://github.com/samgg/clusterSeq/issues
git_url: https://git.bioconductor.org/packages/clusterSeq
git_branch: devel
git_last_commit: 72a65eb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/clusterSeq_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/clusterSeq_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/clusterSeq_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/clusterSeq_1.31.0.tgz
vignettes: vignettes/clusterSeq/inst/doc/clusterSeq.pdf
vignetteTitles: Advanced baySeq analyses
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/clusterSeq/inst/doc/clusterSeq.R
dependencyCount: 42
Package: ClusterSignificance
Version: 1.35.0
Depends: R (>= 3.3.0)
Imports: methods, pracma, princurve (>= 2.0.5), scatterplot3d,
RColorBrewer, grDevices, graphics, utils, stats
Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, plsgenomics,
covr
License: GPL-3
MD5sum: 180828de5b8e7302fa5d0f303328413a
NeedsCompilation: no
Title: The ClusterSignificance package provides tools to assess if
class clusters in dimensionality reduced data representations
have a separation different from permuted data
Description: The ClusterSignificance package provides tools to assess
if class clusters in dimensionality reduced data
representations have a separation different from permuted data.
The term class clusters here refers to, clusters of points
representing known classes in the data. This is particularly
useful to determine if a subset of the variables, e.g. genes in
a specific pathway, alone can separate samples into these
established classes. ClusterSignificance accomplishes this by,
projecting all points onto a one dimensional line. Cluster
separations are then scored and the probability of the seen
separation being due to chance is evaluated using a permutation
method.
biocViews: Clustering, Classification, PrincipalComponent,
StatisticalMethod
Author: Jason T. Serviss [aut, cre], Jesper R. Gadin [aut]
Maintainer: Jason T Serviss <jason.serviss@ki.se>
URL: https://github.com/jasonserviss/ClusterSignificance/
VignetteBuilder: knitr
BugReports: https://github.com/jasonserviss/ClusterSignificance/issues
git_url: https://git.bioconductor.org/packages/ClusterSignificance
git_branch: devel
git_last_commit: 4f0c989
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ClusterSignificance_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ClusterSignificance_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ClusterSignificance_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ClusterSignificance_1.35.0.tgz
vignettes:
vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.html
vignetteTitles: ClusterSignificance Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.R
dependencyCount: 10
Package: clusterStab
Version: 1.79.0
Depends: Biobase (>= 1.4.22), R (>= 1.9.0), methods
Suggests: fibroEset, genefilter
License: Artistic-2.0
MD5sum: 19392e3300dff3428f5a78e0430bd8ae
NeedsCompilation: no
Title: Compute cluster stability scores for microarray data
Description: This package can be used to estimate the number of
clusters in a set of microarray data, as well as test the
stability of these clusters.
biocViews: Clustering
Author: James W. MacDonald, Debashis Ghosh, Mark Smolkin
Maintainer: James W. MacDonald <jmacdon@u.washington.edu>
git_url: https://git.bioconductor.org/packages/clusterStab
git_branch: devel
git_last_commit: a43a209
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/clusterStab_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/clusterStab_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/clusterStab_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/clusterStab_1.79.0.tgz
vignettes: vignettes/clusterStab/inst/doc/clusterStab.pdf
vignetteTitles: clusterStab Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/clusterStab/inst/doc/clusterStab.R
dependencyCount: 7
Package: clustifyr
Version: 1.19.0
Depends: R (>= 2.10)
Imports: cowplot, dplyr, entropy, fgsea, ggplot2, Matrix, rlang,
scales, stringr, tibble, tidyr, stats, methods,
SingleCellExperiment, SummarizedExperiment, SeuratObject,
matrixStats, S4Vectors, proxy, httr, utils
Suggests: ComplexHeatmap, covr, knitr, rmarkdown, testthat, ggrepel,
BiocStyle, BiocManager, remotes, shiny, gprofiler2, purrr,
data.table, R.utils
License: MIT + file LICENSE
MD5sum: cefad5e60f6637a481c2c436c97dab93
NeedsCompilation: no
Title: Classifier for Single-cell RNA-seq Using Cell Clusters
Description: Package designed to aid in classifying cells from
single-cell RNA sequencing data using external reference data
(e.g., bulk RNA-seq, scRNA-seq, microarray, gene lists). A
variety of correlation based methods and gene list enrichment
methods are provided to assist cell type assignment.
biocViews: SingleCell, Annotation, Sequencing, Microarray,
GeneExpression
Author: Rui Fu [cre, aut], Kent Riemondy [aut], Austin Gillen [ctb],
Chengzhe Tian [ctb], Jay Hesselberth [ctb], Yue Hao [ctb],
Michelle Daya [ctb], Sidhant Puntambekar [ctb], RNA Bioscience
Initiative [fnd, cph]
Maintainer: Rui Fu <ray.sinensis@gmail.com>
URL: https://github.com/rnabioco/clustifyr,
https://rnabioco.github.io/clustifyr/
VignetteBuilder: knitr
BugReports: https://github.com/rnabioco/clustifyr/issues
git_url: https://git.bioconductor.org/packages/clustifyr
git_branch: devel
git_last_commit: 01ae82e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/clustifyr_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/clustifyr_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/clustifyr_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/clustifyr_1.19.0.tgz
vignettes: vignettes/clustifyr/inst/doc/clustifyr.html,
vignettes/clustifyr/inst/doc/geo-annotations.html
vignetteTitles: Introduction to clustifyr, geo-annotations
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/clustifyr/inst/doc/clustifyr.R,
vignettes/clustifyr/inst/doc/geo-annotations.R
suggestsMe: clustifyrdatahub
dependencyCount: 98
Package: ClustIRR
Version: 1.5.42
Depends: R (>= 4.3.0)
Imports: blaster, future, future.apply, grDevices, igraph, methods,
Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), reshape2, rstan (>=
2.18.1), rstantools (>= 2.4.0), stats, stringdist, utils,
posterior, visNetwork, dplyr, tidyr, ggplot2, ggforce, scales
LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0),
RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>=
2.18.0)
Suggests: BiocStyle, knitr, testthat, ggplot2, ggrepel, patchwork
License: GPL-3 + file LICENSE
MD5sum: f3ea4890b461cc8fa4f4f436a1454ffe
NeedsCompilation: yes
Title: Clustering of immune receptor repertoires
Description: ClustIRR analyzes repertoires of B- and T-cell receptors.
It starts by identifying communities of immune receptors with
similar specificities, based on the sequences of their
complementarity-determining regions (CDRs). Next, it employs a
Bayesian probabilistic models to quantify differential
community occupancy (DCO) between repertoires, allowing the
identification of expanding or contracting communities in
response to e.g. infection or cancer treatment.
biocViews: Clustering, ImmunoOncology, SingleCell, Software,
Classification
Author: Simo Kitanovski [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2909-5376>), Kai Wollek [aut]
(ORCID: <https://orcid.org/0009-0008-5941-9160>)
Maintainer: Simo Kitanovski <simokitanovski@gmail.com>
URL: https://github.com/snaketron/ClustIRR
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/snaketron/ClustIRR/issues
git_url: https://git.bioconductor.org/packages/ClustIRR
git_branch: devel
git_last_commit: ee730f7
git_last_commit_date: 2025-02-28
Date/Publication: 2025-02-28
source.ver: src/contrib/ClustIRR_1.5.42.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ClustIRR_1.5.42.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ClustIRR_1.5.42.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ClustIRR_1.5.42.tgz
vignettes: vignettes/ClustIRR/inst/doc/User_manual.html
vignetteTitles: Decoding T- and B-cell receptor repertoires with
ClustIRR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ClustIRR/inst/doc/User_manual.R
dependencyCount: 108
Package: CMA
Version: 1.65.0
Depends: R (>= 2.10), methods, stats, Biobase
Suggests: MASS, class, nnet, glmnet, e1071, randomForest, plsgenomics,
gbm, mgcv, corpcor, limma, st, mvtnorm
License: GPL (>= 2)
Archs: x64
MD5sum: 8d31d064263c9630760760b18040c72a
NeedsCompilation: no
Title: Synthesis of microarray-based classification
Description: This package provides a comprehensive collection of
various microarray-based classification algorithms both from
Machine Learning and Statistics. Variable Selection,
Hyperparameter tuning, Evaluation and Comparison can be
performed combined or stepwise in a user-friendly environment.
biocViews: Classification, DecisionTree
Author: Martin Slawski <ms@cs.uni-sb.de>, Anne-Laure Boulesteix
<boulesteix@ibe.med.uni-muenchen.de>, Christoph Bernau
<bernau@ibe.med.uni-muenchen.de>.
Maintainer: Roman Hornung <hornung@ibe.med.uni-muenchen.de>
git_url: https://git.bioconductor.org/packages/CMA
git_branch: devel
git_last_commit: c6f5e4f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CMA_1.65.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CMA_1.65.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CMA_1.65.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CMA_1.65.0.tgz
vignettes: vignettes/CMA/inst/doc/CMA_vignette.pdf
vignetteTitles: CMA_vignette.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CMA/inst/doc/CMA_vignette.R
dependencyCount: 7
Package: cmapR
Version: 1.19.0
Depends: R (>= 4.0)
Imports: methods, rhdf5, data.table, flowCore, SummarizedExperiment,
matrixStats
Suggests: knitr, testthat, BiocStyle, rmarkdown
License: file LICENSE
MD5sum: 6b2fae7ea3217436c52a6a3df64d44c0
NeedsCompilation: no
Title: CMap Tools in R
Description: The Connectivity Map (CMap) is a massive resource of
perturbational gene expression profiles built by researchers at
the Broad Institute and funded by the NIH Library of Integrated
Network-Based Cellular Signatures (LINCS) program. Please visit
https://clue.io for more information. The cmapR package
implements methods to parse, manipulate, and write common CMap
data objects, such as annotated matrices and collections of
gene sets.
biocViews: DataImport, DataRepresentation, GeneExpression
Author: Ted Natoli [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0953-0206>)
Maintainer: Ted Natoli <ted.e.natoli@gmail.com>
URL: https://github.com/cmap/cmapR
VignetteBuilder: knitr
BugReports: https://github.com/cmap/cmapR/issues
git_url: https://git.bioconductor.org/packages/cmapR
git_branch: devel
git_last_commit: 53a0f4e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/cmapR_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cmapR_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cmapR_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cmapR_1.19.0.tgz
vignettes: vignettes/cmapR/inst/doc/tutorial.html
vignetteTitles: cmapR Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/cmapR/inst/doc/tutorial.R
dependencyCount: 46
Package: cn.farms
Version: 1.55.0
Depends: R (>= 3.0), Biobase, methods, ff, oligoClasses, snow
Imports: DBI, affxparser, oligo, DNAcopy, preprocessCore, lattice
Suggests: pd.mapping250k.sty, pd.mapping250k.nsp, pd.genomewidesnp.5,
pd.genomewidesnp.6
License: LGPL (>= 2.0)
MD5sum: c79ca82f6b70afecf231da58a86f756b
NeedsCompilation: yes
Title: cn.FARMS - factor analysis for copy number estimation
Description: This package implements the cn.FARMS algorithm for copy
number variation (CNV) analysis. cn.FARMS allows to analyze the
most common Affymetrix (250K-SNP6.0) array types, supports
high-performance computing using snow and ff.
biocViews: Microarray, CopyNumberVariation
Author: Andreas Mitterecker, Djork-Arne Clevert
Maintainer: Andreas Mitterecker <mitterecker@ml.jku.at>
URL: http://www.bioinf.jku.at/software/cnfarms/cnfarms.html
git_url: https://git.bioconductor.org/packages/cn.farms
git_branch: devel
git_last_commit: 8600141
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cn.farms_1.55.0.tar.gz
vignettes: vignettes/cn.farms/inst/doc/cn.farms.pdf
vignetteTitles: cn.farms: Manual for the R package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cn.farms/inst/doc/cn.farms.R
dependencyCount: 66
Package: cn.mops
Version: 1.53.0
Depends: R (>= 3.5.0), methods, utils, stats, graphics, parallel,
GenomicRanges
Imports: BiocGenerics, Biobase, IRanges, Rsamtools, GenomeInfoDb,
S4Vectors
Suggests: DNAcopy
License: LGPL (>= 2.0)
MD5sum: 4ec460a7d476d3d92a3f7cbe3b052c2a
NeedsCompilation: yes
Title: cn.mops - Mixture of Poissons for CNV detection in NGS data
Description: cn.mops (Copy Number estimation by a Mixture Of PoissonS)
is a data processing pipeline for copy number variations and
aberrations (CNVs and CNAs) from next generation sequencing
(NGS) data. The package supplies functions to convert BAM files
into read count matrices or genomic ranges objects, which are
the input objects for cn.mops. cn.mops models the depths of
coverage across samples at each genomic position. Therefore, it
does not suffer from read count biases along chromosomes. Using
a Bayesian approach, cn.mops decomposes read variations across
samples into integer copy numbers and noise by its mixture
components and Poisson distributions, respectively. cn.mops
guarantees a low FDR because wrong detections are indicated by
high noise and filtered out. cn.mops is very fast and written
in C++.
biocViews: Sequencing, CopyNumberVariation, Homo_sapiens, CellBiology,
HapMap, Genetics
Author: Guenter Klambauer [aut], Gundula Povysil [cre]
Maintainer: Gundula Povysil <povysil@bioinf.jku.at>
URL: http://www.bioinf.jku.at/software/cnmops/cnmops.html
git_url: https://git.bioconductor.org/packages/cn.mops
git_branch: devel
git_last_commit: 3abd26d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cn.mops_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cn.mops_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cn.mops_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cn.mops_1.53.0.tgz
vignettes: vignettes/cn.mops/inst/doc/cn.mops.pdf
vignetteTitles: cn.mops: Manual for the R package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cn.mops/inst/doc/cn.mops.R
dependsOnMe: panelcn.mops
importsMe: CopyNumberPlots
dependencyCount: 40
Package: CNAnorm
Version: 1.53.0
Depends: R (>= 2.10.1), methods
Imports: DNAcopy
License: GPL-2
Archs: x64
MD5sum: 2e8655cb54573cbb88670861a47a189e
NeedsCompilation: yes
Title: A normalization method for Copy Number Aberration in cancer
samples
Description: Performs ratio, GC content correction and normalization of
data obtained using low coverage (one read every 100-10,000 bp)
high troughput sequencing. It performs a "discrete"
normalization looking for the ploidy of the genome. It will
also provide tumour content if at least two ploidy states can
be found.
biocViews: CopyNumberVariation, Sequencing, Coverage, Normalization,
WholeGenome, DNASeq, GenomicVariation
Author: Stefano Berri <sberri@illumina.com>, Henry M. Wood
<H.M.Wood@leeds.ac.uk>, Arief Gusnanto <a.gusnanto@leeds.ac.uk>
Maintainer: Stefano Berri <sberri@illumina.com>
URL: http://www.r-project.org,
git_url: https://git.bioconductor.org/packages/CNAnorm
git_branch: devel
git_last_commit: 65ad348
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CNAnorm_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CNAnorm_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CNAnorm_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CNAnorm_1.53.0.tgz
vignettes: vignettes/CNAnorm/inst/doc/CNAnorm.pdf
vignetteTitles: CNAnorm.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNAnorm/inst/doc/CNAnorm.R
dependencyCount: 2
Package: CNEr
Version: 1.43.0
Depends: R (>= 3.5.0)
Imports: Biostrings (>= 2.33.4), pwalign, DBI (>= 0.7), RSQLite (>=
0.11.4), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.23.16),
rtracklayer (>= 1.25.5), XVector (>= 0.5.4), GenomicAlignments
(>= 1.1.9), methods, S4Vectors (>= 0.13.13), IRanges (>=
2.5.27), readr (>= 0.2.2), BiocGenerics, tools, parallel,
reshape2 (>= 1.4.1), ggplot2 (>= 2.1.0), poweRlaw (>= 0.60.3),
annotate (>= 1.50.0), GO.db (>= 3.3.0), R.utils (>= 2.3.0),
KEGGREST (>= 1.14.0)
LinkingTo: S4Vectors, IRanges, XVector
Suggests: Gviz (>= 1.7.4), BiocStyle, knitr, rmarkdown, testthat,
BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38,
TxDb.Drerio.UCSC.danRer10.refGene, BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Ggallus.UCSC.galGal3
License: GPL-2 | file LICENSE
License_restricts_use: yes
MD5sum: 44968c337893c92471616e4c4995a154
NeedsCompilation: yes
Title: CNE Detection and Visualization
Description: Large-scale identification and advanced visualization of
sets of conserved noncoding elements.
biocViews: GeneRegulation, Visualization, DataImport
Author: Ge Tan <ge_tan@live.com>
Maintainer: Ge Tan <ge_tan@live.com>
URL: https://github.com/ge11232002/CNEr
VignetteBuilder: knitr
BugReports: https://github.com/ge11232002/CNEr/issues
git_url: https://git.bioconductor.org/packages/CNEr
git_branch: devel
git_last_commit: 7b0cff1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CNEr_1.43.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CNEr_1.43.0.tgz
vignettes: vignettes/CNEr/inst/doc/CNEr.html,
vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.html
vignetteTitles: CNE identification and visualisation, Pairwise whole
genome alignment
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CNEr/inst/doc/CNEr.R,
vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.R
dependencyCount: 118
Package: CNORdt
Version: 1.49.0
Depends: R (>= 1.8.0), CellNOptR (>= 0.99), abind
License: GPL-2
MD5sum: 19a2f6f9df4eef59c617526dbe1c32f5
NeedsCompilation: yes
Title: Add-on to CellNOptR: Discretized time treatments
Description: This add-on to the package CellNOptR handles time-course
data, as opposed to steady state data in CellNOptR. It scales
the simulation step to allow comparison and model fitting for
time-course data. Future versions will optimize delays and
strengths for each edge.
biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Proteomics,
TimeCourse
Author: A. MacNamara
Maintainer: A. MacNamara <aidan.macnamara@ebi.ac.uk>
git_url: https://git.bioconductor.org/packages/CNORdt
git_branch: devel
git_last_commit: 7924ad2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CNORdt_1.49.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CNORdt_1.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CNORdt_1.49.0.tgz
vignettes: vignettes/CNORdt/inst/doc/CNORdt-vignette.pdf
vignetteTitles: Using multiple time points to train logic models to
data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNORdt/inst/doc/CNORdt-vignette-example.R,
vignettes/CNORdt/inst/doc/CNORdt-vignette.R
dependencyCount: 73
Package: CNORfeeder
Version: 1.47.2
Depends: R (>= 4.0.0), graph
Imports: CellNOptR (>= 1.4.0)
Suggests: minet, Rgraphviz, RUnit, BiocGenerics, igraph
Enhances: MEIGOR
License: GPL-3
MD5sum: e26114cc26d5774d3a4bbde22940469c
NeedsCompilation: no
Title: Integration of CellNOptR to add missing links
Description: This package integrates literature-constrained and
data-driven methods to infer signalling networks from
perturbation experiments. It permits to extends a given network
with links derived from the data via various inference methods
and uses information on physical interactions of proteins to
guide and validate the integration of links.
biocViews: CellBasedAssays, CellBiology, Proteomics, NetworkInference
Author: Federica Eduati [aut, cre]
Maintainer: Attila Gabor <attila.gabor@uni-heidelberg.de>
git_url: https://git.bioconductor.org/packages/CNORfeeder
git_branch: devel
git_last_commit: 992194d
git_last_commit_date: 2025-03-20
Date/Publication: 2025-03-20
source.ver: src/contrib/CNORfeeder_1.47.2.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CNORfeeder_1.47.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CNORfeeder_1.47.2.tgz
vignettes: vignettes/CNORfeeder/inst/doc/CNORfeeder-vignette.pdf
vignetteTitles: Main vignette:Playing with networks using CNORfeeder
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNORfeeder/inst/doc/CNORfeeder-vignette.R
dependencyCount: 72
Package: CNORfuzzy
Version: 1.49.0
Depends: R (>= 2.15.0), CellNOptR (>= 1.4.0), nloptr (>= 0.8.5)
Suggests: xtable, Rgraphviz, RUnit, BiocGenerics
License: GPL-2
MD5sum: 115431a9454047b57691ec9a305efc56
NeedsCompilation: yes
Title: Addon to CellNOptR: Fuzzy Logic
Description: This package is an extension to CellNOptR. It contains
additional functionality needed to simulate and train a prior
knowledge network to experimental data using constrained fuzzy
logic (cFL, rather than Boolean logic as is the case in
CellNOptR). Additionally, this package will contain functions
to use for the compilation of multiple optimization results
(either Boolean or cFL).
biocViews: Network
Author: M. Morris, T. Cokelaer
Maintainer: T. Cokelaer <cokelaer@ebi.ac.uk>
git_url: https://git.bioconductor.org/packages/CNORfuzzy
git_branch: devel
git_last_commit: 48255bf
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CNORfuzzy_1.49.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CNORfuzzy_1.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CNORfuzzy_1.49.0.tgz
vignettes: vignettes/CNORfuzzy/inst/doc/CNORfuzzy-vignette.pdf
vignetteTitles: Main vignette:Playing with networks using CNORfuzzyl
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNORfuzzy/inst/doc/CNORfuzzy-vignette.R
dependencyCount: 73
Package: CNORode
Version: 1.49.0
Depends: CellNOptR, genalg
Suggests: knitr, rmarkdown
Enhances: doParallel, foreach
License: GPL-2
MD5sum: ec1d475cbe3074334b7791cc310ed5f8
NeedsCompilation: yes
Title: ODE add-on to CellNOptR
Description: Logic based ordinary differential equation (ODE) add-on to
CellNOptR.
biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Proteomics,
Bioinformatics, TimeCourse
Author: David Henriques, Thomas Cokelaer, Attila Gabor, Federica
Eduati, Enio Gjerga
Maintainer: Attila Gabor <attila.gabor@uni-heidelberg.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CNORode
git_branch: devel
git_last_commit: 851f051
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CNORode_1.49.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CNORode_1.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CNORode_1.49.0.tgz
vignettes: vignettes/CNORode/inst/doc/CNORode-vignette.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNORode/inst/doc/CNORode-vignette.R
dependsOnMe: MEIGOR
dependencyCount: 73
Package: CNTools
Version: 1.63.0
Depends: R (>= 2.10), methods, tools, stats, genefilter
License: LGPL
Archs: x64
MD5sum: 9d71b8f05f6cbf1b627df4fb71662b6a
NeedsCompilation: yes
Title: Convert segment data into a region by sample matrix to allow for
other high level computational analyses.
Description: This package provides tools to convert the output of
segmentation analysis using DNAcopy to a matrix structure with
overlapping segments as rows and samples as columns so that
other computational analyses can be applied to segmented data
biocViews: Microarray, CopyNumberVariation
Author: Jianhua Zhang
Maintainer: J. Zhang <jzhang@jimmy.harvard.edu>
git_url: https://git.bioconductor.org/packages/CNTools
git_branch: devel
git_last_commit: 05a59fd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CNTools_1.63.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CNTools_1.63.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CNTools_1.63.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CNTools_1.63.0.tgz
vignettes: vignettes/CNTools/inst/doc/HowTo.pdf
vignetteTitles: NCTools HowTo
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNTools/inst/doc/HowTo.R
dependsOnMe: cghMCR
dependencyCount: 56
Package: CNVfilteR
Version: 1.21.0
Depends: R (>= 4.3)
Imports: IRanges, GenomicRanges, SummarizedExperiment, pracma,
regioneR, assertthat, karyoploteR, CopyNumberPlots, graphics,
utils, VariantAnnotation, Rsamtools, GenomeInfoDb, Biostrings,
methods
Suggests: knitr, BiocStyle, BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Hsapiens.UCSC.hg19.masked, rmarkdown
License: Artistic-2.0
MD5sum: c407ce29093241682735c7830c19a392
NeedsCompilation: no
Title: Identifies false positives of CNV calling tools by using SNV
calls
Description: CNVfilteR identifies those CNVs that can be discarded by
using the single nucleotide variant (SNV) calls that are
usually obtained in common NGS pipelines.
biocViews: CopyNumberVariation, Sequencing, DNASeq, Visualization,
DataImport
Author: Jose Marcos Moreno-Cabrera [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8570-0345>), Bernat Gel [aut]
Maintainer: Jose Marcos Moreno-Cabrera <jpuntomarcos@gmail.com>
URL: https://github.com/jpuntomarcos/CNVfilteR
VignetteBuilder: knitr
BugReports: https://github.com/jpuntomarcos/CNVfilteR/issues
git_url: https://git.bioconductor.org/packages/CNVfilteR
git_branch: devel
git_last_commit: b987028
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CNVfilteR_1.21.0.tar.gz
vignettes: vignettes/CNVfilteR/inst/doc/CNVfilteR.html
vignetteTitles: CNVfilteR vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNVfilteR/inst/doc/CNVfilteR.R
dependencyCount: 149
Package: cnvGSA
Version: 1.51.0
Depends: brglm, doParallel, foreach, GenomicRanges, methods,
splitstackshape
Suggests: cnvGSAdata, org.Hs.eg.db
License: LGPL
Archs: x64
MD5sum: 70216baf9b83c6db8cda98873d63d44c
NeedsCompilation: no
Title: Gene Set Analysis of (Rare) Copy Number Variants
Description: This package is intended to facilitate gene-set
association with rare CNVs in case-control studies.
biocViews: MultipleComparison
Author: Daniele Merico <daniele.merico@gmail.com>, Robert Ziman
<rziman@gmail.com>; packaged by Joseph Lugo
<joseph.r.lugo@gmail.com>
Maintainer: Joseph Lugo <joseph.r.lugo@gmail.com>
git_url: https://git.bioconductor.org/packages/cnvGSA
git_branch: devel
git_last_commit: 66054b1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cnvGSA_1.51.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cnvGSA_1.51.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cnvGSA_1.51.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cnvGSA_1.51.0.tgz
vignettes: vignettes/cnvGSA/inst/doc/cnvGSA-vignette.pdf,
vignettes/cnvGSA/inst/doc/cnvGSAUsersGuide.pdf
vignetteTitles: cnvGSA - Gene-Set Analysis of Rare Copy Number
Variants, cnvGSAUsersGuide.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: cnvGSAdata
dependencyCount: 32
Package: CNViz
Version: 1.15.0
Depends: R (>= 4.0), shiny (>= 1.5.0)
Imports: dplyr, stats, utils, grDevices, plotly, karyoploteR,
CopyNumberPlots, GenomicRanges, magrittr, DT, scales, graphics
Suggests: rmarkdown, knitr
License: Artistic-2.0
MD5sum: d4b0bb354932a774411e4e08511c388f
NeedsCompilation: no
Title: Copy Number Visualization
Description: CNViz takes probe, gene, and segment-level log2 copy
number ratios and launches a Shiny app to visualize your
sample's copy number profile. You can also integrate loss of
heterozygosity (LOH) and single nucleotide variant (SNV) data.
biocViews: Visualization, CopyNumberVariation, Sequencing, DNASeq
Author: Rebecca Greenblatt [aut, cre]
Maintainer: Rebecca Greenblatt <rebecca.greenblatt@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CNViz
git_branch: devel
git_last_commit: 5bf989f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CNViz_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CNViz_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CNViz_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CNViz_1.15.0.tgz
vignettes: vignettes/CNViz/inst/doc/CNViz.html
vignetteTitles: CNViz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNViz/inst/doc/CNViz.R
dependencyCount: 161
Package: CNVMetrics
Version: 1.11.0
Depends: R (>= 4.0)
Imports: GenomicRanges, IRanges, S4Vectors, BiocParallel, methods,
magrittr, stats, pheatmap, gridExtra, grDevices, rBeta2009
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: Artistic-2.0
MD5sum: 9b0c28070b9f28f92b89005a252cc4eb
NeedsCompilation: no
Title: Copy Number Variant Metrics
Description: The CNVMetrics package calculates similarity metrics to
facilitate copy number variant comparison among samples and/or
methods. Similarity metrics can be employed to compare CNV
profiles of genetically unrelated samples as well as those with
a common genetic background. Some metrics are based on the
shared amplified/deleted regions while other metrics rely on
the level of amplification/deletion. The data type used as
input is a plain text file containing the genomic position of
the copy number variations, as well as the status and/or the
log2 ratio values. Finally, a visualization tool is provided to
explore resulting metrics.
biocViews: BiologicalQuestion, Software, CopyNumberVariation
Author: Astrid Deschênes [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7846-6749>), Pascal Belleau [aut]
(ORCID: <https://orcid.org/0000-0002-0802-1071>), David A.
Tuveson [aut] (ORCID: <https://orcid.org/0000-0002-8017-2712>),
Alexander Krasnitz [aut]
Maintainer: Astrid Deschênes <adeschen@hotmail.com>
URL: https://github.com/krasnitzlab/CNVMetrics,
https://krasnitzlab.github.io/CNVMetrics/
VignetteBuilder: knitr
BugReports: https://github.com/krasnitzlab/CNVMetrics/issues
git_url: https://git.bioconductor.org/packages/CNVMetrics
git_branch: devel
git_last_commit: 961ad8c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CNVMetrics_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CNVMetrics_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CNVMetrics_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CNVMetrics_1.11.0.tgz
vignettes: vignettes/CNVMetrics/inst/doc/CNVMetrics.html
vignetteTitles: Copy number variant metrics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNVMetrics/inst/doc/CNVMetrics.R
dependencyCount: 51
Package: CNVPanelizer
Version: 1.39.0
Depends: R (>= 3.2.0), GenomicRanges
Imports: BiocGenerics, S4Vectors, grDevices, stats, utils, NOISeq,
IRanges, Rsamtools, foreach, ggplot2, plyr, GenomeInfoDb,
gplots, reshape2, stringr, testthat, graphics, methods, shiny,
shinyFiles, shinyjs, grid, openxlsx
Suggests: knitr, RUnit
License: GPL-3
MD5sum: 5e071195694f74b7fa1db1a0291f7a33
NeedsCompilation: no
Title: Reliable CNV detection in targeted sequencing applications
Description: A method that allows for the use of a collection of
non-matched normal tissue samples. Our approach uses a
non-parametric bootstrap subsampling of the available reference
samples to estimate the distribution of read counts from
targeted sequencing. As inspired by random forest, this is
combined with a procedure that subsamples the amplicons
associated with each of the targeted genes. The obtained
information allows us to reliably classify the copy number
aberrations on the gene level.
biocViews: Classification, Sequencing, Normalization,
CopyNumberVariation, Coverage
Author: Cristiano Oliveira [aut], Thomas Wolf [aut, cre], Albrecht
Stenzinger [ctb], Volker Endris [ctb], Nicole Pfarr [ctb],
Benedikt Brors [ths], Wilko Weichert [ths]
Maintainer: Thomas Wolf <thomas_wolf71@gmx.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CNVPanelizer
git_branch: devel
git_last_commit: 4aac16f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CNVPanelizer_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CNVPanelizer_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CNVPanelizer_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CNVPanelizer_1.39.0.tgz
vignettes: vignettes/CNVPanelizer/inst/doc/CNVPanelizer.pdf
vignetteTitles: CNVPanelizer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNVPanelizer/inst/doc/CNVPanelizer.R
dependencyCount: 117
Package: CNVRanger
Version: 1.23.0
Depends: GenomicRanges, RaggedExperiment
Imports: BiocGenerics, BiocParallel, GDSArray, GenomeInfoDb, IRanges,
S4Vectors, SNPRelate, SummarizedExperiment, data.table, edgeR,
gdsfmt, grDevices, lattice, limma, methods, plyr, qqman,
rappdirs, reshape2, stats, utils
Suggests: AnnotationHub, BSgenome.Btaurus.UCSC.bosTau6.masked,
BiocStyle, ComplexHeatmap, Gviz, MultiAssayExperiment,
TCGAutils, TxDb.Hsapiens.UCSC.hg19.knownGene, curatedTCGAData,
ensembldb, grid, knitr, org.Hs.eg.db, regioneR, rmarkdown,
statmod
License: Artistic-2.0
MD5sum: 8d9acb74bfd7e5c6a717057c99257f87
NeedsCompilation: no
Title: Summarization and expression/phenotype association of CNV ranges
Description: The CNVRanger package implements a comprehensive tool
suite for CNV analysis. This includes functionality for
summarizing individual CNV calls across a population, assessing
overlap with functional genomic regions, and association
analysis with gene expression and quantitative phenotypes.
biocViews: CopyNumberVariation, DifferentialExpression, GeneExpression,
GenomeWideAssociation, GenomicVariation, Microarray, RNASeq,
SNP
Author: Ludwig Geistlinger [aut, cre], Vinicius Henrique da Silva
[aut], Marcel Ramos [ctb], Levi Waldron [ctb]
Maintainer: Ludwig Geistlinger <ludwig.geistlinger@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/waldronlab/CNVRanger/issues
git_url: https://git.bioconductor.org/packages/CNVRanger
git_branch: devel
git_last_commit: 7cbb1a6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CNVRanger_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CNVRanger_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CNVRanger_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CNVRanger_1.23.0.tgz
vignettes: vignettes/CNVRanger/inst/doc/CNVRanger.html
vignetteTitles: Summarization and quantitative trait analysis of CNV
ranges
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNVRanger/inst/doc/CNVRanger.R
dependencyCount: 74
Package: CNVrd2
Version: 1.45.0
Depends: R (>= 3.0.0), methods, VariantAnnotation, parallel, rjags,
ggplot2, gridExtra
Imports: DNAcopy, IRanges, Rsamtools
Suggests: knitr
License: GPL-2
MD5sum: 46b5295579d7ba61ac80ed920a86bc8d
NeedsCompilation: no
Title: CNVrd2: a read depth-based method to detect and genotype complex
common copy number variants from next generation sequencing
data.
Description: CNVrd2 uses next-generation sequencing data to measure
human gene copy number for multiple samples, indentify SNPs
tagging copy number variants and detect copy number polymorphic
genomic regions.
biocViews: CopyNumberVariation, SNP, Sequencing, Software, Coverage,
LinkageDisequilibrium, Clustering.
Author: Hoang Tan Nguyen, Tony R Merriman and Mik Black
Maintainer: Hoang Tan Nguyen <hoangtannguyenvn@gmail.com>
URL: https://github.com/hoangtn/CNVrd2
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CNVrd2
git_branch: devel
git_last_commit: 55815ed
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CNVrd2_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CNVrd2_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CNVrd2_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CNVrd2_1.45.0.tgz
vignettes: vignettes/CNVrd2/inst/doc/CNVrd2.pdf
vignetteTitles: A Markdown Vignette with knitr
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CNVrd2/inst/doc/CNVrd2.R
dependencyCount: 103
Package: CoCiteStats
Version: 1.79.0
Depends: R (>= 2.0), org.Hs.eg.db
Imports: AnnotationDbi
License: CPL
MD5sum: 5e7abfa5f40b15b26d821f87a97d12da
NeedsCompilation: no
Title: Different test statistics based on co-citation.
Description: A collection of software tools for dealing with
co-citation data.
biocViews: Software
Author: B. Ding and R. Gentleman
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/CoCiteStats
git_branch: devel
git_last_commit: 51ec881
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CoCiteStats_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CoCiteStats_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CoCiteStats_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CoCiteStats_1.79.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 46
Package: COCOA
Version: 2.21.0
Depends: R (>= 3.5), GenomicRanges
Imports: BiocGenerics, S4Vectors, IRanges, data.table, ggplot2,
Biobase, stats, methods, ComplexHeatmap, MIRA, tidyr, grid,
grDevices, simpleCache, fitdistrplus
Suggests: knitr, parallel, testthat, BiocStyle, rmarkdown,
AnnotationHub, LOLA
License: GPL-3
MD5sum: 0b6499cfd3252e7669def3df1dfcf63c
NeedsCompilation: no
Title: Coordinate Covariation Analysis
Description: COCOA is a method for understanding epigenetic variation
among samples. COCOA can be used with epigenetic data that
includes genomic coordinates and an epigenetic signal, such as
DNA methylation and chromatin accessibility data. To describe
the method on a high level, COCOA quantifies inter-sample
variation with either a supervised or unsupervised technique
then uses a database of "region sets" to annotate the variation
among samples. A region set is a set of genomic regions that
share a biological annotation, for instance transcription
factor (TF) binding regions, histone modification regions, or
open chromatin regions. COCOA can identify region sets that are
associated with epigenetic variation between samples and
increase understanding of variation in your data.
biocViews: Epigenetics, DNAMethylation, ATACSeq, DNaseSeq, MethylSeq,
MethylationArray, PrincipalComponent, GenomicVariation,
GeneRegulation, GenomeAnnotation, SystemsBiology,
FunctionalGenomics, ChIPSeq, Sequencing, ImmunoOncology
Author: John Lawson [aut, cre], Nathan Sheffield [aut]
(http://www.databio.org), Jason Smith [ctb]
Maintainer: John Lawson <jtl2hk@virginia.edu>
URL: http://code.databio.org/COCOA/
VignetteBuilder: knitr
BugReports: https://github.com/databio/COCOA
git_url: https://git.bioconductor.org/packages/COCOA
git_branch: devel
git_last_commit: 16ecf4b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/COCOA_2.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/COCOA_2.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/COCOA_2.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/COCOA_2.21.0.tgz
vignettes: vignettes/COCOA/inst/doc/IntroToCOCOA.html
vignetteTitles: Introduction to Coordinate Covariation Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/COCOA/inst/doc/IntroToCOCOA.R
dependencyCount: 127
Package: codelink
Version: 1.75.0
Depends: R (>= 2.10), BiocGenerics (>= 0.3.2), methods, Biobase (>=
2.17.8), limma
Imports: annotate
Suggests: genefilter, parallel, knitr
License: GPL-2
MD5sum: 91bf478103badb5038f9dc258b7bb76e
NeedsCompilation: no
Title: Manipulation of Codelink microarray data
Description: This package facilitates reading, preprocessing and
manipulating Codelink microarray data. The raw data must be
exported as text file using the Codelink software.
biocViews: Microarray, OneChannel, DataImport, Preprocessing
Author: Diego Diez
Maintainer: Diego Diez <diego10ruiz@gmail.com>
URL: https://github.com/ddiez/codelink
VignetteBuilder: knitr
BugReports: https://github.com/ddiez/codelink/issues
git_url: https://git.bioconductor.org/packages/codelink
git_branch: devel
git_last_commit: d620585
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/codelink_1.75.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/codelink_1.75.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/codelink_1.75.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/codelink_1.75.0.tgz
vignettes: vignettes/codelink/inst/doc/Codelink_Introduction.pdf,
vignettes/codelink/inst/doc/Codelink_Legacy.pdf
vignetteTitles: Codelink Intruction, Codelink Legacy
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/codelink/inst/doc/Codelink_Introduction.R,
vignettes/codelink/inst/doc/Codelink_Legacy.R
suggestsMe: MAQCsubset
dependencyCount: 50
Package: CODEX
Version: 1.39.0
Depends: R (>= 3.2.3), Rsamtools, GenomeInfoDb,
BSgenome.Hsapiens.UCSC.hg19, IRanges, Biostrings, S4Vectors
Suggests: WES.1KG.WUGSC
License: GPL-2
MD5sum: e0bde123b3e651d0cf5f3dbfe8651ae1
NeedsCompilation: no
Title: A Normalization and Copy Number Variation Detection Method for
Whole Exome Sequencing
Description: A normalization and copy number variation calling
procedure for whole exome DNA sequencing data. CODEX relies on
the availability of multiple samples processed using the same
sequencing pipeline for normalization, and does not require
matched controls. The normalization model in CODEX includes
terms that specifically remove biases due to GC content, exon
length and targeting and amplification efficiency, and latent
systemic artifacts. CODEX also includes a Poisson
likelihood-based recursive segmentation procedure that
explicitly models the count-based exome sequencing data.
biocViews: ImmunoOncology, ExomeSeq, Normalization, QualityControl,
CopyNumberVariation
Author: Yuchao Jiang, Nancy R. Zhang
Maintainer: Yuchao Jiang <yuchaoj@wharton.upenn.edu>
git_url: https://git.bioconductor.org/packages/CODEX
git_branch: devel
git_last_commit: a803161
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CODEX_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CODEX_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CODEX_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CODEX_1.39.0.tgz
vignettes: vignettes/CODEX/inst/doc/CODEX_vignettes.pdf
vignetteTitles: Using CODEX
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CODEX/inst/doc/CODEX_vignettes.R
dependsOnMe: iCNV
dependencyCount: 60
Package: CoGAPS
Version: 3.27.4
Depends: R (>= 3.5.0)
Imports: BiocParallel, cluster, methods, gplots, graphics, grDevices,
RColorBrewer, Rcpp, S4Vectors, SingleCellExperiment, stats,
SummarizedExperiment, tools, utils, rhdf5, dplyr, fgsea,
forcats, ggplot2
LinkingTo: Rcpp, BH, testthat
Suggests: testthat, knitr, rmarkdown, BiocStyle, SeuratObject,
BiocFileCache, xml2
License: BSD_3_clause + file LICENSE
MD5sum: fc1da315a306c60d3a19db1da28cbaa0
NeedsCompilation: yes
Title: Coordinated Gene Activity in Pattern Sets
Description: Coordinated Gene Activity in Pattern Sets (CoGAPS)
implements a Bayesian MCMC matrix factorization algorithm,
GAPS, and links it to gene set statistic methods to infer
biological process activity. It can be used to perform sparse
matrix factorization on any data, and when this data represents
biomolecules, to do gene set analysis.
biocViews: GeneExpression, Transcription, GeneSetEnrichment,
DifferentialExpression, Bayesian, Clustering, TimeCourse,
RNASeq, Microarray, MultipleComparison, DimensionReduction,
ImmunoOncology
Author: Jeanette Johnson, Ashley Tsang, Jacob Mitchell, Thomas Sherman,
Wai-shing Lee, Conor Kelton, Ondrej Maxian, Jacob Carey,
Genevieve Stein-O'Brien, Michael Considine, Maggie Wodicka,
John Stansfield, Shawn Sivy, Carlo Colantuoni, Alexander
Favorov, Mike Ochs, Elana Fertig
Maintainer: Elana J. Fertig <ejfertig@jhmi.edu>, Thomas D. Sherman
<tomsherman159@gmail.com>, Jeanette Johnson
<jjohn450@jhmi.edu>, Dmitrijs Lvovs <dlvovs1@jh.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CoGAPS
git_branch: devel
git_last_commit: e40a2ab
git_last_commit_date: 2025-03-12
Date/Publication: 2025-03-17
source.ver: src/contrib/CoGAPS_3.27.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CoGAPS_3.27.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CoGAPS_3.27.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CoGAPS_3.27.4.tgz
vignettes: vignettes/CoGAPS/inst/doc/CoGAPS.html
vignetteTitles: CoGAPS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CoGAPS/inst/doc/CoGAPS.R
suggestsMe: projectR, SpaceMarkers
dependencyCount: 105
Package: cogena
Version: 1.41.0
Depends: R (>= 3.6), cluster, ggplot2, kohonen
Imports: methods, class, gplots, mclust, amap, apcluster, foreach,
parallel, doParallel, fastcluster, corrplot, biwt, Biobase,
reshape2, stringr, tibble, tidyr, dplyr, devtools
Suggests: knitr, rmarkdown (>= 2.1)
License: LGPL-3
Archs: x64
MD5sum: 138e5acd3f1e63c2efff7257f301a554
NeedsCompilation: no
Title: co-expressed gene-set enrichment analysis
Description: cogena is a workflow for co-expressed gene-set enrichment
analysis. It aims to discovery smaller scale, but highly
correlated cellular events that may be of great biological
relevance. A novel pipeline for drug discovery and drug
repositioning based on the cogena workflow is proposed.
Particularly, candidate drugs can be predicted based on the
gene expression of disease-related data, or other similar drugs
can be identified based on the gene expression of drug-related
data. Moreover, the drug mode of action can be disclosed by the
associated pathway analysis. In summary, cogena is a flexible
workflow for various gene set enrichment analysis for
co-expressed genes, with a focus on pathway/GO analysis and
drug repositioning.
biocViews: Clustering, GeneSetEnrichment, GeneExpression,
Visualization, Pathways, KEGG, GO, Microarray, Sequencing,
SystemsBiology, DataRepresentation, DataImport
Author: Zhilong Jia [aut, cre], Michael Barnes [aut]
Maintainer: Zhilong Jia <zhilongjia@gmail.com>
URL: https://github.com/zhilongjia/cogena
VignetteBuilder: knitr
BugReports: https://github.com/zhilongjia/cogena/issues
git_url: https://git.bioconductor.org/packages/cogena
git_branch: devel
git_last_commit: 06f4d8f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cogena_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cogena_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cogena_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cogena_1.41.0.tgz
vignettes: vignettes/cogena/inst/doc/cogena-vignette_pdf.pdf,
vignettes/cogena/inst/doc/cogena-vignette_html.html
vignetteTitles: a workflow of cogena, cogena,, a workflow for gene set
enrichment analysis of co-expressed genes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cogena/inst/doc/cogena-vignette_html.R,
vignettes/cogena/inst/doc/cogena-vignette_pdf.R
dependencyCount: 146
Package: cogeqc
Version: 1.11.0
Depends: R (>= 4.2.0)
Imports: utils, graphics, stats, methods, reshape2, ggplot2, scales,
ggtree, patchwork, igraph, rlang, ggbeeswarm, jsonlite,
Biostrings
Suggests: testthat (>= 3.0.0), sessioninfo, knitr, BiocStyle,
rmarkdown, covr
License: GPL-3
MD5sum: d3a9c65194ccb0a197e3249ff4c253bd
NeedsCompilation: no
Title: Systematic quality checks on comparative genomics analyses
Description: cogeqc aims to facilitate systematic quality checks on
standard comparative genomics analyses to help researchers
detect issues and select the most suitable parameters for each
data set. cogeqc can be used to asses: i. genome assembly and
annotation quality with BUSCOs and comparisons of statistics
with publicly available genomes on the NCBI; ii. orthogroup
inference using a protein domain-based approach and; iii.
synteny detection using synteny network properties. There are
also data visualization functions to explore QC summary
statistics.
biocViews: Software, GenomeAssembly, ComparativeGenomics,
FunctionalGenomics, Phylogenetics, QualityControl, Network
Author: FabrÃcio Almeida-Silva [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5314-2964>), Yves Van de Peer
[aut] (ORCID: <https://orcid.org/0000-0003-4327-3730>)
Maintainer: FabrÃcio Almeida-Silva <fabricio_almeidasilva@hotmail.com>
URL: https://github.com/almeidasilvaf/cogeqc
SystemRequirements: BUSCO (>= 5.1.3) <https://busco.ezlab.org/>
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/cogeqc
git_url: https://git.bioconductor.org/packages/cogeqc
git_branch: devel
git_last_commit: 8c60dba
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cogeqc_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cogeqc_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cogeqc_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cogeqc_1.11.0.tgz
vignettes:
vignettes/cogeqc/inst/doc/vignette_01_assessing_genome_assembly.html,
vignettes/cogeqc/inst/doc/vignette_02_assessing_orthogroup_inference.html,
vignettes/cogeqc/inst/doc/vignette_03_assessing_synteny.html
vignetteTitles: Assessing genome assembly and annotation quality,
Assessing orthogroup inference, Assessing synteny
identification
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/cogeqc/inst/doc/vignette_01_assessing_genome_assembly.R,
vignettes/cogeqc/inst/doc/vignette_02_assessing_orthogroup_inference.R,
vignettes/cogeqc/inst/doc/vignette_03_assessing_synteny.R
dependencyCount: 82
Package: Cogito
Version: 1.13.0
Depends: R (>= 4.1), GenomicRanges, jsonlite, GenomicFeatures, entropy
Imports: BiocManager, rmarkdown, GenomeInfoDb, S4Vectors,
AnnotationDbi, graphics, stats, utils, methods, magrittr,
ggplot2, TxDb.Mmusculus.UCSC.mm9.knownGene
Suggests: BiocStyle, knitr, markdown, testthat (>= 3.0.0)
License: LGPL-3
MD5sum: 0a7e1de1df4a48cbd571be91e663bb39
NeedsCompilation: no
Title: Compare genomic intervals tool - Automated, complete,
reproducible and clear report about genomic and epigenomic data
sets
Description: Biological studies often consist of multiple conditions
which are examined with different laboratory set ups like
RNA-sequencing or ChIP-sequencing. To get an overview about the
whole resulting data set, Cogito provides an automated,
complete, reproducible and clear report about all samples and
basic comparisons between all different samples. This report
can be used as documentation about the data set or as starting
point for further custom analysis.
biocViews: FunctionalGenomics, GeneRegulation, Software, Sequencing
Author: Annika Bürger [cre, aut]
Maintainer: Annika Bürger <a.buerger@uni-muenster.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Cogito
git_branch: devel
git_last_commit: 55b1c5a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Cogito_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Cogito_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Cogito_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Cogito_1.13.0.tgz
vignettes: vignettes/Cogito/inst/doc/Cogito.html
vignetteTitles: Cogito: Compare annotated genomic intervals tool
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Cogito/inst/doc/Cogito.R
dependencyCount: 115
Package: coGPS
Version: 1.51.0
Depends: R (>= 2.13.0)
Imports: graphics, grDevices
Suggests: limma
License: GPL-2
MD5sum: 4e3629d613c37c64db8589b97fd59b14
NeedsCompilation: no
Title: cancer outlier Gene Profile Sets
Description: Gene Set Enrichment Analysis of P-value based statistics
for outlier gene detection in dataset merged from multiple
studies
biocViews: Microarray, DifferentialExpression
Author: Yingying Wei, Michael Ochs
Maintainer: Yingying Wei <ywei@jhsph.edu>
git_url: https://git.bioconductor.org/packages/coGPS
git_branch: devel
git_last_commit: 2f7fc38
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/coGPS_1.51.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/coGPS_1.51.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/coGPS_1.51.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/coGPS_1.51.0.tgz
vignettes: vignettes/coGPS/inst/doc/coGPS.pdf
vignetteTitles: coGPS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/coGPS/inst/doc/coGPS.R
dependencyCount: 2
Package: cola
Version: 2.13.1
Depends: R (>= 4.0.0)
Imports: grDevices, graphics, grid, stats, utils, ComplexHeatmap (>=
2.5.4), matrixStats (>= 1.2.0), GetoptLong, circlize (>=
0.4.7), GlobalOptions (>= 0.1.0), clue, parallel, RColorBrewer,
cluster, skmeans, png, mclust, crayon, methods, xml2,
microbenchmark, httr, knitr (>= 1.4.0), markdown (>= 1.6),
digest, impute, brew, Rcpp (>= 0.11.0), BiocGenerics, eulerr,
foreach, doParallel, doRNG, irlba
LinkingTo: Rcpp
Suggests: genefilter, mvtnorm, testthat (>= 0.3), samr, pamr, kohonen,
NMF, WGCNA, Rtsne, umap, clusterProfiler, ReactomePA, DOSE,
AnnotationDbi, gplots, hu6800.db, BiocManager, data.tree,
dendextend, Polychrome, rmarkdown, simplifyEnrichment, cowplot,
flexclust, randomForest, e1071
License: MIT + file LICENSE
MD5sum: e29788f1098117e6a37cf864986c05fc
NeedsCompilation: yes
Title: A Framework for Consensus Partitioning
Description: Subgroup classification is a basic task in genomic data
analysis, especially for gene expression and DNA methylation
data analysis. It can also be used to test the agreement to
known clinical annotations, or to test whether there exist
significant batch effects. The cola package provides a general
framework for subgroup classification by consensus
partitioning. It has the following features: 1. It modularizes
the consensus partitioning processes that various methods can
be easily integrated. 2. It provides rich visualizations for
interpreting the results. 3. It allows running multiple methods
at the same time and provides functionalities to
straightforward compare results. 4. It provides a new method to
extract features which are more efficient to separate
subgroups. 5. It automatically generates detailed reports for
the complete analysis. 6. It allows applying consensus
partitioning in a hierarchical manner.
biocViews: Clustering, GeneExpression, Classification, Software
Author: Zuguang Gu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7395-8709>)
Maintainer: Zuguang Gu <z.gu@dkfz.de>
URL: https://github.com/jokergoo/cola,
https://jokergoo.github.io/cola_collection/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/cola
git_branch: devel
git_last_commit: 1f071f7
git_last_commit_date: 2025-02-06
Date/Publication: 2025-02-09
source.ver: src/contrib/cola_2.13.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cola_2.13.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cola_2.13.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cola_2.13.1.tgz
vignettes: vignettes/cola/inst/doc/cola.html
vignetteTitles: The cola package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
suggestsMe: InteractiveComplexHeatmap, simplifyEnrichment
dependencyCount: 67
Package: comapr
Version: 1.11.1
Depends: R (>= 4.1.0)
Imports: methods, ggplot2, reshape2, dplyr, gridExtra, plotly,
circlize, rlang, GenomicRanges, IRanges, foreach, BiocParallel,
GenomeInfoDb, scales, RColorBrewer, tidyr, S4Vectors, utils,
Matrix, grid, stats, SummarizedExperiment, plyr, Gviz
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0), statmod
License: MIT + file LICENSE
Archs: x64
MD5sum: 7498b3628a641dd647a9d45505e75607
NeedsCompilation: no
Title: Crossover analysis and genetic map construction
Description: comapr detects crossover intervals for single gametes from
their haplotype states sequences and stores the crossovers in
GRanges object. The genetic distances can then be calculated
via the mapping functions using estimated crossover rates for
maker intervals. Visualisation functions for plotting
interval-based genetic map or cumulative genetic distances are
implemented, which help reveal the variation of crossovers
landscapes across the genome and across individuals.
biocViews: Software, SingleCell, Visualization, Genetics
Author: Ruqian Lyu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7736-6612>)
Maintainer: Ruqian Lyu <xiaoru.best@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/comapr
git_branch: devel
git_last_commit: 600d726
git_last_commit_date: 2024-12-21
Date/Publication: 2024-12-22
source.ver: src/contrib/comapr_1.11.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/comapr_1.11.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/comapr_1.11.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/comapr_1.11.1.tgz
vignettes: vignettes/comapr/inst/doc/getStarted.html,
vignettes/comapr/inst/doc/single-sperm-co-analysis.html
vignetteTitles: Get-Started-With-comapr, single-sperm-co-analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/comapr/inst/doc/getStarted.R,
vignettes/comapr/inst/doc/single-sperm-co-analysis.R
dependencyCount: 167
Package: combi
Version: 1.19.0
Depends: R (>= 4.0), DBI
Imports: ggplot2, nleqslv, phyloseq, tensor, stats, limma, Matrix (>=
1.6.0), BB, reshape2, alabama, cobs, Biobase, vegan, grDevices,
graphics, methods, SummarizedExperiment
Suggests: knitr, rmarkdown, testthat
License: GPL-2
MD5sum: a61990a045a86084c0e4668f6e8623fe
NeedsCompilation: no
Title: Compositional omics model based visual integration
Description: This explorative ordination method combines
quasi-likelihood estimation, compositional regression models
and latent variable models for integrative visualization of
several omics datasets. Both unconstrained and constrained
integration are available. The results are shown as
interpretable, compositional multiplots.
biocViews: Metagenomics, DimensionReduction, Microbiome, Visualization,
Metabolomics
Author: Stijn Hawinkel [cre, aut] (ORCID:
<https://orcid.org/0000-0002-4501-5180>)
Maintainer: Stijn Hawinkel <stijn.hawinkel@psb.ugent.be>
VignetteBuilder: knitr
BugReports: https://github.com/CenterForStatistics-UGent/combi/issues
git_url: https://git.bioconductor.org/packages/combi
git_branch: devel
git_last_commit: fb1252a
git_last_commit_date: 2024-10-29
Date/Publication: 2025-01-23
source.ver: src/contrib/combi_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/combi_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/combi_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/combi_1.19.0.tgz
vignettes: vignettes/combi/inst/doc/combi.html
vignetteTitles: Manual for the combi pacakage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/combi/inst/doc/combi.R
dependencyCount: 104
Package: coMethDMR
Version: 1.11.0
Depends: R (>= 4.1)
Imports: AnnotationHub, BiocParallel, bumphunter, ExperimentHub,
GenomicRanges, IRanges, lmerTest, methods, stats, utils
Suggests: BiocStyle, corrplot, knitr, rmarkdown, testthat,
IlluminaHumanMethylation450kanno.ilmn12.hg19,
IlluminaHumanMethylationEPICanno.ilm10b4.hg19
License: GPL-3
MD5sum: 347d785916496ecade0da58251f7e995
NeedsCompilation: no
Title: Accurate identification of co-methylated and differentially
methylated regions in epigenome-wide association studies
Description: coMethDMR identifies genomic regions associated with
continuous phenotypes by optimally leverages covariations among
CpGs within predefined genomic regions. Instead of testing all
CpGs within a genomic region, coMethDMR carries out an
additional step that selects co-methylated sub-regions first
without using any outcome information. Next, coMethDMR tests
association between methylation within the sub-region and
continuous phenotype using a random coefficient mixed effects
model, which models both variations between CpG sites within
the region and differential methylation simultaneously.
biocViews: DNAMethylation, Epigenetics, MethylationArray,
DifferentialMethylation, GenomeWideAssociation
Author: Fernanda Veitzman [cre], Lissette Gomez [aut], Tiago Silva
[aut], Ning Lijiao [ctb], Boissel Mathilde [ctb], Lily Wang
[aut], Gabriel Odom [aut]
Maintainer: Fernanda Veitzman <fveit001@fiu.edu>
URL: https://github.com/TransBioInfoLab/coMethDMR
VignetteBuilder: knitr
BugReports: https://github.com/TransBioInfoLab/coMethDMR/issues
git_url: https://git.bioconductor.org/packages/coMethDMR
git_branch: devel
git_last_commit: 7275ade
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/coMethDMR_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/coMethDMR_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/coMethDMR_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/coMethDMR_1.11.0.tgz
vignettes:
vignettes/coMethDMR/inst/doc/vin1_Introduction_to_coMethDMR_geneBasedPipeline.html,
vignettes/coMethDMR/inst/doc/vin2_BiocParallel_for_coMethDMR_geneBasedPipeline.html
vignetteTitles: "Introduction to coMethDMR", "coMethDMR with Parallel
Computing"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/coMethDMR/inst/doc/vin1_Introduction_to_coMethDMR_geneBasedPipeline.R,
vignettes/coMethDMR/inst/doc/vin2_BiocParallel_for_coMethDMR_geneBasedPipeline.R
dependencyCount: 131
Package: COMPASS
Version: 1.45.0
Depends: R (>= 3.0.3)
Imports: methods, Rcpp, data.table, RColorBrewer, scales, grid, plyr,
knitr, abind, clue, grDevices, utils, pdist, magrittr,
reshape2, dplyr, tidyr, rlang, BiocStyle, rmarkdown, foreach,
coda
LinkingTo: Rcpp (>= 0.11.0)
Suggests: flowWorkspace (>= 3.33.1), flowCore, ncdfFlow, shiny,
testthat, devtools, flowWorkspaceData, ggplot2, progress
License: Artistic-2.0
MD5sum: 059d957ef76dca48274d0435f90dbaf9
NeedsCompilation: yes
Title: Combinatorial Polyfunctionality Analysis of Single Cells
Description: COMPASS is a statistical framework that enables unbiased
analysis of antigen-specific T-cell subsets. COMPASS uses a
Bayesian hierarchical framework to model all observed
cell-subsets and select the most likely to be antigen-specific
while regularizing the small cell counts that often arise in
multi-parameter space. The model provides a posterior
probability of specificity for each cell subset and each
sample, which can be used to profile a subject's immune
response to external stimuli such as infection or vaccination.
biocViews: ImmunoOncology, FlowCytometry
Author: Lynn Lin, Kevin Ushey, Greg Finak, Ravio Kolde (pheatmap)
Maintainer: Greg Finak <gfinak@fhcrc.org>
VignetteBuilder: knitr
BugReports: https://github.com/RGLab/COMPASS/issues
git_url: https://git.bioconductor.org/packages/COMPASS
git_branch: devel
git_last_commit: 2b16d61
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/COMPASS_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/COMPASS_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/COMPASS_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/COMPASS_1.45.0.tgz
vignettes: vignettes/COMPASS/inst/doc/SimpleCOMPASS.pdf,
vignettes/COMPASS/inst/doc/COMPASS.html
vignetteTitles: SimpleCOMPASS, COMPASS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/COMPASS/inst/doc/COMPASS.R,
vignettes/COMPASS/inst/doc/SimpleCOMPASS.R
dependencyCount: 72
Package: compcodeR
Version: 1.43.0
Depends: R (>= 4.0), sm
Imports: knitr (>= 1.2), markdown, ROCR, lattice (>= 0.16), gplots,
gtools, caTools, grid, KernSmooth, MASS, ggplot2, stringr,
modeest, edgeR, limma, vioplot, methods, stats, utils, ape,
phylolm, matrixStats, grDevices, graphics, rmarkdown, shiny,
shinydashboard
Suggests: BiocStyle, EBSeq, DESeq2 (>= 1.1.31), genefilter, NOISeq,
TCC, NBPSeq (>= 0.3.0), phytools, phangorn, testthat, ggtree,
tidytree, statmod, covr, sva, tcltk
Enhances: rpanel, DSS
License: GPL (>= 2)
Archs: x64
MD5sum: 17ef7033e2ea2d39d63316951483bc53
NeedsCompilation: no
Title: RNAseq data simulation, differential expression analysis and
performance comparison of differential expression methods
Description: This package provides extensive functionality for
comparing results obtained by different methods for
differential expression analysis of RNAseq data. It also
contains functions for simulating count data. Finally, it
provides convenient interfaces to several packages for
performing the differential expression analysis. These can also
be used as templates for setting up and running a user-defined
differential analysis workflow within the framework of the
package.
biocViews: ImmunoOncology, RNASeq, DifferentialExpression
Author: Charlotte Soneson [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3833-2169>), Paul Bastide [aut]
(ORCID: <https://orcid.org/0000-0002-8084-9893>), Mélina
Gallopin [aut] (ORCID: <https://orcid.org/0000-0002-2431-7825>)
Maintainer: Charlotte Soneson <charlottesoneson@gmail.com>
URL: https://github.com/csoneson/compcodeR
VignetteBuilder: knitr
BugReports: https://github.com/csoneson/compcodeR/issues
git_url: https://git.bioconductor.org/packages/compcodeR
git_branch: devel
git_last_commit: a538f70
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/compcodeR_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/compcodeR_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/compcodeR_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/compcodeR_1.43.0.tgz
vignettes: vignettes/compcodeR/inst/doc/compcodeR.html,
vignettes/compcodeR/inst/doc/phylocompcodeR.html
vignetteTitles: compcodeR, phylocompcodeR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/compcodeR/inst/doc/compcodeR.R,
vignettes/compcodeR/inst/doc/phylocompcodeR.R
dependencyCount: 107
Package: compEpiTools
Version: 1.41.0
Depends: R (>= 3.5.0), methods, topGO, GenomicRanges
Imports: AnnotationDbi, BiocGenerics, Biostrings, Rsamtools, parallel,
grDevices, gplots, IRanges, GenomicFeatures, XVector,
methylPipe, GO.db, S4Vectors, GenomeInfoDb
Suggests: BSgenome.Mmusculus.UCSC.mm9,
TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, knitr,
rtracklayer
License: GPL
MD5sum: a99e47d82a953db9f01925fc3abe1ebe
NeedsCompilation: no
Title: Tools for computational epigenomics
Description: Tools for computational epigenomics developed for the
analysis, integration and simultaneous visualization of various
(epi)genomics data types across multiple genomic regions in
multiple samples.
biocViews: GeneExpression, Sequencing, Visualization, GenomeAnnotation,
Coverage
Author: Mattia Pelizzola [aut], Kamal Kishore [aut], Mattia Furlan
[ctb, cre]
Maintainer: Mattia Furlan <mattia.furlan@iit.it>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/compEpiTools
git_branch: devel
git_last_commit: 87788a3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/compEpiTools_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/compEpiTools_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/compEpiTools_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/compEpiTools_1.41.0.tgz
vignettes: vignettes/compEpiTools/inst/doc/compEpiTools.pdf
vignetteTitles: compEpiTools.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/compEpiTools/inst/doc/compEpiTools.R
dependencyCount: 168
Package: ComplexHeatmap
Version: 2.23.0
Depends: R (>= 3.5.0), methods, grid, graphics, stats, grDevices
Imports: circlize (>= 0.4.14), GetoptLong, colorspace, clue,
RColorBrewer, GlobalOptions (>= 0.1.0), png, digest, IRanges,
matrixStats, foreach, doParallel, codetools
Suggests: testthat (>= 1.0.0), knitr, markdown, dendsort, jpeg, tiff,
fastcluster, EnrichedHeatmap, dendextend (>= 1.0.1), grImport,
grImport2, glue, GenomicRanges, gridtext, pheatmap (>= 1.0.12),
gridGraphics, gplots, rmarkdown, Cairo, magick
License: MIT + file LICENSE
MD5sum: 0446da886e3ab7b3d190ab7022c5833e
NeedsCompilation: no
Title: Make Complex Heatmaps
Description: Complex heatmaps are efficient to visualize associations
between different sources of data sets and reveal potential
patterns. Here the ComplexHeatmap package provides a highly
flexible way to arrange multiple heatmaps and supports various
annotation graphics.
biocViews: Software, Visualization, Sequencing
Author: Zuguang Gu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7395-8709>)
Maintainer: Zuguang Gu <z.gu@dkfz.de>
URL: https://github.com/jokergoo/ComplexHeatmap,
https://jokergoo.github.io/ComplexHeatmap-reference/book/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ComplexHeatmap
git_branch: devel
git_last_commit: 29bc185
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ComplexHeatmap_2.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ComplexHeatmap_2.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ComplexHeatmap_2.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ComplexHeatmap_2.23.0.tgz
vignettes: vignettes/ComplexHeatmap/inst/doc/complex_heatmap.html,
vignettes/ComplexHeatmap/inst/doc/most_probably_asked_questions.html
vignetteTitles: complex_heatmap.html, Most probably asked questions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles:
vignettes/ComplexHeatmap/inst/doc/most_probably_asked_questions.R
dependsOnMe: AMARETTO, EnrichedHeatmap, InteractiveComplexHeatmap,
multistateQTL, recoup, sechm, countToFPKM
importsMe: airpart, ASURAT, bettr, BindingSiteFinder, BioNERO,
blacksheepr, BloodGen3Module, BulkSignalR, CATALYST, CCPlotR,
celda, CeTF, chevreulPlot, chevreulShiny, ClustAll, COCOA,
cola, COTAN, CRISPRball, CTexploreR, cytoKernel, Damsel, dar,
DEGreport, DEP, diffcyt, diffUTR, dinoR, dominoSignal, ELMER,
ELViS, epiregulon.extra, fCCAC, FLAMES, gCrisprTools, GeDi,
GeneTonic, GenomicPlot, GenomicSuperSignature, geyser,
gINTomics, gmoviz, GRaNIE, hermes, hoodscanR, HybridExpress,
InterCellar, iSEE, MAPFX, MatrixQCvis, MesKit, MOMA, monaLisa,
Moonlight2R, MOSClip, MPAC, MultiRNAflow, muscat, musicatk,
MWASTools, nipalsMCIA, pathlinkR, PathoStat, PeacoQC, pipeComp,
POMA, profileplyr, PRONE, scRNAseqApp, segmenter, signifinder,
simona, simplifyEnrichment, singleCellTK, sparrow, SPONGE,
TBSignatureProfiler, TMSig, Xeva, YAPSA, ProteinGymR,
spatialLIBD, autoGO, bulkAnalyseR, coda4microbiome, conos,
DiscreteGapStatistic, GRIN2, GSSTDA, karyotapR, mineSweepR,
missoNet, MitoHEAR, MKomics, ogrdbstats, Path.Analysis,
PCAPAM50, pkgndep, rKOMICS, rliger, RNAseqQC, RVA, scITD,
SeuratExplorer, sigQC, spiralize, tidyHeatmap, TransProR,
visxhclust, wilson
suggestsMe: artMS, bambu, clustifyr, CNVRanger, demuxSNP, dittoSeq,
EnrichmentBrowser, gtrellis, HilbertCurve, mastR, miaViz,
msImpute, plotgardener, projectR, QFeatures, raer, scDblFinder,
SPIAT, TCGAbiolinks, TCGAutils, weitrix, curatedPCaData,
LegATo, NanoporeRNASeq, BeeBDC, CIARA, circlize, ClustAssess,
ConsensusOPLS, eclust, ggsector, grandR, inferCSN, IOHanalyzer,
metasnf, multipanelfigure, plotthis, scCustomize, SCpubr,
sfcurve, singleCellHaystack, SpatialDDLS, tinyarray
dependencyCount: 29
Package: CompoundDb
Version: 1.11.2
Depends: R (>= 4.1), methods, AnnotationFilter, S4Vectors
Imports: BiocGenerics, ChemmineR, tibble, jsonlite, dplyr, DBI, dbplyr,
RSQLite, Biobase, ProtGenerics (>= 1.35.3), xml2, IRanges,
Spectra (>= 1.15.10), MsCoreUtils, MetaboCoreUtils,
BiocParallel, stringi
Suggests: knitr, rmarkdown, testthat, BiocStyle (>= 2.5.19),
MsBackendMgf
License: Artistic-2.0
MD5sum: 9d53504e87446695ed8ae5f54f50bfbe
NeedsCompilation: no
Title: Creating and Using (Chemical) Compound Annotation Databases
Description: CompoundDb provides functionality to create and use
(chemical) compound annotation databases from a variety of
different sources such as LipidMaps, HMDB, ChEBI or MassBank.
The database format allows to store in addition MS/MS spectra
along with compound information. The package provides also a
backend for Bioconductor's Spectra package and allows thus to
match experimetal MS/MS spectra against MS/MS spectra in the
database. Databases can be stored in SQLite format and are thus
portable.
biocViews: MassSpectrometry, Metabolomics, Annotation
Author: Jan Stanstrup [aut] (ORCID:
<https://orcid.org/0000-0003-0541-7369>), Johannes Rainer [aut,
cre] (ORCID: <https://orcid.org/0000-0002-6977-7147>), Josep M.
Badia [ctb] (ORCID: <https://orcid.org/0000-0002-5704-1124>),
Roger Gine [aut] (ORCID:
<https://orcid.org/0000-0003-0288-9619>), Andrea Vicini [aut]
(ORCID: <https://orcid.org/0000-0001-9438-6909>), Prateek Arora
[ctb] (ORCID: <https://orcid.org/0000-0003-0822-9240>)
Maintainer: Johannes Rainer <johannes.rainer@eurac.edu>
URL: https://github.com/RforMassSpectrometry/CompoundDb
VignetteBuilder: knitr
BugReports: https://github.com/RforMassSpectrometry/CompoundDb/issues
git_url: https://git.bioconductor.org/packages/CompoundDb
git_branch: devel
git_last_commit: 6421537
git_last_commit_date: 2025-01-17
Date/Publication: 2025-01-17
source.ver: src/contrib/CompoundDb_1.11.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CompoundDb_1.11.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CompoundDb_1.11.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CompoundDb_1.11.2.tgz
vignettes: vignettes/CompoundDb/inst/doc/CompoundDb-usage.html,
vignettes/CompoundDb/inst/doc/create-compounddb.html
vignetteTitles: Usage of Annotation Resources with the CompoundDb
Package, Creating CompoundDb annotation resources
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CompoundDb/inst/doc/CompoundDb-usage.R,
vignettes/CompoundDb/inst/doc/create-compounddb.R
importsMe: MetaboAnnotation
suggestsMe: AHMassBank, AnnotationHub, MetMashR
dependencyCount: 119
Package: ComPrAn
Version: 1.15.0
Imports: data.table, dplyr, forcats, ggplot2, magrittr, purrr, tidyr,
rlang, stringr, shiny, DT, RColorBrewer, VennDiagram, rio,
scales, shinydashboard, shinyjs, stats, tibble, grid
Suggests: testthat (>= 2.1.0), knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 4e214249c74bbe4fd223f79acfee271c
NeedsCompilation: no
Title: Complexome Profiling Analysis package
Description: This package is for analysis of SILAC labeled complexome
profiling data. It uses peptide table in tab-delimited format
as an input and produces ready-to-use tables and plots.
biocViews: MassSpectrometry, Proteomics, Visualization
Author: Rick Scavetta [aut], Petra Palenikova [aut, cre] (ORCID:
<https://orcid.org/0000-0002-2465-4370>)
Maintainer: Petra Palenikova <palenikova3@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ComPrAn
git_branch: devel
git_last_commit: 30c8d3c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ComPrAn_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ComPrAn_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ComPrAn_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ComPrAn_1.15.0.tgz
vignettes: vignettes/ComPrAn/inst/doc/fileFormats.html,
vignettes/ComPrAn/inst/doc/proteinWorkflow.html,
vignettes/ComPrAn/inst/doc/SILACcomplexomics.html
vignetteTitles: fileFormats.html, Protein workflow, SILAC complexomics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ComPrAn/inst/doc/fileFormats.R,
vignettes/ComPrAn/inst/doc/proteinWorkflow.R,
vignettes/ComPrAn/inst/doc/SILACcomplexomics.R
dependencyCount: 107
Package: compSPOT
Version: 1.5.0
Depends: R (>= 4.3.0)
Imports: stats, base, ggplot2, plotly, magrittr, ggpubr, gridExtra,
utils, data.table
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0)
License: Artistic-2.0
MD5sum: 3bc6e3c265e9eb3e9019237f0aec72e1
NeedsCompilation: no
Title: compSPOT: Tool for identifying and comparing significantly
mutated genomic hotspots
Description: Clonal cell groups share common mutations within cancer,
precancer, and even clinically normal appearing tissues. The
frequency and location of these mutations may predict prognosis
and cancer risk. It has also been well established that certain
genomic regions have increased sensitivity to acquiring
mutations. Mutation-sensitive genomic regions may therefore
serve as markers for predicting cancer risk. This package
contains multiple functions to establish significantly mutated
hotspots, compare hotspot mutation burden between samples, and
perform exploratory data analysis of the correlation between
hotspot mutation burden and personal risk factors for cancer,
such as age, gender, and history of carcinogen exposure. This
package allows users to identify robust genomic markers to help
establish cancer risk.
biocViews: Software, Technology, Sequencing, DNASeq, WholeGenome,
Classification, SingleCell, Survival, MultipleComparison
Author: Sydney Grant [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1849-5921>), Ella Sampson [aut],
Rhea Rodrigues [aut] (ORCID:
<https://orcid.org/0000-0002-8573-8658>), Gyorgy Paragh [aut]
(ORCID: <https://orcid.org/0000-0002-6612-9267>)
Maintainer: Sydney Grant <Sydney.Grant@roswellpark.org>
URL: https://github.com/sydney-grant/compSPOT
VignetteBuilder: knitr
BugReports: https://github.com/sydney-grant/compSPOT/issues
git_url: https://git.bioconductor.org/packages/compSPOT
git_branch: devel
git_last_commit: 05639f2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/compSPOT_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/compSPOT_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/compSPOT_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/compSPOT_1.5.0.tgz
vignettes: vignettes/compSPOT/inst/doc/compSPOT-vignette.html
vignetteTitles: compSPOT-Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/compSPOT/inst/doc/compSPOT-vignette.R
dependencyCount: 114
Package: concordexR
Version: 1.7.1
Depends: R (>= 4.4.0)
Imports: BiocGenerics, BiocNeighbors, BiocParallel, bluster, cli,
DelayedArray, Matrix, methods, purrr, rlang,
SingleCellExperiment, sparseMatrixStats, SpatialExperiment,
SummarizedExperiment
Suggests: BiocManager, BiocStyle, ggplot2, glue, knitr, mbkmeans,
patchwork, rmarkdown, scater, SFEData,
SpatialFeatureExperiment, TENxPBMCData, testthat (>= 3.0.0)
License: Artistic-2.0
MD5sum: 6187881982886028bf48011284f1a34d
NeedsCompilation: no
Title: Identify Spatial Homogeneous Regions with concordex
Description: Spatial homogeneous regions (SHRs) in tissues are domains
that are homogenous with respect to cell type composition. We
present a method for identifying SHRs using spatial
transcriptomics data, and demonstrate that it is efficient and
effective at finding SHRs for a wide variety of tissue types.
concordex relies on analysis of k-nearest-neighbor (kNN)
graphs. The tool is also useful for analysis of non-spatial
transcriptomics data, and can elucidate the extent of
concordance between partitions of cells derived from clustering
algorithms, and transcriptomic similarity as represented in kNN
graphs.
biocViews: SingleCell, Clustering, Spatial, Transcriptomics
Author: Kayla Jackson [aut, cre] (ORCID:
<https://orcid.org/0000-0001-6483-0108>), A. Sina Booeshaghi
[aut] (ORCID: <https://orcid.org/0000-0002-6442-4502>), Angel
Galvez-Merchan [aut] (ORCID:
<https://orcid.org/0000-0001-7420-8697>), Lambda Moses [aut]
(ORCID: <https://orcid.org/0000-0002-7092-9427>), Alexandra Kim
[ctb], Laura Luebbert [ctb] (ORCID:
<https://orcid.org/0000-0003-1379-2927>), Lior Pachter [aut,
rev, ths] (ORCID: <https://orcid.org/0000-0002-9164-6231>)
Maintainer: Kayla Jackson <kaylajac@caltech.edu>
URL: https://github.com/pachterlab/concordexR,
https://pachterlab.github.io/concordexR/
VignetteBuilder: knitr
BugReports: https://github.com/pachterlab/concordexR/issues
git_url: https://git.bioconductor.org/packages/concordexR
git_branch: devel
git_last_commit: 82b99bd
git_last_commit_date: 2025-01-16
Date/Publication: 2025-01-17
source.ver: src/contrib/concordexR_1.7.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/concordexR_1.7.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/concordexR_1.7.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/concordexR_1.7.1.tgz
vignettes: vignettes/concordexR/inst/doc/concordex-nonspatial.html,
vignettes/concordexR/inst/doc/overview.html
vignetteTitles: concordex-nonspatial, overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/concordexR/inst/doc/concordex-nonspatial.R,
vignettes/concordexR/inst/doc/overview.R
dependencyCount: 87
Package: condiments
Version: 1.15.1
Depends: R (>= 4.0)
Imports: slingshot (>= 1.9), mgcv, RANN, stats, SingleCellExperiment,
SummarizedExperiment, utils, magrittr, dplyr (>= 1.0), Ecume
(>= 0.9.1), methods, pbapply, matrixStats, BiocParallel,
TrajectoryUtils, igraph, distinct
Suggests: knitr, testthat, rmarkdown, covr, viridis, ggplot2,
RColorBrewer, randomForest, tidyr, TSCAN, DelayedMatrixStats
License: MIT + file LICENSE
MD5sum: eb27382855a55ca4771830d71d1c3080
NeedsCompilation: no
Title: Differential Topology, Progression and Differentiation
Description: This package encapsulate many functions to conduct a
differential topology analysis. It focuses on analyzing an
'omic dataset with multiple conditions. While the package is
mostly geared toward scRNASeq, it does not place any
restriction on the actual input format.
biocViews: RNASeq, Sequencing, Software, SingleCell, Transcriptomics,
MultipleComparison, Visualization
Author: Hector Roux de Bezieux [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1489-8339>), Koen Van den Berge
[aut, ctb], Kelly Street [aut, ctb]
Maintainer: Hector Roux de Bezieux <hector.rouxdebezieux@berkeley.edu>
URL: https://hectorrdb.github.io/condiments/index.html
VignetteBuilder: knitr
BugReports: https://github.com/HectorRDB/condiments/issues
git_url: https://git.bioconductor.org/packages/condiments
git_branch: devel
git_last_commit: e681d88
git_last_commit_date: 2024-12-03
Date/Publication: 2024-12-04
source.ver: src/contrib/condiments_1.15.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/condiments_1.15.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/condiments_1.15.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/condiments_1.15.1.tgz
vignettes: vignettes/condiments/inst/doc/condiments.html,
vignettes/condiments/inst/doc/controls.html,
vignettes/condiments/inst/doc/examples.html
vignetteTitles: The condiments workflow, Using condiments, Generating
more examples
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/condiments/inst/doc/condiments.R,
vignettes/condiments/inst/doc/controls.R,
vignettes/condiments/inst/doc/examples.R
dependencyCount: 168
Package: CONFESS
Version: 1.35.0
Depends: R (>= 3.3),grDevices,utils,stats,graphics
Imports: methods,changepoint,cluster,contrast,data.table(>=
1.9.7),ecp,EBImage,flexmix,flowCore,flowClust,flowMeans,flowMerge,flowPeaks,foreach,ggplot2,grid,limma,MASS,moments,outliers,parallel,plotrix,raster,readbitmap,reshape2,SamSPECTRAL,waveslim,wavethresh,zoo
Suggests: BiocStyle, knitr, rmarkdown, CONFESSdata
License: GPL-2
MD5sum: af15b2223a131cd695ba408dc642890d
NeedsCompilation: no
Title: Cell OrderiNg by FluorEScence Signal
Description: Single Cell Fluidigm Spot Detector.
biocViews: ImmunoOncology,
GeneExpression,DataImport,CellBiology,Clustering,RNASeq,QualityControl,Visualization,TimeCourse,Regression,Classification
Author: Diana LOW and Efthimios MOTAKIS
Maintainer: Diana LOW <lowdiana@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CONFESS
git_branch: devel
git_last_commit: be5739f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CONFESS_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CONFESS_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CONFESS_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CONFESS_1.35.0.tgz
vignettes: vignettes/CONFESS/inst/doc/vignette_tex.pdf,
vignettes/CONFESS/inst/doc/vignette.html
vignetteTitles: CONFESS, CONFESS Walkthrough
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CONFESS/inst/doc/vignette_tex.R,
vignettes/CONFESS/inst/doc/vignette.R
dependencyCount: 153
Package: consensus
Version: 1.25.0
Depends: R (>= 3.5), RColorBrewer
Imports: matrixStats, gplots, grDevices, methods, graphics, stats,
utils
Suggests: knitr, RUnit, rmarkdown, BiocGenerics
License: BSD_3_clause + file LICENSE
MD5sum: 6dfc91f751969a46ad60ee049bb9ec07
NeedsCompilation: no
Title: Cross-platform consensus analysis of genomic measurements via
interlaboratory testing method
Description: An implementation of the American Society for Testing and
Materials (ASTM) Standard E691 for interlaboratory testing
procedures, designed for cross-platform genomic measurements.
Given three (3) or more genomic platforms or laboratory
protocols, this package provides interlaboratory testing
procedures giving per-locus comparisons for sensitivity and
precision between platforms.
biocViews: QualityControl, Regression, DataRepresentation,
GeneExpression, Microarray, RNASeq
Author: Tim Peters
Maintainer: Tim Peters <t.peters@garvan.org.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/consensus
git_branch: devel
git_last_commit: c612c54
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/consensus_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/consensus_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/consensus_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/consensus_1.25.0.tgz
vignettes: vignettes/consensus/inst/doc/consensus.pdf
vignetteTitles: Fitting and visualising row-linear models with
\texttt{consensus}
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/consensus/inst/doc/consensus.R
dependencyCount: 12
Package: ConsensusClusterPlus
Version: 1.71.0
Imports: Biobase, ALL, graphics, stats, utils, cluster
License: GPL version 2
MD5sum: 3349265e3f12d49f470447109b61f2fa
NeedsCompilation: no
Title: ConsensusClusterPlus
Description: algorithm for determining cluster count and membership by
stability evidence in unsupervised analysis
biocViews: Software, Clustering
Author: Matt Wilkerson <mdwilkerson@outlook.com>, Peter Waltman
<waltman@soe.ucsc.edu>
Maintainer: Matt Wilkerson <mdwilkerson@outlook.com>
git_url: https://git.bioconductor.org/packages/ConsensusClusterPlus
git_branch: devel
git_last_commit: 21e0b53
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ConsensusClusterPlus_1.71.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ConsensusClusterPlus_1.71.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ConsensusClusterPlus_1.71.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ConsensusClusterPlus_1.71.0.tgz
vignettes:
vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.pdf
vignetteTitles: ConsensusClusterPlus Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.R
importsMe: CATALYST, ChromSCape, DEGreport, FlowSOM, PDATK,
DeSousa2013, iSubGen, longmixr, neatmaps, scRNAtools
suggestsMe: TCGAbiolinks, tidytof
dependencyCount: 10
Package: consensusDE
Version: 1.25.0
Depends: R (>= 3.5), BiocGenerics
Imports: airway, AnnotationDbi, BiocParallel, Biobase, Biostrings,
data.table, dendextend, DESeq2 (>= 1.20.0), EDASeq, ensembldb,
edgeR, EnsDb.Hsapiens.v86, GenomicAlignments, GenomicFeatures,
limma, org.Hs.eg.db, pcaMethods, RColorBrewer, Rsamtools,
RUVSeq, S4Vectors, stats, SummarizedExperiment,
TxDb.Dmelanogaster.UCSC.dm3.ensGene, utils
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: 4d001adcc9619a5e927e8626b73fbc51
NeedsCompilation: no
Title: RNA-seq analysis using multiple algorithms
Description: This package allows users to perform DE analysis using
multiple algorithms. It seeks consensus from multiple methods.
Currently it supports "Voom", "EdgeR" and "DESeq". It uses
RUV-seq (optional) to remove unwanted sources of variation.
biocViews: Transcriptomics, MultipleComparison, Clustering, Sequencing,
Software
Author: Ashley J. Waardenberg [aut, cre], Martha M. Cooper [ctb]
Maintainer: Ashley J. Waardenberg <a.waardenberg@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/consensusDE
git_branch: devel
git_last_commit: 44f18e0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/consensusDE_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/consensusDE_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/consensusDE_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/consensusDE_1.25.0.tgz
vignettes: vignettes/consensusDE/inst/doc/consensusDE.html
vignetteTitles: consensusDE
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/consensusDE/inst/doc/consensusDE.R
dependencyCount: 150
Package: consensusOV
Version: 1.29.0
Depends: R (>= 3.6)
Imports: Biobase, GSVA (>= 1.50.0), gdata, genefu, limma, matrixStats,
randomForest, stats, utils, methods, BiocParallel
Suggests: BiocStyle, ggplot2, knitr, rmarkdown, magick
License: Artistic-2.0
MD5sum: 3c7c4fdea6203168cc01aebe49a67194
NeedsCompilation: no
Title: Gene expression-based subtype classification for high-grade
serous ovarian cancer
Description: This package implements four major subtype classifiers for
high-grade serous (HGS) ovarian cancer as described by Helland
et al. (PLoS One, 2011), Bentink et al. (PLoS One, 2012),
Verhaak et al. (J Clin Invest, 2013), and Konecny et al. (J
Natl Cancer Inst, 2014). In addition, the package implements a
consensus classifier, which consolidates and improves on the
robustness of the proposed subtype classifiers, thereby
providing reliable stratification of patients with HGS ovarian
tumors of clearly defined subtype.
biocViews: Classification, Clustering, DifferentialExpression,
GeneExpression, Microarray, Transcriptomics
Author: Gregory M Chen [aut], Lavanya Kannan [aut], Ludwig Geistlinger
[aut], Victor Kofia [aut], Levi Waldron [aut], Christopher
Eeles [ctb], Benjamin Haibe-Kains [aut, cre]
Maintainer: Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>
URL: http://www.pmgenomics.ca/bhklab/software/consensusOV
VignetteBuilder: knitr
BugReports: https://github.com/bhklab/consensusOV/issues
git_url: https://git.bioconductor.org/packages/consensusOV
git_branch: devel
git_last_commit: fcae035
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-27
source.ver: src/contrib/consensusOV_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/consensusOV_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/consensusOV_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/consensusOV_1.29.0.tgz
vignettes: vignettes/consensusOV/inst/doc/consensusOV.html
vignetteTitles: Molecular subtyping for ovarian cancer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/consensusOV/inst/doc/consensusOV.R
importsMe: signifinder
dependencyCount: 154
Package: consensusSeekeR
Version: 1.35.0
Depends: R (>= 3.5.0), BiocGenerics, IRanges, GenomicRanges,
BiocParallel
Imports: GenomeInfoDb, rtracklayer, stringr, S4Vectors, methods
Suggests: BiocStyle, ggplot2, knitr, rmarkdown, RUnit
License: Artistic-2.0
MD5sum: 4b9f5c30f5d79e4c2cf23cb85e8cb896
NeedsCompilation: no
Title: Detection of consensus regions inside a group of experiences
using genomic positions and genomic ranges
Description: This package compares genomic positions and genomic ranges
from multiple experiments to extract common regions. The size
of the analyzed region is adjustable as well as the number of
experiences in which a feature must be present in a potential
region to tag this region as a consensus region. In genomic
analysis where feature identification generates a position
value surrounded by a genomic range, such as ChIP-Seq peaks and
nucleosome positions, the replication of an experiment may
result in slight differences between predicted values. This
package enables the conciliation of the results into consensus
regions.
biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison,
Transcription, PeakDetection, Sequencing, Coverage
Author: Astrid Deschênes [cre, aut] (ORCID:
<https://orcid.org/0000-0001-7846-6749>), Fabien Claude Lamaze
[ctb], Pascal Belleau [aut] (ORCID:
<https://orcid.org/0000-0002-0802-1071>), Arnaud Droit [aut]
Maintainer: Astrid Deschênes <adeschen@hotmail.com>
URL: https://github.com/adeschen/consensusSeekeR
VignetteBuilder: knitr
BugReports: https://github.com/adeschen/consensusSeekeR/issues
git_url: https://git.bioconductor.org/packages/consensusSeekeR
git_branch: devel
git_last_commit: 937d3ad
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/consensusSeekeR_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/consensusSeekeR_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/consensusSeekeR_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/consensusSeekeR_1.35.0.tgz
vignettes: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.html
vignetteTitles: Detection of consensus regions inside a group of
experiments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.R
importsMe: RJMCMCNucleosomes
suggestsMe: EpiCompare
dependencyCount: 66
Package: consICA
Version: 2.5.0
Depends: R (>= 4.2.0)
Imports: fastICA (>= 1.2.1), sm, org.Hs.eg.db, GO.db, stats,
SummarizedExperiment, BiocParallel, graph, ggplot2, methods,
Rfast, pheatmap, survival, topGO, graphics, grDevices
Suggests: knitr, BiocStyle, rmarkdown, testthat, Seurat
License: MIT + file LICENSE
MD5sum: a71d6b912e70f0fec166c8fc11a12c32
NeedsCompilation: no
Title: consensus Independent Component Analysis
Description: consICA implements a data-driven deconvolution method –
consensus independent component analysis (ICA) to decompose
heterogeneous omics data and extract features suitable for
patient diagnostics and prognostics. The method separates
biologically relevant transcriptional signals from technical
effects and provides information about the cellular composition
and biological processes. The implementation of parallel
computing in the package ensures efficient analysis of modern
multicore systems.
biocViews: Technology, StatisticalMethod, Sequencing, RNASeq,
Transcriptomics, Classification, FeatureExtraction
Author: Petr V. Nazarov [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3443-0298>), Tony Kaoma [aut]
(ORCID: <https://orcid.org/0000-0002-1269-4826>), Maryna
Chepeleva [aut] (ORCID:
<https://orcid.org/0000-0003-3036-4916>)
Maintainer: Petr V. Nazarov <petr.nazarov@lih.lu>
VignetteBuilder: knitr
BugReports: https://github.com/biomod-lih/consICA/issues
git_url: https://git.bioconductor.org/packages/consICA
git_branch: devel
git_last_commit: 7b9bffe
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/consICA_2.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/consICA_2.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/consICA_2.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/consICA_2.5.0.tgz
vignettes: vignettes/consICA/inst/doc/ConsICA.html
vignetteTitles: The consICA package: User’s manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/consICA/inst/doc/ConsICA.R
dependencyCount: 99
Package: CONSTANd
Version: 1.15.0
Depends: R (>= 4.1)
Suggests: BiocStyle, knitr, rmarkdown, tidyr, ggplot2, gridExtra,
magick, Cairo, limma
License: file LICENSE
MD5sum: 1b4c52e21b37fdde59cb00d2f1532063
NeedsCompilation: no
Title: Data normalization by matrix raking
Description: Normalizes a data matrix `data` by raking (using the RAS
method by Bacharach, see references) the Nrows by Ncols matrix
such that the row means and column means equal 1. The result is
a normalized data matrix `K=RAS`, a product of row mulipliers
`R` and column multipliers `S` with the original matrix `A`.
Missing information needs to be presented as `NA` values and
not as zero values, because CONSTANd is able to ignore missing
values when calculating the mean. Using CONSTANd normalization
allows for the direct comparison of values between samples
within the same and even across different CONSTANd-normalized
data matrices.
biocViews: MassSpectrometry, Cheminformatics, Normalization,
Preprocessing, DifferentialExpression, Genetics,
Transcriptomics, Proteomics
Author: Joris Van Houtven [aut, trl], Geert Jan Bex [trl], Dirk
Valkenborg [aut, cre]
Maintainer: Dirk Valkenborg <dirk.valkenborg@uhasselt.be>
URL: qcquan.net/constand
VignetteBuilder: knitr
BugReports: https://github.com/PDiracDelta/CONSTANd/issues
git_url: https://git.bioconductor.org/packages/CONSTANd
git_branch: devel
git_last_commit: 7ad2dec
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CONSTANd_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CONSTANd_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CONSTANd_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CONSTANd_1.15.0.tgz
vignettes: vignettes/CONSTANd/inst/doc/CONSTANd.html
vignetteTitles: CONSTANd
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CONSTANd/inst/doc/CONSTANd.R
dependencyCount: 0
Package: conumee
Version: 1.41.0
Depends: R (>= 3.5.0), minfi,
IlluminaHumanMethylation450kanno.ilmn12.hg19,
IlluminaHumanMethylation450kmanifest,
IlluminaHumanMethylationEPICanno.ilm10b2.hg19,
IlluminaHumanMethylationEPICmanifest
Imports: methods, stats, DNAcopy, rtracklayer, GenomicRanges, IRanges,
GenomeInfoDb
Suggests: BiocStyle, knitr, rmarkdown, minfiData, RCurl
License: GPL (>= 2)
MD5sum: d249d781f88b23ada3ec6eab15e253bf
NeedsCompilation: no
Title: Enhanced copy-number variation analysis using Illumina DNA
methylation arrays
Description: This package contains a set of processing and plotting
methods for performing copy-number variation (CNV) analysis
using Illumina 450k or EPIC methylation arrays.
biocViews: CopyNumberVariation, DNAMethylation, MethylationArray,
Microarray, Normalization, Preprocessing, QualityControl,
Software
Author: Volker Hovestadt, Marc Zapatka
Maintainer: Volker Hovestadt <conumee@hovestadt.bio>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/conumee
git_branch: devel
git_last_commit: f47752d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/conumee_1.41.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/conumee_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/conumee_1.41.0.tgz
vignettes: vignettes/conumee/inst/doc/conumee.html
vignetteTitles: conumee
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/conumee/inst/doc/conumee.R
dependencyCount: 150
Package: convert
Version: 1.83.0
Depends: R (>= 2.6.0), Biobase (>= 1.15.33), limma (>= 1.7.0), marray,
utils, methods
License: LGPL
MD5sum: b81e19c605a1a673a520cbaa50e86a5f
NeedsCompilation: no
Title: Convert Microarray Data Objects
Description: Define coerce methods for microarray data objects.
biocViews: Infrastructure, Microarray, TwoChannel
Author: Gordon Smyth <smyth@wehi.edu.au>, James Wettenhall
<wettenhall@wehi.edu.au>, Yee Hwa (Jean Yang)
<jean@biostat.ucsf.edu>, Martin Morgan
<Martin.Morgan@RoswellPark.org>
Maintainer: Yee Hwa (Jean) Yang <jean@biostat.ucsf.edu>
URL: http://bioinf.wehi.edu.au/limma/convert.html
git_url: https://git.bioconductor.org/packages/convert
git_branch: devel
git_last_commit: 9922d10
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/convert_1.83.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/convert_1.83.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/convert_1.83.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/convert_1.83.0.tgz
vignettes: vignettes/convert/inst/doc/convert.pdf
vignetteTitles: Converting Between Microarray Data Classes
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: TurboNorm
suggestsMe: dyebias, OLIN, dyebiasexamples
dependencyCount: 11
Package: copa
Version: 1.75.0
Depends: Biobase, methods
Suggests: colonCA
License: Artistic-2.0
MD5sum: 1308cacda28659cc583741175183a98d
NeedsCompilation: yes
Title: Functions to perform cancer outlier profile analysis.
Description: COPA is a method to find genes that undergo recurrent
fusion in a given cancer type by finding pairs of genes that
have mutually exclusive outlier profiles.
biocViews: OneChannel, TwoChannel, DifferentialExpression,
Visualization
Author: James W. MacDonald
Maintainer: James W. MacDonald <jmacdon@u.washington.edu>
git_url: https://git.bioconductor.org/packages/copa
git_branch: devel
git_last_commit: 64ecbf8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/copa_1.75.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/copa_1.75.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/copa_1.75.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/copa_1.75.0.tgz
vignettes: vignettes/copa/inst/doc/copa.pdf
vignetteTitles: copa Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/copa/inst/doc/copa.R
dependencyCount: 7
Package: CopyNumberPlots
Version: 1.23.0
Depends: R (>= 3.6), karyoploteR
Imports: regioneR, IRanges, Rsamtools, SummarizedExperiment,
VariantAnnotation, methods, stats, GenomeInfoDb, GenomicRanges,
cn.mops, rhdf5, utils
Suggests: BiocStyle, knitr, rmarkdown, panelcn.mops,
BSgenome.Hsapiens.UCSC.hg19.masked, DNAcopy, testthat
License: Artistic-2.0
MD5sum: 7dbf406abcb5a55d5b2d680ceb17a128
NeedsCompilation: no
Title: Create Copy-Number Plots using karyoploteR functionality
Description: CopyNumberPlots have a set of functions extending
karyoploteRs functionality to create beautiful, customizable
and flexible plots of copy-number related data.
biocViews: Visualization, CopyNumberVariation, Coverage, OneChannel,
DataImport, Sequencing, DNASeq
Author: Bernat Gel <bgel@igtp.cat> and Miriam Magallon
<mmagallon@igtp.cat>
Maintainer: Bernat Gel <bgel@igtp.cat>
URL: https://github.com/bernatgel/CopyNumberPlots
VignetteBuilder: knitr
BugReports: https://github.com/bernatgel/CopyNumberPlots/issues
git_url: https://git.bioconductor.org/packages/CopyNumberPlots
git_branch: devel
git_last_commit: f101583
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CopyNumberPlots_1.23.0.tar.gz
vignettes: vignettes/CopyNumberPlots/inst/doc/CopyNumberPlots.html
vignetteTitles: CopyNumberPlots vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CopyNumberPlots/inst/doc/CopyNumberPlots.R
importsMe: CNVfilteR, CNViz
dependencyCount: 146
Package: coRdon
Version: 1.25.0
Depends: R (>= 3.5)
Imports: methods, stats, utils, Biostrings, Biobase, dplyr, stringr,
purrr, ggplot2, data.table
Suggests: BiocStyle, testthat, knitr, rmarkdown
License: Artistic-2.0
Archs: x64
MD5sum: c01b921d3973e8025df053bce61301f9
NeedsCompilation: no
Title: Codon Usage Analysis and Prediction of Gene Expressivity
Description: Tool for analysis of codon usage in various unannotated or
KEGG/COG annotated DNA sequences. Calculates different measures
of CU bias and CU-based predictors of gene expressivity, and
performs gene set enrichment analysis for annotated sequences.
Implements several methods for visualization of CU and
enrichment analysis results.
biocViews: Software, Metagenomics, GeneExpression, GeneSetEnrichment,
GenePrediction, Visualization, KEGG, Pathways, Genetics
CellBiology, BiomedicalInformatics, ImmunoOncology
Author: Anamaria Elek [cre, aut], Maja Kuzman [aut], Kristian
Vlahovicek [aut]
Maintainer: Anamaria Elek <anamariaelek@gmail.com>
URL: https://github.com/BioinfoHR/coRdon
VignetteBuilder: knitr
BugReports: https://github.com/BioinfoHR/coRdon/issues
git_url: https://git.bioconductor.org/packages/coRdon
git_branch: devel
git_last_commit: c2f3cd0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/coRdon_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/coRdon_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/coRdon_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/coRdon_1.25.0.tgz
vignettes: vignettes/coRdon/inst/doc/coRdon.html
vignetteTitles: coRdon
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/coRdon/inst/doc/coRdon.R
importsMe: vhcub
dependencyCount: 61
Package: CoreGx
Version: 2.11.0
Depends: R (>= 4.1), BiocGenerics, SummarizedExperiment
Imports: Biobase, S4Vectors, MultiAssayExperiment, MatrixGenerics,
piano, BiocParallel, parallel, BumpyMatrix, checkmate, methods,
stats, utils, graphics, grDevices, lsa, data.table, crayon,
glue, rlang, bench
Suggests: pander, markdown, BiocStyle, rmarkdown, knitr, formatR,
testthat
License: GPL (>= 3)
MD5sum: 0e1b81bb4ba70d3a86e7bececd9fbcec
NeedsCompilation: no
Title: Classes and Functions to Serve as the Basis for Other 'Gx'
Packages
Description: A collection of functions and classes which serve as the
foundation for our lab's suite of R packages, such as
'PharmacoGx' and 'RadioGx'. This package was created to
abstract shared functionality from other lab package releases
to increase ease of maintainability and reduce code repetition
in current and future 'Gx' suite programs. Major features
include a 'CoreSet' class, from which 'RadioSet' and
'PharmacoSet' are derived, along with get and set methods for
each respective slot. Additional functions related to fitting
and plotting dose response curves, quantifying statistical
correlation and calculating area under the curve (AUC) or
survival fraction (SF) are included. For more details please
see the included documentation, as well as: Smirnov, P.,
Safikhani, Z., El-Hachem, N., Wang, D., She, A., Olsen, C.,
Freeman, M., Selby, H., Gendoo, D., Grossman, P., Beck, A.,
Aerts, H., Lupien, M., Goldenberg, A. (2015)
<doi:10.1093/bioinformatics/btv723>. Manem, V., Labie, M.,
Smirnov, P., Kofia, V., Freeman, M., Koritzinksy, M., Abazeed,
M., Haibe-Kains, B., Bratman, S. (2018) <doi:10.1101/449793>.
biocViews: Software, Pharmacogenomics, Classification, Survival
Author: Jermiah Joseph [aut], Petr Smirnov [aut], Ian Smith [aut],
Christopher Eeles [aut], Feifei Li [aut], Benjamin Haibe-Kains
[aut, cre]
Maintainer: Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CoreGx
git_branch: devel
git_last_commit: 2e3e61b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CoreGx_2.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CoreGx_2.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CoreGx_2.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CoreGx_2.11.0.tgz
vignettes: vignettes/CoreGx/inst/doc/coreGx.html,
vignettes/CoreGx/inst/doc/TreatmentResponseExperiment.html
vignetteTitles: CoreGx: Class and Function Abstractions, The
TreatmentResponseExperiment Class
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CoreGx/inst/doc/coreGx.R,
vignettes/CoreGx/inst/doc/TreatmentResponseExperiment.R
dependsOnMe: PharmacoGx, RadioGx, ToxicoGx
importsMe: gDRimport, PDATK
dependencyCount: 139
Package: Cormotif
Version: 1.53.0
Depends: R (>= 2.12.0), affy, limma
Imports: affy, graphics, grDevices
License: GPL-2
MD5sum: b1747b4b565bb72daec61db971b5889c
NeedsCompilation: no
Title: Correlation Motif Fit
Description: It fits correlation motif model to multiple studies to
detect study specific differential expression patterns.
biocViews: Microarray, DifferentialExpression
Author: Hongkai Ji, Yingying Wei
Maintainer: Yingying Wei <ywei@jhsph.edu>
git_url: https://git.bioconductor.org/packages/Cormotif
git_branch: devel
git_last_commit: 1746b95
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Cormotif_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Cormotif_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Cormotif_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Cormotif_1.53.0.tgz
vignettes: vignettes/Cormotif/inst/doc/CormotifVignette.pdf
vignetteTitles: Cormotif Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Cormotif/inst/doc/CormotifVignette.R
dependencyCount: 14
Package: corral
Version: 1.17.0
Imports: ggplot2, ggthemes, grDevices, gridExtra, irlba, Matrix,
methods, MultiAssayExperiment, pals, reshape2,
SingleCellExperiment, SummarizedExperiment, transport
Suggests: ade4, BiocStyle, CellBench, DuoClustering2018, knitr,
rmarkdown, scater, testthat
License: GPL-2
MD5sum: 8232ca0692519399f3c70c01cd221139
NeedsCompilation: no
Title: Correspondence Analysis for Single Cell Data
Description: Correspondence analysis (CA) is a matrix factorization
method, and is similar to principal components analysis (PCA).
Whereas PCA is designed for application to continuous,
approximately normally distributed data, CA is appropriate for
non-negative, count-based data that are in the same additive
scale. The corral package implements CA for dimensionality
reduction of a single matrix of single-cell data, as well as a
multi-table adaptation of CA that leverages data-optimized
scaling to align data generated from different sequencing
platforms by projecting into a shared latent space. corral
utilizes sparse matrices and a fast implementation of SVD, and
can be called directly on Bioconductor objects (e.g.,
SingleCellExperiment) for easy pipeline integration. The
package also includes additional options, including variations
of CA to address overdispersion in count data (e.g.,
Freeman-Tukey chi-squared residual), as well as the option to
apply CA-style processing to continuous data (e.g., proteomic
TOF intensities) with the Hellinger distance adaptation of CA.
biocViews: BatchEffect, DimensionReduction, GeneExpression,
Preprocessing, PrincipalComponent, Sequencing, SingleCell,
Software, Visualization
Author: Lauren Hsu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6035-7381>), Aedin Culhane [aut]
(ORCID: <https://orcid.org/0000-0002-1395-9734>)
Maintainer: Lauren Hsu <lrnshoe@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/corral
git_branch: devel
git_last_commit: 284ef14
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/corral_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/corral_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/corral_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/corral_1.17.0.tgz
vignettes: vignettes/corral/inst/doc/corral_dimred.html,
vignettes/corral/inst/doc/corralm_alignment.html
vignetteTitles: dim reduction with corral, alignment with corralm
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/corral/inst/doc/corral_dimred.R,
vignettes/corral/inst/doc/corralm_alignment.R
dependencyCount: 85
Package: coseq
Version: 1.31.0
Depends: R (>= 4.0.0), SummarizedExperiment, S4Vectors
Imports: edgeR, DESeq2, capushe, Rmixmod, e1071, BiocParallel, ggplot2,
scales, HTSFilter, corrplot, HTSCluster, grDevices, graphics,
stats, methods, compositions, mvtnorm
Suggests: Biobase, knitr, rmarkdown, testthat, BiocStyle
License: GPL-3
MD5sum: 9d60f3fe50b515002cab7c22e9837eb7
NeedsCompilation: no
Title: Co-Expression Analysis of Sequencing Data
Description: Co-expression analysis for expression profiles arising
from high-throughput sequencing data. Feature (e.g., gene)
profiles are clustered using adapted transformations and
mixture models or a K-means algorithm, and model selection
criteria (to choose an appropriate number of clusters) are
provided.
biocViews: GeneExpression, RNASeq, Sequencing, Software, ImmunoOncology
Author: Andrea Rau [cre, aut] (ORCID:
<https://orcid.org/0000-0001-6469-488X>), Cathy
Maugis-Rabusseau [ctb], Antoine Godichon-Baggioni [ctb]
Maintainer: Andrea Rau <andrea.rau@inrae.fr>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/coseq
git_branch: devel
git_last_commit: 7cb63f5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/coseq_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/coseq_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/coseq_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/coseq_1.31.0.tgz
vignettes: vignettes/coseq/inst/doc/coseq.html
vignetteTitles: coseq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/coseq/inst/doc/coseq.R
dependencyCount: 95
Package: CoSIA
Version: 1.7.0
Depends: R (>= 4.3.0), methods (>= 4.3.0), ExperimentHub (>= 2.7.0)
Imports: dplyr (>= 1.0.7), magrittr (>= 2.0.1), RColorBrewer (>=
1.1-2), tidyr (>= 1.2.0), plotly (>= 4.10.0), stringr (>=
1.4.0), ggplot2 (>= 3.3.5), tibble (>= 3.1.7), org.Hs.eg.db (>=
3.12.0), org.Mm.eg.db (>= 3.12.0), org.Dr.eg.db (>= 3.12.0),
org.Ce.eg.db (>= 3.12.0), org.Dm.eg.db (>= 3.12.0),
org.Rn.eg.db (>= 3.12.0), AnnotationDbi (>= 1.52.0), biomaRt
(>= 2.46.3), homologene (>= 1.4.68.19), annotationTools (>=
1.64.0), readr (>= 2.1.1), tidyselect (>= 1.1.2), stats (>=
4.1.2)
Suggests: BiocStyle (>= 2.22.0), tidyverse (>= 1.3.1), knitr (>= 1.42),
rmarkdown (>= 2.20), testthat (>= 3.1.6), qpdf (>= 1.3.0)
License: MIT + file LICENSE
MD5sum: 11b6fd8f582ee4cf33e699cd18a5186f
NeedsCompilation: no
Title: An Investigation Across Different Species and Tissues
Description: Cross-Species Investigation and Analysis (CoSIA) is a
package that provides researchers with an alternative
methodology for comparing across species and tissues using
normal wild-type RNA-Seq Gene Expression data from Bgee. Using
RNA-Seq Gene Expression data, CoSIA provides multiple
visualization tools to explore the transcriptome diversity and
variation across genes, tissues, and species. CoSIA uses the
Coefficient of Variation and Shannon Entropy and Specificity to
calculate transcriptome diversity and variation. CoSIA also
provides additional conversion tools and utilities to provide a
streamlined methodology for cross-species comparison.
biocViews: Software, BiologicalQuestion, GeneExpression,
MultipleComparison, ThirdPartyClient, DataImport, GUI
Author: Anisha Haldar [aut] (ORCID:
<https://orcid.org/0000-0002-1395-9793>), Vishal H. Oza [aut]
(ORCID: <https://orcid.org/0000-0001-6990-0267>), Amanda D.
Clark [cre, aut] (ORCID:
<https://orcid.org/0000-0002-1186-3114>), Nathaniel S. DeVoss
[aut] (ORCID: <https://orcid.org/0000-0003-0465-2770>),
Brittany N. Lasseigne [aut] (ORCID:
<https://orcid.org/0000-0002-1642-8904>)
Maintainer: Amanda D. Clark <amanda@freshairfamily.org>
URL: https://www.lasseigne.org/
VignetteBuilder: knitr
BugReports: https://github.com/lasseignelab/CoSIA/issues
git_url: https://git.bioconductor.org/packages/CoSIA
git_branch: devel
git_last_commit: 9a6548e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/CoSIA_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CoSIA_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CoSIA_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CoSIA_1.7.0.tgz
vignettes: vignettes/CoSIA/inst/doc/CoSIA_Intro.html
vignetteTitles: CoSIA_Intro
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CoSIA/inst/doc/CoSIA_Intro.R
dependencyCount: 126
Package: cosmiq
Version: 1.41.0
Depends: R (>= 3.6), Rcpp
Imports: pracma, xcms, MassSpecWavelet, faahKO
Suggests: RUnit, BiocGenerics, BiocStyle
License: GPL-3
MD5sum: 0878dbb37a7eadf4d11d456460a25c11
NeedsCompilation: yes
Title: cosmiq - COmbining Single Masses Into Quantities
Description: cosmiq is a tool for the preprocessing of liquid- or gas -
chromatography mass spectrometry (LCMS/GCMS) data with a focus
on metabolomics or lipidomics applications. To improve the
detection of low abundant signals, cosmiq generates master maps
of the mZ/RT space from all acquired runs before a peak
detection algorithm is applied. The result is a more robust
identification and quantification of low-intensity MS signals
compared to conventional approaches where peak picking is
performed in each LCMS/GCMS file separately. The cosmiq package
builds on the xcmsSet object structure and can be therefore
integrated well with the package xcms as an alternative
preprocessing step.
biocViews: ImmunoOncology, MassSpectrometry, Metabolomics
Author: David Fischer [aut, cre], Christian Panse [aut] (ORCID:
<https://orcid.org/0000-0003-1975-3064>), Endre Laczko [ctb]
Maintainer: David Fischer <dajofischer@googlemail.com>
URL: http://www.bioconductor.org/packages/devel/bioc/html/cosmiq.html
git_url: https://git.bioconductor.org/packages/cosmiq
git_branch: devel
git_last_commit: acb2474
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-05
source.ver: src/contrib/cosmiq_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cosmiq_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cosmiq_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cosmiq_1.41.0.tgz
vignettes: vignettes/cosmiq/inst/doc/cosmiq.pdf
vignetteTitles: cosmiq primer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cosmiq/inst/doc/cosmiq.R
dependencyCount: 148
Package: cosmosR
Version: 1.15.0
Depends: R (>= 4.1)
Imports: CARNIVAL, dorothea, dplyr, GSEABase, igraph, progress, purrr,
rlang, stringr, utils, visNetwork, decoupleR
Suggests: testthat, knitr, rmarkdown, piano, ggplot2
License: GPL-3
Archs: x64
MD5sum: 5ac3ea54b12cef10b28513963d509af3
NeedsCompilation: no
Title: COSMOS (Causal Oriented Search of Multi-Omic Space)
Description: COSMOS (Causal Oriented Search of Multi-Omic Space) is a
method that integrates phosphoproteomics, transcriptomics, and
metabolomics data sets based on prior knowledge of signaling,
metabolic, and gene regulatory networks. It estimated the
activities of transcrption factors and kinases and finds a
network-level causal reasoning. Thereby, COSMOS provides
mechanistic hypotheses for experimental observations across
mulit-omics datasets.
biocViews: CellBiology, Pathways, Network, Proteomics, Metabolomics,
Transcriptomics, GeneSignaling
Author: Aurélien Dugourd [aut] (ORCID:
<https://orcid.org/0000-0002-0714-028X>), Attila Gabor [cre]
(ORCID: <https://orcid.org/0000-0002-0776-1182>), Katharina
Zirngibl [aut] (ORCID: <https://orcid.org/0000-0002-7518-0339>)
Maintainer: Attila Gabor <attila.gabor@uni-heidelberg.de>
URL: https://github.com/saezlab/COSMOSR
VignetteBuilder: knitr
BugReports: https://github.com/saezlab/COSMOSR/issues
git_url: https://git.bioconductor.org/packages/cosmosR
git_branch: devel
git_last_commit: 7c6afd6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cosmosR_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cosmosR_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cosmosR_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cosmosR_1.15.0.tgz
vignettes: vignettes/cosmosR/inst/doc/tutorial.html
vignetteTitles: cosmosR tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/cosmosR/inst/doc/tutorial.R
dependencyCount: 109
Package: COSNet
Version: 1.41.0
Suggests: bionetdata, PerfMeas, RUnit, BiocGenerics
License: GPL (>= 2)
MD5sum: 13b6532c3551c2f7eb93fdfd7b411495
NeedsCompilation: yes
Title: Cost Sensitive Network for node label prediction on graphs with
highly unbalanced labelings
Description: Package that implements the COSNet classification
algorithm. The algorithm predicts node labels in partially
labeled graphs where few positives are available for the class
being predicted.
biocViews: GraphAndNetwork, Classification,Network, NeuralNetwork
Author: Marco Frasca and Giorgio Valentini -- Universita' degli Studi
di Milano
Maintainer: Marco Frasca<frasca@di.unimi.it>
URL: https://github.com/m1frasca/COSNet_GitHub
git_url: https://git.bioconductor.org/packages/COSNet
git_branch: devel
git_last_commit: 3f65459
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/COSNet_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/COSNet_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/COSNet_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/COSNet_1.41.0.tgz
vignettes: vignettes/COSNet/inst/doc/COSNet_v.pdf
vignetteTitles: An R Package for Predicting Binary Labels in
Partially-Labeled Graphs
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/COSNet/inst/doc/COSNet_v.R
dependencyCount: 0
Package: COTAN
Version: 2.7.4
Depends: R (>= 4.3)
Imports: stats, plyr, dplyr, methods, grDevices, Matrix, ggplot2,
ggrepel, ggthemes, graphics, parallel, parallelly, tibble,
tidyr, BiocSingular, parallelDist, ComplexHeatmap, circlize,
grid, scales, RColorBrewer, utils, rlang, Rfast, stringr,
Seurat, umap, dendextend, zeallot, assertthat, withr,
SingleCellExperiment, SummarizedExperiment, S4Vectors
Suggests: testthat (>= 3.2.0), proto, spelling, knitr, data.table,
gsubfn, R.utils, tidyverse, rmarkdown, htmlwidgets, MASS,
Rtsne, plotly, BiocStyle, cowplot, qpdf, GEOquery, sf, torch
License: GPL-3
Archs: x64
MD5sum: 0058b1aeb50144577a329423234dba19
NeedsCompilation: no
Title: COexpression Tables ANalysis
Description: Statistical and computational method to analyze the
co-expression of gene pairs at single cell level. It provides
the foundation for single-cell gene interactome analysis. The
basic idea is studying the zero UMI counts' distribution
instead of focusing on positive counts; this is done with a
generalized contingency tables framework. COTAN can effectively
assess the correlated or anti-correlated expression of gene
pairs. It provides a numerical index related to the correlation
and an approximate p-value for the associated independence
test. COTAN can also evaluate whether single genes are
differentially expressed, scoring them with a newly defined
global differentiation index. Moreover, this approach provides
ways to plot and cluster genes according to their co-expression
pattern with other genes, effectively helping the study of gene
interactions and becoming a new tool to identify cell-identity
marker genes.
biocViews: SystemsBiology, Transcriptomics, GeneExpression, SingleCell
Author: Galfrè Silvia Giulia [aut, cre] (ORCID:
<https://orcid.org/0000-0002-2770-0344>), Morandin Francesco
[aut] (ORCID: <https://orcid.org/0000-0002-2022-2300>),
Fantozzi Marco [aut] (ORCID:
<https://orcid.org/0000-0002-0708-5495>), Pietrosanto Marco
[aut] (ORCID: <https://orcid.org/0000-0001-5129-6065>), Puttini
Daniel [aut] (ORCID: <https://orcid.org/0009-0006-8401-9949>),
Priami Corrado [aut] (ORCID:
<https://orcid.org/0000-0002-3261-6235>), Cremisi Federico
[aut] (ORCID: <https://orcid.org/0000-0003-4925-2703>),
Helmer-Citterich Manuela [aut] (ORCID:
<https://orcid.org/0000-0001-9530-7504>)
Maintainer: Galfrè Silvia Giulia <silvia.galfre@di.unipi.it>
URL: https://github.com/seriph78/COTAN
VignetteBuilder: knitr
BugReports: https://github.com/seriph78/COTAN/issues
git_url: https://git.bioconductor.org/packages/COTAN
git_branch: devel
git_last_commit: bd5f5ce
git_last_commit_date: 2025-02-25
Date/Publication: 2025-02-25
source.ver: src/contrib/COTAN_2.7.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/COTAN_2.7.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/COTAN_2.7.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/COTAN_2.7.4.tgz
vignettes: vignettes/COTAN/inst/doc/Guided_tutorial_v2.html
vignetteTitles: Guided tutorial to COTAN V.2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/COTAN/inst/doc/Guided_tutorial_v2.R
dependencyCount: 203
Package: countsimQC
Version: 1.25.0
Depends: R (>= 3.5)
Imports: rmarkdown (>= 2.5), edgeR, DESeq2 (>= 1.16.0), dplyr, tidyr,
ggplot2, grDevices, tools, SummarizedExperiment, genefilter,
DT, GenomeInfoDbData, caTools, randtests, stats, utils,
methods, ragg
Suggests: knitr, testthat
License: GPL (>=2)
MD5sum: 376604fc69c241e732589a8e1ce4bac5
NeedsCompilation: no
Title: Compare Characteristic Features of Count Data Sets
Description: countsimQC provides functionality to create a
comprehensive report comparing a broad range of characteristics
across a collection of count matrices. One important use case
is the comparison of one or more synthetic count matrices to a
real count matrix, possibly the one underlying the simulations.
However, any collection of count matrices can be compared.
biocViews: Microbiome, RNASeq, SingleCell, ExperimentalDesign,
QualityControl, ReportWriting, Visualization, ImmunoOncology
Author: Charlotte Soneson [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3833-2169>)
Maintainer: Charlotte Soneson <charlottesoneson@gmail.com>
URL: https://github.com/csoneson/countsimQC
VignetteBuilder: knitr
BugReports: https://github.com/csoneson/countsimQC/issues
git_url: https://git.bioconductor.org/packages/countsimQC
git_branch: devel
git_last_commit: be98bfe
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/countsimQC_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/countsimQC_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/countsimQC_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/countsimQC_1.25.0.tgz
vignettes: vignettes/countsimQC/inst/doc/countsimQC.html
vignetteTitles: countsimQC User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/countsimQC/inst/doc/countsimQC.R
suggestsMe: muscat
dependencyCount: 132
Package: covEB
Version: 1.33.0
Depends: R (>= 3.3), mvtnorm, igraph, gsl, Biobase, stats,
LaplacesDemon, Matrix
Suggests: curatedBladderData
License: GPL-3
MD5sum: c6fccb6a6b1575a32a15e3c1dba5f14a
NeedsCompilation: no
Title: Empirical Bayes estimate of block diagonal covariance matrices
Description: Using bayesian methods to estimate correlation matrices
assuming that they can be written and estimated as block
diagonal matrices. These block diagonal matrices are determined
using shrinkage parameters that values below this parameter to
zero.
biocViews: ImmunoOncology, Bayesian, Microarray, RNASeq, Preprocessing,
Software, GeneExpression, StatisticalMethod
Author: C. Pacini
Maintainer: C. Pacini <clarepacini@gmail.com>
git_url: https://git.bioconductor.org/packages/covEB
git_branch: devel
git_last_commit: 9ca408e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/covEB_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/covEB_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/covEB_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/covEB_1.33.0.tgz
vignettes: vignettes/covEB/inst/doc/covEB.pdf
vignetteTitles: covEB
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/covEB/inst/doc/covEB.R
dependencyCount: 24
Package: CoverageView
Version: 1.45.0
Depends: R (>= 2.10), methods, Rsamtools (>= 1.19.17), rtracklayer
Imports: S4Vectors (>= 0.7.21), IRanges(>= 2.3.23), GenomicRanges,
GenomicAlignments, parallel, tools
License: Artistic-2.0
MD5sum: 12800f8e35fa17d577890926a16d0470
NeedsCompilation: no
Title: Coverage visualization package for R
Description: This package provides a framework for the visualization of
genome coverage profiles. It can be used for ChIP-seq
experiments, but it can be also used for genome-wide nucleosome
positioning experiments or other experiment types where it is
important to have a framework in order to inspect how the
coverage distributed across the genome
biocViews: ImmunoOncology,
Visualization,RNASeq,ChIPSeq,Sequencing,Technology,Software
Author: Ernesto Lowy
Maintainer: Ernesto Lowy <ernestolowy@gmail.com>
git_url: https://git.bioconductor.org/packages/CoverageView
git_branch: devel
git_last_commit: b74ea21
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CoverageView_1.45.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CoverageView_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CoverageView_1.45.0.tgz
vignettes: vignettes/CoverageView/inst/doc/CoverageView.pdf
vignetteTitles: Easy visualization of the read coverage
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CoverageView/inst/doc/CoverageView.R
dependencyCount: 58
Package: covRNA
Version: 1.33.0
Depends: ade4, Biobase
Imports: parallel, genefilter, grDevices, stats, graphics
Suggests: BiocStyle, knitr, rmarkdown
License: GPL (>= 2)
MD5sum: f1cda399cab05324cb42217eaf26f7ab
NeedsCompilation: no
Title: Multivariate Analysis of Transcriptomic Data
Description: This package provides the analysis methods fourthcorner
and RLQ analysis for large-scale transcriptomic data.
biocViews: GeneExpression, Transcription
Author: Lara Urban <lara.h.urban@ebi.ac.uk>
Maintainer: Lara Urban <lara.h.urban@ebi.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/covRNA
git_branch: devel
git_last_commit: 84b2985
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/covRNA_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/covRNA_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/covRNA_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/covRNA_1.33.0.tgz
vignettes: vignettes/covRNA/inst/doc/covRNA.html
vignetteTitles: An Introduction to covRNA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/covRNA/inst/doc/covRNA.R
dependencyCount: 63
Package: CPSM
Version: 0.99.7
Depends: R (>= 3.5.0)
Imports: SummarizedExperiment, grDevices, reshape2 , survival ,
survminer , ggplot2 , MTLR , pec , glmnet , rms ,
preprocessCore , survivalROC , Matrix , svglite, stats, Hmisc,
ROCR, ggfortify, MASS,
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle
License: GPL-3 | file LICENSE
MD5sum: e2e10bca27b0afe789839bea6ef91480
NeedsCompilation: no
Title: CPSM: Cancer patient survival model
Description: The CPSM package provides a comprehensive computational
pipeline for predicting the survival probability of cancer
patients. It offers a series of steps including data
processing, splitting data into training and test subsets, and
normalization of data. The package enables the selection of
significant features based on univariate survival analysis and
generates a LASSO prognostic index score. It supports the
development of predictive models for survival probability using
various features and provides visualization tools to draw
survival curves based on predicted survival probabilities.
Additionally, SPM includes functionalities for generating bar
plots that depict the predicted mean and median survival times
of patients, making it a versatile tool for survival analysis
in cancer research.
biocViews: GeneExpression, Normalization, Survival
Author: Harpreet Kaur [aut, cre] (ORCID:
<https://orcid.org/0000-0003-0421-8341>), Pijush Das [aut],
Kevin Camphausen [aut], Uma Shankavaram [aut]
Maintainer: Harpreet Kaur <hks04180@gmail.com>
URL: https://github.com/hks5august/CPSM/
VignetteBuilder: knitr
BugReports: https://github.com/hks5august/CPSM/issues
git_url: https://git.bioconductor.org/packages/CPSM
git_branch: devel
git_last_commit: f9ecfc0
git_last_commit_date: 2025-03-11
Date/Publication: 2025-03-12
source.ver: src/contrib/CPSM_0.99.7.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CPSM_0.99.7.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CPSM_0.99.7.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CPSM_0.99.7.tgz
vignettes: vignettes/CPSM/inst/doc/CPSM.html
vignetteTitles: CPSM: Cancer patient survival model
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CPSM/inst/doc/CPSM.R
dependencyCount: 195
Package: cpvSNP
Version: 1.39.0
Depends: R (>= 3.5.0), GenomicFeatures, GSEABase (>= 1.24.0)
Imports: methods, corpcor, BiocParallel, ggplot2, plyr
Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, BiocGenerics,
ReportingTools, BiocStyle
License: Artistic-2.0
MD5sum: 288efb00e0a32ab594883f7e6aed7321
NeedsCompilation: no
Title: Gene set analysis methods for SNP association p-values that lie
in genes in given gene sets
Description: Gene set analysis methods exist to combine SNP-level
association p-values into gene sets, calculating a single
association p-value for each gene set. This package implements
two such methods that require only the calculated SNP p-values,
the gene set(s) of interest, and a correlation matrix (if
desired). One method (GLOSSI) requires independent SNPs and the
other (VEGAS) can take into account correlation (LD) among the
SNPs. Built-in plotting functions are available to help users
visualize results.
biocViews: Genetics, StatisticalMethod, Pathways, GeneSetEnrichment,
GenomicVariation
Author: Caitlin McHugh, Jessica Larson, and Jason Hackney
Maintainer: Caitlin McHugh <mchughc@uw.edu>
git_url: https://git.bioconductor.org/packages/cpvSNP
git_branch: devel
git_last_commit: 644afbb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cpvSNP_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cpvSNP_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cpvSNP_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cpvSNP_1.39.0.tgz
vignettes: vignettes/cpvSNP/inst/doc/cpvSNP.pdf
vignetteTitles: Running gene set analyses with the "cpvSNP" package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cpvSNP/inst/doc/cpvSNP.R
dependencyCount: 104
Package: cqn
Version: 1.53.0
Depends: R (>= 2.10.0), mclust
Imports: splines, graphics, nor1mix, stats, quantreg
Suggests: scales, edgeR
License: Artistic-2.0
MD5sum: edc24e14bb0377e8ea01b2aa167cf4ba
NeedsCompilation: no
Title: Conditional quantile normalization
Description: A normalization tool for RNA-Seq data, implementing the
conditional quantile normalization method.
biocViews: ImmunoOncology, RNASeq, Preprocessing,
DifferentialExpression
Author: Jean (Zhijin) Wu, Kasper Daniel Hansen
Maintainer: Kasper Daniel Hansen <kasperdanielhansen@gmail.com>
git_url: https://git.bioconductor.org/packages/cqn
git_branch: devel
git_last_commit: 1daa019
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cqn_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cqn_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cqn_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cqn_1.53.0.tgz
vignettes: vignettes/cqn/inst/doc/cqn.pdf
vignetteTitles: CQN (Conditional Quantile Normalization)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cqn/inst/doc/cqn.R
dependsOnMe: KnowSeq
importsMe: GeoTcgaData, tweeDEseq
dependencyCount: 16
Package: CRImage
Version: 1.55.0
Depends: EBImage, DNAcopy, aCGH
Imports: MASS, e1071, foreach, sgeostat
License: Artistic-2.0
MD5sum: 9bed7aaaa326b01d053433a61605cf6b
NeedsCompilation: no
Title: CRImage a package to classify cells and calculate tumour
cellularity
Description: CRImage provides functionality to process and analyze
images, in particular to classify cells in biological images.
Furthermore, in the context of tumor images, it provides
functionality to calculate tumour cellularity.
biocViews: CellBiology, Classification
Author: Henrik Failmezger <failmezger@mpipz.mpg.de>, Yinyin Yuan
<Yinyin.Yuan@cancer.org.uk>, Oscar Rueda
<oscar.rueda@cancer.org.uk>, Florian Markowetz
<Florian.Markowetz@cancer.org.uk>
Maintainer: Henrik Failmezger <failmezger@mpipz.mpg.de>, Yinyin Yuan
<Yinyin.Yuan@cancer.org.uk>
git_url: https://git.bioconductor.org/packages/CRImage
git_branch: devel
git_last_commit: 181767b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CRImage_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CRImage_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CRImage_1.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CRImage_1.55.0.tgz
vignettes: vignettes/CRImage/inst/doc/CRImage.pdf
vignetteTitles: CRImage Manual
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CRImage/inst/doc/CRImage.R
dependencyCount: 63
Package: CRISPRball
Version: 1.3.2
Depends: R (>= 4.4.0), shinyBS
Imports: DT, shiny, grid, ComplexHeatmap, InteractiveComplexHeatmap,
graphics, stats, ggplot2, plotly, shinyWidgets,
shinycssloaders, shinyjqui, dittoSeq, matrixStats,
colourpicker, shinyjs, circlize, PCAtools, utils, grDevices,
htmlwidgets, methods
Suggests: BiocStyle, msigdbr, depmap, pool, RSQLite, mygene, testthat
(>= 3.0.0), knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 3991eec395fe58c76c2fd2ebb9ab0833
NeedsCompilation: no
Title: Shiny Application for Interactive CRISPR Screen Visualization,
Exploration, Comparison, and Filtering
Description: A Shiny application for visualization, exploration,
comparison, and filtering of CRISPR screens analyzed with
MAGeCK RRA or MLE. Features include interactive plots with
on-click labeling, full customization of plot aesthetics, data
upload and/or download, and much more. Quickly and easily
explore your CRISPR screen results and generate
publication-quality figures in seconds.
biocViews: Software, ShinyApps, CRISPR, QualityControl, Visualization,
GUI
Author: Jared Andrews [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0780-6248>), Jacob Steele [ctb]
(ORCID: <https://orcid.org/0000-0001-9924-2226>)
Maintainer: Jared Andrews <jared.andrews07@gmail.com>
URL: https://github.com/j-andrews7/CRISPRball
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/
git_url: https://git.bioconductor.org/packages/CRISPRball
git_branch: devel
git_last_commit: 1f91c68
git_last_commit_date: 2024-12-19
Date/Publication: 2024-12-19
source.ver: src/contrib/CRISPRball_1.3.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CRISPRball_1.3.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CRISPRball_1.3.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CRISPRball_1.3.2.tgz
vignettes: vignettes/CRISPRball/inst/doc/CRISPRball.html
vignetteTitles: CRISPRball Quick Start
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CRISPRball/inst/doc/CRISPRball.R
dependencyCount: 157
Package: crisprBase
Version: 1.11.0
Depends: utils, methods, R (>= 4.1)
Imports: BiocGenerics, Biostrings, GenomicRanges, graphics, IRanges,
S4Vectors, stringr
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: MIT + file LICENSE
MD5sum: 56280a7a77d5518eda790d6ade5b1fd0
NeedsCompilation: no
Title: Base functions and classes for CRISPR gRNA design
Description: Provides S4 classes for general nucleases, CRISPR
nucleases, CRISPR nickases, and base editors.Several
CRISPR-specific genome arithmetic functions are implemented to
help extract genomic coordinates of spacer and protospacer
sequences. Commonly-used CRISPR nuclease objects are provided
that can be readily used in other packages. Both DNA- and
RNA-targeting nucleases are supported.
biocViews: CRISPR, FunctionalGenomics
Author: Jean-Philippe Fortin [aut, cre]
Maintainer: Jean-Philippe Fortin <fortin946@gmail.com>
URL: https://github.com/crisprVerse/crisprBase
VignetteBuilder: knitr
BugReports: https://github.com/crisprVerse/crisprBase/issues
git_url: https://git.bioconductor.org/packages/crisprBase
git_branch: devel
git_last_commit: d22a715
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/crisprBase_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/crisprBase_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/crisprBase_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/crisprBase_1.11.0.tgz
vignettes: vignettes/crisprBase/inst/doc/crisprBase.html
vignetteTitles: Introduction to crisprBase
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/crisprBase/inst/doc/crisprBase.R
dependsOnMe: crisprDesign, crisprViz
importsMe: crisprBowtie, crisprBwa, crisprShiny, crisprVerse
dependencyCount: 34
Package: crisprBowtie
Version: 1.11.0
Depends: methods
Imports: BiocGenerics, Biostrings, BSgenome, crisprBase (>= 0.99.15),
GenomeInfoDb, GenomicRanges, IRanges, Rbowtie, readr, stats,
stringr, utils
Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown,
testthat
License: MIT + file LICENSE
MD5sum: 84ee442017cf27cb568ba2cd8a6b3ce2
NeedsCompilation: no
Title: Bowtie-based alignment of CRISPR gRNA spacer sequences
Description: Provides a user-friendly interface to map on-targets and
off-targets of CRISPR gRNA spacer sequences using bowtie. The
alignment is fast, and can be performed using either
commonly-used or custom CRISPR nucleases. The alignment can
work with any reference or custom genomes. Both DNA- and
RNA-targeting nucleases are supported.
biocViews: CRISPR, FunctionalGenomics, Alignment
Author: Jean-Philippe Fortin [aut, cre]
Maintainer: Jean-Philippe Fortin <fortin946@gmail.com>
URL: https://github.com/crisprVerse/crisprBowtie
VignetteBuilder: knitr
BugReports: https://github.com/crisprVerse/crisprBowtie/issues
git_url: https://git.bioconductor.org/packages/crisprBowtie
git_branch: devel
git_last_commit: fa0a52f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/crisprBowtie_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/crisprBowtie_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/crisprBowtie_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/crisprBowtie_1.11.0.tgz
vignettes: vignettes/crisprBowtie/inst/doc/crisprBowtie.html
vignetteTitles: Introduction to crisprBowtie
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/crisprBowtie/inst/doc/crisprBowtie.R
importsMe: crisprDesign, crisprVerse
dependencyCount: 85
Package: crisprBwa
Version: 1.11.0
Depends: methods
Imports: BiocGenerics, BSgenome, crisprBase (>= 0.99.15), GenomeInfoDb,
Rbwa, readr, stats, stringr, utils
Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown,
testthat
License: MIT + file LICENSE
OS_type: unix
MD5sum: 8700c84502a861f5dc6dc0e54af9b86a
NeedsCompilation: no
Title: BWA-based alignment of CRISPR gRNA spacer sequences
Description: Provides a user-friendly interface to map on-targets and
off-targets of CRISPR gRNA spacer sequences using bwa. The
alignment is fast, and can be performed using either
commonly-used or custom CRISPR nucleases. The alignment can
work with any reference or custom genomes. Currently not
supported on Windows machines.
biocViews: CRISPR, FunctionalGenomics, Alignment
Author: Jean-Philippe Fortin [aut, cre]
Maintainer: Jean-Philippe Fortin <fortin946@gmail.com>
URL: https://github.com/crisprVerse/crisprBwa
VignetteBuilder: knitr
BugReports: https://github.com/crisprVerse/crisprBwa/issues
git_url: https://git.bioconductor.org/packages/crisprBwa
git_branch: devel
git_last_commit: 3b1e984
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/crisprBwa_1.11.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/crisprBwa_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/crisprBwa_1.11.0.tgz
vignettes: vignettes/crisprBwa/inst/doc/crisprBwa.html
vignetteTitles: Introduction to crisprBwa
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/crisprBwa/inst/doc/crisprBwa.R
suggestsMe: crisprDesign
dependencyCount: 85
Package: crisprDesign
Version: 1.9.0
Depends: R (>= 4.2.0), crisprBase (>= 1.1.3)
Imports: AnnotationDbi, BiocGenerics, Biostrings, BSgenome,
crisprBowtie (>= 0.99.8), crisprScore (>= 1.1.6), GenomeInfoDb,
GenomicFeatures, GenomicRanges (>= 1.38.0), IRanges, Matrix,
MatrixGenerics, methods, rtracklayer, S4Vectors, stats,
txdbmaker, utils, VariantAnnotation
Suggests: biomaRt, BSgenome.Hsapiens.UCSC.hg38,
BSgenome.Mmusculus.UCSC.mm10, BiocStyle, crisprBwa (>= 0.99.7),
knitr, rmarkdown, Rbowtie, Rbwa, RCurl, testthat
License: MIT + file LICENSE
MD5sum: ccb317fdeef48fb0ce281c58cf547910
NeedsCompilation: no
Title: Comprehensive design of CRISPR gRNAs for nucleases and base
editors
Description: Provides a comprehensive suite of functions to design and
annotate CRISPR guide RNA (gRNAs) sequences. This includes on-
and off-target search, on-target efficiency scoring, off-target
scoring, full gene and TSS contextual annotations, and SNP
annotation (human only). It currently support five types of
CRISPR modalities (modes of perturbations): CRISPR knockout,
CRISPR activation, CRISPR inhibition, CRISPR base editing, and
CRISPR knockdown. All types of CRISPR nucleases are supported,
including DNA- and RNA-target nucleases such as Cas9, Cas12a,
and Cas13d. All types of base editors are also supported. gRNA
design can be performed on reference genomes, transcriptomes,
and custom DNA and RNA sequences. Both unpaired and paired gRNA
designs are enabled.
biocViews: CRISPR, FunctionalGenomics, GeneTarget
Author: Jean-Philippe Fortin [aut, cre], Luke Hoberecht [aut]
Maintainer: Jean-Philippe Fortin <fortin946@gmail.com>
URL: https://github.com/crisprVerse/crisprDesign
VignetteBuilder: knitr
BugReports: https://github.com/crisprVerse/crisprDesign/issues
git_url: https://git.bioconductor.org/packages/crisprDesign
git_branch: devel
git_last_commit: 61a8bea
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/crisprDesign_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/crisprDesign_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/crisprDesign_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/crisprDesign_1.9.0.tgz
vignettes: vignettes/crisprDesign/inst/doc/intro.html
vignetteTitles: Introduction to crisprDesign
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/crisprDesign/inst/doc/intro.R
dependsOnMe: crisprViz
importsMe: crisprShiny, crisprVerse
dependencyCount: 125
Package: crisprScore
Version: 1.11.0
Depends: R (>= 4.1), crisprScoreData (>= 1.1.3)
Imports: basilisk (>= 1.9.2), basilisk.utils (>= 1.9.1), BiocGenerics,
Biostrings, IRanges, methods, randomForest, reticulate,
stringr, utils, XVector
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: MIT + file LICENSE
MD5sum: 8ad0900366dc4839417cbb4e31c1113b
NeedsCompilation: no
Title: On-Target and Off-Target Scoring Algorithms for CRISPR gRNAs
Description: Provides R wrappers of several on-target and off-target
scoring methods for CRISPR guide RNAs (gRNAs). The following
nucleases are supported: SpCas9, AsCas12a, enAsCas12a, and
RfxCas13d (CasRx). The available on-target cutting efficiency
scoring methods are RuleSet1, Azimuth, DeepHF, DeepCpf1,
enPAM+GB, and CRISPRscan. Both the CFD and MIT scoring methods
are available for off-target specificity prediction. The
package also provides a Lindel-derived score to predict the
probability of a gRNA to produce indels inducing a frameshift
for the Cas9 nuclease. Note that DeepHF, DeepCpf1 and enPAM+GB
are not available on Windows machines.
biocViews: CRISPR, FunctionalGenomics, FunctionalPrediction
Author: Jean-Philippe Fortin [aut, cre, cph], Aaron Lun [aut], Luke
Hoberecht [ctb], Pirunthan Perampalam [ctb]
Maintainer: Jean-Philippe Fortin <fortin946@gmail.com>
URL: https://github.com/crisprVerse/crisprScore/issues
VignetteBuilder: knitr
BugReports: https://github.com/crisprVerse/crisprScore
git_url: https://git.bioconductor.org/packages/crisprScore
git_branch: devel
git_last_commit: d409224
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/crisprScore_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/crisprScore_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/crisprScore_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/crisprScore_1.11.0.tgz
vignettes: vignettes/crisprScore/inst/doc/crisprScore.html
vignetteTitles: crisprScore
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/crisprScore/inst/doc/crisprScore.R
importsMe: crisprDesign, crisprShiny, crisprVerse
dependencyCount: 80
Package: CRISPRseek
Version: 1.47.1
Depends: R (>= 3.5.0), BiocGenerics, Biostrings, GenomicFeatures
Imports: parallel, data.table, seqinr, S4Vectors (>= 0.9.25), IRanges,
BSgenome, hash, methods,reticulate,rhdf5,XVector, DelayedArray,
GenomeInfoDb, GenomicRanges, dplyr, keras, mltools, gtools,
openxlsx, rio, rlang, stringr
Suggests: RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19,
TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db,
BSgenome.Mmusculus.UCSC.mm10,
TxDb.Mmusculus.UCSC.mm10.knownGene, org.Mm.eg.db, lattice,
MASS, tensorflow, BSgenome.Hsapiens.UCSC.hg38, BiocFileCache,
TxDb.Hsapiens.UCSC.hg38.knownGene, testthat, knitr
License: file LICENSE
MD5sum: d9c206d3cc46582821ba5896aba8f16f
NeedsCompilation: no
Title: Design of guide RNAs in CRISPR genome-editing systems
Description: The package encompasses functions to find potential guide
RNAs for the CRISPR-based genome-editing systems including the
Base Editors and the Prime Editors when supplied with target
sequences as input. Users have the flexibility to filter
resulting guide RNAs based on parameters such as the absence of
restriction enzyme cut sites or the lack of paired guide RNAs.
The package also facilitates genome-wide exploration for
off-targets, offering features to score and rank off-targets,
retrieve flanking sequences, and indicate whether the hits are
located within exon regions. All detected guide RNAs are
annotated with the cumulative scores of the top5 and topN
off-targets together with the detailed information such as
mismatch sites and restrictuion enzyme cut sites. The package
also outputs INDELs and their frequencies for Cas9 targeted
sites.
biocViews: ImmunoOncology, GeneRegulation, SequenceMatching, CRISPR
Author: Lihua Julie Zhu Paul Scemama Benjamin R. Holmes Hervé Pagès Kai
Hu Hui Mao Michael Lawrence Isana Veksler-Lublinsky Victor
Ambros Neil Aronin Michael Brodsky Devin M Burris
Maintainer: Lihua Julie Zhu <julie.zhu@umassmed.edu> Kai Hu
<kai.hu@umassmed.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CRISPRseek
git_branch: devel
git_last_commit: 2a471fe
git_last_commit_date: 2025-03-04
Date/Publication: 2025-03-05
source.ver: src/contrib/CRISPRseek_1.47.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CRISPRseek_1.47.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CRISPRseek_1.47.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CRISPRseek_1.47.1.tgz
vignettes: vignettes/CRISPRseek/inst/doc/CRISPRseek.html
vignetteTitles: CRISPRseek: guide RNA design and off-target analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CRISPRseek/inst/doc/CRISPRseek.R
importsMe: GUIDEseq, multicrispr
dependencyCount: 141
Package: crisprShiny
Version: 1.3.0
Depends: R (>= 4.4.0), shiny
Imports: BiocGenerics, Biostrings, BSgenome, crisprBase, crisprDesign,
crisprScore, crisprViz, DT, GenomeInfoDb, htmlwidgets, methods,
pwalign, S4Vectors, shinyBS, shinyjs, utils, waiter
Suggests: BiocStyle, knitr, rmarkdown, shinyFeedback, testthat (>=
3.0.0), BSgenome.Hsapiens.UCSC.hg38
License: MIT + file LICENSE
MD5sum: fb8117cd14c2537a4f851a667ea1fc8a
NeedsCompilation: no
Title: Exploring curated CRISPR gRNAs via Shiny
Description: Provides means to interactively visualize guide RNAs
(gRNAs) in GuideSet objects via Shiny application. This GUI can
be self-contained or as a module within a larger Shiny app. The
content of the app reflects the annotations present in the
passed GuideSet object, and includes intuitive tools to
examine, filter, and export gRNAs, thereby making gRNA design
more user-friendly.
biocViews: CRISPR, FunctionalGenomics, GeneTarget, GUI
Author: Jean-Philippe Fortin [aut, cre], Luke Hoberecht [aut]
Maintainer: Jean-Philippe Fortin <fortin946@gmail.com>
URL: https://github.com/crisprVerse/crisprShiny
VignetteBuilder: knitr
BugReports: https://github.com/crisprVerse/crisprShiny/issues
git_url: https://git.bioconductor.org/packages/crisprShiny
git_branch: devel
git_last_commit: d16fcf6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/crisprShiny_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/crisprShiny_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/crisprShiny_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/crisprShiny_1.3.0.tgz
vignettes: vignettes/crisprShiny/inst/doc/intro.html
vignetteTitles: Introduction to crisprShiny
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/crisprShiny/inst/doc/intro.R
dependencyCount: 193
Package: CrispRVariants
Version: 1.35.0
Depends: R (>= 4.3.0), ggplot2 (>= 2.2.0)
Imports: AnnotationDbi, BiocParallel, Biostrings, methods,
GenomeInfoDb, GenomicAlignments, GenomicRanges, grDevices,
grid, gridExtra, IRanges, reshape2, Rsamtools, S4Vectors (>=
0.9.38), utils
Suggests: BiocStyle, GenomicFeatures, knitr, rmarkdown, readxl,
rtracklayer, sangerseqR, testthat, VariantAnnotation
License: GPL-2
MD5sum: 16614aef4e1e9473b10f90a6e1cb0bfa
NeedsCompilation: no
Title: Tools for counting and visualising mutations in a target
location
Description: CrispRVariants provides tools for analysing the results of
a CRISPR-Cas9 mutagenesis sequencing experiment, or other
sequencing experiments where variants within a given region are
of interest. These tools allow users to localize variant allele
combinations with respect to any genomic location (e.g. the
Cas9 cut site), plot allele combinations and calculate mutation
rates with flexible filtering of unrelated variants.
biocViews: ImmunoOncology, CRISPR, GenomicVariation, VariantDetection,
GeneticVariability, DataRepresentation, Visualization,
Sequencing
Author: Helen Lindsay [aut, cre]
Maintainer: Helen Lindsay <helen.lindsay@chuv.ch>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CrispRVariants
git_branch: devel
git_last_commit: aa34499
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CrispRVariants_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CrispRVariants_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CrispRVariants_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CrispRVariants_1.35.0.tgz
vignettes: vignettes/CrispRVariants/inst/doc/user_guide.pdf,
vignettes/CrispRVariants/inst/doc/user_guide.html
vignetteTitles: CrispRVariants, CrispRVariants
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CrispRVariants/inst/doc/user_guide.R
dependencyCount: 95
Package: crisprVerse
Version: 1.9.0
Depends: R (>= 4.2.0)
Imports: BiocManager, cli, crisprBase, crisprBowtie, crisprScore,
crisprScoreData, crisprDesign, crisprViz, rlang, tools, utils
Suggests: BiocStyle, knitr, testthat
License: MIT + file LICENSE
MD5sum: b37f0da41757af2da8a0501da714b933
NeedsCompilation: no
Title: Easily install and load the crisprVerse ecosystem for CRISPR
gRNA design
Description: The crisprVerse is a modular ecosystem of R packages
developed for the design and manipulation of CRISPR guide RNAs
(gRNAs). All packages share a common language and design
principles. This package is designed to make it easy to install
and load the crisprVerse packages in a single step. To learn
more about the crisprVerse, visit
<https://www.github.com/crisprVerse>.
biocViews: CRISPR, FunctionalGenomics, GeneTarget
Author: Jean-Philippe Fortin [aut, cre]
Maintainer: Jean-Philippe Fortin <fortin946@gmail.com>
URL: https://github.com/crisprVerse/crisprVerse
VignetteBuilder: knitr
BugReports: https://github.com/crisprVerse/crisprVerse/issues
git_url: https://git.bioconductor.org/packages/crisprVerse
git_branch: devel
git_last_commit: e57a5dc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/crisprVerse_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/crisprVerse_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/crisprVerse_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/crisprVerse_1.9.0.tgz
vignettes: vignettes/crisprVerse/inst/doc/crisprVerse.html
vignetteTitles: crisprVerse
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/crisprVerse/inst/doc/crisprVerse.R
dependencyCount: 180
Package: crisprViz
Version: 1.9.0
Depends: R (>= 4.2.0), crisprBase (>= 0.99.15), crisprDesign (>=
0.99.77)
Imports: BiocGenerics, Biostrings, BSgenome, GenomeInfoDb,
GenomicFeatures, GenomicRanges, grDevices, Gviz, IRanges,
methods, S4Vectors, txdbmaker
Suggests: AnnotationHub, BiocStyle, BSgenome.Hsapiens.UCSC.hg38, knitr,
rmarkdown, rtracklayer, testthat, utils
License: MIT + file LICENSE
MD5sum: aa9701a8a430094afeb617342fc3af7f
NeedsCompilation: no
Title: Visualization Functions for CRISPR gRNAs
Description: Provides functionalities to visualize and contextualize
CRISPR guide RNAs (gRNAs) on genomic tracks across nucleases
and applications. Works in conjunction with the crisprBase and
crisprDesign Bioconductor packages. Plots are produced using
the Gviz framework.
biocViews: CRISPR, FunctionalGenomics, GeneTarget
Author: Jean-Philippe Fortin [aut, cre], Luke Hoberecht [aut]
Maintainer: Jean-Philippe Fortin <fortin946@gmail.com>
URL: https://github.com/crisprVerse/crisprViz
VignetteBuilder: knitr
BugReports: https://github.com/crisprVerse/crisprViz/issues
git_url: https://git.bioconductor.org/packages/crisprViz
git_branch: devel
git_last_commit: aa8d738
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/crisprViz_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/crisprViz_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/crisprViz_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/crisprViz_1.9.0.tgz
vignettes: vignettes/crisprViz/inst/doc/intro.html
vignetteTitles: Introduction to crisprViz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/crisprViz/inst/doc/intro.R
importsMe: crisprShiny, crisprVerse
dependencyCount: 179
Package: crlmm
Version: 1.65.1
Depends: R (>= 2.14.0), oligoClasses (>= 1.21.12), preprocessCore (>=
1.17.7)
Imports: methods, Biobase (>= 2.15.4), BiocGenerics, affyio (>=
1.23.2), illuminaio, ellipse, mvtnorm, splines, stats, utils,
lattice, ff, foreach, RcppEigen (>= 0.3.1.2.1), matrixStats,
VGAM, parallel, graphics, limma, beanplot
LinkingTo: preprocessCore (>= 1.17.7)
Suggests: hapmapsnp6, genomewidesnp6Crlmm (>= 1.0.7), snpStats, RUnit
License: Artistic-2.0
MD5sum: 1d7c5d7d55bc7609b235053deb2e0419
NeedsCompilation: yes
Title: Genotype Calling (CRLMM) and Copy Number Analysis tool for
Affymetrix SNP 5.0 and 6.0 and Illumina arrays
Description: Faster implementation of CRLMM specific to SNP 5.0 and 6.0
arrays, as well as a copy number tool specific to 5.0, 6.0, and
Illumina platforms.
biocViews: Microarray, Preprocessing, SNP, CopyNumberVariation
Author: Benilton S Carvalho, Robert Scharpf, Matt Ritchie, Ingo
Ruczinski, Rafael A Irizarry
Maintainer: Benilton S Carvalho <benilton@unicamp.br>, Robert Scharpf
<rscharpf@jhsph.edu>, Matt Ritchie <mritchie@wehi.EDU.AU>
git_url: https://git.bioconductor.org/packages/crlmm
git_branch: devel
git_last_commit: 9b175a9
git_last_commit_date: 2025-03-04
Date/Publication: 2025-03-04
source.ver: src/contrib/crlmm_1.65.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/crlmm_1.65.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/crlmm_1.65.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/crlmm_1.65.1.tgz
vignettes: vignettes/crlmm/inst/doc/AffyGW.pdf,
vignettes/crlmm/inst/doc/CopyNumberOverview.pdf,
vignettes/crlmm/inst/doc/genotyping.pdf,
vignettes/crlmm/inst/doc/gtypeDownstream.pdf,
vignettes/crlmm/inst/doc/IlluminaPreprocessCN.pdf,
vignettes/crlmm/inst/doc/Infrastructure.pdf
vignetteTitles: Copy number estimation, Overview of copy number
vignettes, crlmm Vignette - Genotyping, crlmm Vignette -
Downstream Analysis, Preprocessing and genotyping Illumina
arrays for copy number analysis, Infrastructure for copy number
analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/crlmm/inst/doc/genotyping.R
dependsOnMe: MAGAR
importsMe: VanillaICE
suggestsMe: oligoClasses, hapmap370k
dependencyCount: 73
Package: CSAR
Version: 1.59.0
Depends: R (>= 2.15.0), S4Vectors, IRanges, GenomeInfoDb, GenomicRanges
Imports: stats, utils
Suggests: ShortRead, Biostrings
License: Artistic-2.0
Archs: x64
MD5sum: ef25f502898ca57975345f02a6ad161b
NeedsCompilation: yes
Title: Statistical tools for the analysis of ChIP-seq data
Description: Statistical tools for ChIP-seq data analysis. The package
includes the statistical method described in Kaufmann et al.
(2009) PLoS Biology: 7(4):e1000090. Briefly, Taking the average
DNA fragment size subjected to sequencing into account, the
software calculates genomic single-nucleotide read-enrichment
values. After normalization, sample and control are compared
using a test based on the Poisson distribution. Test statistic
thresholds to control the false discovery rate are obtained
through random permutation.
biocViews: ChIPSeq, Transcription, Genetics
Author: Jose M Muino
Maintainer: Jose M Muino <jose.muino@live.com>
git_url: https://git.bioconductor.org/packages/CSAR
git_branch: devel
git_last_commit: 43ca1bc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CSAR_1.59.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CSAR_1.59.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CSAR_1.59.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CSAR_1.59.0.tgz
vignettes: vignettes/CSAR/inst/doc/CSAR.pdf
vignetteTitles: CSAR Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CSAR/inst/doc/CSAR.R
dependencyCount: 23
Package: csaw
Version: 1.41.2
Depends: R (>= 3.5.0), GenomicRanges, SummarizedExperiment
Imports: Rcpp, Matrix, BiocGenerics, Rsamtools, edgeR, limma, methods,
S4Vectors, IRanges, GenomeInfoDb, stats, BiocParallel, metapod,
utils
LinkingTo: Rhtslib, Rcpp
Suggests: AnnotationDbi, org.Mm.eg.db,
TxDb.Mmusculus.UCSC.mm10.knownGene, testthat, GenomicFeatures,
GenomicAlignments, knitr, BiocStyle, rmarkdown, BiocManager
License: GPL-3
Archs: x64
MD5sum: 9eaa27985aa48f4c5d4449f4f734e5c0
NeedsCompilation: yes
Title: ChIP-Seq Analysis with Windows
Description: Detection of differentially bound regions in ChIP-seq data
with sliding windows, with methods for normalization and proper
FDR control.
biocViews: MultipleComparison, ChIPSeq, Normalization, Sequencing,
Coverage, Genetics, Annotation, DifferentialPeakCalling
Author: Aaron Lun [aut, cre], Gordon Smyth [aut]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
SystemRequirements: C++11, GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/csaw
git_branch: devel
git_last_commit: 75cff36
git_last_commit_date: 2025-01-29
Date/Publication: 2025-01-29
source.ver: src/contrib/csaw_1.41.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/csaw_1.41.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/csaw_1.41.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/csaw_1.41.2.tgz
vignettes: vignettes/csaw/inst/doc/csaw.html
vignetteTitles: Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/csaw/inst/doc/csaw.R
dependsOnMe: csawBook
importsMe: diffHic, epigraHMM, extraChIPs, icetea, NADfinder, vulcan,
treediff
suggestsMe: DiffBind, GRaNIE, chipseqDB
dependencyCount: 56
Package: csdR
Version: 1.13.4
Depends: R (>= 4.1.0)
Imports: WGCNA, glue, RhpcBLASctl, matrixStats, Rcpp
LinkingTo: Rcpp
Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle, magrittr,
igraph, dplyr
License: GPL-3
MD5sum: 9bba81cf0d087f309581dbcb1638d9b8
NeedsCompilation: yes
Title: Differential gene co-expression
Description: This package contains functionality to run differential
gene co-expression across two different conditions. The
algorithm is inspired by Voigt et al. 2017 and finds Conserved,
Specific and Differentiated genes (hence the name CSD). This
package include efficient and variance calculation by
bootstrapping and Welford's algorithm.
biocViews: DifferentialExpression, GraphAndNetwork, GeneExpression,
Network
Author: Jakob Peder Pettersen [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3485-1634>)
Maintainer: Jakob Peder Pettersen <jakobpeder.pettersen@gmail.com>
URL: https://almaaslab.github.io/csdR,
https://github.com/AlmaasLab/csdR
VignetteBuilder: knitr
BugReports: https://github.com/AlmaasLab/csdR/issues
git_url: https://git.bioconductor.org/packages/csdR
git_branch: devel
git_last_commit: 13fefea
git_last_commit_date: 2025-03-26
Date/Publication: 2025-03-26
source.ver: src/contrib/csdR_1.13.4.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/csdR_1.13.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/csdR_1.13.4.tgz
vignettes: vignettes/csdR/inst/doc/csdR.html
vignetteTitles: csdR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/csdR/inst/doc/csdR.R
dependencyCount: 115
Package: CSSQ
Version: 1.19.0
Depends: SummarizedExperiment, GenomicRanges, IRanges, S4Vectors,
rtracklayer
Imports: GenomicAlignments, GenomicFeatures, Rsamtools, ggplot2,
grDevices, stats, utils
Suggests: BiocStyle, knitr, rmarkdown, markdown
License: Artistic-2.0
Archs: x64
MD5sum: 39349e7e129091c130a2e88de9d72f51
NeedsCompilation: no
Title: Chip-seq Signal Quantifier Pipeline
Description: This package is desgined to perform statistical analysis
to identify statistically significant differentially bound
regions between multiple groups of ChIP-seq dataset.
biocViews: ChIPSeq, DifferentialPeakCalling, Sequencing, Normalization
Author: Ashwath Kumar [aut], Michael Y Hu [aut], Yajun Mei [aut],
Yuhong Fan [aut]
Maintainer: Fan Lab at Georgia Institute of Technology
<yuhong.fan@biology.gatech.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CSSQ
git_branch: devel
git_last_commit: 9eae002
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CSSQ_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CSSQ_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CSSQ_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CSSQ_1.19.0.tgz
vignettes: vignettes/CSSQ/inst/doc/CSSQ.html
vignetteTitles: Introduction to CSSQ
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CSSQ/inst/doc/CSSQ.R
dependencyCount: 97
Package: ctc
Version: 1.81.0
Depends: amap
License: GPL-2
MD5sum: cdb6d6e5d5c38d19bfa84312444b4acf
NeedsCompilation: no
Title: Cluster and Tree Conversion.
Description: Tools for export and import classification trees and
clusters to other programs
biocViews: Microarray, Clustering, Classification, DataImport,
Visualization
Author: Antoine Lucas <antoinelucas@gmail.com>, Laurent Gautier
<laurent@cbs.dtu.dk>
Maintainer: Antoine Lucas <antoinelucas@gmail.com>
URL: http://antoinelucas.free.fr/ctc
git_url: https://git.bioconductor.org/packages/ctc
git_branch: devel
git_last_commit: d5fd1b9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ctc_1.81.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ctc_1.81.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ctc_1.81.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ctc_1.81.0.tgz
vignettes: vignettes/ctc/inst/doc/ctc.pdf
vignetteTitles: Introduction to ctc
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ctc/inst/doc/ctc.R
importsMe: miRLAB, multiClust
dependencyCount: 1
Package: CTdata
Version: 1.7.0
Depends: R (>= 4.2)
Imports: ExperimentHub, utils
Suggests: testthat (>= 3.0.0), DT, BiocStyle, knitr, rmarkdown,
SummarizedExperiment, SingleCellExperiment
License: Artistic-2.0
MD5sum: 57472576a9d787b182165170180c68d6
NeedsCompilation: no
Title: Data companion to CTexploreR
Description: Data from publicly available databases (GTEx, CCLE, TCGA
and ENCODE) that go with CTexploreR in order to re-define a
comprehensive and thoroughly curated list of CT genes and their
main characteristics.
biocViews: Transcriptomics, Epigenetics, GeneExpression, DataImport,
ExperimentHubSoftware
Author: Axelle Loriot [aut] (ORCID:
<https://orcid.org/0000-0002-5288-8561>), Julie Devis [aut]
(ORCID: <https://orcid.org/0000-0001-5525-5666>), Anna
Diacofotaki [ctb], Charles De Smet [ths], Laurent Gatto [aut,
ths, cre] (ORCID: <https://orcid.org/0000-0002-1520-2268>)
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
VignetteBuilder: knitr
BugReports: https://github.com/UCLouvain-CBIO/CTdata/issues
git_url: https://git.bioconductor.org/packages/CTdata
git_branch: devel
git_last_commit: 0cd0dfa
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CTdata_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CTdata_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CTdata_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CTdata_1.7.0.tgz
vignettes: vignettes/CTdata/inst/doc/CTdata.html
vignetteTitles: Cancer Testis Datasets
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CTdata/inst/doc/CTdata.R
dependsOnMe: CTexploreR
dependencyCount: 66
Package: CTDquerier
Version: 2.15.0
Depends: R (>= 4.1)
Imports: RCurl, stringr, S4Vectors, stringdist, ggplot2, igraph, utils,
grid, gridExtra, methods, stats, BiocFileCache
Suggests: BiocStyle, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: b55b85b456b8de093160d6d21fd3851b
NeedsCompilation: no
Title: Package for CTDbase data query, visualization and downstream
analysis
Description: Package to retrieve and visualize data from the
Comparative Toxicogenomics Database (http://ctdbase.org/). The
downloaded data is formated as DataFrames for further
downstream analyses.
biocViews: Software, BiomedicalInformatics, Infrastructure, DataImport,
DataRepresentation, GeneSetEnrichment, NetworkEnrichment,
Pathways, Network, GO, KEGG
Author: Carles Hernandez-Ferrer [aut], Juan R. Gonzalez [aut], Xavier
Escribà -Montagut [cre]
Maintainer: Xavier Escribà -Montagut <xavier.escriba@isglobal.org>
VignetteBuilder: rmarkdown
git_url: https://git.bioconductor.org/packages/CTDquerier
git_branch: devel
git_last_commit: b79a567
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CTDquerier_2.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CTDquerier_2.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CTDquerier_2.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CTDquerier_2.15.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
dependencyCount: 72
Package: CTexploreR
Version: 1.3.0
Depends: R (>= 4.3), CTdata (>= 1.5.3)
Imports: BiocGenerics, ComplexHeatmap, grid, SummarizedExperiment,
GenomicRanges, IRanges, dplyr, tidyr, tibble, ggplot2, rlang,
grDevices, stats, circlize, ggrepel, SingleCellExperiment,
MatrixGenerics
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0),
InteractiveComplexHeatmap
License: Artistic-2.0
MD5sum: d86618c1ff57aeab00bbb65162cbc8ee
NeedsCompilation: no
Title: Explores Cancer Testis Genes
Description: The CTexploreR package re-defines the list of Cancer
Testis/Germline (CT) genes. It is based on publicly available
RNAseq databases (GTEx, CCLE and TCGA) and summarises CT genes'
main characteristics. Several visualisation functions allow to
explore their expression in different types of tissues and
cancer cells, or to inspect the methylation status of their
promoters in normal tissues.
biocViews: Transcriptomics, Epigenetics, DifferentialExpression,
GeneExpression, DNAMethylation, ExperimentHubSoftware,
DataImport
Author: Axelle Loriot [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5288-8561>), Julie Devis [aut]
(ORCID: <https://orcid.org/0000-0001-5525-5666>), Anna
Diacofotaki [ctb], Charles De Smet [ths], Laurent Gatto [aut,
ths] (ORCID: <https://orcid.org/0000-0002-1520-2268>)
Maintainer: Axelle Loriot <axelle.loriot@uclouvain.be>
URL: https://github.com/UCLouvain-CBIO/CTexploreR
VignetteBuilder: knitr
BugReports: https://github.com/UCLouvain-CBIO/CTexploreR/issues
git_url: https://git.bioconductor.org/packages/CTexploreR
git_branch: devel
git_last_commit: b8dffc6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-12-20
source.ver: src/contrib/CTexploreR_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CTexploreR_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CTexploreR_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CTexploreR_1.3.0.tgz
vignettes: vignettes/CTexploreR/inst/doc/CTexploreR.html
vignetteTitles: Cancer Testis Explorer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CTexploreR/inst/doc/CTexploreR.R
dependencyCount: 109
Package: cTRAP
Version: 1.25.0
Depends: R (>= 4.0)
Imports: AnnotationDbi, AnnotationHub, binr, cowplot, data.table,
dplyr, DT, fastmatch, fgsea, ggplot2, ggrepel, graphics,
highcharter, htmltools, httr, limma, methods, parallel,
pbapply, purrr, qs, R.utils, readxl, reshape2, rhdf5, rlang,
scales, shiny (>= 1.7.0), shinycssloaders, stats, tibble,
tools, utils
Suggests: testthat, knitr, covr, rmarkdown, spelling, biomaRt, remotes
License: MIT + file LICENSE
MD5sum: 79b60308ddc324c283f6c40a249ea43e
NeedsCompilation: no
Title: Identification of candidate causal perturbations from
differential gene expression data
Description: Compare differential gene expression results with those
from known cellular perturbations (such as gene knock-down,
overexpression or small molecules) derived from the
Connectivity Map. Such analyses allow not only to infer the
molecular causes of the observed difference in gene expression
but also to identify small molecules that could drive or revert
specific transcriptomic alterations.
biocViews: DifferentialExpression, GeneExpression, RNASeq,
Transcriptomics, Pathways, ImmunoOncology, GeneSetEnrichment
Author: Bernardo P. de Almeida [aut], Nuno Saraiva-Agostinho [aut,
cre], Nuno L. Barbosa-Morais [aut, led]
Maintainer: Nuno Saraiva-Agostinho <nunodanielagostinho@gmail.com>
URL: https://nuno-agostinho.github.io/cTRAP,
https://github.com/nuno-agostinho/cTRAP
VignetteBuilder: knitr
BugReports: https://github.com/nuno-agostinho/cTRAP/issues
git_url: https://git.bioconductor.org/packages/cTRAP
git_branch: devel
git_last_commit: b3d1df5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cTRAP_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cTRAP_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cTRAP_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cTRAP_1.25.0.tgz
vignettes: vignettes/cTRAP/inst/doc/cTRAP.html
vignetteTitles: cTRAP: identifying candidate causal perturbations from
differential gene expression data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/cTRAP/inst/doc/cTRAP.R
dependencyCount: 159
Package: ctsGE
Version: 1.33.0
Depends: R (>= 3.2)
Imports: ccaPP, ggplot2, limma, reshape2, shiny, stats, stringr, utils
Suggests: BiocStyle, dplyr, DT, GEOquery, knitr, pander, rmarkdown,
testthat
License: GPL-2
MD5sum: 4773503d4302b596e2660d7ecc6bbf37
NeedsCompilation: no
Title: Clustering of Time Series Gene Expression data
Description: Methodology for supervised clustering of potentially many
predictor variables, such as genes etc., in time series
datasets Provides functions that help the user assigning genes
to predefined set of model profiles.
biocViews: ImmunoOncology, GeneExpression, Transcription,
DifferentialExpression, GeneSetEnrichment, Genetics, Bayesian,
Clustering, TimeCourse, Sequencing, RNASeq
Author: Michal Sharabi-Schwager [aut, cre], Ron Ophir [aut]
Maintainer: Michal Sharabi-Schwager <michalsharabi@gmail.com>
URL: https://github.com/michalsharabi/ctsGE
VignetteBuilder: knitr
BugReports: https://github.com/michalsharabi/ctsGE/issues
git_url: https://git.bioconductor.org/packages/ctsGE
git_branch: devel
git_last_commit: ae475ee
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ctsGE_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ctsGE_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ctsGE_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ctsGE_1.33.0.tgz
vignettes: vignettes/ctsGE/inst/doc/ctsGE.html
vignetteTitles: ctsGE Package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ctsGE/inst/doc/ctsGE.R
dependencyCount: 72
Package: CTSV
Version: 1.9.0
Depends: R (>= 4.2),
Imports: stats, pscl, qvalue, BiocParallel, methods, knitr,
SpatialExperiment, SummarizedExperiment
Suggests: testthat, BiocStyle
License: GPL-3
MD5sum: b074ec849f6ba7aa4fcbb2ddaac6e3df
NeedsCompilation: yes
Title: Identification of cell-type-specific spatially variable genes
accounting for excess zeros
Description: The R package CTSV implements the CTSV approach developed
by Jinge Yu and Xiangyu Luo that detects cell-type-specific
spatially variable genes accounting for excess zeros. CTSV
directly models sparse raw count data through a zero-inflated
negative binomial regression model, incorporates cell-type
proportions, and performs hypothesis testing based on R package
pscl. The package outputs p-values and q-values for genes in
each cell type, and CTSV is scalable to datasets with tens of
thousands of genes measured on hundreds of spots. CTSV can be
installed in Windows, Linux, and Mac OS.
biocViews: GeneExpression, StatisticalMethod, Regression, Spatial,
Genetics
Author: Jinge Yu Developer [aut, cre], Xiangyu Luo Developer [aut]
Maintainer: Jinge Yu Developer <yjgruc@ruc.edu.cn>
URL: https://github.com/jingeyu/CTSV
VignetteBuilder: knitr
BugReports: https://github.com/jingeyu/CTSV/issues
git_url: https://git.bioconductor.org/packages/CTSV
git_branch: devel
git_last_commit: cd7cd49
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CTSV_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CTSV_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CTSV_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CTSV_1.9.0.tgz
vignettes: vignettes/CTSV/inst/doc/CTSV.html
vignetteTitles: Basic Usage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CTSV/inst/doc/CTSV.R
dependencyCount: 105
Package: cummeRbund
Version: 2.49.0
Depends: R (>= 2.7.0), BiocGenerics (>= 0.3.2), RSQLite, ggplot2,
reshape2, fastcluster, rtracklayer, Gviz
Imports: methods, plyr, BiocGenerics, S4Vectors (>= 0.9.25), Biobase
Suggests: cluster, plyr, NMFN, stringr, GenomicFeatures, GenomicRanges,
rjson
License: Artistic-2.0
MD5sum: 1d077049bdbb56c7ad659b1c494cbbf1
NeedsCompilation: no
Title: Analysis, exploration, manipulation, and visualization of
Cufflinks high-throughput sequencing data.
Description: Allows for persistent storage, access, exploration, and
manipulation of Cufflinks high-throughput sequencing data. In
addition, provides numerous plotting functions for commonly
used visualizations.
biocViews: HighThroughputSequencing, HighThroughputSequencingData,
RNAseq, RNAseqData, GeneExpression, DifferentialExpression,
Infrastructure, DataImport, DataRepresentation, Visualization,
Bioinformatics, Clustering, MultipleComparisons, QualityControl
Author: L. Goff, C. Trapnell, D. Kelley
Maintainer: Loyal A. Goff <lgoff@csail.mit.edu>
git_url: https://git.bioconductor.org/packages/cummeRbund
git_branch: devel
git_last_commit: ddf62c2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cummeRbund_2.49.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cummeRbund_2.49.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cummeRbund_2.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cummeRbund_2.49.0.tgz
vignettes:
vignettes/cummeRbund/inst/doc/cummeRbund-example-workflow.pdf,
vignettes/cummeRbund/inst/doc/cummeRbund-manual.pdf
vignetteTitles: Sample cummeRbund workflow, CummeRbund User Guide
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cummeRbund/inst/doc/cummeRbund-example-workflow.R,
vignettes/cummeRbund/inst/doc/cummeRbund-manual.R
dependencyCount: 159
Package: CuratedAtlasQueryR
Version: 1.5.0
Depends: R (>= 4.2.0)
Imports: dplyr, SummarizedExperiment, SingleCellExperiment, purrr (>=
1.0.0), BiocGenerics, glue, HDF5Array, DBI, tools, httr, cli,
assertthat, SeuratObject, Seurat, methods, rlang, stats,
S4Vectors, tibble, utils, dbplyr (>= 2.3.0), duckdb, stringr
Suggests: zellkonverter, rmarkdown, knitr, testthat, basilisk, arrow,
reticulate, spelling, forcats, ggplot2,
tidySingleCellExperiment, rprojroot
License: GPL-3
MD5sum: 110141ee34456ae28fcc1e4b52c6e2e0
NeedsCompilation: no
Title: Queries the Human Cell Atlas
Description: Provides access to a copy of the Human Cell Atlas, but
with harmonised metadata. This allows for uniform querying
across numerous datasets within the Atlas using common fields
such as cell type, tissue type, and patient ethnicity. Usage
involves first querying the metadata table for cells of
interest, and then downloading the corresponding cells into a
SingleCellExperiment object.
biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression,
GeneExpression, Normalization, Clustering, QualityControl,
Sequencing, Transcription, Transcriptomics
Author: Stefano Mangiola [aut, cre, rev] (ORCID:
<https://orcid.org/0000-0001-7474-836X>), Michael Milton [aut,
rev] (ORCID: <https://orcid.org/0000-0002-8965-2595>), Martin
Morgan [ctb, rev], Vincent Carey [ctb, rev], Julie Iskander
[rev], Tony Papenfuss [rev], Silicon Valley Foundation
CZF2019-002443 [fnd], NIH NHGRI 5U24HG004059-18 [fnd], Victoria
Cancer Agency ECRF21036 [fnd], NHMRC 1116955 [fnd]
Maintainer: Stefano Mangiola <mangiolastefano@gmail.com>
URL: https://github.com/stemangiola/CuratedAtlasQueryR
VignetteBuilder: knitr
BugReports: https://github.com/stemangiola/CuratedAtlasQueryR/issues
git_url: https://git.bioconductor.org/packages/CuratedAtlasQueryR
git_branch: devel
git_last_commit: 7febec3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-05
source.ver: src/contrib/CuratedAtlasQueryR_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CuratedAtlasQueryR_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CuratedAtlasQueryR_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CuratedAtlasQueryR_1.5.0.tgz
vignettes: vignettes/CuratedAtlasQueryR/inst/doc/Introduction.html
vignetteTitles: CuratedAtlasQueryR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CuratedAtlasQueryR/inst/doc/Introduction.R
dependencyCount: 181
Package: customCMPdb
Version: 1.17.0
Depends: R (>= 4.0)
Imports: AnnotationHub, RSQLite, XML, utils, ChemmineR, methods, stats,
rappdirs, BiocFileCache
Suggests: knitr, rmarkdown, testthat, BiocStyle
License: Artistic-2.0
MD5sum: c69d45b43d8f59f29c2181b8d4055339
NeedsCompilation: no
Title: Customize and Query Compound Annotation Database
Description: This package serves as a query interface for important
community collections of small molecules, while also allowing
users to include custom compound collections.
biocViews: Software, Cheminformatics,AnnotationHubSoftware
Author: Yuzhu Duan [aut, cre], Thomas Girke [aut]
Maintainer: Yuzhu Duan <yduan004@ucr.edu>
URL: https://github.com/yduan004/customCMPdb/
VignetteBuilder: knitr
BugReports: https://github.com/yduan004/customCMPdb/issues
git_url: https://git.bioconductor.org/packages/customCMPdb
git_branch: devel
git_last_commit: c0072d4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/customCMPdb_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/customCMPdb_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/customCMPdb_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/customCMPdb_1.17.0.tgz
vignettes: vignettes/customCMPdb/inst/doc/customCMPdb.html
vignetteTitles: customCMPdb
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/customCMPdb/inst/doc/customCMPdb.R
dependencyCount: 112
Package: customProDB
Version: 1.47.0
Depends: R (>= 3.5.0), IRanges, AnnotationDbi, biomaRt (>= 2.17.1)
Imports: S4Vectors (>= 0.9.25), DBI, GenomeInfoDb, GenomicRanges,
Rsamtools (>= 1.10.2), GenomicAlignments, Biostrings (>=
2.26.3), GenomicFeatures (>= 1.32.0), stringr, RCurl, plyr,
VariantAnnotation (>= 1.13.44), rtracklayer, RSQLite,
txdbmaker, AhoCorasickTrie, methods
Suggests: RMariaDB, BSgenome.Hsapiens.UCSC.hg19
License: Artistic-2.0
MD5sum: 8f984d30426ec22018c61ed5257593a5
NeedsCompilation: no
Title: Generate customized protein database from NGS data, with a focus
on RNA-Seq data, for proteomics search
Description: Database search is the most widely used approach for
peptide and protein identification in mass spectrometry-based
proteomics studies. Our previous study showed that
sample-specific protein databases derived from RNA-Seq data can
better approximate the real protein pools in the samples and
thus improve protein identification. More importantly, single
nucleotide variations, short insertion and deletions and novel
junctions identified from RNA-Seq data make protein database
more complete and sample-specific. Here, we report an R package
customProDB that enables the easy generation of customized
databases from RNA-Seq data for proteomics search. This work
bridges genomics and proteomics studies and facilitates
cross-omics data integration.
biocViews: ImmunoOncology, Sequencing, MassSpectrometry, Proteomics,
SNP, RNASeq, Software, Transcription, AlternativeSplicing,
FunctionalGenomics
Author: Xiaojing Wang
Maintainer: Xiaojing Wang <xwang.research@gmail.com> Bo Wen
<wenbostar@gmail.com>
git_url: https://git.bioconductor.org/packages/customProDB
git_branch: devel
git_last_commit: 695c102
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/customProDB_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/customProDB_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/customProDB_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/customProDB_1.47.0.tgz
vignettes: vignettes/customProDB/inst/doc/customProDB.pdf
vignetteTitles: Introduction to customProDB
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/customProDB/inst/doc/customProDB.R
dependencyCount: 106
Package: cyanoFilter
Version: 1.15.0
Depends: R(>= 4.1.0)
Imports: Biobase, flowCore, flowDensity, flowClust, cytometree,
ggplot2, GGally, graphics, grDevices, methods, mrfDepth, stats,
utils
Suggests: magrittr, dplyr, purrr, knitr, stringr, rmarkdown, tidyr
License: MIT + file LICENSE
MD5sum: ed0c631580079d5e35eb08d724362b66
NeedsCompilation: no
Title: Phytoplankton Population Identification using Cell Pigmentation
and/or Complexity
Description: An approach to filter out and/or identify phytoplankton
cells from all particles measured via flow cytometry pigment
and cell complexity information. It does this using a sequence
of one-dimensional gates on pre-defined channels measuring
certain pigmentation and complexity. The package is especially
tuned for cyanobacteria, but will work fine for phytoplankton
communities where there is at least one cell characteristic
that differentiates every phytoplankton in the community.
biocViews: FlowCytometry, Clustering, OneChannel
Author: Oluwafemi Olusoji [cre, aut], Aerts Marc [ctb], Delaender
Frederik [ctb], Neyens Thomas [ctb], Spaak jurg [aut]
Maintainer: Oluwafemi Olusoji <oluwafemi.olusoji@uhasselt.be>
URL: https://github.com/fomotis/cyanoFilter
VignetteBuilder: knitr
BugReports: https://github.com/fomotis/cyanoFilter/issues
git_url: https://git.bioconductor.org/packages/cyanoFilter
git_branch: devel
git_last_commit: aa13f66
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cyanoFilter_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cyanoFilter_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cyanoFilter_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cyanoFilter_1.15.0.tgz
vignettes: vignettes/cyanoFilter/inst/doc/cyanoFilter.html
vignetteTitles: cyanoFilter: A Semi-Automated Framework for Identifying
Phytplanktons and Cyanobacteria Population in Flow Cytometry
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/cyanoFilter/inst/doc/cyanoFilter.R
dependencyCount: 120
Package: cycle
Version: 1.61.0
Depends: R (>= 2.10.0), Mfuzz
Imports: Biobase, stats
License: GPL-2
MD5sum: c079f6a45d40d08a9587a25d193ade97
NeedsCompilation: no
Title: Significance of periodic expression pattern in time-series data
Description: Package for assessing the statistical significance of
periodic expression based on Fourier analysis and comparison
with data generated by different background models
biocViews: Microarray, TimeCourse
Author: Matthias Futschik <mfutschik@ualg.pt>
Maintainer: Matthias Futschik <mfutschik@ualg.pt>
URL: http://cycle.sysbiolab.eu
git_url: https://git.bioconductor.org/packages/cycle
git_branch: devel
git_last_commit: 40c4cd1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cycle_1.61.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cycle_1.61.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cycle_1.61.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cycle_1.61.0.tgz
vignettes: vignettes/cycle/inst/doc/cycle.pdf
vignetteTitles: Introduction to cycle
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cycle/inst/doc/cycle.R
dependencyCount: 18
Package: cydar
Version: 1.31.0
Depends: SingleCellExperiment
Imports: viridis, methods, shiny, graphics, stats, grDevices, utils,
BiocGenerics, S4Vectors, BiocParallel, SummarizedExperiment,
flowCore, Biobase, Rcpp, BiocNeighbors
LinkingTo: Rcpp
Suggests: ncdfFlow, testthat, rmarkdown, knitr, edgeR, limma, glmnet,
BiocStyle, flowStats
License: GPL-3
MD5sum: ac1cf0b54fabbf2c5d0e9da41ef3d74d
NeedsCompilation: yes
Title: Using Mass Cytometry for Differential Abundance Analyses
Description: Identifies differentially abundant populations between
samples and groups in mass cytometry data. Provides methods for
counting cells into hyperspheres, controlling the spatial false
discovery rate, and visualizing changes in abundance in the
high-dimensional marker space.
biocViews: ImmunoOncology, FlowCytometry, MultipleComparison,
Proteomics, SingleCell
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/cydar
git_branch: devel
git_last_commit: 8dc413e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cydar_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cydar_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cydar_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cydar_1.31.0.tgz
vignettes: vignettes/cydar/inst/doc/cydar.html
vignetteTitles: Detecting differential abundance
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cydar/inst/doc/cydar.R
dependencyCount: 101
Package: cypress
Version: 1.3.0
Depends: R(>= 4.4.0)
Imports: stats, abind, sirt, MASS,TOAST, tibble, parallel,
preprocessCore, SummarizedExperiment, TCA, PROPER,
methods,dplyr, utils, RColorBrewer, graphics, edgeR,
BiocParallel, checkmate, mvtnorm, DESeq2, rlang, e1071
Suggests: knitr, rmarkdown, MatrixGenerics, htmltools, RUnit,
BiocGenerics, BiocManager, BiocStyle, Biobase
License: GPL-2 | GPL-3
Archs: x64
MD5sum: 0f2d0c3caf6728c436ced1e6eec80710
NeedsCompilation: no
Title: Cell-Type-Specific Power Assessment
Description: CYPRESS is a cell-type-specific power tool. This package
aims to perform power analysis for the cell-type-specific data.
It calculates FDR, FDC, and power, under various study design
parameters, including but not limited to sample size, and
effect size. It takes the input of a SummarizeExperimental(SE)
object with observed mixture data (feature by sample matrix),
and the cell-type mixture proportions (sample by cell-type
matrix). It can solve the cell-type mixture proportions from
the reference free panel from TOAST and conduct tests to
identify cell-type-specific differential expression (csDE)
genes.
biocViews: Software, GeneExpression, DataImport, RNASeq, Sequencing
Author: Shilin Yu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5494-1960>), Guanqun Meng [aut],
Wen Tang [aut]
Maintainer: Shilin Yu <sy597@georgetown.edu>
URL: https://github.com/renlyly/cypress
VignetteBuilder: knitr
BugReports: https://github.com/renlyly/cypress/issues
git_url: https://git.bioconductor.org/packages/cypress
git_branch: devel
git_last_commit: f8d19ce
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cypress_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cypress_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cypress_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cypress_1.3.0.tgz
vignettes: vignettes/cypress/inst/doc/cypress.html
vignetteTitles: cypress Package User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cypress/inst/doc/cypress.R
dependencyCount: 128
Package: CytoDx
Version: 1.27.0
Depends: R (>= 3.5)
Imports: doParallel, dplyr, glmnet, rpart, rpart.plot, stats,
flowCore,grDevices, graphics, utils
Suggests: knitr, rmarkdown
License: GPL-2
MD5sum: a76e2818defebc6ee52d7db89ca05aba
NeedsCompilation: no
Title: Robust prediction of clinical outcomes using cytometry data
without cell gating
Description: This package provides functions that predict clinical
outcomes using single cell data (such as flow cytometry data,
RNA single cell sequencing data) without the requirement of
cell gating or clustering.
biocViews: ImmunoOncology, CellBiology, FlowCytometry,
StatisticalMethod, Software, CellBasedAssays, Regression,
Classification, Survival
Author: Zicheng Hu
Maintainer: Zicheng Hu <zicheng.hu@ucsf.edu>
VignetteBuilder: knitr, rmarkdown
git_url: https://git.bioconductor.org/packages/CytoDx
git_branch: devel
git_last_commit: 05557f1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CytoDx_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CytoDx_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CytoDx_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CytoDx_1.27.0.tgz
vignettes: vignettes/CytoDx/inst/doc/CytoDx_Vignette.pdf
vignetteTitles: Introduction to CytoDx
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CytoDx/inst/doc/CytoDx_Vignette.R
dependencyCount: 48
Package: cytofQC
Version: 1.7.0
Imports: CATALYST, flowCore, e1071, EZtune, gbm, ggplot2, hrbrthemes,
matrixStats, randomForest, rmarkdown, SingleCellExperiment,
stats, SummarizedExperiment, ssc, S4Vectors, graphics, methods
Suggests: gridExtra, knitr, RColorBrewer, testthat, uwot
License: Artistic-2.0
MD5sum: eae97fef9f8d5cad360eab08db0efebc
NeedsCompilation: no
Title: Labels normalized cells for CyTOF data and assigns probabilities
for each label
Description: cytofQC is a package for initial cleaning of CyTOF data.
It uses a semi-supervised approach for labeling cells with
their most likely data type (bead, doublet, debris, dead) and
the probability that they belong to each label type. This
package does not remove data from the dataset, but provides
labels and information to aid the data user in cleaning their
data. Our algorithm is able to distinguish between doublets and
large cells.
biocViews: Software, SingleCell, Annotation
Author: Jill Lundell [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6048-4700>), Kelly Street [aut]
(ORCID: <https://orcid.org/0000-0001-6379-5013>)
Maintainer: Jill Lundell <jflundell@gmail.com>
URL: https://github.com/jillbo1000/cytofQC
VignetteBuilder: knitr
BugReports: https://github.com/jillbo1000/cytofQC/issues
git_url: https://git.bioconductor.org/packages/cytofQC
git_branch: devel
git_last_commit: c4c778e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cytofQC_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cytofQC_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cytofQC_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cytofQC_1.7.0.tgz
vignettes: vignettes/cytofQC/inst/doc/cytofQC.html
vignetteTitles: Workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cytofQC/inst/doc/cytofQC.R
dependencyCount: 230
Package: CytoGLMM
Version: 1.15.0
Imports: stats, methods, BiocParallel, RColorBrewer, cowplot,
doParallel, dplyr, factoextra, flexmix, ggplot2, magrittr,
mbest, pheatmap, stringr, strucchange, tibble, ggrepel, MASS,
logging, Matrix, tidyr, caret, rlang, grDevices
Suggests: knitr, rmarkdown, testthat, BiocStyle
License: LGPL-3
MD5sum: 9a982cf44a2bf596af4ebf47a05cbd7c
NeedsCompilation: no
Title: Conditional Differential Analysis for Flow and Mass Cytometry
Experiments
Description: The CytoGLMM R package implements two multiple regression
strategies: A bootstrapped generalized linear model (GLM) and a
generalized linear mixed model (GLMM). Most current data
analysis tools compare expressions across many computationally
discovered cell types. CytoGLMM focuses on just one cell type.
Our narrower field of application allows us to define a more
specific statistical model with easier to control statistical
guarantees. As a result, CytoGLMM finds differential proteins
in flow and mass cytometry data while reducing biases arising
from marker correlations and safeguarding against false
discoveries induced by patient heterogeneity.
biocViews: FlowCytometry, Proteomics, SingleCell, CellBasedAssays,
CellBiology, ImmunoOncology, Regression, StatisticalMethod,
Software
Author: Christof Seiler [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8802-3642>)
Maintainer: Christof Seiler <christof.seiler@maastrichtuniversity.nl>
URL: https://christofseiler.github.io/CytoGLMM,
https://github.com/ChristofSeiler/CytoGLMM
VignetteBuilder: knitr
BugReports: https://github.com/ChristofSeiler/CytoGLMM/issues
git_url: https://git.bioconductor.org/packages/CytoGLMM
git_branch: devel
git_last_commit: a3b4bb6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CytoGLMM_1.15.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CytoGLMM_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CytoGLMM_1.15.0.tgz
vignettes: vignettes/CytoGLMM/inst/doc/CytoGLMM.html
vignetteTitles: CytoGLMM Workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/CytoGLMM/inst/doc/CytoGLMM.R
dependencyCount: 174
Package: cytoKernel
Version: 1.13.0
Depends: R (>= 4.1)
Imports: Rcpp, SummarizedExperiment, utils, methods, ComplexHeatmap,
circlize, ashr, data.table, BiocParallel, dplyr, stats,
magrittr, rlang, S4Vectors
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, BiocStyle, testthat
License: GPL-3
MD5sum: 19ac987980b40a8b3af152f64249d956
NeedsCompilation: yes
Title: Differential expression using kernel-based score test
Description: cytoKernel implements a kernel-based score test to
identify differentially expressed features in high-dimensional
biological experiments. This approach can be applied across
many different high-dimensional biological data including gene
expression data and dimensionally reduced cytometry-based
marker expression data. In this R package, we implement
functions that compute the feature-wise p values and their
corresponding adjusted p values. Additionally, it also computes
the feature-wise shrunk effect sizes and their corresponding
shrunken effect size. Further, it calculates the percent of
differentially expressed features and plots user-friendly
heatmap of the top differentially expressed features on the
rows and samples on the columns.
biocViews: ImmunoOncology, Proteomics, SingleCell, Software,
OneChannel, FlowCytometry, DifferentialExpression,
GeneExpression, Clustering
Author: Tusharkanti Ghosh [aut, cre], Victor Lui [aut], Pratyaydipta
Rudra [aut], Souvik Seal [aut], Thao Vu [aut], Elena Hsieh
[aut], Debashis Ghosh [aut, cph]
Maintainer: Tusharkanti Ghosh <tusharkantighosh30@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/Ghoshlab/cytoKernel/issues
git_url: https://git.bioconductor.org/packages/cytoKernel
git_branch: devel
git_last_commit: ee6062c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cytoKernel_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cytoKernel_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cytoKernel_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cytoKernel_1.13.0.tgz
vignettes: vignettes/cytoKernel/inst/doc/cytoKernel.html
vignetteTitles: The CytoK user's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cytoKernel/inst/doc/cytoKernel.R
dependencyCount: 85
Package: cytolib
Version: 2.19.3
Depends: R (>= 3.4)
Imports: RProtoBufLib
LinkingTo: BH(>= 1.84.0.0), RProtoBufLib(>= 2.13.1),Rhdf5lib
Suggests: knitr, rmarkdown
License: AGPL-3.0-only
License_restricts_use: no
MD5sum: bffbd552b642fe8c962ef625a6d4a27e
NeedsCompilation: yes
Title: C++ infrastructure for representing and interacting with the
gated cytometry data
Description: This package provides the core data structure and API to
represent and interact with the gated cytometry data.
biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing,
DataRepresentation
Author: Mike Jiang
Maintainer: Mike Jiang <mike@ozette.com>
SystemRequirements: GNU make, C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/cytolib
git_branch: devel
git_last_commit: 1f886d9
git_last_commit_date: 2025-01-29
Date/Publication: 2025-01-29
source.ver: src/contrib/cytolib_2.19.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cytolib_2.19.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cytolib_2.19.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cytolib_2.19.3.tgz
vignettes: vignettes/cytolib/inst/doc/cytolib.html
vignetteTitles: Using cytolib
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/cytolib/inst/doc/cytolib.R
importsMe: CytoML, flowCore, flowWorkspace
linksToMe: CytoML, flowCore, flowWorkspace
dependencyCount: 3
Package: cytomapper
Version: 1.19.0
Depends: R (>= 4.0), EBImage, SingleCellExperiment, methods
Imports: SpatialExperiment, S4Vectors, BiocParallel, HDF5Array,
DelayedArray, RColorBrewer, viridis, utils,
SummarizedExperiment, tools, graphics, raster, grDevices,
stats, ggplot2, ggbeeswarm, svgPanZoom, svglite, shiny,
shinydashboard, matrixStats, rhdf5, nnls
Suggests: BiocStyle, knitr, rmarkdown, markdown, cowplot, testthat,
shinytest
License: GPL (>= 2)
Archs: x64
MD5sum: 72bc35f6b440d126a9e33854b8c7671d
NeedsCompilation: no
Title: Visualization of highly multiplexed imaging data in R
Description: Highly multiplexed imaging acquires the single-cell
expression of selected proteins in a spatially-resolved
fashion. These measurements can be visualised across multiple
length-scales. First, pixel-level intensities represent the
spatial distributions of feature expression with highest
resolution. Second, after segmentation, expression values or
cell-level metadata (e.g. cell-type information) can be
visualised on segmented cell areas. This package contains
functions for the visualisation of multiplexed read-outs and
cell-level information obtained by multiplexed imaging
technologies. The main functions of this package allow 1. the
visualisation of pixel-level information across multiple
channels, 2. the display of cell-level information (expression
and/or metadata) on segmentation masks and 3. gating and
visualisation of single cells.
biocViews: ImmunoOncology, Software, SingleCell, OneChannel,
TwoChannel, MultipleComparison, Normalization, DataImport
Author: Nils Eling [aut] (ORCID:
<https://orcid.org/0000-0002-4711-1176>), Nicolas Damond [aut]
(ORCID: <https://orcid.org/0000-0003-3027-8989>), Tobias Hoch
[ctb], Lasse Meyer [cre, ctb] (ORCID:
<https://orcid.org/0000-0002-1660-1199>)
Maintainer: Lasse Meyer <lasse.meyer@dqbm.uzh.ch>
URL: https://github.com/BodenmillerGroup/cytomapper
VignetteBuilder: knitr
BugReports: https://github.com/BodenmillerGroup/cytomapper/issues
git_url: https://git.bioconductor.org/packages/cytomapper
git_branch: devel
git_last_commit: ded2c6e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cytomapper_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cytomapper_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cytomapper_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cytomapper_1.19.0.tgz
vignettes: vignettes/cytomapper/inst/doc/cytomapper_ondisk.html,
vignettes/cytomapper/inst/doc/cytomapper.html
vignetteTitles: "On disk storage of images", "Visualization of imaging
cytometry data in R"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cytomapper/inst/doc/cytomapper_ondisk.R,
vignettes/cytomapper/inst/doc/cytomapper.R
importsMe: cytoviewer, imcRtools, simpleSeg
dependencyCount: 145
Package: CytoMDS
Version: 1.3.5
Depends: R (>= 4.4), Biobase
Imports: methods, stats, rlang, pracma, withr, flowCore, reshape2,
ggplot2, ggrepel, ggforce, patchwork, transport, smacof,
BiocParallel, CytoPipeline
Suggests: testthat (>= 3.0.0), vdiffr, diffviewer, knitr, rmarkdown,
BiocStyle, HDCytoData
License: GPL-3
Archs: x64
MD5sum: cc9a3b5dee8e70dd9d10a7cb1266a12c
NeedsCompilation: no
Title: Low Dimensions projection of cytometry samples
Description: This package implements a low dimensional visualization of
a set of cytometry samples, in order to visually assess the
'distances' between them. This, in turn, can greatly help the
user to identify quality issues like batch effects or outlier
samples, and/or check the presence of potential sample clusters
that might align with the exeprimental design. The CytoMDS
algorithm combines, on the one hand, the concept of Earth
Mover's Distance (EMD), a.k.a. Wasserstein metric and, on the
other hand, the Multi Dimensional Scaling (MDS) algorithm for
the low dimensional projection. Also, the package provides some
diagnostic tools for both checking the quality of the MDS
projection, as well as tools to help with the interpretation of
the axes of the projection.
biocViews: FlowCytometry, QualityControl, DimensionReduction,
MultidimensionalScaling, Software, Visualization
Author: Philippe Hauchamps [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2865-1852>), Laurent Gatto [aut]
(ORCID: <https://orcid.org/0000-0002-1520-2268>), Dan Lin [ctb]
Maintainer: Philippe Hauchamps <philippe.hauchamps@uclouvain.be>
URL: https://uclouvain-cbio.github.io/CytoMDS
VignetteBuilder: knitr
BugReports: https://github.com/UCLouvain-CBIO/CytoMDS/issues
git_url: https://git.bioconductor.org/packages/CytoMDS
git_branch: devel
git_last_commit: c4f0405
git_last_commit_date: 2025-01-21
Date/Publication: 2025-01-21
source.ver: src/contrib/CytoMDS_1.3.5.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CytoMDS_1.3.5.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CytoMDS_1.3.5.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CytoMDS_1.3.5.tgz
vignettes: vignettes/CytoMDS/inst/doc/CytoMDS.html
vignetteTitles: Low Dimensional Projection of Cytometry Samples
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CytoMDS/inst/doc/CytoMDS.R
dependencyCount: 194
Package: cytoMEM
Version: 1.11.0
Depends: R (>= 4.2.0)
Imports: gplots, tools, flowCore, grDevices, stats, utils, matrixStats,
methods
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: 6b6b20b55ff738e2d72a61333965a930
NeedsCompilation: no
Title: Marker Enrichment Modeling (MEM)
Description: MEM, Marker Enrichment Modeling, automatically generates
and displays quantitative labels for cell populations that have
been identified from single-cell data. The input for MEM is a
dataset that has pre-clustered or pre-gated populations with
cells in rows and features in columns. Labels convey a list of
measured features and the features' levels of relative
enrichment on each population. MEM can be applied to a wide
variety of data types and can compare between MEM labels from
flow cytometry, mass cytometry, single cell RNA-seq, and
spectral flow cytometry using RMSD.
biocViews: Proteomics, SystemsBiology, Classification, FlowCytometry,
DataRepresentation, DataImport, CellBiology, SingleCell,
Clustering
Author: Sierra Lima [aut] (ORCID:
<https://orcid.org/0000-0001-5944-750X>), Kirsten Diggins [aut]
(ORCID: <https://orcid.org/0000-0003-1622-0158>), Jonathan
Irish [aut, cre] (ORCID:
<https://orcid.org/0000-0001-9428-8866>)
Maintainer: Jonathan Irish <jonathan.irish@vanderbilt.edu>
URL: https://github.com/cytolab/cytoMEM
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/cytoMEM
git_branch: devel
git_last_commit: d2f1603
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cytoMEM_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cytoMEM_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cytoMEM_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cytoMEM_1.11.0.tgz
vignettes:
vignettes/cytoMEM/inst/doc/Intro_to_Marker_Enrichment_Modeling_Analysis.html
vignetteTitles: Intro_to_Marker_Enrichment_Modeling_Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/cytoMEM/inst/doc/Intro_to_Marker_Enrichment_Modeling_Analysis.R
dependencyCount: 24
Package: CytoML
Version: 2.19.3
Depends: R (>= 3.5.0)
Imports: cytolib(>= 2.3.10), flowCore (>= 1.99.10), flowWorkspace (>=
4.1.8), openCyto (>= 1.99.2), XML, data.table, jsonlite, RBGL,
Rgraphviz, Biobase, methods, graph, graphics, utils, jsonlite,
dplyr, grDevices, methods, ggcyto (>= 1.11.4), yaml, stats,
tibble
LinkingTo: cpp11, BH(>= 1.62.0-1), RProtoBufLib, cytolib, Rhdf5lib,
flowWorkspace
Suggests: testthat, flowWorkspaceData , knitr, rmarkdown, parallel
License: AGPL-3.0-only
License_restricts_use: no
MD5sum: 07065e21b84d7ec84a5ac7528e660d10
NeedsCompilation: yes
Title: A GatingML Interface for Cross Platform Cytometry Data Sharing
Description: Uses platform-specific implemenations of the GatingML2.0
standard to exchange gated cytometry data with other software
platforms.
biocViews: ImmunoOncology, FlowCytometry, DataImport,
DataRepresentation
Author: Mike Jiang, Jake Wagner
Maintainer: Mike Jiang <mike@ozette.com>
URL: https://github.com/RGLab/CytoML
SystemRequirements: xml2, GNU make, C++11
VignetteBuilder: knitr
BugReports: https://github.com/RGLab/CytoML/issues
git_url: https://git.bioconductor.org/packages/CytoML
git_branch: devel
git_last_commit: d9df748
git_last_commit_date: 2025-03-06
Date/Publication: 2025-03-25
source.ver: src/contrib/CytoML_2.19.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CytoML_2.19.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CytoML_2.19.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CytoML_2.19.3.tgz
vignettes: vignettes/CytoML/inst/doc/cytobank2GatingSet.html,
vignettes/CytoML/inst/doc/flowjo_to_gatingset.html,
vignettes/CytoML/inst/doc/HowToExportGatingSet.html
vignetteTitles: How to import Cytobank into a GatingSet, flowJo parser,
How to export a GatingSet to GatingML
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CytoML/inst/doc/cytobank2GatingSet.R,
vignettes/CytoML/inst/doc/flowjo_to_gatingset.R,
vignettes/CytoML/inst/doc/HowToExportGatingSet.R
suggestsMe: FlowSOM, flowWorkspace, openCyto
dependencyCount: 84
Package: CytoPipeline
Version: 1.7.0
Depends: R (>= 4.4)
Imports: methods, stats, utils, withr, rlang, ggplot2 (>= 3.4.1),
ggcyto, BiocFileCache, BiocParallel, flowCore, PeacoQC, flowAI,
diagram, jsonlite, scales
Suggests: testthat (>= 3.0.0), vdiffr, diffviewer, knitr, rmarkdown,
BiocStyle, reshape2, dplyr, CytoPipelineGUI
License: GPL-3
MD5sum: 9b9c1c2ebaf363445d3211f740a84dea
NeedsCompilation: no
Title: Automation and visualization of flow cytometry data analysis
pipelines
Description: This package provides support for automation and
visualization of flow cytometry data analysis pipelines. In the
current state, the package focuses on the preprocessing and
quality control part. The framework is based on two main S4
classes, i.e. CytoPipeline and CytoProcessingStep. The pipeline
steps are linked to corresponding R functions - that are either
provided in the CytoPipeline package itself, or exported from a
third party package, or coded by the user her/himself. The
processing steps need to be specified centrally and explicitly
using either a json input file or through step by step creation
of a CytoPipeline object with dedicated methods. After having
run the pipeline, obtained results at all steps can be
retrieved and visualized thanks to file caching (the running
facility uses a BiocFileCache implementation). The package
provides also specific visualization tools like pipeline
workflow summary display, and 1D/2D comparison plots of
obtained flowFrames at various steps of the pipeline.
biocViews: FlowCytometry, Preprocessing, QualityControl, WorkflowStep,
ImmunoOncology, Software, Visualization
Author: Philippe Hauchamps [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2865-1852>), Laurent Gatto [aut]
(ORCID: <https://orcid.org/0000-0002-1520-2268>), Dan Lin [ctb]
Maintainer: Philippe Hauchamps <philippe.hauchamps@uclouvain.be>
URL: https://uclouvain-cbio.github.io/CytoPipeline
VignetteBuilder: knitr
BugReports: https://github.com/UCLouvain-CBIO/CytoPipeline/issues
git_url: https://git.bioconductor.org/packages/CytoPipeline
git_branch: devel
git_last_commit: 9ee3877
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CytoPipeline_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CytoPipeline_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CytoPipeline_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CytoPipeline_1.7.0.tgz
vignettes: vignettes/CytoPipeline/inst/doc/CytoPipeline.html,
vignettes/CytoPipeline/inst/doc/Demo.html
vignetteTitles: Automation and Visualization of Flow Cytometry Data
Analysis Pipelines, Demonstration of the CytoPipeline R package
suite functionalities
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CytoPipeline/inst/doc/CytoPipeline.R,
vignettes/CytoPipeline/inst/doc/Demo.R
dependsOnMe: CytoPipelineGUI
importsMe: CytoMDS
dependencyCount: 135
Package: CytoPipelineGUI
Version: 1.5.0
Depends: R (>= 4.4), CytoPipeline
Imports: shiny, plotly, ggplot2, flowCore
Suggests: testthat (>= 3.0.0), vdiffr, diffviewer, knitr, rmarkdown,
BiocStyle, patchwork
License: GPL-3
Archs: x64
MD5sum: e82fb0ecbb5cb5f48ba97ae619db8923
NeedsCompilation: no
Title: GUI's for visualization of flow cytometry data analysis
pipelines
Description: This package is the companion of the `CytoPipeline`
package. It provides GUI's (shiny apps) for the visualization
of flow cytometry data analysis pipelines that are run with
`CytoPipeline`. Two shiny applications are provided, i.e. an
interactive flow frame assessment and comparison tool and an
interactive scale transformations visualization and adjustment
tool.
biocViews: FlowCytometry, Preprocessing, QualityControl, WorkflowStep,
ImmunoOncology, Software, Visualization, GUI, ShinyApps
Author: Philippe Hauchamps [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2865-1852>), Laurent Gatto [aut]
(ORCID: <https://orcid.org/0000-0002-1520-2268>), Dan Lin [ctb]
Maintainer: Philippe Hauchamps <philippe.hauchamps@uclouvain.be>
URL: https://uclouvain-cbio.github.io/CytoPipelineGUI
VignetteBuilder: knitr
BugReports: https://github.com/UCLouvain-CBIO/CytoPipelineGUI/issues
git_url: https://git.bioconductor.org/packages/CytoPipelineGUI
git_branch: devel
git_last_commit: d7627a8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/CytoPipelineGUI_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/CytoPipelineGUI_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CytoPipelineGUI_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CytoPipelineGUI_1.5.0.tgz
vignettes: vignettes/CytoPipelineGUI/inst/doc/CytoPipelineGUI.html,
vignettes/CytoPipelineGUI/inst/doc/Demo.html
vignetteTitles: CytoPipelineGUI : visualization of Flow Cytometry Data
Analysis Pipelines run with CytoPipeline, Demonstration of the
CytoPipeline R package suite functionalities
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/CytoPipelineGUI/inst/doc/CytoPipelineGUI.R,
vignettes/CytoPipelineGUI/inst/doc/Demo.R
suggestsMe: CytoPipeline
dependencyCount: 147
Package: cytoviewer
Version: 1.7.0
Imports: shiny, shinydashboard, utils, colourpicker, shinycssloaders,
svgPanZoom, viridis, archive, grDevices, RColorBrewer, svglite,
EBImage, methods, cytomapper, SingleCellExperiment, S4Vectors,
SummarizedExperiment
Suggests: BiocStyle, knitr, rmarkdown, markdown, testthat
License: GPL-3
MD5sum: 387a359545f6a1d55d1aceaf1ab1da9d
NeedsCompilation: no
Title: An interactive multi-channel image viewer for R
Description: This R package supports interactive visualization of
multi-channel images and segmentation masks generated by
imaging mass cytometry and other highly multiplexed imaging
techniques using shiny. The cytoviewer interface is divided
into image-level (Composite and Channels) and cell-level
visualization (Masks). It allows users to overlay individual
images with segmentation masks, integrates well with
SingleCellExperiment and SpatialExperiment objects for metadata
visualization and supports image downloads.
biocViews: ImmunoOncology, Software, SingleCell, OneChannel,
TwoChannel, MultiChannel, Spatial, DataImport
Author: Lasse Meyer [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1660-1199>), Nils Eling [aut]
(ORCID: <https://orcid.org/0000-0002-4711-1176>)
Maintainer: Lasse Meyer <lasse.meyer@dqbm.uzh.ch>
URL: https://github.com/BodenmillerGroup/cytoviewer
VignetteBuilder: knitr
BugReports: https://github.com/BodenmillerGroup/cytoviewer/issues
git_url: https://git.bioconductor.org/packages/cytoviewer
git_branch: devel
git_last_commit: 43409c7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/cytoviewer_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/cytoviewer_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/cytoviewer_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/cytoviewer_1.7.0.tgz
vignettes: vignettes/cytoviewer/inst/doc/cytoviewer.html
vignetteTitles: "Interactive multi-channel image visualization in R"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/cytoviewer/inst/doc/cytoviewer.R
dependencyCount: 151
Package: dada2
Version: 1.35.0
Depends: R (>= 3.4.0), Rcpp (>= 0.12.0), methods (>= 3.4.0)
Imports: Biostrings (>= 2.42.1), ggplot2 (>= 2.1.0), reshape2 (>=
1.4.1), ShortRead (>= 1.32.0), RcppParallel (>= 4.3.0),
parallel (>= 3.2.0), IRanges (>= 2.6.0), XVector (>= 0.16.0),
BiocGenerics (>= 0.22.0)
LinkingTo: Rcpp, RcppParallel
Suggests: BiocStyle, knitr, rmarkdown
License: LGPL-2
MD5sum: d767058a803a99a0088bcdc1b36d0a0b
NeedsCompilation: yes
Title: Accurate, high-resolution sample inference from amplicon
sequencing data
Description: The dada2 package infers exact amplicon sequence variants
(ASVs) from high-throughput amplicon sequencing data, replacing
the coarser and less accurate OTU clustering approach. The
dada2 pipeline takes as input demultiplexed fastq files, and
outputs the sequence variants and their sample-wise abundances
after removing substitution and chimera errors. Taxonomic
classification is available via a native implementation of the
RDP naive Bayesian classifier, and species-level assignment to
16S rRNA gene fragments by exact matching.
biocViews: ImmunoOncology, Microbiome, Sequencing, Classification,
Metagenomics
Author: Benjamin Callahan <benjamin.j.callahan@gmail.com>, Paul
McMurdie, Susan Holmes
Maintainer: Benjamin Callahan <benjamin.j.callahan@gmail.com>
URL: http://benjjneb.github.io/dada2/
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/benjjneb/dada2/issues
git_url: https://git.bioconductor.org/packages/dada2
git_branch: devel
git_last_commit: 3d68997
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/dada2_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/dada2_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/dada2_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/dada2_1.35.0.tgz
vignettes: vignettes/dada2/inst/doc/dada2-intro.html
vignetteTitles: Introduction to dada2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/dada2/inst/doc/dada2-intro.R
dependsOnMe: MiscMetabar
importsMe: Rbec, DBTC
suggestsMe: mia
dependencyCount: 92
Package: dagLogo
Version: 1.45.1
Depends: R (>= 3.0.1), methods, grid
Imports: pheatmap, Biostrings, UniProt.ws, BiocGenerics, utils,
biomaRt, motifStack, httr
Suggests: XML, grImport, grImport2, BiocStyle, knitr, rmarkdown,
testthat
License: GPL (>=2)
MD5sum: 5d4015ae86b074fdb5bfd362fa410efa
NeedsCompilation: no
Title: dagLogo: a Bioconductor package for visualizing conserved amino
acid sequence pattern in groups based on probability theory
Description: Visualize significant conserved amino acid sequence
pattern in groups based on probability theory.
biocViews: SequenceMatching, Visualization
Author: Jianhong Ou, Haibo Liu, Alexey Stukalov, Niraj Nirala, Usha
Acharya, Lihua Julie Zhu
Maintainer: Jianhong Ou <jou@morgridge.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/dagLogo
git_branch: devel
git_last_commit: d2c077c
git_last_commit_date: 2025-01-03
Date/Publication: 2025-01-03
source.ver: src/contrib/dagLogo_1.45.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/dagLogo_1.45.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/dagLogo_1.45.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/dagLogo_1.45.1.tgz
vignettes: vignettes/dagLogo/inst/doc/dagLogo.html
vignetteTitles: dagLogo Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/dagLogo/inst/doc/dagLogo.R
dependencyCount: 146
Package: daMA
Version: 1.79.0
Imports: MASS, stats
License: GPL (>= 2)
Archs: x64
MD5sum: 6e4f9cd46aede1142190567da6366c7a
NeedsCompilation: no
Title: Efficient design and analysis of factorial two-colour microarray
data
Description: This package contains functions for the efficient design
of factorial two-colour microarray experiments and for the
statistical analysis of factorial microarray data. Statistical
details are described in Bretz et al. (2003, submitted)
biocViews: Microarray, TwoChannel, DifferentialExpression
Author: Jobst Landgrebe <jlandgr1@gwdg.de> and Frank Bretz
<bretz@bioinf.uni-hannover.de>
Maintainer: Jobst Landgrebe <jlandgr1@gwdg.de>
URL: http://www.microarrays.med.uni-goettingen.de
git_url: https://git.bioconductor.org/packages/daMA
git_branch: devel
git_last_commit: d03098c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/daMA_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/daMA_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/daMA_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/daMA_1.79.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 6
Package: DAMEfinder
Version: 1.19.0
Depends: R (>= 4.0)
Imports: stats, GenomeInfoDb, GenomicRanges, IRanges, S4Vectors, readr,
SummarizedExperiment, GenomicAlignments, stringr, plyr,
VariantAnnotation, parallel, ggplot2, Rsamtools, BiocGenerics,
methods, limma, bumphunter, Biostrings, reshape2, cowplot,
utils
Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer,
BSgenome.Hsapiens.UCSC.hg19
License: MIT + file LICENSE
MD5sum: f6b04e6869776783be1f6b7d096b5020
NeedsCompilation: no
Title: Finds DAMEs - Differential Allelicly MEthylated regions
Description: 'DAMEfinder' offers functionality for taking methtuple or
bismark outputs to calculate ASM scores and compute DAMEs. It
also offers nice visualization of methyl-circle plots.
biocViews: DNAMethylation, DifferentialMethylation, Coverage
Author: Stephany Orjuela [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1508-461X>), Dania Machlab [aut],
Mark Robinson [aut]
Maintainer: Stephany Orjuela <sorjuelal@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/markrobinsonuzh/DAMEfinder/issues
git_url: https://git.bioconductor.org/packages/DAMEfinder
git_branch: devel
git_last_commit: faefd56
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DAMEfinder_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DAMEfinder_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DAMEfinder_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DAMEfinder_1.19.0.tgz
vignettes: vignettes/DAMEfinder/inst/doc/DAMEfinder_workflow.html
vignetteTitles: DAMEfinder Workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/DAMEfinder/inst/doc/DAMEfinder_workflow.R
dependencyCount: 122
Package: DaMiRseq
Version: 2.19.0
Depends: R (>= 3.5.0), SummarizedExperiment, ggplot2
Imports: DESeq2, limma, EDASeq, RColorBrewer, sva, Hmisc, pheatmap,
FactoMineR, corrplot, randomForest, e1071, caret, MASS,
lubridate, plsVarSel, kknn, FSelector, methods, stats, utils,
graphics, grDevices, reshape2, ineq, arm, pls, RSNNS, edgeR,
plyr
Suggests: BiocStyle, knitr, testthat
License: GPL (>= 2)
MD5sum: a0f37a727c6ec9e1a4da2479ef478c5a
NeedsCompilation: no
Title: Data Mining for RNA-seq data: normalization, feature selection
and classification
Description: The DaMiRseq package offers a tidy pipeline of data mining
procedures to identify transcriptional biomarkers and exploit
them for both binary and multi-class classification purposes.
The package accepts any kind of data presented as a table of
raw counts and allows including both continous and factorial
variables that occur with the experimental setting. A series of
functions enable the user to clean up the data by filtering
genomic features and samples, to adjust data by identifying and
removing the unwanted source of variation (i.e. batches and
confounding factors) and to select the best predictors for
modeling. Finally, a "stacking" ensemble learning technique is
applied to build a robust classification model. Every step
includes a checkpoint that the user may exploit to assess the
effects of data management by looking at diagnostic plots, such
as clustering and heatmaps, RLE boxplots, MDS or correlation
plot.
biocViews: Sequencing, RNASeq, Classification, ImmunoOncology
Author: Mattia Chiesa <mattia.chiesa@cardiologicomonzino.it>, Luca
Piacentini <luca.piacentini@cardiologicomonzino.it>
Maintainer: Mattia Chiesa <mattia.chiesa@cardiologicomonzino.it>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DaMiRseq
git_branch: devel
git_last_commit: 83ca733
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DaMiRseq_2.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DaMiRseq_2.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DaMiRseq_2.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DaMiRseq_2.19.0.tgz
vignettes: vignettes/DaMiRseq/inst/doc/DaMiRseq.pdf
vignetteTitles: Data Mining for RNA-seq data: normalization,, features
selection and classification - DaMiRseq package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DaMiRseq/inst/doc/DaMiRseq.R
importsMe: GARS
dependencyCount: 254
Package: Damsel
Version: 1.3.0
Depends: R (>= 4.4.0)
Imports: AnnotationDbi, Biostrings, ComplexHeatmap, dplyr, edgeR,
GenomeInfoDb, GenomicFeatures, GenomicRanges, ggbio, ggplot2,
goseq, magrittr, patchwork, plyranges, reshape2, rlang,
Rsamtools, Rsubread, stats, stringr, tidyr, utils
Suggests: BiocStyle, biomaRt, biovizBase,
BSgenome.Dmelanogaster.UCSC.dm6, knitr, limma, org.Dm.eg.db,
rmarkdown, testthat (>= 3.0.0),
TxDb.Dmelanogaster.UCSC.dm6.ensGene
License: MIT + file LICENSE
MD5sum: e70cdd63daa72624366351094f6647bf
NeedsCompilation: no
Title: Damsel: an end to end analysis of DamID
Description: Damsel provides an end to end analysis of DamID data.
Damsel takes bam files from Dam-only control and fusion samples
and counts the reads matching to each GATC region. edgeR is
utilised to identify regions of enrichment in the fusion
relative to the control. Enriched regions are combined into
peaks, and are associated with nearby genes. Damsel allows for
IGV style plots to be built as the results build, inspired by
ggcoverage, and using the functionality and layering ability of
ggplot2. Damsel also conducts gene ontology testing with bias
correction through goseq, and future versions of Damsel will
also incorporate motif enrichment analysis. Overall, Damsel is
the first package allowing for an end to end analysis with
visual capabilities. The goal of Damsel was to bring all the
analysis into one place, and allow for exploratory analysis
within R.
biocViews: DifferentialMethylation, PeakDetection, GenePrediction,
GeneSetEnrichment
Author: Caitlin Page [aut, cre] (ORCID:
<https://orcid.org/0009-0004-7949-8143>)
Maintainer: Caitlin Page <caitlin.page@petermac.org>
URL: https://github.com/Oshlack/Damsel
VignetteBuilder: knitr
BugReports: https://github.com/Oshlack/Damsel
git_url: https://git.bioconductor.org/packages/Damsel
git_branch: devel
git_last_commit: 7bdd2de
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Damsel_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Damsel_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Damsel_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Damsel_1.3.0.tgz
vignettes: vignettes/Damsel/inst/doc/Damsel-workflow.html
vignetteTitles: Damsel-workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Damsel/inst/doc/Damsel-workflow.R
dependencyCount: 181
Package: dandelionR
Version: 0.99.11
Depends: R (>= 4.4.0)
Imports: BiocGenerics, bluster, destiny, igraph, MASS, Matrix, methods,
miloR, purrr, rlang, S4Vectors, SingleCellExperiment, spam,
stats, SummarizedExperiment, uwot
Suggests: BiocStyle, knitr, rmarkdown, RColorBrewer, scater,
scRepertoire, testthat
License: MIT + file LICENSE
MD5sum: dfe41eaf15e4ee20f7cf411d4001b257
NeedsCompilation: no
Title: Single-cell Immune Repertoire Trajectory Analysis in R
Description: dandelionR is an R package for performing single-cell
immune repertoire trajectory analysis, based on the original
python implementation. It provides the necessary functions to
interface with scRepertoire and a custom implementation of an
absorbing Markov chain for pseudotime inference, inspired by
the Palantir Python package.
biocViews: Software, ImmunoOncology, SingleCell
Author: Jiawei Yu [aut] (ORCID:
<https://orcid.org/0009-0005-9170-7881>), Nicholas Borcherding
[aut] (ORCID: <https://orcid.org/0000-0003-1427-6342>), Kelvin
Tuong [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6735-6808>)
Maintainer: Kelvin Tuong <z.tuong@uq.edu.au>
URL: https://www.github.com/tuonglab/dandelionR/
VignetteBuilder: knitr
BugReports: https://www.github.com/tuonglab/dandelionR/issues
git_url: https://git.bioconductor.org/packages/dandelionR
git_branch: devel
git_last_commit: babae48
git_last_commit_date: 2025-03-02
Date/Publication: 2025-03-03
source.ver: src/contrib/dandelionR_0.99.11.tar.gz
win.binary.ver: bin/windows/contrib/4.5/dandelionR_0.99.11.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/dandelionR_0.99.11.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/dandelionR_0.99.11.tgz
vignettes: vignettes/dandelionR/inst/doc/dandelionR.html,
vignettes/dandelionR/inst/doc/vignette_reproduce_original.html
vignetteTitles: Single-cell immune repertoire trajectory analysis with
dandelionR, vignette_reproduce_original.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/dandelionR/inst/doc/dandelionR.R,
vignettes/dandelionR/inst/doc/vignette_reproduce_original.R
dependencyCount: 174
Package: DAPAR
Version: 1.39.0
Depends: R (>= 4.3.0)
Imports: Biobase, MSnbase, DAPARdata (>= 1.30.0), utils, highcharter,
foreach
Suggests: testthat, BiocStyle, AnnotationDbi, clusterProfiler, graph,
diptest, cluster, vioplot, visNetwork, vsn, igraph, FactoMineR,
factoextra, dendextend, parallel, doParallel, Mfuzz, apcluster,
forcats, readxl, openxlsx, multcomp, purrr, tibble, knitr,
norm, scales, tidyverse, cp4p, imp4p (>= 1.1),lme4, dplyr,
limma, preprocessCore, stringr, tidyr, impute, gplots,
grDevices, reshape2, graphics, stats, methods, ggplot2,
RColorBrewer, Matrix, org.Sc.sgd.db
License: Artistic-2.0
MD5sum: 4a962ed160ce46e3fa72e9d6a9fdf327
NeedsCompilation: no
Title: Tools for the Differential Analysis of Proteins Abundance with R
Description: The package DAPAR is a Bioconductor distributed R package
which provides all the necessary functions to analyze
quantitative data from label-free proteomics experiments.
Contrarily to most other similar R packages, it is endowed with
rich and user-friendly graphical interfaces, so that no
programming skill is required (see `Prostar` package).
biocViews: Proteomics, Normalization, Preprocessing, MassSpectrometry,
QualityControl, GO, DataImport
Author: c(person(given = "Samuel", family = "Wieczorek", email =
"samuel.wieczorek@cea.fr", role = c("aut","cre")), person(given
= "Florence", family ="Combes", email =
"florence.combes@cea.fr", role = "aut"), person(given =
"Thomas", family ="Burger", email = "thomas.burger@cea.fr",
role = "aut"), person(given = "Vasile-Cosmin", family ="Lazar",
email = "vcosminlazar@gmail.com", role = "ctb"), person(given =
"Enora", family ="Fremy", email = "enora.fremy@cea.fr", role =
"ctb"), person(given = "Helene", family ="Borges", email =
"helene.borges@cea.fr", role = "ctb"))
Maintainer: Samuel Wieczorek <samuel.wieczorek@cea.fr>
URL: http://www.prostar-proteomics.org/
VignetteBuilder: knitr
BugReports: https://github.com/edyp-lab/DAPAR/issues
git_url: https://git.bioconductor.org/packages/DAPAR
git_branch: devel
git_last_commit: 2500d2a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-05
source.ver: src/contrib/DAPAR_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DAPAR_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DAPAR_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DAPAR_1.39.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
importsMe: Prostar
suggestsMe: DAPARdata, mi4p
dependencyCount: 149
Package: dar
Version: 1.3.3
Depends: R (>= 4.5.0)
Imports: cli, ComplexHeatmap, crayon, dplyr, generics, ggplot2, glue,
gplots, heatmaply, magrittr, methods, mia, phyloseq, purrr,
readr, rlang (>= 0.4.11), scales, stringr, tibble, tidyr,
UpSetR, waldo
Suggests: ALDEx2, ANCOMBC, apeglm, ashr, Biobase, corncob, covr,
DESeq2, devtools, furrr, future, knitr, lefser, limma,
Maaslin2, metagenomeSeq, microbiome, rmarkdown, roxygen2,
roxyglobals, roxytest, rstatix, SummarizedExperiment,
TreeSummarizedExperiment, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: 10eb9421bcd53329d0750f0f0891a3c0
NeedsCompilation: no
Title: Differential Abundance Analysis by Consensus
Description: Differential abundance testing in microbiome data
challenges both parametric and non-parametric statistical
methods, due to its sparsity, high variability and
compositional nature. Microbiome-specific statistical methods
often assume classical distribution models or take into account
compositional specifics. These produce results that range
within the specificity vs sensitivity space in such a way that
type I and type II error that are difficult to ascertain in
real microbiome data when a single method is used. Recently, a
consensus approach based on multiple differential abundance
(DA) methods was recently suggested in order to increase
robustness. With dar, you can use dplyr-like pipeable sequences
of DA methods and then apply different consensus strategies. In
this way we can obtain more reliable results in a fast,
consistent and reproducible way.
biocViews: Software, Sequencing, Microbiome, Metagenomics,
MultipleComparison, Normalization
Author: Francesc Catala-Moll [aut, cre] (ORCID:
<https://orcid.org/0000-0002-2354-8648>)
Maintainer: Francesc Catala-Moll <fcatala@irsicaixa.es>
URL: https://github.com/MicrobialGenomics-IrsicaixaOrg/dar,
https://microbialgenomics-irsicaixaorg.github.io/dar/
VignetteBuilder: knitr
BugReports:
https://github.com/MicrobialGenomics-IrsicaixaOrg/dar/issues
git_url: https://git.bioconductor.org/packages/dar
git_branch: devel
git_last_commit: 2925316
git_last_commit_date: 2025-03-15
Date/Publication: 2025-03-17
source.ver: src/contrib/dar_1.3.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/dar_1.3.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/dar_1.3.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/dar_1.3.3.tgz
vignettes: vignettes/dar/inst/doc/article.html,
vignettes/dar/inst/doc/bioinformatics_vignette.html,
vignettes/dar/inst/doc/dar.html,
vignettes/dar/inst/doc/data_import.html,
vignettes/dar/inst/doc/filtering_subsetting.html,
vignettes/dar/inst/doc/import_export_recipes.html
vignetteTitles: Workflow with real data, Workflow with real data,
Introduction to dar, Data Import, Filtering and Subsetting,
Reproducibility in Microbiome Data Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/dar/inst/doc/article.R,
vignettes/dar/inst/doc/bioinformatics_vignette.R,
vignettes/dar/inst/doc/dar.R,
vignettes/dar/inst/doc/data_import.R,
vignettes/dar/inst/doc/filtering_subsetting.R,
vignettes/dar/inst/doc/import_export_recipes.R
dependencyCount: 233
Package: DART
Version: 1.55.0
Depends: R (>= 2.10.0), igraph (>= 0.6.0)
Suggests: breastCancerVDX, breastCancerMAINZ, Biobase
License: GPL-2
MD5sum: 36a2a2fcff52bb2bf06ce58191b633de
NeedsCompilation: no
Title: Denoising Algorithm based on Relevance network Topology
Description: Denoising Algorithm based on Relevance network Topology
(DART) is an algorithm designed to evaluate the consistency of
prior information molecular signatures (e.g in-vitro
perturbation expression signatures) in independent molecular
data (e.g gene expression data sets). If consistent, a pruning
network strategy is then used to infer the activation status of
the molecular signature in individual samples.
biocViews: GeneExpression, DifferentialExpression, GraphAndNetwork,
Pathways
Author: Yan Jiao, Katherine Lawler, Andrew E Teschendorff, Charles
Shijie Zheng
Maintainer: Charles Shijie Zheng <charles_zheng@live.com>
git_url: https://git.bioconductor.org/packages/DART
git_branch: devel
git_last_commit: 06d231f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DART_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DART_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DART_1.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DART_1.55.0.tgz
vignettes: vignettes/DART/inst/doc/DART.pdf
vignetteTitles: DART Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DART/inst/doc/DART.R
dependencyCount: 17
Package: dcanr
Version: 1.23.0
Depends: R (>= 3.6.0)
Imports: igraph, foreach, plyr, stringr, reshape2, methods, Matrix,
graphics, stats, RColorBrewer, circlize, doRNG
Suggests: EBcoexpress, testthat, EBarrays, GeneNet, mclust, minqa,
SummarizedExperiment, Biobase, knitr, rmarkdown, BiocStyle,
edgeR
Enhances: parallel, doSNOW, doParallel
License: GPL-3
MD5sum: 8d6ed7913505b790d657ee2838168868
NeedsCompilation: no
Title: Differential co-expression/association network analysis
Description: This package implements methods and an evaluation
framework to infer differential co-expression/association
networks. Various methods are implemented and can be evaluated
using simulated datasets. Inference of differential
co-expression networks can allow identification of networks
that are altered between two conditions (e.g., health and
disease).
biocViews: NetworkInference, GraphAndNetwork, DifferentialExpression,
Network
Author: Dharmesh D. Bhuva [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6398-9157>)
Maintainer: Dharmesh D. Bhuva <bhuva.d@wehi.edu.au>
URL: https://davislaboratory.github.io/dcanr/,
https://github.com/DavisLaboratory/dcanr
VignetteBuilder: knitr
BugReports: https://github.com/DavisLaboratory/dcanr/issues
git_url: https://git.bioconductor.org/packages/dcanr
git_branch: devel
git_last_commit: 1a99f0d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/dcanr_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/dcanr_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/dcanr_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/dcanr_1.23.0.tgz
vignettes: vignettes/dcanr/inst/doc/dcanr_evaluation_vignette.html,
vignettes/dcanr/inst/doc/dcanr_vignette.html
vignetteTitles: 2. DC method evaluation, 1. Differential co-expression
analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/dcanr/inst/doc/dcanr_evaluation_vignette.R,
vignettes/dcanr/inst/doc/dcanr_vignette.R
importsMe: ClassifyR, multiWGCNA, SingscoreAMLMutations
dependencyCount: 35
Package: DCATS
Version: 1.5.0
Depends: R (>= 4.1.0), stats
Imports: MCMCpack, matrixStats, robustbase, aod, e1071
Suggests: testthat (>= 3.0.0), knitr, Seurat, SeuratObject, tidyverse,
rmarkdown, BiocStyle
License: MIT + file LICENSE
MD5sum: 1faa56842660f648d975a7435404ab5e
NeedsCompilation: no
Title: Differential Composition Analysis Transformed by a Similarity
matrix
Description: Methods to detect the differential composition abundances
between conditions in singel-cell RNA-seq experiments, with or
without replicates. It aims to correct bias introduced by
missclaisification and enable controlling of confounding
covariates. To avoid the influence of proportion change from
big cell types, DCATS can use either total cell number or
specific reference group as normalization term.
biocViews: SingleCell, Normalization
Author: Xinyi Lin [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7780-2461>), Chuen Chau [aut],
Yuanhua Huang [aut], Joshua W.K. Ho [aut]
Maintainer: Xinyi Lin <linxy29@connect.hku.hk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DCATS
git_branch: devel
git_last_commit: 61682fd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DCATS_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DCATS_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DCATS_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DCATS_1.5.0.tgz
vignettes: vignettes/DCATS/inst/doc/Intro_to_DCATS.html
vignetteTitles: Differential Composition Analysis with DCATS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/DCATS/inst/doc/Intro_to_DCATS.R
dependencyCount: 24
Package: dce
Version: 1.13.0
Depends: R (>= 4.1)
Imports: stats, methods, assertthat, graph, pcalg, purrr, tidyverse,
Matrix, ggraph, tidygraph, ggplot2, rlang, expm, MASS, edgeR,
epiNEM, igraph, metap, mnem, naturalsort, ppcor, glm2,
graphite, reshape2, dplyr, magrittr, glue, Rgraphviz,
harmonicmeanp, org.Hs.eg.db, logger, shadowtext
Suggests: knitr, rmarkdown, testthat (>= 2.1.0), BiocStyle, formatR,
cowplot, ggplotify, dagitty, lmtest, sandwich, devtools,
curatedTCGAData, TCGAutils, SummarizedExperiment, RcppParallel,
docopt, CARNIVAL
License: GPL-3
MD5sum: a369874285bc5f4d10415002ecdc1f03
NeedsCompilation: no
Title: Pathway Enrichment Based on Differential Causal Effects
Description: Compute differential causal effects (dce) on (biological)
networks. Given observational samples from a control experiment
and non-control (e.g., cancer) for two genes A and B, we can
compute differential causal effects with a (generalized) linear
regression. If the causal effect of gene A on gene B in the
control samples is different from the causal effect in the
non-control samples the dce will differ from zero. We
regularize the dce computation by the inclusion of prior
network information from pathway databases such as KEGG.
biocViews: Software, StatisticalMethod, GraphAndNetwork, Regression,
GeneExpression, DifferentialExpression, NetworkEnrichment,
Network, KEGG
Author: Kim Philipp Jablonski [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4166-4343>), Martin Pirkl [aut]
Maintainer: Kim Philipp Jablonski <kim.philipp.jablonski@gmail.com>
URL: https://github.com/cbg-ethz/dce
VignetteBuilder: knitr
BugReports: https://github.com/cbg-ethz/dce/issues
git_url: https://git.bioconductor.org/packages/dce
git_branch: devel
git_last_commit: 642d99a
git_last_commit_date: 2024-04-30
Date/Publication: 2024-10-25
source.ver: src/contrib/dce_1.13.0.tar.gz
vignettes: vignettes/dce/inst/doc/dce.html,
vignettes/dce/inst/doc/pathway_databases.html
vignetteTitles: Get started, Overview of pathway network databases
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/dce/inst/doc/dce.R,
vignettes/dce/inst/doc/pathway_databases.R
dependencyCount: 242
Package: dcGSA
Version: 1.35.0
Depends: R (>= 3.3), Matrix
Imports: BiocParallel
Suggests: knitr
License: GPL-2
MD5sum: 8b69f62dae97659decd1474bf8dc025a
NeedsCompilation: no
Title: Distance-correlation based Gene Set Analysis for longitudinal
gene expression profiles
Description: Distance-correlation based Gene Set Analysis for
longitudinal gene expression profiles. In longitudinal studies,
the gene expression profiles were collected at each visit from
each subject and hence there are multiple measurements of the
gene expression profiles for each subject. The dcGSA package
could be used to assess the associations between gene sets and
clinical outcomes of interest by fully taking advantage of the
longitudinal nature of both the gene expression profiles and
clinical outcomes.
biocViews: ImmunoOncology, GeneSetEnrichment,Microarray,
StatisticalMethod, Sequencing, RNASeq, GeneExpression
Author: Jiehuan Sun [aut, cre], Jose Herazo-Maya [aut], Xiu Huang
[aut], Naftali Kaminski [aut], and Hongyu Zhao [aut]
Maintainer: Jiehuan sun <jiehuan.sun@yale.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/dcGSA
git_branch: devel
git_last_commit: a50f905
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/dcGSA_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/dcGSA_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/dcGSA_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/dcGSA_1.35.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 18
Package: ddCt
Version: 1.63.0
Depends: R (>= 2.3.0), methods
Imports: Biobase (>= 1.10.0), RColorBrewer (>= 0.1-3), xtable, lattice,
BiocGenerics
Suggests: testthat (>= 3.0.0), RUnit
License: LGPL-3
MD5sum: 504a29359b77f2c92062c045efc9ccd8
NeedsCompilation: no
Title: The ddCt Algorithm for the Analysis of Quantitative Real-Time
PCR (qRT-PCR)
Description: The Delta-Delta-Ct (ddCt) Algorithm is an approximation
method to determine relative gene expression with quantitative
real-time PCR (qRT-PCR) experiments. Compared to other
approaches, it requires no standard curve for each
primer-target pair, therefore reducing the working load and yet
returning accurate enough results as long as the assumptions of
the amplification efficiency hold. The ddCt package implements
a pipeline to collect, analyse and visualize qRT-PCR results,
for example those from TaqMan SDM software, mainly using the
ddCt method. The pipeline can be either invoked by a script in
command-line or through the API consisting of S4-Classes,
methods and functions.
biocViews: GeneExpression, DifferentialExpression,
MicrotitrePlateAssay, qPCR
Author: Jitao David Zhang, Rudolf Biczok, and Markus Ruschhaupt
Maintainer: Jitao David Zhang <jitao_david.zhang@roche.com>
git_url: https://git.bioconductor.org/packages/ddCt
git_branch: devel
git_last_commit: 3e97954
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ddCt_1.63.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ddCt_1.63.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ddCt_1.63.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ddCt_1.63.0.tgz
vignettes: vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.pdf,
vignettes/ddCt/inst/doc/rtPCR-usage.pdf,
vignettes/ddCt/inst/doc/rtPCR.pdf
vignetteTitles: How to apply the ddCt method, Analyse RT-PCR data with
the end-to-end script in ddCt package, Introduction to the ddCt
method for qRT-PCR data analysis: background,, algorithm and
example
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.R,
vignettes/ddCt/inst/doc/rtPCR-usage.R,
vignettes/ddCt/inst/doc/rtPCR.R
dependencyCount: 12
Package: ddPCRclust
Version: 1.27.0
Depends: R (>= 3.5)
Imports: plotrix, clue, parallel, ggplot2, openxlsx, R.utils, flowCore,
flowDensity (>= 1.13.3), SamSPECTRAL, flowPeaks
Suggests: BiocStyle
License: Artistic-2.0
MD5sum: fcbab27498957051a01a8cfd2d6bdd08
NeedsCompilation: no
Title: Clustering algorithm for ddPCR data
Description: The ddPCRclust algorithm can automatically quantify the
CPDs of non-orthogonal ddPCR reactions with up to four targets.
In order to determine the correct droplet count for each
target, it is crucial to both identify all clusters and label
them correctly based on their position. For more information on
what data can be analyzed and how a template needs to be
formatted, please check the vignette.
biocViews: ddPCR, Clustering
Author: Benedikt G. Brink [aut, cre], Justin Meskas [ctb], Ryan R.
Brinkman [ctb]
Maintainer: Benedikt G. Brink <bbrink@cebitec.uni-bielefeld.de>
URL: https://github.com/bgbrink/ddPCRclust
BugReports: https://github.com/bgbrink/ddPCRclust/issues
git_url: https://git.bioconductor.org/packages/ddPCRclust
git_branch: devel
git_last_commit: b05f1a6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ddPCRclust_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ddPCRclust_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ddPCRclust_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ddPCRclust_1.27.0.tgz
vignettes: vignettes/ddPCRclust/inst/doc/ddPCRclust.pdf
vignetteTitles: Bioconductor LaTeX Style
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ddPCRclust/inst/doc/ddPCRclust.R
suggestsMe: Polytect
dependencyCount: 107
Package: dearseq
Version: 1.19.0
Depends: R (>= 3.6.0)
Imports: CompQuadForm, dplyr, ggplot2, KernSmooth, magrittr,
matrixStats, methods, patchwork, parallel, pbapply, reshape2,
rlang, scattermore, stats, statmod, survey, tibble, viridisLite
Suggests: Biobase, BiocManager, BiocSet, edgeR, DESeq2, GEOquery, GSA,
knitr, limma, readxl, rmarkdown, S4Vectors,
SummarizedExperiment, testthat, covr
License: GPL-2 | file LICENSE
MD5sum: d844621512866203027e15927a6ae97d
NeedsCompilation: no
Title: Differential Expression Analysis for RNA-seq data through a
robust variance component test
Description: Differential Expression Analysis RNA-seq data with
variance component score test accounting for data
heteroscedasticity through precision weights. Perform both
gene-wise and gene set analyses, and can deal with repeated or
longitudinal data. Methods are detailed in: i) Agniel D &
Hejblum BP (2017) Variance component score test for time-course
gene set analysis of longitudinal RNA-seq data, Biostatistics,
18(4):589-604 ; and ii) Gauthier M, Agniel D, Thiébaut R &
Hejblum BP (2020) dearseq: a variance component score test for
RNA-Seq differential analysis that effectively controls the
false discovery rate, NAR Genomics and Bioinformatics,
2(4):lqaa093.
biocViews: BiomedicalInformatics, CellBiology, DifferentialExpression,
DNASeq, GeneExpression, Genetics, GeneSetEnrichment,
ImmunoOncology, KEGG, Regression, RNASeq, Sequencing,
SystemsBiology, TimeCourse, Transcription, Transcriptomics
Author: Denis Agniel [aut], Boris P. Hejblum [aut, cre] (ORCID:
<https://orcid.org/0000-0003-0646-452X>), Marine Gauthier
[aut], Mélanie Huchon [ctb]
Maintainer: Boris P. Hejblum <boris.hejblum@u-bordeaux.fr>
VignetteBuilder: knitr
BugReports: https://github.com/borishejblum/dearseq/issues
git_url: https://git.bioconductor.org/packages/dearseq
git_branch: devel
git_last_commit: 2e8a88c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/dearseq_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/dearseq_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/dearseq_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/dearseq_1.19.0.tgz
vignettes: vignettes/dearseq/inst/doc/dearseqUserguide.html
vignetteTitles: dearseqUserguide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/dearseq/inst/doc/dearseqUserguide.R
importsMe: benchdamic
suggestsMe: GeoTcgaData, TcGSA
dependencyCount: 59
Package: debCAM
Version: 1.25.0
Depends: R (>= 3.5)
Imports: methods, rJava, BiocParallel, stats, Biobase,
SummarizedExperiment, corpcor, geometry, NMF, nnls, DMwR2,
pcaPP, apcluster, graphics
Suggests: knitr, rmarkdown, BiocStyle, testthat, GEOquery, rgl
License: GPL-2
MD5sum: 5a9e48ce4f179d28d875fcf6934a5c8f
NeedsCompilation: no
Title: Deconvolution by Convex Analysis of Mixtures
Description: An R package for fully unsupervised deconvolution of
complex tissues. It provides basic functions to perform
unsupervised deconvolution on mixture expression profiles by
Convex Analysis of Mixtures (CAM) and some auxiliary functions
to help understand the subpopulation-specific results. It also
implements functions to perform supervised deconvolution based
on prior knowledge of molecular markers, S matrix or A matrix.
Combining molecular markers from CAM and from prior knowledge
can achieve semi-supervised deconvolution of mixtures.
biocViews: Software, CellBiology, GeneExpression
Author: Lulu Chen <luluchen@vt.edu>
Maintainer: Lulu Chen <luluchen@vt.edu>
SystemRequirements: Java (>= 1.8)
VignetteBuilder: knitr
BugReports: https://github.com/Lululuella/debCAM/issues
git_url: https://git.bioconductor.org/packages/debCAM
git_branch: devel
git_last_commit: f694119
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/debCAM_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/debCAM_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/debCAM_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/debCAM_1.25.0.tgz
vignettes: vignettes/debCAM/inst/doc/debcam.html
vignetteTitles: debCAM User Manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/debCAM/inst/doc/debcam.R
dependencyCount: 117
Package: debrowser
Version: 1.35.0
Depends: R (>= 3.5.0),
Imports: shiny, jsonlite, shinyjs, shinydashboard, shinyBS, gplots, DT,
ggplot2, RColorBrewer, annotate, AnnotationDbi, DESeq2, DOSE,
igraph, grDevices, graphics, stats, utils, GenomicRanges,
IRanges, S4Vectors, SummarizedExperiment, stringi, reshape2,
org.Hs.eg.db, org.Mm.eg.db, limma, edgeR, clusterProfiler,
methods, sva, RCurl, enrichplot, colourpicker, plotly,
heatmaply, Harman, pathview, apeglm, ashr
Suggests: testthat, rmarkdown, knitr
License: GPL-3 + file LICENSE
MD5sum: a507a1fa8f86e785267477165496caaa
NeedsCompilation: no
Title: Interactive Differential Expresion Analysis Browser
Description: Bioinformatics platform containing interactive plots and
tables for differential gene and region expression studies.
Allows visualizing expression data much more deeply in an
interactive and faster way. By changing the parameters, users
can easily discover different parts of the data that like never
have been done before. Manually creating and looking these
plots takes time. With DEBrowser users can prepare plots
without writing any code. Differential expression, PCA and
clustering analysis are made on site and the results are shown
in various plots such as scatter, bar, box, volcano, ma plots
and Heatmaps.
biocViews: Sequencing, ChIPSeq, RNASeq, DifferentialExpression,
GeneExpression, Clustering, ImmunoOncology
Author: Alper Kucukural <alper.kucukural@umassmed.edu>, Onur Yukselen
<onur.yukselen@umassmed.edu>, Manuel Garber
<manuel.garber@umassmed.edu>
Maintainer: Alper Kucukural <alper.kucukural@umassmed.edu>
URL: https://github.com/UMMS-Biocore/debrowser
VignetteBuilder: knitr, rmarkdown
BugReports: https://github.com/UMMS-Biocore/debrowser/issues/new
git_url: https://git.bioconductor.org/packages/debrowser
git_branch: devel
git_last_commit: d896173
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/debrowser_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/debrowser_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/debrowser_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/debrowser_1.35.0.tgz
vignettes: vignettes/debrowser/inst/doc/DEBrowser.html
vignetteTitles: DEBrowser Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/debrowser/inst/doc/DEBrowser.R
dependencyCount: 225
Package: DECIPHER
Version: 3.3.4
Depends: R (>= 3.5.0), Biostrings (>= 2.59.1), stats
Imports: methods, DBI, S4Vectors, IRanges, XVector
LinkingTo: Biostrings, S4Vectors, IRanges, XVector
Suggests: RSQLite (>= 1.1)
License: GPL-3
MD5sum: 904e4cf814acb92b57f9e8330b8b02e3
NeedsCompilation: yes
Title: Tools for curating, analyzing, and manipulating biological
sequences
Description: A toolset for deciphering and managing biological
sequences.
biocViews: Clustering, Genetics, Sequencing, DataImport, Visualization,
Microarray, QualityControl, qPCR, Alignment, WholeGenome,
Microbiome, ImmunoOncology, GenePrediction
Author: Erik Wright
Maintainer: Erik Wright <eswright@pitt.edu>
URL: http://DECIPHER.codes
git_url: https://git.bioconductor.org/packages/DECIPHER
git_branch: devel
git_last_commit: cbe06a1
git_last_commit_date: 2025-03-11
Date/Publication: 2025-03-11
source.ver: src/contrib/DECIPHER_3.3.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DECIPHER_3.3.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DECIPHER_3.3.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DECIPHER_3.3.4.tgz
vignettes: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.pdf,
vignettes/DECIPHER/inst/doc/ClassifySequences.pdf,
vignettes/DECIPHER/inst/doc/ClusteringSequences.pdf,
vignettes/DECIPHER/inst/doc/DECIPHERing.pdf,
vignettes/DECIPHER/inst/doc/DesignMicroarray.pdf,
vignettes/DECIPHER/inst/doc/DesignPrimers.pdf,
vignettes/DECIPHER/inst/doc/DesignProbes.pdf,
vignettes/DECIPHER/inst/doc/DesignSignatures.pdf,
vignettes/DECIPHER/inst/doc/FindChimeras.pdf,
vignettes/DECIPHER/inst/doc/FindingGenes.pdf,
vignettes/DECIPHER/inst/doc/FindingNonCodingRNAs.pdf,
vignettes/DECIPHER/inst/doc/GrowingTrees.pdf,
vignettes/DECIPHER/inst/doc/RepeatRepeat.pdf,
vignettes/DECIPHER/inst/doc/SearchForResearch.pdf
vignetteTitles: The Art of Multiple Sequence Alignment in R, Classify
Sequences, Upsize Your Clustering with Clusterize, Getting
Started DECIPHERing, Design Microarray Probes, Design
Group-Specific Primers, Design Group-Specific FISH Probes,
Design Primers that Yield Group-Specific Signatures, Finding
Chimeric Sequences, The Magic of Gene Finding, The Double Life
of RNA: Uncovering Non-Coding RNAs, Growing Phylogenetic Trees
with Treeline, Detecting Obscure Tandem Repeats in Sequences,
Searching biological sequences
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.R,
vignettes/DECIPHER/inst/doc/ClassifySequences.R,
vignettes/DECIPHER/inst/doc/ClusteringSequences.R,
vignettes/DECIPHER/inst/doc/DECIPHERing.R,
vignettes/DECIPHER/inst/doc/DesignMicroarray.R,
vignettes/DECIPHER/inst/doc/DesignPrimers.R,
vignettes/DECIPHER/inst/doc/DesignProbes.R,
vignettes/DECIPHER/inst/doc/DesignSignatures.R,
vignettes/DECIPHER/inst/doc/FindChimeras.R,
vignettes/DECIPHER/inst/doc/FindingGenes.R,
vignettes/DECIPHER/inst/doc/FindingNonCodingRNAs.R,
vignettes/DECIPHER/inst/doc/GrowingTrees.R,
vignettes/DECIPHER/inst/doc/RepeatRepeat.R,
vignettes/DECIPHER/inst/doc/SearchForResearch.R
dependsOnMe: AssessORF, sangeranalyseR, SynExtend
importsMe: mia, openPrimeR, scifer, AssessORFData, copyseparator,
ensembleTax
suggestsMe: MicrobiotaProcess, microbial, MiscMetabar
dependencyCount: 26
Package: decompTumor2Sig
Version: 2.23.0
Depends: R(>= 4.0), ggplot2
Imports: methods, Matrix, quadprog(>= 1.5-5), GenomicRanges, stats,
GenomicFeatures, Biostrings, BiocGenerics, S4Vectors, plyr,
utils, graphics, BSgenome.Hsapiens.UCSC.hg19,
TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation,
SummarizedExperiment, ggseqlogo, gridExtra, data.table,
GenomeInfoDb, readxl
Suggests: knitr, rmarkdown, BiocStyle
License: GPL-2
MD5sum: 62e274e3d8f966ccd7083d3209ee96ec
NeedsCompilation: no
Title: Decomposition of individual tumors into mutational signatures by
signature refitting
Description: Uses quadratic programming for signature refitting, i.e.,
to decompose the mutation catalog from an individual tumor
sample into a set of given mutational signatures (either
Alexandrov-model signatures or Shiraishi-model signatures),
computing weights that reflect the contributions of the
signatures to the mutation load of the tumor.
biocViews: Software, SNP, Sequencing, DNASeq, GenomicVariation,
SomaticMutation, BiomedicalInformatics, Genetics,
BiologicalQuestion, StatisticalMethod
Author: Rosario M. Piro [aut, cre], Sandra Krueger [ctb]
Maintainer: Rosario M. Piro <rmpiro@gmail.com>
URL: http://rmpiro.net/decompTumor2Sig/,
https://github.com/rmpiro/decompTumor2Sig
VignetteBuilder: knitr
BugReports: https://github.com/rmpiro/decompTumor2Sig/issues
git_url: https://git.bioconductor.org/packages/decompTumor2Sig
git_branch: devel
git_last_commit: 7907da8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/decompTumor2Sig_2.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/decompTumor2Sig_2.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/decompTumor2Sig_2.23.0.tgz
vignettes: vignettes/decompTumor2Sig/inst/doc/decompTumor2Sig.html
vignetteTitles: A brief introduction to decompTumor2Sig
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/decompTumor2Sig/inst/doc/decompTumor2Sig.R
importsMe: musicatk
dependencyCount: 113
Package: DeconRNASeq
Version: 1.49.0
Depends: R (>= 2.14.0), limSolve, pcaMethods, ggplot2, grid
License: GPL-2
Archs: x64
MD5sum: ffe78f38b7c3ff729592fb67d6863f24
NeedsCompilation: no
Title: Deconvolution of Heterogeneous Tissue Samples for mRNA-Seq data
Description: DeconSeq is an R package for deconvolution of
heterogeneous tissues based on mRNA-Seq data. It modeled
expression levels from heterogeneous cell populations in
mRNA-Seq as the weighted average of expression from different
constituting cell types and predicted cell type proportions of
single expression profiles.
biocViews: DifferentialExpression
Author: Ting Gong <tinggong@gmail.com> Joseph D. Szustakowski
<joseph.szustakowski@novartis.com>
Maintainer: Ting Gong <tinggong@gmail.com>
git_url: https://git.bioconductor.org/packages/DeconRNASeq
git_branch: devel
git_last_commit: 3a944d5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DeconRNASeq_1.49.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DeconRNASeq_1.49.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DeconRNASeq_1.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DeconRNASeq_1.49.0.tgz
vignettes: vignettes/DeconRNASeq/inst/doc/DeconRNASeq.pdf
vignetteTitles: DeconRNASeq Demo
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DeconRNASeq/inst/doc/DeconRNASeq.R
suggestsMe: ADAPTS
dependencyCount: 43
Package: decontam
Version: 1.27.0
Depends: R (>= 3.4.1), methods (>= 3.4.1)
Imports: ggplot2 (>= 2.1.0), reshape2 (>= 1.4.1), stats
Suggests: BiocStyle, knitr, rmarkdown, phyloseq
License: Artistic-2.0
MD5sum: 7ac6c33f8479c403a0c1f20f6861f2b4
NeedsCompilation: no
Title: Identify Contaminants in Marker-gene and Metagenomics Sequencing
Data
Description: Simple statistical identification of contaminating
sequence features in marker-gene or metagenomics data. Works on
any kind of feature derived from environmental sequencing data
(e.g. ASVs, OTUs, taxonomic groups, MAGs,...). Requires DNA
quantitation data or sequenced negative control samples.
biocViews: ImmunoOncology, Microbiome, Sequencing, Classification,
Metagenomics
Author: Benjamin Callahan [aut, cre], Nicole Marie Davis [aut], Felix
G.M. Ernst [ctb] (ORCID:
<https://orcid.org/0000-0001-5064-0928>)
Maintainer: Benjamin Callahan <benjamin.j.callahan@gmail.com>
URL: https://github.com/benjjneb/decontam
VignetteBuilder: knitr
BugReports: https://github.com/benjjneb/decontam/issues
git_url: https://git.bioconductor.org/packages/decontam
git_branch: devel
git_last_commit: 2222eff
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/decontam_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/decontam_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/decontam_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/decontam_1.27.0.tgz
vignettes: vignettes/decontam/inst/doc/decontam_intro.html
vignetteTitles: Introduction to dada2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/decontam/inst/doc/decontam_intro.R
importsMe: mia
dependencyCount: 41
Package: decontX
Version: 1.5.1
Depends: R (>= 4.3.0)
Imports: celda, dbscan, DelayedArray, ggplot2, Matrix (>= 1.5.3),
MCMCprecision, methods, patchwork, plyr, Rcpp (>= 0.12.0),
RcppParallel (>= 5.0.1), reshape2, rstan (>= 2.18.1),
rstantools (>= 2.2.0), S4Vectors, scater, Seurat,
SingleCellExperiment, SummarizedExperiment, withr
LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0),
RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>=
2.18.0)
Suggests: BiocStyle, dplyr, knitr, rmarkdown, scran,
SingleCellMultiModal, TENxPBMCData, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: 2f43d08271493c33985ba3100b071c53
NeedsCompilation: yes
Title: Decontamination of single cell genomics data
Description: This package contains implementation of DecontX (Yang et
al. 2020), a decontamination algorithm for single-cell RNA-seq,
and DecontPro (Yin et al. 2023), a decontamination algorithm
for single cell protein expression data. DecontX is a novel
Bayesian method to computationally estimate and remove RNA
contamination in individual cells without empty droplet
information. DecontPro is a Bayesian method that estimates the
level of contamination from ambient and background sources in
CITE-seq ADT dataset and decontaminate the dataset.
biocViews: SingleCell, Bayesian
Author: Yuan Yin [aut] (ORCID:
<https://orcid.org/0000-0001-9261-6061>), Masanao Yajima [aut]
(ORCID: <https://orcid.org/0000-0002-7583-3707>), Joshua
Campbell [aut, cre] (ORCID:
<https://orcid.org/0000-0003-0780-8662>)
Maintainer: Joshua Campbell <camp@bu.edu>
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/decontX
git_branch: devel
git_last_commit: 310ac52
git_last_commit_date: 2025-02-16
Date/Publication: 2025-02-17
source.ver: src/contrib/decontX_1.5.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/decontX_1.5.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/decontX_1.5.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/decontX_1.5.1.tgz
vignettes: vignettes/decontX/inst/doc/decontPro.html,
vignettes/decontX/inst/doc/decontX.html
vignetteTitles: decontPro, Estimate and remove cross-contamination from
ambient RNA in single-cell data with DecontX
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/decontX/inst/doc/decontPro.R,
vignettes/decontX/inst/doc/decontX.R
dependencyCount: 241
Package: deconvR
Version: 1.13.0
Depends: R (>= 4.1), data.table (>= 1.14.0)
Imports: S4Vectors (>= 0.30.0), methylKit (>= 1.18.0), IRanges (>=
2.26.0), GenomicRanges (>= 1.44.0), BiocGenerics (>= 0.38.0),
stats, methods, foreach (>= 1.5.1), magrittr (>= 2.0.1),
matrixStats (>= 0.61.0), e1071 (>= 1.7.9), quadprog (>= 1.5.8),
nnls (>= 1.4), rsq (>= 2.2), MASS, utils, dplyr (>= 1.0.7),
tidyr (>= 1.1.3), assertthat, minfi
Suggests: testthat (>= 3.0.0), roxygen2 (>= 7.1.2), doParallel (>=
1.0.16), parallel, knitr (>= 1.34), BiocStyle (>= 2.20.2),
reshape2 (>= 1.4.4), ggplot2 (>= 3.3.5), rmarkdown, devtools
(>= 2.4.2), sessioninfo (>= 1.1.1), covr, granulator,
RefManageR
License: Artistic-2.0
MD5sum: 53e436b5a8ba6a51a18e5c53f4e4f3da
NeedsCompilation: no
Title: Simulation and Deconvolution of Omic Profiles
Description: This package provides a collection of functions designed
for analyzing deconvolution of the bulk sample(s) using an
atlas of reference omic signature profiles and a user-selected
model. Users are given the option to create or extend a
reference atlas and,also simulate the desired size of the bulk
signature profile of the reference cell types.The package
includes the cell-type-specific methylation atlas and, Illumina
Epic B5 probe ids that can be used in deconvolution.
Additionally,we included BSmeth2Probe, to make mapping WGBS
data to their probe IDs easier.
biocViews: DNAMethylation, Regression, GeneExpression, RNASeq,
SingleCell, StatisticalMethod, Transcriptomics
Author: Irem B. Gündüz [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2641-0916>), Veronika Ebenal [aut]
(ORCID: <https://orcid.org/0000-0001-7976-3964>), Altuna Akalin
[aut] (ORCID: <https://orcid.org/0000-0002-0468-0117>)
Maintainer: Irem B. Gündüz <irembgunduz@gmail.com>
URL: https://github.com/BIMSBbioinfo/deconvR
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/deconvR
git_url: https://git.bioconductor.org/packages/deconvR
git_branch: devel
git_last_commit: 02212ba
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-27
source.ver: src/contrib/deconvR_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/deconvR_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/deconvR_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/deconvR_1.13.0.tgz
vignettes: vignettes/deconvR/inst/doc/deconvRVignette.html
vignetteTitles: deconvRVignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/deconvR/inst/doc/deconvRVignette.R
dependencyCount: 186
Package: decoupleR
Version: 2.13.0
Depends: R (>= 4.0)
Imports: BiocParallel, broom, dplyr, magrittr, Matrix, parallelly,
purrr, rlang, stats, stringr, tibble, tidyr, tidyselect, withr
Suggests: glmnet (>= 4.1-7), GSVA, viper, fgsea (>= 1.15.4), AUCell,
SummarizedExperiment, rpart, ranger, BiocStyle, covr, knitr,
pkgdown, RefManageR, rmarkdown, roxygen2, sessioninfo,
pheatmap, testthat, OmnipathR, Seurat, ggplot2, ggrepel,
patchwork, magick
License: GPL-3 + file LICENSE
Archs: x64
MD5sum: b062e20601a2d330e2a75b63cb7c56c2
NeedsCompilation: no
Title: decoupleR: Ensemble of computational methods to infer biological
activities from omics data
Description: Many methods allow us to extract biological activities
from omics data using information from prior knowledge
resources, reducing the dimensionality for increased
statistical power and better interpretability. Here, we present
decoupleR, a Bioconductor package containing different
statistical methods to extract these signatures within a
unified framework. decoupleR allows the user to flexibly test
any method with any resource. It incorporates methods that take
into account the sign and weight of network interactions.
decoupleR can be used with any omic, as long as its features
can be linked to a biological process based on prior knowledge.
For example, in transcriptomics gene sets regulated by a
transcription factor, or in phospho-proteomics phosphosites
that are targeted by a kinase.
biocViews: DifferentialExpression, FunctionalGenomics, GeneExpression,
GeneRegulation, Network, Software, StatisticalMethod,
Transcription,
Author: Pau Badia-i-Mompel [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1004-3923>), Jesús Vélez-Santiago
[aut] (ORCID: <https://orcid.org/0000-0001-5128-3838>), Jana
Braunger [aut] (ORCID:
<https://orcid.org/0000-0003-0820-9987>), Celina Geiss [aut]
(ORCID: <https://orcid.org/0000-0002-8740-706X>), Daniel
Dimitrov [aut] (ORCID:
<https://orcid.org/0000-0002-5197-2112>), Sophia Müller-Dott
[aut] (ORCID: <https://orcid.org/0000-0002-9710-1865>), Petr
Taus [aut] (ORCID: <https://orcid.org/0000-0003-3764-9033>),
Aurélien Dugourd [aut] (ORCID:
<https://orcid.org/0000-0002-0714-028X>), Christian H. Holland
[aut] (ORCID: <https://orcid.org/0000-0002-3060-5786>), Ricardo
O. Ramirez Flores [aut] (ORCID:
<https://orcid.org/0000-0003-0087-371X>), Julio Saez-Rodriguez
[aut] (ORCID: <https://orcid.org/0000-0002-8552-8976>)
Maintainer: Pau Badia-i-Mompel <pau.badia@uni-heidelberg.de>
URL: https://saezlab.github.io/decoupleR/
VignetteBuilder: knitr
BugReports: https://github.com/saezlab/decoupleR/issues
git_url: https://git.bioconductor.org/packages/decoupleR
git_branch: devel
git_last_commit: 1d86763
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/decoupleR_2.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/decoupleR_2.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/decoupleR_2.13.0.tgz
vignettes: vignettes/decoupleR/inst/doc/decoupleR.html,
vignettes/decoupleR/inst/doc/pw_bk.html,
vignettes/decoupleR/inst/doc/pw_sc.html,
vignettes/decoupleR/inst/doc/tf_bk.html,
vignettes/decoupleR/inst/doc/tf_sc.html
vignetteTitles: Introduction, Pathway activity inference in bulk
RNA-seq, Pathway activity activity inference from scRNA-seq,
Transcription factor activity inference in bulk RNA-seq,
Transcription factor activity inference from scRNA-seq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/decoupleR/inst/doc/decoupleR.R,
vignettes/decoupleR/inst/doc/pw_bk.R,
vignettes/decoupleR/inst/doc/pw_sc.R,
vignettes/decoupleR/inst/doc/tf_bk.R,
vignettes/decoupleR/inst/doc/tf_sc.R
importsMe: cosmosR, easier, progeny
suggestsMe: SCpubr
dependencyCount: 42
Package: DeepPINCS
Version: 1.15.0
Depends: keras, R (>= 4.1)
Imports: tensorflow, CatEncoders, matlab, rcdk, stringdist, tokenizers,
webchem, purrr, ttgsea, PRROC, reticulate, stats
Suggests: knitr, testthat, rmarkdown
License: Artistic-2.0
MD5sum: 9b9c27503b422628bfaf8f426062b3fc
NeedsCompilation: no
Title: Protein Interactions and Networks with Compounds based on
Sequences using Deep Learning
Description: The identification of novel compound-protein interaction
(CPI) is important in drug discovery. Revealing unknown
compound-protein interactions is useful to design a new drug
for a target protein by screening candidate compounds. The
accurate CPI prediction assists in effective drug discovery
process. To identify potential CPI effectively, prediction
methods based on machine learning and deep learning have been
developed. Data for sequences are provided as discrete symbolic
data. In the data, compounds are represented as SMILES
(simplified molecular-input line-entry system) strings and
proteins are sequences in which the characters are amino acids.
The outcome is defined as a variable that indicates how strong
two molecules interact with each other or whether there is an
interaction between them. In this package, a deep-learning
based model that takes only sequence information of both
compounds and proteins as input and the outcome as output is
used to predict CPI. The model is implemented by using compound
and protein encoders with useful features. The CPI model also
supports other modeling tasks, including protein-protein
interaction (PPI), chemical-chemical interaction (CCI), or
single compounds and proteins. Although the model is designed
for proteins, DNA and RNA can be used if they are represented
as sequences.
biocViews: Software, Network, GraphAndNetwork, NeuralNetwork
Author: Dongmin Jung [cre, aut] (ORCID:
<https://orcid.org/0000-0001-7499-8422>)
Maintainer: Dongmin Jung <dmdmjung@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DeepPINCS
git_branch: devel
git_last_commit: 70f36a9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DeepPINCS_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DeepPINCS_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DeepPINCS_1.15.0.tgz
vignettes: vignettes/DeepPINCS/inst/doc/DeepPINCS.html
vignetteTitles: DeepPINCS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DeepPINCS/inst/doc/DeepPINCS.R
importsMe: GenProSeq, VAExprs
dependencyCount: 145
Package: deepSNV
Version: 1.53.0
Depends: R (>= 2.13.0), methods, graphics, parallel, IRanges,
GenomicRanges, SummarizedExperiment, Biostrings, VGAM,
VariantAnnotation (>= 1.27.6),
Imports: Rhtslib
LinkingTo: Rhtslib (>= 1.13.1)
Suggests: RColorBrewer, knitr, rmarkdown
License: GPL-3
MD5sum: a66619f626641914eacc934bd78c3126
NeedsCompilation: yes
Title: Detection of subclonal SNVs in deep sequencing data.
Description: This package provides provides quantitative variant
callers for detecting subclonal mutations in ultra-deep (>=100x
coverage) sequencing experiments. The deepSNV algorithm is used
for a comparative setup with a control experiment of the same
loci and uses a beta-binomial model and a likelihood ratio test
to discriminate sequencing errors and subclonal SNVs. The
shearwater algorithm computes a Bayes classifier based on a
beta-binomial model for variant calling with multiple samples
for precisely estimating model parameters - such as local error
rates and dispersion - and prior knowledge, e.g. from variation
data bases such as COSMIC.
biocViews: GeneticVariability, SNP, Sequencing, Genetics, DataImport
Author: Niko Beerenwinkel [ths], Raul Alcantara [ctb], David Jones
[ctb], John Marshall [ctb], Inigo Martincorena [ctb], Moritz
Gerstung [aut, cre]
Maintainer: Moritz Gerstung <moritz.gerstung@ebi.ac.uk>
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/deepSNV
git_branch: devel
git_last_commit: d480433
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/deepSNV_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/deepSNV_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/deepSNV_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/deepSNV_1.53.0.tgz
vignettes: vignettes/deepSNV/inst/doc/deepSNV.pdf,
vignettes/deepSNV/inst/doc/shearwater.pdf,
vignettes/deepSNV/inst/doc/shearwaterML.html
vignetteTitles: An R package for detecting low frequency variants in
deep sequencing experiments, Subclonal variant calling with
multiple samples and prior knowledge using shearwater,
Shearwater ML
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/deepSNV/inst/doc/deepSNV.R,
vignettes/deepSNV/inst/doc/shearwater.R,
vignettes/deepSNV/inst/doc/shearwaterML.R
importsMe: mitoClone2
suggestsMe: GenomicFiles
dependencyCount: 81
Package: DeepTarget
Version: 1.1.0
Depends: R (>= 4.2.0)
Imports: fgsea, ggplot2, stringr, ggpubr, BiocParallel, pROC, stats,
grDevices, graphics, depmap, readr, dplyr
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-2
MD5sum: b47b6fabcd4147b8f5e8b2f50c9ec4ba
NeedsCompilation: no
Title: Deep characterization of cancer drugs
Description: This package predicts a drug’s primary target(s) or
secondary target(s) by integrating large-scale genetic and drug
screens from the Cancer Dependency Map project run by the Broad
Institute. It further investigates whether the drug
specifically targets the wild-type or mutated target forms. To
show how to use this package in practice, we provided sample
data along with step-by-step example.
biocViews: GeneTarget, GenePrediction,Pathways, GeneExpression, RNASeq,
ImmunoOncology,DifferentialExpression, GeneSetEnrichment,
ReportWriting,CRISPR
Author: Sanju Sinha [aut], Trinh Nguyen [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6606-6948>), Ying Hu [aut]
Maintainer: Trinh Nguyen <tinh.nguyen@nih.gov>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DeepTarget
git_branch: devel
git_last_commit: 3398b5d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DeepTarget_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DeepTarget_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DeepTarget_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DeepTarget_1.1.0.tgz
vignettes: vignettes/DeepTarget/inst/doc/DeepTarget_Vignette.html
vignetteTitles: Workflow Demonstration for Deep characterization of
cancer drugs
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DeepTarget/inst/doc/DeepTarget_Vignette.R
dependencyCount: 141
Package: DEFormats
Version: 1.35.0
Imports: checkmate, data.table, DESeq2, edgeR (>= 3.13.4),
GenomicRanges, methods, S4Vectors, stats, SummarizedExperiment
Suggests: BiocStyle (>= 1.8.0), knitr, rmarkdown, testthat
License: GPL-3
Archs: x64
MD5sum: 54bcb46a47b211c2be35f0279b5f98be
NeedsCompilation: no
Title: Differential gene expression data formats converter
Description: Convert between different data formats used by
differential gene expression analysis tools.
biocViews: ImmunoOncology, DifferentialExpression, GeneExpression,
RNASeq, Sequencing, Transcription
Author: Andrzej OleÅ›
Maintainer: Andrzej OleÅ› <andrzej.oles@gmail.com>
URL: https://github.com/aoles/DEFormats
VignetteBuilder: knitr
BugReports: https://github.com/aoles/DEFormats/issues
git_url: https://git.bioconductor.org/packages/DEFormats
git_branch: devel
git_last_commit: 05370ad
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DEFormats_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DEFormats_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DEFormats_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DEFormats_1.35.0.tgz
vignettes: vignettes/DEFormats/inst/doc/DEFormats.html
vignetteTitles: Differential gene expression data formats converter
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DEFormats/inst/doc/DEFormats.R
importsMe: regionReport
suggestsMe: ideal
dependencyCount: 82
Package: DegCre
Version: 1.3.0
Depends: R (>= 4.4)
Imports: GenomicRanges, InteractionSet, plotgardener, S4Vectors, stats,
graphics, grDevices, BiocGenerics, GenomeInfoDb, IRanges,
BiocParallel, qvalue, TxDb.Hsapiens.UCSC.hg38.knownGene,
org.Hs.eg.db, utils
Suggests: BSgenome, BSgenome.Hsapiens.UCSC.hg38, BiocStyle, magick,
knitr, rmarkdown, TxDb.Mmusculus.UCSC.mm10.knownGene, testthat
(>= 3.0.0)
License: MIT + file LICENSE
MD5sum: 53dd1a5436d63aef7abdbe68af6231ca
NeedsCompilation: no
Title: Probabilistic association of DEGs to CREs from differential data
Description: DegCre generates associations between differentially
expressed genes (DEGs) and cis-regulatory elements (CREs) based
on non-parametric concordance between differential data. The
user provides GRanges of DEG TSS and CRE regions with
differential p-value and optionally log-fold changes and DegCre
returns an annotated Hits object with associations and their
calculated probabilities. Additionally, the package provides
functionality for visualization and conversion to other
formats.
biocViews: GeneExpression, GeneRegulation, ATACSeq, ChIPSeq, DNaseSeq,
RNASeq
Author: Brian S. Roberts [aut, cre] (ORCID:
<https://orcid.org/0009-0001-2914-6826>)
Maintainer: Brian S. Roberts <brianroberts1976@yahoo.com>
URL: https://github.com/brianSroberts/DegCre
VignetteBuilder: knitr
BugReports: https://github.com/brianSroberts/DegCre/issues
git_url: https://git.bioconductor.org/packages/DegCre
git_branch: devel
git_last_commit: a35e1e0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DegCre_1.3.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DegCre_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DegCre_1.3.0.tgz
vignettes:
vignettes/DegCre/inst/doc/degcre_introduction_and_examples.html
vignetteTitles: DegCre Introduction and Examples
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/DegCre/inst/doc/degcre_introduction_and_examples.R
dependencyCount: 121
Package: DegNorm
Version: 1.17.0
Depends: R (>= 4.0.0), methods
Imports: Rcpp (>= 1.0.2),GenomicFeatures, txdbmaker, parallel, foreach,
S4Vectors, doParallel, Rsamtools (>= 1.31.2),
GenomicAlignments, heatmaply, data.table, stats, ggplot2,
GenomicRanges, IRanges, plyr, plotly, utils,viridis
LinkingTo: Rcpp, RcppArmadillo,S4Vectors,IRanges
Suggests: knitr,rmarkdown,formatR
License: LGPL (>= 3)
MD5sum: a270193c5a572ef23c5e4f5507f920ad
NeedsCompilation: yes
Title: DegNorm: degradation normalization for RNA-seq data
Description: This package performs degradation normalization in bulk
RNA-seq data to improve differential expression analysis
accuracy.
biocViews: RNASeq, Normalization, GeneExpression, Alignment,Coverage,
DifferentialExpression, BatchEffect,Software,Sequencing,
ImmunoOncology, QualityControl, DataImport
Author: Bin Xiong and Ji-Ping Wang
Maintainer: Ji-Ping Wang <jzwang@northwestern.edu>
VignetteBuilder: knitr
BugReports: https://github.com/jipingw/DegNorm/issues
git_url: https://git.bioconductor.org/packages/DegNorm
git_branch: devel
git_last_commit: 3be95e6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DegNorm_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DegNorm_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DegNorm_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DegNorm_1.17.0.tgz
vignettes: vignettes/DegNorm/inst/doc/DegNorm.html
vignetteTitles: DegNorm
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DegNorm/inst/doc/DegNorm.R
dependencyCount: 162
Package: DEGraph
Version: 1.59.0
Depends: R (>= 2.10.0), R.utils
Imports: graph, KEGGgraph, lattice, mvtnorm, R.methodsS3, RBGL,
Rgraphviz, rrcov, NCIgraph
Suggests: corpcor, fields, graph, KEGGgraph, lattice, marray, RBGL,
rrcov, Rgraphviz, NCIgraph
License: GPL-3
MD5sum: ffc8b9f4519a263767159b1f9372e139
NeedsCompilation: no
Title: Two-sample tests on a graph
Description: DEGraph implements recent hypothesis testing methods which
directly assess whether a particular gene network is
differentially expressed between two conditions. This is to be
contrasted with the more classical two-step approaches which
first test individual genes, then test gene sets for enrichment
in differentially expressed genes. These recent methods take
into account the topology of the network to yield more powerful
detection procedures. DEGraph provides methods to easily test
all KEGG pathways for differential expression on any gene
expression data set and tools to visualize the results.
biocViews: Microarray, DifferentialExpression, GraphAndNetwork,
Network, NetworkEnrichment, DecisionTree
Author: Laurent Jacob, Pierre Neuvial and Sandrine Dudoit
Maintainer: Laurent Jacob <laurent.jacob@gmail.com>
git_url: https://git.bioconductor.org/packages/DEGraph
git_branch: devel
git_last_commit: 6c12c51
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DEGraph_1.59.0.tar.gz
vignettes: vignettes/DEGraph/inst/doc/DEGraph.pdf
vignetteTitles: DEGraph: differential expression testing for gene
networks
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DEGraph/inst/doc/DEGraph.R
dependencyCount: 65
Package: DEGreport
Version: 1.43.0
Depends: R (>= 4.0.0)
Imports: utils, methods, Biobase, BiocGenerics, broom, circlize,
ComplexHeatmap, cowplot, ConsensusClusterPlus, cluster,
dendextend, DESeq2, dplyr, edgeR, ggplot2, ggdendro, grid,
ggrepel, grDevices, knitr, logging, magrittr, psych,
RColorBrewer, reshape, rlang, scales, stats, stringr, stringi,
S4Vectors, SummarizedExperiment, tidyr, tibble
Suggests: BiocStyle, AnnotationDbi, limma, pheatmap, rmarkdown,
statmod, testthat
License: MIT + file LICENSE
MD5sum: 867aaed2658d76b50baa36f5492074d2
NeedsCompilation: no
Title: Report of DEG analysis
Description: Creation of ready-to-share figures of differential
expression analyses of count data. It integrates some of the
code mentioned in DESeq2 and edgeR vignettes, and report a
ranked list of genes according to the fold changes mean and
variability for each selected gene.
biocViews: DifferentialExpression, Visualization, RNASeq,
ReportWriting, GeneExpression, ImmunoOncology
Author: Lorena Pantano [aut, cre], John Hutchinson [ctb], Victor
Barrera [ctb], Mary Piper [ctb], Radhika Khetani [ctb], Kenneth
Daily [ctb], Thanneer Malai Perumal [ctb], Rory Kirchner [ctb],
Michael Steinbaugh [ctb], Ivo Zeller [ctb]
Maintainer: Lorena Pantano <lorena.pantano@gmail.com>
URL: http://lpantano.github.io/DEGreport/
VignetteBuilder: knitr
BugReports: https://github.com/lpantano/DEGreport/issues
git_url: https://git.bioconductor.org/packages/DEGreport
git_branch: devel
git_last_commit: d93b674
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DEGreport_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DEGreport_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DEGreport_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DEGreport_1.43.0.tgz
vignettes: vignettes/DEGreport/inst/doc/DEGreport.html
vignetteTitles: QC and downstream analysis for differential expression
RNA-seq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/DEGreport/inst/doc/DEGreport.R
importsMe: isomiRs
dependencyCount: 120
Package: DEGseq
Version: 1.61.0
Depends: R (>= 2.8.0), qvalue, methods
Imports: graphics, grDevices, methods, stats, utils
License: LGPL (>=2)
Archs: x64
MD5sum: eefebb29199988229ea722e8d7759939
NeedsCompilation: yes
Title: Identify Differentially Expressed Genes from RNA-seq data
Description: DEGseq is an R package to identify differentially
expressed genes from RNA-Seq data.
biocViews: RNASeq, Preprocessing, GeneExpression,
DifferentialExpression, ImmunoOncology
Author: Likun Wang <wanglk@pku.edu.cn>, Xiaowo Wang
<xwwang@tsinghua.edu.cn> and Xuegong Zhang
<zhangxg@tsinghua.edu.cn>.
Maintainer: Likun Wang <wanglk@pku.edu.cn>
git_url: https://git.bioconductor.org/packages/DEGseq
git_branch: devel
git_last_commit: 8770bb2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DEGseq_1.61.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DEGseq_1.61.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DEGseq_1.61.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DEGseq_1.61.0.tgz
vignettes: vignettes/DEGseq/inst/doc/DEGseq.pdf
vignetteTitles: DEGseq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DEGseq/inst/doc/DEGseq.R
dependencyCount: 42
Package: DelayedArray
Version: 0.33.6
Depends: R (>= 4.0.0), methods, stats4, Matrix, BiocGenerics (>=
0.53.3), MatrixGenerics (>= 1.1.3), S4Vectors (>= 0.27.2),
IRanges (>= 2.17.3), S4Arrays (>= 1.5.4), SparseArray (>=
1.7.5)
Imports: stats
LinkingTo: S4Vectors
Suggests: BiocParallel, HDF5Array (>= 1.17.12), genefilter,
SummarizedExperiment, airway, lobstr, DelayedMatrixStats,
knitr, rmarkdown, BiocStyle, RUnit
License: Artistic-2.0
MD5sum: 418e4de726bc01cc1597c597879dc9dd
NeedsCompilation: yes
Title: A unified framework for working transparently with on-disk and
in-memory array-like datasets
Description: Wrapping an array-like object (typically an on-disk
object) in a DelayedArray object allows one to perform common
array operations on it without loading the object in memory. In
order to reduce memory usage and optimize performance,
operations on the object are either delayed or executed using a
block processing mechanism. Note that this also works on
in-memory array-like objects like DataFrame objects (typically
with Rle columns), Matrix objects, ordinary arrays and, data
frames.
biocViews: Infrastructure, DataRepresentation, Annotation,
GenomeAnnotation
Author: Hervé Pagès [aut, cre], Aaron Lun [ctb], Peter Hickey [ctb]
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/DelayedArray
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/DelayedArray/issues
git_url: https://git.bioconductor.org/packages/DelayedArray
git_branch: devel
git_last_commit: e170345
git_last_commit_date: 2025-02-13
Date/Publication: 2025-02-14
source.ver: src/contrib/DelayedArray_0.33.6.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DelayedArray_0.33.6.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DelayedArray_0.33.6.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DelayedArray_0.33.6.tgz
vignettes:
vignettes/DelayedArray/inst/doc/A-Working_with_large_arrays.pdf,
vignettes/DelayedArray/inst/doc/C-DelayedArray_HDF5Array_update.pdf,
vignettes/DelayedArray/inst/doc/B-Implementing_a_backend.html
vignetteTitles: 1. Working with large arrays in R (slides from July
2017), 3. A DelayedArray / HDF5Array update (slides from April
2021), 2. Implementing A DelayedArray Backend
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DelayedArray/inst/doc/A-Working_with_large_arrays.R,
vignettes/DelayedArray/inst/doc/C-DelayedArray_HDF5Array_update.R
dependsOnMe: chihaya, DelayedDataFrame, DelayedMatrixStats,
DelayedRandomArray, GDSArray, HDF5Array, Rarr, rhdf5client,
SCArray, singleCellTK, SQLDataFrame, TileDBArray, VCFArray
importsMe: adverSCarial, alabaster.matrix, AUCell, batchelor, beachmat,
beachmat.hdf5, beachmat.tiledb, BiocSingular, bsseq, celaref,
celda, Cepo, ChromSCape, clusterExperiment, concordexR,
CRISPRseek, cytomapper, decontX, DelayedTensor, DEScan2,
dreamlet, DropletUtils, ELMER, EWCE, FLAMES, flowWorkspace,
FRASER, GenomicScores, glmGamPoi, GSVA, hipathia,
LoomExperiment, Macarron, mariner, mbkmeans, methodical,
MethReg, methrix, methylSig, mia, miaViz, minfi, MOFA2, MuData,
MultiAssayExperiment, mumosa, NetActivity, netSmooth, NewWave,
orthogene, orthos, PCAtools, ResidualMatrix, RTCGAToolbox,
ScaledMatrix, SCArray.sat, scater, scDblFinder, scFeatures,
scMerge, scmeth, scPCA, scran, scrapper, scry, scuttle,
signatureSearch, SingleCellAlleleExperiment,
SingleCellExperiment, SingleR, sketchR, SpliceWiz,
SummarizedExperiment, transformGamPoi, TSCAN,
VariantExperiment, velociraptor, Voyager, weitrix, xcore,
zellkonverter, ZygosityPredictor, celldex, imcdatasets,
scRNAseq, ebvcube, scDiffCom
suggestsMe: BiocGenerics, ChIPpeakAnno, gwascat, hermes, iSEE, lute,
MAST, MatrixGenerics, ProteoDisco, S4Arrays, S4Vectors, satuRn,
scone, SPOTlight, TrajectoryUtils, Seurat, SeuratObject,
SpatialDDLS
dependencyCount: 21
Package: DelayedDataFrame
Version: 1.23.0
Depends: R (>= 3.6), S4Vectors (>= 0.23.19), DelayedArray (>= 0.7.5)
Imports: methods, stats, BiocGenerics
Suggests: testthat, knitr, rmarkdown, BiocStyle, SeqArray, GDSArray
License: GPL-3
Archs: x64
MD5sum: aa4af3adaf7326d0ed26d2e320ca864b
NeedsCompilation: no
Title: Delayed operation on DataFrame using standard DataFrame metaphor
Description: Based on the standard DataFrame metaphor, we are trying to
implement the feature of delayed operation on the
DelayedDataFrame, with a slot of lazyIndex, which saves the
mapping indexes for each column of DelayedDataFrame. Methods
like show, validity check, [/[[ subsetting, rbind/cbind are
implemented for DelayedDataFrame to be operated around
lazyIndex. The listData slot stays untouched until a
realization call e.g., DataFrame constructor OR as.list() is
invoked.
biocViews: Infrastructure, DataRepresentation
Author: Qian Liu [aut, cre], Hervé Pagès [aut], Martin Morgan [aut]
Maintainer: Qian Liu <Qian.Liu@roswellpark.org>
URL: https://github.com/Bioconductor/DelayedDataFrame
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/DelayedDataFrame/issues
git_url: https://git.bioconductor.org/packages/DelayedDataFrame
git_branch: devel
git_last_commit: 049374e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DelayedDataFrame_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DelayedDataFrame_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DelayedDataFrame_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DelayedDataFrame_1.23.0.tgz
vignettes: vignettes/DelayedDataFrame/inst/doc/DelayedDataFrame.html
vignetteTitles: DelayedDataFrame
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DelayedDataFrame/inst/doc/DelayedDataFrame.R
importsMe: VariantExperiment
dependencyCount: 22
Package: DelayedMatrixStats
Version: 1.29.1
Depends: MatrixGenerics (>= 1.15.1), DelayedArray (>= 0.31.7)
Imports: methods, sparseMatrixStats (>= 1.13.2), Matrix (>= 1.5-0),
S4Vectors (>= 0.17.5), IRanges (>= 2.25.10), SparseArray (>=
1.5.19)
Suggests: testthat, knitr, rmarkdown, BiocStyle, microbenchmark,
profmem, HDF5Array, matrixStats (>= 1.0.0)
License: MIT + file LICENSE
Archs: x64
MD5sum: 485aab0debc97cee1e62ae1d93b7ae54
NeedsCompilation: no
Title: Functions that Apply to Rows and Columns of 'DelayedMatrix'
Objects
Description: A port of the 'matrixStats' API for use with DelayedMatrix
objects from the 'DelayedArray' package. High-performing
functions operating on rows and columns of DelayedMatrix
objects, e.g. col / rowMedians(), col / rowRanks(), and col /
rowSds(). Functions optimized per data type and for subsetted
calculations such that both memory usage and processing time is
minimized.
biocViews: Infrastructure, DataRepresentation, Software
Author: Peter Hickey [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8153-6258>), Hervé Pagès [ctb],
Aaron Lun [ctb]
Maintainer: Peter Hickey <peter.hickey@gmail.com>
URL: https://github.com/PeteHaitch/DelayedMatrixStats
VignetteBuilder: knitr
BugReports: https://github.com/PeteHaitch/DelayedMatrixStats/issues
git_url: https://git.bioconductor.org/packages/DelayedMatrixStats
git_branch: devel
git_last_commit: eb0b432
git_last_commit_date: 2025-01-08
Date/Publication: 2025-01-09
source.ver: src/contrib/DelayedMatrixStats_1.29.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DelayedMatrixStats_1.29.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DelayedMatrixStats_1.29.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DelayedMatrixStats_1.29.1.tgz
vignettes:
vignettes/DelayedMatrixStats/inst/doc/DelayedMatrixStatsOverview.html
vignetteTitles: Overview of DelayedMatrixStats
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles:
vignettes/DelayedMatrixStats/inst/doc/DelayedMatrixStatsOverview.R
importsMe: AUCell, batchelor, biscuiteer, bsseq, Cepo, dmrseq,
dreamlet, DropletUtils, FRASER, glmGamPoi, GSVA, lemur,
methrix, methylSig, mia, minfi, mumosa, NetActivity, PCAtools,
recountmethylation, SCArray, scFeatures, scMerge, scone,
singleCellTK, SingleR, sparrow, SpliceWiz, SVP, weitrix,
celldex
suggestsMe: condiments, DelayedArray, EWCE, HDF5Array, lute,
MatrixGenerics, mbkmeans, ScaledMatrix, scater, scPCA, scran,
scuttle, slingshot, SplineDV, tradeSeq, TrajectoryUtils,
Voyager, ClustAssess, SpatialDDLS
dependencyCount: 24
Package: DelayedRandomArray
Version: 1.15.0
Depends: SparseArray (>= 1.5.15), DelayedArray (>= 0.31.6)
Imports: methods, dqrng, Rcpp
LinkingTo: dqrng, BH, Rcpp
Suggests: testthat, knitr, BiocStyle, rmarkdown, Matrix
License: GPL-3
MD5sum: 2d3c6f79e034dd9e1809382d6cd8a0cd
NeedsCompilation: yes
Title: Delayed Arrays of Random Values
Description: Implements a DelayedArray of random values where the
realization of the sampled values is delayed until they are
needed. Reproducible sampling within any subarray is achieved
by chunking where each chunk is initialized with a different
random seed and stream. The usual distributions in the stats
package are supported, along with scalar, vector and arrays for
the parameters.
biocViews: DataRepresentation
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://github.com/LTLA/DelayedRandomArray
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/LTLA/DelayedRandomArray/issues
git_url: https://git.bioconductor.org/packages/DelayedRandomArray
git_branch: devel
git_last_commit: 7ae113b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DelayedRandomArray_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DelayedRandomArray_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DelayedRandomArray_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DelayedRandomArray_1.15.0.tgz
vignettes: vignettes/DelayedRandomArray/inst/doc/userguide.html
vignetteTitles: User's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DelayedRandomArray/inst/doc/userguide.R
importsMe: DelayedTensor
dependencyCount: 26
Package: DelayedTensor
Version: 1.13.0
Depends: R (>= 4.1.0)
Imports: methods, utils, S4Arrays, SparseArray, DelayedArray (>=
0.31.8), HDF5Array, BiocSingular, rTensor, DelayedRandomArray
(>= 1.13.1), irlba, Matrix, einsum,
Suggests: markdown, rmarkdown, BiocStyle, knitr, testthat, magrittr,
dplyr, reticulate
License: Artistic-2.0
MD5sum: c2708e6eab7fa0e20accee421c0d3116
NeedsCompilation: no
Title: R package for sparse and out-of-core arithmetic and
decomposition of Tensor
Description: DelayedTensor operates Tensor arithmetic directly on
DelayedArray object. DelayedTensor provides some generic
function related to Tensor arithmetic/decompotision and
dispatches it on the DelayedArray class. DelayedTensor also
suppors Tensor contraction by einsum function, which is
inspired by numpy einsum.
biocViews: Software, Infrastructure, DataRepresentation,
DimensionReduction
Author: Koki Tsuyuzaki [aut, cre]
Maintainer: Koki Tsuyuzaki <k.t.the-answer@hotmail.co.jp>
VignetteBuilder: knitr
BugReports: https://github.com/rikenbit/DelayedTensor/issues
git_url: https://git.bioconductor.org/packages/DelayedTensor
git_branch: devel
git_last_commit: 35f745f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DelayedTensor_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DelayedTensor_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DelayedTensor_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DelayedTensor_1.13.0.tgz
vignettes: vignettes/DelayedTensor/inst/doc/DelayedTensor_1.html,
vignettes/DelayedTensor/inst/doc/DelayedTensor_2.html,
vignettes/DelayedTensor/inst/doc/DelayedTensor_3.html,
vignettes/DelayedTensor/inst/doc/DelayedTensor_4.html
vignetteTitles: DelayedTensor, TensorArithmetic, TensorDecomposition,
Einsum
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DelayedTensor/inst/doc/DelayedTensor_1.R,
vignettes/DelayedTensor/inst/doc/DelayedTensor_2.R,
vignettes/DelayedTensor/inst/doc/DelayedTensor_3.R,
vignettes/DelayedTensor/inst/doc/DelayedTensor_4.R
dependencyCount: 51
Package: DELocal
Version: 1.7.0
Imports: DESeq2, dplyr, reshape2, limma, SummarizedExperiment, ggplot2,
matrixStats, stats
Suggests: biomaRt, knitr, rmarkdown, stringr, BiocStyle
License: MIT + file LICENSE
Archs: x64
MD5sum: 6f70db054ef9559d18011b75f96f899d
NeedsCompilation: no
Title: Identifies differentially expressed genes with respect to other
local genes
Description: The goal of DELocal is to identify DE genes compared to
their neighboring genes from the same chromosomal location. It
has been shown that genes of related functions are generally
very far from each other in the chromosome. DELocal utilzes
this information to identify DE genes comparing with their
neighbouring genes.
biocViews: GeneExpression, DifferentialExpression, RNASeq,
Transcriptomics
Author: Rishi Das Roy [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3276-7279>)
Maintainer: Rishi Das Roy <rishi.dasroy@gmail.com>
URL: https://github.com/dasroy/DELocal
VignetteBuilder: knitr
BugReports: https://github.com/dasroy/DELocal/issues
git_url: https://git.bioconductor.org/packages/DELocal
git_branch: devel
git_last_commit: b84dfd4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DELocal_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DELocal_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DELocal_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DELocal_1.7.0.tgz
vignettes: vignettes/DELocal/inst/doc/DELocal.html
vignetteTitles: DELocal
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/DELocal/inst/doc/DELocal.R
importsMe: broadSeq
dependencyCount: 84
Package: deltaCaptureC
Version: 1.21.0
Depends: R (>= 3.6)
Imports: IRanges, GenomicRanges, SummarizedExperiment, ggplot2, DESeq2,
tictoc
Suggests: knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 906a86248863e4b8cadcc85421302d3c
NeedsCompilation: no
Title: This Package Discovers Meso-scale Chromatin Remodeling from 3C
Data
Description: This package discovers meso-scale chromatin remodelling
from 3C data. 3C data is local in nature. It givens
interaction counts between restriction enzyme digestion
fragments and a preferred 'viewpoint' region. By binning this
data and using permutation testing, this package can test
whether there are statistically significant changes in the
interaction counts between the data from two cell types or two
treatments.
biocViews: BiologicalQuestion, StatisticalMethod
Author: Michael Shapiro [aut, cre] (ORCID:
<https://orcid.org/0000-0002-2769-9320>)
Maintainer: Michael Shapiro <michael.shapiro@crick.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/deltaCaptureC
git_branch: devel
git_last_commit: 10a66b3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/deltaCaptureC_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/deltaCaptureC_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/deltaCaptureC_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/deltaCaptureC_1.21.0.tgz
vignettes: vignettes/deltaCaptureC/inst/doc/deltaCaptureC.html
vignetteTitles: Delta Capture-C
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/deltaCaptureC/inst/doc/deltaCaptureC.R
dependencyCount: 77
Package: deltaGseg
Version: 1.47.0
Depends: R (>= 2.15.1), methods, ggplot2, changepoint, wavethresh,
tseries, pvclust, fBasics, grid, reshape, scales
Suggests: knitr
License: GPL-2
MD5sum: b0aaec8022fe71ee6b63e03006d0fe0f
NeedsCompilation: no
Title: deltaGseg
Description: Identifying distinct subpopulations through multiscale
time series analysis
biocViews: Proteomics, TimeCourse, Visualization, Clustering
Author: Diana Low, Efthymios Motakis
Maintainer: Diana Low <lowdiana@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/deltaGseg
git_branch: devel
git_last_commit: 6042fb0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/deltaGseg_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/deltaGseg_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/deltaGseg_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/deltaGseg_1.47.0.tgz
vignettes: vignettes/deltaGseg/inst/doc/deltaGseg.pdf
vignetteTitles: deltaGseg
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/deltaGseg/inst/doc/deltaGseg.R
dependencyCount: 55
Package: DeMAND
Version: 1.37.0
Depends: R (>= 2.14.0), KernSmooth, methods
License: file LICENSE
MD5sum: f7b6b188d3d60f7a336c680b767601ff
NeedsCompilation: no
Title: DeMAND
Description: DEMAND predicts Drug MoA by interrogating a cell context
specific regulatory network with a small number (N >= 6) of
compound-induced gene expression signatures, to elucidate
specific proteins whose interactions in the network is
dysregulated by the compound.
biocViews: SystemsBiology, NetworkEnrichment, GeneExpression,
StatisticalMethod, Network
Author: Jung Hoon Woo <jw2853@columbia.edu>, Yishai Shimoni
<ys2559@columbia.edu>
Maintainer: Jung Hoon Woo <jw2853@columbia.edu>, Mariano Alvarez
<reef103@gmail.com>
git_url: https://git.bioconductor.org/packages/DeMAND
git_branch: devel
git_last_commit: 4822c1c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DeMAND_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DeMAND_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DeMAND_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DeMAND_1.37.0.tgz
vignettes: vignettes/DeMAND/inst/doc/DeMAND.pdf
vignetteTitles: Using DeMAND
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/DeMAND/inst/doc/DeMAND.R
dependencyCount: 3
Package: DeMixT
Version: 1.23.0
Depends: R (>= 3.6.0), parallel, Rcpp (>= 1.0.0), SummarizedExperiment,
knitr, KernSmooth, matrixcalc, rmarkdown, DSS, dendextend,
psych, sva
Imports: matrixStats, stats, truncdist, base64enc, ggplot2
LinkingTo: Rcpp
License: GPL-3
MD5sum: 45925f5de82015e41f7daf8d1cb783f4
NeedsCompilation: yes
Title: Cell type-specific deconvolution of heterogeneous tumor samples
with two or three components using expression data from RNAseq
or microarray platforms
Description: DeMixT is a software package that performs deconvolution
on transcriptome data from a mixture of two or three
components.
biocViews: Software, StatisticalMethod, Classification, GeneExpression,
Sequencing, Microarray, TissueMicroarray, Coverage
Author: Zeya Wang <zw17.rice@gmail.com>, Shaolong
Cao<scao@mdanderson.org>, Wenyi Wang <wwang7@@mdanderson.org>
Maintainer: Ruonan Li <RLi10@mdanderson.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DeMixT
git_branch: devel
git_last_commit: eb1e461
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DeMixT_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DeMixT_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DeMixT_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DeMixT_1.23.0.tgz
vignettes: vignettes/DeMixT/inst/doc/demixt.html
vignetteTitles: DeMixT.Rmd
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DeMixT/inst/doc/demixt.R
dependencyCount: 150
Package: demuxmix
Version: 1.9.0
Depends: R (>= 4.0.0)
Imports: stats, MASS, Matrix, ggplot2, gridExtra, methods
Suggests: BiocStyle, cowplot, DropletUtils, knitr, reshape2, rmarkdown,
testthat (>= 3.0.0)
License: Artistic-2.0
MD5sum: 33a16f9d3f8904d577533fa81e5513ad
NeedsCompilation: no
Title: Demultiplexing oligo-barcoded scRNA-seq data using regression
mixture models
Description: A package for demultiplexing single-cell sequencing
experiments of pooled cells labeled with barcode
oligonucleotides. The package implements methods to fit
regression mixture models for a probabilistic classification of
cells, including multiplet detection. Demultiplexing error
rates can be estimated, and methods for quality control are
provided.
biocViews: SingleCell, Sequencing, Preprocessing, Classification,
Regression
Author: Hans-Ulrich Klein [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6382-9428>)
Maintainer: Hans-Ulrich Klein <hansulrich.klein@gmail.com>
URL: https://github.com/huklein/demuxmix
VignetteBuilder: knitr
BugReports: https://github.com/huklein/demuxmix/issues
git_url: https://git.bioconductor.org/packages/demuxmix
git_branch: devel
git_last_commit: 6169ab1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/demuxmix_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/demuxmix_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/demuxmix_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/demuxmix_1.9.0.tgz
vignettes: vignettes/demuxmix/inst/doc/demuxmix.html
vignetteTitles: Demultiplexing cells with demuxmix
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/demuxmix/inst/doc/demuxmix.R
importsMe: demuxSNP
dependencyCount: 36
Package: demuxSNP
Version: 1.5.0
Depends: R (>= 4.3.0), SingleCellExperiment, VariantAnnotation,
ensembldb
Imports: MatrixGenerics, BiocGenerics, class, GenomeInfoDb, IRanges,
Matrix, SummarizedExperiment, demuxmix, methods, KernelKnn,
dplyr
Suggests: knitr, rmarkdown, ComplexHeatmap, viridisLite, ggpubr,
dittoSeq, EnsDb.Hsapiens.v86, BiocStyle, RefManageR, testthat
(>= 3.0.0), Seurat
License: GPL-3
Archs: x64
MD5sum: 9f3c3723cc7f3284256bda2e68cf5f1c
NeedsCompilation: no
Title: scRNAseq demultiplexing using cell hashing and SNPs
Description: This package assists in demultiplexing scRNAseq data using
both cell hashing and SNPs data. The SNP profile of each group
os learned using high confidence assignments from the cell
hashing data. Cells which cannot be assigned with high
confidence from the cell hashing data are assigned to their
most similar group based on their SNPs. We also provide some
helper function to optimise SNP selection, create training data
and merge SNP data into the SingleCellExperiment framework.
biocViews: Classification, SingleCell
Author: Michael Lynch [aut, cre] (ORCID:
<https://orcid.org/0000-0001-9535-6461>), Aedin Culhane [aut]
(ORCID: <https://orcid.org/0000-0002-1395-9734>)
Maintainer: Michael Lynch <michael.lynch@ul.ie>
URL: https://github.com/michaelplynch/demuxSNP
VignetteBuilder: knitr
BugReports: https://github.com/michaelplynch/demuxSNP/issues
git_url: https://git.bioconductor.org/packages/demuxSNP
git_branch: devel
git_last_commit: 5ce455e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/demuxSNP_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/demuxSNP_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/demuxSNP_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/demuxSNP_1.5.0.tgz
vignettes: vignettes/demuxSNP/inst/doc/supervised_demultiplexing.html
vignetteTitles: Supervised Demultiplexing using Cell Hashing and SNPs
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/demuxSNP/inst/doc/supervised_demultiplexing.R
dependencyCount: 112
Package: densvis
Version: 1.17.0
Imports: Rcpp, basilisk, assertthat, reticulate, Rtsne, irlba
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, BiocStyle, ggplot2, uwot, testthat
License: MIT + file LICENSE
MD5sum: b377783448749a39b9503547d67058be
NeedsCompilation: yes
Title: Density-Preserving Data Visualization via Non-Linear
Dimensionality Reduction
Description: Implements the density-preserving modification to t-SNE
and UMAP described by Narayan et al. (2020)
<doi:10.1101/2020.05.12.077776>. The non-linear dimensionality
reduction techniques t-SNE and UMAP enable users to summarise
complex high-dimensional sequencing data such as single cell
RNAseq using lower dimensional representations. These lower
dimensional representations enable the visualisation of
discrete transcriptional states, as well as continuous
trajectory (for example, in early development). However, these
methods focus on the local neighbourhood structure of the data.
In some cases, this results in misleading visualisations, where
the density of cells in the low-dimensional embedding does not
represent the transcriptional heterogeneity of data in the
original high-dimensional space. den-SNE and densMAP aim to
enable more accurate visual interpretation of high-dimensional
datasets by producing lower-dimensional embeddings that
accurately represent the heterogeneity of the original
high-dimensional space, enabling the identification of
homogeneous and heterogeneous cell states. This accuracy is
accomplished by including in the optimisation process a term
which considers the local density of points in the original
high-dimensional space. This can help to create visualisations
that are more representative of heterogeneity in the original
high-dimensional space.
biocViews: DimensionReduction, Visualization, Software, SingleCell,
Sequencing
Author: Alan O'Callaghan [aut, cre], Ashwinn Narayan [aut], Hyunghoon
Cho [aut]
Maintainer: Alan O'Callaghan <alan.ocallaghan@outlook.com>
URL: https://bioconductor.org/packages/densvis
VignetteBuilder: knitr
BugReports: https://github.com/Alanocallaghan/densvis/issues
git_url: https://git.bioconductor.org/packages/densvis
git_branch: devel
git_last_commit: af1be7b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/densvis_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/densvis_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/densvis_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/densvis_1.17.0.tgz
vignettes: vignettes/densvis/inst/doc/densvis.html
vignetteTitles: Introduction to densvis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/densvis/inst/doc/densvis.R
suggestsMe: scater
dependencyCount: 27
Package: DEP
Version: 1.29.0
Depends: R (>= 3.5)
Imports: ggplot2, dplyr, purrr, readr, tibble, tidyr,
SummarizedExperiment (>= 1.11.5), MSnbase, limma, vsn, fdrtool,
ggrepel, ComplexHeatmap, RColorBrewer, circlize, shiny,
shinydashboard, DT, rmarkdown, assertthat, gridExtra, grid,
stats, imputeLCMD, cluster
Suggests: testthat, enrichR, knitr, BiocStyle
License: Artistic-2.0
MD5sum: 034d1a7a29c1259dd8ebf6245721a0ff
NeedsCompilation: no
Title: Differential Enrichment analysis of Proteomics data
Description: This package provides an integrated analysis workflow for
robust and reproducible analysis of mass spectrometry
proteomics data for differential protein expression or
differential enrichment. It requires tabular input (e.g. txt
files) as generated by quantitative analysis softwares of raw
mass spectrometry data, such as MaxQuant or IsobarQuant.
Functions are provided for data preparation, filtering,
variance normalization and imputation of missing values, as
well as statistical testing of differentially enriched /
expressed proteins. It also includes tools to check
intermediate steps in the workflow, such as normalization and
missing values imputation. Finally, visualization tools are
provided to explore the results, including heatmap, volcano
plot and barplot representations. For scientists with limited
experience in R, the package also contains wrapper functions
that entail the complete analysis workflow and generate a
report. Even easier to use are the interactive Shiny apps that
are provided by the package.
biocViews: ImmunoOncology, Proteomics, MassSpectrometry,
DifferentialExpression, DataRepresentation
Author: Arne Smits [cre, aut], Wolfgang Huber [aut]
Maintainer: Arne Smits <smits.arne@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DEP
git_branch: devel
git_last_commit: 44e537d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DEP_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DEP_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DEP_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DEP_1.29.0.tgz
vignettes: vignettes/DEP/inst/doc/DEP.html,
vignettes/DEP/inst/doc/MissingValues.html
vignetteTitles: DEP: Introduction, DEP: Missing value handling
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DEP/inst/doc/DEP.R,
vignettes/DEP/inst/doc/MissingValues.R
suggestsMe: proDA, RforProteomics
dependencyCount: 170
Package: DepecheR
Version: 1.23.0
Depends: R (>= 4.0)
Imports: ggplot2 (>= 3.1.0), MASS (>= 7.3.51), Rcpp (>= 1.0.0), dplyr
(>= 0.7.8), gplots (>= 3.0.1), viridis (>= 0.5.1), foreach (>=
1.4.4), doSNOW (>= 1.0.16), matrixStats (>= 0.54.0), mixOmics
(>= 6.6.1), moments (>= 0.14), grDevices (>= 3.5.2), graphics
(>= 3.5.2), stats (>= 3.5.2), utils (>= 3.5), methods (>= 3.5),
parallel (>= 3.5.2), reshape2 (>= 1.4.3), beanplot (>= 1.2),
FNN (>= 1.1.3), robustbase (>= 0.93.5), gmodels (>= 2.18.1),
collapse (>= 1.9.2), ClusterR (>= 1.3.2)
LinkingTo: Rcpp, RcppEigen
Suggests: uwot, testthat, knitr, rmarkdown, BiocStyle
License: MIT + file LICENSE
Archs: x64
MD5sum: d631e6d53a21f8deda581c7b425ce05f
NeedsCompilation: yes
Title: Determination of essential phenotypic elements of clusters in
high-dimensional entities
Description: The purpose of this package is to identify traits in a
dataset that can separate groups. This is done on two levels.
First, clustering is performed, using an implementation of
sparse K-means. Secondly, the generated clusters are used to
predict outcomes of groups of individuals based on their
distribution of observations in the different clusters. As
certain clusters with separating information will be
identified, and these clusters are defined by a sparse number
of variables, this method can reduce the complexity of data, to
only emphasize the data that actually matters.
biocViews:
Software,CellBasedAssays,Transcription,DifferentialExpression,
DataRepresentation,ImmunoOncology,Transcriptomics,Classification,Clustering,
DimensionReduction,FeatureExtraction,FlowCytometry,RNASeq,SingleCell,
Visualization
Author: Jakob Theorell [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8752-3151>), Axel Theorell [aut]
Maintainer: Jakob Theorell <jakob.theorell@ki.se>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DepecheR
git_branch: devel
git_last_commit: 68cbb01
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DepecheR_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DepecheR_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DepecheR_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DepecheR_1.23.0.tgz
vignettes: vignettes/DepecheR/inst/doc/DepecheR_test.html,
vignettes/DepecheR/inst/doc/GroupProbPlot_usage.html
vignetteTitles: Example of a cytometry data analysis with DepecheR,
Using the groupProbPlot plot function for single-cell
probability display
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/DepecheR/inst/doc/DepecheR_test.R,
vignettes/DepecheR/inst/doc/GroupProbPlot_usage.R
suggestsMe: flowSpecs
dependencyCount: 111
Package: DepInfeR
Version: 1.11.0
Depends: R (>= 4.2.0)
Imports: matrixStats, glmnet, stats, BiocParallel
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, dplyr, tidyr, tibble,
ggplot2, missForest, pheatmap, RColorBrewer, ggrepel,
BiocStyle, ggbeeswarm
License: GPL-3
MD5sum: 77423bf73f100a9bcfa5ac7085169be5
NeedsCompilation: no
Title: Inferring tumor-specific cancer dependencies through integrating
ex-vivo drug response assays and drug-protein profiling
Description: DepInfeR integrates two experimentally accessible input
data matrices: the drug sensitivity profiles of cancer cell
lines or primary tumors ex-vivo (X), and the drug affinities of
a set of proteins (Y), to infer a matrix of molecular protein
dependencies of the cancers (ß). DepInfeR deconvolutes the
protein inhibition effect on the viability phenotype by using
regularized multivariate linear regression. It assigns a
“dependence coefficient†to each protein and each sample, and
therefore could be used to gain a causal and accurate
understanding of functional consequences of genomic aberrations
in a heterogeneous disease, as well as to guide the choice of
pharmacological intervention for a specific cancer type,
sub-type, or an individual patient. For more information,
please read out preprint on bioRxiv:
https://doi.org/10.1101/2022.01.11.475864.
biocViews: Software, Regression, Pharmacogenetics, Pharmacogenomics,
FunctionalGenomics
Author: Junyan Lu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-9211-0746>), Alina Batzilla [aut]
Maintainer: Junyan Lu <jylu1118@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/Huber-group-EMBL/DepInfeR/issues
git_url: https://git.bioconductor.org/packages/DepInfeR
git_branch: devel
git_last_commit: 903c5e9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DepInfeR_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DepInfeR_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DepInfeR_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DepInfeR_1.11.0.tgz
vignettes: vignettes/DepInfeR/inst/doc/vignette.html
vignetteTitles: DepInfeR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DepInfeR/inst/doc/vignette.R
dependencyCount: 27
Package: DEqMS
Version: 1.25.0
Depends: R(>= 3.5),graphics,stats,ggplot2,matrixStats,limma(>= 3.34)
Suggests:
BiocStyle,knitr,rmarkdown,markdown,plyr,reshape2,utils,ggrepel,ExperimentHub,LSD
License: LGPL
Archs: x64
MD5sum: 10ce878650a4980d9f0c2d86d60f34cf
NeedsCompilation: no
Title: a tool to perform statistical analysis of differential protein
expression for quantitative proteomics data.
Description: DEqMS is developped on top of Limma. However, Limma
assumes same prior variance for all genes. In proteomics, the
accuracy of protein abundance estimates varies by the number of
peptides/PSMs quantified in both label-free and labelled data.
Proteins quantification by multiple peptides or PSMs are more
accurate. DEqMS package is able to estimate different prior
variances for proteins quantified by different number of
PSMs/peptides, therefore acchieving better accuracy. The
package can be applied to analyze both label-free and labelled
proteomics data.
biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Preprocessing,
DifferentialExpression,
MultipleComparison,Normalization,Bayesian,ExperimentHubSoftware
Author: Yafeng Zhu
Maintainer: Yafeng Zhu <yafeng.zhu@outlook.com>
VignetteBuilder: knitr
BugReports: https://github.com/yafeng/DEqMS/issues
git_url: https://git.bioconductor.org/packages/DEqMS
git_branch: devel
git_last_commit: bdb1c60
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DEqMS_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DEqMS_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DEqMS_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DEqMS_1.25.0.tgz
vignettes: vignettes/DEqMS/inst/doc/DEqMS-package-vignette.html
vignetteTitles: DEqMS R Markdown vignettes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DEqMS/inst/doc/DEqMS-package-vignette.R
importsMe: PRONE
dependencyCount: 38
Package: derfinder
Version: 1.41.0
Depends: R (>= 3.5.0)
Imports: BiocGenerics (>= 0.25.1), AnnotationDbi (>= 1.27.9),
BiocParallel (>= 1.15.15), bumphunter (>= 1.9.2),
derfinderHelper (>= 1.1.0), GenomeInfoDb (>= 1.3.3),
GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges
(>= 1.17.40), Hmisc, IRanges (>= 2.3.23), methods, qvalue (>=
1.99.0), Rsamtools (>= 1.25.0), rtracklayer, S4Vectors (>=
0.23.19), stats, utils
Suggests: BiocStyle (>= 2.5.19), sessioninfo, derfinderData (>=
0.99.0), derfinderPlot, DESeq2, ggplot2, knitr (>= 1.6), limma,
RefManageR, rmarkdown (>= 0.3.3), testthat (>= 2.1.0),
TxDb.Hsapiens.UCSC.hg19.knownGene, covr
License: Artistic-2.0
MD5sum: ae6046cca4147a0c7695b4ee55312cbf
NeedsCompilation: no
Title: Annotation-agnostic differential expression analysis of RNA-seq
data at base-pair resolution via the DER Finder approach
Description: This package provides functions for annotation-agnostic
differential expression analysis of RNA-seq data. Two
implementations of the DER Finder approach are included in this
package: (1) single base-level F-statistics and (2) DER
identification at the expressed regions-level. The DER Finder
approach can also be used to identify differentially bounded
ChIP-seq peaks.
biocViews: DifferentialExpression, Sequencing, RNASeq, ChIPSeq,
DifferentialPeakCalling, Software, ImmunoOncology, Coverage
Author: Leonardo Collado-Torres [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2140-308X>), Alyssa C. Frazee
[ctb], Andrew E. Jaffe [aut] (ORCID:
<https://orcid.org/0000-0001-6886-1454>), Jeffrey T. Leek [aut,
ths] (ORCID: <https://orcid.org/0000-0002-2873-2671>)
Maintainer: Leonardo Collado-Torres <lcolladotor@gmail.com>
URL: https://github.com/lcolladotor/derfinder
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/derfinder/
git_url: https://git.bioconductor.org/packages/derfinder
git_branch: devel
git_last_commit: 5234647
git_last_commit_date: 2024-12-13
Date/Publication: 2024-12-13
source.ver: src/contrib/derfinder_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/derfinder_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/derfinder_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/derfinder_1.41.0.tgz
vignettes: vignettes/derfinder/inst/doc/derfinder-quickstart.html,
vignettes/derfinder/inst/doc/derfinder-users-guide.html
vignetteTitles: derfinder quick start guide, derfinder users guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/derfinder/inst/doc/derfinder-quickstart.R,
vignettes/derfinder/inst/doc/derfinder-users-guide.R
importsMe: derfinderPlot, recount, regionReport, GenomicState,
recountWorkflow
suggestsMe: megadepth
dependencyCount: 144
Package: derfinderHelper
Version: 1.41.0
Depends: R(>= 3.2.2)
Imports: IRanges (>= 1.99.27), Matrix, methods, S4Vectors (>= 0.2.2)
Suggests: sessioninfo, knitr (>= 1.6), BiocStyle (>= 2.5.19),
RefManageR, rmarkdown (>= 0.3.3), testthat, covr
License: Artistic-2.0
MD5sum: 1373aa17454dfa24799b599d1b3704e8
NeedsCompilation: no
Title: derfinder helper package
Description: Helper package for speeding up the derfinder package when
using multiple cores. This package is particularly useful when
using BiocParallel and it helps reduce the time spent loading
the full derfinder package when running the F-statistics
calculation in parallel.
biocViews: DifferentialExpression, Sequencing, RNASeq, Software,
ImmunoOncology
Author: Leonardo Collado-Torres [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2140-308X>), Andrew E. Jaffe [aut]
(ORCID: <https://orcid.org/0000-0001-6886-1454>), Jeffrey T.
Leek [aut, ths] (ORCID:
<https://orcid.org/0000-0002-2873-2671>)
Maintainer: Leonardo Collado-Torres <lcolladotor@gmail.com>
URL: https://github.com/leekgroup/derfinderHelper
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/derfinderHelper
git_url: https://git.bioconductor.org/packages/derfinderHelper
git_branch: devel
git_last_commit: 4dbfb34
git_last_commit_date: 2024-12-12
Date/Publication: 2024-12-13
source.ver: src/contrib/derfinderHelper_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/derfinderHelper_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/derfinderHelper_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/derfinderHelper_1.41.0.tgz
vignettes: vignettes/derfinderHelper/inst/doc/derfinderHelper.html
vignetteTitles: Introduction to derfinderHelper
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/derfinderHelper/inst/doc/derfinderHelper.R
importsMe: derfinder
dependencyCount: 13
Package: derfinderPlot
Version: 1.41.0
Depends: R(>= 3.2)
Imports: derfinder (>= 1.1.0), GenomeInfoDb (>= 1.3.3),
GenomicFeatures, GenomicRanges (>= 1.17.40), ggbio (>=
1.13.13), ggplot2, graphics, grDevices, IRanges (>= 1.99.28),
limma, methods, plyr, RColorBrewer, reshape2, S4Vectors (>=
0.9.38), scales, utils
Suggests: biovizBase (>= 1.27.2), bumphunter (>= 1.7.6), derfinderData
(>= 0.99.0), sessioninfo, knitr (>= 1.6), BiocStyle (>=
2.5.19), org.Hs.eg.db, RefManageR, rmarkdown (>= 0.3.3),
testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, covr
License: Artistic-2.0
MD5sum: f565592709ac0744a07ccb059f820f86
NeedsCompilation: no
Title: Plotting functions for derfinder
Description: This package provides plotting functions for results from
the derfinder package. This helps separate the graphical
dependencies required for making these plots from the core
functionality of derfinder.
biocViews: DifferentialExpression, Sequencing, RNASeq, Software,
Visualization, ImmunoOncology
Author: Leonardo Collado-Torres [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2140-308X>), Andrew E. Jaffe [aut]
(ORCID: <https://orcid.org/0000-0001-6886-1454>), Jeffrey T.
Leek [aut, ths] (ORCID:
<https://orcid.org/0000-0002-2873-2671>)
Maintainer: Leonardo Collado-Torres <lcolladotor@gmail.com>
URL: https://github.com/leekgroup/derfinderPlot
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/derfinderPlot
git_url: https://git.bioconductor.org/packages/derfinderPlot
git_branch: devel
git_last_commit: b8a1580
git_last_commit_date: 2024-12-12
Date/Publication: 2024-12-13
source.ver: src/contrib/derfinderPlot_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/derfinderPlot_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/derfinderPlot_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/derfinderPlot_1.41.0.tgz
vignettes: vignettes/derfinderPlot/inst/doc/derfinderPlot.html
vignetteTitles: Introduction to derfinderPlot
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/derfinderPlot/inst/doc/derfinderPlot.R
importsMe: recountWorkflow
suggestsMe: derfinder, regionReport, GenomicState
dependencyCount: 174
Package: DEScan2
Version: 1.27.0
Depends: R (>= 3.5), GenomicRanges
Imports: BiocParallel, BiocGenerics, ChIPpeakAnno, data.table,
DelayedArray, GenomeInfoDb, GenomicAlignments, glue, IRanges,
plyr, Rcpp (>= 0.12.13), rtracklayer, S4Vectors (>= 0.23.19),
SummarizedExperiment, tools, utils
LinkingTo: Rcpp, RcppArmadillo
Suggests: BiocStyle, knitr, rmarkdown, testthat, edgeR, limma, EDASeq,
RUVSeq, RColorBrewer, statmod
License: Artistic-2.0
MD5sum: abac8e353578145f5909d40f4fdad9ca
NeedsCompilation: yes
Title: Differential Enrichment Scan 2
Description: Integrated peak and differential caller, specifically
designed for broad epigenomic signals.
biocViews: ImmunoOncology, PeakDetection, Epigenetics, Software,
Sequencing, Coverage
Author: Dario Righelli [aut, cre], John Koberstein [aut], Bruce Gomes
[aut], Nancy Zhang [aut], Claudia Angelini [aut], Lucia Peixoto
[aut], Davide Risso [aut]
Maintainer: Dario Righelli <dario.righelli@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DEScan2
git_branch: devel
git_last_commit: b34d6d0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DEScan2_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DEScan2_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DEScan2_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DEScan2_1.27.0.tgz
vignettes: vignettes/DEScan2/inst/doc/DEScan2.html
vignetteTitles: DEScan2 Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DEScan2/inst/doc/DEScan2.R
dependencyCount: 134
Package: DESeq2
Version: 1.47.5
Depends: S4Vectors (>= 0.23.18), IRanges, GenomicRanges,
SummarizedExperiment (>= 1.1.6)
Imports: BiocGenerics (>= 0.7.5), Biobase, BiocParallel, matrixStats,
methods, stats4, locfit, ggplot2 (>= 3.4.0), Rcpp (>= 0.11.0),
MatrixGenerics
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, knitr, rmarkdown, vsn, pheatmap, RColorBrewer,
apeglm, ashr, tximport, tximeta, tximportData, readr, pbapply,
airway, glmGamPoi, BiocManager
License: LGPL (>= 3)
MD5sum: 6788aa6f015bf7dc97a881a9292da299
NeedsCompilation: yes
Title: Differential gene expression analysis based on the negative
binomial distribution
Description: Estimate variance-mean dependence in count data from
high-throughput sequencing assays and test for differential
expression based on a model using the negative binomial
distribution.
biocViews: Sequencing, RNASeq, ChIPSeq, GeneExpression, Transcription,
Normalization, DifferentialExpression, Bayesian, Regression,
PrincipalComponent, Clustering, ImmunoOncology
Author: Michael Love [aut, cre], Constantin Ahlmann-Eltze [ctb], Kwame
Forbes [ctb], Simon Anders [aut, ctb], Wolfgang Huber [aut,
ctb], RADIANT EU FP7 [fnd], NIH NHGRI [fnd], CZI [fnd]
Maintainer: Michael Love <michaelisaiahlove@gmail.com>
URL: https://github.com/thelovelab/DESeq2
VignetteBuilder: knitr, rmarkdown
git_url: https://git.bioconductor.org/packages/DESeq2
git_branch: devel
git_last_commit: f22b9d2
git_last_commit_date: 2025-03-05
Date/Publication: 2025-03-05
source.ver: src/contrib/DESeq2_1.47.5.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DESeq2_1.47.5.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DESeq2_1.47.5.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DESeq2_1.47.5.tgz
vignettes: vignettes/DESeq2/inst/doc/DESeq2.html
vignetteTitles: Analyzing RNA-seq data with DESeq2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DESeq2/inst/doc/DESeq2.R
dependsOnMe: DEWSeq, DEXSeq, metaseqR2, octad, rgsepd, SeqGSEA, TCC,
tRanslatome, rnaseqDTU, rnaseqGene, Anaconda, DRomics,
ordinalbayes
importsMe: Anaquin, animalcules, APAlyzer, BatchQC, benchdamic,
broadSeq, CeTF, circRNAprofiler, CleanUpRNAseq, consensusDE,
coseq, countsimQC, cypress, DaMiRseq, debrowser, DEFormats,
DEGreport, DELocal, deltaCaptureC, DEsubs, DiffBind, easier,
EBSEA, ERSSA, GDCRNATools, GeneTonic, gg4way, Glimma, GRaNIE,
hermes, HTSFilter, HybridExpress, icetea, ideal, INSPEcT,
IntEREst, iSEEde, isomiRs, kissDE, magpie, microbiomeExplorer,
MIRit, MLSeq, mobileRNA, mosdef, MultiRNAflow, muscat, NBAMSeq,
NetActivity, ORFik, OUTRIDER, pairedGSEA, PathoStat,
pcaExplorer, phantasus, POMA, proActiv, RegEnrich,
regionReport, ReportingTools, RiboDiPA, Rmmquant, saseR, scBFA,
scGPS, scQTLtools, SEtools, singleCellTK, SNPhood, srnadiff,
SurfR, systemPipeTools, TBSignatureProfiler, TEKRABber,
terapadog, UMI4Cats, vidger, vulcan, zitools,
BloodCancerMultiOmics2017, FieldEffectCrc,
homosapienDEE2CellScore, IHWpaper, ExpHunterSuite,
recountWorkflow, autoGO, bulkAnalyseR, cinaR, ExpGenetic,
HeritSeq, HEssRNA, limorhyde2, microbial, RCPA, RNAseqQC,
sRNAGenetic, TransProR, wilson
suggestsMe: aggregateBioVar, apeglm, bambu, BindingSiteFinder,
biobroom, BiocGenerics, BioCor, BiocSet, BioNERO, CAGEr,
compcodeR, dar, dearseq, derfinder, dittoSeq, EDASeq,
EnhancedVolcano, EnrichmentBrowser, EWCE, extraChIPs, fishpond,
gage, GeDi, GenomicAlignments, GenomicRanges, GeoTcgaData,
geyser, glmGamPoi, HiCDCPlus, IHW, InteractiveComplexHeatmap,
methodical, OPWeight, pathlinkR, PCAtools, phyloseq, progeny,
raer, recount, ribosomeProfilingQC, roastgsa, RUVSeq, Rvisdiff,
scran, sparrow, spatialHeatmap, SpliceWiz, subSeq, systemPipeR,
systemPipeShiny, TFEA.ChIP, tidybulk, topconfects, tximeta,
tximport, variancePartition, Wrench, zinbwave,
curatedAdipoChIP, curatedAdipoRNA, GSE62944, RegParallel,
Single.mTEC.Transcriptomes, CAGEWorkflow, fluentGenomics, bakR,
cellpypes, conos, FateID, GiANT, glmmSeq, grandR, lfc, LorMe,
metacoder, metaRNASeq, MiscMetabar, pctax, pmartR, RaceID,
rliger, SCdeconR, seqgendiff, Seurat, SeuratExplorer, SQMtools,
volcano3D
dependencyCount: 75
Package: DEsingle
Version: 1.27.0
Depends: R (>= 3.4.0)
Imports: stats, Matrix (>= 1.2-14), MASS (>= 7.3-45), VGAM (>= 1.0-2),
bbmle (>= 1.0.18), gamlss (>= 4.4-0), maxLik (>= 1.3-4), pscl
(>= 1.4.9), BiocParallel (>= 1.12.0),
Suggests: knitr, rmarkdown, SingleCellExperiment
License: GPL-2
Archs: x64
MD5sum: 814f7f28c08dc3ef74ca533d4f359bd1
NeedsCompilation: no
Title: DEsingle for detecting three types of differential expression in
single-cell RNA-seq data
Description: DEsingle is an R package for differential expression (DE)
analysis of single-cell RNA-seq (scRNA-seq) data. It defines
and detects 3 types of differentially expressed genes between
two groups of single cells, with regard to different expression
status (DEs), differential expression abundance (DEa), and
general differential expression (DEg). DEsingle employs
Zero-Inflated Negative Binomial model to estimate the
proportion of real and dropout zeros and to define and detect
the 3 types of DE genes. Results showed that DEsingle
outperforms existing methods for scRNA-seq DE analysis, and can
reveal different types of DE genes that are enriched in
different biological functions.
biocViews: DifferentialExpression, GeneExpression, SingleCell,
ImmunoOncology, RNASeq, Transcriptomics, Sequencing,
Preprocessing, Software
Author: Zhun Miao <miaoz13@tsinghua.org.cn>
Maintainer: Zhun Miao <miaoz13@tsinghua.org.cn>
URL: https://miaozhun.github.io/DEsingle/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DEsingle
git_branch: devel
git_last_commit: 56ad390
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DEsingle_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DEsingle_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DEsingle_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DEsingle_1.27.0.tgz
vignettes: vignettes/DEsingle/inst/doc/DEsingle.html
vignetteTitles: DEsingle
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DEsingle/inst/doc/DEsingle.R
dependencyCount: 39
Package: DESpace
Version: 1.99.2
Depends: R (>= 4.5.0)
Imports: edgeR, limma, dplyr, stats, Matrix, SpatialExperiment,
ggplot2, SummarizedExperiment, S4Vectors, BiocGenerics,
data.table, assertthat, terra, sf, spatstat.explore,
spatstat.geom, ggforce, ggnewscale, patchwork, BiocParallel,
methods, scales, scuttle
Suggests: knitr, rmarkdown, testthat, BiocStyle, muSpaData,
ExperimentHub, spatialLIBD, purrr, reshape2, tidyverse,
concaveman
License: GPL-3
MD5sum: 0827ad3bc31f99bbe1bbd92f37f05ae6
NeedsCompilation: no
Title: DESpace: a framework to discover spatially variable genes and
differential spatial patterns across conditions
Description: Intuitive framework for identifying spatially variable
genes (SVGs) and differential spatial variable pattern (DSP)
between conditions via edgeR, a popular method for performing
differential expression analyses. Based on pre-annotated
spatial clusters as summarized spatial information, DESpace
models gene expression using a negative binomial (NB), via
edgeR, with spatial clusters as covariates. SVGs are then
identified by testing the significance of spatial clusters. For
multi-sample, multi-condition datasets, we again fit a NB model
via edgeR, incorporating spatial clusters, conditions and their
interactions as covariates. DSP genes-representing differences
in spatial gene expression patterns across experimental
conditions-are identified by testing the interaction between
spatial clusters and conditions.
biocViews: Spatial, SingleCell, RNASeq, Transcriptomics,
GeneExpression, Sequencing,
DifferentialExpression,StatisticalMethod, Visualization
Author: Peiying Cai [aut, cre] (ORCID:
<https://orcid.org/0009-0001-9229-2244>), Simone Tiberi [aut]
(ORCID: <https://orcid.org/0000-0002-3054-9964>)
Maintainer: Peiying Cai <peiying.cai@uzh.ch>
URL: https://github.com/peicai/DESpace,
https://peicai.github.io/DESpace/
VignetteBuilder: knitr
BugReports: https://github.com/peicai/DESpace/issues
git_url: https://git.bioconductor.org/packages/DESpace
git_branch: devel
git_last_commit: 3ec00b4
git_last_commit_date: 2025-03-28
Date/Publication: 2025-03-28
source.ver: src/contrib/DESpace_1.99.2.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DESpace_1.99.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DESpace_1.99.2.tgz
vignettes: vignettes/DESpace/inst/doc/DSP.html,
vignettes/DESpace/inst/doc/SVG.html
vignetteTitles: Differential Spatial Pattern between conditions, A
framework to discover spatially variable genes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DESpace/inst/doc/DSP.R,
vignettes/DESpace/inst/doc/SVG.R
dependencyCount: 254
Package: destiny
Version: 3.21.0
Depends: R (>= 3.4.0)
Imports: methods, graphics, grDevices, grid, utils, stats, Matrix, Rcpp
(>= 0.10.3), RcppEigen, RSpectra (>= 0.14-0), irlba,
pcaMethods, Biobase, BiocGenerics, SummarizedExperiment,
SingleCellExperiment, ggplot2, ggplot.multistats, rlang, tidyr,
tidyselect, ggthemes, VIM, knn.covertree, proxy, RcppHNSW,
smoother, scales, scatterplot3d
LinkingTo: Rcpp, RcppEigen, grDevices
Suggests: knitr, rmarkdown, igraph, testthat, FNN, tidyverse,
gridExtra, cowplot, conflicted, viridis, rgl, scRNAseq,
org.Mm.eg.db, scran, repr
Enhances: rgl, SingleCellExperiment
License: GPL-3
Archs: x64
MD5sum: a2a7e04846f826461005fca7b86ba56b
NeedsCompilation: yes
Title: Creates diffusion maps
Description: Create and plot diffusion maps.
biocViews: CellBiology, CellBasedAssays, Clustering, Software,
Visualization
Author: Philipp Angerer [cre, aut] (ORCID:
<https://orcid.org/0000-0002-0369-2888>), Laleh Haghverdi
[ctb], Maren Büttner [ctb] (ORCID:
<https://orcid.org/0000-0002-6189-3792>), Fabian Theis [ctb]
(ORCID: <https://orcid.org/0000-0002-2419-1943>), Carsten Marr
[ctb] (ORCID: <https://orcid.org/0000-0003-2154-4552>), Florian
Büttner [ctb] (ORCID: <https://orcid.org/0000-0001-5587-6761>)
Maintainer: Philipp Angerer <phil.angerer@gmail.com>
URL: https://theislab.github.io/destiny/,
https://github.com/theislab/destiny/,
https://www.helmholtz-muenchen.de/icb/destiny,
https://bioconductor.org/packages/destiny,
https://doi.org/10.1093/bioinformatics/btv715
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/theislab/destiny/issues
git_url: https://git.bioconductor.org/packages/destiny
git_branch: devel
git_last_commit: 339d0ba
git_last_commit_date: 2024-11-15
Date/Publication: 2024-11-15
source.ver: src/contrib/destiny_3.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/destiny_3.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/destiny_3.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/destiny_3.21.0.tgz
vignettes: vignettes/destiny/inst/doc/Diffusion-Map-recap.html,
vignettes/destiny/inst/doc/Diffusion-Maps.html,
vignettes/destiny/inst/doc/DPT.html,
vignettes/destiny/inst/doc/Gene-Relevance.html,
vignettes/destiny/inst/doc/Global-Sigma.html,
vignettes/destiny/inst/doc/tidyverse.html
vignetteTitles: Reproduce the Diffusion Map vignette with the supplied
data(), destiny main vignette: Start here!, destiny 2.0 brought
the Diffusion Pseudo Time (DPT) class, detecting relevant genes
with destiny 3, The effects of a global vs. local kernel,
tidyverse and ggplot integration with destiny
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/destiny/inst/doc/Diffusion-Map-recap.R,
vignettes/destiny/inst/doc/Diffusion-Maps.R,
vignettes/destiny/inst/doc/DPT.R,
vignettes/destiny/inst/doc/Gene-Relevance.R,
vignettes/destiny/inst/doc/Global-Sigma.R,
vignettes/destiny/inst/doc/tidyverse.R
importsMe: dandelionR
suggestsMe: CelliD, CellTrails, monocle
dependencyCount: 122
Package: DEsubs
Version: 1.33.0
Depends: R (>= 3.3), locfit
Imports: graph, igraph, RBGL, circlize, limma, edgeR, EBSeq, NBPSeq,
stats, grDevices, graphics, pheatmap, utils, ggplot2, Matrix,
jsonlite, tools, DESeq2, methods
Suggests: RUnit, BiocGenerics, knitr, rmarkdown
License: GPL-3
MD5sum: 7482de722eeb342c9260cc190dacba9b
NeedsCompilation: no
Title: DEsubs: an R package for flexible identification of
differentially expressed subpathways using RNA-seq expression
experiments
Description: DEsubs is a network-based systems biology package that
extracts disease-perturbed subpathways within a pathway network
as recorded by RNA-seq experiments. It contains an extensive
and customizable framework covering a broad range of operation
modes at all stages of the subpathway analysis, enabling a
case-specific approach. The operation modes refer to the
pathway network construction and processing, the subpathway
extraction, visualization and enrichment analysis with regard
to various biological and pharmacological features. Its
capabilities render it a tool-guide for both the modeler and
experimentalist for the identification of more robust
systems-level biomarkers for complex diseases.
biocViews: SystemsBiology, GraphAndNetwork, Pathways, KEGG,
GeneExpression, NetworkEnrichment, Network, RNASeq,
DifferentialExpression, Normalization, ImmunoOncology
Author: Aristidis G. Vrahatis and Panos Balomenos
Maintainer: Aristidis G. Vrahatis <agvrahatis@upatras.gr>, Panos
Balomenos <balomenos@upatras.gr>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DEsubs
git_branch: devel
git_last_commit: e463abc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DEsubs_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DEsubs_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DEsubs_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DEsubs_1.33.0.tgz
vignettes: vignettes/DEsubs/inst/doc/DEsubs.pdf
vignetteTitles: DEsubs
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DEsubs/inst/doc/DEsubs.R
dependencyCount: 115
Package: DEWSeq
Version: 1.21.0
Depends: R(>= 4.0.0), R.utils, DESeq2, BiocParallel
Imports: BiocGenerics, data.table(>= 1.11.8), GenomeInfoDb,
GenomicRanges, methods, S4Vectors, SummarizedExperiment, stats,
utils
Suggests: knitr, tidyverse, rmarkdown, testthat, BiocStyle, IHW
License: LGPL (>= 3)
Archs: x64
MD5sum: d2871376b909562c5dfbbde475c44038
NeedsCompilation: no
Title: Differential Expressed Windows Based on Negative Binomial
Distribution
Description: DEWSeq is a sliding window approach for the analysis of
differentially enriched binding regions eCLIP or iCLIP next
generation sequencing data.
biocViews: Sequencing, GeneRegulation, FunctionalGenomics,
DifferentialExpression
Author: Sudeep Sahadevan [aut], Thomas Schwarzl [aut], bioinformatics
team Hentze [aut, cre]
Maintainer: bioinformatics team Hentze <biohentze@embl.de>
URL: https://github.com/EMBL-Hentze-group/DEWSeq/
VignetteBuilder: knitr
BugReports: https://github.com/EMBL-Hentze-group/DEWSeq/issues
git_url: https://git.bioconductor.org/packages/DEWSeq
git_branch: devel
git_last_commit: 93a7801
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DEWSeq_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DEWSeq_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DEWSeq_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DEWSeq_1.21.0.tgz
vignettes: vignettes/DEWSeq/inst/doc/DEWSeq.html
vignetteTitles: Analyzing eCLIP/iCLIP data with DEWSeq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DEWSeq/inst/doc/DEWSeq.R
dependencyCount: 80
Package: DExMA
Version: 1.15.0
Depends: R (>= 4.1), DExMAdata
Imports: Biobase, GEOquery, impute, limma, pheatmap, plyr, scales,
snpStats, sva, swamp, stats, methods, utils, bnstruct,
RColorBrewer, grDevices
Suggests: BiocStyle, qpdf, BiocGenerics, RUnit
License: GPL-2
MD5sum: f2fba0b54ad74ec858b2416c0e2a9d8a
NeedsCompilation: no
Title: Differential Expression Meta-Analysis
Description: performing all the steps of gene expression meta-analysis
considering the possible existence of missing genes. It
provides the necessary functions to be able to perform the
different methods of gene expression meta-analysis. In
addition, it contains functions to apply quality controls,
download GEO datasets and show graphical representations of the
results.
biocViews: DifferentialExpression, GeneExpression, StatisticalMethod,
QualityControl
Author: Juan Antonio Villatoro-GarcÃa [aut, cre], Pedro Carmona-Sáez
[aut]
Maintainer: Juan Antonio Villatoro-GarcÃa
<juanantoniovillatorogarcia@gmail.com>
git_url: https://git.bioconductor.org/packages/DExMA
git_branch: devel
git_last_commit: 655549d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DExMA_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DExMA_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DExMA_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DExMA_1.15.0.tgz
vignettes: vignettes/DExMA/inst/doc/DExMA.pdf
vignetteTitles: Differential Expression Meta-Analysis with DExMA
package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DExMA/inst/doc/DExMA.R
dependencyCount: 133
Package: DEXSeq
Version: 1.53.1
Depends: BiocParallel, Biobase, SummarizedExperiment, IRanges (>=
2.5.17), GenomicRanges (>= 1.23.7), DESeq2 (>= 1.39.6),
AnnotationDbi, RColorBrewer, S4Vectors (>= 0.23.18)
Imports: BiocGenerics, biomaRt, hwriter, methods, stringr, Rsamtools,
statmod, geneplotter, genefilter
Suggests: GenomicFeatures, txdbmaker, pasilla (>= 0.2.22), BiocStyle,
knitr, rmarkdown, testthat, pasillaBamSubset,
GenomicAlignments, roxygen2, glmGamPoi
License: GPL (>= 3)
MD5sum: f0430bed9f13e66716a31f17d4f9502c
NeedsCompilation: no
Title: Inference of differential exon usage in RNA-Seq
Description: The package is focused on finding differential exon usage
using RNA-seq exon counts between samples with different
experimental designs. It provides functions that allows the
user to make the necessary statistical tests based on a model
that uses the negative binomial distribution to estimate the
variance between biological replicates and generalized linear
models for testing. The package also provides functions for the
visualization and exploration of the results.
biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression,
AlternativeSplicing, DifferentialSplicing, GeneExpression,
Visualization
Author: Simon Anders <sanders@fs.tum.de> and Alejandro Reyes
<alejandro.reyes.ds@gmail.com>
Maintainer: Alejandro Reyes <alejandro.reyes.ds@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DEXSeq
git_branch: devel
git_last_commit: 7e80cb2
git_last_commit_date: 2025-02-28
Date/Publication: 2025-03-06
source.ver: src/contrib/DEXSeq_1.53.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DEXSeq_1.53.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DEXSeq_1.53.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DEXSeq_1.53.1.tgz
vignettes: vignettes/DEXSeq/inst/doc/DEXSeq.html
vignetteTitles: Inferring differential exon usage in RNA-Seq data with
the DEXSeq package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DEXSeq/inst/doc/DEXSeq.R
dependsOnMe: IsoformSwitchAnalyzeR, pasilla, rnaseqDTU
importsMe: diffUTR, IntEREst, pairedGSEA, saseR
suggestsMe: bambu, GenomicRanges, satuRn, stageR, subSeq, BioPlex
dependencyCount: 117
Package: DFP
Version: 1.65.0
Depends: methods, Biobase (>= 2.5.5)
License: GPL-2
MD5sum: 43ae91652af37f4fccf82df9d3083404
NeedsCompilation: no
Title: Gene Selection
Description: This package provides a supervised technique able to
identify differentially expressed genes, based on the
construction of \emph{Fuzzy Patterns} (FPs). The Fuzzy Patterns
are built by means of applying 3 Membership Functions to
discretized gene expression values.
biocViews: Microarray, DifferentialExpression
Author: R. Alvarez-Gonzalez, D. Glez-Pena, F. Diaz, F. Fdez-Riverola
Maintainer: Rodrigo Alvarez-Glez <rodrigo.djv@uvigo.es>
git_url: https://git.bioconductor.org/packages/DFP
git_branch: devel
git_last_commit: c67c641
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DFP_1.65.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DFP_1.65.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DFP_1.65.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DFP_1.65.0.tgz
vignettes: vignettes/DFP/inst/doc/DFP.pdf
vignetteTitles: Howto: Discriminat Fuzzy Pattern
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DFP/inst/doc/DFP.R
dependencyCount: 7
Package: DFplyr
Version: 1.1.0
Depends: dplyr
Imports: BiocGenerics, methods, rlang, S4Vectors, tidyselect
Suggests: BiocStyle, GenomeInfoDb, GenomicRanges, IRanges, knitr,
rmarkdown, sessioninfo, testthat (>= 3.0.0), tibble
License: GPL-3
MD5sum: 1ef95960b26acdafe17983cf70bbde94
NeedsCompilation: no
Title: A `DataFrame` (`S4Vectors`) backend for `dplyr`
Description: Provides `dplyr` verbs (`mutate`, `select`, `filter`,
etc...) supporting `S4Vectors::DataFrame` objects. Importantly,
this is achieved without conversion to an intermediate
`tibble`. Adds grouping infrastructure to `DataFrame` which is
respected by the transformation verbs.
biocViews: DataRepresentation, Infrastructure, Software
Author: Jonathan Carroll [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1404-5264>)
Maintainer: Jonathan Carroll <rpkg@jcarroll.com.au>
URL: https://github.com/jonocarroll/DFplyr
VignetteBuilder: knitr
BugReports: https://github.com/jonocarroll/DFplyr/issues
git_url: https://git.bioconductor.org/packages/DFplyr
git_branch: devel
git_last_commit: c81f630
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DFplyr_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DFplyr_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DFplyr_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DFplyr_1.1.0.tgz
vignettes: vignettes/DFplyr/inst/doc/example_usage.html
vignetteTitles: Example Usage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DFplyr/inst/doc/example_usage.R
dependencyCount: 24
Package: DiffBind
Version: 3.17.3
Depends: R (>= 4.0), GenomicRanges, SummarizedExperiment
Imports: RColorBrewer, amap, gplots, grDevices, limma,
GenomicAlignments, locfit, stats, utils, IRanges, lattice,
systemPipeR, tools, Rcpp, dplyr, ggplot2, BiocParallel,
parallel, S4Vectors, Rsamtools (>= 2.13.1), DESeq2, methods,
graphics, ggrepel, apeglm, ashr, GreyListChIP
LinkingTo: Rhtslib (>= 1.99.1), Rcpp
Suggests: BiocStyle, testthat, xtable, rgl, XLConnect, edgeR, csaw,
BSgenome, GenomeInfoDb, profileplyr, rtracklayer, grid
License: Artistic-2.0
MD5sum: 1cebec86d6181968db0d4f39e1f42d82
NeedsCompilation: yes
Title: Differential Binding Analysis of ChIP-Seq Peak Data
Description: Compute differentially bound sites from multiple ChIP-seq
experiments using affinity (quantitative) data. Also enables
occupancy (overlap) analysis and plotting functions.
biocViews: Sequencing, ChIPSeq,ATACSeq, DNaseSeq, MethylSeq, RIPSeq,
DifferentialPeakCalling, DifferentialMethylation,
GeneRegulation, HistoneModification, PeakDetection,
BiomedicalInformatics, CellBiology, MultipleComparison,
Normalization, ReportWriting, Epigenetics, FunctionalGenomics
Author: Rory Stark [aut, cre], Gord Brown [aut]
Maintainer: Rory Stark <bioconductor@starkhome.com>
URL:
https://www.cruk.cam.ac.uk/core-facilities/bioinformatics-core/software/DiffBind
SystemRequirements: GNU make
git_url: https://git.bioconductor.org/packages/DiffBind
git_branch: devel
git_last_commit: ffd064b
git_last_commit_date: 2025-01-20
Date/Publication: 2025-01-20
source.ver: src/contrib/DiffBind_3.17.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DiffBind_3.17.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DiffBind_3.17.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DiffBind_3.17.3.tgz
vignettes: vignettes/DiffBind/inst/doc/DiffBind.pdf
vignetteTitles: DiffBind: Differential binding analysis of ChIP-Seq
peak data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DiffBind/inst/doc/DiffBind.R
dependsOnMe: ChIPQC, vulcan
dependencyCount: 147
Package: diffcoexp
Version: 1.27.0
Depends: R (>= 3.5), WGCNA, SummarizedExperiment
Imports: stats, DiffCorr, psych, igraph, BiocGenerics
Suggests: GEOquery, RUnit
License: GPL (>2)
MD5sum: cf939b233c0dac583cea3d983c38aff4
NeedsCompilation: no
Title: Differential Co-expression Analysis
Description: A tool for the identification of differentially
coexpressed links (DCLs) and differentially coexpressed genes
(DCGs). DCLs are gene pairs with significantly different
correlation coefficients under two conditions. DCGs are genes
with significantly more DCLs than by chance.
biocViews: GeneExpression, DifferentialExpression, Transcription,
Microarray, OneChannel, TwoChannel, RNASeq, Sequencing,
Coverage, ImmunoOncology
Author: Wenbin Wei, Sandeep Amberkar, Winston Hide
Maintainer: Wenbin Wei <wenbin.wei2@durham.ac.uk>
URL: https://github.com/hidelab/diffcoexp
git_url: https://git.bioconductor.org/packages/diffcoexp
git_branch: devel
git_last_commit: 8501abb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/diffcoexp_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/diffcoexp_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/diffcoexp_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/diffcoexp_1.27.0.tgz
vignettes: vignettes/diffcoexp/inst/doc/diffcoexp.pdf
vignetteTitles: About diffcoexp
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/diffcoexp/inst/doc/diffcoexp.R
importsMe: ExpHunterSuite
dependencyCount: 129
Package: diffcyt
Version: 1.27.3
Depends: R (>= 3.4.0)
Imports: flowCore, FlowSOM, SummarizedExperiment, S4Vectors, limma,
edgeR, lme4, multcomp, dplyr, tidyr, reshape2, magrittr, stats,
methods, utils, grDevices, graphics, ComplexHeatmap, circlize,
grid
Suggests: BiocStyle, knitr, rmarkdown, testthat, HDCytoData, CATALYST
License: MIT + file LICENSE
Archs: x64
MD5sum: facd085338172675c3c80db9ceda2882
NeedsCompilation: no
Title: Differential discovery in high-dimensional cytometry via
high-resolution clustering
Description: Statistical methods for differential discovery analyses in
high-dimensional cytometry data (including flow cytometry, mass
cytometry or CyTOF, and oligonucleotide-tagged cytometry),
based on a combination of high-resolution clustering and
empirical Bayes moderated tests adapted from transcriptomics.
biocViews: ImmunoOncology, FlowCytometry, Proteomics, SingleCell,
CellBasedAssays, CellBiology, Clustering, FeatureExtraction,
Software
Author: Lukas M. Weber [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3282-1730>)
Maintainer: Lukas M. Weber <lmweb012@gmail.com>
URL: https://github.com/lmweber/diffcyt
VignetteBuilder: knitr
BugReports: https://github.com/lmweber/diffcyt/issues
git_url: https://git.bioconductor.org/packages/diffcyt
git_branch: devel
git_last_commit: a4da914
git_last_commit_date: 2025-02-08
Date/Publication: 2025-02-09
source.ver: src/contrib/diffcyt_1.27.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/diffcyt_1.27.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/diffcyt_1.27.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/diffcyt_1.27.3.tgz
vignettes: vignettes/diffcyt/inst/doc/diffcyt_workflow.html
vignetteTitles: diffcyt workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/diffcyt/inst/doc/diffcyt_workflow.R
dependsOnMe: censcyt, cytofWorkflow
importsMe: treeclimbR, treekoR
suggestsMe: CATALYST, tidytof
dependencyCount: 146
Package: DifferentialRegulation
Version: 2.5.0
Depends: R (>= 4.3.0)
Imports: methods, Rcpp, doRNG, MASS, data.table, doParallel, parallel,
foreach, stats, BANDITS, Matrix, SingleCellExperiment,
SummarizedExperiment, ggplot2, tximport, gridExtra
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, testthat, BiocStyle
License: GPL-3
MD5sum: 36e977958c3e1e23013c3f3b38872253
NeedsCompilation: yes
Title: Differentially regulated genes from scRNA-seq data
Description: DifferentialRegulation is a method for detecting
differentially regulated genes between two groups of samples
(e.g., healthy vs. disease, or treated vs. untreated samples),
by targeting differences in the balance of spliced and
unspliced mRNA abundances, obtained from single-cell
RNA-sequencing (scRNA-seq) data. From a mathematical point of
view, DifferentialRegulation accounts for the sample-to-sample
variability, and embeds multiple samples in a Bayesian
hierarchical model. Furthermore, our method also deals with two
major sources of mapping uncertainty: i) 'ambiguous' reads,
compatible with both spliced and unspliced versions of a gene,
and ii) reads mapping to multiple genes. In particular,
ambiguous reads are treated separately from spliced and
unsplced reads, while reads that are compatible with multiple
genes are allocated to the gene of origin. Parameters are
inferred via Markov chain Monte Carlo (MCMC) techniques
(Metropolis-within-Gibbs).
biocViews: DifferentialSplicing, Bayesian, Genetics, RNASeq,
Sequencing, DifferentialExpression, GeneExpression,
MultipleComparison, Software, Transcription, StatisticalMethod,
Visualization, SingleCell, GeneTarget
Author: Simone Tiberi [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3054-9964>), Charlotte Soneson
[aut] (ORCID: <https://orcid.org/0000-0003-3833-2169>)
Maintainer: Simone Tiberi <simone.tiberi@unibo.it>
URL: https://github.com/SimoneTiberi/DifferentialRegulation
SystemRequirements: C++17
VignetteBuilder: knitr
BugReports:
https://github.com/SimoneTiberi/DifferentialRegulation/issues
git_url: https://git.bioconductor.org/packages/DifferentialRegulation
git_branch: devel
git_last_commit: f41d98e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DifferentialRegulation_2.5.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.5/DifferentialRegulation_2.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DifferentialRegulation_2.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DifferentialRegulation_2.5.0.tgz
vignettes:
vignettes/DifferentialRegulation/inst/doc/DifferentialRegulation.html
vignetteTitles: DifferentialRegulation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/DifferentialRegulation/inst/doc/DifferentialRegulation.R
dependencyCount: 97
Package: diffGeneAnalysis
Version: 1.89.0
Imports: graphics, grDevices, minpack.lm (>= 1.0-4), stats, utils
License: GPL
MD5sum: 7d75d52a1424532b6ce561b33bfa0894
NeedsCompilation: no
Title: Performs differential gene expression Analysis
Description: Analyze microarray data
biocViews: Microarray, DifferentialExpression
Author: Choudary Jagarlamudi
Maintainer: Choudary Jagarlamudi <choudary.jagar@swosu.edu>
git_url: https://git.bioconductor.org/packages/diffGeneAnalysis
git_branch: devel
git_last_commit: 0f55259
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/diffGeneAnalysis_1.89.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/diffGeneAnalysis_1.89.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/diffGeneAnalysis_1.89.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/diffGeneAnalysis_1.89.0.tgz
vignettes: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.pdf
vignetteTitles: Documentation on diffGeneAnalysis
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.R
dependencyCount: 5
Package: diffHic
Version: 1.39.1
Depends: R (>= 3.5), GenomicRanges, InteractionSet,
SummarizedExperiment
Imports: Rsamtools, Rhtslib, Biostrings, BSgenome, rhdf5, edgeR, limma,
csaw, locfit, methods, IRanges, S4Vectors, GenomeInfoDb,
BiocGenerics, grDevices, graphics, stats, utils, Rcpp,
rtracklayer
LinkingTo: Rhtslib (>= 1.13.1), Rcpp
Suggests: BSgenome.Ecoli.NCBI.20080805, Matrix, testthat
License: GPL-3
Archs: x64
MD5sum: c23c38db84cda9ca6cfffc79e47efb21
NeedsCompilation: yes
Title: Differential Analysis of Hi-C Data
Description: Detects differential interactions across biological
conditions in a Hi-C experiment. Methods are provided for read
alignment and data pre-processing into interaction counts.
Statistical analysis is based on edgeR and supports
normalization and filtering. Several visualization options are
also available.
biocViews: MultipleComparison, Preprocessing, Sequencing, Coverage,
Alignment, Normalization, Clustering, HiC
Author: Aaron Lun, Gordon Smyth
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>,
Gordon Smyth <smyth@wehi.edu.au>, Hannah Coughlin
<coughlin.h@wehi.edu.au>
SystemRequirements: C++, GNU make
git_url: https://git.bioconductor.org/packages/diffHic
git_branch: devel
git_last_commit: de5cfac
git_last_commit_date: 2024-12-16
Date/Publication: 2024-12-16
source.ver: src/contrib/diffHic_1.39.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/diffHic_1.39.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/diffHic_1.39.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/diffHic_1.39.1.tgz
vignettes: vignettes/diffHic/inst/doc/diffHic.pdf,
vignettes/diffHic/inst/doc/diffHicUsersGuide.pdf
vignetteTitles: diffHic Vignette, diffHicUsersGuide.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
importsMe: OHCA
dependencyCount: 70
Package: DiffLogo
Version: 2.31.0
Depends: R (>= 3.4), stats, cba
Imports: grDevices, graphics, utils, tools
Suggests: knitr, testthat, seqLogo, MotifDb
License: GPL (>= 2)
MD5sum: adcf5ba9d4beca94614d75128f3af703
NeedsCompilation: no
Title: DiffLogo: A comparative visualisation of biooligomer motifs
Description: DiffLogo is an easy-to-use tool to visualize motif
differences.
biocViews: Software, SequenceMatching, MultipleComparison,
MotifAnnotation, Visualization, Alignment
Author: c( person("Martin", "Nettling", role = c("aut", "cre"), email =
"martin.nettling@informatik.uni-halle.de"), person("Hendrik",
"Treutler", role = c("aut", "cre"), email =
"hendrik.treutler@ipb-halle.de"), person("Jan", "Grau", role =
c("aut", "ctb"), email = "grau@informatik.uni-halle.de"),
person("Andrey", "Lando", role = c("aut", "ctb"), email =
"dronte@autosome.ru"), person("Jens", "Keilwagen", role =
c("aut", "ctb"), email = "jens.keilwagen@julius-kuehn.de"),
person("Stefan", "Posch", role = "aut", email =
"posch@informatik.uni-halle.de"), person("Ivo", "Grosse", role
= "aut", email = "grosse@informatik.uni-halle.de"))
Maintainer: Hendrik Treutler<hendrik.treutler@gmail.com>
URL: https://github.com/mgledi/DiffLogo/
BugReports: https://github.com/mgledi/DiffLogo/issues
git_url: https://git.bioconductor.org/packages/DiffLogo
git_branch: devel
git_last_commit: 33af0b4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DiffLogo_2.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DiffLogo_2.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DiffLogo_2.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DiffLogo_2.31.0.tgz
vignettes: vignettes/DiffLogo/inst/doc/DiffLogoBasics.pdf
vignetteTitles: Basics of the DiffLogo package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DiffLogo/inst/doc/DiffLogoBasics.R
dependencyCount: 9
Package: diffuStats
Version: 1.27.0
Depends: R (>= 3.4)
Imports: grDevices, stats, methods, Matrix, MASS, checkmate, expm,
igraph, Rcpp, RcppArmadillo, RcppParallel, plyr, precrec
LinkingTo: Rcpp, RcppArmadillo, RcppParallel
Suggests: testthat, knitr, rmarkdown, ggplot2, ggsci, igraphdata,
BiocStyle, reshape2, utils
License: GPL-3
Archs: x64
MD5sum: f654b32696e6377036c61ecd5a983910
NeedsCompilation: yes
Title: Diffusion scores on biological networks
Description: Label propagation approaches are a widely used procedure
in computational biology for giving context to molecular
entities using network data. Node labels, which can derive from
gene expression, genome-wide association studies, protein
domains or metabolomics profiling, are propagated to their
neighbours in the network, effectively smoothing the scores
through prior annotated knowledge and prioritising novel
candidates. The R package diffuStats contains a collection of
diffusion kernels and scoring approaches that facilitates their
computation, characterisation and benchmarking.
biocViews: Network, GeneExpression, GraphAndNetwork, Metabolomics,
Transcriptomics, Proteomics, Genetics, GenomeWideAssociation,
Normalization
Author: Sergio Picart-Armada [aut, cre], Alexandre Perera-Lluna [aut]
Maintainer: Sergio Picart-Armada <sergi.picart@upc.edu>
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/diffuStats
git_branch: devel
git_last_commit: 6ad732c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/diffuStats_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/diffuStats_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/diffuStats_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/diffuStats_1.27.0.tgz
vignettes: vignettes/diffuStats/inst/doc/diffuStats.pdf,
vignettes/diffuStats/inst/doc/intro.html
vignetteTitles: Case study: predicting protein function, Quick start
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/diffuStats/inst/doc/diffuStats.R,
vignettes/diffuStats/inst/doc/intro.R
dependencyCount: 49
Package: diffUTR
Version: 1.15.0
Depends: R (>= 4.0)
Imports: S4Vectors, SummarizedExperiment, limma, edgeR, DEXSeq,
GenomicRanges, Rsubread, ggplot2, rtracklayer, ComplexHeatmap,
ggrepel, stringi, methods, stats, GenomeInfoDb, dplyr,
matrixStats, IRanges, ensembldb, viridisLite
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-3
MD5sum: 1c6d7bfb16eaa85c24d894ed83dce55b
NeedsCompilation: no
Title: diffUTR: Streamlining differential exon and 3' UTR usage
Description: The diffUTR package provides a uniform interface and
plotting functions for limma/edgeR/DEXSeq -powered differential
bin/exon usage. It includes in addition an improved version of
the limma::diffSplice method. Most importantly, diffUTR further
extends the application of these frameworks to differential UTR
usage analysis using poly-A site databases.
biocViews: GeneExpression
Author: Pierre-Luc Germain [cre, aut] (ORCID:
<https://orcid.org/0000-0003-3418-4218>), Stefan Gerber [aut]
Maintainer: Pierre-Luc Germain <pierre-luc.germain@hest.ethz.ch>
VignetteBuilder: knitr
BugReports: https://github.com/ETHZ-INS/diffUTR
git_url: https://git.bioconductor.org/packages/diffUTR
git_branch: devel
git_last_commit: 43fce84
git_last_commit_date: 2024-10-29
Date/Publication: 2025-03-06
source.ver: src/contrib/diffUTR_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/diffUTR_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/diffUTR_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/diffUTR_1.15.0.tgz
vignettes: vignettes/diffUTR/inst/doc/diffSplice2.html,
vignettes/diffUTR/inst/doc/diffUTR.html
vignetteTitles: diffUTR_diffSplice2, 1_diffUTR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/diffUTR/inst/doc/diffSplice2.R,
vignettes/diffUTR/inst/doc/diffUTR.R
dependencyCount: 144
Package: diggit
Version: 1.39.0
Depends: R (>= 3.0.2), Biobase, methods
Imports: ks, viper(>= 1.3.1), parallel
Suggests: diggitdata
License: file LICENSE
Archs: x64
MD5sum: d9922171ff24749c3baf83676c272561
NeedsCompilation: no
Title: Inference of Genetic Variants Driving Cellular Phenotypes
Description: Inference of Genetic Variants Driving Cellullar Phenotypes
by the DIGGIT algorithm
biocViews: SystemsBiology, NetworkEnrichment, GeneExpression,
FunctionalPrediction, GeneRegulation
Author: Mariano J Alvarez <reef103@gmail.com>
Maintainer: Mariano J Alvarez <reef103@gmail.com>
git_url: https://git.bioconductor.org/packages/diggit
git_branch: devel
git_last_commit: e13e6e5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/diggit_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/diggit_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/diggit_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/diggit_1.39.0.tgz
vignettes: vignettes/diggit/inst/doc/diggit.pdf
vignetteTitles: Using DIGGIT
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/diggit/inst/doc/diggit.R
dependencyCount: 96
Package: Dino
Version: 1.13.0
Depends: R (>= 4.0.0)
Imports: BiocParallel, BiocSingular, SummarizedExperiment,
SingleCellExperiment, S4Vectors, Matrix, Seurat, matrixStats,
parallel, scran, grDevices, stats, methods
Suggests: testthat (>= 2.1.0), knitr, rmarkdown, BiocStyle, devtools,
ggplot2, gridExtra, ggpubr, grid, magick, hexbin
License: GPL-3
MD5sum: b91dcfe0bdea54f9e11a651daa96e00c
NeedsCompilation: no
Title: Normalization of Single-Cell mRNA Sequencing Data
Description: Dino normalizes single-cell, mRNA sequencing data to
correct for technical variation, particularly sequencing depth,
prior to downstream analysis. The approach produces a matrix of
corrected expression for which the dependency between
sequencing depth and the full distribution of normalized
expression; many existing methods aim to remove only the
dependency between sequencing depth and the mean of the
normalized expression. This is particuarly useful in the
context of highly sparse datasets such as those produced by 10X
genomics and other uninque molecular identifier (UMI) based
microfluidics protocols for which the depth-dependent
proportion of zeros in the raw expression data can otherwise
present a challenge.
biocViews: Software, Normalization, RNASeq, SingleCell, Sequencing,
GeneExpression, Transcriptomics, Regression, CellBasedAssays
Author: Jared Brown [aut, cre] (ORCID:
<https://orcid.org/0000-0002-9151-4386>), Christina Kendziorski
[ctb]
Maintainer: Jared Brown <brownj@ds.dfci.harvard.edu>
URL: https://github.com/JBrownBiostat/Dino
VignetteBuilder: knitr
BugReports: https://github.com/JBrownBiostat/Dino/issues
git_url: https://git.bioconductor.org/packages/Dino
git_branch: devel
git_last_commit: 8abb017
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Dino_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Dino_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Dino_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Dino_1.13.0.tgz
vignettes: vignettes/Dino/inst/doc/Dino.html
vignetteTitles: Normalization by distributional resampling of high
throughput single-cell RNA-sequencing data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Dino/inst/doc/Dino.R
dependencyCount: 191
Package: dinoR
Version: 1.3.0
Depends: R (>= 4.3.0), SummarizedExperiment
Imports: BiocGenerics, circlize, ComplexHeatmap, cowplot, dplyr, edgeR,
GenomicRanges, ggplot2, Matrix, methods, rlang, stats, stringr,
tibble, tidyr, tidyselect
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: 228edc55576b1c5285b00e67f0413dd8
NeedsCompilation: no
Title: Differential NOMe-seq analysis
Description: dinoR tests for significant differences in NOMe-seq
footprints between two conditions, using genomic regions of
interest (ROI) centered around a landmark, for example a
transcription factor (TF) motif. This package takes NOMe-seq
data (GCH methylation/protection) in the form of a Ranged
Summarized Experiment as input. dinoR can be used to group
sequencing fragments into 3 or 5 categories representing
characteristic footprints (TF bound, nculeosome bound, open
chromatin), plot the percentage of fragments in each category
in a heatmap, or averaged across different ROI groups, for
example, containing a common TF motif. It is designed to
compare footprints between two sample groups, using edgeR's
quasi-likelihood methods on the total fragment counts per ROI,
sample, and footprint category.
biocViews: NucleosomePositioning, Epigenetics, MethylSeq,
DifferentialMethylation, Coverage, Transcription, Sequencing,
Software
Author: Michaela Schwaiger [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4522-7810>)
Maintainer: Michaela Schwaiger <michaela.schwaiger@fmi.ch>
URL: https://github.com/xxxmichixxx/dinoR
VignetteBuilder: knitr
BugReports: https://github.com/xxxmichixxx/dinoR/issues
git_url: https://git.bioconductor.org/packages/dinoR
git_branch: devel
git_last_commit: eaf277e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/dinoR_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/dinoR_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/dinoR_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/dinoR_1.3.0.tgz
vignettes: vignettes/dinoR/inst/doc/dinoR-vignette.html
vignetteTitles: dinoR-vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/dinoR/inst/doc/dinoR-vignette.R
dependencyCount: 89
Package: dir.expiry
Version: 1.15.0
Imports: utils, filelock
Suggests: rmarkdown, knitr, testthat, BiocStyle
License: GPL-3
MD5sum: 8de15560fbcd2b1d639c5ee0b836c098
NeedsCompilation: no
Title: Managing Expiration for Cache Directories
Description: Implements an expiration system for access to versioned
directories. Directories that have not been accessed by a
registered function within a certain time frame are deleted.
This aims to reduce disk usage by eliminating obsolete caches
generated by old versions of packages.
biocViews: Software, Infrastructure
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/dir.expiry
git_branch: devel
git_last_commit: 42c6808
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/dir.expiry_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/dir.expiry_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/dir.expiry_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/dir.expiry_1.15.0.tgz
vignettes: vignettes/dir.expiry/inst/doc/userguide.html
vignetteTitles: Managing directory expiration
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/dir.expiry/inst/doc/userguide.R
importsMe: basilisk, basilisk.utils, biocmake, rebook
dependencyCount: 2
Package: Director
Version: 1.33.1
Depends: R (>= 4.0)
Imports: htmltools, utils, grDevices
License: GPL-3 + file LICENSE
MD5sum: dc8e499dbb5399b8198de3ac5748aeb0
NeedsCompilation: no
Title: A dynamic visualization tool of multi-level data
Description: Director is an R package designed to streamline the
visualization of molecular effects in regulatory cascades. It
utilizes the R package htmltools and a modified Sankey plugin
of the JavaScript library D3 to provide a fast and easy,
browser-enabled solution to discovering potentially interesting
downstream effects of regulatory and/or co-expressed molecules.
The diagrams are robust, interactive, and packaged as
highly-portable HTML files that eliminate the need for
third-party software to view. This enables a straightforward
approach for scientists to interpret the data produced, and
bioinformatics developers an alternative means to present
relevant data.
biocViews: Visualization
Author: Katherine Icay [aut, cre]
Maintainer: Katherine Icay <kat.icay@gmail.com>
URL: https://github.com/kzouchka/Director
BugReports: https://github.com/kzouchka/Director/issues
PackageStatus: Deprecated
git_url: https://git.bioconductor.org/packages/Director
git_branch: devel
git_last_commit: 2152c5d
git_last_commit_date: 2025-02-18
Date/Publication: 2025-02-18
source.ver: src/contrib/Director_1.33.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Director_1.33.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Director_1.33.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Director_1.33.1.tgz
vignettes: vignettes/Director/inst/doc/vignette.pdf
vignetteTitles: Using Director
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Director/inst/doc/vignette.R
dependencyCount: 7
Package: DirichletMultinomial
Version: 1.49.0
Depends: S4Vectors, IRanges
Imports: stats4, methods, BiocGenerics
Suggests: lattice, parallel, MASS, RColorBrewer, DT, knitr, rmarkdown,
BiocStyle
License: LGPL-3
MD5sum: 8bc79040f15522671a6e389d2dd9d382
NeedsCompilation: yes
Title: Dirichlet-Multinomial Mixture Model Machine Learning for
Microbiome Data
Description: Dirichlet-multinomial mixture models can be used to
describe variability in microbial metagenomic data. This
package is an interface to code originally made available by
Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15, as
discussed further in the man page for this package,
?DirichletMultinomial.
biocViews: ImmunoOncology, Microbiome, Sequencing, Clustering,
Classification, Metagenomics
Author: Martin Morgan [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5874-8148>)
Maintainer: Martin Morgan <mtmorgan.xyz@gmail.com>
URL: https://mtmorgan.github.io/DirichletMultinomial/
SystemRequirements: gsl
VignetteBuilder: knitr
BugReports: https://github.com/mtmorgan/DirichletMultinomial/issues
git_url: https://git.bioconductor.org/packages/DirichletMultinomial
git_branch: devel
git_last_commit: 776fd59
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DirichletMultinomial_1.49.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DirichletMultinomial_1.49.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DirichletMultinomial_1.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DirichletMultinomial_1.49.0.tgz
vignettes:
vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.html
vignetteTitles: DirichletMultinomial for Clustering and Classification
of Microbiome Data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.R
importsMe: mia, miaViz, TFBSTools
suggestsMe: bluster, MicrobiotaProcess
dependencyCount: 9
Package: discordant
Version: 1.31.0
Depends: R (>= 4.1.0)
Imports: Rcpp, Biobase, stats, biwt, gtools, MASS, tools, dplyr,
methods, utils
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, testthat (>= 3.0.0)
License: GPL-3
MD5sum: 38398e7f1ec6460034588e43e091245d
NeedsCompilation: yes
Title: The Discordant Method: A Novel Approach for Differential
Correlation
Description: Discordant is an R package that identifies pairs of
features that correlate differently between phenotypic groups,
with application to -omics data sets. Discordant uses a mixture
model that “bins†molecular feature pairs based on their type
of coexpression or coabbundance. Algorithm is explained further
in "Differential Correlation for Sequencing Data"" (Siska et
al. 2016).
biocViews: ImmunoOncology, BiologicalQuestion, StatisticalMethod,
mRNAMicroarray, Microarray, Genetics, RNASeq
Author: Charlotte Siska [aut], McGrath Max [aut, cre], Katerina Kechris
[aut, cph, ths]
Maintainer: McGrath Max <max.mcgrath@ucdenver.edu>
URL: https://github.com/siskac/discordant
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/discordant
git_branch: devel
git_last_commit: e2c5e77
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/discordant_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/discordant_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/discordant_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/discordant_1.31.0.tgz
vignettes: vignettes/discordant/inst/doc/Using_discordant.html
vignetteTitles: The discordant R Package: A Novel Approach to
Differential Correlation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/discordant/inst/doc/Using_discordant.R
dependencyCount: 30
Package: DiscoRhythm
Version: 1.23.0
Depends: R (>= 3.6.0)
Imports: matrixTests, matrixStats, MetaCycle (>= 1.2.0), data.table,
ggplot2, ggExtra, dplyr, broom, shiny, shinyBS,
shinycssloaders, shinydashboard, shinyjs, BiocStyle, rmarkdown,
knitr, kableExtra, magick, VennDiagram, UpSetR, heatmaply,
viridis, plotly, DT, gridExtra, methods, stats,
SummarizedExperiment, BiocGenerics, S4Vectors, zip, reshape2
Suggests: testthat
License: GPL-3
Archs: x64
MD5sum: 347eb7f593ffb6f5c5303992d5c06470
NeedsCompilation: no
Title: Interactive Workflow for Discovering Rhythmicity in Biological
Data
Description: Set of functions for estimation of cyclical
characteristics, such as period, phase, amplitude, and
statistical significance in large temporal datasets. Supporting
functions are available for quality control, dimensionality
reduction, spectral analysis, and analysis of experimental
replicates. Contains a R Shiny web interface to execute all
workflow steps.
biocViews: Software, TimeCourse, QualityControl, Visualization, GUI,
PrincipalComponent
Author: Matthew Carlucci [aut, cre], Algimantas KriÅ¡ÄiÅ«nas [aut],
Haohan Li [aut], Povilas Gibas [aut], Karolis KonceviÄius
[aut], Art Petronis [aut], Gabriel Oh [aut]
Maintainer: Matthew Carlucci <Matthew.Carlucci@camh.ca>
URL: https://github.com/matthewcarlucci/DiscoRhythm
SystemRequirements: To generate html reports pandoc
(http://pandoc.org/installing.html) is required.
VignetteBuilder: knitr
BugReports: https://github.com/matthewcarlucci/DiscoRhythm/issues
git_url: https://git.bioconductor.org/packages/DiscoRhythm
git_branch: devel
git_last_commit: 9e4ec1f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DiscoRhythm_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DiscoRhythm_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DiscoRhythm_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DiscoRhythm_1.23.0.tgz
vignettes: vignettes/DiscoRhythm/inst/doc/disco_workflow_vignette.html
vignetteTitles: Introduction to DiscoRhythm
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DiscoRhythm/inst/doc/disco_workflow_vignette.R
dependencyCount: 158
Package: distinct
Version: 1.19.0
Depends: R (>= 4.3)
Imports: Rcpp, stats, SummarizedExperiment, SingleCellExperiment,
methods, Matrix, foreach, parallel, doParallel, doRNG, ggplot2,
limma, scater
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, testthat, UpSetR, BiocStyle
License: GPL (>= 3)
Archs: x64
MD5sum: 63a3b0ed24cc1214e69100dbd71f6189
NeedsCompilation: yes
Title: distinct: a method for differential analyses via hierarchical
permutation tests
Description: distinct is a statistical method to perform differential
testing between two or more groups of distributions;
differential testing is performed via hierarchical
non-parametric permutation tests on the cumulative distribution
functions (cdfs) of each sample. While most methods for
differential expression target differences in the mean
abundance between conditions, distinct, by comparing full cdfs,
identifies, both, differential patterns involving changes in
the mean, as well as more subtle variations that do not involve
the mean (e.g., unimodal vs. bi-modal distributions with the
same mean). distinct is a general and flexible tool: due to its
fully non-parametric nature, which makes no assumptions on how
the data was generated, it can be applied to a variety of
datasets. It is particularly suitable to perform differential
state analyses on single cell data (i.e., differential analyses
within sub-populations of cells), such as single cell RNA
sequencing (scRNA-seq) and high-dimensional flow or mass
cytometry (HDCyto) data. To use distinct one needs data from
two or more groups of samples (i.e., experimental conditions),
with at least 2 samples (i.e., biological replicates) per
group.
biocViews: Genetics, RNASeq, Sequencing, DifferentialExpression,
GeneExpression, MultipleComparison, Software, Transcription,
StatisticalMethod, Visualization, SingleCell, FlowCytometry,
GeneTarget
Author: Simone Tiberi [aut, cre].
Maintainer: Simone Tiberi <simone.tiberi@uzh.ch>
URL: https://github.com/SimoneTiberi/distinct
SystemRequirements: C++17
VignetteBuilder: knitr
BugReports: https://github.com/SimoneTiberi/distinct/issues
git_url: https://git.bioconductor.org/packages/distinct
git_branch: devel
git_last_commit: ce83fb1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/distinct_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/distinct_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/distinct_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/distinct_1.19.0.tgz
vignettes: vignettes/distinct/inst/doc/distinct.html
vignetteTitles: distinct: a method for differential analyses via
hierarchical permutation tests
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/distinct/inst/doc/distinct.R
importsMe: condiments
dependencyCount: 116
Package: dittoSeq
Version: 1.19.0
Depends: ggplot2
Imports: methods, colorspace (>= 1.4), gridExtra, cowplot, reshape2,
pheatmap, grDevices, ggrepel, ggridges, stats, utils,
SummarizedExperiment, SingleCellExperiment, S4Vectors
Suggests: plotly, testthat, Seurat (>= 2.2), DESeq2, edgeR,
ggplot.multistats, knitr, rmarkdown, BiocStyle, scRNAseq,
ggrastr (>= 0.2.0), ComplexHeatmap, bluster, scater, scran
License: MIT + file LICENSE
MD5sum: 7a7d0203aff164b521fc73203bf6c67b
NeedsCompilation: no
Title: User Friendly Single-Cell and Bulk RNA Sequencing Visualization
Description: A universal, user friendly, single-cell and bulk RNA
sequencing visualization toolkit that allows highly
customizable creation of color blindness friendly,
publication-quality figures. dittoSeq accepts both
SingleCellExperiment (SCE) and Seurat objects, as well as the
import and usage, via conversion to an SCE, of
SummarizedExperiment or DGEList bulk data. Visualizations
include dimensionality reduction plots, heatmaps, scatterplots,
percent composition or expression across groups, and more.
Customizations range from size and title adjustments to
automatic generation of annotations for heatmaps, overlay of
trajectory analysis onto any dimensionality reduciton plot,
hidden data overlay upon cursor hovering via ggplotly
conversion, and many more. All with simple, discrete inputs.
Color blindness friendliness is powered by legend adjustments
(enlarged keys), and by allowing the use of shapes or
letter-overlay in addition to the carefully selected
dittoColors().
biocViews: Software, Visualization, RNASeq, SingleCell, GeneExpression,
Transcriptomics, DataImport
Author: Daniel Bunis [aut, cre], Jared Andrews [aut, ctb]
Maintainer: Daniel Bunis <daniel.bunis@ucsf.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/dittoSeq
git_branch: devel
git_last_commit: 849ef42
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/dittoSeq_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/dittoSeq_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/dittoSeq_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/dittoSeq_1.19.0.tgz
vignettes: vignettes/dittoSeq/inst/doc/dittoSeq.html
vignetteTitles: Annotating scRNA-seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/dittoSeq/inst/doc/dittoSeq.R
importsMe: CRISPRball, SPIAT
suggestsMe: demuxSNP, tidySingleCellExperiment, magmaR, scCustomize
dependencyCount: 73
Package: divergence
Version: 1.23.0
Depends: R (>= 3.6), SummarizedExperiment
Suggests: knitr, rmarkdown
License: GPL-2
Archs: x64
MD5sum: a9d2f36a82c152f847a183ef9e72c1ca
NeedsCompilation: no
Title: Divergence: Functionality for assessing omics data by divergence
with respect to a baseline
Description: This package provides functionality for performing
divergence analysis as presented in Dinalankara et al,
"Digitizing omics profiles by divergence from a baseline", PANS
2018. This allows the user to simplify high dimensional omics
data into a binary or ternary format which encapsulates how the
data is divergent from a specified baseline group with the same
univariate or multivariate features.
biocViews: Software, StatisticalMethod
Author: Wikum Dinalankara <wdd4001@med.cornell.edu>, Luigi Marchionni
<marchion@jhu.edu>, Qian Ke <qke1@jhu.edu>
Maintainer: Wikum Dinalankara <wdd4001@med.cornell.edu>, Luigi
Marchionni <marchion@jhu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/divergence
git_branch: devel
git_last_commit: ef1c130
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/divergence_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/divergence_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/divergence_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/divergence_1.23.0.tgz
vignettes: vignettes/divergence/inst/doc/divergence.html
vignetteTitles: Performing Divergence Analysis
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/divergence/inst/doc/divergence.R
dependencyCount: 36
Package: dks
Version: 1.53.0
Depends: R (>= 2.8)
Imports: cubature
License: GPL
MD5sum: 92a497c5055c5637e5c1d5e515654ba3
NeedsCompilation: no
Title: The double Kolmogorov-Smirnov package for evaluating multiple
testing procedures.
Description: The dks package consists of a set of diagnostic functions
for multiple testing methods. The functions can be used to
determine if the p-values produced by a multiple testing
procedure are correct. These functions are designed to be
applied to simulated data. The functions require the entire set
of p-values from multiple simulated studies, so that the joint
distribution can be evaluated.
biocViews: MultipleComparison, QualityControl
Author: Jeffrey T. Leek <jleek@jhsph.edu>
Maintainer: Jeffrey T. Leek <jleek@jhsph.edu>
git_url: https://git.bioconductor.org/packages/dks
git_branch: devel
git_last_commit: 7806109
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/dks_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/dks_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/dks_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/dks_1.53.0.tgz
vignettes: vignettes/dks/inst/doc/dks.pdf
vignetteTitles: dksTutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/dks/inst/doc/dks.R
dependencyCount: 4
Package: DMCFB
Version: 1.21.0
Depends: R (>= 4.0.0), SummarizedExperiment, methods, S4Vectors,
BiocParallel, GenomicRanges, IRanges
Imports: utils, stats, speedglm, MASS, data.table, splines, arm,
rtracklayer, benchmarkme, tibble, matrixStats, fastDummies,
graphics
Suggests: testthat, knitr, rmarkdown, BiocStyle
License: GPL-3
MD5sum: e79afca1aa5471d1baadcb7bb1f6d4d2
NeedsCompilation: no
Title: Differentially Methylated Cytosines via a Bayesian Functional
Approach
Description: DMCFB is a pipeline for identifying differentially
methylated cytosines using a Bayesian functional regression
model in bisulfite sequencing data. By using a functional
regression data model, it tries to capture position-specific,
group-specific and other covariates-specific methylation
patterns as well as spatial correlation patterns and unknown
underlying models of methylation data. It is robust and
flexible with respect to the true underlying models and
inclusion of any covariates, and the missing values are imputed
using spatial correlation between positions and samples. A
Bayesian approach is adopted for estimation and inference in
the proposed method.
biocViews: DifferentialMethylation, Sequencing, Coverage, Bayesian,
Regression
Author: Farhad Shokoohi [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6224-2609>)
Maintainer: Farhad Shokoohi <shokoohi@icloud.com>
VignetteBuilder: knitr
BugReports: https://github.com/shokoohi/DMCFB/issues
git_url: https://git.bioconductor.org/packages/DMCFB
git_branch: devel
git_last_commit: 931dcfd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DMCFB_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DMCFB_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DMCFB_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DMCFB_1.21.0.tgz
vignettes: vignettes/DMCFB/inst/doc/DMCFB.html
vignetteTitles: Identifying DMCs using Bayesian functional regressions
in BS-Seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DMCFB/inst/doc/DMCFB.R
dependencyCount: 99
Package: DMCHMM
Version: 1.29.0
Depends: R (>= 4.1.0), SummarizedExperiment, methods, S4Vectors,
BiocParallel, GenomicRanges, IRanges, fdrtool
Imports: utils, stats, grDevices, rtracklayer, multcomp, calibrate,
graphics
Suggests: testthat, knitr, rmarkdown
License: GPL-3
MD5sum: bae753a2f5f3cafc1e4c36e655ddba86
NeedsCompilation: no
Title: Differentially Methylated CpG using Hidden Markov Model
Description: A pipeline for identifying differentially methylated CpG
sites using Hidden Markov Model in bisulfite sequencing data.
DNA methylation studies have enabled researchers to understand
methylation patterns and their regulatory roles in biological
processes and disease. However, only a limited number of
statistical approaches have been developed to provide formal
quantitative analysis. Specifically, a few available methods do
identify differentially methylated CpG (DMC) sites or regions
(DMR), but they suffer from limitations that arise mostly due
to challenges inherent in bisulfite sequencing data. These
challenges include: (1) that read-depths vary considerably
among genomic positions and are often low; (2) both methylation
and autocorrelation patterns change as regions change; and (3)
CpG sites are distributed unevenly. Furthermore, there are
several methodological limitations: almost none of these tools
is capable of comparing multiple groups and/or working with
missing values, and only a few allow continuous or multiple
covariates. The last of these is of great interest among
researchers, as the goal is often to find which regions of the
genome are associated with several exposures and traits. To
tackle these issues, we have developed an efficient DMC
identification method based on Hidden Markov Models (HMMs)
called “DMCHMM†which is a three-step approach (model
selection, prediction, testing) aiming to address the
aforementioned drawbacks.
biocViews: DifferentialMethylation, Sequencing, HiddenMarkovModel,
Coverage
Author: Farhad Shokoohi
Maintainer: Farhad Shokoohi <shokoohi@icloud.com>
VignetteBuilder: knitr
BugReports: https://github.com/shokoohi/DMCHMM/issues
git_url: https://git.bioconductor.org/packages/DMCHMM
git_branch: devel
git_last_commit: d270747
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DMCHMM_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DMCHMM_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DMCHMM_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DMCHMM_1.29.0.tgz
vignettes: vignettes/DMCHMM/inst/doc/DMCHMM.html
vignetteTitles: DMCHMM: Differentially Methylated CpG using Hidden
Markov Model
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DMCHMM/inst/doc/DMCHMM.R
dependencyCount: 68
Package: DMRcaller
Version: 1.39.0
Depends: R (>= 3.5), GenomicRanges, IRanges, S4Vectors (>= 0.23.10)
Imports: parallel, Rcpp, RcppRoll, betareg, grDevices, graphics,
methods, stats, utils
Suggests: knitr, RUnit, BiocGenerics
License: GPL-3
MD5sum: 819431e473505cab31a0f8f4e70629af
NeedsCompilation: no
Title: Differentially Methylated Regions caller
Description: Uses Bisulfite sequencing data in two conditions and
identifies differentially methylated regions between the
conditions in CG and non-CG context. The input is the CX report
files produced by Bismark and the output is a list of DMRs
stored as GRanges objects.
biocViews: DifferentialMethylation, DNAMethylation, Software,
Sequencing, Coverage
Author: Nicolae Radu Zabet <n.r.zabet@gen.cam.ac.uk>, Jonathan Michael
Foonlan Tsang <jmft2@cam.ac.uk>, Alessandro Pio Greco
<apgrec@essex.ac.uk> and Ryan Merritt <rmerri@essex.ac.uk>
Maintainer: Nicolae Radu Zabet <n.r.zabet@gen.cam.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DMRcaller
git_branch: devel
git_last_commit: 13a82ad
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DMRcaller_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DMRcaller_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DMRcaller_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DMRcaller_1.39.0.tgz
vignettes: vignettes/DMRcaller/inst/doc/DMRcaller.pdf
vignetteTitles: DMRcaller
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DMRcaller/inst/doc/DMRcaller.R
dependencyCount: 37
Package: DMRcate
Version: 3.3.1
Depends: R (>= 4.3.0)
Imports: AnnotationHub, ExperimentHub, bsseq, GenomeInfoDb, limma,
edgeR, minfi, missMethyl, GenomicRanges, plyr, Gviz, IRanges,
stats, utils, S4Vectors, methods, graphics,
SummarizedExperiment, biomaRt, grDevices
Suggests: knitr, RUnit, BiocGenerics,
IlluminaHumanMethylation450kanno.ilmn12.hg19,
IlluminaHumanMethylationEPICanno.ilm10b4.hg19,
IlluminaHumanMethylationEPICv2anno.20a1.hg38,
FlowSorted.Blood.EPIC, tissueTreg, DMRcatedata, EPICv2manifest
License: file LICENSE
MD5sum: 0a19832d3f5a7d5a07cbe6a2e2f8cc77
NeedsCompilation: no
Title: Methylation array and sequencing spatial analysis methods
Description: De novo identification and extraction of differentially
methylated regions (DMRs) from the human genome using Whole
Genome Bisulfite Sequencing (WGBS) and Illumina Infinium Array
(450K and EPIC) data. Provides functionality for filtering
probes possibly confounded by SNPs and cross-hybridisation.
Includes GRanges generation and plotting functions.
biocViews: DifferentialMethylation, GeneExpression, Microarray,
MethylationArray, Genetics, DifferentialExpression,
GenomeAnnotation, DNAMethylation, OneChannel, TwoChannel,
MultipleComparison, QualityControl, TimeCourse, Sequencing,
WholeGenome, Epigenetics, Coverage, Preprocessing, DataImport
Author: Tim Peters
Maintainer: Tim Peters <t.peters@garvan.org.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DMRcate
git_branch: devel
git_last_commit: 52f38aa
git_last_commit_date: 2024-12-16
Date/Publication: 2024-12-18
source.ver: src/contrib/DMRcate_3.3.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DMRcate_3.3.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DMRcate_3.3.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DMRcate_3.3.1.tgz
vignettes: vignettes/DMRcate/inst/doc/EPICv1_and_450K.pdf,
vignettes/DMRcate/inst/doc/EPICv2.pdf,
vignettes/DMRcate/inst/doc/sequencing.pdf
vignetteTitles: DMRcate for EPICv1 and 450K assays, DMR calling from
EPICv2 arrays, DMRcate for bisulfite sequencing assays (WGBS
and RRBS)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/DMRcate/inst/doc/EPICv1_and_450K.R,
vignettes/DMRcate/inst/doc/EPICv2.R,
vignettes/DMRcate/inst/doc/sequencing.R
suggestsMe: missMethyl
dependencyCount: 224
Package: DMRScan
Version: 1.29.0
Depends: R (>= 3.6.0)
Imports: Matrix, MASS, RcppRoll,GenomicRanges, IRanges, GenomeInfoDb,
methods, mvtnorm, stats, parallel
Suggests: knitr, rmarkdown, BiocStyle, BiocManager
License: GPL-3
MD5sum: f4cc438825b0b1fe65a1781fb2cd864c
NeedsCompilation: no
Title: Detection of Differentially Methylated Regions
Description: This package detects significant differentially methylated
regions (for both qualitative and quantitative traits), using a
scan statistic with underlying Poisson heuristics. The scan
statistic will depend on a sequence of window sizes (# of CpGs
within each window) and on a threshold for each window size.
This threshold can be calculated by three different means: i)
analytically using Siegmund et.al (2012) solution (preferred),
ii) an important sampling as suggested by Zhang (2008), and a
iii) full MCMC modeling of the data, choosing between a number
of different options for modeling the dependency between each
CpG.
biocViews: Software, Technology, Sequencing, WholeGenome
Author: Christian M Page [aut, cre], Linda Vos [aut], Trine B Rounge
[ctb, dtc], Hanne F Harbo [ths], Bettina K Andreassen [aut]
Maintainer: Christian M Page <page.ntnu@gmail.com>
URL: https://github.com/christpa/DMRScan
VignetteBuilder: knitr
BugReports: https://github.com/christpa/DMRScan/issues
PackageStatus: Active
git_url: https://git.bioconductor.org/packages/DMRScan
git_branch: devel
git_last_commit: b96365d
git_last_commit_date: 2024-10-29
Date/Publication: 2025-01-23
source.ver: src/contrib/DMRScan_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DMRScan_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DMRScan_1.29.0.tgz
vignettes: vignettes/DMRScan/inst/doc/DMRScan_vignette.html
vignetteTitles: DMR Scan Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DMRScan/inst/doc/DMRScan_vignette.R
dependencyCount: 32
Package: dmrseq
Version: 1.27.0
Depends: R (>= 3.5), bsseq
Imports: GenomicRanges, nlme, ggplot2, S4Vectors, RColorBrewer,
bumphunter, DelayedMatrixStats (>= 1.1.13), matrixStats,
BiocParallel, outliers, methods, locfit, IRanges, grDevices,
graphics, stats, utils, annotatr, AnnotationHub, rtracklayer,
GenomeInfoDb, splines
Suggests: knitr, rmarkdown, BiocStyle,
TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db
License: MIT + file LICENSE
MD5sum: 8b6eff5f6dce9ecc2873c58d938eed38
NeedsCompilation: no
Title: Detection and inference of differentially methylated regions
from Whole Genome Bisulfite Sequencing
Description: This package implements an approach for scanning the
genome to detect and perform accurate inference on
differentially methylated regions from Whole Genome Bisulfite
Sequencing data. The method is based on comparing detected
regions to a pooled null distribution, that can be implemented
even when as few as two samples per population are available.
Region-level statistics are obtained by fitting a generalized
least squares (GLS) regression model with a nested
autoregressive correlated error structure for the effect of
interest on transformed methylation proportions.
biocViews: ImmunoOncology, DNAMethylation, Epigenetics,
MultipleComparison, Software, Sequencing,
DifferentialMethylation, WholeGenome, Regression,
FunctionalGenomics
Author: Keegan Korthauer [cre, aut] (ORCID:
<https://orcid.org/0000-0002-4565-1654>), Rafael Irizarry [aut]
(ORCID: <https://orcid.org/0000-0002-3944-4309>), Yuval
Benjamini [aut], Sutirtha Chakraborty [aut]
Maintainer: Keegan Korthauer <keegan@stat.ubc.ca>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/dmrseq
git_branch: devel
git_last_commit: ea859e7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/dmrseq_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/dmrseq_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/dmrseq_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/dmrseq_1.27.0.tgz
vignettes: vignettes/dmrseq/inst/doc/dmrseq.html
vignetteTitles: Analyzing Bisulfite-seq data with dmrseq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/dmrseq/inst/doc/dmrseq.R
importsMe: biscuiteer
dependencyCount: 149
Package: DNABarcodeCompatibility
Version: 1.23.0
Depends: R (>= 3.6.0)
Imports: dplyr, tidyr, numbers, purrr, stringr, stats, utils, methods,
Rcpp (>= 0.11.2), BH
LinkingTo: Rcpp, BH
Suggests: knitr, rmarkdown, BiocStyle, testthat
License: file LICENSE
MD5sum: 20e9daec0e78088b5d751c8dbb81912f
NeedsCompilation: yes
Title: A Tool for Optimizing Combinations of DNA Barcodes Used in
Multiplexed Experiments on Next Generation Sequencing Platforms
Description: The package allows one to obtain optimised combinations of
DNA barcodes to be used for multiplex sequencing. In each
barcode combination, barcodes are pooled with respect to
Illumina chemistry constraints. Combinations can be filtered to
keep those that are robust against substitution and
insertion/deletion errors thereby facilitating the
demultiplexing step. In addition, the package provides an
optimiser function to further favor the selection of barcode
combinations with least heterogeneity in barcode usage.
biocViews: Preprocessing, Sequencing
Author: Céline Trébeau [cre] (ORCID:
<https://orcid.org/0000-0001-6795-5379>), Jacques Boutet de
Monvel [aut] (ORCID: <https://orcid.org/0000-0001-6182-3527>),
Fabienne Wong Jun Tai [ctb], Raphaël Etournay [aut] (ORCID:
<https://orcid.org/0000-0002-2441-9274>)
Maintainer: Céline Trébeau <ctrebeau@pasteur.fr>
URL: https://dnabarcodecompatibility.pasteur.fr/
VignetteBuilder: knitr
BugReports:
https://gitlab.pasteur.fr/ida-public/dnabarcodecompatibility/-/issues
git_url: https://git.bioconductor.org/packages/DNABarcodeCompatibility
git_branch: devel
git_last_commit: 2a16130
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DNABarcodeCompatibility_1.23.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.5/DNABarcodeCompatibility_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DNABarcodeCompatibility_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DNABarcodeCompatibility_1.23.0.tgz
vignettes: vignettes/DNABarcodeCompatibility/inst/doc/introduction.html
vignetteTitles: Introduction to DNABarcodeCompatibility
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/DNABarcodeCompatibility/inst/doc/introduction.R
dependencyCount: 30
Package: DNABarcodes
Version: 1.37.0
Depends: Matrix, parallel
Imports: Rcpp (>= 0.11.2), BH
LinkingTo: Rcpp, BH
Suggests: knitr, BiocStyle, rmarkdown
License: GPL-2
MD5sum: cc7e4318d686a7875a8a28486082e4e0
NeedsCompilation: yes
Title: A tool for creating and analysing DNA barcodes used in Next
Generation Sequencing multiplexing experiments
Description: The package offers a function to create DNA barcode sets
capable of correcting insertion, deletion, and substitution
errors. Existing barcodes can be analysed regarding their
minimal, maximal and average distances between barcodes.
Finally, reads that start with a (possibly mutated) barcode can
be demultiplexed, i.e., assigned to their original reference
barcode.
biocViews: Preprocessing, Sequencing
Author: Tilo Buschmann <tilo.buschmann.ac@gmail.com>
Maintainer: Tilo Buschmann <tilo.buschmann.ac@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DNABarcodes
git_branch: devel
git_last_commit: 0b621bc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DNABarcodes_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DNABarcodes_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DNABarcodes_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DNABarcodes_1.37.0.tgz
vignettes: vignettes/DNABarcodes/inst/doc/DNABarcodes.html
vignetteTitles: DNABarcodes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DNABarcodes/inst/doc/DNABarcodes.R
dependencyCount: 11
Package: DNAcopy
Version: 1.81.0
License: GPL (>= 2)
Archs: x64
MD5sum: fc6cac7f73017c8148f7d12f64f76aaf
NeedsCompilation: yes
Title: DNA Copy Number Data Analysis
Description: Implements the circular binary segmentation (CBS)
algorithm to segment DNA copy number data and identify genomic
regions with abnormal copy number.
biocViews: Microarray, CopyNumberVariation
Author: Venkatraman E. Seshan, Adam Olshen
Maintainer: Venkatraman E. Seshan <seshanv@mskcc.org>
git_url: https://git.bioconductor.org/packages/DNAcopy
git_branch: devel
git_last_commit: 8c04c59
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DNAcopy_1.81.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DNAcopy_1.81.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DNAcopy_1.81.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DNAcopy_1.81.0.tgz
vignettes: vignettes/DNAcopy/inst/doc/DNAcopy.pdf
vignetteTitles: DNAcopy
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DNAcopy/inst/doc/DNAcopy.R
dependsOnMe: CGHcall, cghMCR, CRImage, PureCN, CSclone, ParDNAcopy,
saasCNV
importsMe: ADaCGH2, AneuFinder, ChAMP, cn.farms, CNAnorm, CNVrd2,
conumee, GWASTools, maftools, MDTS, MEDIPS, MinimumDistance,
QDNAseq, Repitools, SCOPE, jointseg, PSCBS
suggestsMe: cn.mops, CopyNumberPlots, fastseg, nullranges, sesame,
ACNE, aroma.cn, aroma.core, calmate
dependencyCount: 0
Package: DNAcycP2
Version: 0.99.7
Depends: R (>= 4.4.0)
Imports: basilisk, reticulate
Suggests: knitr, rmarkdown, BiocGenerics, RUnit, tinytest, BiocStyle,
Biostrings
License: Artistic-2.0
Archs: x64
MD5sum: 57fe5c65528dd5a6f002ca7d992c0487
NeedsCompilation: no
Title: DNA Cyclizability Prediction
Description: This package performs prediction of intrinsic
cyclizability of of every 50-bp subsequence in a DNA sequence.
The input could be a file either in FASTA or text format. The
output will be the C-score, the estimated intrinsic
cyclizability score for each 50 bp sequences in each entry of
the sequence set.
biocViews: NeuralNetwork, StructuralPrediction
Author: Ji-Ping Wang [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8398-939X>)
Maintainer: Ji-Ping Wang <jzwang@northwestern.edu>
URL: https://github.com/jipingw/DNAcycP2
VignetteBuilder: knitr
BugReports: https://github.com/jipingw/DNAcycP2
git_url: https://git.bioconductor.org/packages/DNAcycP2
git_branch: devel
git_last_commit: 5c1bc33
git_last_commit_date: 2025-03-25
Date/Publication: 2025-03-26
source.ver: src/contrib/DNAcycP2_0.99.7.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DNAcycP2_0.99.7.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DNAcycP2_0.99.7.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DNAcycP2_0.99.7.tgz
vignettes: vignettes/DNAcycP2/inst/doc/dnacycp2.html
vignetteTitles: DNAcycP2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DNAcycP2/inst/doc/dnacycp2.R
dependencyCount: 24
Package: DNAfusion
Version: 1.9.0
Depends: R (>= 4.2.0)
Imports: bamsignals, GenomicRanges, IRanges, Rsamtools,
GenomicAlignments, BiocBaseUtils, S4Vectors, GenomicFeatures,
TxDb.Hsapiens.UCSC.hg38.knownGene, BiocGenerics
Suggests: knitr, rmarkdown, testthat, sessioninfo, BiocStyle
License: GPL-3
Archs: x64
MD5sum: 3e97235bbc3b7d7c9fbc50f7b970789f
NeedsCompilation: no
Title: Identification of gene fusions using paired-end sequencing
Description: DNAfusion can identify gene fusions such as EML4-ALK based
on paired-end sequencing results. This package was developed
using position deduplicated BAM files generated with the AVENIO
Oncology Analysis Software. These files are made using the
AVENIO ctDNA surveillance kit and Illumina Nextseq 500
sequencing. This is a targeted hybridization NGS approach and
includes ALK-specific but not EML4-specific probes.
biocViews: TargetedResequencing, Genetics, GeneFusionDetection,
Sequencing
Author: Christoffer Trier Maansson [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3071-3437>), Emma Roger Andersen
[ctb, rev], Maiken Parm Ulhoi [dtc], Peter Meldgaard [dtc], Boe
Sandahl Sorensen [rev, fnd]
Maintainer: Christoffer Trier Maansson <ctm@clin.au.dk>
URL: https://github.com/CTrierMaansson/DNAfusion
VignetteBuilder: knitr
BugReports: https://github.com/CTrierMaansson/DNAfusion/issues
git_url: https://git.bioconductor.org/packages/DNAfusion
git_branch: devel
git_last_commit: b215285
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DNAfusion_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DNAfusion_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DNAfusion_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DNAfusion_1.9.0.tgz
vignettes: vignettes/DNAfusion/inst/doc/Introduction_to_DNAfusion.html
vignetteTitles: Introduction to DNAfusion
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DNAfusion/inst/doc/Introduction_to_DNAfusion.R
dependencyCount: 82
Package: DNAshapeR
Version: 1.35.0
Depends: R (>= 3.4), GenomicRanges
Imports: Rcpp (>= 0.12.1), Biostrings, fields
LinkingTo: Rcpp
Suggests: AnnotationHub, knitr, rmarkdown, testthat,
BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Hsapiens.UCSC.hg19,
caret
License: GPL-2
MD5sum: b738a616d0475594a94b8959702b98ed
NeedsCompilation: yes
Title: High-throughput prediction of DNA shape features
Description: DNAhapeR is an R/BioConductor package for ultra-fast,
high-throughput predictions of DNA shape features. The package
allows to predict, visualize and encode DNA shape features for
statistical learning.
biocViews: StructuralPrediction, DNA3DStructure, Software
Author: Tsu-Pei Chiu and Federico Comoglio
Maintainer: Tsu-Pei Chiu <tsupeich@usc.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DNAshapeR
git_branch: devel
git_last_commit: 090780d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DNAshapeR_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DNAshapeR_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DNAshapeR_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DNAshapeR_1.35.0.tgz
vignettes: vignettes/DNAshapeR/inst/doc/DNAshapeR.html
vignetteTitles: DNAshapeR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DNAshapeR/inst/doc/DNAshapeR.R
dependencyCount: 33
Package: DominoEffect
Version: 1.27.0
Depends: R(>= 3.5)
Imports: biomaRt, data.table, utils, stats, Biostrings, pwalign,
SummarizedExperiment, VariantAnnotation, AnnotationDbi,
GenomeInfoDb, IRanges, GenomicRanges, methods
Suggests: knitr, testthat, rmarkdown
License: GPL (>= 3)
MD5sum: 215d286429324a0ef85f364e17ba890a
NeedsCompilation: no
Title: Identification and Annotation of Protein Hotspot Residues
Description: The functions support identification and annotation of
hotspot residues in proteins. These are individual amino acids
that accumulate mutations at a much higher rate than their
surrounding regions.
biocViews: Software, SomaticMutation, Proteomics, SequenceMatching,
Alignment
Author: Marija Buljan and Peter Blattmann
Maintainer: Marija Buljan <marija.buljan.2@gmail.com>, Peter Blattmann
<peter_blattmann@bluewin.ch>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DominoEffect
git_branch: devel
git_last_commit: deeeeac
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DominoEffect_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DominoEffect_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DominoEffect_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DominoEffect_1.27.0.tgz
vignettes: vignettes/DominoEffect/inst/doc/Vignette.html
vignetteTitles: Vignette for DominoEffect package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DominoEffect/inst/doc/Vignette.R
dependencyCount: 104
Package: dominoSignal
Version: 1.1.5
Depends: R(>= 4.2.0),
Imports: biomaRt, ComplexHeatmap, circlize, ggpubr, grDevices, grid,
igraph, Matrix, methods, plyr, stats, utils, magrittr, purrr,
dplyr
Suggests: knitr, patchwork, rmarkdown, Seurat, testthat, formatR,
BiocFileCache, SingleCellExperiment
License: GPL-3 | file LICENSE
Archs: x64
MD5sum: e8e3ac17cfe8cffbe8451abf30ae3af3
NeedsCompilation: no
Title: Cell Communication Analysis for Single Cell RNA Sequencing
Description: dominoSignal is a package developed to analyze cell
signaling through ligand - receptor - transcription factor
networks in scRNAseq data. It takes as input information
transcriptomic data, requiring counts, z-scored counts, and
cluster labels, as well as information on transcription factor
activation (such as from SCENIC) and a database of ligand and
receptor pairings (such as from CellPhoneDB). This package
creates an object storing ligand - receptor - transcription
factor linkages by cluster and provides several methods for
exploring, summarizing, and visualizing the analysis.
biocViews: SystemsBiology, SingleCell, Transcriptomics, Network
Author: Christopher Cherry [aut] (ORCID:
<https://orcid.org/0000-0002-5481-0055>), Jacob T Mitchell
[aut, cre] (ORCID: <https://orcid.org/0000-0002-5370-9692>),
Sushma Nagaraj [aut] (ORCID:
<https://orcid.org/0000-0001-5166-1309>), Kavita Krishnan [aut]
(ORCID: <https://orcid.org/0000-0003-1345-0249>), Dmitrijs
Lvovs [aut], Elana Fertig [ctb] (ORCID:
<https://orcid.org/0000-0003-3204-342X>), Jennifer Elisseeff
[ctb] (ORCID: <https://orcid.org/0000-0002-5066-1996>)
Maintainer: Jacob T Mitchell <jmitch81@jhmi.edu>
URL: https://fertiglab.github.io/dominoSignal/
VignetteBuilder: knitr
BugReports: https://github.com/FertigLab/dominoSignal/issues
git_url: https://git.bioconductor.org/packages/dominoSignal
git_branch: devel
git_last_commit: ee1b992
git_last_commit_date: 2025-03-17
Date/Publication: 2025-03-18
source.ver: src/contrib/dominoSignal_1.1.5.tar.gz
win.binary.ver: bin/windows/contrib/4.5/dominoSignal_1.1.5.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/dominoSignal_1.1.5.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/dominoSignal_1.1.5.tgz
vignettes: vignettes/dominoSignal/inst/doc/domino_object_vignette.html,
vignettes/dominoSignal/inst/doc/dominoSignal.html,
vignettes/dominoSignal/inst/doc/plotting_vignette.html
vignetteTitles: Interacting with domino Objects, Get Started with
dominoSignal, Plotting Functions and Options
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/dominoSignal/inst/doc/domino_object_vignette.R,
vignettes/dominoSignal/inst/doc/dominoSignal.R,
vignettes/dominoSignal/inst/doc/plotting_vignette.R
dependencyCount: 136
Package: doppelgangR
Version: 1.35.0
Depends: R (>= 3.5.0), Biobase, BiocParallel
Imports: sva, impute, digest, mnormt, methods, grDevices, graphics,
stats, SummarizedExperiment, utils
Suggests: BiocStyle, knitr, rmarkdown, curatedOvarianData, testthat
License: GPL (>=2.0)
MD5sum: 45b4f71a9c59d512e32d61984907348f
NeedsCompilation: no
Title: Identify likely duplicate samples from genomic or meta-data
Description: The main function is doppelgangR(), which takes as minimal
input a list of ExpressionSet object, and searches all list
pairs for duplicated samples. The search is based on the
genomic data (exprs(eset)), phenotype/clinical data
(pData(eset)), and "smoking guns" - supposedly unique
identifiers found in pData(eset).
biocViews: ImmunoOncology, RNASeq, Microarray, GeneExpression,
QualityControl
Author: Levi Waldron [aut, cre], Markus Reister [aut, ctb], Marcel
Ramos [ctb]
Maintainer: Levi Waldron <lwaldron.research@gmail.com>
URL: https://github.com/lwaldron/doppelgangR
VignetteBuilder: knitr
BugReports: https://github.com/lwaldron/doppelgangR/issues
git_url: https://git.bioconductor.org/packages/doppelgangR
git_branch: devel
git_last_commit: 0cd0783
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/doppelgangR_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/doppelgangR_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/doppelgangR_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/doppelgangR_1.35.0.tgz
vignettes: vignettes/doppelgangR/inst/doc/doppelgangR.html
vignetteTitles: doppelgangR vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/doppelgangR/inst/doc/doppelgangR.R
dependencyCount: 81
Package: Doscheda
Version: 1.29.0
Depends: R (>= 3.4)
Imports: methods, drc, stats, httr, jsonlite, reshape2 , vsn, affy,
limma, stringr, ggplot2, graphics, grDevices, calibrate,
corrgram, gridExtra, DT, shiny, shinydashboard, readxl,
prodlim, matrixStats
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: GPL-3
MD5sum: 071c4ec8871c7fe965fa78607f4525c4
NeedsCompilation: no
Title: A DownStream Chemo-Proteomics Analysis Pipeline
Description: Doscheda focuses on quantitative chemoproteomics used to
determine protein interaction profiles of small molecules from
whole cell or tissue lysates using Mass Spectrometry data. The
package provides a shiny application to run the pipeline,
several visualisations and a downloadable report of an
experiment.
biocViews: Proteomics, Normalization, Preprocessing, MassSpectrometry,
QualityControl, DataImport, Regression
Author: Bruno Contrino, Piero Ricchiuto
Maintainer: Bruno Contrino <br1contrino@yahoo.co.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Doscheda
git_branch: devel
git_last_commit: 305a40a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-01
source.ver: src/contrib/Doscheda_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Doscheda_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Doscheda_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Doscheda_1.29.0.tgz
vignettes: vignettes/Doscheda/inst/doc/Doscheda.html
vignetteTitles: Doscheda
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Doscheda/inst/doc/Doscheda.R
dependencyCount: 154
Package: DOSE
Version: 4.1.0
Depends: R (>= 3.5.0)
Imports: AnnotationDbi, BiocParallel, fgsea, ggplot2, GOSemSim (>=
2.31.2), methods, qvalue, reshape2, stats, utils, yulab.utils
(>= 0.1.6)
Suggests: prettydoc, clusterProfiler, gson (>= 0.0.5), knitr, memoise,
org.Hs.eg.db, rmarkdown, testthat
License: Artistic-2.0
MD5sum: 9141c32d267c2d1e45095490348bd546
NeedsCompilation: no
Title: Disease Ontology Semantic and Enrichment analysis
Description: This package implements five methods proposed by Resnik,
Schlicker, Jiang, Lin and Wang respectively for measuring
semantic similarities among DO terms and gene products.
Enrichment analyses including hypergeometric model and gene set
enrichment analysis are also implemented for discovering
disease associations of high-throughput biological data.
biocViews: Annotation, Visualization, MultipleComparison,
GeneSetEnrichment, Pathways, Software
Author: Guangchuang Yu [aut, cre], Li-Gen Wang [ctb], Vladislav Petyuk
[ctb], Giovanni Dall'Olio [ctb]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
URL: https://yulab-smu.top/contribution-knowledge-mining/
VignetteBuilder: knitr
BugReports: https://github.com/GuangchuangYu/DOSE/issues
git_url: https://git.bioconductor.org/packages/DOSE
git_branch: devel
git_last_commit: 898262a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DOSE_4.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DOSE_4.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DOSE_4.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DOSE_4.1.0.tgz
vignettes: vignettes/DOSE/inst/doc/DOSE.html
vignetteTitles: DOSE
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DOSE/inst/doc/DOSE.R
importsMe: bioCancer, clusterProfiler, debrowser, enrichplot,
enrichViewNet, GDCRNATools, meshes, miRSM, miRspongeR,
Moonlight2R, MoonlightR, Pigengene, ReactomePA, RegEnrich,
scTensor, signatureSearch, SVMDO, TDbasedUFEadv,
ExpHunterSuite, GseaVis, immcp
suggestsMe: cola, GOSemSim, GRaNIE, rrvgo, scGPS
dependencyCount: 95
Package: doseR
Version: 1.23.0
Depends: R (>= 3.6)
Imports: edgeR, methods, stats, graphics, matrixStats, mclust, lme4,
RUnit, SummarizedExperiment, digest, S4Vectors
Suggests: BiocStyle, knitr, rmarkdown
License: GPL
MD5sum: c2109bc5e0b7e49fc9ca57d93d9f7596
NeedsCompilation: no
Title: doseR
Description: doseR package is a next generation sequencing package for
sex chromosome dosage compensation which can be applied broadly
to detect shifts in gene expression among an arbitrary number
of pre-defined groups of loci. doseR is a differential gene
expression package for count data, that detects directional
shifts in expression for multiple, specific subsets of genes,
broad utility in systems biology research. doseR has been
prepared to manage the nature of the data and the desired set
of inferences. doseR uses S4 classes to store count data from
sequencing experiment. It contains functions to normalize and
filter count data, as well as to plot and calculate statistics
of count data. It contains a framework for linear modeling of
count data. The package has been tested using real and
simulated data.
biocViews: Infrastructure, Software, DataRepresentation, Sequencing,
GeneExpression, SystemsBiology, DifferentialExpression
Author: AJ Vaestermark, JR Walters.
Maintainer: ake.vastermark <ake.vastermark@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/doseR
git_branch: devel
git_last_commit: 44de376
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/doseR_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/doseR_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/doseR_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/doseR_1.23.0.tgz
vignettes: vignettes/doseR/inst/doc/doseR.html
vignetteTitles: "doseR"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/doseR/inst/doc/doseR.R
dependencyCount: 56
Package: doubletrouble
Version: 1.7.1
Depends: R (>= 4.2.0)
Imports: syntenet, GenomicRanges, Biostrings, mclust, MSA2dist (>=
1.1.5), ggplot2, rlang, stats, utils, AnnotationDbi,
GenomicFeatures
Suggests: txdbmaker, testthat (>= 3.0.0), knitr, feature, patchwork,
BiocStyle, rmarkdown, covr, sessioninfo
License: GPL-3
MD5sum: 1a810382e738043eeefeaff240dc9354
NeedsCompilation: no
Title: Identification and classification of duplicated genes
Description: doubletrouble aims to identify duplicated genes from
whole-genome protein sequences and classify them based on their
modes of duplication. The duplication modes are i. segmental
duplication (SD); ii. tandem duplication (TD); iii. proximal
duplication (PD); iv. transposed duplication (TRD) and; v.
dispersed duplication (DD). Transposon-derived duplicates (TRD)
can be further subdivided into rTRD (retrotransposon-derived
duplication) and dTRD (DNA transposon-derived duplication). If
users want a simpler classification scheme, duplicates can also
be classified into SD- and SSD-derived (small-scale
duplication) gene pairs. Besides classifying gene pairs, users
can also classify genes, so that each gene is assigned a unique
mode of duplication. Users can also calculate substitution
rates per substitution site (i.e., Ka and Ks) from duplicate
pairs, find peaks in Ks distributions with Gaussian Mixture
Models (GMMs), and classify gene pairs into age groups based on
Ks peaks.
biocViews: Software, WholeGenome, ComparativeGenomics,
FunctionalGenomics, Phylogenetics, Network, Classification
Author: FabrÃcio Almeida-Silva [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5314-2964>), Yves Van de Peer
[aut] (ORCID: <https://orcid.org/0000-0003-4327-3730>)
Maintainer: FabrÃcio Almeida-Silva <fabricio_almeidasilva@hotmail.com>
URL: https://github.com/almeidasilvaf/doubletrouble
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/doubletrouble
git_url: https://git.bioconductor.org/packages/doubletrouble
git_branch: devel
git_last_commit: f68b4af
git_last_commit_date: 2025-03-13
Date/Publication: 2025-03-13
source.ver: src/contrib/doubletrouble_1.7.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/doubletrouble_1.7.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/doubletrouble_1.7.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/doubletrouble_1.7.1.tgz
vignettes: vignettes/doubletrouble/inst/doc/doubletrouble_vignette.html
vignetteTitles: Identification and classification of duplicated genes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/doubletrouble/inst/doc/doubletrouble_vignette.R
dependencyCount: 143
Package: drawProteins
Version: 1.27.0
Depends: R (>= 4.0)
Imports: ggplot2, httr, dplyr, readr, tidyr
Suggests: covr, testthat, knitr, rmarkdown, BiocStyle
License: MIT + file LICENSE
MD5sum: 935d75853df050f5a744bad0dd7d516e
NeedsCompilation: no
Title: Package to Draw Protein Schematics from Uniprot API output
Description: This package draws protein schematics from Uniprot API
output. From the JSON returned by the GET command, it creates a
dataframe from the Uniprot Features API. This dataframe can
then be used by geoms based on ggplot2 and base R to draw
protein schematics.
biocViews: Visualization, FunctionalPrediction, Proteomics
Author: Paul Brennan [aut, cre]
Maintainer: Paul Brennan <brennanpincardiff@gmail.com>
URL: https://github.com/brennanpincardiff/drawProteins
VignetteBuilder: knitr
BugReports:
https://github.com/brennanpincardiff/drawProteins/issues/new
git_url: https://git.bioconductor.org/packages/drawProteins
git_branch: devel
git_last_commit: fc1cfc1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/drawProteins_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/drawProteins_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/drawProteins_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/drawProteins_1.27.0.tgz
vignettes: vignettes/drawProteins/inst/doc/drawProteins_BiocStyle.html,
vignettes/drawProteins/inst/doc/drawProteins_extract_transcripts_BiocStyle.html
vignetteTitles: Using drawProteins, Using extract_transcripts in
drawProteins
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/drawProteins/inst/doc/drawProteins_BiocStyle.R,
vignettes/drawProteins/inst/doc/drawProteins_extract_transcripts_BiocStyle.R
importsMe: factR
dependencyCount: 61
Package: dreamlet
Version: 1.5.1
Depends: R (>= 4.3.0), variancePartition (>= 1.36.1),
SingleCellExperiment, ggplot2
Imports: edgeR, SummarizedExperiment, DelayedMatrixStats,
sparseMatrixStats, MatrixGenerics, Matrix, methods, purrr,
GSEABase, data.table, zenith (>= 1.1.2), mashr (>= 0.2.52),
ashr, dplyr, BiocParallel, ggbeeswarm, S4Vectors, IRanges,
irlba, limma, metafor, remaCor, broom, tidyr, rlang,
BiocGenerics, S4Arrays, SparseArray, DelayedArray, gtools,
reshape2, ggrepel, scattermore, Rcpp, lme4 (>= 1.1-33), MASS,
Rdpack, utils, stats
LinkingTo: Rcpp, beachmat
Suggests: BiocStyle, knitr, pander, rmarkdown, muscat, ExperimentHub,
RUnit, muscData, scater, scuttle
License: Artistic-2.0
MD5sum: 7a0c61377f92750b7fa6f9d607c77fcf
NeedsCompilation: yes
Title: Scalable differential expression analysis of single cell
transcriptomics datasets with complex study designs
Description: Recent advances in single cell/nucleus transcriptomic
technology has enabled collection of cohort-scale datasets to
study cell type specific gene expression differences associated
disease state, stimulus, and genetic regulation. The scale of
these data, complex study designs, and low read count per cell
mean that characterizing cell type specific molecular
mechanisms requires a user-frieldly, purpose-build analytical
framework. We have developed the dreamlet package that applies
a pseudobulk approach and fits a regression model for each gene
and cell cluster to test differential expression across
individuals associated with a trait of interest. Use of
precision-weighted linear mixed models enables accounting for
repeated measures study designs, high dimensional batch
effects, and varying sequencing depth or observed cells per
biosample.
biocViews: RNASeq, GeneExpression, DifferentialExpression, BatchEffect,
QualityControl, Regression, GeneSetEnrichment, GeneRegulation,
Epigenetics, FunctionalGenomics, Transcriptomics,
Normalization, SingleCell, Preprocessing, Sequencing,
ImmunoOncology, Software
Author: Gabriel Hoffman [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0957-0224>)
Maintainer: Gabriel Hoffman <gabriel.hoffman@mssm.edu>
URL: https://DiseaseNeurogenomics.github.io/dreamlet
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/DiseaseNeurogenomics/dreamlet/issues
git_url: https://git.bioconductor.org/packages/dreamlet
git_branch: devel
git_last_commit: 9517428
git_last_commit_date: 2025-03-25
Date/Publication: 2025-03-26
source.ver: src/contrib/dreamlet_1.5.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/dreamlet_1.5.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/dreamlet_1.5.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/dreamlet_1.5.1.tgz
vignettes: vignettes/dreamlet/inst/doc/cell_covs.html,
vignettes/dreamlet/inst/doc/dreamlet.html,
vignettes/dreamlet/inst/doc/errors.html,
vignettes/dreamlet/inst/doc/h5ad_on_disk.html,
vignettes/dreamlet/inst/doc/mashr.html,
vignettes/dreamlet/inst/doc/non_lin_eff.html
vignetteTitles: Modeling continuous cell-level covariates, Dreamlet
analysis of single cell RNA-seq, Error handling, Loading
large-scale H5AD datasets, mashr analysis following dreamlet,
Testing non-linear effects
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/dreamlet/inst/doc/cell_covs.R,
vignettes/dreamlet/inst/doc/dreamlet.R,
vignettes/dreamlet/inst/doc/errors.R,
vignettes/dreamlet/inst/doc/h5ad_on_disk.R,
vignettes/dreamlet/inst/doc/non_lin_eff.R
dependencyCount: 192
Package: DRIMSeq
Version: 1.35.0
Depends: R (>= 3.4.0)
Imports: utils, stats, MASS, GenomicRanges, IRanges, S4Vectors,
BiocGenerics, methods, BiocParallel, limma, edgeR, ggplot2,
reshape2
Suggests: PasillaTranscriptExpr, GeuvadisTranscriptExpr, grid,
BiocStyle, knitr, testthat
License: GPL (>= 3)
MD5sum: 8a625cf608e725315651690307d2e9e0
NeedsCompilation: no
Title: Differential transcript usage and tuQTL analyses with
Dirichlet-multinomial model in RNA-seq
Description: The package provides two frameworks. One for the
differential transcript usage analysis between different
conditions and one for the tuQTL analysis. Both are based on
modeling the counts of genomic features (i.e., transcripts)
with the Dirichlet-multinomial distribution. The package also
makes available functions for visualization and exploration of
the data and results.
biocViews: ImmunoOncology, SNP, AlternativeSplicing,
DifferentialSplicing, Genetics, RNASeq, Sequencing,
WorkflowStep, MultipleComparison, GeneExpression,
DifferentialExpression
Author: Malgorzata Nowicka [aut, cre]
Maintainer: Malgorzata Nowicka <gosia.nowicka.uzh@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DRIMSeq
git_branch: devel
git_last_commit: bffc717
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DRIMSeq_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DRIMSeq_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DRIMSeq_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DRIMSeq_1.35.0.tgz
vignettes: vignettes/DRIMSeq/inst/doc/DRIMSeq.pdf
vignetteTitles: Differential transcript usage and transcript usage QTL
analyses in RNA-seq with the DRIMSeq package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DRIMSeq/inst/doc/DRIMSeq.R
dependsOnMe: rnaseqDTU
importsMe: BANDITS
dependencyCount: 72
Package: DriverNet
Version: 1.47.0
Depends: R (>= 2.10), methods
License: GPL-3
Archs: x64
MD5sum: 5de66b21ba86c160a6fd6769b7aa0bcd
NeedsCompilation: no
Title: Drivernet: uncovering somatic driver mutations modulating
transcriptional networks in cancer
Description: DriverNet is a package to predict functional important
driver genes in cancer by integrating genome data (mutation and
copy number variation data) and transcriptome data (gene
expression data). The different kinds of data are combined by
an influence graph, which is a gene-gene interaction network
deduced from pathway data. A greedy algorithm is used to find
the possible driver genes, which may mutated in a larger number
of patients and these mutations will push the gene expression
values of the connected genes to some extreme values.
biocViews: Network
Author: Ali Bashashati, Reza Haffari, Jiarui Ding, Gavin Ha, Kenneth
Liu, Jamie Rosner and Sohrab Shah
Maintainer: Jiarui Ding <jiaruid@cs.ubc.ca>
git_url: https://git.bioconductor.org/packages/DriverNet
git_branch: devel
git_last_commit: a5b4009
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DriverNet_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DriverNet_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DriverNet_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DriverNet_1.47.0.tgz
vignettes: vignettes/DriverNet/inst/doc/DriverNet-Overview.pdf
vignetteTitles: An introduction to DriverNet
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DriverNet/inst/doc/DriverNet-Overview.R
dependencyCount: 1
Package: DropletUtils
Version: 1.27.2
Depends: SingleCellExperiment
Imports: utils, stats, methods, Matrix, Rcpp, BiocGenerics, S4Vectors,
IRanges, GenomicRanges, SummarizedExperiment, BiocParallel,
SparseArray (>= 1.5.18), DelayedArray (>= 0.31.9),
DelayedMatrixStats, HDF5Array, rhdf5, edgeR, R.utils, dqrng,
beachmat, scuttle
LinkingTo: Rcpp, beachmat, Rhdf5lib, BH, dqrng, scuttle
Suggests: testthat, knitr, BiocStyle, rmarkdown, jsonlite,
DropletTestFiles
License: GPL-3
MD5sum: 0d0f51d02dd01250c0ef55f9828f33d8
NeedsCompilation: yes
Title: Utilities for Handling Single-Cell Droplet Data
Description: Provides a number of utility functions for handling
single-cell (RNA-seq) data from droplet technologies such as
10X Genomics. This includes data loading from count matrices or
molecule information files, identification of cells from empty
droplets, removal of barcode-swapped pseudo-cells, and
downsampling of the count matrix.
biocViews: ImmunoOncology, SingleCell, Sequencing, RNASeq,
GeneExpression, Transcriptomics, DataImport, Coverage
Author: Aaron Lun [aut], Jonathan Griffiths [ctb, cre], Davis McCarthy
[ctb], Dongze He [ctb], Rob Patro [ctb]
Maintainer: Jonathan Griffiths <jonathan.griffiths.94@gmail.com>
SystemRequirements: C++11, GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DropletUtils
git_branch: devel
git_last_commit: 5aea486
git_last_commit_date: 2024-12-10
Date/Publication: 2024-12-18
source.ver: src/contrib/DropletUtils_1.27.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DropletUtils_1.27.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DropletUtils_1.27.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DropletUtils_1.27.2.tgz
vignettes: vignettes/DropletUtils/inst/doc/DropletUtils.html
vignetteTitles: Utilities for handling droplet-based single-cell
RNA-seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DropletUtils/inst/doc/DropletUtils.R
dependsOnMe: OSCA.intro, OSCA.multisample, OSCA.workflows
importsMe: FLAMES, scCB2, scPipe, singleCellTK, Spaniel,
SpatialExperimentIO, SpatialFeatureExperiment, visiumStitched
suggestsMe: alabaster.spatial, demuxmix, mumosa, Nebulosa, OSTA.data,
SingleCellAlleleExperiment, SpatialExperiment, SPOTlight, SVP,
tidySpatialExperiment, DropletTestFiles, MerfishData, muscData,
spatialLIBD, scCustomize, SoupX
dependencyCount: 67
Package: drugTargetInteractions
Version: 1.15.0
Depends: methods, R (>= 4.1)
Imports: utils, RSQLite, UniProt.ws, biomaRt,ensembldb,
BiocFileCache,dplyr,rappdirs, AnnotationFilter, S4Vectors
Suggests: RUnit, BiocStyle, knitr, rmarkdown, ggplot2, reshape2, DT,
EnsDb.Hsapiens.v86
License: Artistic-2.0
MD5sum: 5d8be70766cd4f6bb9bb405e08d0bc00
NeedsCompilation: no
Title: Drug-Target Interactions
Description: Provides utilities for identifying drug-target
interactions for sets of small molecule or gene/protein
identifiers. The required drug-target interaction information
is obained from a local SQLite instance of the ChEMBL database.
ChEMBL has been chosen for this purpose, because it provides
one of the most comprehensive and best annotatated knowledge
resources for drug-target information available in the public
domain.
biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics,
Pharmacogenomics, Proteomics, Metabolomics
Author: Thomas Girke [cre, aut]
Maintainer: Thomas Girke <thomas.girke@ucr.edu>
URL: https://github.com/girke-lab/drugTargetInteractions
VignetteBuilder: knitr
BugReports: https://github.com/girke-lab/drugTargetInteractions
git_url: https://git.bioconductor.org/packages/drugTargetInteractions
git_branch: devel
git_last_commit: 262cba9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/drugTargetInteractions_1.15.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.5/drugTargetInteractions_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/drugTargetInteractions_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/drugTargetInteractions_1.15.0.tgz
vignettes:
vignettes/drugTargetInteractions/inst/doc/drugTargetInteractions.html
vignetteTitles: Drug-Target Interactions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/drugTargetInteractions/inst/doc/drugTargetInteractions.R
dependencyCount: 108
Package: DrugVsDisease
Version: 2.49.0
Depends: R (>= 2.10), affy, limma, biomaRt, ArrayExpress, GEOquery,
DrugVsDiseasedata, cMap2data, qvalue
Imports: annotate, hgu133a.db, hgu133a2.db, hgu133plus2.db, RUnit,
BiocGenerics, xtable
License: GPL-3
Archs: x64
MD5sum: bbe8b86958c3e739fccedd85bd652d7a
NeedsCompilation: no
Title: Comparison of disease and drug profiles using Gene set
Enrichment Analysis
Description: This package generates ranked lists of differential gene
expression for either disease or drug profiles. Input data can
be downloaded from Array Express or GEO, or from local CEL
files. Ranked lists of differential expression and associated
p-values are calculated using Limma. Enrichment scores
(Subramanian et al. PNAS 2005) are calculated to a reference
set of default drug or disease profiles, or a set of custom
data supplied by the user. Network visualisation of significant
scores are output in Cytoscape format.
biocViews: Microarray, GeneExpression, Clustering
Author: C. Pacini
Maintainer: j. Saez-Rodriguez <saezrodriguez@ebi.ac.uk>
git_url: https://git.bioconductor.org/packages/DrugVsDisease
git_branch: devel
git_last_commit: a631f4f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DrugVsDisease_2.49.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DrugVsDisease_2.49.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DrugVsDisease_2.49.0.tgz
vignettes: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.pdf
vignetteTitles: DrugVsDisease
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.R
dependencyCount: 133
Package: DSS
Version: 2.55.0
Depends: R (>= 3.5.0), methods, Biobase, BiocParallel, bsseq, parallel
Imports: utils, graphics, stats, splines
Suggests: BiocStyle, knitr, rmarkdown, edgeR
License: GPL
MD5sum: 672b711143db263aef806d6733ba75ff
NeedsCompilation: yes
Title: Dispersion shrinkage for sequencing data
Description: DSS is an R library performing differntial analysis for
count-based sequencing data. It detectes differentially
expressed genes (DEGs) from RNA-seq, and differentially
methylated loci or regions (DML/DMRs) from bisulfite sequencing
(BS-seq). The core of DSS is a new dispersion shrinkage method
for estimating the dispersion parameter from Gamma-Poisson or
Beta-Binomial distributions.
biocViews: Sequencing, RNASeq, DNAMethylation,GeneExpression,
DifferentialExpression,DifferentialMethylation
Author: Hao Wu<hao.wu@emory.edu>, Hao Feng<hxf155@case.edu>
Maintainer: Hao Wu <hao.wu@emory.edu>, Hao Feng <hxf155@case.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/DSS
git_branch: devel
git_last_commit: 23fba50
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DSS_2.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DSS_2.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DSS_2.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DSS_2.55.0.tgz
vignettes: vignettes/DSS/inst/doc/DSS.html
vignetteTitles: The DSS User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DSS/inst/doc/DSS.R
dependsOnMe: DeMixT
importsMe: borealis, kissDE, metaseqR2, methylSig
suggestsMe: biscuiteer, methrix, NanoMethViz
dependencyCount: 91
Package: dStruct
Version: 1.13.0
Depends: R (>= 4.1)
Imports: zoo, ggplot2, purrr, reshape2, parallel, IRanges, S4Vectors,
rlang, grDevices, stats, utils
Suggests: BiocStyle, knitr, rmarkdown, tidyverse, testthat (>= 3.0.0)
License: GPL (>= 2)
MD5sum: 8890f0c7e8384e1e5ee6a78f17b6d491
NeedsCompilation: no
Title: Identifying differentially reactive regions from RNA structurome
profiling data
Description: dStruct identifies differentially reactive regions from
RNA structurome profiling data. dStruct is compatible with a
broad range of structurome profiling technologies, e.g.,
SHAPE-MaP, DMS-MaPseq, Structure-Seq, SHAPE-Seq, etc. See
Choudhary et al., Genome Biology, 2019 for the underlying
method.
biocViews: StatisticalMethod, StructuralPrediction, Sequencing,
Software
Author: Krishna Choudhary [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7966-1527>), Sharon Aviran [aut]
(ORCID: <https://orcid.org/0000-0003-1872-9820>)
Maintainer: Krishna Choudhary <kchoudhary@ucdavis.edu>
URL: https://github.com/dataMaster-Kris/dStruct
VignetteBuilder: knitr
BugReports: https://github.com/dataMaster-Kris/dStruct/issues
git_url: https://git.bioconductor.org/packages/dStruct
git_branch: devel
git_last_commit: 32829a3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/dStruct_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/dStruct_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/dStruct_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/dStruct_1.13.0.tgz
vignettes: vignettes/dStruct/inst/doc/dStruct.html
vignetteTitles: Differential RNA structurome analysis using `dStruct`
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/dStruct/inst/doc/dStruct.R
dependencyCount: 49
Package: DTA
Version: 2.53.0
Depends: R (>= 2.10), LSD
Imports: scatterplot3d
License: Artistic-2.0
Archs: x64
MD5sum: 1582cef58372e425bb8732a036e7a966
NeedsCompilation: no
Title: Dynamic Transcriptome Analysis
Description: Dynamic Transcriptome Analysis (DTA) can monitor the
cellular response to perturbations with higher sensitivity and
temporal resolution than standard transcriptomics. The package
implements the underlying kinetic modeling approach capable of
the precise determination of synthesis- and decay rates from
individual microarray or RNAseq measurements.
biocViews: Microarray, DifferentialExpression, GeneExpression,
Transcription
Author: Bjoern Schwalb, Benedikt Zacher, Sebastian Duemcke, Achim
Tresch
Maintainer: Bjoern Schwalb <schwalb@lmb.uni-muenchen.de>
git_url: https://git.bioconductor.org/packages/DTA
git_branch: devel
git_last_commit: 3ccd65f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DTA_2.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DTA_2.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DTA_2.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DTA_2.53.0.tgz
vignettes: vignettes/DTA/inst/doc/DTA.pdf
vignetteTitles: A guide to Dynamic Transcriptome Analysis (DTA)
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DTA/inst/doc/DTA.R
importsMe: rifiComparative
dependencyCount: 5
Package: Dune
Version: 1.19.0
Depends: R (>= 3.6)
Imports: BiocParallel, SummarizedExperiment, utils, ggplot2, dplyr,
tidyr, RColorBrewer, magrittr, gganimate, purrr, aricode
Suggests: knitr, rmarkdown, testthat (>= 2.1.0)
License: MIT + file LICENSE
MD5sum: 469581728c1a9070af78518ebca71180
NeedsCompilation: no
Title: Improving replicability in single-cell RNA-Seq cell type
discovery
Description: Given a set of clustering labels, Dune merges pairs of
clusters to increase mean ARI between labels, improving
replicability.
biocViews: Clustering, GeneExpression, RNASeq, Software, SingleCell,
Transcriptomics, Visualization
Author: Hector Roux de Bezieux [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1489-8339>), Kelly Street [aut]
Maintainer: Hector Roux de Bezieux <hector.rouxdebezieux@berkeley.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Dune
git_branch: devel
git_last_commit: 4205bcf
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Dune_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Dune_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Dune_1.19.0.tgz
vignettes: vignettes/Dune/inst/doc/Dune.html
vignetteTitles: Dune Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Dune/inst/doc/Dune.R
dependencyCount: 97
Package: DuplexDiscovereR
Version: 1.1.2
Depends: R (>= 4.4), InteractionSet
Imports: Gviz, Biostrings, rtracklayer, GenomicAlignments,
GenomicRanges, ggsci, igraph, rlang, scales, stringr, dplyr,
tibble, tidyr, purrr, methods, grDevices, stats, utils
Suggests: knitr, UpSetR, BiocStyle, rmarkdown, testthat (>= 3.0.0)
License: GPL-3
MD5sum: 21aea95f937846d377d44ad34e429596
NeedsCompilation: no
Title: Analysis of the data from RNA duplex probing experiments
Description: DuplexDiscovereR is a package designed for analyzing data
from RNA cross-linking and proximity ligation protocols such as
SPLASH, PARIS, LIGR-seq, and others. DuplexDiscovereR accepts
input in the form of chimerically or split-aligned reads. It
includes procedures for alignment classification, filtering,
and efficient clustering of individual chimeric reads into
duplex groups (DGs). Once DGs are identified, the package
predicts RNA duplex formation and their hybridization energies.
Additional metrics, such as p-values for random ligation
hypothesis or mean DG alignment scores, can be calculated to
rank final set of RNA duplexes. Data from multiple experiments
or replicates can be processed separately and further compared
to check the reproducibility of the experimental method.
biocViews: Sequencing, Transcriptomics, StructuralPrediction,
Clustering, SplicedAlignment
Author: Egor Semenchenko [aut, cre, cph] (ORCID:
<https://orcid.org/0009-0007-5306-076X>), Volodymyr Tsybulskyi
[ctb] (ORCID: <https://orcid.org/0009-0002-4141-6291>),
Irmtraud M. Meyer [aut, cph] (ORCID:
<https://orcid.org/0000-0002-4048-3479>)
Maintainer: Egor Semenchenko <yegor.smb@gmail.com>
URL: https://github.com/Egors01/DuplexDiscovereR/
VignetteBuilder: knitr
BugReports: https://github.com/Egors01/DuplexDiscovereR/issues/
git_url: https://git.bioconductor.org/packages/DuplexDiscovereR
git_branch: devel
git_last_commit: be2a890
git_last_commit_date: 2025-03-24
Date/Publication: 2025-03-26
source.ver: src/contrib/DuplexDiscovereR_1.1.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DuplexDiscovereR_1.1.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DuplexDiscovereR_1.1.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DuplexDiscovereR_1.1.2.tgz
vignettes: vignettes/DuplexDiscovereR/inst/doc/DuplexDiscovereR.html
vignetteTitles: DuplexDiscovereR tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/DuplexDiscovereR/inst/doc/DuplexDiscovereR.R
dependencyCount: 159
Package: dupRadar
Version: 1.37.0
Depends: R (>= 3.2.0)
Imports: Rsubread (>= 1.14.1), KernSmooth
Suggests: BiocStyle, knitr, rmarkdown, AnnotationHub
License: GPL-3
MD5sum: c90e148780dae1051ad7153e868bef3d
NeedsCompilation: no
Title: Assessment of duplication rates in RNA-Seq datasets
Description: Duplication rate quality control for RNA-Seq datasets.
biocViews: Technology, Sequencing, RNASeq, QualityControl,
ImmunoOncology
Author: Sergi Sayols <sergisayolspuig@gmail.com>, Holger Klein
<holger.klein@gmail.com>
Maintainer: Sergi Sayols <sergisayolspuig@gmail.com>, Holger Klein
<holger.klein@gmail.com>
URL: https://www.bioconductor.org/packages/dupRadar,
https://ssayols.github.io/dupRadar/index.html
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/dupRadar
git_branch: devel
git_last_commit: 9afe62b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/dupRadar_1.37.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/dupRadar_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/dupRadar_1.37.0.tgz
vignettes: vignettes/dupRadar/inst/doc/dupRadar.html
vignetteTitles: Using dupRadar
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/dupRadar/inst/doc/dupRadar.R
dependencyCount: 10
Package: dyebias
Version: 1.67.0
Depends: R (>= 1.4.1), marray, Biobase
Suggests: limma, convert, GEOquery, dyebiasexamples, methods
License: GPL-3
MD5sum: 933ca5288ff17d17d76952f9da9cda9d
NeedsCompilation: no
Title: The GASSCO method for correcting for slide-dependent
gene-specific dye bias
Description: Many two-colour hybridizations suffer from a dye bias that
is both gene-specific and slide-specific. The former depends on
the content of the nucleotide used for labeling; the latter
depends on the labeling percentage. The slide-dependency was
hitherto not recognized, and made addressing the artefact
impossible. Given a reasonable number of dye-swapped pairs of
hybridizations, or of same vs. same hybridizations, both the
gene- and slide-biases can be estimated and corrected using the
GASSCO method (Margaritis et al., Mol. Sys. Biol. 5:266 (2009),
doi:10.1038/msb.2009.21)
biocViews: Microarray, TwoChannel, QualityControl, Preprocessing
Author: Philip Lijnzaad and Thanasis Margaritis
Maintainer: Philip Lijnzaad <plijnzaad@gmail.com>
URL: http://www.holstegelab.nl/publications/margaritis_lijnzaad
git_url: https://git.bioconductor.org/packages/dyebias
git_branch: devel
git_last_commit: c649c70
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/dyebias_1.67.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/dyebias_1.67.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/dyebias_1.67.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/dyebias_1.67.0.tgz
vignettes: vignettes/dyebias/inst/doc/dyebias-vignette.pdf
vignetteTitles: dye bias correction
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/dyebias/inst/doc/dyebias-vignette.R
suggestsMe: dyebiasexamples
dependencyCount: 11
Package: DynDoc
Version: 1.85.0
Depends: methods, utils
Imports: methods
License: Artistic-2.0
MD5sum: b802c3d469d9f5fd94ad4073a8dd1b98
NeedsCompilation: no
Title: Dynamic document tools
Description: A set of functions to create and interact with dynamic
documents and vignettes.
biocViews: ReportWriting, Infrastructure
Author: R. Gentleman, Jeff Gentry
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/DynDoc
git_branch: devel
git_last_commit: 9092b54
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/DynDoc_1.85.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/DynDoc_1.85.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DynDoc_1.85.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/DynDoc_1.85.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: tkWidgets
dependencyCount: 2
Package: easier
Version: 1.13.0
Depends: R (>= 4.2.0)
Imports: progeny, easierData, dorothea (>= 1.6.0), decoupleR,
quantiseqr, ROCR, grDevices, stats, graphics, ggplot2, ggpubr,
DESeq2, utils, dplyr, tidyr, tibble, matrixStats, rlang,
BiocParallel, reshape2, rstatix, ggrepel, magrittr, coin
Suggests: knitr, rmarkdown, BiocStyle, testthat, SummarizedExperiment,
viper
License: MIT + file LICENSE
MD5sum: 21842e3166ea7bbdfbc679475b71edbb
NeedsCompilation: no
Title: Estimate Systems Immune Response from RNA-seq data
Description: This package provides a workflow for the use of EaSIeR
tool, developed to assess patients' likelihood to respond to
ICB therapies providing just the patients' RNA-seq data as
input. We integrate RNA-seq data with different types of prior
knowledge to extract quantitative descriptors of the tumor
microenvironment from several points of view, including
composition of the immune repertoire, and activity of intra-
and extra-cellular communications. Then, we use multi-task
machine learning trained in TCGA data to identify how these
descriptors can simultaneously predict several state-of-the-art
hallmarks of anti-cancer immune response. In this way we derive
cancer-specific models and identify cancer-specific systems
biomarkers of immune response. These biomarkers have been
experimentally validated in the literature and the performance
of EaSIeR predictions has been validated using independent
datasets form four different cancer types with patients treated
with anti-PD1 or anti-PDL1 therapy.
biocViews: GeneExpression, Software, Transcription, SystemsBiology,
Pathways, GeneSetEnrichment, ImmunoOncology, Epigenetics,
Classification, BiomedicalInformatics, Regression,
ExperimentHubSoftware
Author: Oscar Lapuente-Santana [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1995-8393>), Federico Marini [aut]
(ORCID: <https://orcid.org/0000-0003-3252-7758>), Arsenij
Ustjanzew [aut] (ORCID:
<https://orcid.org/0000-0002-1014-4521>), Francesca Finotello
[aut] (ORCID: <https://orcid.org/0000-0003-0712-4658>),
Federica Eduati [aut] (ORCID:
<https://orcid.org/0000-0002-7822-3867>)
Maintainer: Oscar Lapuente-Santana <o.lapuente.santana@tue.nl>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/easier
git_branch: devel
git_last_commit: 060a509
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/easier_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/easier_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/easier_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/easier_1.13.0.tgz
vignettes: vignettes/easier/inst/doc/easier_user_manual.html
vignetteTitles: easier User Manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/easier/inst/doc/easier_user_manual.R
dependencyCount: 164
Package: EasyCellType
Version: 1.9.0
Depends: R (>= 4.2.0)
Imports: clusterProfiler, dplyr, forcats, ggplot2, magrittr, rlang,
stats, org.Hs.eg.db, org.Mm.eg.db, AnnotationDbi, vctrs (>=
0.6.4), BiocStyle
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), Seurat, BiocManager,
devtools, BiocStyle
License: Artistic-2.0
MD5sum: 18fb6a1f24cdb6fd5cf2b95bbef18f8b
NeedsCompilation: no
Title: Annotate cell types for scRNA-seq data
Description: We developed EasyCellType which can automatically examine
the input marker lists obtained from existing software such as
Seurat over the cell markerdatabases. Two quantification
approaches to annotate cell types are provided: Gene set
enrichment analysis (GSEA) and a modified versio of Fisher's
exact test. The function presents annotation recommendations in
graphical outcomes: bar plots for each cluster showing
candidate cell types, as well as a dot plot summarizing the top
5 significant annotations for each cluster.
biocViews: SingleCell, Software, GeneExpression, GeneSetEnrichment
Author: Ruoxing Li [aut, cre, ctb], Ziyi Li [ctb]
Maintainer: Ruoxing Li <ruoxingli@outlook.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/EasyCellType
git_branch: devel
git_last_commit: 902a85b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/EasyCellType_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EasyCellType_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EasyCellType_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EasyCellType_1.9.0.tgz
vignettes: vignettes/EasyCellType/inst/doc/my-vignette.html
vignetteTitles: my-vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EasyCellType/inst/doc/my-vignette.R
dependencyCount: 143
Package: easylift
Version: 1.5.0
Depends: GenomicRanges, BiocFileCache
Imports: rtracklayer, GenomeInfoDb, R.utils, tools, methods
Suggests: testthat (>= 3.0.0), IRanges, knitr, BiocStyle, rmarkdown
License: MIT + file LICENSE
MD5sum: af65b22b32498b63c0a920611bff70ba
NeedsCompilation: no
Title: An R package to perform genomic liftover
Description: The easylift package provides a convenient tool for
genomic liftover operations between different genome
assemblies. It seamlessly works with Bioconductor's GRanges
objects and chain files from the UCSC Genome Browser, allowing
for straightforward handling of genomic ranges across various
genome versions. One noteworthy feature of easylift is its
integration with the BiocFileCache package. This integration
automates the management and caching of chain files necessary
for liftover operations. Users no longer need to manually
specify chain file paths in their function calls, reducing the
complexity of the liftover process.
biocViews: Software, WorkflowStep, Sequencing, Coverage,
GenomeAssembly, DataImport
Author: Abdullah Al Nahid [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4390-0768>), Hervé Pagès [aut,
rev], Michael Love [aut, rev] (ORCID:
<https://orcid.org/0000-0001-8401-0545>)
Maintainer: Abdullah Al Nahid <abdnahid56@gmail.com>
URL: https://github.com/nahid18/easylift,
https://nahid18.github.io/easylift
VignetteBuilder: knitr
BugReports: https://github.com/nahid18/easylift/issues
git_url: https://git.bioconductor.org/packages/easylift
git_branch: devel
git_last_commit: 16c9c5b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/easylift_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/easylift_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/easylift_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/easylift_1.5.0.tgz
vignettes: vignettes/easylift/inst/doc/easylift.html
vignetteTitles: Perform Genomic Liftover
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/easylift/inst/doc/easylift.R
dependencyCount: 91
Package: easyreporting
Version: 1.19.0
Depends: R (>= 3.5.0)
Imports: rmarkdown, methods, tools, shiny, rlang
Suggests: distill, BiocStyle, knitr, readxl, edgeR, limma, EDASeq,
statmod
License: Artistic-2.0
MD5sum: 3cee64fa0654e5d7e6a1cd6ff1bc5717
NeedsCompilation: no
Title: Helps creating report for improving Reproducible Computational
Research
Description: An S4 class for facilitating the automated creation of
rmarkdown files inside other packages/software even without
knowing rmarkdown language. Best if implemented in functions as
"recursive" style programming.
biocViews: ReportWriting
Author: Dario Righelli [cre, aut]
Maintainer: Dario Righelli <dario.righelli@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/drighelli/easyreporting/issues
git_url: https://git.bioconductor.org/packages/easyreporting
git_branch: devel
git_last_commit: 0f50bcf
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/easyreporting_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/easyreporting_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/easyreporting_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/easyreporting_1.19.0.tgz
vignettes: vignettes/easyreporting/inst/doc/bio_usage.html,
vignettes/easyreporting/inst/doc/standard_usage.html
vignetteTitles: bio_usage.html, standard_usage.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/easyreporting/inst/doc/bio_usage.R,
vignettes/easyreporting/inst/doc/standard_usage.R
dependencyCount: 43
Package: easyRNASeq
Version: 2.43.0
Imports: Biobase (>= 2.64.0), BiocFileCache (>= 2.12.0), BiocGenerics
(>= 0.50.0), BiocParallel (>= 1.38.0), biomaRt (>= 2.60.1),
Biostrings (>= 2.72.1), edgeR (>= 4.2.1), GenomeInfoDb (>=
1.40.1), genomeIntervals (>= 1.60.0), GenomicAlignments (>=
1.40.0), GenomicRanges (>= 1.56.1), SummarizedExperiment (>=
1.34.0), graphics, IRanges (>= 2.38.1), LSD (>= 4.1-0),
methods, parallel, rappdirs (>= 0.3.3), Rsamtools (>= 2.20.0),
S4Vectors (>= 0.42.1), ShortRead (>= 1.62.0), utils
Suggests: BiocStyle (>= 2.32.1), BSgenome (>= 1.72.0),
BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.4.0), curl, knitr,
rmarkdown, RUnit (>= 0.4.33)
License: Artistic-2.0
MD5sum: ae7b560cb851f0aaa2434bed507c2003
NeedsCompilation: no
Title: Count summarization and normalization for RNA-Seq data
Description: Calculates the coverage of high-throughput short-reads
against a genome of reference and summarizes it per feature of
interest (e.g. exon, gene, transcript). The data can be
normalized as 'RPKM' or by the 'DESeq' or 'edgeR' package.
biocViews: GeneExpression, RNASeq, Genetics, Preprocessing,
ImmunoOncology
Author: Nicolas Delhomme, Ismael Padioleau, Bastian Schiffthaler,
Niklas Maehler
Maintainer: Nicolas Delhomme <nicolas.delhomme@umu.se>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/easyRNASeq
git_branch: devel
git_last_commit: d32c68e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/easyRNASeq_2.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/easyRNASeq_2.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/easyRNASeq_2.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/easyRNASeq_2.43.0.tgz
vignettes: vignettes/easyRNASeq/inst/doc/easyRNASeq.pdf,
vignettes/easyRNASeq/inst/doc/simpleRNASeq.html
vignetteTitles: R / Bioconductor for High Throughput Sequence Analysis,
geneNetworkR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/easyRNASeq/inst/doc/easyRNASeq.R,
vignettes/easyRNASeq/inst/doc/simpleRNASeq.R
importsMe: msgbsR
dependencyCount: 110
Package: EBarrays
Version: 2.71.0
Depends: R (>= 1.8.0), Biobase, lattice, methods
Imports: Biobase, cluster, graphics, grDevices, lattice, methods, stats
License: GPL (>= 2)
MD5sum: 844beddb438532c29a8da6bc00c53498
NeedsCompilation: yes
Title: Unified Approach for Simultaneous Gene Clustering and
Differential Expression Identification
Description: EBarrays provides tools for the analysis of
replicated/unreplicated microarray data.
biocViews: Clustering, DifferentialExpression
Author: Ming Yuan, Michael Newton, Deepayan Sarkar and Christina
Kendziorski
Maintainer: Ming Yuan <myuan@isye.gatech.edu>
git_url: https://git.bioconductor.org/packages/EBarrays
git_branch: devel
git_last_commit: 3c3b71c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/EBarrays_2.71.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EBarrays_2.71.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EBarrays_2.71.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EBarrays_2.71.0.tgz
vignettes: vignettes/EBarrays/inst/doc/vignette.pdf
vignetteTitles: Introduction to EBarrays
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EBarrays/inst/doc/vignette.R
dependsOnMe: EBcoexpress, gaga, geNetClassifier
importsMe: casper
suggestsMe: Category, dcanr
dependencyCount: 11
Package: EBcoexpress
Version: 1.51.0
Depends: EBarrays, mclust, minqa
Suggests: graph, igraph, colorspace
License: GPL (>= 2)
MD5sum: b40535a5279edc92df3b541d69da44b5
NeedsCompilation: yes
Title: EBcoexpress for Differential Co-Expression Analysis
Description: An Empirical Bayesian Approach to Differential
Co-Expression Analysis at the Gene-Pair Level
biocViews: Bayesian
Author: John A. Dawson
Maintainer: John A. Dawson <jadawson@wisc.edu>
git_url: https://git.bioconductor.org/packages/EBcoexpress
git_branch: devel
git_last_commit: 7d83d90
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/EBcoexpress_1.51.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EBcoexpress_1.51.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EBcoexpress_1.51.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EBcoexpress_1.51.0.tgz
vignettes: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.pdf
vignetteTitles: EBcoexpress Demo
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.R
suggestsMe: dcanr
dependencyCount: 15
Package: EBImage
Version: 4.49.0
Depends: methods
Imports: BiocGenerics (>= 0.7.1), graphics, grDevices, stats, abind,
tiff, jpeg, png, locfit, fftwtools (>= 0.9-7), utils,
htmltools, htmlwidgets, RCurl
Suggests: BiocStyle, digest, knitr, rmarkdown, shiny
License: LGPL
Archs: x64
MD5sum: 2fb13cb9b0acedabee242aae27ebfde6
NeedsCompilation: yes
Title: Image processing and analysis toolbox for R
Description: EBImage provides general purpose functionality for image
processing and analysis. In the context of (high-throughput)
microscopy-based cellular assays, EBImage offers tools to
segment cells and extract quantitative cellular descriptors.
This allows the automation of such tasks using the R
programming language and facilitates the use of other tools in
the R environment for signal processing, statistical modeling,
machine learning and visualization with image data.
biocViews: Visualization
Author: Andrzej OleÅ›, Gregoire Pau, Mike Smith, Oleg Sklyar, Wolfgang
Huber, with contributions from Joseph Barry and Philip A.
Marais
Maintainer: Andrzej OleÅ› <andrzej.oles@gmail.com>
URL: https://github.com/aoles/EBImage
VignetteBuilder: knitr
BugReports: https://github.com/aoles/EBImage/issues
git_url: https://git.bioconductor.org/packages/EBImage
git_branch: devel
git_last_commit: ad8004f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/EBImage_4.49.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EBImage_4.49.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EBImage_4.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EBImage_4.49.0.tgz
vignettes: vignettes/EBImage/inst/doc/EBImage-introduction.html
vignetteTitles: Introduction to EBImage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EBImage/inst/doc/EBImage-introduction.R
dependsOnMe: CRImage, cytomapper, flowcatchR, DonaPLLP2013, furrowSeg,
MerfishData, nucim
importsMe: alabaster.sfe, bnbc, Cardinal, CatsCradle, cytoviewer,
flowCHIC, heatmaps, imcRtools, MoleculeExperiment, RBioFormats,
simpleSeg, sosta, SpatialFeatureExperiment,
SpatialOmicsOverlay, synapsis, xenLite, yamss, BioImageDbs,
bioimagetools, GoogleImage2Array, LFApp, LOMAR, ProxReg,
RockFab, SAFARI
suggestsMe: HilbertVis, Voyager, aroma.core, cooltools, glow, ijtiff,
juicr, lidR, metagear, pliman, rcaiman
dependencyCount: 45
Package: EBSEA
Version: 1.35.0
Depends: R (>= 4.0.0)
Imports: DESeq2, graphics, stats, EmpiricalBrownsMethod
Suggests: knitr, rmarkdown
License: GPL-2
Archs: x64
MD5sum: bbd1e1144c92bdb4472877b4dc09e5b8
NeedsCompilation: no
Title: Exon Based Strategy for Expression Analysis of genes
Description: Calculates differential expression of genes based on exon
counts of genes obtained from RNA-seq sequencing data.
biocViews: Software, DifferentialExpression, GeneExpression, Sequencing
Author: Arfa Mehmood, Asta Laiho, Laura L. Elo
Maintainer: Arfa Mehmood <arfa.mehmood@utu.fi>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/EBSEA
git_branch: devel
git_last_commit: be7ea14
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/EBSEA_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EBSEA_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EBSEA_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EBSEA_1.35.0.tgz
vignettes: vignettes/EBSEA/inst/doc/EBSEA.html
vignetteTitles: EBSEA
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EBSEA/inst/doc/EBSEA.R
dependencyCount: 77
Package: EBSeq
Version: 2.5.2
Depends: blockmodeling, gplots, testthat, R (>= 3.0.0)
Imports: Rcpp (>= 0.12.11), RcppEigen (>= 0.3.2.9.0)
LinkingTo: Rcpp,RcppEigen,BH
License: Artistic-2.0
Archs: x64
MD5sum: 7d931ecc0c74de57375c09b4686cfc45
NeedsCompilation: yes
Title: An R package for gene and isoform differential expression
analysis of RNA-seq data
Description: Differential Expression analysis at both gene and isoform
level using RNA-seq data
biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression,
MultipleComparison, RNASeq, Sequencing
Author: Xiuyu Ma [cre, aut], Ning Leng [aut], Christina Kendziorski
[ctb], Michael A. Newton [ctb]
Maintainer: Xiuyu Ma <watsonforfun@gmail.com>
SystemRequirements: c++14
git_url: https://git.bioconductor.org/packages/EBSeq
git_branch: devel
git_last_commit: e80d996
git_last_commit_date: 2025-01-28
Date/Publication: 2025-01-28
source.ver: src/contrib/EBSeq_2.5.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EBSeq_2.5.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EBSeq_2.5.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EBSeq_2.5.2.tgz
vignettes: vignettes/EBSeq/inst/doc/EBSeq_Vignette.pdf
vignetteTitles: EBSeq Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EBSeq/inst/doc/EBSeq_Vignette.R
dependsOnMe: Oscope
importsMe: BatchQC, broadSeq, DEsubs, scDD
suggestsMe: compcodeR
dependencyCount: 43
Package: ecolitk
Version: 1.79.0
Depends: R (>= 2.10)
Imports: Biobase, graphics, methods
Suggests: ecoliLeucine, ecolicdf, graph, multtest, affy
License: GPL (>= 2)
MD5sum: 7f2b281de463d6f765a678230b0de291
NeedsCompilation: no
Title: Meta-data and tools for E. coli
Description: Meta-data and tools to work with E. coli. The tools are
mostly plotting functions to work with circular genomes. They
can used with other genomes/plasmids.
biocViews: Annotation, Visualization
Author: Laurent Gautier
Maintainer: Laurent Gautier <lgautier@gmail.com>
git_url: https://git.bioconductor.org/packages/ecolitk
git_branch: devel
git_last_commit: 926c981
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ecolitk_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ecolitk_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ecolitk_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ecolitk_1.79.0.tgz
vignettes: vignettes/ecolitk/inst/doc/ecolitk.pdf
vignetteTitles: ecolitk
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ecolitk/inst/doc/ecolitk.R
dependencyCount: 7
Package: EDASeq
Version: 2.41.0
Depends: Biobase (>= 2.15.1), ShortRead (>= 1.11.42)
Imports: methods, graphics, BiocGenerics, IRanges (>= 1.13.9),
aroma.light, Rsamtools (>= 1.5.75), biomaRt, Biostrings,
AnnotationDbi, GenomicFeatures, GenomicRanges, BiocManager
Suggests: BiocStyle, knitr, yeastRNASeq, leeBamViews, edgeR,
KernSmooth, testthat, DESeq2, rmarkdown
License: Artistic-2.0
Archs: x64
MD5sum: eddb6c6de525dcb3c56a13b682eea9a6
NeedsCompilation: no
Title: Exploratory Data Analysis and Normalization for RNA-Seq
Description: Numerical and graphical summaries of RNA-Seq read data.
Within-lane normalization procedures to adjust for GC-content
effect (or other gene-level effects) on read counts: loess
robust local regression, global-scaling, and full-quantile
normalization (Risso et al., 2011). Between-lane normalization
procedures to adjust for distributional differences between
lanes (e.g., sequencing depth): global-scaling and
full-quantile normalization (Bullard et al., 2010).
biocViews: ImmunoOncology, Sequencing, RNASeq, Preprocessing,
QualityControl, DifferentialExpression
Author: Davide Risso [aut, cre, cph], Sandrine Dudoit [aut], Ludwig
Geistlinger [ctb]
Maintainer: Davide Risso <risso.davide@gmail.com>
URL: https://github.com/drisso/EDASeq
VignetteBuilder: knitr
BugReports: https://github.com/drisso/EDASeq/issues
git_url: https://git.bioconductor.org/packages/EDASeq
git_branch: devel
git_last_commit: 68d2eba
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/EDASeq_2.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EDASeq_2.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EDASeq_2.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EDASeq_2.41.0.tgz
vignettes: vignettes/EDASeq/inst/doc/EDASeq.html
vignetteTitles: EDASeq Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EDASeq/inst/doc/EDASeq.R
dependsOnMe: RUVSeq
importsMe: consensusDE, DaMiRseq, metaseqR2, octad, ribosomeProfilingQC
suggestsMe: awst, DEScan2, easyreporting, GRaNIE, HTSFilter, MOSClip,
TCGAbiolinks
dependencyCount: 116
Package: edge
Version: 2.39.0
Depends: R(>= 3.1.0), Biobase
Imports: methods, splines, sva, qvalue(>= 1.99.0), MASS
Suggests: testthat, knitr, ggplot2, reshape2
License: MIT + file LICENSE
MD5sum: dbed0bf91790338911dc048ce00641ca
NeedsCompilation: yes
Title: Extraction of Differential Gene Expression
Description: The edge package implements methods for carrying out
differential expression analyses of genome-wide gene expression
studies. Significance testing using the optimal discovery
procedure and generalized likelihood ratio tests (equivalent to
F-tests and t-tests) are implemented for general study designs.
Special functions are available to facilitate the analysis of
common study designs, including time course experiments. Other
packages such as sva and qvalue are integrated in edge to
provide a wide range of tools for gene expression analysis.
biocViews: MultipleComparison, DifferentialExpression, TimeCourse,
Regression, GeneExpression, DataImport
Author: John D. Storey, Jeffrey T. Leek and Andrew J. Bass
Maintainer: John D. Storey <jstorey@princeton.edu>, Andrew J. Bass
<ajbass@emory.edu>
URL: https://github.com/jdstorey/edge
VignetteBuilder: knitr
BugReports: https://github.com/jdstorey/edge/issues
git_url: https://git.bioconductor.org/packages/edge
git_branch: devel
git_last_commit: b391071
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/edge_2.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/edge_2.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/edge_2.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/edge_2.39.0.tgz
vignettes: vignettes/edge/inst/doc/edge.pdf
vignetteTitles: edge Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/edge/inst/doc/edge.R
dependencyCount: 95
Package: edgeR
Version: 4.5.9
Depends: R (>= 3.6.0), limma (>= 3.63.6)
Imports: methods, graphics, stats, utils, locfit
Suggests: jsonlite, knitr, Matrix, readr, rhdf5, SeuratObject, splines,
AnnotationDbi, Biobase, BiocStyle, org.Hs.eg.db,
SummarizedExperiment
License: GPL (>=2)
MD5sum: 4c8ef10dc1b4e22a5cbcc136fa0178f7
NeedsCompilation: yes
Title: Empirical Analysis of Digital Gene Expression Data in R
Description: Differential expression analysis of sequence count data.
Implements a range of statistical methodology based on the
negative binomial distributions, including empirical Bayes
estimation, exact tests, generalized linear models,
quasi-likelihood, and gene set enrichment. Can perform
differential analyses of any type of omics data that produces
read counts, including RNA-seq, ChIP-seq, ATAC-seq,
Bisulfite-seq, SAGE, CAGE, metabolomics, or proteomics spectral
counts. RNA-seq analyses can be conducted at the gene or
isoform level, and tests can be conducted for differential exon
or transcript usage.
biocViews: AlternativeSplicing, BatchEffect, Bayesian,
BiomedicalInformatics, CellBiology, ChIPSeq, Clustering,
Coverage, DifferentialExpression, DifferentialMethylation,
DifferentialSplicing, DNAMethylation, Epigenetics,
FunctionalGenomics, GeneExpression, GeneSetEnrichment,
Genetics, Genetics, ImmunoOncology, MultipleComparison,
Normalization, Pathways, Proteomics, QualityControl,
Regression, RNASeq, SAGE, Sequencing, SingleCell,
SystemsBiology, TimeCourse, Transcription, Transcriptomics
Author: Yunshun Chen, Lizhong Chen, Aaron TL Lun, Davis J McCarthy,
Pedro Baldoni, Matthew E Ritchie, Belinda Phipson, Yifang Hu,
Xiaobei Zhou, Mark D Robinson, Gordon K Smyth
Maintainer: Yunshun Chen <yuchen@wehi.edu.au>, Gordon Smyth
<smyth@wehi.edu.au>, Aaron Lun
<infinite.monkeys.with.keyboards@gmail.com>, Mark Robinson
<mark.robinson@imls.uzh.ch>
URL: https://bioinf.wehi.edu.au/edgeR/,
https://bioconductor.org/packages/edgeR
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/edgeR
git_branch: devel
git_last_commit: d37f300
git_last_commit_date: 2025-03-11
Date/Publication: 2025-03-11
source.ver: src/contrib/edgeR_4.5.9.tar.gz
win.binary.ver: bin/windows/contrib/4.5/edgeR_4.5.9.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/edgeR_4.5.9.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/edgeR_4.5.9.tgz
vignettes: vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf,
vignettes/edgeR/inst/doc/intro.html
vignetteTitles: edgeR User's Guide, A brief introduction to edgeR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/edgeR/inst/doc/intro.R
dependsOnMe: ASpli, IntEREst, methylMnM, miloR, octad, RUVSeq, TCC,
tRanslatome, ReactomeGSA.data, EGSEA123, RNAseq123, rnaseqDTU,
RnaSeqGeneEdgeRQL, csawBook, OSCA.multisample, OSCA.workflows,
babel, BALLI, BioInsight, SCdeconR
importsMe: affycoretools, ATACseqQC, autonomics, AWFisher, baySeq,
beer, benchdamic, BioQC, broadSeq, censcyt, ChromSCape,
circRNAprofiler, CleanUpRNAseq, clusterExperiment, CNVRanger,
compcodeR, consensusDE, coseq, countsimQC, csaw, cypress,
DaMiRseq, Damsel, dce, debrowser, DEFormats, DEGreport,
DESpace, DEsubs, diffcyt, diffHic, diffUTR, dinoR, DMRcate,
doseR, dreamlet, DRIMSeq, DropletUtils, easyRNASeq, EGSEA,
eisaR, EnrichmentBrowser, erccdashboard, ERSSA, extraChIPs,
GDCRNATools, GenomicPlot, GEOexplorer, gg4way, gINTomics,
Glimma, GSEABenchmarkeR, hermes, HTSFilter, icetea, infercnv,
iSEEde, IsoformSwitchAnalyzeR, KnowSeq, Maaslin2, mastR, MEB,
MEDIPS, metaseqR2, MIRit, MLSeq, moanin, mobileRNA, MOSim,
Motif2Site, msgbsR, msmsTests, multiHiCcompare, muscat,
PathoStat, phantasus, PhIPData, ppcseq, PRONE, PROPER,
psichomics, RCM, regsplice, Repitools, RNAseqCovarImpute,
ROSeq, Rvisdiff, saseR, scCB2, scde, scone, scran, ScreenR,
SEtools, SIMD, SingleCellSignalR, singscore, SpaNorm, sparrow,
spatialHeatmap, speckle, splatter, SPsimSeq, srnadiff, sSNAPPY,
standR, STATegRa, SurfR, sva, TBSignatureProfiler, TCseq,
tradeSeq, treeclimbR, treekoR, tweeDEseq, vidger, xcore, yarn,
zinbwave, emtdata, spatialLIBD, ExpHunterSuite,
recountWorkflow, SingscoreAMLMutations, aIc, bulkAnalyseR,
CAMML, CIDER, cinaR, CoreMicrobiomeR, cpam, HTSCluster,
idiffomix, microbial, RCPA, RVA, scITD, SCRIP, scRNAtools,
SPUTNIK, ssizeRNA, TransProR, TSGS
suggestsMe: ABSSeq, biobroom, ClassifyR, cqn, cydar, dcanr, dearseq,
DEScan2, DiffBind, dittoSeq, DSS, easyreporting, EDASeq, gage,
gCrisprTools, GenomicAlignments, GenomicRanges, GeoTcgaData,
glmGamPoi, goseq, GSAR, GSVA, ideal, iSEEpathways, iSEEu,
lemur, missMethyl, MoonlightR, multiMiR, raer, recount,
regionReport, ribosomeProfilingQC, satuRn, scider, SeqGate,
signifinder, SpliceWiz, stageR, subSeq, systemPipeR,
TCGAbiolinks, tidybulk, topconfects, transmogR, tximeta,
tximport, variancePartition, weitrix, Wrench, zenith, zFPKM,
leeBamViews, CAGEWorkflow, chipseqDB, DGEobj, DGEobj.utils,
DiPALM, easybio, glmmSeq, MiscMetabar, palasso, pctax, pmartR,
seqgendiff, SIBERG, volcano3D
dependencyCount: 10
Package: EDIRquery
Version: 1.7.0
Depends: R (>= 4.2.0)
Imports: tibble (>= 3.1.6), tictoc (>= 1.0.1), utils (>= 4.1.3), stats
(>= 4.1.3), readr (>= 2.1.2), InteractionSet (>= 1.22.0),
GenomicRanges (>= 1.46.1)
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
License: GPL-3
Archs: x64
MD5sum: aca8b76dd94c493cf6639e75c1827145
NeedsCompilation: no
Title: Query the EDIR Database For Specific Gene
Description: EDIRquery provides a tool to search for genes of interest
within the Exome Database of Interspersed Repeats (EDIR). A
gene name is a required input, and users can additionally
specify repeat sequence lengths, minimum and maximum distance
between sequences, and whether to allow a 1-bp mismatch.
Outputs include a summary of results by repeat length, as well
as a dataframe of query results. Example data provided includes
a subset of the data for the gene GAA (ENSG00000171298). To
query the full database requires providing a path to the
downloaded database files as a parameter.
biocViews: Genetics, SequenceMatching
Author: Laura D.T. Vo Ngoc [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1597-900X>)
Maintainer: Laura D.T. Vo Ngoc <doan.vongoc@vub.be>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/EDIRquery
git_branch: devel
git_last_commit: 6892942
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/EDIRquery_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EDIRquery_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EDIRquery_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EDIRquery_1.7.0.tgz
vignettes: vignettes/EDIRquery/inst/doc/EDIRquery.pdf
vignetteTitles: EDIRquery
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EDIRquery/inst/doc/EDIRquery.R
dependencyCount: 62
Package: eds
Version: 1.9.0
Depends: Matrix
Imports: Rcpp
LinkingTo: Rcpp
Suggests: knitr, tximportData, testthat (>= 3.0.0)
License: GPL-2
Archs: x64
MD5sum: a222a8466fd5c23685395d8a47b63f03
NeedsCompilation: yes
Title: eds: Low-level reader for Alevin EDS format
Description: This packages provides a single function, readEDS. This is
a low-level utility for reading in Alevin EDS format into R.
This function is not designed for end-users but instead the
package is predominantly for simplifying package dependency
graph for other Bioconductor packages.
biocViews: Sequencing, RNASeq, GeneExpression, SingleCell
Author: Avi Srivastava [aut, cre], Michael Love [aut, ctb]
Maintainer: Avi Srivastava <asrivastava@cs.stonybrook.edu>
URL: https://github.com/mikelove/eds
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/eds
git_branch: devel
git_last_commit: 9cf419e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/eds_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/eds_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/eds_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/eds_1.9.0.tgz
vignettes: vignettes/eds/inst/doc/eds.html
vignetteTitles: eds: Low-level reader function for Alevin EDS format
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/eds/inst/doc/eds.R
importsMe: singleCellTK
suggestsMe: tximport
dependencyCount: 9
Package: EGAD
Version: 1.35.0
Depends: R(>= 3.5)
Imports: gplots, Biobase, GEOquery, limma, impute, RColorBrewer, zoo,
igraph, plyr, MASS, RCurl, methods
Suggests: knitr, testthat, rmarkdown, markdown
License: GPL-2
MD5sum: 7364c6d2370e1ec73ee96a12f4898d12
NeedsCompilation: no
Title: Extending guilt by association by degree
Description: The package implements a series of highly efficient tools
to calculate functional properties of networks based on guilt
by association methods.
biocViews: Software, FunctionalGenomics, SystemsBiology,
GenePrediction, FunctionalPrediction, NetworkEnrichment,
GraphAndNetwork, Network
Author: Sara Ballouz [aut, cre], Melanie Weber [aut, ctb], Paul
Pavlidis [aut], Jesse Gillis [aut, ctb]
Maintainer: Sara Ballouz <sarahballouz@gmail.com>
VignetteBuilder: rmarkdown
git_url: https://git.bioconductor.org/packages/EGAD
git_branch: devel
git_last_commit: 65e168f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/EGAD_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EGAD_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EGAD_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EGAD_1.35.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 91
Package: EGSEA
Version: 1.35.0
Depends: R (>= 4.3.0), Biobase, gage (>= 2.14.4), AnnotationDbi, topGO
(>= 2.16.0), pathview (>= 1.4.2)
Imports: PADOG (>= 1.6.0), GSVA (>= 1.12.0), globaltest (>= 5.18.0),
limma (>= 3.20.9), edgeR (>= 3.6.8), HTMLUtils (>= 0.1.5),
hwriter (>= 1.2.2), gplots (>= 2.14.2), ggplot2 (>= 1.0.0),
safe (>= 3.4.0), stringi (>= 0.5.0), parallel, stats, metap,
grDevices, graphics, utils, org.Hs.eg.db, org.Mm.eg.db,
org.Rn.eg.db, RColorBrewer, methods, EGSEAdata (>= 1.3.1),
htmlwidgets, plotly, DT
Suggests: BiocStyle, knitr, testthat
License: GPL-3
Archs: x64
MD5sum: 652146426f4da9f7a18ac171137e1518
NeedsCompilation: no
Title: Ensemble of Gene Set Enrichment Analyses
Description: This package implements the Ensemble of Gene Set
Enrichment Analyses (EGSEA) method for gene set testing. EGSEA
algorithm utilizes the analysis results of twelve prominent GSE
algorithms in the literature to calculate collective
significance scores for each gene set.
biocViews: ImmunoOncology, DifferentialExpression, GO, GeneExpression,
GeneSetEnrichment, Genetics, Microarray, MultipleComparison,
OneChannel, Pathways, RNASeq, Sequencing, Software,
SystemsBiology, TwoChannel,Metabolomics, Proteomics, KEGG,
GraphAndNetwork, GeneSignaling, GeneTarget, NetworkEnrichment,
Network, Classification
Author: Monther Alhamdoosh [aut, cre], Luyi Tian [aut], Milica Ng
[aut], Matthew Ritchie [ctb]
Maintainer: Monther Alhamdoosh <m.hamdoosh@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/EGSEA
git_branch: devel
git_last_commit: bc1e988
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/EGSEA_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EGSEA_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EGSEA_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EGSEA_1.35.0.tgz
vignettes: vignettes/EGSEA/inst/doc/EGSEA.pdf
vignetteTitles: EGSEA vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EGSEA/inst/doc/EGSEA.R
dependsOnMe: EGSEA123
suggestsMe: tidybulk, EGSEAdata
dependencyCount: 200
Package: eiR
Version: 1.47.1
Depends: R (>= 2.10.0), ChemmineR (>= 2.15.15), methods, DBI
Imports: snow, tools, snowfall, RUnit, methods, ChemmineR, RCurl,
digest, BiocGenerics, RcppAnnoy (>= 0.0.9)
Suggests: BiocStyle, knitcitations, knitr,
knitrBootstrap,rmarkdown,RSQLite,codetools
License: Artistic-2.0
MD5sum: 1598cdacf238a10b5dcbf6539dd3885c
NeedsCompilation: yes
Title: Accelerated similarity searching of small molecules
Description: The eiR package provides utilities for accelerated
structure similarity searching of very large small molecule
data sets using an embedding and indexing approach.
biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics,
Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays,
Visualization, Infrastructure, DataImport, Clustering,
Proteomics, Metabolomics
Author: Kevin Horan, Yiqun Cao and Tyler Backman
Maintainer: Thomas Girke <thomas.girke@ucr.edu>
URL: https://github.com/girke-lab/eiR
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/eiR
git_branch: devel
git_last_commit: 5af151f
git_last_commit_date: 2025-02-11
Date/Publication: 2025-02-11
source.ver: src/contrib/eiR_1.47.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/eiR_1.47.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/eiR_1.47.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/eiR_1.47.1.tgz
vignettes: vignettes/eiR/inst/doc/eiR.html
vignetteTitles: eiR: Accelerated Similarity Searching of Small
Molecules
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: TRUE
Rfiles: vignettes/eiR/inst/doc/eiR.R
dependencyCount: 81
Package: eisaR
Version: 1.19.0
Depends: R (>= 4.1)
Imports: graphics, stats, GenomicRanges, S4Vectors, IRanges, limma,
edgeR (>= 4.0), methods, SummarizedExperiment, BiocGenerics,
utils
Suggests: knitr, rmarkdown, testthat, BiocStyle, QuasR, Rbowtie,
Rhisat2, Biostrings, BSgenome, BSgenome.Hsapiens.UCSC.hg38,
ensembldb, AnnotationDbi, GenomicFeatures, txdbmaker,
rtracklayer
License: GPL-3
MD5sum: cee013ad7592666590e492b498defb7b
NeedsCompilation: no
Title: Exon-Intron Split Analysis (EISA) in R
Description: Exon-intron split analysis (EISA) uses ordinary RNA-seq
data to measure changes in mature RNA and pre-mRNA reads across
different experimental conditions to quantify transcriptional
and post-transcriptional regulation of gene expression. For
details see Gaidatzis et al., Nat Biotechnol 2015. doi:
10.1038/nbt.3269. eisaR implements the major steps of EISA in
R.
biocViews: Transcription, GeneExpression, GeneRegulation,
FunctionalGenomics, Transcriptomics, Regression, RNASeq
Author: Michael Stadler [aut, cre], Dimos Gaidatzis [aut], Lukas Burger
[aut], Charlotte Soneson [aut]
Maintainer: Michael Stadler <michael.stadler@fmi.ch>
URL: https://github.com/fmicompbio/eisaR
VignetteBuilder: knitr
BugReports: https://github.com/fmicompbio/eisaR/issues
git_url: https://git.bioconductor.org/packages/eisaR
git_branch: devel
git_last_commit: d8c11e0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/eisaR_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/eisaR_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/eisaR_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/eisaR_1.19.0.tgz
vignettes: vignettes/eisaR/inst/doc/eisaR.html,
vignettes/eisaR/inst/doc/rna-velocity.html
vignetteTitles: Using eisaR for Exon-Intron Split Analysis (EISA),
Generating reference files for spliced and unspliced abundance
estimation with alignment-free methods
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/eisaR/inst/doc/eisaR.R,
vignettes/eisaR/inst/doc/rna-velocity.R
dependencyCount: 40
Package: ELMER
Version: 2.31.0
Depends: R (>= 3.4.0), ELMER.data (>= 2.9.3)
Imports: GenomicRanges, ggplot2, reshape, grid, grDevices, graphics,
methods, parallel, stats, utils, IRanges, GenomeInfoDb,
S4Vectors, GenomicFeatures, TCGAbiolinks (>= 2.23.7), plyr,
Matrix, dplyr, Gviz, ComplexHeatmap, circlize,
MultiAssayExperiment, SummarizedExperiment, biomaRt,
doParallel, downloader, ggrepel, lattice, magrittr, readr,
scales, rvest, xml2, plotly, gridExtra, rmarkdown, stringr,
tibble, tidyr, progress, purrr, reshape2, ggpubr, rtracklayer
(>= 1.61.2), DelayedArray
Suggests: BiocStyle, AnnotationHub, ExperimentHub, knitr, testthat,
data.table, DT, GenomicInteractions, webshot, R.utils, covr,
sesameData
License: GPL-3
Archs: x64
MD5sum: eac9ad812c49757fa74ccb07d72ff339
NeedsCompilation: no
Title: Inferring Regulatory Element Landscapes and Transcription Factor
Networks Using Cancer Methylomes
Description: ELMER is designed to use DNA methylation and gene
expression from a large number of samples to infere regulatory
element landscape and transcription factor network in primary
tissue.
biocViews: DNAMethylation, GeneExpression, MotifAnnotation, Software,
GeneRegulation, Transcription, Network
Author: Tiago Chedraoui Silva [aut, cre], Lijing Yao [aut], Simon
Coetzee [aut], Nicole Gull [ctb], Hui Shen [ctb], Peter Laird
[ctb], Peggy Farnham [aut], Dechen Li [ctb], Benjamin Berman
[aut]
Maintainer: Tiago Chedraoui Silva <tiagochst@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ELMER
git_branch: devel
git_last_commit: 1f88893
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ELMER_2.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ELMER_2.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ELMER_2.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ELMER_2.31.0.tgz
vignettes: vignettes/ELMER/inst/doc/analysis_data_input.html,
vignettes/ELMER/inst/doc/analysis_diff_meth.html,
vignettes/ELMER/inst/doc/analysis_get_pair.html,
vignettes/ELMER/inst/doc/analysis_gui.html,
vignettes/ELMER/inst/doc/analysis_motif_enrichment.html,
vignettes/ELMER/inst/doc/analysis_regulatory_tf.html,
vignettes/ELMER/inst/doc/index.html,
vignettes/ELMER/inst/doc/input.html,
vignettes/ELMER/inst/doc/pipe.html,
vignettes/ELMER/inst/doc/plots_heatmap.html,
vignettes/ELMER/inst/doc/plots_motif_enrichment.html,
vignettes/ELMER/inst/doc/plots_scatter.html,
vignettes/ELMER/inst/doc/plots_schematic.html,
vignettes/ELMER/inst/doc/plots_TF.html,
vignettes/ELMER/inst/doc/usecase.html
vignetteTitles: "3.1 - Data input - Creating MAE object", "3.2 -
Identifying differentially methylated probes", "3.3 -
Identifying putative probe-gene pairs", 5 - Integrative
analysis workshop with TCGAbiolinks and ELMER - Analysis GUI,
"3.4 - Motif enrichment analysis on the selected probes", "3.5
- Identifying regulatory TFs", "1 - ELMER v.2: An
R/Bioconductor package to reconstruct gene regulatory networks
from DNA methylation and transcriptome profiles", "2 -
Introduction: Input data", "3.6 - TCGA.pipe: Running ELMER for
TCGA data in a compact way", "4.5 - Heatmap plots", "4.3 -
Motif enrichment plots", "4.1 - Scatter plots", "4.2 -
Schematic plots", "4.4 - Regulatory TF plots", "11 - ELMER: Use
case"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ELMER/inst/doc/analysis_data_input.R,
vignettes/ELMER/inst/doc/analysis_diff_meth.R,
vignettes/ELMER/inst/doc/analysis_get_pair.R,
vignettes/ELMER/inst/doc/analysis_gui.R,
vignettes/ELMER/inst/doc/analysis_motif_enrichment.R,
vignettes/ELMER/inst/doc/analysis_regulatory_tf.R,
vignettes/ELMER/inst/doc/index.R,
vignettes/ELMER/inst/doc/input.R,
vignettes/ELMER/inst/doc/pipe.R,
vignettes/ELMER/inst/doc/plots_heatmap.R,
vignettes/ELMER/inst/doc/plots_motif_enrichment.R,
vignettes/ELMER/inst/doc/plots_scatter.R,
vignettes/ELMER/inst/doc/plots_schematic.R,
vignettes/ELMER/inst/doc/plots_TF.R,
vignettes/ELMER/inst/doc/usecase.R
dependencyCount: 215
Package: ELViS
Version: 0.99.13
Depends: R (>= 4.5.0)
Imports: basilisk, BiocGenerics, circlize, ComplexHeatmap, data.table,
dplyr, GenomicFeatures, GenomicRanges, ggplot2, glue, graphics,
grDevices, igraph, IRanges, magrittr, memoise, methods,
parallel, patchwork, scales, segclust2d, stats, stringr,
txdbmaker, utils, uuid, zoo
Suggests: Rsamtools, BiocManager, knitr, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: d1bff1f6e69f4a1b588bbc0772346317
NeedsCompilation: no
Title: An R Package for Estimating Copy Number Levels of Viral Genome
Segments Using Base-Resolution Read Depth Profile
Description: Base-resolution copy number analysis of viral genome.
Utilizes base-resolution read depth data over viral genome to
find copy number segments with two-dimensional segmentation
approach. Provides publish-ready figures, including histograms
of read depths, coverage line plots over viral genome annotated
with copy number change events and viral genes, and heatmaps
showing multiple types of data with integrative clustering of
samples.
biocViews: CopyNumberVariation, Coverage, GenomicVariation,
BiomedicalInformatics, Sequencing, Normalization,
Visualization, Clustering
Author: Hyo Young Choi [aut, cph] (ORCID:
<https://orcid.org/0000-0002-7627-8493>), Jin-Young Lee [aut,
cre, cph] (ORCID: <https://orcid.org/0000-0002-5366-7488>),
Xiaobei Zhao [ctb] (ORCID:
<https://orcid.org/0000-0002-5277-0846>), Jeremiah R. Holt
[ctb] (ORCID: <https://orcid.org/0000-0002-5201-5015>),
Katherine A. Hoadley [aut] (ORCID:
<https://orcid.org/0000-0002-1216-477X>), D. Neil Hayes [aut,
fnd, cph] (ORCID: <https://orcid.org/0000-0001-6203-7771>)
Maintainer: Jin-Young Lee <jlee307@uthsc.edu>
URL: https://github.com/hyochoi/ELViS
VignetteBuilder: knitr
BugReports: https://github.com/hyochoi/ELViS/issues
git_url: https://git.bioconductor.org/packages/ELViS
git_branch: devel
git_last_commit: ed429d6
git_last_commit_date: 2025-03-04
Date/Publication: 2025-03-05
source.ver: src/contrib/ELViS_0.99.13.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ELViS_0.99.13.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ELViS_0.99.13.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ELViS_0.99.13.tgz
vignettes: vignettes/ELViS/inst/doc/ELViS_Toy_Example.html
vignetteTitles: Authoring R Markdown vignettes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ELViS/inst/doc/ELViS_Toy_Example.R
dependencyCount: 142
Package: EMDomics
Version: 2.37.0
Depends: R (>= 3.2.1)
Imports: emdist, BiocParallel, matrixStats, ggplot2, CDFt,
preprocessCore
Suggests: knitr
License: MIT + file LICENSE
MD5sum: 45681860bf8d9c9364cb4ba03999d0b8
NeedsCompilation: no
Title: Earth Mover's Distance for Differential Analysis of Genomics
Data
Description: The EMDomics algorithm is used to perform a supervised
multi-class analysis to measure the magnitude and statistical
significance of observed continuous genomics data between
groups. Usually the data will be gene expression values from
array-based or sequence-based experiments, but data from other
types of experiments can also be analyzed (e.g. copy number
variation). Traditional methods like Significance Analysis of
Microarrays (SAM) and Linear Models for Microarray Data (LIMMA)
use significance tests based on summary statistics (mean and
standard deviation) of the distributions. This approach lacks
power to identify expression differences between groups that
show high levels of intra-group heterogeneity. The Earth
Mover's Distance (EMD) algorithm instead computes the "work"
needed to transform one distribution into another, thus
providing a metric of the overall difference in shape between
two distributions. Permutation of sample labels is used to
generate q-values for the observed EMD scores. This package
also incorporates the Komolgorov-Smirnov (K-S) test and the
Cramer von Mises test (CVM), which are both common distribution
comparison tests.
biocViews: Software, DifferentialExpression, GeneExpression, Microarray
Author: Sadhika Malladi [aut, cre], Daniel Schmolze [aut, cre], Andrew
Beck [aut], Sheida Nabavi [aut]
Maintainer: Sadhika Malladi <contact@sadhikamalladi.com> and Daniel
Schmolze <emd@schmolze.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/EMDomics
git_branch: devel
git_last_commit: 593aa0c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/EMDomics_2.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EMDomics_2.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EMDomics_2.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EMDomics_2.37.0.tgz
vignettes: vignettes/EMDomics/inst/doc/EMDomics.html
vignetteTitles: EMDomics Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/EMDomics/inst/doc/EMDomics.R
dependencyCount: 49
Package: EmpiricalBrownsMethod
Version: 1.35.0
Depends: R (>= 3.2.0)
Suggests: BiocStyle, testthat, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 2ac0d9bd7a5d53286c2d235669a255b5
NeedsCompilation: no
Title: Uses Brown's method to combine p-values from dependent tests
Description: Combining P-values from multiple statistical tests is
common in bioinformatics. However, this procedure is
non-trivial for dependent P-values. This package implements an
empirical adaptation of Brown’s Method (an extension of
Fisher’s Method) for combining dependent P-values which is
appropriate for highly correlated data sets found in
high-throughput biological experiments.
biocViews: StatisticalMethod, GeneExpression, Pathways
Author: William Poole
Maintainer: David Gibbs <dgibbs@systemsbiology.org>
URL: https://github.com/IlyaLab/CombiningDependentPvaluesUsingEBM.git
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/EmpiricalBrownsMethod
git_branch: devel
git_last_commit: 73676a6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/EmpiricalBrownsMethod_1.35.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.5/EmpiricalBrownsMethod_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EmpiricalBrownsMethod_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EmpiricalBrownsMethod_1.35.0.tgz
vignettes: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.html
vignetteTitles: Empirical Browns Method
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.R
dependsOnMe: poolVIM
importsMe: EBSEA
dependencyCount: 0
Package: EnhancedVolcano
Version: 1.25.0
Depends: ggplot2, ggrepel
Imports: methods
Suggests: ggalt, ggrastr, RUnit, BiocGenerics, knitr, DESeq2, pasilla,
airway, org.Hs.eg.db, gridExtra, magrittr, rmarkdown
License: GPL-3
MD5sum: be827e677511fbbd2aeb29bf3110daef
NeedsCompilation: no
Title: Publication-ready volcano plots with enhanced colouring and
labeling
Description: Volcano plots represent a useful way to visualise the
results of differential expression analyses. Here, we present a
highly-configurable function that produces publication-ready
volcano plots. EnhancedVolcano will attempt to fit as many
point labels in the plot window as possible, thus avoiding
'clogging' up the plot with labels that could not otherwise
have been read. Other functionality allows the user to identify
up to 4 different types of attributes in the same plot space
via colour, shape, size, and shade parameter configurations.
biocViews: RNASeq, GeneExpression, Transcription,
DifferentialExpression, ImmunoOncology
Author: Kevin Blighe [aut, cre], Sharmila Rana [aut], Emir Turkes
[ctb], Benjamin Ostendorf [ctb], Andrea Grioni [ctb], Myles
Lewis [aut]
Maintainer: Kevin Blighe <kevin@clinicalbioinformatics.co.uk>
URL: https://github.com/kevinblighe/EnhancedVolcano
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/EnhancedVolcano
git_branch: devel
git_last_commit: 88a8e6a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/EnhancedVolcano_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EnhancedVolcano_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EnhancedVolcano_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EnhancedVolcano_1.25.0.tgz
vignettes: vignettes/EnhancedVolcano/inst/doc/EnhancedVolcano.html
vignetteTitles: Publication-ready volcano plots with enhanced colouring
and labeling
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EnhancedVolcano/inst/doc/EnhancedVolcano.R
importsMe: chevreulShiny, ExpHunterSuite
suggestsMe: xCell2, rliger
dependencyCount: 37
Package: enhancerHomologSearch
Version: 1.13.2
Depends: R (>= 4.1.0), methods
Imports: BiocGenerics, Biostrings, BSgenome, BiocParallel,
BiocFileCache, GenomeInfoDb, GenomicRanges, httr, IRanges,
jsonlite, motifmatchr, Matrix, pwalign, rtracklayer, Rcpp,
S4Vectors, stats, utils
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, BSgenome.Drerio.UCSC.danRer10,
BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10,
TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db,
TxDb.Mmusculus.UCSC.mm10.knownGene, org.Mm.eg.db, MotifDb,
testthat, TFBSTools
License: GPL (>= 2)
MD5sum: b0909c4a9aedf5701001ad4f11d9b5fa
NeedsCompilation: yes
Title: Identification of putative mammalian orthologs to given enhancer
Description: Get ENCODE data of enhancer region via H3K4me1 peaks and
search homolog regions for given sequences. The candidates of
enhancer homolog regions can be filtered by distance to target
TSS. The top candidates from human and mouse will be aligned to
each other and then exported as multiple alignments with given
enhancer.
biocViews: Sequencing, GeneRegulation, Alignment
Author: Jianhong Ou [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8652-2488>), Valentina Cigliola
[dtc], Kenneth Poss [fnd]
Maintainer: Jianhong Ou <jou@morgridge.org>
URL: https://jianhong.github.io/enhancerHomologSearch
VignetteBuilder: knitr
BugReports: https://github.com/jianhong/enhancerHomologSearch/issues
git_url: https://git.bioconductor.org/packages/enhancerHomologSearch
git_branch: devel
git_last_commit: 27b3973
git_last_commit_date: 2025-01-03
Date/Publication: 2025-01-03
source.ver: src/contrib/enhancerHomologSearch_1.13.2.tar.gz
win.binary.ver:
bin/windows/contrib/4.5/enhancerHomologSearch_1.13.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/enhancerHomologSearch_1.13.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/enhancerHomologSearch_1.13.2.tgz
vignettes:
vignettes/enhancerHomologSearch/inst/doc/enhancerHomologSearch.html
vignetteTitles: enhancerHomologSearch Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/enhancerHomologSearch/inst/doc/enhancerHomologSearch.R
dependencyCount: 99
Package: EnMCB
Version: 1.19.0
Depends: R (>= 4.0)
Imports: survivalROC, glmnet, rms, mboost, Matrix, igraph, methods,
survivalsvm, ggplot2, boot, e1071, survival, BiocFileCache
Suggests: SummarizedExperiment, testthat, Biobase, survminer,
affycoretools, knitr, plotROC, limma, rmarkdown
License: GPL-2
MD5sum: 115608284ef76981ca7edc3067aca08f
NeedsCompilation: no
Title: Predicting Disease Progression Based on Methylation Correlated
Blocks using Ensemble Models
Description: Creation of the correlated blocks using DNA methylation
profiles. Machine learning models can be constructed to predict
differentially methylated blocks and disease progression.
biocViews: Normalization, DNAMethylation, MethylationArray,
SupportVectorMachine
Author: Xin Yu
Maintainer: Xin Yu <whirlsyu@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/whirlsyu/EnMCB/issues
git_url: https://git.bioconductor.org/packages/EnMCB
git_branch: devel
git_last_commit: a5bc079
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/EnMCB_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EnMCB_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EnMCB_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EnMCB_1.19.0.tgz
vignettes: vignettes/EnMCB/inst/doc/vignette.html
vignetteTitles: vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EnMCB/inst/doc/vignette.R
dependencyCount: 128
Package: ENmix
Version: 1.43.4
Depends: parallel,doParallel,foreach,SummarizedExperiment,stats,R (>=
3.5.0)
Imports: grDevices,graphics,matrixStats,methods,utils,irlba,
geneplotter,impute,minfi,RPMM,illuminaio,dynamicTreeCut,IRanges,gtools,
Biobase,ExperimentHub,AnnotationHub,genefilter,gplots,quadprog,S4Vectors
Suggests: minfiData, RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown
License: Artistic-2.0
Archs: x64
MD5sum: d3d7c0d14d6f7b167e7d21cba917be24
NeedsCompilation: no
Title: Quality control and analysis tools for Illumina DNA methylation
BeadChip
Description: Tools for quanlity control, analysis and visulization of
Illumina DNA methylation array data.
biocViews: DNAMethylation, Preprocessing, QualityControl, TwoChannel,
Microarray, OneChannel, MethylationArray, BatchEffect,
Normalization, DataImport, Regression,
PrincipalComponent,Epigenetics, MultiChannel,
DifferentialMethylation, ImmunoOncology
Author: Zongli Xu [cre, aut], Liang Niu [aut], Jack Taylor [ctb]
Maintainer: Zongli Xu <xuz@niehs.nih.gov>
URL: https://github.com/Bioconductor/ENmix
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/ENmix/issues
git_url: https://git.bioconductor.org/packages/ENmix
git_branch: devel
git_last_commit: 1c378be
git_last_commit_date: 2025-03-14
Date/Publication: 2025-03-17
source.ver: src/contrib/ENmix_1.43.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ENmix_1.43.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ENmix_1.43.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ENmix_1.43.4.tgz
vignettes: vignettes/ENmix/inst/doc/ENmix.html
vignetteTitles: ENmix User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ENmix/inst/doc/ENmix.R
dependencyCount: 163
Package: EnrichDO
Version: 1.1.1
Depends: R (>= 4.0.0)
Imports: BiocGenerics, Rgraphviz, clusterProfiler, hash, S4Vectors,
dplyr, ggplot2, graph, magrittr, methods, pheatmap, graphics,
utils, purrr, tidyr, stats
Suggests: knitr, rmarkdown, org.Hs.eg.db, testthat (>= 3.0.0),
BiocStyle
License: MIT + file LICENSE
MD5sum: 7f906e0b549aa2bfdef20f22c1b27465
NeedsCompilation: no
Title: a Global Weighted Model for Disease Ontology Enrichment Analysis
Description: To implement disease ontology (DO) enrichment analysis,
this package is designed and presents a double weighted model
based on the latest annotations of the human genome with DO
terms, by integrating the DO graph topology on a global scale.
This package exhibits high accuracy that it can identify more
specific DO terms, which alleviates the over enriched problem.
The package includes various statistical models and
visualization schemes for discovering the associations between
genes and diseases from biological big data.
biocViews: Annotation, Visualization, GeneSetEnrichment, Software
Author: Liang Cheng [aut], Haixiu Yang [aut], Hongyu Fu [cre]
Maintainer: Hongyu Fu <2287531995@qq.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/EnrichDO
git_branch: devel
git_last_commit: ef3d0ef
git_last_commit_date: 2024-11-27
Date/Publication: 2024-11-29
source.ver: src/contrib/EnrichDO_1.1.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EnrichDO_1.1.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EnrichDO_1.1.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EnrichDO_1.1.1.tgz
vignettes: vignettes/EnrichDO/inst/doc/EnrichDO.html
vignetteTitles: EnrichDO: Disease Ontology Enrichment Analysis
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/EnrichDO/inst/doc/EnrichDO.R
dependencyCount: 127
Package: EnrichedHeatmap
Version: 1.37.0
Depends: R (>= 3.6.0), methods, grid, ComplexHeatmap (>= 2.11.0),
GenomicRanges
Imports: matrixStats, stats, GetoptLong, Rcpp, utils, locfit, circlize
(>= 0.4.5), IRanges
LinkingTo: Rcpp
Suggests: testthat (>= 0.3), knitr, markdown, rmarkdown, genefilter,
RColorBrewer
License: MIT + file LICENSE
MD5sum: 4c2c644daf9ec8f4dfa8b53aecc3cf79
NeedsCompilation: yes
Title: Making Enriched Heatmaps
Description: Enriched heatmap is a special type of heatmap which
visualizes the enrichment of genomic signals on specific target
regions. Here we implement enriched heatmap by ComplexHeatmap
package. Since this type of heatmap is just a normal heatmap
but with some special settings, with the functionality of
ComplexHeatmap, it would be much easier to customize the
heatmap as well as concatenating to a list of heatmaps to show
correspondance between different data sources.
biocViews: Software, Visualization, Sequencing, GenomeAnnotation,
Coverage
Author: Zuguang Gu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7395-8709>)
Maintainer: Zuguang Gu <z.gu@dkfz.de>
URL: https://github.com/jokergoo/EnrichedHeatmap
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/EnrichedHeatmap
git_branch: devel
git_last_commit: 6e3469a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/EnrichedHeatmap_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EnrichedHeatmap_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EnrichedHeatmap_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EnrichedHeatmap_1.37.0.tgz
vignettes: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.html,
vignettes/EnrichedHeatmap/inst/doc/roadmap.html,
vignettes/EnrichedHeatmap/inst/doc/row_odering.html,
vignettes/EnrichedHeatmap/inst/doc/visualize_categorical_signals_wrapper.html
vignetteTitles: 1. Make Enriched Heatmaps, 4. Visualize Comprehensive
Associations in Roadmap dataset, 3. Compare row ordering
methods, 2. Visualize Categorical Signals
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.R,
vignettes/EnrichedHeatmap/inst/doc/roadmap.R,
vignettes/EnrichedHeatmap/inst/doc/row_odering.R,
vignettes/EnrichedHeatmap/inst/doc/visualize_categorical_signals_wrapper.R
importsMe: profileplyr
suggestsMe: ComplexHeatmap, epistack, extraChIPs,
InteractiveComplexHeatmap
dependencyCount: 47
Package: EnrichmentBrowser
Version: 2.37.0
Depends: SummarizedExperiment, graph
Imports: AnnotationDbi, BiocFileCache, BiocManager, GSEABase, GO.db,
KEGGREST, KEGGgraph, Rgraphviz, S4Vectors, SPIA, edgeR,
graphite, hwriter, limma, methods, pathview, safe
Suggests: ALL, BiocStyle, ComplexHeatmap, DESeq2, ReportingTools,
airway, biocGraph, hgu95av2.db, geneplotter, knitr, msigdbr,
rmarkdown, statmod
License: Artistic-2.0
MD5sum: b99ec3f4af1dbbca85ed27e822948cb1
NeedsCompilation: no
Title: Seamless navigation through combined results of set-based and
network-based enrichment analysis
Description: The EnrichmentBrowser package implements essential
functionality for the enrichment analysis of gene expression
data. The analysis combines the advantages of set-based and
network-based enrichment analysis in order to derive
high-confidence gene sets and biological pathways that are
differentially regulated in the expression data under
investigation. Besides, the package facilitates the
visualization and exploration of such sets and pathways.
biocViews: ImmunoOncology, Microarray, RNASeq, GeneExpression,
DifferentialExpression, Pathways, GraphAndNetwork, Network,
GeneSetEnrichment, NetworkEnrichment, Visualization,
ReportWriting
Author: Ludwig Geistlinger [aut, cre], Gergely Csaba [aut], Mara
Santarelli [ctb], Mirko Signorelli [ctb], Rohit Satyam [ctb],
Marcel Ramos [ctb], Levi Waldron [ctb], Ralf Zimmer [aut]
Maintainer: Ludwig Geistlinger <ludwig.geistlinger@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/lgeistlinger/EnrichmentBrowser/issues
git_url: https://git.bioconductor.org/packages/EnrichmentBrowser
git_branch: devel
git_last_commit: 0601e6c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/EnrichmentBrowser_2.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EnrichmentBrowser_2.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EnrichmentBrowser_2.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EnrichmentBrowser_2.37.0.tgz
vignettes: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.html
vignetteTitles: Seamless navigation through combined results of set- &
network-based enrichment analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.R
importsMe: GSEABenchmarkeR, zenith
suggestsMe: GenomicSuperSignature, roastgsa, bugphyzz
dependencyCount: 94
Package: enrichplot
Version: 1.27.4
Depends: R (>= 3.5.0)
Imports: aplot (>= 0.2.1), DOSE (>= 3.31.2), ggfun (>= 0.1.7),
ggnewscale, ggplot2, ggrepel (>= 0.9.0), ggtangle (>= 0.0.5),
graphics, grid, igraph, methods, plyr, purrr, RColorBrewer,
reshape2, rlang, stats, utils, scatterpie, GOSemSim (>=
2.31.2), magrittr, ggtree, yulab.utils (>= 0.1.6)
Suggests: clusterProfiler, dplyr, europepmc, ggarchery, ggupset, glue,
knitr, rmarkdown, org.Hs.eg.db, prettydoc, tibble, tidyr,
ggforce, ggHoriPlot, AnnotationDbi, ggplotify, ggridges,
grDevices, gridExtra, ggstar, scales, ggtreeExtra, tidydr
License: Artistic-2.0
Archs: x64
MD5sum: 9a02bf2a524c017790609b51c15fddcd
NeedsCompilation: no
Title: Visualization of Functional Enrichment Result
Description: The 'enrichplot' package implements several visualization
methods for interpreting functional enrichment results obtained
from ORA or GSEA analysis. It is mainly designed to work with
the 'clusterProfiler' package suite. All the visualization
methods are developed based on 'ggplot2' graphics.
biocViews: Annotation, GeneSetEnrichment, GO, KEGG, Pathways, Software,
Visualization
Author: Guangchuang Yu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6485-8781>), Chun-Hui Gao [ctb]
(ORCID: <https://orcid.org/0000-0002-1445-7939>)
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
URL: https://yulab-smu.top/contribution-knowledge-mining/
VignetteBuilder: knitr
BugReports: https://github.com/GuangchuangYu/enrichplot/issues
git_url: https://git.bioconductor.org/packages/enrichplot
git_branch: devel
git_last_commit: 2ee77c6
git_last_commit_date: 2025-01-07
Date/Publication: 2025-01-08
source.ver: src/contrib/enrichplot_1.27.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/enrichplot_1.27.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/enrichplot_1.27.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/enrichplot_1.27.4.tgz
vignettes: vignettes/enrichplot/inst/doc/enrichplot.html
vignetteTitles: enrichplot
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: maEndToEnd
importsMe: CBNplot, ChIPseeker, clusterProfiler, debrowser,
enrichViewNet, meshes, MicrobiomeProfiler, ReactomePA,
TDbasedUFEadv, ExpHunterSuite
suggestsMe: GeoTcgaData, mastR, methylGSA, ReporterScore, SCpubr
dependencyCount: 120
Package: enrichViewNet
Version: 1.5.0
Depends: R (>= 4.2.0)
Imports: gprofiler2, strex, RCy3, jsonlite, stringr, enrichplot, DOSE,
methods
Suggests: BiocStyle, knitr, rmarkdown, ggplot2, testthat, magick
License: Artistic-2.0
MD5sum: bed8197731a8b8d52b0dd546efdd59eb
NeedsCompilation: no
Title: From functional enrichment results to biological networks
Description: This package enables the visualization of functional
enrichment results as network graphs. First the package enables
the visualization of enrichment results, in a format
corresponding to the one generated by gprofiler2, as a
customizable Cytoscape network. In those networks, both gene
datasets (GO terms/pathways/protein complexes) and genes
associated to the datasets are represented as nodes. While the
edges connect each gene to its dataset(s). The package also
provides the option to create enrichment maps from functional
enrichment results. Enrichment maps enable the visualization of
enriched terms into a network with edges connecting overlapping
genes.
biocViews: BiologicalQuestion, Software, Network, NetworkEnrichment, GO
Author: Astrid Deschênes [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7846-6749>), Pascal Belleau [aut]
(ORCID: <https://orcid.org/0000-0002-0802-1071>), Robert L.
Faure [aut] (ORCID: <https://orcid.org/0000-0003-1798-4723>),
Maria J. Fernandes [aut] (ORCID:
<https://orcid.org/0000-0002-3973-025X>), Alexander Krasnitz
[aut], David A. Tuveson [aut] (ORCID:
<https://orcid.org/0000-0002-8017-2712>)
Maintainer: Astrid Deschênes <adeschen@hotmail.com>
URL: https://github.com/adeschen/enrichViewNet,
https://adeschen.github.io/enrichViewNet/
VignetteBuilder: knitr
BugReports: https://github.com/adeschen/enrichViewNet/issues
git_url: https://git.bioconductor.org/packages/enrichViewNet
git_branch: devel
git_last_commit: b9b8a23
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/enrichViewNet_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/enrichViewNet_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/enrichViewNet_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/enrichViewNet_1.5.0.tgz
vignettes: vignettes/enrichViewNet/inst/doc/enrichViewNet.html
vignetteTitles: From functional enrichment results to biological
networks
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/enrichViewNet/inst/doc/enrichViewNet.R
dependencyCount: 161
Package: ensembldb
Version: 2.31.0
Depends: R (>= 3.5.0), BiocGenerics (>= 0.15.10), GenomicRanges (>=
1.31.18), GenomicFeatures (>= 1.49.6), AnnotationFilter (>=
1.5.2)
Imports: methods, RSQLite (>= 1.1), DBI, Biobase, GenomeInfoDb,
AnnotationDbi (>= 1.31.19), rtracklayer, S4Vectors (>=
0.23.10), Rsamtools, IRanges (>= 2.13.24), ProtGenerics,
Biostrings (>= 2.47.9), curl
Suggests: BiocStyle, knitr, EnsDb.Hsapiens.v86 (>= 0.99.8), testthat,
BSgenome.Hsapiens.NCBI.GRCh38, ggbio (>= 1.24.0), Gviz (>=
1.20.0), rmarkdown, AnnotationHub
Enhances: RMariaDB, shiny
License: LGPL
MD5sum: cc0c3b5af780df595713644ad36a0dcb
NeedsCompilation: no
Title: Utilities to create and use Ensembl-based annotation databases
Description: The package provides functions to create and use
transcript centric annotation databases/packages. The
annotation for the databases are directly fetched from Ensembl
using their Perl API. The functionality and data is similar to
that of the TxDb packages from the GenomicFeatures package,
but, in addition to retrieve all gene/transcript models and
annotations from the database, ensembldb provides a filter
framework allowing to retrieve annotations for specific entries
like genes encoded on a chromosome region or transcript models
of lincRNA genes. EnsDb databases built with ensembldb contain
also protein annotations and mappings between proteins and
their encoding transcripts. Finally, ensembldb provides
functions to map between genomic, transcript and protein
coordinates.
biocViews: Genetics, AnnotationData, Sequencing, Coverage
Author: Johannes Rainer <johannes.rainer@eurac.edu> with contributions
from Tim Triche, Sebastian Gibb, Laurent Gatto Christian
Weichenberger and Boyu Yu.
Maintainer: Johannes Rainer <johannes.rainer@eurac.edu>
URL: https://github.com/jorainer/ensembldb
VignetteBuilder: knitr
BugReports: https://github.com/jorainer/ensembldb/issues
git_url: https://git.bioconductor.org/packages/ensembldb
git_branch: devel
git_last_commit: 9c06161
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ensembldb_2.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ensembldb_2.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ensembldb_2.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ensembldb_2.31.0.tgz
vignettes:
vignettes/ensembldb/inst/doc/coordinate-mapping-use-cases.html,
vignettes/ensembldb/inst/doc/coordinate-mapping.html,
vignettes/ensembldb/inst/doc/ensembldb.html,
vignettes/ensembldb/inst/doc/MySQL-backend.html,
vignettes/ensembldb/inst/doc/proteins.html
vignetteTitles: Use cases for coordinate mapping with ensembldb,
Mapping between genome,, transcript and protein coordinates,
Generating an using Ensembl based annotation packages, Using a
MariaDB/MySQL server backend, Querying protein features
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ensembldb/inst/doc/coordinate-mapping-use-cases.R,
vignettes/ensembldb/inst/doc/coordinate-mapping.R,
vignettes/ensembldb/inst/doc/ensembldb.R,
vignettes/ensembldb/inst/doc/MySQL-backend.R,
vignettes/ensembldb/inst/doc/proteins.R
dependsOnMe: chimeraviz, demuxSNP, AHEnsDbs, EnsDb.Hsapiens.v75,
EnsDb.Hsapiens.v79, EnsDb.Hsapiens.v86, EnsDb.Mmusculus.v75,
EnsDb.Mmusculus.v79, EnsDb.Rnorvegicus.v75,
EnsDb.Rnorvegicus.v79
importsMe: biovizBase, BUSpaRse, chevreulProcess, ChIPpeakAnno,
CleanUpRNAseq, consensusDE, diffUTR, epimutacions, epivizrData,
ggbio, GRaNIE, Gviz, RAIDS, RITAN, scanMiRApp, scFeatures,
signifinder, singleCellTK, TVTB, tximeta,
GenomicDistributionsData, scRNAseq, crosstalkr, GRIN2,
locuszoomr, RNAseqQC
suggestsMe: AlphaMissenseR, AnnotationHub, autonomics, CNVRanger,
eisaR, EpiTxDb, fishpond, GenomicFeatures, ldblock,
multicrispr, nullranges, satuRn, txdbmaker, wiggleplotr,
celldex, gaawr2, GeneSelectR, pQTLdata
dependencyCount: 80
Package: epialleleR
Version: 1.15.3
Depends: R (>= 4.1)
Imports: stats, methods, utils, data.table, BiocGenerics,
GenomicRanges, Rcpp
LinkingTo: Rcpp, BH, Rhtslib
Suggests: GenomeInfoDb, SummarizedExperiment, VariantAnnotation, RUnit,
knitr, rmarkdown, ggplot2
License: Artistic-2.0
MD5sum: f60b3536c1b2d08b60fe848d246f09fa
NeedsCompilation: yes
Title: Fast, Epiallele-Aware Methylation Caller and Reporter
Description: Epialleles are specific DNA methylation patterns that are
mitotically and/or meiotically inherited. This package calls
and reports cytosine methylation as well as frequencies of
hypermethylated epialleles at the level of genomic regions or
individual cytosines in next-generation sequencing data using
binary alignment map (BAM) files as an input. Among other
things, this package can also extract and visualise methylation
patterns and assess allele specificity of methylation.
biocViews: DNAMethylation, Epigenetics, MethylSeq, LongRead
Author: Oleksii Nikolaienko [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5910-4934>)
Maintainer: Oleksii Nikolaienko <oleksii.nikolaienko@gmail.com>
URL: https://github.com/BBCG/epialleleR
SystemRequirements: C++17, GNU make
VignetteBuilder: knitr
BugReports: https://github.com/BBCG/epialleleR/issues
git_url: https://git.bioconductor.org/packages/epialleleR
git_branch: devel
git_last_commit: 98760d3
git_last_commit_date: 2025-03-05
Date/Publication: 2025-03-05
source.ver: src/contrib/epialleleR_1.15.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/epialleleR_1.15.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/epialleleR_1.15.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/epialleleR_1.15.3.tgz
vignettes: vignettes/epialleleR/inst/doc/epialleleR.html,
vignettes/epialleleR/inst/doc/values.html
vignetteTitles: epialleleR, values
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/epialleleR/inst/doc/epialleleR.R,
vignettes/epialleleR/inst/doc/values.R
dependencyCount: 27
Package: EpiCompare
Version: 1.11.3
Depends: R (>= 4.2.0)
Imports: AnnotationHub, ChIPseeker, data.table, genomation,
GenomicRanges, IRanges (>= 2.41.3), GenomeInfoDb, ggplot2 (>=
3.5.0), htmltools, methods, plotly, reshape2, rmarkdown,
rtracklayer, stats, stringr, utils, BiocGenerics, downloadthis,
parallel
Suggests: rworkflows, BiocFileCache, BiocParallel, BiocStyle,
clusterProfiler, GenomicAlignments, grDevices, knitr,
org.Hs.eg.db, testthat (>= 3.0.0), tidyr,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Hsapiens.UCSC.hg38.knownGene,
TxDb.Mmusculus.UCSC.mm9.knownGene,
TxDb.Mmusculus.UCSC.mm10.knownGene,
BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38,
BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm10,
ComplexUpset, plyranges, scales, Matrix, consensusSeekeR,
heatmaply, viridis
License: GPL-3
MD5sum: 15aad566183a908e93111b6a17229d50
NeedsCompilation: no
Title: Comparison, Benchmarking & QC of Epigenomic Datasets
Description: EpiCompare is used to compare and analyse epigenetic
datasets for quality control and benchmarking purposes. The
package outputs an HTML report consisting of three sections:
(1. General metrics) Metrics on peaks (percentage of
blacklisted and non-standard peaks, and peak widths) and
fragments (duplication rate) of samples, (2. Peak overlap)
Percentage and statistical significance of overlapping and
non-overlapping peaks. Also includes upset plot and (3.
Functional annotation) functional annotation (ChromHMM,
ChIPseeker and enrichment analysis) of peaks. Also includes
peak enrichment around TSS.
biocViews: Epigenetics, Genetics, QualityControl, ChIPSeq,
MultipleComparison, FunctionalGenomics, ATACSeq, DNaseSeq
Author: Sera Choi [aut] (ORCID:
<https://orcid.org/0000-0002-5077-1984>), Brian Schilder [aut]
(ORCID: <https://orcid.org/0000-0001-5949-2191>), Leyla
Abbasova [aut], Alan Murphy [aut] (ORCID:
<https://orcid.org/0000-0002-2487-8753>), Nathan Skene [aut]
(ORCID: <https://orcid.org/0000-0002-6807-3180>), Thomas
Roberts [ctb], Hiranyamaya Dash [cre] (ORCID:
<https://orcid.org/0009-0005-5514-505X>)
Maintainer: Hiranyamaya Dash <hdash.work@gmail.com>
URL: https://github.com/neurogenomics/EpiCompare
VignetteBuilder: knitr
BugReports: https://github.com/neurogenomics/EpiCompare/issues
git_url: https://git.bioconductor.org/packages/EpiCompare
git_branch: devel
git_last_commit: 65484db
git_last_commit_date: 2025-02-14
Date/Publication: 2025-02-14
source.ver: src/contrib/EpiCompare_1.11.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EpiCompare_1.11.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EpiCompare_1.11.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EpiCompare_1.11.3.tgz
vignettes: vignettes/EpiCompare/inst/doc/docker.html,
vignettes/EpiCompare/inst/doc/EpiCompare.html,
vignettes/EpiCompare/inst/doc/example_report.html
vignetteTitles: docker, EpiCompare, example_report
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EpiCompare/inst/doc/docker.R,
vignettes/EpiCompare/inst/doc/EpiCompare.R,
vignettes/EpiCompare/inst/doc/example_report.R
dependencyCount: 192
Package: epidecodeR
Version: 1.15.0
Depends: R (>= 3.1.0)
Imports: EnvStats, ggplot2, rtracklayer, GenomicRanges, IRanges,
rstatix, ggpubr, methods, stats, utils, dplyr
Suggests: knitr, rmarkdown
License: GPL-3
Archs: x64
MD5sum: 110d6263f49ca2fc1fd37b817180e819
NeedsCompilation: no
Title: epidecodeR: a functional exploration tool for epigenetic and
epitranscriptomic regulation
Description: epidecodeR is a package capable of analysing impact of
degree of DNA/RNA epigenetic chemical modifications on
dysregulation of genes or proteins. This package integrates
chemical modification data generated from a host of epigenomic
or epitranscriptomic techniques such as ChIP-seq, ATAC-seq,
m6A-seq, etc. and dysregulated gene lists in the form of
differential gene expression, ribosome occupancy or
differential protein translation and identify impact of
dysregulation of genes caused due to varying degrees of
chemical modifications associated with the genes. epidecodeR
generates cumulative distribution function (CDF) plots showing
shifts in trend of overall log2FC between genes divided into
groups based on the degree of modification associated with the
genes. The tool also tests for significance of difference in
log2FC between groups of genes.
biocViews: DifferentialExpression, GeneRegulation, HistoneModification,
FunctionalPrediction, Transcription, GeneExpression,
Epitranscriptomics, Epigenetics, FunctionalGenomics,
SystemsBiology, Transcriptomics, ChipOnChip
Author: Kandarp Joshi [aut, cre], Dan Ohtan Wang [aut]
Maintainer: Kandarp Joshi <kandarpbioinfo@gmail.com>
URL: https://github.com/kandarpRJ/epidecodeR,
https://epidecoder.shinyapps.io/shinyapp
VignetteBuilder: knitr
BugReports: https://github.com/kandarpRJ/epidecodeR/issues
git_url: https://git.bioconductor.org/packages/epidecodeR
git_branch: devel
git_last_commit: 787612b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/epidecodeR_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/epidecodeR_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/epidecodeR_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/epidecodeR_1.15.0.tgz
vignettes: vignettes/epidecodeR/inst/doc/epidecodeR.html
vignetteTitles: epidecodeR: a functional exploration tool for
epigenetic and epitranscriptomic regulation
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/epidecodeR/inst/doc/epidecodeR.R
dependencyCount: 126
Package: EpiDISH
Version: 2.23.1
Depends: R (>= 4.1)
Imports: MASS, e1071, quadprog, parallel, stats, matrixStats, stringr,
locfdr, Matrix
Suggests: roxygen2, GEOquery, BiocStyle, knitr, rmarkdown, Biobase,
testthat
License: GPL-2
MD5sum: 31ba6a7dc7c1bf5904d6b9ba13154405
NeedsCompilation: no
Title: Epigenetic Dissection of Intra-Sample-Heterogeneity
Description: EpiDISH is a R package to infer the proportions of a
priori known cell-types present in a sample representing a
mixture of such cell-types. Right now, the package can be used
on DNAm data of blood-tissue of any age, from birth to old-age,
generic epithelial tissue and breast tissue. Besides, the
package provides a function that allows the identification of
differentially methylated cell-types and their directionality
of change in Epigenome-Wide Association Studies.
biocViews: DNAMethylation, MethylationArray, Epigenetics,
DifferentialMethylation, ImmunoOncology
Author: Andrew E. Teschendorff [aut], Shijie C. Zheng [aut, cre]
Maintainer: Shijie C. Zheng <shijieczheng@gmail.com>
URL: https://github.com/sjczheng/EpiDISH
VignetteBuilder: knitr
BugReports: https://github.com/sjczheng/EpiDISH/issues
git_url: https://git.bioconductor.org/packages/EpiDISH
git_branch: devel
git_last_commit: 150c50f
git_last_commit_date: 2024-11-11
Date/Publication: 2024-11-11
source.ver: src/contrib/EpiDISH_2.23.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EpiDISH_2.23.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EpiDISH_2.23.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EpiDISH_2.23.1.tgz
vignettes: vignettes/EpiDISH/inst/doc/EpiDISH.html
vignetteTitles: Epigenetic Dissection of Intra-Sample-Heterogeneity
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EpiDISH/inst/doc/EpiDISH.R
dependsOnMe: TOAST
suggestsMe: planet
dependencyCount: 26
Package: epigenomix
Version: 1.47.0
Depends: R (>= 3.5.0), methods, Biobase, S4Vectors, IRanges,
GenomicRanges, SummarizedExperiment
Imports: BiocGenerics, MCMCpack, Rsamtools, parallel, GenomeInfoDb,
beadarray
License: LGPL-3
MD5sum: c1c8152a61c4c779d2682db6ad7373e7
NeedsCompilation: no
Title: Epigenetic and gene transcription data normalization and
integration with mixture models
Description: A package for the integrative analysis of RNA-seq or
microarray based gene transcription and histone modification
data obtained by ChIP-seq. The package provides methods for
data preprocessing and matching as well as methods for fitting
bayesian mixture models in order to detect genes with
differences in both data types.
biocViews: ChIPSeq, GeneExpression, DifferentialExpression,
Classification
Author: Hans-Ulrich Klein, Martin Schaefer
Maintainer: Hans-Ulrich Klein <h.klein@uni-muenster.de>
git_url: https://git.bioconductor.org/packages/epigenomix
git_branch: devel
git_last_commit: 6b8078f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/epigenomix_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/epigenomix_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/epigenomix_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/epigenomix_1.47.0.tgz
vignettes: vignettes/epigenomix/inst/doc/epigenomix.pdf
vignetteTitles: epigenomix package vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/epigenomix/inst/doc/epigenomix.R
dependencyCount: 107
Package: epigraHMM
Version: 1.15.0
Depends: R (>= 3.5.0)
Imports: Rcpp, magrittr, data.table, SummarizedExperiment, methods,
GenomeInfoDb, GenomicRanges, rtracklayer, IRanges, Rsamtools,
bamsignals, csaw, S4Vectors, limma, stats, Rhdf5lib, rhdf5,
Matrix, MASS, scales, ggpubr, ggplot2, GreyListChIP, pheatmap,
grDevices
LinkingTo: Rcpp, RcppArmadillo, Rhdf5lib
Suggests: testthat, knitr, rmarkdown, BiocStyle,
BSgenome.Rnorvegicus.UCSC.rn4, gcapc, chromstaRData
License: MIT + file LICENSE
MD5sum: 0e85fd394d77a5e1684fd675387d6cc1
NeedsCompilation: yes
Title: Epigenomic R-based analysis with hidden Markov models
Description: epigraHMM provides a set of tools for the analysis of
epigenomic data based on hidden Markov Models. It contains two
separate peak callers, one for consensus peaks from biological
or technical replicates, and one for differential peaks from
multi-replicate multi-condition experiments. In differential
peak calling, epigraHMM provides window-specific posterior
probabilities associated with every possible combinatorial
pattern of read enrichment across conditions.
biocViews: ChIPSeq, ATACSeq, DNaseSeq, HiddenMarkovModel, Epigenetics
Author: Pedro Baldoni [aut, cre]
Maintainer: Pedro Baldoni <pedrobaldoni@gmail.com>
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/epigraHMM
git_branch: devel
git_last_commit: 524cd3d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/epigraHMM_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/epigraHMM_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/epigraHMM_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/epigraHMM_1.15.0.tgz
vignettes: vignettes/epigraHMM/inst/doc/epigraHMM.html
vignetteTitles: Consensus and Differential Peak Calling With epigraHMM
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/epigraHMM/inst/doc/epigraHMM.R
dependencyCount: 140
Package: EpiMix
Version: 1.9.0
Depends: R (>= 4.2.0), EpiMix.data (>= 1.2.2)
Imports: AnnotationHub, AnnotationDbi, Biobase, biomaRt, data.table,
doParallel, doSNOW, downloader, dplyr, ELMER.data,
ExperimentHub, foreach, GenomeInfoDb, GenomicFeatures,
GenomicRanges, ggplot2, graphics, grDevices, impute, IRanges,
limma, methods, parallel, plyr, progress, R.matlab,
RColorBrewer, RCurl, rlang, RPMM, S4Vectors, stats,
SummarizedExperiment, tibble, tidyr, utils
Suggests: BiocStyle, clusterProfiler, DT, GEOquery, karyoploteR, knitr,
org.Hs.eg.db, regioneR, Seurat, survival, survminer,
TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, BiocGenerics,
multiMiR, miRBaseConverter
License: GPL-3
MD5sum: 3e493ced5bc2ab34dab0301693c3a12e
NeedsCompilation: no
Title: EpiMix: an integrative tool for the population-level analysis of
DNA methylation
Description: EpiMix is a comprehensive tool for the integrative
analysis of high-throughput DNA methylation data and gene
expression data. EpiMix enables automated data downloading
(from TCGA or GEO), preprocessing, methylation modeling,
interactive visualization and functional annotation.To identify
hypo- or hypermethylated CpG sites across physiological or
pathological conditions, EpiMix uses a beta mixture modeling to
identify the methylation states of each CpG probe and compares
the methylation of the experimental group to the control
group.The output from EpiMix is the functional DNA methylation
that is predictive of gene expression. EpiMix incorporates
specialized algorithms to identify functional DNA methylation
at various genetic elements, including proximal cis-regulatory
elements of protein-coding genes, distal enhancers, and genes
encoding microRNAs and lncRNAs.
biocViews: Software, Epigenetics, Preprocessing, DNAMethylation,
GeneExpression, DifferentialMethylation
Author: Yuanning Zheng [aut, cre], Markus Sujansky [aut], John Jun
[aut], Olivier Gevaert [aut]
Maintainer: Yuanning Zheng <eric2021@stanford.edu>
VignetteBuilder: knitr
BugReports: https://github.com/gevaertlab/EpiMix/issues
git_url: https://git.bioconductor.org/packages/EpiMix
git_branch: devel
git_last_commit: 0b423e1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/EpiMix_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EpiMix_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EpiMix_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EpiMix_1.9.0.tgz
vignettes: vignettes/EpiMix/inst/doc/Methylation_Modeling.html
vignetteTitles: vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EpiMix/inst/doc/Methylation_Modeling.R
importsMe: Moonlight2R
dependencyCount: 137
Package: epimutacions
Version: 1.11.0
Depends: R (>= 4.3.0), epimutacionsData
Imports: minfi, bumphunter, isotree, robustbase, ggplot2,
GenomicRanges, GenomicFeatures, IRanges, SummarizedExperiment,
stats, matrixStats, BiocGenerics, S4Vectors, utils, biomaRt,
BiocParallel, GenomeInfoDb, Homo.sapiens, purrr, tibble, Gviz,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Hsapiens.UCSC.hg18.knownGene,
TxDb.Hsapiens.UCSC.hg38.knownGene, rtracklayer, AnnotationDbi,
AnnotationHub, ExperimentHub, reshape2, grid, ensembldb,
gridExtra, IlluminaHumanMethylation450kmanifest,
IlluminaHumanMethylationEPICmanifest,
IlluminaHumanMethylation450kanno.ilmn12.hg19,
IlluminaHumanMethylationEPICanno.ilm10b2.hg19, ggrepel
Suggests: testthat, knitr, rmarkdown, BiocStyle, a4Base, kableExtra,
methods, grDevices
License: MIT + file LICENSE
Archs: x64
MD5sum: 3b139888cb6d8d8bfc12f55729a25eaa
NeedsCompilation: yes
Title: Robust outlier identification for DNA methylation data
Description: The package includes some statistical outlier detection
methods for epimutations detection in DNA methylation data. The
methods included in the package are MANOVA, Multivariate linear
models, isolation forest, robust mahalanobis distance, quantile
and beta. The methods compare a case sample with a suspected
disease against a reference panel (composed of healthy
individuals) to identify epimutations in the given case sample.
It also contains functions to annotate and visualize the
identified epimutations.
biocViews: DNAMethylation, BiologicalQuestion, Preprocessing,
StatisticalMethod, Normalization
Author: Dolors Pelegri-Siso [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5993-3003>), Juan R. Gonzalez
[aut] (ORCID: <https://orcid.org/0000-0003-3267-2146>), Carlos
Ruiz-Arenas [aut] (ORCID:
<https://orcid.org/0000-0002-6014-3498>), Carles
Hernandez-Ferrer [aut] (ORCID:
<https://orcid.org/0000-0002-8029-7160>), Leire Abarrategui
[aut] (ORCID: <https://orcid.org/0000-0002-1175-038X>)
Maintainer: Dolors Pelegri-Siso <dolors.pelegri@isglobal.org>
URL: https://github.com/isglobal-brge/epimutacions
VignetteBuilder: knitr
BugReports: https://github.com/isglobal-brge/epimutacions/issues
git_url: https://git.bioconductor.org/packages/epimutacions
git_branch: devel
git_last_commit: 8a6e409
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/epimutacions_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/epimutacions_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/epimutacions_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/epimutacions_1.11.0.tgz
vignettes: vignettes/epimutacions/inst/doc/epimutacions.html
vignetteTitles: Detection of epimutations with state of the art methods
in methylation data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/epimutacions/inst/doc/epimutacions.R
dependencyCount: 224
Package: epiNEM
Version: 1.31.0
Depends: R (>= 4.1)
Imports: BoutrosLab.plotting.general, BoolNet, e1071, gtools, stats,
igraph, utils, lattice, latticeExtra, RColorBrewer, pcalg,
minet, grDevices, graph, mnem, latex2exp
Suggests: knitr, RUnit, BiocGenerics, STRINGdb, devtools, rmarkdown,
GOSemSim, AnnotationHub, org.Sc.sgd.db, BiocStyle
License: GPL-3
MD5sum: ecb80a0c5c040677b87a82f5df5b9a99
NeedsCompilation: no
Title: epiNEM
Description: epiNEM is an extension of the original Nested Effects
Models (NEM). EpiNEM is able to take into account double
knockouts and infer more complex network signalling pathways.
It is tailored towards large scale double knock-out screens.
biocViews: Pathways, SystemsBiology, NetworkInference, Network
Author: Madeline Diekmann & Martin Pirkl
Maintainer: Martin Pirkl <martinpirkl@yahoo.de>
URL: https://github.com/cbg-ethz/epiNEM/
VignetteBuilder: knitr
BugReports: https://github.com/cbg-ethz/epiNEM/issues
git_url: https://git.bioconductor.org/packages/epiNEM
git_branch: devel
git_last_commit: 6d6ef3a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/epiNEM_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/epiNEM_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/epiNEM_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/epiNEM_1.31.0.tgz
vignettes: vignettes/epiNEM/inst/doc/epiNEM.html
vignetteTitles: epiNEM
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/epiNEM/inst/doc/epiNEM.R
importsMe: bnem, dce, nempi
suggestsMe: mnem
dependencyCount: 113
Package: EpipwR
Version: 1.1.0
Depends: R (>= 4.4.0)
Imports: EpipwR.data, ExperimentHub (>= 2.10.0), ggplot2
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), sessioninfo
License: Artistic-2.0
Archs: x64
MD5sum: e18f3eae20a1c90e2185739208b8bfbd
NeedsCompilation: no
Title: Efficient Power Analysis for EWAS with Continuous or Binary
Outcomes
Description: A quasi-simulation based approach to performing power
analysis for EWAS (Epigenome-wide association studies) with
continuous or binary outcomes. 'EpipwR' relies on empirical
EWAS datasets to determine power at specific sample sizes while
keeping computational cost low. EpipwR can be run with a
variety of standard statistical tests, controlling for either a
false discovery rate or a family-wise type I error rate.
biocViews: Epigenetics, ExperimentalDesign
Author: Jackson Barth [aut, cre] (ORCID:
<https://orcid.org/0009-0009-6307-9928>), Austin Reynolds
[aut], Mary Lauren Benton [ctb], Carissa Fong [ctb]
Maintainer: Jackson Barth <Jackson_Barth@Baylor.edu>
URL: https://github.com/jbarth216/EpipwR
VignetteBuilder: knitr
BugReports: https://github.com/jbarth216/EpipwR
git_url: https://git.bioconductor.org/packages/EpipwR
git_branch: devel
git_last_commit: d393930
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/EpipwR_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EpipwR_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EpipwR_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EpipwR_1.1.0.tgz
vignettes: vignettes/EpipwR/inst/doc/EpipwR.html
vignetteTitles: EpipwR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EpipwR/inst/doc/EpipwR.R
dependencyCount: 84
Package: epiregulon
Version: 1.3.6
Depends: R (>= 4.4), SingleCellExperiment
Imports: AnnotationHub, BiocParallel, ExperimentHub, Matrix, Rcpp,
S4Vectors, SummarizedExperiment, bluster, checkmate, entropy,
lifecycle, methods, scran, scuttle, stats, utils, scMultiome,
GenomeInfoDb, GenomicRanges, AUCell,
BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38,
BSgenome.Mmusculus.UCSC.mm10, motifmatchr, IRanges, beachmat
LinkingTo: Rcpp, beachmat, assorthead
Suggests: knitr, rmarkdown, parallel, BiocStyle, testthat (>= 3.0.0),
coin, scater, beachmat.hdf5
License: MIT + file LICENSE
MD5sum: 666eb1d7cb3b6ec12cfca7c41d4db5c2
NeedsCompilation: yes
Title: Gene regulatory network inference from single cell epigenomic
data
Description: Gene regulatory networks model the underlying gene
regulation hierarchies that drive gene expression and observed
phenotypes. Epiregulon infers TF activity in single cells by
constructing a gene regulatory network (regulons). This is
achieved through integration of scATAC-seq and scRNA-seq data
and incorporation of public bulk TF ChIP-seq data. Links
between regulatory elements and their target genes are
established by computing correlations between chromatin
accessibility and gene expressions.
biocViews: SingleCell, GeneRegulation,NetworkInference,Network,
GeneExpression, Transcription, GeneTarget
Author: Xiaosai Yao [aut, cre] (ORCID:
<https://orcid.org/0000-0001-9729-0726>), Tomasz WÅ‚odarczyk
[aut] (ORCID: <https://orcid.org/0000-0003-1554-9699>), Aaron
Lun [aut], Shang-Yang Chen [aut]
Maintainer: Xiaosai Yao <xiaosai.yao@gmail.com>
URL: https://github.com/xiaosaiyao/epiregulon/
VignetteBuilder: knitr
BugReports: https://github.com/xiaosaiyao/epiregulon/issues
git_url: https://git.bioconductor.org/packages/epiregulon
git_branch: devel
git_last_commit: b201d70
git_last_commit_date: 2025-03-10
Date/Publication: 2025-03-13
source.ver: src/contrib/epiregulon_1.3.6.tar.gz
win.binary.ver: bin/windows/contrib/4.5/epiregulon_1.3.6.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/epiregulon_1.3.6.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/epiregulon_1.3.6.tgz
vignettes: vignettes/epiregulon/inst/doc/multiome.mae.html
vignetteTitles: Epiregulon tutorial with MultiAssayExperiment
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/epiregulon/inst/doc/multiome.mae.R
suggestsMe: epiregulon.extra
dependencyCount: 202
Package: epiregulon.extra
Version: 1.3.3
Depends: R (>= 4.4), SingleCellExperiment
Imports: scran, ComplexHeatmap, Matrix, SummarizedExperiment,
checkmate, circlize, clusterProfiler, ggplot2, ggraph, igraph,
patchwork, reshape2, scales, scater
Suggests: epiregulon, knitr, rmarkdown, parallel, BiocStyle, testthat
(>= 3.0.0), msigdb, GSEABase, dorothea, scMultiome, S4Vectors,
scuttle, vdiffr, ggrastr, ggrepel
License: MIT + file LICENSE
MD5sum: c6e33c5fe3dc03fe48bd51f711907d28
NeedsCompilation: no
Title: Companion package to epiregulon with additional plotting,
differential and graph functions
Description: Gene regulatory networks model the underlying gene
regulation hierarchies that drive gene expression and observed
phenotypes. Epiregulon infers TF activity in single cells by
constructing a gene regulatory network (regulons). This is
achieved through integration of scATAC-seq and scRNA-seq data
and incorporation of public bulk TF ChIP-seq data. Links
between regulatory elements and their target genes are
established by computing correlations between chromatin
accessibility and gene expressions.
biocViews: GeneRegulation, Network, GeneExpression, Transcription,
ChipOnChip, DifferentialExpression, GeneTarget, Normalization,
GraphAndNetwork
Author: Xiaosai Yao [aut, cre] (ORCID:
<https://orcid.org/0000-0001-9729-0726>), Tomasz WÅ‚odarczyk
[aut] (ORCID: <https://orcid.org/0000-0003-1554-9699>), Timothy
Keyes [aut], Shang-Yang Chen [aut]
Maintainer: Xiaosai Yao <xiaosai.yao@gmail.com>
URL: https://github.com/xiaosaiyao/epiregulon.extra/
VignetteBuilder: knitr
BugReports: https://github.com/xiaosaiyao/epiregulon.extra/issues
git_url: https://git.bioconductor.org/packages/epiregulon.extra
git_branch: devel
git_last_commit: 3568f4f
git_last_commit_date: 2025-03-19
Date/Publication: 2025-03-19
source.ver: src/contrib/epiregulon.extra_1.3.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/epiregulon.extra_1.3.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/epiregulon.extra_1.3.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/epiregulon.extra_1.3.3.tgz
vignettes: vignettes/epiregulon.extra/inst/doc/Data_visualization.html
vignetteTitles: Data visualization with epiregulon.extra
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/epiregulon.extra/inst/doc/Data_visualization.R
dependencyCount: 184
Package: epistack
Version: 1.13.0
Depends: R (>= 4.1)
Imports: GenomicRanges, SummarizedExperiment, BiocGenerics, S4Vectors,
IRanges, graphics, plotrix, grDevices, stats, methods
Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown,
EnrichedHeatmap, biomaRt, rtracklayer, covr, vdiffr, magick
License: MIT + file LICENSE
Archs: x64
MD5sum: 4fe2007e8a3a5219e937f574ce839987
NeedsCompilation: no
Title: Heatmaps of Stack Profiles from Epigenetic Signals
Description: The epistack package main objective is the visualizations
of stacks of genomic tracks (such as, but not restricted to,
ChIP-seq, ATAC-seq, DNA methyation or genomic conservation
data) centered at genomic regions of interest. epistack needs
three different inputs: 1) a genomic score objects, such as
ChIP-seq coverage or DNA methylation values, provided as a
`GRanges` (easily obtained from `bigwig` or `bam` files). 2) a
list of feature of interest, such as peaks or transcription
start sites, provided as a `GRanges` (easily obtained from
`gtf` or `bed` files). 3) a score to sort the features, such as
peak height or gene expression value.
biocViews: RNASeq, Preprocessing, ChIPSeq, GeneExpression, Coverage
Author: SACI Safia [aut], DEVAILLY Guillaume [cre, aut]
Maintainer: DEVAILLY Guillaume <gdevailly@hotmail.com>
URL: https://github.com/GenEpi-GenPhySE/epistack
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/epistack
git_branch: devel
git_last_commit: dab3b9d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/epistack_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/epistack_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/epistack_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/epistack_1.13.0.tgz
vignettes: vignettes/epistack/inst/doc/using_epistack.html
vignetteTitles: Using epistack
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/epistack/inst/doc/using_epistack.R
dependencyCount: 37
Package: epistasisGA
Version: 1.9.0
Depends: R (>= 4.2)
Imports: BiocParallel, data.table, matrixStats, stats, survival,
igraph, batchtools, qgraph, grDevices, parallel, ggplot2, grid,
bigmemory, graphics, utils
LinkingTo: Rcpp, RcppArmadillo, BH, bigmemory
Suggests: BiocStyle, knitr, rmarkdown, magrittr, kableExtra, testthat
(>= 3.0.0)
License: GPL-3
MD5sum: eaa51002720a5d4f3b2e4f071a9ed4a8
NeedsCompilation: yes
Title: An R package to identify multi-snp effects in nuclear family
studies using the GADGETS method
Description: This package runs the GADGETS method to identify epistatic
effects in nuclear family studies. It also provides functions
for permutation-based inference and graphical visualization of
the results.
biocViews: Genetics, SNP, GeneticVariability
Author: Michael Nodzenski [aut, cre], Juno Krahn [ctb]
Maintainer: Michael Nodzenski <michael.nodzenski@gmail.com>
URL: https://github.com/mnodzenski/epistasisGA
VignetteBuilder: knitr
BugReports: https://github.com/mnodzenski/epistasisGA/issues
git_url: https://git.bioconductor.org/packages/epistasisGA
git_branch: devel
git_last_commit: a8c940b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/epistasisGA_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/epistasisGA_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/epistasisGA_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/epistasisGA_1.9.0.tgz
vignettes: vignettes/epistasisGA/inst/doc/E_GADGETS.html,
vignettes/epistasisGA/inst/doc/GADGETS.html,
vignettes/epistasisGA/inst/doc/Including_Maternal_SNPs.html
vignetteTitles: E-GADGETS, GADGETS, Detecting Maternal-SNP Interactions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/epistasisGA/inst/doc/E_GADGETS.R,
vignettes/epistasisGA/inst/doc/GADGETS.R,
vignettes/epistasisGA/inst/doc/Including_Maternal_SNPs.R
dependencyCount: 117
Package: EpiTxDb
Version: 1.19.0
Depends: R (>= 4.0), AnnotationDbi, Modstrings
Imports: methods, utils, httr, xml2, curl, rex, GenomicFeatures,
txdbmaker, GenomicRanges, GenomeInfoDb, BiocGenerics,
BiocFileCache, S4Vectors, IRanges, RSQLite, DBI, Biostrings,
tRNAdbImport
Suggests: BiocStyle, knitr, rmarkdown, testthat, httptest,
AnnotationHub, ensembldb, ggplot2, EpiTxDb.Hs.hg38,
BSgenome.Hsapiens.UCSC.hg38, BSgenome.Scerevisiae.UCSC.sacCer3,
TxDb.Hsapiens.UCSC.hg38.knownGene
License: Artistic-2.0
MD5sum: 37b0707c145961ac3d61bdd9aa073432
NeedsCompilation: no
Title: Storing and accessing epitranscriptomic information using the
AnnotationDbi interface
Description: EpiTxDb facilitates the storage of epitranscriptomic
information. More specifically, it can keep track of
modification identity, position, the enzyme for introducing it
on the RNA, a specifier which determines the position on the
RNA to be modified and the literature references each
modification is associated with.
biocViews: Software, Epitranscriptomics
Author: Felix G.M. Ernst [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5064-0928>)
Maintainer: Felix G.M. Ernst <felix.gm.ernst@outlook.com>
URL: https://github.com/FelixErnst/EpiTxDb
VignetteBuilder: knitr
BugReports: https://github.com/FelixErnst/EpiTxDb/issues
git_url: https://git.bioconductor.org/packages/EpiTxDb
git_branch: devel
git_last_commit: 49a7f33
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/EpiTxDb_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EpiTxDb_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EpiTxDb_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EpiTxDb_1.19.0.tgz
vignettes: vignettes/EpiTxDb/inst/doc/EpiTxDb-creation.html,
vignettes/EpiTxDb/inst/doc/EpiTxDb.html
vignetteTitles: EpiTxDb-creation, EpiTxDb
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EpiTxDb/inst/doc/EpiTxDb-creation.R,
vignettes/EpiTxDb/inst/doc/EpiTxDb.R
dependsOnMe: EpiTxDb.Hs.hg38, EpiTxDb.Mm.mm10, EpiTxDb.Sc.sacCer3
dependencyCount: 121
Package: epivizr
Version: 2.37.0
Depends: R (>= 3.5.0), methods
Imports: epivizrServer (>= 1.1.1), epivizrData (>= 1.3.4),
GenomicRanges, S4Vectors, IRanges, bumphunter, GenomeInfoDb
Suggests: testthat, roxygen2, knitr, Biobase, SummarizedExperiment,
antiProfilesData, hgu133plus2.db, Mus.musculus, BiocStyle,
minfi, rmarkdown
License: Artistic-2.0
Archs: x64
MD5sum: 146b736e811b17d71e0877d27069811e
NeedsCompilation: no
Title: R Interface to epiviz web app
Description: This package provides connections to the epiviz web app
(http://epiviz.cbcb.umd.edu) for interactive visualization of
genomic data. Objects in R/bioc interactive sessions can be
displayed in genome browser tracks or plots to be explored by
navigation through genomic regions. Fundamental Bioconductor
data structures are supported (e.g., GenomicRanges and
RangedSummarizedExperiment objects), while providing an easy
mechanism to support other data structures (through package
epivizrData). Visualizations (using d3.js) can be easily added
to the web app as well.
biocViews: Visualization, Infrastructure, GUI
Author: Hector Corrada Bravo, Florin Chelaru, Llewellyn Smith, Naomi
Goldstein, Jayaram Kancherla, Morgan Walter, Brian Gottfried
Maintainer: Hector Corrada Bravo <hcorrada@gmail.com>
VignetteBuilder: knitr
Video: https://www.youtube.com/watch?v=099c4wUxozA
git_url: https://git.bioconductor.org/packages/epivizr
git_branch: devel
git_last_commit: 4241f76
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/epivizr_2.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/epivizr_2.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/epivizr_2.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/epivizr_2.37.0.tgz
vignettes: vignettes/epivizr/inst/doc/IntroToEpivizr.html
vignetteTitles: Introduction to epivizr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/epivizr/inst/doc/IntroToEpivizr.R
dependsOnMe: epivizrStandalone, scTreeViz
dependencyCount: 123
Package: epivizrChart
Version: 1.29.0
Depends: R (>= 3.5.0)
Imports: epivizrData (>= 1.5.1), epivizrServer, htmltools, rjson,
methods, BiocGenerics
Suggests: testthat, roxygen2, knitr, Biobase, GenomicRanges, S4Vectors,
IRanges, SummarizedExperiment, antiProfilesData,
hgu133plus2.db, Mus.musculus, BiocStyle, Homo.sapiens, shiny,
minfi, Rsamtools, rtracklayer, RColorBrewer, magrittr,
AnnotationHub
License: Artistic-2.0
MD5sum: e59a113e0396dcb233c1eacefcd0bb33
NeedsCompilation: no
Title: R interface to epiviz web components
Description: This package provides an API for interactive visualization
of genomic data using epiviz web components. Objects in
R/BioConductor can be used to generate interactive R
markdown/notebook documents or can be visualized in the R
Studio's default viewer.
biocViews: Visualization, GUI
Author: Brian Gottfried [aut], Jayaram Kancherla [aut], Hector Corrada
Bravo [aut, cre]
Maintainer: Hector Corrada Bravo <hcorrada@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/epivizrChart
git_branch: devel
git_last_commit: 08a6956
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/epivizrChart_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/epivizrChart_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/epivizrChart_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/epivizrChart_1.29.0.tgz
vignettes: vignettes/epivizrChart/inst/doc/IntegrationWithIGVjs.html,
vignettes/epivizrChart/inst/doc/IntegrationWithShiny.html,
vignettes/epivizrChart/inst/doc/IntroToEpivizrChart.html,
vignettes/epivizrChart/inst/doc/VisualizeSumExp.html
vignetteTitles: Visualizing Files with epivizrChart, Visualizing
genomic data in Shiny Apps using epivizrChart, Introduction to
epivizrChart, Visualizing `RangeSummarizedExperiment` objects
Shiny Apps using epivizrChart
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/epivizrChart/inst/doc/IntegrationWithIGVjs.R,
vignettes/epivizrChart/inst/doc/IntegrationWithShiny.R,
vignettes/epivizrChart/inst/doc/IntroToEpivizrChart.R,
vignettes/epivizrChart/inst/doc/VisualizeSumExp.R
dependencyCount: 117
Package: epivizrData
Version: 1.35.0
Depends: R (>= 3.4), methods, epivizrServer (>= 1.1.1), Biobase
Imports: S4Vectors, GenomicRanges, SummarizedExperiment (>= 0.2.0),
OrganismDbi, GenomicFeatures, GenomeInfoDb, IRanges, ensembldb
Suggests: testthat, roxygen2, bumphunter, hgu133plus2.db, Mus.musculus,
TxDb.Mmusculus.UCSC.mm10.knownGene, rjson, knitr, rmarkdown,
BiocStyle, EnsDb.Mmusculus.v79, AnnotationHub, rtracklayer,
utils, RMySQL, DBI, matrixStats
License: MIT + file LICENSE
MD5sum: dd767cfce4b72d751c4a911d0f880c50
NeedsCompilation: no
Title: Data Management API for epiviz interactive visualization app
Description: Serve data from Bioconductor Objects through a WebSocket
connection.
biocViews: Infrastructure, Visualization
Author: Hector Corrada Bravo [aut, cre], Florin Chelaru [aut]
Maintainer: Hector Corrada Bravo <hcorrada@gmail.com>
URL: http://epiviz.github.io
VignetteBuilder: knitr
BugReports: https://github.com/epiviz/epivizrData/issues
git_url: https://git.bioconductor.org/packages/epivizrData
git_branch: devel
git_last_commit: 0d48393
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/epivizrData_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/epivizrData_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/epivizrData_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/epivizrData_1.35.0.tgz
vignettes: vignettes/epivizrData/inst/doc/epivizrData.html
vignetteTitles: epivizrData Usage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/epivizrData/inst/doc/epivizrData.R
importsMe: epivizr, epivizrChart, scTreeViz
dependencyCount: 114
Package: epivizrServer
Version: 1.35.0
Depends: R (>= 3.2.3), methods
Imports: httpuv (>= 1.3.0), R6 (>= 2.0.0), rjson, mime (>= 0.2)
Suggests: testthat, knitr, rmarkdown, BiocStyle
License: MIT + file LICENSE
MD5sum: f71c0e0802fd906a9585b087585fa6a4
NeedsCompilation: no
Title: WebSocket server infrastructure for epivizr apps and packages
Description: This package provides objects to manage WebSocket
connections to epiviz apps. Other epivizr package use this
infrastructure.
biocViews: Infrastructure, Visualization
Author: Hector Corrada Bravo [aut, cre]
Maintainer: Hector Corrada Bravo <hcorrada@gmail.com>
URL: https://epiviz.github.io
VignetteBuilder: knitr
BugReports: https://github.com/epiviz/epivizrServer
git_url: https://git.bioconductor.org/packages/epivizrServer
git_branch: devel
git_last_commit: c1b4447
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-31
source.ver: src/contrib/epivizrServer_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/epivizrServer_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/epivizrServer_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/epivizrServer_1.35.0.tgz
vignettes: vignettes/epivizrServer/inst/doc/epivizrServer.html
vignetteTitles: epivizrServer Usage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
dependsOnMe: epivizrData
importsMe: epivizr, epivizrChart, epivizrStandalone, scTreeViz
dependencyCount: 14
Package: epivizrStandalone
Version: 1.35.0
Depends: R (>= 3.2.3), epivizr (>= 2.3.6), methods
Imports: git2r, epivizrServer, GenomeInfoDb, BiocGenerics,
GenomicFeatures, S4Vectors
Suggests: testthat, knitr, rmarkdown, OrganismDbi (>= 1.13.9),
Mus.musculus, Biobase, BiocStyle
License: MIT + file LICENSE
MD5sum: 9d327d681d5c95be00f790dc1699711d
NeedsCompilation: no
Title: Run Epiviz Interactive Genomic Data Visualization App within R
Description: This package imports the epiviz visualization JavaScript
app for genomic data interactive visualization. The
'epivizrServer' package is used to provide a web server running
completely within R. This standalone version allows to browse
arbitrary genomes through genome annotations provided by
Bioconductor packages.
biocViews: Visualization, Infrastructure, GUI
Author: Hector Corrada Bravo, Jayaram Kancherla
Maintainer: Hector Corrada Bravo <hcorrada@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/epivizrStandalone
git_branch: devel
git_last_commit: d990d4e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/epivizrStandalone_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/epivizrStandalone_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/epivizrStandalone_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/epivizrStandalone_1.35.0.tgz
vignettes: vignettes/epivizrStandalone/inst/doc/EpivizrStandalone.html
vignetteTitles: Introduction to epivizrStandalone
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
suggestsMe: scTreeViz
dependencyCount: 125
Package: erccdashboard
Version: 1.41.0
Depends: R (>= 4.0), ggplot2 (>= 2.1.0), gridExtra (>= 2.0.0)
Imports: edgeR, gplots, grid, gtools, limma, locfit, MASS, plyr,
qvalue, reshape2, ROCR, scales, stringr, knitr
Suggests: BiocStyle, knitr, rmarkdown
License: GPL (>=2)
MD5sum: 90ca6a92236ef5e17be6cce4b48fbb1e
NeedsCompilation: no
Title: Assess Differential Gene Expression Experiments with ERCC
Controls
Description: Technical performance metrics for differential gene
expression experiments using External RNA Controls Consortium
(ERCC) spike-in ratio mixtures.
biocViews: ImmunoOncology, GeneExpression, Transcription,
AlternativeSplicing, DifferentialExpression,
DifferentialSplicing, Genetics, Microarray, mRNAMicroarray,
RNASeq, BatchEffect, MultipleComparison, QualityControl
Author: Sarah Munro, Steve Lund
Maintainer: Sarah Munro <sarah.munro@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/erccdashboard
git_branch: devel
git_last_commit: e3aeeef
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/erccdashboard_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/erccdashboard_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/erccdashboard_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/erccdashboard_1.41.0.tgz
vignettes: vignettes/erccdashboard/inst/doc/erccdashboard.html
vignetteTitles: erccdashboard introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/erccdashboard/inst/doc/erccdashboard.R
dependencyCount: 58
Package: erma
Version: 1.23.1
Depends: R (>= 3.1), methods, Homo.sapiens, GenomicFiles (>= 1.5.2)
Imports: rtracklayer (>= 1.38.1), S4Vectors (>= 0.23.18), BiocGenerics,
GenomicRanges, SummarizedExperiment, ggplot2, GenomeInfoDb,
Biobase, shiny, BiocParallel, IRanges, AnnotationDbi
Suggests: rmarkdown, BiocStyle, knitr, GO.db, png, DT, doParallel
License: Artistic-2.0
MD5sum: c97f6df6f4e02e616eb24e8bb6e0bdae
NeedsCompilation: no
Title: epigenomic road map adventures
Description: Software and data to support epigenomic road map
adventures.
Author: VJ Carey <stvjc@channing.harvard.edu>
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
PackageStatus: Deprecated
git_url: https://git.bioconductor.org/packages/erma
git_branch: devel
git_last_commit: 079b2c4
git_last_commit_date: 2025-03-26
Date/Publication: 2025-03-26
source.ver: src/contrib/erma_1.23.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/erma_1.23.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/erma_1.23.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/erma_1.23.1.tgz
vignettes: vignettes/erma/inst/doc/erma.html
vignetteTitles: ermaInteractive
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/erma/inst/doc/erma.R
dependencyCount: 141
Package: ERSSA
Version: 1.25.0
Depends: R (>= 4.0.0)
Imports: edgeR (>= 3.23.3), DESeq2 (>= 1.21.16), ggplot2 (>= 3.0.0),
RColorBrewer (>= 1.1-2), plyr (>= 1.8.4), BiocParallel (>=
1.15.8), apeglm (>= 1.4.2), grDevices, stats, utils
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-3 | file LICENSE
MD5sum: 0f93d4ccb606a9742da91832aec42e63
NeedsCompilation: no
Title: Empirical RNA-seq Sample Size Analysis
Description: The ERSSA package takes user supplied RNA-seq differential
expression dataset and calculates the number of differentially
expressed genes at varying biological replicate levels. This
allows the user to determine, without relying on any a priori
assumptions, whether sufficient differential detection has been
acheived with their RNA-seq dataset.
biocViews: ImmunoOncology, GeneExpression, Transcription,
DifferentialExpression, RNASeq, MultipleComparison,
QualityControl
Author: Zixuan Shao [aut, cre]
Maintainer: Zixuan Shao <Zixuanshao.zach@gmail.com>
URL: https://github.com/zshao1/ERSSA
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ERSSA
git_branch: devel
git_last_commit: 63a52f8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ERSSA_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ERSSA_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ERSSA_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ERSSA_1.25.0.tgz
vignettes: vignettes/ERSSA/inst/doc/ERSSA.html
vignetteTitles: ERSSA Package Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ERSSA/inst/doc/ERSSA.R
dependencyCount: 89
Package: esATAC
Version: 1.29.0
Depends: R (>= 4.0.0), Rsamtools, GenomicRanges, ShortRead, pipeFrame
Imports: Rcpp (>= 0.12.11), methods, knitr, Rbowtie2, rtracklayer,
ggplot2, Biostrings, ChIPseeker, clusterProfiler, igraph,
rJava, magrittr, digest, BSgenome, AnnotationDbi,
GenomicAlignments, GenomicFeatures, R.utils, GenomeInfoDb,
BiocGenerics, S4Vectors, IRanges, rmarkdown, tools,
VennDiagram, grid, JASPAR2018, TFBSTools, grDevices, graphics,
stats, utils, parallel, corrplot, BiocManager, motifmatchr
LinkingTo: Rcpp
Suggests: BSgenome.Hsapiens.UCSC.hg19,
TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, testthat,
webshot, prettydoc
License: GPL-3 | file LICENSE
MD5sum: 53d1c1e27d82624f135ff31aa35ba99f
NeedsCompilation: yes
Title: An Easy-to-use Systematic pipeline for ATACseq data analysis
Description: This package provides a framework and complete preset
pipeline for quantification and analysis of ATAC-seq Reads. It
covers raw sequencing reads preprocessing (FASTQ files), reads
alignment (Rbowtie2), aligned reads file operations (SAM, BAM,
and BED files), peak calling (F-seq), genome annotations
(Motif, GO, SNP analysis) and quality control report. The
package is managed by dataflow graph. It is easy for user to
pass variables seamlessly between processes and understand the
workflow. Users can process FASTQ files through end-to-end
preset pipeline which produces a pretty HTML report for quality
control and preliminary statistical results, or customize
workflow starting from any intermediate stages with esATAC
functions easily and flexibly.
biocViews: ImmunoOncology, Sequencing, DNASeq, QualityControl,
Alignment, Preprocessing, Coverage, ATACSeq, DNaseSeq
Author: Zheng Wei, Wei Zhang
Maintainer: Zheng Wei <wzweizheng@qq.com>
URL: https://github.com/wzthu/esATAC
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/wzthu/esATAC/issues
git_url: https://git.bioconductor.org/packages/esATAC
git_branch: devel
git_last_commit: 508f345
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/esATAC_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/esATAC_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/esATAC_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/esATAC_1.29.0.tgz
vignettes: vignettes/esATAC/inst/doc/esATAC-Introduction.html
vignetteTitles: esATAC: an Easy-to-use Systematic pipeline for ATAC-seq
data analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/esATAC/inst/doc/esATAC-Introduction.R
dependencyCount: 187
Package: escape
Version: 2.3.0
Depends: R (>= 4.1)
Imports: AUCell, BiocParallel, grDevices, dplyr, ggdist, ggplot2,
ggpointdensity, GSEABase, GSVA, SingleCellExperiment, ggridges,
msigdbr, stats, reshape2, patchwork, MatrixGenerics, utils,
SummarizedExperiment, UCell, stringr, methods, SeuratObject,
Matrix
Suggests: Seurat, hexbin, scran, knitr, rmarkdown, markdown, BiocStyle,
RColorBrewer, rlang, spelling, testthat (>= 3.0.0), vdiffr
License: MIT + file LICENSE
MD5sum: 3a8549a14c3930ae5df297f3813d72e7
NeedsCompilation: no
Title: Easy single cell analysis platform for enrichment
Description: A bridging R package to facilitate gene set enrichment
analysis (GSEA) in the context of single-cell RNA sequencing.
Using raw count information, Seurat objects, or
SingleCellExperiment format, users can perform and visualize
ssGSEA, GSVA, AUCell, and UCell-based enrichment calculations
across individual cells.
biocViews: Software, SingleCell, Classification, Annotation,
GeneSetEnrichment, Sequencing, GeneSignaling, Pathways
Author: Nick Borcherding [aut, cre], Jared Andrews [aut], Alexei
Martsinkovskiy [ctb]
Maintainer: Nick Borcherding <ncborch@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/escape
git_branch: devel
git_last_commit: 78d6ceb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/escape_2.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/escape_2.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/escape_2.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/escape_2.3.0.tgz
vignettes: vignettes/escape/inst/doc/vignette.html
vignetteTitles: Escape-ingToWork
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/escape/inst/doc/vignette.R
suggestsMe: Cepo
dependencyCount: 174
Package: escheR
Version: 1.7.0
Depends: ggplot2, R (>= 4.3)
Imports: SpatialExperiment (>= 1.6.1), SingleCellExperiment, rlang,
SummarizedExperiment
Suggests: STexampleData, BumpyMatrix, knitr, rmarkdown, BiocStyle,
ggpubr, scran, scater, scuttle, Seurat, hexbin
License: MIT + file LICENSE
Archs: x64
MD5sum: 6c6b1f456e96392582fdc25e12cfa86a
NeedsCompilation: no
Title: Unified multi-dimensional visualizations with Gestalt principles
Description: The creation of effective visualizations is a fundamental
component of data analysis. In biomedical research, new
challenges are emerging to visualize multi-dimensional data in
a 2D space, but current data visualization tools have limited
capabilities. To address this problem, we leverage Gestalt
principles to improve the design and interpretability of
multi-dimensional data in 2D data visualizations, layering
aesthetics to display multiple variables. The proposed
visualization can be applied to spatially-resolved
transcriptomics data, but also broadly to data visualized in 2D
space, such as embedding visualizations. We provide this open
source R package escheR, which is built off of the
state-of-the-art ggplot2 visualization framework and can be
seamlessly integrated into genomics toolboxes and workflows.
biocViews: Spatial, SingleCell, Transcriptomics, Visualization,
Software
Author: Boyi Guo [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2950-2349>), Stephanie C. Hicks
[aut] (ORCID: <https://orcid.org/0000-0002-7858-0231>), Erik D.
Nelson [ctb] (ORCID: <https://orcid.org/0000-0001-8477-0982>)
Maintainer: Boyi Guo <boyi.guo.work@gmail.com>
URL: https://github.com/boyiguo1/escheR
VignetteBuilder: knitr
BugReports: https://github.com/boyiguo1/escheR/issues
git_url: https://git.bioconductor.org/packages/escheR
git_branch: devel
git_last_commit: 6c6c207
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/escheR_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/escheR_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/escheR_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/escheR_1.7.0.tgz
vignettes: vignettes/escheR/inst/doc/more_than_visium.html,
vignettes/escheR/inst/doc/SRT_eg.html
vignetteTitles: beyond_visium, Getting Start with `escheR`
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/escheR/inst/doc/more_than_visium.R,
vignettes/escheR/inst/doc/SRT_eg.R
importsMe: SpotSweeper
suggestsMe: tpSVG
dependencyCount: 86
Package: esetVis
Version: 1.33.0
Imports: mpm, hexbin, Rtsne, MLP, grid, Biobase, MASS, stats, utils,
grDevices, methods
Suggests: ggplot2, ggvis, plotly, ggrepel, knitr, rmarkdown, ALL,
hgu95av2.db, AnnotationDbi, pander, SummarizedExperiment, GO.db
License: GPL-3
MD5sum: a7a2593f81e68ecaa4f63134e69b7b82
NeedsCompilation: no
Title: Visualizations of expressionSet Bioconductor object
Description: Utility functions for visualization of expressionSet (or
SummarizedExperiment) Bioconductor object, including spectral
map, tsne and linear discriminant analysis. Static plot via the
ggplot2 package or interactive via the ggvis or rbokeh packages
are available.
biocViews: Visualization, DataRepresentation, DimensionReduction,
PrincipalComponent, Pathways
Author: Laure Cougnaud <laure.cougnaud@openanalytics.eu>
Maintainer: Laure Cougnaud <laure.cougnaud@openanalytics.eu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/esetVis
git_branch: devel
git_last_commit: 84fc5bd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/esetVis_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/esetVis_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/esetVis_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/esetVis_1.33.0.tgz
vignettes: vignettes/esetVis/inst/doc/esetVis-vignette.html
vignetteTitles: esetVis package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/esetVis/inst/doc/esetVis-vignette.R
dependencyCount: 58
Package: eudysbiome
Version: 1.37.0
Depends: R (>= 3.1.0)
Imports: plyr, Rsamtools, R.utils, Biostrings
License: GPL-2
MD5sum: 29232592154e028bfecf74ee3d043260
NeedsCompilation: no
Title: Cartesian plot and contingency test on 16S Microbial data
Description: eudysbiome a package that permits to annotate the
differential genera as harmful/harmless based on their ability
to contribute to host diseases (as indicated in literature) or
unknown based on their ambiguous genus classification. Further,
the package statistically measures the eubiotic (harmless
genera increase or harmful genera decrease) or
dysbiotic(harmless genera decrease or harmful genera increase)
impact of a given treatment or environmental change on the
(gut-intestinal, GI) microbiome in comparison to the microbiome
of the reference condition.
Author: Xiaoyuan Zhou, Christine Nardini
Maintainer: Xiaoyuan Zhou <zhouxiaoyuan@picb.ac.cn>
git_url: https://git.bioconductor.org/packages/eudysbiome
git_branch: devel
git_last_commit: 89cab1f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/eudysbiome_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/eudysbiome_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/eudysbiome_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/eudysbiome_1.37.0.tgz
vignettes: vignettes/eudysbiome/inst/doc/eudysbiome.pdf
vignetteTitles: eudysbiome User Manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/eudysbiome/inst/doc/eudysbiome.R
dependencyCount: 44
Package: evaluomeR
Version: 1.23.0
Depends: R (>= 3.6), SummarizedExperiment, MultiAssayExperiment,
cluster (>= 2.0.9), fpc (>= 2.2-3), randomForest (>= 4.6.14),
flexmix (>= 2.3.15), RSKC (>= 2.4.2), sparcl (>= 1.0.4)
Imports: corrplot (>= 0.84), grDevices, graphics, reshape2, ggplot2,
ggdendro, plotrix, stats, matrixStats, Rdpack, MASS, class,
prabclus, mclust, kableExtra, dplyr, dendextend (>= 1.16.0)
Suggests: BiocStyle, knitr, rmarkdown, magrittr
License: GPL-3
MD5sum: 7f35c5e339a0764bfeed7433cd9137ff
NeedsCompilation: no
Title: Evaluation of Bioinformatics Metrics
Description: Evaluating the reliability of your own metrics and the
measurements done on your own datasets by analysing the
stability and goodness of the classifications of such metrics.
biocViews: Clustering, Classification, FeatureExtraction
Author: José Antonio Bernabé-DÃaz [aut, cre], Manuel Franco [aut],
Juana-MarÃa Vivo [aut], Manuel Quesada-MartÃnez [aut], Astrid
Duque-Ramos [aut], Jesualdo Tomás Fernández-Breis [aut]
Maintainer: José Antonio Bernabé-DÃaz <joseantonio.bernabe1@um.es>
URL: https://github.com/neobernad/evaluomeR
VignetteBuilder: knitr
BugReports: https://github.com/neobernad/evaluomeR/issues
git_url: https://git.bioconductor.org/packages/evaluomeR
git_branch: devel
git_last_commit: b2ae8f5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/evaluomeR_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/evaluomeR_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/evaluomeR_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/evaluomeR_1.23.0.tgz
vignettes: vignettes/evaluomeR/inst/doc/manual.html
vignetteTitles: Evaluation of Bioinformatics Metrics with evaluomeR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/evaluomeR/inst/doc/manual.R
dependencyCount: 124
Package: EventPointer
Version: 3.15.0
Depends: R (>= 3.5.0), SGSeq, Matrix, SummarizedExperiment
Imports: GenomicFeatures, stringr, GenomeInfoDb, igraph, MASS, nnls,
limma, matrixStats, RBGL, prodlim, graph, methods, utils,
stats, doParallel, foreach, affxparser, GenomicRanges,
S4Vectors, IRanges, qvalue, cobs, rhdf5, BSgenome, Biostrings,
glmnet, abind, iterators, lpSolve, poibin, speedglm, tximport,
fgsea
Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, dplyr,
kableExtra
License: Artistic-2.0
MD5sum: c4f7f8128c0d830799b4c4f13915f9b4
NeedsCompilation: yes
Title: An effective identification of alternative splicing events using
junction arrays and RNA-Seq data
Description: EventPointer is an R package to identify alternative
splicing events that involve either simple (case-control
experiment) or complex experimental designs such as time course
experiments and studies including paired-samples. The algorithm
can be used to analyze data from either junction arrays
(Affymetrix Arrays) or sequencing data (RNA-Seq). The software
returns a data.frame with the detected alternative splicing
events: gene name, type of event (cassette, alternative
3',...,etc), genomic position, statistical significance and
increment of the percent spliced in (Delta PSI) for all the
events. The algorithm can generate a series of files to
visualize the detected alternative splicing events in IGV. This
eases the interpretation of results and the design of primers
for standard PCR validation.
biocViews: AlternativeSplicing, DifferentialSplicing, mRNAMicroarray,
RNASeq, Transcription, Sequencing, TimeCourse, ImmunoOncology
Author: Juan Pablo Romero [aut], Juan A. Ferrer-Bonsoms [aut, cre],
Pablo Sacristan [aut], Ander Muniategui [aut], Fernando Carazo
[aut], Ander Aramburu [aut], Angel Rubio [aut]
Maintainer: Juan A. Ferrer-Bonsoms <jafhernandez@tecnun.es>
VignetteBuilder: knitr
BugReports: https://github.com/jpromeror/EventPointer/issues
git_url: https://git.bioconductor.org/packages/EventPointer
git_branch: devel
git_last_commit: 8a000b5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/EventPointer_3.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EventPointer_3.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EventPointer_3.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EventPointer_3.15.0.tgz
vignettes: vignettes/EventPointer/inst/doc/EventPointer.html
vignetteTitles: EventPointer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EventPointer/inst/doc/EventPointer.R
dependencyCount: 149
Package: EWCE
Version: 1.15.1
Depends: R (>= 4.2), RNOmni (>= 1.0)
Imports: stats, utils, methods, ewceData (>= 1.7.1), dplyr, ggplot2,
reshape2, limma, stringr, HGNChelper, Matrix, parallel,
SingleCellExperiment, SummarizedExperiment, DelayedArray,
BiocParallel, orthogene (>= 0.99.8), data.table
Suggests: rworkflows, remotes, knitr, BiocStyle, rmarkdown, testthat
(>= 3.0.0), readxl, memoise, markdown, sctransform, DESeq2,
MAST, DelayedMatrixStats, ggdendro, scales, patchwork
License: GPL-3
MD5sum: 4e6fdd30a084a4f35b799f80c7d1a300
NeedsCompilation: no
Title: Expression Weighted Celltype Enrichment
Description: Used to determine which cell types are enriched within
gene lists. The package provides tools for testing enrichments
within simple gene lists (such as human disease associated
genes) and those resulting from differential expression
studies. The package does not depend upon any particular Single
Cell Transcriptome dataset and user defined datasets can be
loaded in and used in the analyses.
biocViews: GeneExpression, Transcription, DifferentialExpression,
GeneSetEnrichment, Genetics, Microarray, mRNAMicroarray,
OneChannel, RNASeq, BiomedicalInformatics, Proteomics,
Visualization, FunctionalGenomics, SingleCell
Author: Alan Murphy [cre] (ORCID:
<https://orcid.org/0000-0002-2487-8753>), Brian Schilder [aut]
(ORCID: <https://orcid.org/0000-0001-5949-2191>), Nathan Skene
[aut] (ORCID: <https://orcid.org/0000-0002-6807-3180>)
Maintainer: Alan Murphy <alanmurph94@hotmail.com>
URL: https://github.com/NathanSkene/EWCE
VignetteBuilder: knitr
BugReports: https://github.com/NathanSkene/EWCE/issues
git_url: https://git.bioconductor.org/packages/EWCE
git_branch: devel
git_last_commit: a86e84a
git_last_commit_date: 2025-02-14
Date/Publication: 2025-02-14
source.ver: src/contrib/EWCE_1.15.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/EWCE_1.15.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/EWCE_1.15.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/EWCE_1.15.1.tgz
vignettes: vignettes/EWCE/inst/doc/EWCE.html,
vignettes/EWCE/inst/doc/extended.html
vignetteTitles: Getting started, Extended examples
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/EWCE/inst/doc/EWCE.R,
vignettes/EWCE/inst/doc/extended.R
dependencyCount: 194
Package: ExCluster
Version: 1.25.0
Depends: Rsubread, GenomicRanges, rtracklayer, matrixStats, IRanges
Imports: stats, methods, grDevices, graphics, utils
License: GPL-3
MD5sum: d644973362555239eb94a7e45af0d753
NeedsCompilation: no
Title: ExCluster robustly detects differentially expressed exons
between two conditions of RNA-seq data, requiring at least two
independent biological replicates per condition
Description: ExCluster flattens Ensembl and GENCODE GTF files into GFF
files, which are used to count reads per non-overlapping exon
bin from BAM files. This read counting is done using the
function featureCounts from the package Rsubread. Library sizes
are normalized across all biological replicates, and ExCluster
then compares two different conditions to detect signifcantly
differentially spliced genes. This process requires at least
two independent biological repliates per condition, and
ExCluster accepts only exactly two conditions at a time.
ExCluster ultimately produces false discovery rates (FDRs) per
gene, which are used to detect significance. Exon log2 fold
change (log2FC) means and variances may be plotted for each
significantly differentially spliced gene, which helps
scientists develop hypothesis and target differential splicing
events for RT-qPCR validation in the wet lab.
biocViews: ImmunoOncology, DifferentialSplicing, RNASeq, Software
Author: R. Matthew Tanner, William L. Stanford, and Theodore J. Perkins
Maintainer: R. Matthew Tanner <rtann038@uottawa.ca>
git_url: https://git.bioconductor.org/packages/ExCluster
git_branch: devel
git_last_commit: 7e5c843
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ExCluster_1.25.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ExCluster_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ExCluster_1.25.0.tgz
vignettes: vignettes/ExCluster/inst/doc/ExCluster.pdf
vignetteTitles: ExCluster Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ExCluster/inst/doc/ExCluster.R
dependencyCount: 59
Package: ExiMiR
Version: 2.49.0
Depends: R (>= 2.10), Biobase (>= 2.5.5), affy (>= 1.26.1), limma
Imports: affyio(>= 1.13.3), Biobase(>= 2.5.5), preprocessCore(>=
1.10.0)
Suggests: mirna10cdf
License: GPL-2
Archs: x64
MD5sum: eb76e2b35dac49e6cf07656891c13a83
NeedsCompilation: no
Title: R functions for the normalization of Exiqon miRNA array data
Description: This package contains functions for reading raw data in
ImaGene TXT format obtained from Exiqon miRCURY LNA arrays,
annotating them with appropriate GAL files, and normalizing
them using a spike-in probe-based method. Other platforms and
data formats are also supported.
biocViews: Microarray, OneChannel, TwoChannel, Preprocessing,
GeneExpression, Transcription
Author: Sylvain Gubian <DL.RSupport@pmi.com>, Alain Sewer
<DL.RSupport@pmi.com>, PMP SA
Maintainer: Sylvain Gubian <DL.RSupport@pmi.com>
git_url: https://git.bioconductor.org/packages/ExiMiR
git_branch: devel
git_last_commit: 4ca7e42
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ExiMiR_2.49.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ExiMiR_2.49.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ExiMiR_2.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ExiMiR_2.49.0.tgz
vignettes: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.pdf
vignetteTitles: Description of ExiMiR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.R
dependencyCount: 14
Package: ExperimentHub
Version: 2.15.0
Depends: methods, BiocGenerics (>= 0.15.10), AnnotationHub (>= 3.3.6),
BiocFileCache (>= 1.5.1)
Imports: utils, S4Vectors, BiocManager, rappdirs
Suggests: knitr, BiocStyle, rmarkdown, HubPub, GenomicAlignments
Enhances: ExperimentHubData
License: Artistic-2.0
MD5sum: e937c1b2175175ea0f5bc0821b6275e6
NeedsCompilation: no
Title: Client to access ExperimentHub resources
Description: This package provides a client for the Bioconductor
ExperimentHub web resource. ExperimentHub provides a central
location where curated data from experiments, publications or
training courses can be accessed. Each resource has associated
metadata, tags and date of modification. The client creates and
manages a local cache of files retrieved enabling quick and
reproducible access.
biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient
Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut],
Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb],
Valerie Oberchain [ctb], Kayla Morrell [ctb], Lori Shepherd
[aut]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/ExperimentHub/issues
git_url: https://git.bioconductor.org/packages/ExperimentHub
git_branch: devel
git_last_commit: dace2c4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ExperimentHub_2.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ExperimentHub_2.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ExperimentHub_2.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ExperimentHub_2.15.0.tgz
vignettes: vignettes/ExperimentHub/inst/doc/ExperimentHub.html
vignetteTitles: ExperimentHub: Access the ExperimentHub Web Service
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ExperimentHub/inst/doc/ExperimentHub.R
dependsOnMe: adductomicsR, CoSIA, iSEEhub, LRcell, octad, SeqSQC,
BeadSorted.Saliva.EPIC, benchmarkfdrData2019, biscuiteerData,
bodymapRat, CellMapperData, clustifyrdatahub, CoSIAdata,
crisprScoreData, curatedAdipoChIP, CytoMethIC, DMRcatedata,
eoPredData, EpiMix.data, ewceData, FlowSorted.Blood.EPIC,
FlowSorted.CordBloodCombined.450k, HDCytoData, HiContactsData,
HighlyReplicatedRNASeq, HumanAffyData, mcsurvdata,
MetaGxBreast, MetaGxOvarian, MetaGxPancreas, multiWGCNAdata,
muscData, muSpaData, NanoporeRNASeq, NestLink, nullrangesData,
ObMiTi, octad.db, RNAmodR.Data, scMultiome, scpdata,
sesameData, SimBenchData, SpatialDatasets, spatialDmelxsim,
STexampleData, tartare, TENxVisiumData, TENxXeniumData,
VectraPolarisData, WeberDivechaLCdata
importsMe: BiocHubsShiny, BloodGen3Module, CBNplot, coMethDMR, CTdata,
DMRcate, EpiMix, epimutacions, EpipwR, epiregulon,
ExperimentHubData, GSEABenchmarkeR, hpar, m6Aboost, MACSr,
MatrixQCvis, methodical, MethReg, methylclock, Moonlight2R,
MsDataHub, orthos, PhyloProfile, signatureSearch, singleCellTK,
spatialFDA, TENET, adductData, BioImageDbs, celldex,
cfToolsData, chipseqDBData, CLLmethylation,
curatedMetagenomicData, curatedPCaData, curatedTBData,
curatedTCGAData, depmap, DropletTestFiles, DuoClustering2018,
easierData, emtdata, EpipwR.data, FieldEffectCrc,
gDNAinRNAseqData, GenomicDistributionsData, HarmonizedTCGAData,
HCAData, HCATonsilData, HMP16SData, HMP2Data,
homosapienDEE2CellScore, humanHippocampus2024, imcdatasets,
JohnsonKinaseData, LRcellTypeMarkers, marinerData, MerfishData,
methylclockData, MethylSeqData, microbiomeDataSets,
MouseAgingData, MouseGastrulationData, MouseThymusAgeing,
msigdb, NxtIRFdata, orthosData, PhyloProfileData,
preciseTADhub, ProteinGymR, raerdata, scaeData, scRNAseq,
SFEData, signatureSearchData, SingleCellMultiModal,
SingleMoleculeFootprintingData, spatialLIBD, TabulaMurisData,
TabulaMurisSenisData, TENET.ExperimentHub, TENxBrainData,
TENxBUSData, TENxPBMCData, tuberculosis, TumourMethData,
xcoredata
suggestsMe: AlphaMissenseR, ANF, AnnotationHub, bambu, Banksy, celaref,
CellMapper, DESpace, dreamlet, ELMER, genomicInstability,
h5mread, HDF5Array, jazzPanda, lute, mariner, missMethyl,
MsBackendRawFileReader, multiWGCNA, muscat, nullranges, planet,
quantiseqr, rawDiag, rawrr, recountmethylation,
SingleMoleculeFootprinting, sosta, SparseArray, SPOTlight,
standR, TCGAbiolinks, TENxIO, Voyager, xcore, BioPlex,
celarefData, curatedAdipoArray, epimutacionsData, GSE103322,
GSE13015, GSE159526, GSE62944, muleaData, smokingMouse,
SubcellularSpatialData, tissueTreg, TransOmicsData, FLASHMM
dependencyCount: 65
Package: ExperimentHubData
Version: 1.33.0
Depends: utils, BiocGenerics (>= 0.15.10), S4Vectors, AnnotationHubData
(>= 1.21.3)
Imports: methods, ExperimentHub, BiocManager, DBI, httr, curl
Suggests: GenomeInfoDb, RUnit, knitr, BiocStyle, rmarkdown, HubPub
License: Artistic-2.0
MD5sum: f59c22bba53e21b309f77e18dd443ff0
NeedsCompilation: no
Title: Add resources to ExperimentHub
Description: Functions to add metadata to ExperimentHub db and resource
files to AWS S3 buckets.
biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient
Author: Bioconductor Maintainer [cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ExperimentHubData
git_branch: devel
git_last_commit: c820abe
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ExperimentHubData_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ExperimentHubData_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ExperimentHubData_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ExperimentHubData_1.33.0.tgz
vignettes: vignettes/ExperimentHubData/inst/doc/ExperimentHubData.html
vignetteTitles: Introduction to ExperimentHubData
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: RNAmodR.Data
importsMe: methylclockData
suggestsMe: HubPub, MsDataHub, cfToolsData, homosapienDEE2CellScore,
humanHippocampus2024, JohnsonKinaseData, marinerData,
scMultiome, smokingMouse, TENET.ExperimentHub
dependencyCount: 124
Package: ExperimentSubset
Version: 1.17.0
Depends: R (>= 4.0.0), SummarizedExperiment, SingleCellExperiment,
SpatialExperiment, TreeSummarizedExperiment
Imports: methods, Matrix, S4Vectors
Suggests: BiocStyle, knitr, rmarkdown, testthat, covr, stats, scran,
scater, scds, TENxPBMCData, airway
License: MIT + file LICENSE
MD5sum: 3476fc9334c565b2acb699bb85215539
NeedsCompilation: no
Title: Manages subsets of data with Bioconductor Experiment objects
Description: Experiment objects such as the SummarizedExperiment or
SingleCellExperiment are data containers for one or more
matrix-like assays along with the associated row and column
data. Often only a subset of the original data is needed for
down-stream analysis. For example, filtering out poor quality
samples will require excluding some columns before analysis.
The ExperimentSubset object is a container to efficiently
manage different subsets of the same data without having to
make separate objects for each new subset.
biocViews: Infrastructure, Software, DataImport, DataRepresentation
Author: Irzam Sarfraz [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8121-792X>), Muhammad Asif [aut,
ths] (ORCID: <https://orcid.org/0000-0003-1839-2527>), Joshua
D. Campbell [aut] (ORCID:
<https://orcid.org/0000-0003-0780-8662>)
Maintainer: Irzam Sarfraz <irzam9095@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ExperimentSubset
git_branch: devel
git_last_commit: 48fa542
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-05
source.ver: src/contrib/ExperimentSubset_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ExperimentSubset_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ExperimentSubset_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ExperimentSubset_1.17.0.tgz
vignettes: vignettes/ExperimentSubset/inst/doc/ExperimentSubset.html
vignetteTitles: An introduction to ExperimentSubset class
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ExperimentSubset/inst/doc/ExperimentSubset.R
dependencyCount: 91
Package: ExploreModelMatrix
Version: 1.19.2
Imports: shiny (>= 1.5.0), shinydashboard, DT, cowplot, utils, dplyr,
magrittr, tidyr, ggplot2, stats, methods, rintrojs, scales,
tibble, MASS, limma, S4Vectors, shinyjs
Suggests: testthat (>= 2.1.0), knitr, rmarkdown, htmltools, BiocStyle
License: MIT + file LICENSE
MD5sum: 7d63b4029f877f6aed5c2b5b99c387c7
NeedsCompilation: no
Title: Graphical Exploration of Design Matrices
Description: Given a sample data table and a design formula,
ExploreModelMatrix generates an interactive application for
exploration of the resulting design matrix. This can be helpful
for interpreting model coefficients and constructing
appropriate contrasts in (generalized) linear models. Static
visualizations can also be generated.
biocViews: ExperimentalDesign, Regression, DifferentialExpression,
ShinyApps
Author: Charlotte Soneson [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3833-2169>), Federico Marini [aut]
(ORCID: <https://orcid.org/0000-0003-3252-7758>), Michael Love
[aut] (ORCID: <https://orcid.org/0000-0001-8401-0545>), Florian
Geier [aut] (ORCID: <https://orcid.org/0000-0002-9076-9264>),
Michael Stadler [aut] (ORCID:
<https://orcid.org/0000-0002-2269-4934>)
Maintainer: Charlotte Soneson <charlottesoneson@gmail.com>
URL: https://github.com/csoneson/ExploreModelMatrix
VignetteBuilder: knitr
BugReports: https://github.com/csoneson/ExploreModelMatrix/issues
git_url: https://git.bioconductor.org/packages/ExploreModelMatrix
git_branch: devel
git_last_commit: dce1f59
git_last_commit_date: 2024-12-31
Date/Publication: 2024-12-31
source.ver: src/contrib/ExploreModelMatrix_1.19.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ExploreModelMatrix_1.19.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ExploreModelMatrix_1.19.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ExploreModelMatrix_1.19.2.tgz
vignettes: vignettes/ExploreModelMatrix/inst/doc/EMMdeploy.html,
vignettes/ExploreModelMatrix/inst/doc/ExploreModelMatrix.html
vignetteTitles: ExploreModelMatrix-deploy, ExploreModelMatrix
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ExploreModelMatrix/inst/doc/EMMdeploy.R,
vignettes/ExploreModelMatrix/inst/doc/ExploreModelMatrix.R
dependencyCount: 87
Package: ExpressionAtlas
Version: 1.35.0
Depends: R (>= 4.2.0), methods, Biobase, SummarizedExperiment, limma,
S4Vectors, xml2, RCurl, jsonlite, BiocStyle
Imports: utils, XML, httr
Suggests: knitr, testthat, rmarkdown
License: GPL (>= 3)
MD5sum: 09ba95f3fdcf829380ebd84da9b7dbd1
NeedsCompilation: no
Title: Download datasets from EMBL-EBI Expression Atlas
Description: This package is for searching for datasets in EMBL-EBI
Expression Atlas, and downloading them into R for further
analysis. Each Expression Atlas dataset is represented as a
SimpleList object with one element per platform. Sequencing
data is contained in a SummarizedExperiment object, while
microarray data is contained in an ExpressionSet or MAList
object.
biocViews: ExpressionData, ExperimentData, SequencingData,
MicroarrayData, ArrayExpress
Author: Maria Keays [aut] (ORCID:
<https://orcid.org/0000-0003-2034-601X>), Pedro Madrigal [cre]
(ORCID: <https://orcid.org/0000-0003-1959-8199>)
Maintainer: Pedro Madrigal <pmadrigal@ebi.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ExpressionAtlas
git_branch: devel
git_last_commit: 08329fa
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ExpressionAtlas_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ExpressionAtlas_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ExpressionAtlas_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ExpressionAtlas_1.35.0.tgz
vignettes: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.html
vignetteTitles: ExpressionAtlas
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.R
suggestsMe: spatialHeatmap
dependencyCount: 68
Package: extraChIPs
Version: 1.11.1
Depends: BiocParallel, R (>= 4.2.0), GenomicRanges, ggplot2 (>= 3.5.0),
ggside (>= 0.3.1), SummarizedExperiment, tibble
Imports: csaw, dplyr (>= 1.1.1), edgeR (>= 4.0), forcats, GenomeInfoDb,
glue, ggrepel, InteractionSet, IRanges, matrixStats, methods,
patchwork, RColorBrewer, rlang, Rsamtools, rtracklayer,
S4Vectors, scales, stats, stringr, tidyr, tidyselect, vctrs
Suggests: apeglm, BiocStyle, ComplexUpset, covr, DESeq2,
EnrichedHeatmap, GenomicAlignments, GenomicInteractions, Gviz,
ggforce, harmonicmeanp, here, knitr, limma, magrittr,
plyranges, quantro, rmarkdown, testthat (>= 3.0.0), tidyverse,
VennDiagram
License: GPL-3
MD5sum: 78feb850a8fac16d489412f30ea026b4
NeedsCompilation: yes
Title: Additional functions for working with ChIP-Seq data
Description: This package builds on existing tools and adds some simple
but extremely useful capabilities for working wth ChIP-Seq
data. The focus is on detecting differential binding
windows/regions. One set of functions focusses on
set-operations retaining mcols for GRanges objects, whilst
another group of functions are to aid visualisation of results.
Coercion to tibble objects is also implemented.
biocViews: ChIPSeq, HiC, Sequencing, Coverage
Author: Stevie Pederson [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8197-3303>)
Maintainer: Stevie Pederson <stephen.pederson.au@gmail.com>
URL: https://github.com/smped/extraChIPs
VignetteBuilder: knitr
BugReports: https://github.com/smped/extraChIPs/issues
git_url: https://git.bioconductor.org/packages/extraChIPs
git_branch: devel
git_last_commit: 4035072
git_last_commit_date: 2025-03-01
Date/Publication: 2025-03-02
source.ver: src/contrib/extraChIPs_1.11.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/extraChIPs_1.11.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/extraChIPs_1.11.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/extraChIPs_1.11.1.tgz
vignettes:
vignettes/extraChIPs/inst/doc/differential_signal_fixed.html,
vignettes/extraChIPs/inst/doc/differential_signal_sliding.html,
vignettes/extraChIPs/inst/doc/range_based_functions.html
vignetteTitles: Differential Signal Analysis (Fixed-Width Windows),
Differential Signal Analysis (Sliding Windows), Range-Based
Operations
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/extraChIPs/inst/doc/differential_signal_fixed.R,
vignettes/extraChIPs/inst/doc/differential_signal_sliding.R,
vignettes/extraChIPs/inst/doc/range_based_functions.R
suggestsMe: motifTestR, transmogR
dependencyCount: 102
Package: fabia
Version: 2.53.0
Depends: R (>= 3.6.0), Biobase
Imports: methods, graphics, grDevices, stats, utils
License: LGPL (>= 2.1)
MD5sum: 6f88996bb803626d6b467569484b3ce4
NeedsCompilation: yes
Title: FABIA: Factor Analysis for Bicluster Acquisition
Description: Biclustering by "Factor Analysis for Bicluster
Acquisition" (FABIA). FABIA is a model-based technique for
biclustering, that is clustering rows and columns
simultaneously. Biclusters are found by factor analysis where
both the factors and the loading matrix are sparse. FABIA is a
multiplicative model that extracts linear dependencies between
samples and feature patterns. It captures realistic
non-Gaussian data distributions with heavy tails as observed in
gene expression measurements. FABIA utilizes well understood
model selection techniques like the EM algorithm and
variational approaches and is embedded into a Bayesian
framework. FABIA ranks biclusters according to their
information content and separates spurious biclusters from true
biclusters. The code is written in C.
biocViews: StatisticalMethod, Microarray, DifferentialExpression,
MultipleComparison, Clustering, Visualization
Author: Sepp Hochreiter <hochreit@bioinf.jku.at>
Maintainer: Andreas Mitterecker <mitterecker@bioinf.jku.at>
URL: http://www.bioinf.jku.at/software/fabia/fabia.html
git_url: https://git.bioconductor.org/packages/fabia
git_branch: devel
git_last_commit: c680892
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/fabia_2.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/fabia_2.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/fabia_2.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/fabia_2.53.0.tgz
vignettes: vignettes/fabia/inst/doc/fabia.pdf
vignetteTitles: FABIA: Manual for the R package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fabia/inst/doc/fabia.R
dependsOnMe: hapFabia, RcmdrPlugin.BiclustGUI, superbiclust
importsMe: miRSM, mosbi, BcDiag
suggestsMe: fabiaData
dependencyCount: 8
Package: factDesign
Version: 1.83.0
Depends: Biobase (>= 2.5.5)
Imports: stats
Suggests: affy, genefilter, multtest
License: LGPL
Archs: x64
MD5sum: bc9f865a0992acc847d3be2ff52cf188
NeedsCompilation: no
Title: Factorial designed microarray experiment analysis
Description: This package provides a set of tools for analyzing data
from a factorial designed microarray experiment, or any
microarray experiment for which a linear model is appropriate.
The functions can be used to evaluate tests of contrast of
biological interest and perform single outlier detection.
biocViews: Microarray, DifferentialExpression
Author: Denise Scholtens
Maintainer: Denise Scholtens <dscholtens@northwestern.edu>
git_url: https://git.bioconductor.org/packages/factDesign
git_branch: devel
git_last_commit: f7b7be7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/factDesign_1.83.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/factDesign_1.83.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/factDesign_1.83.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/factDesign_1.83.0.tgz
vignettes: vignettes/factDesign/inst/doc/factDesign.pdf
vignetteTitles: factDesign
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/factDesign/inst/doc/factDesign.R
dependencyCount: 7
Package: factR
Version: 1.9.0
Depends: R (>= 4.2)
Imports: BiocGenerics (>= 0.46), Biostrings (>= 2.68), GenomeInfoDb (>=
1.36), dplyr (>= 1.1), GenomicFeatures (>= 1.52), GenomicRanges
(>= 1.52), IRanges (>= 2.34), purrr (>= 1.0), rtracklayer (>=
1.60), tidyr (>= 1.3), methods (>= 4.3), BiocParallel (>=
1.34), S4Vectors (>= 0.38), data.table (>= 1.14), rlang (>=
1.1), tibble (>= 3.2), wiggleplotr (>= 1.24), RCurl (>= 1.98),
XML (>= 3.99), drawProteins (>= 1.20), ggplot2 (>= 3.4),
stringr (>= 1.5), pbapply (>= 1.7), stats (>= 4.3), utils (>=
4.3), graphics (>= 4.3), crayon (>= 1.5)
Suggests: AnnotationHub (>= 2.22), BSgenome (>= 1.58),
BSgenome.Mmusculus.UCSC.mm10, testthat, knitr, rmarkdown,
markdown, zeallot, rmdformats, bio3d (>= 2.4), signalHsmm (>=
1.5), tidyverse (>= 1.3), covr, patchwork
License: file LICENSE
Archs: x64
MD5sum: 8de097d63ba83be56f44f612a5bd6995
NeedsCompilation: no
Title: Functional Annotation of Custom Transcriptomes
Description: factR contain tools to process and interact with
custom-assembled transcriptomes (GTF). At its core, factR
constructs CDS information on custom transcripts and
subsequently predicts its functional output. In addition, factR
has tools capable of plotting transcripts, correcting
chromosome and gene information and shortlisting new
transcripts.
biocViews: AlternativeSplicing, FunctionalPrediction, GenePrediction
Author: Fursham Hamid [aut, cre]
Maintainer: Fursham Hamid <fursham.h@gmail.com>
URL: https://fursham-h.github.io/factR/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/factR
git_branch: devel
git_last_commit: c6ff9f0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/factR_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/factR_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/factR_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/factR_1.9.0.tgz
vignettes: vignettes/factR/inst/doc/factR.html
vignetteTitles: factR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/factR/inst/doc/factR.R
dependencyCount: 116
Package: faers
Version: 1.3.0
Depends: R (>= 3.5.0)
Imports: BiocParallel, brio, cli, curl (>= 5.0.0), data.table, httr2
(>= 1.0.0), MCMCpack, methods, openEBGM, rlang (>= 1.1.0),
rvest, tools, utils, vroom, xml2
Suggests: BiocStyle, countrycode, knitr, rmarkdown, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: c659ef70405a1b857be76f4dce0bf4f5
NeedsCompilation: no
Title: R interface for FDA Adverse Event Reporting System
Description: The FDA Adverse Event Reporting System (FAERS) is a
database used for the spontaneous reporting of adverse events
and medication errors related to human drugs and therapeutic
biological products. faers pacakge serves as the interface
between the FAERS database and R. Furthermore, faers pacakge
offers a standardized approach for performing pharmacovigilance
analysis.
biocViews: Software, DataImport, BiomedicalInformatics,
Pharmacogenomics, Pharmacogenomics
Author: Yun Peng [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2801-3332>), YuXuan Song [aut],
Caipeng Qin [aut], JiaXing Lin [aut]
Maintainer: Yun Peng <yunyunp96@163.com>
VignetteBuilder: knitr
BugReports: https://github.com/Yunuuuu/faers
git_url: https://git.bioconductor.org/packages/faers
git_branch: devel
git_last_commit: f960e35
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/faers_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/faers_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/faers_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/faers_1.3.0.tgz
vignettes: vignettes/faers/inst/doc/FAERS-Pharmacovigilance.html
vignetteTitles: FAERS-Pharmacovigilance
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/faers/inst/doc/FAERS-Pharmacovigilance.R
dependencyCount: 79
Package: FamAgg
Version: 1.35.0
Depends: methods, kinship2, igraph
Imports: gap (>= 1.1-17), Matrix, BiocGenerics, utils, survey
Suggests: BiocStyle, knitr, RUnit, rmarkdown
License: MIT + file LICENSE
MD5sum: 682aa0a5035619b1f74690d3a0d09ed7
NeedsCompilation: no
Title: Pedigree Analysis and Familial Aggregation
Description: Framework providing basic pedigree analysis and plotting
utilities as well as a variety of methods to evaluate familial
aggregation of traits in large pedigrees.
biocViews: Genetics
Author: J. Rainer, D. Taliun, C.X. Weichenberger
Maintainer: Johannes Rainer <johannes.rainer@eurac.edu>
URL: https://github.com/EuracBiomedicalResearch/FamAgg
VignetteBuilder: knitr
BugReports: https://github.com/EuracBiomedicalResearch/FamAgg/issues
git_url: https://git.bioconductor.org/packages/FamAgg
git_branch: devel
git_last_commit: 898172e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/FamAgg_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/FamAgg_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/FamAgg_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/FamAgg_1.35.0.tgz
vignettes: vignettes/FamAgg/inst/doc/FamAgg.html
vignetteTitles: Pedigree Analysis and Familial Aggregation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/FamAgg/inst/doc/FamAgg.R
dependencyCount: 93
Package: famat
Version: 1.17.0
Depends: R (>= 4.0)
Imports: KEGGREST, mgcv, stats, BiasedUrn, dplyr, gprofiler2,
rWikiPathways, reactome.db, stringr, GO.db, ontologyIndex,
tidyr, shiny, shinydashboard, shinyBS, plotly, magrittr, DT,
clusterProfiler, org.Hs.eg.db
Suggests: BiocStyle, knitr, rmarkdown, testthat, BiocManager
License: GPL-3
MD5sum: 1fc08d15147121d5eb1f202772106df0
NeedsCompilation: no
Title: Functional analysis of metabolic and transcriptomic data
Description: Famat is made to collect data about lists of genes and
metabolites provided by user, and to visualize it through a
Shiny app. Information collected is: - Pathways containing some
of the user's genes and metabolites (obtained using a pathway
enrichment analysis). - Direct interactions between user's
elements inside pathways. - Information about elements (their
identifiers and descriptions). - Go terms enrichment analysis
performed on user's genes. The Shiny app is composed of: -
information about genes, metabolites, and direct interactions
between them inside pathways. - an heatmap showing which
elements from the list are in pathways (pathways are structured
in hierarchies). - hierarchies of enriched go terms using
Molecular Function and Biological Process.
biocViews: FunctionalPrediction, GeneSetEnrichment, Pathways, GO,
Reactome, KEGG
Author: Mathieu Charles [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5343-6324>)
Maintainer: Mathieu Charles <mathieu.charles@inrae.fr>
URL: https://github.com/emiliesecherre/famat
VignetteBuilder: knitr
BugReports: https://github.com/emiliesecherre/famat/issues
git_url: https://git.bioconductor.org/packages/famat
git_branch: devel
git_last_commit: 3a4f53d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/famat_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/famat_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/famat_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/famat_1.17.0.tgz
vignettes: vignettes/famat/inst/doc/famat.html
vignetteTitles: famat
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/famat/inst/doc/famat.R
dependencyCount: 170
Package: fastLiquidAssociation
Version: 1.43.0
Depends: methods, LiquidAssociation, parallel, doParallel, stats,
Hmisc, utils
Imports: WGCNA, impute, preprocessCore
Suggests: GOstats, yeastCC, org.Sc.sgd.db
License: GPL-2
MD5sum: c996aa12578383ae42e455c1c742fbdd
NeedsCompilation: no
Title: functions for genome-wide application of Liquid Association
Description: This package extends the function of the LiquidAssociation
package for genome-wide application. It integrates a screening
method into the LA analysis to reduce the number of triplets to
be examined for a high LA value and provides code for use in
subsequent significance analyses.
biocViews: Software, GeneExpression, Genetics, Pathways, CellBiology
Author: Tina Gunderson
Maintainer: Tina Gunderson <gunderson.tina@gmail.com>
git_url: https://git.bioconductor.org/packages/fastLiquidAssociation
git_branch: devel
git_last_commit: 62d6792
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/fastLiquidAssociation_1.43.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.5/fastLiquidAssociation_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/fastLiquidAssociation_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/fastLiquidAssociation_1.43.0.tgz
vignettes:
vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.pdf
vignetteTitles: fastLiquidAssociation Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.R
dependencyCount: 123
Package: FastqCleaner
Version: 1.25.0
Imports: methods, shiny, stats, IRanges, Biostrings, ShortRead, DT,
S4Vectors, graphics, htmltools, shinyBS, Rcpp (>= 0.12.12)
LinkingTo: Rcpp
Suggests: BiocStyle, testthat, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 0d04173e47e0b2486ca4ab67d51df272
NeedsCompilation: yes
Title: A Shiny Application for Quality Control, Filtering and Trimming
of FASTQ Files
Description: An interactive web application for quality control,
filtering and trimming of FASTQ files. This user-friendly tool
combines a pipeline for data processing based on Biostrings and
ShortRead infrastructure, with a cutting-edge visual
environment. Single-Read and Paired-End files can be locally
processed. Diagnostic interactive plots (CG content, per-base
sequence quality, etc.) are provided for both the input and
output files.
biocViews:
QualityControl,Sequencing,Software,SangerSeq,SequenceMatching
Author: Leandro Roser [aut, cre], Fernán Agüero [aut], Daniel Sánchez
[aut]
Maintainer: Leandro Roser <learoser@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/FastqCleaner
git_branch: devel
git_last_commit: 2a95050
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/FastqCleaner_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/FastqCleaner_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/FastqCleaner_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/FastqCleaner_1.25.0.tgz
vignettes: vignettes/FastqCleaner/inst/doc/Overview.html
vignetteTitles: An Introduction to FastqCleaner
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/FastqCleaner/inst/doc/Overview.R
dependencyCount: 100
Package: fastreeR
Version: 1.11.0
Depends: R (>= 4.4)
Imports: ape, data.table, dynamicTreeCut, methods, R.utils, rJava,
stats, stringr, utils
Suggests: BiocFileCache, BiocStyle, graphics, knitr, memuse, rmarkdown,
spelling, testthat (>= 3.0.0)
License: GPL-3
MD5sum: b599d5d7903295ab2aa4c4789d92c9dc
NeedsCompilation: no
Title: Phylogenetic, Distance and Other Calculations on VCF and Fasta
Files
Description: Calculate distances, build phylogenetic trees or perform
hierarchical clustering between the samples of a VCF or FASTA
file. Functions are implemented in Java and called via rJava.
Parallel implementation that operates directly on the VCF or
FASTA file for fast execution.
biocViews: Phylogenetics, Metagenomics, Clustering
Author: Anestis Gkanogiannis [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6441-0688>)
Maintainer: Anestis Gkanogiannis <anestis@gkanogiannis.com>
URL: https://github.com/gkanogiannis/fastreeR,
https://github.com/gkanogiannis/BioInfoJava-Utils
SystemRequirements: Java (>= 8)
VignetteBuilder: knitr
BugReports: https://github.com/gkanogiannis/fastreeR/issues
git_url: https://git.bioconductor.org/packages/fastreeR
git_branch: devel
git_last_commit: 5904abc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/fastreeR_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/fastreeR_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/fastreeR_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/fastreeR_1.11.0.tgz
vignettes: vignettes/fastreeR/inst/doc/fastreeR_vignette.html
vignetteTitles: fastreeR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fastreeR/inst/doc/fastreeR_vignette.R
dependencyCount: 27
Package: fastseg
Version: 1.53.3
Depends: R (>= 2.13), GenomicRanges, Biobase
Imports: methods, graphics, grDevices, stats, BiocGenerics, S4Vectors,
IRanges
Suggests: DNAcopy, BiocStyle, knitr
License: LGPL (>= 2.0)
Archs: x64
MD5sum: 0dddfd0075087eb5ae6bb417e70116c7
NeedsCompilation: yes
Title: fastseg - a fast segmentation algorithm
Description: fastseg implements a very fast and efficient segmentation
algorithm. It has similar functionality as DNACopy (Olshen and
Venkatraman 2004), but is considerably faster and more
flexible. fastseg can segment data from DNA microarrays and
data from next generation sequencing for example to detect copy
number segments. Further it can segment data from RNA
microarrays like tiling arrays to identify transcripts. Most
generally, it can segment data given as a matrix or as a
vector. Various data formats can be used as input to fastseg
like expression set objects for microarrays or GRanges for
sequencing data. The segmentation criterion of fastseg is based
on a statistical test in a Bayesian framework, namely the cyber
t-test (Baldi 2001). The speed-up arises from the facts, that
sampling is not necessary in for fastseg and that a dynamic
programming approach is used for calculation of the segments'
first and higher order moments.
biocViews: Classification, CopyNumberVariation
Author: Guenter Klambauer [aut], Sonali Kumari [ctb], Alexander Blume
[cre]
Maintainer: Alexander Blume <alex.gos90@gmail.com>
URL: http://www.bioinf.jku.at/software/fastseg/index.html
VignetteBuilder: knitr
BugReports: https://github.com/alexg9010/fastseg/issues
git_url: https://git.bioconductor.org/packages/fastseg
git_branch: devel
git_last_commit: 44f2c84
git_last_commit_date: 2025-02-06
Date/Publication: 2025-02-09
source.ver: src/contrib/fastseg_1.53.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/fastseg_1.53.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/fastseg_1.53.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/fastseg_1.53.3.tgz
vignettes: vignettes/fastseg/inst/doc/fastseg.html
vignetteTitles: An R Package for fast segmentation
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fastseg/inst/doc/fastseg.R
importsMe: methylKit
dependencyCount: 25
Package: fCCAC
Version: 1.33.0
Depends: R (>= 4.2.0), S4Vectors, IRanges, GenomicRanges, grid
Imports: fda, RColorBrewer, genomation, ggplot2, ComplexHeatmap,
grDevices, stats, utils
Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown
License: Artistic-2.0
MD5sum: b0eca5ee92acc13b0201d00211388ed6
NeedsCompilation: no
Title: functional Canonical Correlation Analysis to evaluate Covariance
between nucleic acid sequencing datasets
Description: Computational evaluation of variability across DNA or RNA
sequencing datasets is a crucial step in genomics, as it allows
both to evaluate reproducibility of replicates, and to compare
different datasets to identify potential correlations. fCCAC
applies functional Canonical Correlation Analysis to allow the
assessment of: (i) reproducibility of biological or technical
replicates, analyzing their shared covariance in higher order
components; and (ii) the associations between different
datasets. fCCAC represents a more sophisticated approach that
complements Pearson correlation of genomic coverage.
biocViews: Epigenetics, Transcription, Sequencing, Coverage, ChIPSeq,
FunctionalGenomics, RNASeq, ATACSeq, MNaseSeq
Author: Pedro Madrigal [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1959-8199>)
Maintainer: Pedro Madrigal <pmadrigal@ebi.ac.uk>
git_url: https://git.bioconductor.org/packages/fCCAC
git_branch: devel
git_last_commit: 55a4b1b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/fCCAC_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/fCCAC_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/fCCAC_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/fCCAC_1.33.0.tgz
vignettes: vignettes/fCCAC/inst/doc/fCCAC.pdf
vignetteTitles: fCCAC Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fCCAC/inst/doc/fCCAC.R
dependencyCount: 134
Package: fCI
Version: 1.37.0
Depends: R (>= 3.1),FNN, psych, gtools, zoo, rgl, grid, VennDiagram
Suggests: knitr, rmarkdown, BiocStyle
License: GPL (>= 2)
Archs: x64
MD5sum: f1ee633226c453188dc69b085d214810
NeedsCompilation: no
Title: f-divergence Cutoff Index for Differential Expression Analysis
in Transcriptomics and Proteomics
Description: (f-divergence Cutoff Index), is to find DEGs in the
transcriptomic & proteomic data, and identify DEGs by computing
the difference between the distribution of fold-changes for the
control-control and remaining (non-differential) case-control
gene expression ratio data. fCI provides several advantages
compared to existing methods.
biocViews: Proteomics
Author: Shaojun Tang
Maintainer: Shaojun Tang <tangshao2008@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/fCI
git_branch: devel
git_last_commit: 81521f6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/fCI_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/fCI_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/fCI_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/fCI_1.37.0.tgz
vignettes: vignettes/fCI/inst/doc/fCI.html
vignetteTitles: fCI
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fCI/inst/doc/fCI.R
dependencyCount: 50
Package: fcScan
Version: 1.21.0
Imports: stats, plyr, VariantAnnotation, SummarizedExperiment,
rtracklayer, GenomicRanges, methods, IRanges, foreach,
doParallel, parallel
Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown
License: Artistic-2.0
MD5sum: 8acd48c61ad91b0a91f0df8a849a4c11
NeedsCompilation: no
Title: fcScan for detecting clusters of coordinates with user defined
options
Description: This package is used to detect combination of genomic
coordinates falling within a user defined window size along
with user defined overlap between identified neighboring
clusters. It can be used for genomic data where the clusters
are built on a specific chromosome or specific strand.
Clustering can be performed with a "greedy" option allowing
thus the presence of additional sites within the allowed window
size.
biocViews: GenomeAnnotation, Clustering
Author: Abdullah El-Kurdi [aut], Ghiwa khalil [aut], Georges Khazen
[ctb], Pierre Khoueiry [aut, cre]
Maintainer: Pierre Khoueiry <pk17@aub.edu.lb> Abdullah El-Kurdi
<ak161@aub.edu.lb>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/fcScan
git_branch: devel
git_last_commit: 59bb726
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/fcScan_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/fcScan_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/fcScan_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/fcScan_1.21.0.tgz
vignettes: vignettes/fcScan/inst/doc/fcScan_vignette.html
vignetteTitles: fcScan
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fcScan/inst/doc/fcScan_vignette.R
dependencyCount: 84
Package: fdrame
Version: 1.79.0
Imports: tcltk, graphics, grDevices, stats, utils
License: GPL (>= 2)
MD5sum: 161d5e89b1a0b09b7f980909eab8d915
NeedsCompilation: yes
Title: FDR adjustments of Microarray Experiments (FDR-AME)
Description: This package contains two main functions. The first is
fdr.ma which takes normalized expression data array,
experimental design and computes adjusted p-values It returns
the fdr adjusted p-values and plots, according to the methods
described in (Reiner, Yekutieli and Benjamini 2002). The
second, is fdr.gui() which creates a simple graphic user
interface to access fdr.ma
biocViews: Microarray, DifferentialExpression, MultipleComparison
Author: Yoav Benjamini, Effi Kenigsberg, Anat Reiner, Daniel Yekutieli
Maintainer: Effi Kenigsberg <effiken.fdrame@gmail.com>
git_url: https://git.bioconductor.org/packages/fdrame
git_branch: devel
git_last_commit: 0ca5353
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/fdrame_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/fdrame_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/fdrame_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/fdrame_1.79.0.tgz
vignettes: vignettes/fdrame/inst/doc/fdrame.pdf
vignetteTitles: Annotation Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 5
Package: FEAST
Version: 1.15.0
Depends: R (>= 4.1), mclust, BiocParallel, SummarizedExperiment
Imports: SingleCellExperiment, methods, stats, utils, irlba, TSCAN,
SC3, matrixStats
Suggests: rmarkdown, Seurat, ggpubr, knitr, testthat (>= 3.0.0),
BiocStyle
License: GPL-2
MD5sum: 2d1a2bf5cc520ae800a334356b8e4c0d
NeedsCompilation: yes
Title: FEAture SelcTion (FEAST) for Single-cell clustering
Description: Cell clustering is one of the most important and commonly
performed tasks in single-cell RNA sequencing (scRNA-seq) data
analysis. An important step in cell clustering is to select a
subset of genes (referred to as “featuresâ€), whose expression
patterns will then be used for downstream clustering. A good
set of features should include the ones that distinguish
different cell types, and the quality of such set could have
significant impact on the clustering accuracy. FEAST is an R
library for selecting most representative features before
performing the core of scRNA-seq clustering. It can be used as
a plug-in for the etablished clustering algorithms such as SC3,
TSCAN, SHARP, SIMLR, and Seurat. The core of FEAST algorithm
includes three steps: 1. consensus clustering; 2. gene-level
significance inference; 3. validation of an optimized feature
set.
biocViews: Sequencing, SingleCell, Clustering, FeatureExtraction
Author: Kenong Su [aut, cre], Hao Wu [aut]
Maintainer: Kenong Su <kenong.su@emory.edu>
VignetteBuilder: knitr
BugReports: https://github.com/suke18/FEAST/issues
git_url: https://git.bioconductor.org/packages/FEAST
git_branch: devel
git_last_commit: 169e892
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/FEAST_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/FEAST_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/FEAST_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/FEAST_1.15.0.tgz
vignettes: vignettes/FEAST/inst/doc/FEAST.html
vignetteTitles: The FEAST User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FEAST/inst/doc/FEAST.R
dependencyCount: 125
Package: FeatSeekR
Version: 1.7.2
Imports: pheatmap, MASS, pracma, stats, SummarizedExperiment, methods
Suggests: rmarkdown, knitr, BiocStyle, testthat (>= 3.0.0)
License: GPL-3
Archs: x64
MD5sum: 530a8a1213ffba97d6b537f76c4dd711
NeedsCompilation: no
Title: FeatSeekR an R package for unsupervised feature selection
Description: FeatSeekR performs unsupervised feature selection using
replicated measurements. It iteratively selects features with
the highest reproducibility across replicates, after projecting
out those dimensions from the data that are spanned by the
previously selected features. The selected a set of features
has a high replicate reproducibility and a high degree of
uniqueness.
biocViews: Software, StatisticalMethod, FeatureExtraction,
MassSpectrometry
Author: Tuemay Capraz [cre, aut] (ORCID:
<https://orcid.org/0000-0002-2547-067X>)
Maintainer: Tuemay Capraz <tuemay.capraz@embl.de>
URL: https://github.com/tcapraz/FeatSeekR
VignetteBuilder: knitr
BugReports: https://github.com/tcapraz/FeatSeekR/issues
git_url: https://git.bioconductor.org/packages/FeatSeekR
git_branch: devel
git_last_commit: 37d7e96
git_last_commit_date: 2025-01-28
Date/Publication: 2025-01-28
source.ver: src/contrib/FeatSeekR_1.7.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/FeatSeekR_1.7.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/FeatSeekR_1.7.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/FeatSeekR_1.7.2.tgz
vignettes: vignettes/FeatSeekR/inst/doc/FeatSeekR-vignette.html
vignetteTitles: `FeatSeekR` user guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FeatSeekR/inst/doc/FeatSeekR-vignette.R
dependencyCount: 51
Package: fedup
Version: 1.15.0
Depends: R (>= 4.1)
Imports: openxlsx, tibble, dplyr, data.table, ggplot2, ggthemes,
forcats, RColorBrewer, RCy3, utils, stats
Suggests: biomaRt, tidyr, testthat, knitr, rmarkdown, devtools, covr
License: MIT + file LICENSE
Archs: x64
MD5sum: f99a7f5d80e5f8a428934551f48721b0
NeedsCompilation: no
Title: Fisher's Test for Enrichment and Depletion of User-Defined
Pathways
Description: An R package that tests for enrichment and depletion of
user-defined pathways using a Fisher's exact test. The method
is designed for versatile pathway annotation formats (eg. gmt,
txt, xlsx) to allow the user to run pathway analysis on custom
annotations. This package is also integrated with Cytoscape to
provide network-based pathway visualization that enhances the
interpretability of the results.
biocViews: GeneSetEnrichment, Pathways, NetworkEnrichment, Network
Author: Catherine Ross [aut, cre]
Maintainer: Catherine Ross <catherinem.ross@mail.utoronto.ca>
URL: https://github.com/rosscm/fedup
VignetteBuilder: knitr
BugReports: https://github.com/rosscm/fedup/issues
git_url: https://git.bioconductor.org/packages/fedup
git_branch: devel
git_last_commit: 6f5f84f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/fedup_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/fedup_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/fedup_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/fedup_1.15.0.tgz
vignettes: vignettes/fedup/inst/doc/fedup_doubleTest.html,
vignettes/fedup/inst/doc/fedup_mutliTest.html,
vignettes/fedup/inst/doc/fedup_singleTest.html
vignetteTitles: fedup_doubleTest.html, fedup_mutliTest.html,
fedup_singleTest.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/fedup/inst/doc/fedup_doubleTest.R,
vignettes/fedup/inst/doc/fedup_mutliTest.R,
vignettes/fedup/inst/doc/fedup_singleTest.R
dependencyCount: 81
Package: FELLA
Version: 1.27.0
Depends: R (>= 3.5.0)
Imports: methods, igraph, Matrix, KEGGREST, plyr, stats, graphics,
utils
Suggests: shiny, DT, magrittr, visNetwork, knitr, BiocStyle, rmarkdown,
testthat, biomaRt, org.Hs.eg.db, org.Mm.eg.db, AnnotationDbi,
GOSemSim
License: GPL-3
MD5sum: 6746a4f3dc2949cd600dcd7a5dc6154a
NeedsCompilation: no
Title: Interpretation and enrichment for metabolomics data
Description: Enrichment of metabolomics data using KEGG entries. Given
a set of affected compounds, FELLA suggests affected reactions,
enzymes, modules and pathways using label propagation in a
knowledge model network. The resulting subnetwork can be
visualised and exported.
biocViews: Software, Metabolomics, GraphAndNetwork, KEGG, GO, Pathways,
Network, NetworkEnrichment
Author: Sergio Picart-Armada [aut, cre], Francesc Fernandez-Albert
[aut], Alexandre Perera-Lluna [aut]
Maintainer: Sergio Picart-Armada <sergi.picart@upc.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/FELLA
git_branch: devel
git_last_commit: d1ba32c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/FELLA_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/FELLA_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/FELLA_1.27.0.tgz
vignettes: vignettes/FELLA/inst/doc/FELLA.pdf,
vignettes/FELLA/inst/doc/musmusculus.pdf,
vignettes/FELLA/inst/doc/zebrafish.pdf,
vignettes/FELLA/inst/doc/quickstart.html
vignetteTitles: FELLA, Example: a fatty liver study on Mus musculus,
Example: oxybenzone exposition in gilt-head bream, Quick start
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FELLA/inst/doc/FELLA.R,
vignettes/FELLA/inst/doc/musmusculus.R,
vignettes/FELLA/inst/doc/quickstart.R,
vignettes/FELLA/inst/doc/zebrafish.R
dependencyCount: 41
Package: fenr
Version: 1.5.2
Depends: R (>= 4.1.0)
Imports: tools, methods, assertthat, rlang, dplyr, tidyr, tidyselect,
tibble, purrr, readr, stringr, httr2, rvest, progress,
BiocFileCache, shiny, ggplot2
Suggests: BiocStyle, testthat, knitr, rmarkdown, topGO
License: MIT + file LICENSE
MD5sum: 64b0fe4a4d1d474b3997012c0f772c8b
NeedsCompilation: no
Title: Fast functional enrichment for interactive applications
Description: Perform fast functional enrichment on feature lists (like
genes or proteins) using the hypergeometric distribution.
Tailored for speed, this package is ideal for interactive
platforms such as Shiny. It supports the retrieval of
functional data from sources like GO, KEGG, Reactome, Bioplanet
and WikiPathways. By downloading and preparing data first, it
allows for rapid successive tests on various feature selections
without the need for repetitive, time-consuming preparatory
steps typical of other packages.
biocViews: FunctionalPrediction, DifferentialExpression,
GeneSetEnrichment, GO, KEGG, Reactome, Proteomics
Author: Marek Gierlinski [aut, cre] (ORCID:
<https://orcid.org/0000-0001-9149-3514>)
Maintainer: Marek Gierlinski <M.Gierlinski@dundee.ac.uk>
URL: https://github.com/bartongroup/fenr
VignetteBuilder: knitr
BugReports: https://github.com/bartongroup/fenr/issues
git_url: https://git.bioconductor.org/packages/fenr
git_branch: devel
git_last_commit: e0fb642
git_last_commit_date: 2025-02-20
Date/Publication: 2025-02-20
source.ver: src/contrib/fenr_1.5.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/fenr_1.5.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/fenr_1.5.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/fenr_1.5.2.tgz
vignettes: vignettes/fenr/inst/doc/fenr.html
vignetteTitles: Fast functional enrichment
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/fenr/inst/doc/fenr.R
dependencyCount: 93
Package: ffpe
Version: 1.51.0
Depends: R (>= 2.10.0), TTR, methods
Imports: Biobase, BiocGenerics, affy, lumi, methylumi, sfsmisc
Suggests: genefilter, ffpeExampleData
License: GPL (>2)
MD5sum: 8fe98a07b548c031c65ff2581ce583b3
NeedsCompilation: no
Title: Quality assessment and control for FFPE microarray expression
data
Description: Identify low-quality data using metrics developed for
expression data derived from Formalin-Fixed, Paraffin-Embedded
(FFPE) data. Also a function for making Concordance at the Top
plots (CAT-plots).
biocViews: Microarray, GeneExpression, QualityControl
Author: Levi Waldron
Maintainer: Levi Waldron <lwaldron.research@gmail.com>
git_url: https://git.bioconductor.org/packages/ffpe
git_branch: devel
git_last_commit: a03eb82
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ffpe_1.51.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ffpe_1.51.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ffpe_1.51.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ffpe_1.51.0.tgz
vignettes: vignettes/ffpe/inst/doc/ffpe.pdf
vignetteTitles: ffpe package user guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ffpe/inst/doc/ffpe.R
dependencyCount: 170
Package: fgga
Version: 1.15.0
Depends: R (>= 4.3), RBGL
Imports: graph, stats, e1071, methods, gRbase, jsonlite, BiocFileCache,
curl, igraph
Suggests: knitr, rmarkdown, GOstats, GO.db, BiocGenerics, pROC, RUnit,
BiocStyle
License: GPL-3
MD5sum: 82c13438996d2226e908550f702bdeec
NeedsCompilation: no
Title: Hierarchical ensemble method based on factor graph
Description: Package that implements the FGGA algorithm. This package
provides a hierarchical ensemble method based ob factor graphs
for the consistent cross-ontology annotation of protein coding
genes. FGGA embodies elements of predicate logic, communication
theory, supervised learning and inference in graphical models.
biocViews: Software, StatisticalMethod, Classification, Network,
NetworkInference, SupportVectorMachine, GraphAndNetwork, GO
Author: Flavio Spetale [aut, cre]
Maintainer: Flavio Spetale <spetale@cifasis-conicet.gov.ar>
URL: https://github.com/fspetale/fgga
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/fgga
git_branch: devel
git_last_commit: df19642
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/fgga_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/fgga_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/fgga_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/fgga_1.15.0.tgz
vignettes: vignettes/fgga/inst/doc/fgga.html
vignetteTitles: FGGA: Factor Graph GO Annotation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fgga/inst/doc/fgga.R
dependencyCount: 63
Package: FGNet
Version: 3.41.0
Depends: R (>= 4.2.0)
Imports: igraph (>= 0.6), hwriter, R.utils, XML, plotrix, reshape2,
RColorBrewer, png, methods, stats, utils, graphics, grDevices
Suggests: RCurl, gage, topGO, GO.db, reactome.db, RUnit, BiocGenerics,
org.Sc.sgd.db, knitr, rmarkdown, AnnotationDbi, BiocManager
License: GPL (>= 2)
MD5sum: 296bb447f054a159036041ac14debaa1
NeedsCompilation: no
Title: Functional Gene Networks derived from biological enrichment
analyses
Description: Build and visualize functional gene and term networks from
clustering of enrichment analyses in multiple annotation
spaces. The package includes a graphical user interface (GUI)
and functions to perform the functional enrichment analysis
through DAVID, GeneTerm Linker, gage (GSEA) and topGO.
biocViews: Annotation, GO, Pathways, GeneSetEnrichment, Network,
Visualization, FunctionalGenomics, NetworkEnrichment,
Clustering
Author: Sara Aibar, Celia Fontanillo, Conrad Droste and Javier De Las
Rivas.
Maintainer: Sara Aibar <saibar@usal.es>
URL: http://www.cicancer.org
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/FGNet
git_branch: devel
git_last_commit: a782bd5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/FGNet_3.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/FGNet_3.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/FGNet_3.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/FGNet_3.41.0.tgz
vignettes: vignettes/FGNet/inst/doc/FGNet.html
vignetteTitles: FGNet
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FGNet/inst/doc/FGNet.R
importsMe: IntramiRExploreR
dependencyCount: 31
Package: fgsea
Version: 1.33.4
Depends: R (>= 4.1)
Imports: Rcpp, data.table, BiocParallel, stats, ggplot2 (>= 2.2.0),
cowplot, grid, fastmatch, Matrix, scales, utils
LinkingTo: Rcpp, BH
Suggests: testthat, knitr, rmarkdown, reactome.db, AnnotationDbi,
parallel, org.Mm.eg.db, limma, GEOquery, msigdbr, aggregation,
Seurat
License: MIT + file LICENCE
MD5sum: 3f88f9e7174fc587a88e7d23e62e353a
NeedsCompilation: yes
Title: Fast Gene Set Enrichment Analysis
Description: The package implements an algorithm for fast gene set
enrichment analysis. Using the fast algorithm allows to make
more permutations and get more fine grained p-values, which
allows to use accurate stantard approaches to multiple
hypothesis correction.
biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment,
Pathways
Author: Gennady Korotkevich [aut], Vladimir Sukhov [aut], Nikolay Budin
[ctb], Nikita Gusak [ctb], Zieman Mark [ctb], Alexey
Sergushichev [aut, cre]
Maintainer: Alexey Sergushichev <alsergbox@gmail.com>
URL: https://github.com/ctlab/fgsea/
VignetteBuilder: knitr
BugReports: https://github.com/ctlab/fgsea/issues
git_url: https://git.bioconductor.org/packages/fgsea
git_branch: devel
git_last_commit: 237247f
git_last_commit_date: 2025-03-19
Date/Publication: 2025-03-21
source.ver: src/contrib/fgsea_1.33.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/fgsea_1.33.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/fgsea_1.33.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/fgsea_1.33.4.tgz
vignettes: vignettes/fgsea/inst/doc/fgsea-tutorial.html,
vignettes/fgsea/inst/doc/geseca-tutorial.html
vignetteTitles: Using fgsea package, Gene set co-regulation analysis
tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fgsea/inst/doc/fgsea-tutorial.R,
vignettes/fgsea/inst/doc/geseca-tutorial.R
dependsOnMe: gsean, metapone, PPInfer
importsMe: BioNAR, CelliD, CEMiTool, clustifyr, CoGAPS, cTRAP,
DeepTarget, DOSE, EventPointer, fobitools, lipidr, mCSEA,
MIRit, MPAC, multiGSEA, NanoTube, nipalsMCIA, omicsViewer,
pairedGSEA, pathlinkR, phantasus, piano, POMA, projectR,
RegEnrich, RegionalST, signatureSearch, ViSEAGO, cinaR, DTSEA,
mulea, scITD
suggestsMe: Cepo, decoupleR, gatom, gCrisprTools, iSEEpathways, mdp,
sparrow, SpliceWiz, ttgsea, easybio, genekitr, GeneNMF, goat,
grandR, Platypus, RCPA, rliger
dependencyCount: 49
Package: FilterFFPE
Version: 1.17.0
Imports: foreach, doParallel, GenomicRanges, IRanges, Rsamtools,
parallel, S4Vectors
Suggests: BiocStyle
License: LGPL-3
MD5sum: 95b123bc3a68ee984bd769e45d91b124
NeedsCompilation: no
Title: FFPE Artificial Chimeric Read Filter for NGS data
Description: This package finds and filters artificial chimeric reads
specifically generated in next-generation sequencing (NGS)
process of formalin-fixed paraffin-embedded (FFPE) tissues.
These artificial chimeric reads can lead to a large number of
false positive structural variation (SV) calls. The required
input is an indexed BAM file of a FFPE sample.
biocViews: StructuralVariation, Sequencing, Alignment, QualityControl,
Preprocessing
Author: Lanying Wei [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4281-8017>)
Maintainer: Lanying Wei <lanying.wei@uni-muenster.de>
git_url: https://git.bioconductor.org/packages/FilterFFPE
git_branch: devel
git_last_commit: 494b4cf
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/FilterFFPE_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/FilterFFPE_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/FilterFFPE_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/FilterFFPE_1.17.0.tgz
vignettes: vignettes/FilterFFPE/inst/doc/FilterFFPE.pdf
vignetteTitles: An introduction to FilterFFPE
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FilterFFPE/inst/doc/FilterFFPE.R
dependencyCount: 42
Package: findIPs
Version: 1.3.1
Depends: graphics, R (>= 4.4.0)
Imports: Biobase, BiocParallel, parallel, stats, SummarizedExperiment,
survival, utils
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: GPL-3
Archs: x64
MD5sum: ba9d75cf5c14d21a1742aaab907909be
NeedsCompilation: no
Title: Influential Points Detection for Feature Rankings
Description: Feature rankings can be distorted by a single case in the
context of high-dimensional data. The cases exerts abnormal
influence on feature rankings are called influential points
(IPs). The package aims at detecting IPs based on case deletion
and quantifies their effects by measuring the rank changes
(DOI:10.48550/arXiv.2303.10516). The package applies a novel
rank comparing measure using the adaptive weights that stress
the top-ranked important features and adjust the weights to
ranking properties.
biocViews: GeneExpression, DifferentialExpression, Regression, Survival
Author: Shuo Wang [aut, cre] (ORCID:
<https://orcid.org/0009-0000-0424-2160>), Junyan Lu [aut]
Maintainer: Shuo Wang <wangsures@foxmail.com>
URL: https://github.com/ShuoStat/findIPs
VignetteBuilder: knitr
BugReports: https://github.com/ShuoStat/findIPs
git_url: https://git.bioconductor.org/packages/findIPs
git_branch: devel
git_last_commit: 342a7ad
git_last_commit_date: 2024-11-21
Date/Publication: 2024-11-21
source.ver: src/contrib/findIPs_1.3.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/findIPs_1.3.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/findIPs_1.3.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/findIPs_1.3.1.tgz
vignettes: vignettes/findIPs/inst/doc/findIPs.html
vignetteTitles: Introduction to package findIPs
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/findIPs/inst/doc/findIPs.R
dependencyCount: 48
Package: FindIT2
Version: 1.13.0
Depends: GenomicRanges, R (>= 3.5.0)
Imports: withr, BiocGenerics, GenomeInfoDb, rtracklayer, S4Vectors,
GenomicFeatures, dplyr, rlang, patchwork, ggplot2,
BiocParallel, qvalue, stringr, utils, stats, ggrepel, tibble,
tidyr, SummarizedExperiment, MultiAssayExperiment, IRanges,
progress, purrr, glmnet, methods
Suggests: BiocStyle, knitr, rmarkdown, sessioninfo, testthat (>=
3.0.0), TxDb.Athaliana.BioMart.plantsmart28
License: Artistic-2.0
MD5sum: 0a6681e6670668ad7b6148d15007c16c
NeedsCompilation: no
Title: find influential TF and Target based on multi-omics data
Description: This package implements functions to find influential TF
and target based on different input type. It have five module:
Multi-peak multi-gene annotaion(mmPeakAnno module), Calculate
regulation potential(calcRP module), Find influential Target
based on ChIP-Seq and RNA-Seq data(Find influential Target
module), Find influential TF based on different input(Find
influential TF module), Calculate peak-gene or peak-peak
correlation(peakGeneCor module). And there are also some other
useful function like integrate different source information,
calculate jaccard similarity for your TF.
biocViews: Software, Annotation, ChIPSeq, ATACSeq, GeneRegulation,
MultipleComparison, GeneTarget
Author: Guandong Shang [aut, cre] (ORCID:
<https://orcid.org/0000-0002-9509-0314>)
Maintainer: Guandong Shang <shangguandong1996@163.com>
URL: https://github.com/shangguandong1996/FindIT2
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/FindIT2
git_url: https://git.bioconductor.org/packages/FindIT2
git_branch: devel
git_last_commit: 30235ec
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/FindIT2_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/FindIT2_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/FindIT2_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/FindIT2_1.13.0.tgz
vignettes: vignettes/FindIT2/inst/doc/FindIT2.html
vignetteTitles: Introduction to FindIT2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FindIT2/inst/doc/FindIT2.R
dependencyCount: 120
Package: FISHalyseR
Version: 1.41.0
Depends: EBImage,abind
Suggests: knitr
License: Artistic-2.0
MD5sum: 78b4d81d5aabf926774d3630e118e6a0
NeedsCompilation: no
Title: FISHalyseR a package for automated FISH quantification
Description: FISHalyseR provides functionality to process and analyse
digital cell culture images, in particular to quantify FISH
probes within nuclei. Furthermore, it extract the spatial
location of each nucleus as well as each probe enabling spatial
co-localisation analysis.
biocViews: CellBiology
Author: Karesh Arunakirinathan <akaresh88@gmail.com>, Andreas Heindl
<andreas.heindl@icr.ac.uk>
Maintainer: Karesh Arunakirinathan <akaresh88@gmail.com>, Andreas
Heindl <andreas.heindl@icr.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/FISHalyseR
git_branch: devel
git_last_commit: 51ff6ab
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/FISHalyseR_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/FISHalyseR_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/FISHalyseR_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/FISHalyseR_1.41.0.tgz
vignettes: vignettes/FISHalyseR/inst/doc/FISHalyseR.pdf
vignetteTitles: FISHAlyseR Automated fluorescence in situ hybridisation
quantification in R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FISHalyseR/inst/doc/FISHalyseR.R
dependencyCount: 46
Package: fishpond
Version: 2.13.0
Imports: graphics, stats, utils, methods, abind, gtools, qvalue,
S4Vectors, IRanges, SummarizedExperiment, GenomicRanges,
matrixStats, svMisc, Matrix, SingleCellExperiment, jsonlite
Suggests: testthat, knitr, rmarkdown, macrophage, tximeta,
org.Hs.eg.db, samr, DESeq2, apeglm, tximportData, limma,
ensembldb, EnsDb.Hsapiens.v86, GenomicFeatures, AnnotationDbi,
pheatmap, Gviz, GenomeInfoDb, data.table
License: GPL-2
MD5sum: e20339fe0e023b304ef15f4525418f04
NeedsCompilation: no
Title: Fishpond: downstream methods and tools for expression data
Description: Fishpond contains methods for differential transcript and
gene expression analysis of RNA-seq data using inferential
replicates for uncertainty of abundance quantification, as
generated by Gibbs sampling or bootstrap sampling. Also the
package contains a number of utilities for working with Salmon
and Alevin quantification files.
biocViews: Sequencing, RNASeq, GeneExpression, Transcription,
Normalization, Regression, MultipleComparison, BatchEffect,
Visualization, DifferentialExpression, DifferentialSplicing,
AlternativeSplicing, SingleCell
Author: Anqi Zhu [aut, ctb], Michael Love [aut, cre], Avi Srivastava
[aut, ctb], Rob Patro [aut, ctb], Joseph Ibrahim [aut, ctb],
Hirak Sarkar [ctb], Euphy Wu [ctb], Noor Pratap Singh [ctb],
Scott Van Buren [ctb], Dongze He [ctb], Steve Lianoglou [ctb],
Wes Wilson [ctb], Jeroen Gilis [ctb]
Maintainer: Michael Love <michaelisaiahlove@gmail.com>
URL: https://thelovelab.github.io/fishpond,
https://thelovelab.com/mikelove/fishpond
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/tag/fishpond
git_url: https://git.bioconductor.org/packages/fishpond
git_branch: devel
git_last_commit: df45f79
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/fishpond_2.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/fishpond_2.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/fishpond_2.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/fishpond_2.13.0.tgz
vignettes: vignettes/fishpond/inst/doc/allelic.html,
vignettes/fishpond/inst/doc/swish.html
vignetteTitles: 2. SEESAW - Allelic expression analysis with Salmon and
Swish, 1. Swish: DE analysis accounting for inferential
uncertainty
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fishpond/inst/doc/allelic.R,
vignettes/fishpond/inst/doc/swish.R
suggestsMe: tximeta
dependencyCount: 71
Package: FitHiC
Version: 1.33.0
Imports: data.table, fdrtool, grDevices, graphics, Rcpp, stats, utils
LinkingTo: Rcpp
Suggests: knitr, rmarkdown
License: GPL (>= 2)
MD5sum: 77f4113d1dd1bebcfb0b6a79964b17e3
NeedsCompilation: yes
Title: Confidence estimation for intra-chromosomal contact maps
Description: Fit-Hi-C is a tool for assigning statistical confidence
estimates to intra-chromosomal contact maps produced by
genome-wide genome architecture assays such as Hi-C.
biocViews: DNA3DStructure, Software
Author: Ferhat Ay [aut] (Python original,
https://noble.gs.washington.edu/proj/fit-hi-c/), Timothy L.
Bailey [aut], William S. Noble [aut], Ruyu Tan [aut, cre, trl]
(R port)
Maintainer: Ruyu Tan <rut003@ucsd.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/FitHiC
git_branch: devel
git_last_commit: 75885eb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/FitHiC_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/FitHiC_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/FitHiC_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/FitHiC_1.33.0.tgz
vignettes: vignettes/FitHiC/inst/doc/fithic.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FitHiC/inst/doc/fithic.R
dependencyCount: 8
Package: flagme
Version: 1.63.0
Depends: gcspikelite, xcms, CAMERA
Imports: gplots, graphics, MASS, methods, SparseM, stats, utils
License: LGPL (>= 2)
MD5sum: 591c172145b311782550652a081ff340
NeedsCompilation: yes
Title: Analysis of Metabolomics GC/MS Data
Description: Fragment-level analysis of gas
chromatography-massspectrometry metabolomics data.
biocViews: DifferentialExpression, MassSpectrometry
Author: Mark Robinson <mark.robinson@imls.uzh.ch>, Riccardo Romoli
<riccardo.romoli@unifi.it>
Maintainer: Mark Robinson <mark.robinson@imls.uzh.ch>, Riccardo Romoli
<riccardo.romoli@unifi.it>
git_url: https://git.bioconductor.org/packages/flagme
git_branch: devel
git_last_commit: 7b496c7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flagme_1.63.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flagme_1.63.0.zip
vignettes: vignettes/flagme/inst/doc/flagme-knitr.pdf,
vignettes/flagme/inst/doc/flagme.pdf
vignetteTitles: Using flagme -- Fragment-level analysis of GC-MS-based
metabolomics data, \texttt{flagme}: Fragment-level analysis of
\\ GC-MS-based metabolomics data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flagme/inst/doc/flagme-knitr.R,
vignettes/flagme/inst/doc/flagme.R
dependencyCount: 167
Package: FLAMES
Version: 2.1.7
Depends: R (>= 4.2.0)
Imports: basilisk, bambu, BiocParallel, Biostrings, BiocGenerics,
circlize, ComplexHeatmap, cowplot, dplyr, DelayedArray,
DropletUtils, GenomicRanges, GenomicFeatures, txdbmaker,
GenomicAlignments, GenomeInfoDb, ggplot2, ggbio, grid,
gridExtra, igraph, jsonlite, magrittr, magick, Matrix,
MatrixGenerics, readr, reticulate, Rsamtools, rtracklayer,
RColorBrewer, SingleCellExperiment, SummarizedExperiment,
SpatialExperiment, scater, scatterpie, S4Vectors, scuttle,
stats, scran, stringr, tidyr, utils, withr, future, methods,
tibble, tidyselect, IRanges
LinkingTo: Rcpp, Rhtslib, testthat
Suggests: BiocStyle, GEOquery, knitr, rmarkdown, BiocFileCache,
R.utils, ShortRead, uwot, testthat (>= 3.0.0), xml2
License: GPL (>= 3)
MD5sum: 51fe17c336353f3e22c86cc1e71124ac
NeedsCompilation: yes
Title: FLAMES: Full Length Analysis of Mutations and Splicing in long
read RNA-seq data
Description: Semi-supervised isoform detection and annotation from both
bulk and single-cell long read RNA-seq data. Flames provides
automated pipelines for analysing isoforms, as well as
intermediate functions for manual execution.
biocViews: RNASeq, SingleCell, Transcriptomics, DataImport,
DifferentialSplicing, AlternativeSplicing, GeneExpression,
LongRead
Author: Luyi Tian [aut], Changqing Wang [aut, cre], Yupei You [aut],
Oliver Voogd [aut], Jakob Schuster [aut], Shian Su [aut],
Matthew Ritchie [ctb]
Maintainer: Changqing Wang <wang.ch@wehi.edu.au>
URL: https://mritchielab.github.io/FLAMES
SystemRequirements: GNU make, C++17, samtools (>= 1.19), minimap2 (>=
2.17)
VignetteBuilder: knitr
BugReports: https://github.com/mritchielab/FLAMES/issues
git_url: https://git.bioconductor.org/packages/FLAMES
git_branch: devel
git_last_commit: b2a3a0d
git_last_commit_date: 2025-03-10
Date/Publication: 2025-03-11
source.ver: src/contrib/FLAMES_2.1.7.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/FLAMES_2.1.7.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/FLAMES_2.1.7.tgz
vignettes: vignettes/FLAMES/inst/doc/FLAMES_vignette.html
vignetteTitles: FLAMES
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FLAMES/inst/doc/FLAMES_vignette.R
dependencyCount: 258
Package: flowAI
Version: 1.37.0
Depends: R (>= 4.3.0)
Imports: ggplot2, flowCore, plyr, changepoint, knitr, reshape2,
RColorBrewer, scales, methods, graphics, stats, utils,
rmarkdown
Suggests: testthat, shiny, BiocStyle
License: GPL (>= 2)
Archs: x64
MD5sum: aa15855427ebd549be0f2ace4824d8a4
NeedsCompilation: no
Title: Automatic and interactive quality control for flow cytometry
data
Description: The package is able to perform an automatic or interactive
quality control on FCS data acquired using flow cytometry
instruments. By evaluating three different properties: 1) flow
rate, 2) signal acquisition, 3) dynamic range, the quality
control enables the detection and removal of anomalies.
biocViews: FlowCytometry, QualityControl, BiomedicalInformatics,
ImmunoOncology
Author: Gianni Monaco [aut], Chen Hao [ctb]
Maintainer: Gianni Monaco <mongianni1@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/flowAI
git_branch: devel
git_last_commit: 1a2a8aa
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowAI_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowAI_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowAI_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowAI_1.37.0.tgz
vignettes: vignettes/flowAI/inst/doc/flowAI.html
vignetteTitles: Automatic and GUI methods to do quality control on Flow
cytometry Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowAI/inst/doc/flowAI.R
importsMe: CytoPipeline
dependencyCount: 76
Package: flowBeads
Version: 1.45.0
Depends: R (>= 2.15.0), methods, Biobase, rrcov, flowCore
Imports: flowCore, rrcov, knitr, xtable
Suggests: flowViz
License: Artistic-2.0
Archs: x64
MD5sum: 2f63d77151964c63750f7d7530c54a0f
NeedsCompilation: no
Title: flowBeads: Analysis of flow bead data
Description: This package extends flowCore to provide functionality
specific to bead data. One of the goals of this package is to
automate analysis of bead data for the purpose of
normalisation.
biocViews: ImmunoOncology, Infrastructure, FlowCytometry,
CellBasedAssays
Author: Nikolas Pontikos
Maintainer: Nikolas Pontikos <n.pontikos@gmail.com>
git_url: https://git.bioconductor.org/packages/flowBeads
git_branch: devel
git_last_commit: e4e2a74
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowBeads_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowBeads_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowBeads_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowBeads_1.45.0.tgz
vignettes: vignettes/flowBeads/inst/doc/HowTo-flowBeads.pdf
vignetteTitles: Analysis of Flow Cytometry Bead Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowBeads/inst/doc/HowTo-flowBeads.R
dependencyCount: 32
Package: flowBin
Version: 1.43.0
Depends: methods, flowCore, flowFP, R (>= 2.10)
Imports: class, limma, snow, BiocGenerics
Suggests: parallel
License: Artistic-2.0
MD5sum: f169c6bcf9a0e52c910a6c3724ce992d
NeedsCompilation: no
Title: Combining multitube flow cytometry data by binning
Description: Software to combine flow cytometry data that has been
multiplexed into multiple tubes with common markers between
them, by establishing common bins across tubes in terms of the
common markers, then determining expression within each tube
for each bin in terms of the tube-specific markers.
biocViews: ImmunoOncology, CellBasedAssays, FlowCytometry
Author: Kieran O'Neill
Maintainer: Kieran O'Neill <koneill@bccrc.ca>
git_url: https://git.bioconductor.org/packages/flowBin
git_branch: devel
git_last_commit: 6f2acd3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowBin_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowBin_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowBin_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowBin_1.43.0.tgz
vignettes: vignettes/flowBin/inst/doc/flowBin.pdf
vignetteTitles: flowBin
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowBin/inst/doc/flowBin.R
dependencyCount: 37
Package: flowcatchR
Version: 1.41.2
Depends: R (>= 2.10), methods, EBImage
Imports: colorRamps, abind, BiocParallel, graphics, stats, utils,
plotly, shiny
Suggests: BiocStyle, knitr, rmarkdown
License: BSD_3_clause + file LICENSE
MD5sum: 8b33589b5bfff2904f2576fac678a057
NeedsCompilation: no
Title: Tools to analyze in vivo microscopy imaging data focused on
tracking flowing blood cells
Description: flowcatchR is a set of tools to analyze in vivo microscopy
imaging data, focused on tracking flowing blood cells. It
guides the steps from segmentation to calculation of features,
filtering out particles not of interest, providing also a set
of utilities to help checking the quality of the performed
operations (e.g. how good the segmentation was). It allows
investigating the issue of tracking flowing cells such as in
blood vessels, to categorize the particles in flowing, rolling
and adherent. This classification is applied in the study of
phenomena such as hemostasis and study of thrombosis
development. Moreover, flowcatchR presents an integrated
workflow solution, based on the integration with a Shiny App
and Jupyter notebooks, which is delivered alongside the
package, and can enable fully reproducible bioimage analysis in
the R environment.
biocViews: Software, Visualization, CellBiology, Classification,
Infrastructure, GUI, ShinyApps
Author: Federico Marini [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3252-7758>)
Maintainer: Federico Marini <marinif@uni-mainz.de>
URL: https://github.com/federicomarini/flowcatchR,
https://federicomarini.github.io/flowcatchR/
SystemRequirements: ImageMagick
VignetteBuilder: knitr
BugReports: https://github.com/federicomarini/flowcatchR/issues
git_url: https://git.bioconductor.org/packages/flowcatchR
git_branch: devel
git_last_commit: d498f26
git_last_commit_date: 2024-12-20
Date/Publication: 2024-12-20
source.ver: src/contrib/flowcatchR_1.41.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowcatchR_1.41.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowcatchR_1.41.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowcatchR_1.41.2.tgz
vignettes: vignettes/flowcatchR/inst/doc/flowcatchr_vignette.html
vignetteTitles: flowcatchR: tracking and analyzing cells in time lapse
microscopy images
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/flowcatchR/inst/doc/flowcatchr_vignette.R
dependencyCount: 104
Package: flowCHIC
Version: 1.41.0
Depends: R (>= 3.1.0)
Imports: methods, flowCore, EBImage, vegan, hexbin, ggplot2, grid
License: GPL-2
MD5sum: e5cbe7d93d74ff61e7d1067b4574e905
NeedsCompilation: no
Title: Analyze flow cytometric data using histogram information
Description: A package to analyze flow cytometric data of complex
microbial communities based on histogram images
biocViews: ImmunoOncology, CellBasedAssays, Clustering, FlowCytometry,
Software, Visualization
Author: Joachim Schumann <joachim.schumann@ufz.de>, Christin Koch
<christin.koch@ufz.de>, Ingo Fetzer
<info.fetzer@stockholmresilience.su.se>, Susann Müller
<susann.mueller@ufz.de>
Maintainer: Author: Joachim Schumann <joachim.schumann@ufz.de>
URL: http://www.ufz.de/index.php?en=16773
git_url: https://git.bioconductor.org/packages/flowCHIC
git_branch: devel
git_last_commit: 837b51d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowCHIC_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowCHIC_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowCHIC_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowCHIC_1.41.0.tgz
vignettes: vignettes/flowCHIC/inst/doc/flowCHICmanual.pdf
vignetteTitles: Analyze flow cytometric data using histogram
information
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowCHIC/inst/doc/flowCHICmanual.R
dependencyCount: 84
Package: flowClean
Version: 1.45.0
Depends: R (>= 2.15.0), flowCore
Imports: bit, changepoint, sfsmisc
Suggests: flowViz, grid, gridExtra
License: Artistic-2.0
MD5sum: 3cf491041e7731167da60244d31a92fa
NeedsCompilation: no
Title: flowClean
Description: A quality control tool for flow cytometry data based on
compositional data analysis.
biocViews: FlowCytometry, QualityControl, ImmunoOncology
Author: Kipper Fletez-Brant
Maintainer: Kipper Fletez-Brant <cafletezbrant@gmail.com>
git_url: https://git.bioconductor.org/packages/flowClean
git_branch: devel
git_last_commit: c666a91
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowClean_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowClean_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowClean_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowClean_1.45.0.tgz
vignettes: vignettes/flowClean/inst/doc/flowClean.pdf
vignetteTitles: flowClean
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowClean/inst/doc/flowClean.R
dependencyCount: 25
Package: flowClust
Version: 3.45.0
Depends: R(>= 2.5.0)
Imports: BiocGenerics, methods, Biobase, graph, flowCore, parallel
Suggests: testthat, flowWorkspace, flowWorkspaceData, knitr, rmarkdown,
openCyto, flowStats(>= 4.7.1)
License: MIT
MD5sum: 57819f2b9b3b23e073714c8bd6cf28af
NeedsCompilation: yes
Title: Clustering for Flow Cytometry
Description: Robust model-based clustering using a t-mixture model with
Box-Cox transformation. Note: users should have GSL installed.
Windows users: 'consult the README file available in the inst
directory of the source distribution for necessary
configuration instructions'.
biocViews: ImmunoOncology, Clustering, Visualization, FlowCytometry
Author: Raphael Gottardo, Kenneth Lo <c.lo@stat.ubc.ca>, Greg Finak
<greg@ozette.ai>
Maintainer: Greg Finak <greg@ozette.ai>, Mike Jiang <mike@ozette.ai>
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/flowClust
git_branch: devel
git_last_commit: ace5f62
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowClust_3.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowClust_3.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowClust_3.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowClust_3.45.0.tgz
vignettes: vignettes/flowClust/inst/doc/flowClust.html
vignetteTitles: Robust Model-based Clustering of Flow Cytometry Data\\
The flowClust package
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowClust/inst/doc/flowClust.R
importsMe: cyanoFilter, flowTrans, openCyto
suggestsMe: BiocGenerics, flowTime, segmenTier
dependencyCount: 20
Package: flowCore
Version: 2.19.0
Depends: R (>= 3.5.0)
Imports: Biobase, BiocGenerics (>= 0.29.2), grDevices, graphics,
methods, stats, utils, stats4, Rcpp, matrixStats, cytolib (>=
2.13.1), S4Vectors
LinkingTo: cpp11, BH(>= 1.81.0.0), cytolib, RProtoBufLib
Suggests: Rgraphviz, flowViz, flowStats (>= 3.43.4), testthat,
flowWorkspace, flowWorkspaceData, openCyto, knitr, ggcyto,
gridExtra
License: Artistic-2.0
MD5sum: 8a3b99be4b7fba3f88b1188adb7f6b8a
NeedsCompilation: yes
Title: flowCore: Basic structures for flow cytometry data
Description: Provides S4 data structures and basic functions to deal
with flow cytometry data.
biocViews: ImmunoOncology, Infrastructure, FlowCytometry,
CellBasedAssays
Author: B Ellis [aut], Perry Haaland [aut], Florian Hahne [aut],
Nolwenn Le Meur [aut], Nishant Gopalakrishnan [aut], Josef
Spidlen [aut], Mike Jiang [aut, cre], Greg Finak [aut], Samuel
Granjeaud [ctb]
Maintainer: Mike Jiang <mike@ozette.com>
SystemRequirements: GNU make, C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/flowCore
git_branch: devel
git_last_commit: 46f519e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowCore_2.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowCore_2.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowCore_2.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowCore_2.19.0.tgz
vignettes: vignettes/flowCore/inst/doc/HowTo-flowCore.pdf,
vignettes/flowCore/inst/doc/fcs3.html,
vignettes/flowCore/inst/doc/hyperlog.notice.html
vignetteTitles: Basic Functions for Flow Cytometry Data, fcs3.html,
hyperlog.notice.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowCore/inst/doc/HowTo-flowCore.R
dependsOnMe: flowBeads, flowBin, flowClean, flowCut, flowFP, flowMatch,
flowTime, flowTrans, flowViz, flowVS, ggcyto, immunoClust,
infinityFlow, ncdfFlow, HDCytoData, healthyFlowData,
highthroughputassays
importsMe: CATALYST, cmapR, cyanoFilter, cydar, cytofQC, CytoMDS,
cytoMEM, CytoML, CytoPipeline, CytoPipelineGUI, ddPCRclust,
diffcyt, flowAI, flowBeads, flowCHIC, flowClust, flowDensity,
flowGate, flowMeans, flowPloidy, FlowSOM, flowSpecs, flowStats,
flowTrans, flowViz, flowWorkspace, GateFinder, MAPFX, MetaCyto,
openCyto, PeacoQC, scDataviz, scifer, Sconify, tidyFlowCore,
tidytof
suggestsMe: COMPASS, flowPeaks, flowPloidyData, hypergate, MuPETFlow,
segmenTier
dependencyCount: 17
Package: flowCut
Version: 1.17.0
Depends: R (>= 3.4), flowCore
Imports: flowDensity (>= 1.13.1), Cairo, e1071, grDevices, graphics,
stats,methods
Suggests: RUnit, BiocGenerics, knitr, markdown, rmarkdown
License: Artistic-2.0
Archs: x64
MD5sum: 93d8aa49eeba59903cc361bd17c37b03
NeedsCompilation: no
Title: Automated Removal of Outlier Events and Flagging of Files Based
on Time Versus Fluorescence Analysis
Description: Common techinical complications such as clogging can
result in spurious events and fluorescence intensity shifting,
flowCut is designed to detect and remove technical artifacts
from your data by removing segments that show statistical
differences from other segments.
biocViews: FlowCytometry, Preprocessing, QualityControl,
CellBasedAssays
Author: Justin Meskas [cre, aut], Sherrie Wang [aut]
Maintainer: Justin Meskas <justinmeskas@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/flowCut
git_branch: devel
git_last_commit: d6a0c68
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowCut_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowCut_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowCut_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowCut_1.17.0.tgz
vignettes: vignettes/flowCut/inst/doc/flowCut.html
vignetteTitles: _**flowCut**_: Precise and Accurate Automated Removal
of Outlier Events and Flagging of Files Based on Time Versus
Fluorescence Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowCut/inst/doc/flowCut.R
dependencyCount: 101
Package: flowCyBar
Version: 1.43.0
Depends: R (>= 3.0.0)
Imports: gplots, vegan, methods
License: GPL-2
Archs: x64
MD5sum: 271f0dc9f3e3c45ab610787efb57aac5
NeedsCompilation: no
Title: Analyze flow cytometric data using gate information
Description: A package to analyze flow cytometric data using gate
information to follow population/community dynamics
biocViews: ImmunoOncology, CellBasedAssays, Clustering, FlowCytometry,
Software, Visualization
Author: Joachim Schumann <joachim.schumann@ufz.de>, Christin Koch
<christin.koch@ufz.de>, Susanne Günther
<susanne.guenther@ufz.de>, Ingo Fetzer
<ingo.fetzer@stockholmresilience.su.se>, Susann Müller
<susann.mueller@ufz.de>
Maintainer: Joachim Schumann <joachim.schumann@ufz.de>
URL: http://www.ufz.de/index.php?de=16773
git_url: https://git.bioconductor.org/packages/flowCyBar
git_branch: devel
git_last_commit: 2c9345f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowCyBar_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowCyBar_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowCyBar_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowCyBar_1.43.0.tgz
vignettes: vignettes/flowCyBar/inst/doc/flowCyBar-manual.pdf
vignetteTitles: Analyze flow cytometric data using gate information
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowCyBar/inst/doc/flowCyBar-manual.R
dependencyCount: 20
Package: flowDensity
Version: 1.41.0
Imports: flowCore, graphics, flowViz (>= 1.42), car, polyclip, gplots,
methods, stats, grDevices
Suggests: knitr,rmarkdown
License: Artistic-2.0
MD5sum: 2d73b52b7ae3ade91d2388ae8973733c
NeedsCompilation: no
Title: Sequential Flow Cytometry Data Gating
Description: This package provides tools for automated sequential
gating analogous to the manual gating strategy based on the
density of the data.
biocViews: Bioinformatics, FlowCytometry, CellBiology, Clustering,
Cancer, FlowCytData, DataRepresentation, StemCell,
DensityGating
Author: Mehrnoush Malek,M. Jafar Taghiyar
Maintainer: Mehrnoush Malek <mehrmalek@gmail.com>
SystemRequirements: xml2, GNU make, C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/flowDensity
git_branch: devel
git_last_commit: 77d7444
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowDensity_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowDensity_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowDensity_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowDensity_1.41.0.tgz
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowDensity/inst/doc/flowDensity.R
importsMe: cyanoFilter, ddPCRclust, flowCut
dependencyCount: 96
Package: flowFP
Version: 1.65.0
Depends: R (>= 2.10), flowCore, flowViz
Imports: Biobase, BiocGenerics (>= 0.1.6), graphics, grDevices,
methods, stats, stats4
Suggests: RUnit
License: Artistic-2.0
MD5sum: 8960d5dd75c50f0bfb78b56d40df4d43
NeedsCompilation: yes
Title: Fingerprinting for Flow Cytometry
Description: Fingerprint generation of flow cytometry data, used to
facilitate the application of machine learning and datamining
tools for flow cytometry.
biocViews: FlowCytometry, CellBasedAssays, Clustering, Visualization
Author: Herb Holyst <holyst@pennmedicine.upenn.edu>, Wade Rogers
<wade.rogers@spcytomics.com>
Maintainer: Herb Holyst <holyst@pennmedicine.upenn.edu>, Wade Rogers
<wade.rogers@spcytomics.com>
git_url: https://git.bioconductor.org/packages/flowFP
git_branch: devel
git_last_commit: a7c3d15
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowFP_1.65.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowFP_1.65.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowFP_1.65.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowFP_1.65.0.tgz
vignettes: vignettes/flowFP/inst/doc/flowFP_HowTo.pdf
vignetteTitles: Fingerprinting for Flow Cytometry
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowFP/inst/doc/flowFP_HowTo.R
dependsOnMe: flowBin
importsMe: GateFinder
dependencyCount: 32
Package: flowGate
Version: 1.7.0
Depends: flowWorkspace (>= 4.0.6), ggcyto (>= 1.16.0), R (>= 4.2)
Imports: shiny (>= 1.5.0), BiocManager (>= 1.30.10), flowCore (>=
2.0.1), dplyr (>= 1.0.0), ggplot2 (>= 3.3.2), rlang (>= 0.4.7),
purrr, tibble, methods
Suggests: knitr, rmarkdown, stringr, tidyverse, testthat
License: MIT + file LICENSE
MD5sum: ef344f078f9cfd659489233cd4308d4c
NeedsCompilation: no
Title: Interactive Cytometry Gating in R
Description: flowGate adds an interactive Shiny app to allow manual
GUI-based gating of flow cytometry data in R. Using flowGate,
you can draw 1D and 2D span/rectangle gates, quadrant gates,
and polygon gates on flow cytometry data by interactively
drawing the gates on a plot of your data, rather than by
specifying gate coordinates. This package is especially geared
toward wet-lab cytometerists looking to take advantage of R for
cytometry analysis, without necessarily having a lot of R
experience.
biocViews: Software, WorkflowStep, FlowCytometry, Preprocessing,
ImmunoOncology, DataImport
Author: Andrew Wight [aut, cre], Harvey Cantor [aut, ldr]
Maintainer: Andrew Wight <andrew.wight10@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/flowGate
git_branch: devel
git_last_commit: abd76a4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowGate_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowGate_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowGate_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowGate_1.7.0.tgz
vignettes: vignettes/flowGate/inst/doc/flowGate.html
vignetteTitles: flowGate
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/flowGate/inst/doc/flowGate.R
dependencyCount: 93
Package: flowGraph
Version: 1.15.0
Depends: R (>= 4.1)
Imports: effsize, furrr, future, purrr, ggiraph, ggrepel, ggplot2,
igraph, Matrix, matrixStats, stats, utils, visNetwork,
htmlwidgets, grDevices, methods, stringr, stringi, Rdpack,
data.table (>= 1.9.5), gridExtra,
Suggests: BiocStyle, dplyr, knitr, rmarkdown, testthat (>= 2.1.0)
License: Artistic-2.0
Archs: x64
MD5sum: 9aa6068aa8cf283d729cba4f4c0d23ea
NeedsCompilation: no
Title: Identifying differential cell populations in flow cytometry data
accounting for marker frequency
Description: Identifies maximal differential cell populations in flow
cytometry data taking into account dependencies between cell
populations; flowGraph calculates and plots SpecEnr abundance
scores given cell population cell counts.
biocViews: FlowCytometry, StatisticalMethod, ImmunoOncology, Software,
CellBasedAssays, Visualization
Author: Alice Yue [aut, cre]
Maintainer: Alice Yue <aya43@sfu.ca>
URL: https://github.com/aya49/flowGraph
VignetteBuilder: knitr
BugReports: https://github.com/aya49/flowGraph/issues
git_url: https://git.bioconductor.org/packages/flowGraph
git_branch: devel
git_last_commit: dd112bf
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowGraph_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowGraph_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowGraph_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowGraph_1.15.0.tgz
vignettes: vignettes/flowGraph/inst/doc/flowGraph.html
vignetteTitles: flowGraph
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowGraph/inst/doc/flowGraph.R
dependencyCount: 82
Package: flowMatch
Version: 1.43.0
Depends: R (>= 3.0.0), Rcpp (>= 0.11.0), methods, flowCore
Imports: Biobase
LinkingTo: Rcpp
Suggests: healthyFlowData
License: Artistic-2.0
MD5sum: aafae8ac42f571d3ddee11b18cf8504f
NeedsCompilation: yes
Title: Matching and meta-clustering in flow cytometry
Description: Matching cell populations and building meta-clusters and
templates from a collection of FC samples.
biocViews: ImmunoOncology, Clustering, FlowCytometry
Author: Ariful Azad
Maintainer: Ariful Azad <azad@lbl.gov>
git_url: https://git.bioconductor.org/packages/flowMatch
git_branch: devel
git_last_commit: 8cc0506
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowMatch_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowMatch_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowMatch_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowMatch_1.43.0.tgz
vignettes: vignettes/flowMatch/inst/doc/flowMatch.pdf
vignetteTitles: flowMatch: Cell population matching and meta-clustering
in Flow Cytometry
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowMatch/inst/doc/flowMatch.R
dependencyCount: 18
Package: flowMeans
Version: 1.67.0
Depends: R (>= 2.10.0)
Imports: Biobase, graphics, grDevices, methods, rrcov, stats, feature,
flowCore
License: Artistic-2.0
MD5sum: e57e6b9cfe2dd6923cf6a5051c1922e1
NeedsCompilation: no
Title: Non-parametric Flow Cytometry Data Gating
Description: Identifies cell populations in Flow Cytometry data using
non-parametric clustering and segmented-regression-based change
point detection. Note: R 2.11.0 or newer is required.
biocViews: ImmunoOncology, FlowCytometry, CellBiology, Clustering
Author: Nima Aghaeepour
Maintainer: Nima Aghaeepour <naghaeep@gmail.com>
git_url: https://git.bioconductor.org/packages/flowMeans
git_branch: devel
git_last_commit: e87c7ac
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowMeans_1.67.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowMeans_1.67.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowMeans_1.67.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowMeans_1.67.0.tgz
vignettes: vignettes/flowMeans/inst/doc/flowMeans.pdf
vignetteTitles: flowMeans: Non-parametric Flow Cytometry Data Gating
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowMeans/inst/doc/flowMeans.R
importsMe: optimalFlow
dependencyCount: 40
Package: flowMerge
Version: 2.55.0
Depends: graph,feature,flowClust,Rgraphviz,foreach,snow
Imports: rrcov,flowCore, graphics, methods, stats, utils
Suggests: knitr, rmarkdown
Enhances: doMC, multicore
License: Artistic-2.0
Archs: x64
MD5sum: 04b4264419548b5dc4f1522582913c5a
NeedsCompilation: no
Title: Cluster Merging for Flow Cytometry Data
Description: Merging of mixture components for model-based automated
gating of flow cytometry data using the flowClust framework.
Note: users should have a working copy of flowClust 2.0
installed.
biocViews: ImmunoOncology, Clustering, FlowCytometry
Author: Greg Finak <gfinak@fhcrc.org>, Raphael Gottardo
<rgottard@fhcrc.org>
Maintainer: Greg Finak <gfinak@fhcrc.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/flowMerge
git_branch: devel
git_last_commit: e5ea0ae
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowMerge_2.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowMerge_2.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowMerge_2.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowMerge_2.55.0.tgz
vignettes: vignettes/flowMerge/inst/doc/flowmerge.html
vignetteTitles: Merging Mixture Components for Cell Population
Identification in Flow Cytometry Data The flowMerge Package.
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowMerge/inst/doc/flowmerge.R
suggestsMe: segmenTier
dependencyCount: 48
Package: flowPeaks
Version: 1.53.2
Depends: R (>= 2.12.0)
Suggests: flowCore
License: Artistic-1.0
MD5sum: 4b7d0b472113651726bbecbbace04a11
NeedsCompilation: yes
Title: An R package for flow data clustering
Description: A fast and automatic clustering to classify the cells into
subpopulations based on finding the peaks from the overall
density function generated by K-means.
biocViews: ImmunoOncology, FlowCytometry, Clustering, Gating
Author: Yongchao Ge<yongchao.ge@gmail.com>
Maintainer: Yongchao Ge<yongchao.ge@gmail.com>
SystemRequirements: gsl
git_url: https://git.bioconductor.org/packages/flowPeaks
git_branch: devel
git_last_commit: 8974c0d
git_last_commit_date: 2024-11-21
Date/Publication: 2024-11-22
source.ver: src/contrib/flowPeaks_1.53.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowPeaks_1.53.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowPeaks_1.53.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowPeaks_1.53.2.tgz
vignettes: vignettes/flowPeaks/inst/doc/flowPeaks-guide.pdf
vignetteTitles: Tutorial of flowPeaks package
hasREADME: TRUE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowPeaks/inst/doc/flowPeaks-guide.R
importsMe: ddPCRclust, Polytect
dependencyCount: 0
Package: flowPloidy
Version: 1.33.0
Imports: flowCore, car, caTools, knitr, rmarkdown, minpack.lm, shiny,
methods, graphics, stats, utils
Suggests: flowPloidyData, testthat
License: GPL-3
MD5sum: 6af5ce927d0f25adbf60b75066a2a0b6
NeedsCompilation: no
Title: Analyze flow cytometer data to determine sample ploidy
Description: Determine sample ploidy via flow cytometry histogram
analysis. Reads Flow Cytometry Standard (FCS) files via the
flowCore bioconductor package, and provides functions for
determining the DNA ploidy of samples based on internal
standards.
biocViews: FlowCytometry, GUI, Regression, Visualization
Author: Tyler Smith <tyler@plantarum.ca>
Maintainer: Tyler Smith <tyler@plantarum.ca>
URL: https://github.com/plantarum/flowPloidy
VignetteBuilder: knitr
BugReports: https://github.com/plantarum/flowPloidy/issues
git_url: https://git.bioconductor.org/packages/flowPloidy
git_branch: devel
git_last_commit: 1117775
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowPloidy_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowPloidy_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowPloidy_1.33.0.tgz
vignettes: vignettes/flowPloidy/inst/doc/flowPloidy-gettingStarted.pdf,
vignettes/flowPloidy/inst/doc/histogram-tour.pdf
vignetteTitles: flowPloidy: Getting Started, flowPloidy: FCM Histograms
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowPloidy/inst/doc/flowPloidy-gettingStarted.R,
vignettes/flowPloidy/inst/doc/histogram-tour.R
dependencyCount: 114
Package: flowPlots
Version: 1.55.0
Depends: R (>= 2.13.0), methods
Suggests: vcd
License: Artistic-2.0
Archs: x64
MD5sum: ab78689d9417b284d2f745a0c6034449
NeedsCompilation: no
Title: flowPlots: analysis plots and data class for gated flow
cytometry data
Description: Graphical displays with embedded statistical tests for
gated ICS flow cytometry data, and a data class which stores
"stacked" data and has methods for computing summary measures
on stacked data, such as marginal and polyfunctional degree
data.
biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays,
Visualization, DataRepresentation
Author: N. Hawkins, S. Self
Maintainer: N. Hawkins <hawkins@fhcrc.org>
git_url: https://git.bioconductor.org/packages/flowPlots
git_branch: devel
git_last_commit: bd7ddaf
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowPlots_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowPlots_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowPlots_1.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowPlots_1.55.0.tgz
vignettes: vignettes/flowPlots/inst/doc/flowPlots.pdf
vignetteTitles: Plots with Embedded Tests for Gated Flow Cytometry Data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowPlots/inst/doc/flowPlots.R
dependencyCount: 1
Package: FlowSOM
Version: 2.15.0
Depends: R (>= 4.0), igraph
Imports: stats, utils, colorRamps, ConsensusClusterPlus, dplyr,
flowCore, ggforce, ggnewscale, ggplot2, ggpubr, grDevices,
magrittr, methods, rlang, Rtsne, tidyr, BiocGenerics, XML
Suggests: BiocStyle, testthat, CytoML, flowWorkspace, ggrepel,
scattermore, pheatmap, ggpointdensity
License: GPL (>= 2)
MD5sum: ae5fca973d7c296c74a4584bb3c6ccf1
NeedsCompilation: yes
Title: Using self-organizing maps for visualization and interpretation
of cytometry data
Description: FlowSOM offers visualization options for cytometry data,
by using Self-Organizing Map clustering and Minimal Spanning
Trees.
biocViews: CellBiology, FlowCytometry, Clustering, Visualization,
Software, CellBasedAssays
Author: Sofie Van Gassen [aut, cre], Artuur Couckuyt [aut], Katrien
Quintelier [aut], Annelies Emmaneel [aut], Britt Callebaut
[aut], Yvan Saeys [aut]
Maintainer: Sofie Van Gassen <sofie.vangassen@ugent.be>
URL: http://www.r-project.org, http://dambi.ugent.be
git_url: https://git.bioconductor.org/packages/FlowSOM
git_branch: devel
git_last_commit: b617807
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/FlowSOM_2.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/FlowSOM_2.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/FlowSOM_2.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/FlowSOM_2.15.0.tgz
vignettes: vignettes/FlowSOM/inst/doc/FlowSOM.pdf
vignetteTitles: FlowSOM
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FlowSOM/inst/doc/FlowSOM.R
importsMe: CATALYST, diffcyt
suggestsMe: tidytof, HDCytoData
dependencyCount: 103
Package: flowSpecs
Version: 1.21.0
Depends: R (>= 4.0)
Imports: ggplot2 (>= 3.1.0), BiocGenerics (>= 0.30.0), BiocParallel (>=
1.18.1), Biobase (>= 2.48.0), reshape2 (>= 1.4.3), flowCore (>=
1.50.0), zoo (>= 1.8.6), stats (>= 3.6.0), methods (>= 3.6.0)
Suggests: testthat, knitr, rmarkdown, BiocStyle, DepecheR
License: MIT + file LICENSE
MD5sum: e89e12aae367e03e3c36c9cc3c915c85
NeedsCompilation: no
Title: Tools for processing of high-dimensional cytometry data
Description: This package is intended to fill the role of conventional
cytometry pre-processing software, for spectral decomposition,
transformation, visualization and cleanup, and to aid further
downstream analyses, such as with DepecheR, by enabling
transformation of flowFrames and flowSets to dataframes.
Functions for flowCore-compliant automatic 1D-gating/filtering
are in the pipe line. The package name has been chosen both as
it will deal with spectral cytometry and as it will hopefully
give the user a nice pair of spectacles through which to view
their data.
biocViews: Software,CellBasedAssays,DataRepresentation,ImmunoOncology,
FlowCytometry,SingleCell,Visualization,Normalization,DataImport
Author: Jakob Theorell [aut, cre]
Maintainer: Jakob Theorell <jakob.theorell@ki.se>
VignetteBuilder: knitr
BugReports: https://github.com/jtheorell/flowSpecs/issues
git_url: https://git.bioconductor.org/packages/flowSpecs
git_branch: devel
git_last_commit: 5ac3e4e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowSpecs_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowSpecs_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowSpecs_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowSpecs_1.21.0.tgz
vignettes: vignettes/flowSpecs/inst/doc/flowSpecs_vinjette.html
vignetteTitles: Example workflow for processing of raw spectral
cytometry files
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/flowSpecs/inst/doc/flowSpecs_vinjette.R
dependencyCount: 62
Package: flowStats
Version: 4.19.0
Depends: R (>= 3.0.2)
Imports: BiocGenerics, MASS, flowCore (>= 1.99.6), flowWorkspace,
ncdfFlow(>= 2.19.5), flowViz, fda (>= 2.2.6), Biobase, methods,
grDevices, graphics, stats, cluster, utils, KernSmooth,
lattice, ks, RColorBrewer, rrcov, corpcor, mnormt, clue
Suggests: xtable, testthat, openCyto, ggcyto, ggridges
Enhances: RBGL,graph
License: Artistic-2.0
Archs: x64
MD5sum: 38e375e92a235038ab9ea6f5b20abeac
NeedsCompilation: no
Title: Statistical methods for the analysis of flow cytometry data
Description: Methods and functionality to analyse flow data that is
beyond the basic infrastructure provided by the flowCore
package.
biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays
Author: Florian Hahne, Nishant Gopalakrishnan, Alireza Hadj
Khodabakhshi, Chao-Jen Wong, Kyongryun Lee
Maintainer: Greg Finak <greg@ozette.com>, Mike Jiang <mike@ozette.com>
URL: http://www.github.com/RGLab/flowStats
BugReports: http://www.github.com/RGLab/flowStats/issues
git_url: https://git.bioconductor.org/packages/flowStats
git_branch: devel
git_last_commit: 463f04e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowStats_4.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowStats_4.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowStats_4.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowStats_4.19.0.tgz
vignettes: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.pdf
vignetteTitles: flowStats Overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.R
dependsOnMe: flowVS, highthroughputassays
suggestsMe: cydar, flowClust, flowCore, flowTime, flowViz, ggcyto,
openCyto
dependencyCount: 100
Package: flowTime
Version: 1.31.0
Depends: R (>= 3.4), flowCore
Imports: utils, dplyr (>= 1.0.0), tibble, magrittr, plyr, rlang
Suggests: knitr, rmarkdown, flowViz, ggplot2, BiocGenerics, stats,
flowClust, openCyto, flowStats, ggcyto
License: Artistic-2.0
MD5sum: 479db96b4a29d2fe0986cbf3e1bcd91b
NeedsCompilation: no
Title: Annotation and analysis of biological dynamical systems using
flow cytometry
Description: This package facilitates analysis of both timecourse and
steady state flow cytometry experiments. This package was
originially developed for quantifying the function of gene
regulatory networks in yeast (strain W303) expressing
fluorescent reporter proteins using BD Accuri C6 and SORP
cytometers. However, the functions are for the most part
general and may be adapted for analysis of other organisms
using other flow cytometers. Functions in this package
facilitate the annotation of flow cytometry data with
experimental metadata, as often required for publication and
general ease-of-reuse. Functions for creating, saving and
loading gate sets are also included. In the past, we have
typically generated summary statistics for each flowset for
each timepoint and then annotated and analyzed these summary
statistics. This method loses a great deal of the power that
comes from the large amounts of individual cell data generated
in flow cytometry, by essentially collapsing this data into a
bulk measurement after subsetting. In addition to these summary
functions, this package also contains functions to facilitate
annotation and analysis of steady-state or time-lapse data
utilizing all of the data collected from the thousands of
individual cells in each sample.
biocViews: FlowCytometry, TimeCourse, Visualization, DataImport,
CellBasedAssays, ImmunoOncology
Author: R. Clay Wright [aut, cre], Nick Bolten [aut], Edith
Pierre-Jerome [aut]
Maintainer: R. Clay Wright <wright.clay@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/flowTime
git_branch: devel
git_last_commit: e95fbb1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowTime_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowTime_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowTime_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowTime_1.31.0.tgz
vignettes: vignettes/flowTime/inst/doc/gating-vignette.html,
vignettes/flowTime/inst/doc/steady-state-vignette.html,
vignettes/flowTime/inst/doc/time-course-vignette.html
vignetteTitles: Yeast gating, Steady-state analysis of flow cytometry
data, Time course analysis of flow cytometry data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowTime/inst/doc/gating-vignette.R,
vignettes/flowTime/inst/doc/steady-state-vignette.R,
vignettes/flowTime/inst/doc/time-course-vignette.R
dependencyCount: 34
Package: flowTrans
Version: 1.59.0
Depends: R (>= 2.11.0), flowCore, flowViz,flowClust
Imports: flowCore, methods, flowViz, stats, flowClust
License: Artistic-2.0
MD5sum: 22ca6fd59a4043a981a23cb2654a9379
NeedsCompilation: no
Title: Parameter Optimization for Flow Cytometry Data Transformation
Description: Profile maximum likelihood estimation of parameters for
flow cytometry data transformations.
biocViews: ImmunoOncology, FlowCytometry
Author: Greg Finak <gfinak@fredhutch.org>, Juan Manuel-Perez
<jperez@ircm.qc.ca>, Raphael Gottardo <rgottard@fredhutch.org>
Maintainer: Greg Finak <gfinak@fredhutch.org>
git_url: https://git.bioconductor.org/packages/flowTrans
git_branch: devel
git_last_commit: 0b7c905
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowTrans_1.59.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowTrans_1.59.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowTrans_1.59.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowTrans_1.59.0.tgz
vignettes: vignettes/flowTrans/inst/doc/flowTrans.pdf
vignetteTitles: flowTrans package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowTrans/inst/doc/flowTrans.R
dependencyCount: 35
Package: flowViz
Version: 1.71.0
Depends: R (>= 2.7.0), flowCore(>= 1.41.9), lattice
Imports: stats4, Biobase, flowCore, graphics, grDevices, grid,
KernSmooth, lattice, latticeExtra, MASS, methods, RColorBrewer,
stats, utils, hexbin,IDPmisc
Suggests: colorspace, flowStats, knitr, rmarkdown, markdown, testthat
License: Artistic-2.0
MD5sum: ab8abd5113b81f5b3fa613108d1aac8b
NeedsCompilation: no
Title: Visualization for flow cytometry
Description: Provides visualization tools for flow cytometry data.
biocViews: ImmunoOncology, Infrastructure, FlowCytometry,
CellBasedAssays, Visualization
Author: B. Ellis, R. Gentleman, F. Hahne, N. Le Meur, D. Sarkar, M.
Jiang
Maintainer: Mike Jiang <mike@ozette.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/flowViz
git_branch: devel
git_last_commit: c55bec5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowViz_1.71.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowViz_1.71.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowViz_1.71.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowViz_1.71.0.tgz
vignettes: vignettes/flowViz/inst/doc/filters.html
vignetteTitles: Visualizing Gates with Flow Cytometry Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowViz/inst/doc/filters.R
dependsOnMe: flowFP, flowVS
importsMe: flowDensity, flowStats, flowTrans, openCyto
suggestsMe: flowBeads, flowClean, flowCore, flowTime, ggcyto
dependencyCount: 31
Package: flowVS
Version: 1.39.0
Depends: R (>= 3.2), methods, flowCore, flowViz, flowStats
Suggests: knitr, vsn,
License: Artistic-2.0
MD5sum: bf5414dd3af2812e81c55dc17431a53f
NeedsCompilation: no
Title: Variance stabilization in flow cytometry (and microarrays)
Description: Per-channel variance stabilization from a collection of
flow cytometry samples by Bertlett test for homogeneity of
variances. The approach is applicable to microarrays data as
well.
biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Microarray
Author: Ariful Azad
Maintainer: Ariful Azad <azad@iu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/flowVS
git_branch: devel
git_last_commit: 0702a66
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/flowVS_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowVS_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowVS_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowVS_1.39.0.tgz
vignettes: vignettes/flowVS/inst/doc/flowVS.pdf
vignetteTitles: flowVS: Cell population matching and meta-clustering in
Flow Cytometry
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/flowVS/inst/doc/flowVS.R
dependencyCount: 101
Package: flowWorkspace
Version: 4.19.1
Depends: R (>= 3.5.0)
Imports: Biobase, BiocGenerics, cytolib (>= 2.13.1), XML, ggplot2,
graph, graphics, grDevices, methods, stats, stats4, utils,
RBGL, tools, Rgraphviz, data.table, dplyr, scales(>= 1.3.0),
matrixStats, RProtoBufLib, flowCore(>= 2.1.1), ncdfFlow(>=
2.25.4), DelayedArray, S4Vectors
LinkingTo: cpp11, BH(>= 1.62.0-1), RProtoBufLib(>= 1.99.4), cytolib (>=
2.3.7),Rhdf5lib
Suggests: testthat, flowWorkspaceData (>= 2.23.2), knitr, rmarkdown,
ggcyto, parallel, CytoML, openCyto
License: AGPL-3.0-only
License_restricts_use: no
MD5sum: 338d781a4081dd14f69a5f83c1152498
NeedsCompilation: yes
Title: Infrastructure for representing and interacting with gated and
ungated cytometry data sets.
Description: This package is designed to facilitate comparison of
automated gating methods against manual gating done in flowJo.
This package allows you to import basic flowJo workspaces into
BioConductor and replicate the gating from flowJo using the
flowCore functionality. Gating hierarchies, groups of samples,
compensation, and transformation are performed so that the
output matches the flowJo analysis.
biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing,
DataRepresentation
Author: Greg Finak, Mike Jiang
Maintainer: Greg Finak <greg@ozette.com>, Mike Jiang <mike@ozette.com>
SystemRequirements: GNU make, C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/flowWorkspace
git_branch: devel
git_last_commit: 9fb294c
git_last_commit_date: 2025-03-06
Date/Publication: 2025-03-25
source.ver: src/contrib/flowWorkspace_4.19.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/flowWorkspace_4.19.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/flowWorkspace_4.19.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/flowWorkspace_4.19.1.tgz
vignettes:
vignettes/flowWorkspace/inst/doc/flowWorkspace-Introduction.html,
vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.html
vignetteTitles: flowWorkspace Introduction: A Package to store and
maninpulate gated flow data, How to merge GatingSets
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: TRUE
Rfiles: vignettes/flowWorkspace/inst/doc/flowWorkspace-Introduction.R,
vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.R
dependsOnMe: flowGate, ggcyto, highthroughputassays
importsMe: CytoML, flowStats, openCyto, PeacoQC
suggestsMe: CATALYST, COMPASS, flowClust, flowCore, FlowSOM
linksToMe: CytoML
dependencyCount: 65
Package: fmcsR
Version: 1.49.0
Depends: R (>= 2.10.0), ChemmineR, methods
Imports: RUnit, methods, ChemmineR, BiocGenerics, parallel
Suggests: BiocStyle, knitr, knitcitations, knitrBootstrap,rmarkdown,
codetools
License: Artistic-2.0
MD5sum: 570a38181982605e9ce2d04bb7a15496
NeedsCompilation: yes
Title: Mismatch Tolerant Maximum Common Substructure Searching
Description: The fmcsR package introduces an efficient maximum common
substructure (MCS) algorithms combined with a novel matching
strategy that allows for atom and/or bond mismatches in the
substructures shared among two small molecules. The resulting
flexible MCSs (FMCSs) are often larger than strict MCSs,
resulting in the identification of more common features in
their source structures, as well as a higher sensitivity in
finding compounds with weak structural similarities. The fmcsR
package provides several utilities to use the FMCS algorithm
for pairwise compound comparisons, structure similarity
searching and clustering.
biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics,
Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays,
Visualization, Infrastructure, DataImport, Clustering,
Proteomics, Metabolomics
Author: Yan Wang, Tyler Backman, Kevin Horan, Thomas Girke
Maintainer: Thomas Girke <thomas.girke@ucr.edu>
URL: https://github.com/girke-lab/fmcsR
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/fmcsR
git_branch: devel
git_last_commit: aacfd16
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/fmcsR_1.49.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/fmcsR_1.49.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/fmcsR_1.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/fmcsR_1.49.0.tgz
vignettes: vignettes/fmcsR/inst/doc/fmcsR.html
vignetteTitles: fmcsR
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fmcsR/inst/doc/fmcsR.R
importsMe: chemodiv
suggestsMe: ChemmineR, xnet
dependencyCount: 79
Package: fmrs
Version: 1.17.0
Depends: R (>= 4.3.0)
Imports: methods, survival, stats
Suggests: BiocGenerics, testthat, knitr, utils
License: GPL-3
Archs: x64
MD5sum: 44e030a991f8b261b65a9abd563ab28d
NeedsCompilation: yes
Title: Variable Selection in Finite Mixture of AFT Regression and FMR
Models
Description: The package obtains parameter estimation, i.e., maximum
likelihood estimators (MLE), via the Expectation-Maximization
(EM) algorithm for the Finite Mixture of Regression (FMR)
models with Normal distribution, and MLE for the Finite Mixture
of Accelerated Failure Time Regression (FMAFTR) subject to
right censoring with Log-Normal and Weibull distributions via
the EM algorithm and the Newton-Raphson algorithm (for Weibull
distribution). More importantly, the package obtains the
maximum penalized likelihood (MPLE) for both FMR and FMAFTR
models (collectively called FMRs). A component-wise tuning
parameter selection based on a component-wise BIC is
implemented in the package. Furthermore, this package provides
Ridge Regression and Elastic Net.
biocViews: Survival, Regression, DimensionReduction
Author: Farhad Shokoohi [aut, cre]
(<https://orcid.org/0000-0002-6224-2609>)
Maintainer: Farhad Shokoohi <shokoohi@icloud.com>
VignetteBuilder: knitr
BugReports: https://github.com/shokoohi/fmrs/issues
git_url: https://git.bioconductor.org/packages/fmrs
git_branch: devel
git_last_commit: 6030d8e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/fmrs_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/fmrs_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/fmrs_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/fmrs_1.17.0.tgz
vignettes: vignettes/fmrs/inst/doc/usingfmrs.html
vignetteTitles: Using fmrs package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fmrs/inst/doc/usingfmrs.R
importsMe: HhP
dependencyCount: 10
Package: fobitools
Version: 1.15.1
Depends: R (>= 4.1)
Imports: clisymbols, crayon, dplyr, fgsea, ggplot2, ggraph, magrittr,
ontologyIndex, purrr, RecordLinkage, stringr, textclean,
tictoc, tidygraph, tidyr, vroom
Suggests: BiocStyle, covr, ggrepel, kableExtra, knitr,
metabolomicsWorkbenchR, POMA, rmarkdown, rvest,
SummarizedExperiment, testthat (>= 2.3.2), tidyverse
License: GPL-3
Archs: x64
MD5sum: d2b13350066900f6f6646133ae5a9f66
NeedsCompilation: no
Title: Tools for Manipulating the FOBI Ontology
Description: A set of tools for interacting with the Food-Biomarker
Ontology (FOBI). A collection of basic manipulation tools for
biological significance analysis, graphs, and text mining
strategies for annotating nutritional data.
biocViews: MassSpectrometry, Metabolomics, Software, Visualization,
BiomedicalInformatics, GraphAndNetwork, Annotation,
Cheminformatics, Pathways, GeneSetEnrichment
Author: Pol Castellano-Escuder [aut, cre] (ORCID:
<https://orcid.org/0000-0001-6466-877X>), Alex Sánchez-Pla
[aut] (ORCID: <https://orcid.org/0000-0002-8673-7737>)
Maintainer: Pol Castellano-Escuder <polcaes@gmail.com>
URL: https://github.com/pcastellanoescuder/fobitools/
VignetteBuilder: knitr
BugReports: https://github.com/pcastellanoescuder/fobitools/issues
git_url: https://git.bioconductor.org/packages/fobitools
git_branch: devel
git_last_commit: fc658a6
git_last_commit_date: 2024-11-27
Date/Publication: 2024-11-29
source.ver: src/contrib/fobitools_1.15.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/fobitools_1.15.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/fobitools_1.15.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/fobitools_1.15.1.tgz
vignettes: vignettes/fobitools/inst/doc/Dietary_data_annotation.html,
vignettes/fobitools/inst/doc/food_enrichment_analysis.html,
vignettes/fobitools/inst/doc/MW_ST000291_enrichment.html,
vignettes/fobitools/inst/doc/MW_ST000629_enrichment.html
vignetteTitles: Dietary text annotation, Simple food ORA, Use case
ST000291, Use case ST000629
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/fobitools/inst/doc/Dietary_data_annotation.R,
vignettes/fobitools/inst/doc/food_enrichment_analysis.R,
vignettes/fobitools/inst/doc/MW_ST000291_enrichment.R,
vignettes/fobitools/inst/doc/MW_ST000629_enrichment.R
dependencyCount: 127
Package: FRASER
Version: 2.3.0
Depends: BiocParallel, data.table, Rsamtools, SummarizedExperiment
Imports: AnnotationDbi, BBmisc, Biobase, BiocGenerics, biomaRt,
BSgenome, cowplot, DelayedArray (>= 0.5.11),
DelayedMatrixStats, extraDistr, generics, GenomeInfoDb,
GenomicAlignments, GenomicFeatures, GenomicRanges, IRanges,
grDevices, ggplot2, ggrepel, HDF5Array, matrixStats, methods,
OUTRIDER, pcaMethods, pheatmap, plotly, PRROC, RColorBrewer,
rhdf5, Rsubread, R.utils, S4Vectors, stats, tibble, tools,
utils, VGAM
LinkingTo: RcppArmadillo, Rcpp
Suggests: magick, BiocStyle, knitr, rmarkdown, testthat, covr,
TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, rtracklayer,
SGSeq, ggbio, biovizBase
License: MIT + file LICENSE
Archs: x64
MD5sum: c4af61b252d78b8febe1bb202fee9fb9
NeedsCompilation: yes
Title: Find RAre Splicing Events in RNA-Seq Data
Description: Detection of rare aberrant splicing events in
transcriptome profiles. Read count ratio expectations are
modeled by an autoencoder to control for confounding factors in
the data. Given these expectations, the ratios are assumed to
follow a beta-binomial distribution with a junction specific
dispersion. Outlier events are then identified as read-count
ratios that deviate significantly from this distribution.
FRASER is able to detect alternative splicing, but also intron
retention. The package aims to support diagnostics in the field
of rare diseases where RNA-seq is performed to identify
aberrant splicing defects.
biocViews: RNASeq, AlternativeSplicing, Sequencing, Software, Genetics,
Coverage
Author: Christian Mertes [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1091-205X>), Ines Scheller [aut]
(ORCID: <https://orcid.org/0000-0003-4533-7857>), Karoline Lutz
[ctb], Vicente Yepez [aut] (ORCID:
<https://orcid.org/0000-0001-7916-3643>), Julien Gagneur [aut]
(ORCID: <https://orcid.org/0000-0002-8924-8365>)
Maintainer: Christian Mertes <mertes@in.tum.de>
URL: https://github.com/gagneurlab/FRASER
VignetteBuilder: knitr
BugReports: https://github.com/gagneurlab/FRASER/issues
git_url: https://git.bioconductor.org/packages/FRASER
git_branch: devel
git_last_commit: 1b4ac46
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/FRASER_2.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/FRASER_2.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/FRASER_2.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/FRASER_2.3.0.tgz
vignettes: vignettes/FRASER/inst/doc/FRASER.pdf
vignetteTitles: FRASER: Find RAre Splicing Events in RNA-seq Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/FRASER/inst/doc/FRASER.R
dependencyCount: 186
Package: frenchFISH
Version: 1.19.0
Imports: utils, MCMCpack, NHPoisson
Suggests: knitr, rmarkdown, testthat
License: Artistic-2.0
MD5sum: 4b0aa958dc81d8afdb6942de979bf35c
NeedsCompilation: no
Title: Poisson Models for Quantifying DNA Copy-number from FISH Images
of Tissue Sections
Description: FrenchFISH comprises a nuclear volume correction method
coupled with two types of Poisson models: either a Poisson
model for improved manual spot counting without the need for
control probes; or a homogenous Poisson Point Process model for
automated spot counting.
biocViews: Software, BiomedicalInformatics, CellBiology, Genetics,
HiddenMarkovModel, Preprocessing
Author: Adam Berman, Geoff Macintyre
Maintainer: Adam Berman <agb61@cam.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/frenchFISH
git_branch: devel
git_last_commit: 4b9c985
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/frenchFISH_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/frenchFISH_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/frenchFISH_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/frenchFISH_1.19.0.tgz
vignettes: vignettes/frenchFISH/inst/doc/frenchFISH.html
vignetteTitles: Correcting FISH probe counts with frenchFISH
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/frenchFISH/inst/doc/frenchFISH.R
dependencyCount: 77
Package: FRGEpistasis
Version: 1.43.0
Depends: R (>= 2.15), MASS, fda, methods, stats
Imports: utils
License: GPL-2
Archs: x64
MD5sum: e939af95744d1936771efd0b41c98493
NeedsCompilation: no
Title: Epistasis Analysis for Quantitative Traits by Functional
Regression Model
Description: A Tool for Epistasis Analysis Based on Functional
Regression Model
biocViews: Genetics, NetworkInference, GeneticVariability, Software
Author: Futao Zhang
Maintainer: Futao Zhang <futoaz@gmail.com>
git_url: https://git.bioconductor.org/packages/FRGEpistasis
git_branch: devel
git_last_commit: 4b9608b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/FRGEpistasis_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/FRGEpistasis_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/FRGEpistasis_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/FRGEpistasis_1.43.0.tgz
vignettes: vignettes/FRGEpistasis/inst/doc/FRGEpistasis.pdf
vignetteTitles: FRGEpistasis: A Tool for Epistasis Analysis Based on
Functional Regression Model
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FRGEpistasis/inst/doc/FRGEpistasis.R
dependencyCount: 55
Package: frma
Version: 1.59.0
Depends: R (>= 2.10.0), Biobase (>= 2.6.0)
Imports: Biobase, MASS, DBI, affy, methods, oligo, oligoClasses,
preprocessCore, utils, BiocGenerics
Suggests: hgu133afrmavecs, frmaExampleData
License: GPL (>= 2)
MD5sum: bd9965e9b5f1d422f4620cf2677a002d
NeedsCompilation: no
Title: Frozen RMA and Barcode
Description: Preprocessing and analysis for single microarrays and
microarray batches.
biocViews: Software, Microarray, Preprocessing
Author: Matthew N. McCall <mccallm@gmail.com>, Rafael A. Irizarry
<rafa@jhu.edu>, with contributions from Terry Therneau
Maintainer: Matthew N. McCall <mccallm@gmail.com>
URL: http://bioconductor.org
git_url: https://git.bioconductor.org/packages/frma
git_branch: devel
git_last_commit: 93b1951
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/frma_1.59.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/frma_1.59.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/frma_1.59.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/frma_1.59.0.tgz
vignettes: vignettes/frma/inst/doc/frma.pdf
vignetteTitles: frma: Preprocessing for single arrays and array batches
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/frma/inst/doc/frma.R
importsMe: ChIPXpress, rat2302frmavecs, DeSousa2013
suggestsMe: frmaTools, ath1121501frmavecs, antiProfilesData
dependencyCount: 66
Package: frmaTools
Version: 1.59.0
Depends: R (>= 2.10.0), affy
Imports: Biobase, DBI, methods, preprocessCore, stats, utils
Suggests: oligo, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, frma, affyPLM,
hgu133aprobe, hgu133atagprobe, hgu133plus2probe, hgu133acdf,
hgu133atagcdf, hgu133plus2cdf, hgu133afrmavecs, frmaExampleData
License: GPL (>= 2)
MD5sum: 66320a23dca0b8dea82d0c62c9dc7783
NeedsCompilation: no
Title: Frozen RMA Tools
Description: Tools for advanced use of the frma package.
biocViews: Software, Microarray, Preprocessing
Author: Matthew N. McCall <mccallm@gmail.com>, Rafael A. Irizarry
<rafa@jhu.edu>
Maintainer: Matthew N. McCall <mccallm@gmail.com>
URL: http://bioconductor.org
git_url: https://git.bioconductor.org/packages/frmaTools
git_branch: devel
git_last_commit: 3414630
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/frmaTools_1.59.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/frmaTools_1.59.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/frmaTools_1.59.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/frmaTools_1.59.0.tgz
vignettes: vignettes/frmaTools/inst/doc/frmaTools.pdf
vignetteTitles: frmaTools: Create packages containing the vectors used
by frma.
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/frmaTools/inst/doc/frmaTools.R
importsMe: DeSousa2013
dependencyCount: 13
Package: funOmics
Version: 1.1.0
Depends: R (>= 4.4.0), NMF
Imports: NMF, pathifier, stats, KEGGREST, AnnotationDbi, org.Hs.eg.db,
dplyr, stringr
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), MultiAssayExperiment,
SummarizedExperiment, airway
License: MIT + file LICENSE
MD5sum: 27e7da30ae165ca5272166bd8573f58d
NeedsCompilation: no
Title: Aggregating Omics Data into Higher-Level Functional
Representations
Description: The 'funOmics' package ggregates or summarizes omics data
into higher level functional representations such as GO terms
gene sets or KEGG metabolic pathways. The aggregated data
matrix represents functional activity scores that facilitate
the analysis of functional molecular sets while allowing to
reduce dimensionality and provide easier and faster biological
interpretations. Coordinated functional activity scores can be
as informative as single molecules!
biocViews: Software, Transcriptomics, Metabolomics, Proteomics,
Pathways, GO, KEGG
Author: Elisa Gomez de Lope [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7115-7393>), Enrico Glaab [ctb]
(ORCID: <https://orcid.org/0000-0003-3977-7469>)
Maintainer: Elisa Gomez de Lope <elisa.gomezdelope@uni.lu>
URL: https://github.com/elisagdelope/funomics
VignetteBuilder: knitr
BugReports: https://github.com/elisagdelope/funomics/issues
git_url: https://git.bioconductor.org/packages/funOmics
git_branch: devel
git_last_commit: f088c60
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/funOmics_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/funOmics_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/funOmics_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/funOmics_1.1.0.tgz
vignettes: vignettes/funOmics/inst/doc/funomics_vignette.html
vignetteTitles: funOmics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/funOmics/inst/doc/funomics_vignette.R
dependencyCount: 92
Package: funtooNorm
Version: 1.31.0
Depends: R(>= 3.4)
Imports: pls, matrixStats, minfi, methods,
IlluminaHumanMethylation450kmanifest,
IlluminaHumanMethylation450kanno.ilmn12.hg19, GenomeInfoDb,
grDevices, graphics, stats
Suggests: prettydoc, minfiData, knitr, rmarkdown
License: GPL-3
MD5sum: f2ce3720f6da37be24985d0077aa07d9
NeedsCompilation: no
Title: Normalization Procedure for Infinium HumanMethylation450
BeadChip Kit
Description: Provides a function to normalize Illumina Infinium Human
Methylation 450 BeadChip (Illumina 450K), correcting for tissue
and/or cell type.
biocViews: DNAMethylation, Preprocessing, Normalization
Author: Celia Greenwood <celia.greenwood@mcgill.ca>,Stepan Grinek
<stepan.grinek@ladydavis.ca>, Maxime Turgeon
<maxime.turgeon@mail.mcgill.ca>, Kathleen Klein
<kathleen.klein@mail.mcgill.ca>
Maintainer: Kathleen Klein <kathleen.klein@mail.mcgill.ca>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/funtooNorm
git_branch: devel
git_last_commit: 4e274a7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/funtooNorm_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/funtooNorm_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/funtooNorm_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/funtooNorm_1.31.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 148
Package: FuseSOM
Version: 1.9.0
Depends: R (>= 4.2.0)
Imports: psych, FCPS, analogue, coop, pheatmap, ggplotify, fastcluster,
fpc, ggplot2, stringr, ggpubr, proxy, cluster, diptest,
methods, SummarizedExperiment, stats, S4Vectors
LinkingTo: Rcpp
Suggests: knitr, BiocStyle, rmarkdown, SingleCellExperiment
License: GPL-2
MD5sum: b3c0a643a6c5d34eb4134f0c04744a09
NeedsCompilation: yes
Title: A Correlation Based Multiview Self Organizing Maps Clustering
For IMC Datasets
Description: A correlation-based multiview self-organizing map for the
characterization of cell types in highly multiplexed in situ
imaging cytometry assays (`FuseSOM`) is a tool for unsupervised
clustering. `FuseSOM` is robust and achieves high accuracy by
combining a `Self Organizing Map` architecture and a
`Multiview` integration of correlation based metrics. This
allows FuseSOM to cluster highly multiplexed in situ imaging
cytometry assays.
biocViews: SingleCell, CellBasedAssays, Clustering, Spatial
Author: Elijah Willie [aut, cre]
Maintainer: Elijah Willie <ewil3501@uni.sydney.edu.au>
VignetteBuilder: knitr
BugReports: https://github.com/ecool50/FuseSOM/issues
git_url: https://git.bioconductor.org/packages/FuseSOM
git_branch: devel
git_last_commit: 5ce2297
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/FuseSOM_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/FuseSOM_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/FuseSOM_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/FuseSOM_1.9.0.tgz
vignettes: vignettes/FuseSOM/inst/doc/Introduction.html
vignetteTitles: FuseSOM package manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/FuseSOM/inst/doc/Introduction.R
dependencyCount: 140
Package: G4SNVHunter
Version: 0.99.4
Depends: R (>= 4.3.0)
Imports: Biostrings, stats, GenomicRanges, IRanges, Rcpp, RcppRoll,
data.table, GenomeInfoDb, S4Vectors, ggplot2, cowplot,
progress, ggseqlogo, viridis, ggpointdensity
LinkingTo: Rcpp
Suggests: knitr, BiocStyle, rmarkdown, BiocManager,
BSgenome.Hsapiens.UCSC.hg19, DT, rtracklayer, testthat (>=
3.0.0), RBGL
License: MIT + file LICENSE
MD5sum: 19219bfbb767c723400420bf5bfa6d7d
NeedsCompilation: yes
Title: Evaluating SNV-Induced Disruption of G-Quadruplex Structures
Description: G-quadruplexes (G4s) are unique nucleic acid secondary
structures predominantly found in guanine-rich regions and have
been shown to be involved in various biological regulatory
processes. G4SNVHunter is an R package designed to rapidly
identify genomic sequences with G4-forming potential and
accurately screen user-provided single nucleotide variants
(also applicable to single nucleotide polymorphisms) that may
destabilize these structures. This enables users to screen key
variants for further experimental study, investigating how
these variants may influence biological functions, such as gene
regulation, by altering G4 formation.
biocViews: Epigenetics, SNP
Author: Rongxin Zhang [cre, aut] (ORCID:
<https://orcid.org/0000-0002-1643-1185>)
Maintainer: Rongxin Zhang <rongxinzhang@outlook.com>
URL: https://github.com/rongxinzh/G4SNVHunter
VignetteBuilder: knitr
BugReports: https://github.com/rongxinzh/G4SNVHunter/issues
git_url: https://git.bioconductor.org/packages/G4SNVHunter
git_branch: devel
git_last_commit: 73edf5e
git_last_commit_date: 2024-12-12
Date/Publication: 2024-12-12
source.ver: src/contrib/G4SNVHunter_0.99.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/G4SNVHunter_0.99.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/G4SNVHunter_0.99.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/G4SNVHunter_0.99.4.tgz
vignettes: vignettes/G4SNVHunter/inst/doc/G4SNVHunter.html
vignetteTitles: Introduction to G4SNVHunter
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/G4SNVHunter/inst/doc/G4SNVHunter.R
dependencyCount: 66
Package: GA4GHclient
Version: 1.31.0
Depends: R (>= 3.5.0), S4Vectors
Imports: BiocGenerics, Biostrings, dplyr, GenomeInfoDb, GenomicRanges,
httr, IRanges, jsonlite, methods, VariantAnnotation
Suggests: AnnotationDbi, BiocStyle, DT, knitr, org.Hs.eg.db, rmarkdown,
testthat, TxDb.Hsapiens.UCSC.hg19.knownGene
License: GPL (>= 2)
Archs: x64
MD5sum: cac55083f03f2e232bdb4215ce11baa6
NeedsCompilation: no
Title: A Bioconductor package for accessing GA4GH API data servers
Description: GA4GHclient provides an easy way to access public data
servers through Global Alliance for Genomics and Health (GA4GH)
genomics API. It provides low-level access to GA4GH API and
translates response data into Bioconductor-based class objects.
biocViews: DataRepresentation, ThirdPartyClient
Author: Welliton Souza [aut, cre], Benilton Carvalho [ctb], Cristiane
Rocha [ctb]
Maintainer: Welliton Souza <well309@gmail.com>
URL: https://github.com/labbcb/GA4GHclient
VignetteBuilder: knitr
BugReports: https://github.com/labbcb/GA4GHclient/issues
git_url: https://git.bioconductor.org/packages/GA4GHclient
git_branch: devel
git_last_commit: 7451330
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GA4GHclient_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GA4GHclient_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GA4GHclient_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GA4GHclient_1.31.0.tgz
vignettes: vignettes/GA4GHclient/inst/doc/GA4GHclient.html
vignetteTitles: GA4GHclient
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GA4GHclient/inst/doc/GA4GHclient.R
dependsOnMe: GA4GHshiny
dependencyCount: 87
Package: GA4GHshiny
Version: 1.29.0
Depends: GA4GHclient
Imports: AnnotationDbi, BiocGenerics, dplyr, DT, GenomeInfoDb,
openxlsx, GenomicFeatures, methods, purrr, S4Vectors, shiny,
shinyjs, tidyr, shinythemes
Suggests: BiocStyle, org.Hs.eg.db, knitr, rmarkdown, testthat,
TxDb.Hsapiens.UCSC.hg19.knownGene
License: GPL-3
Archs: x64
MD5sum: 2ed68886abf2042d1c3692dc8aa81f2f
NeedsCompilation: no
Title: Shiny application for interacting with GA4GH-based data servers
Description: GA4GHshiny package provides an easy way to interact with
data servers based on Global Alliance for Genomics and Health
(GA4GH) genomics API through a Shiny application. It also
integrates with Beacon Network.
biocViews: GUI
Author: Welliton Souza [aut, cre], Benilton Carvalho [ctb], Cristiane
Rocha [ctb], Elizabeth Borgognoni [ctb]
Maintainer: Welliton Souza <well309@gmail.com>
URL: https://github.com/labbcb/GA4GHshiny
VignetteBuilder: knitr
BugReports: https://github.com/labbcb/GA4GHshiny/issues
git_url: https://git.bioconductor.org/packages/GA4GHshiny
git_branch: devel
git_last_commit: 145a45d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GA4GHshiny_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GA4GHshiny_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GA4GHshiny_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GA4GHshiny_1.29.0.tgz
vignettes: vignettes/GA4GHshiny/inst/doc/GA4GHshiny.html
vignetteTitles: GA4GHshiny
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GA4GHshiny/inst/doc/GA4GHshiny.R
dependencyCount: 123
Package: gaga
Version: 2.53.1
Depends: R (>= 2.8.0), Biobase, coda, EBarrays, mgcv
Enhances: parallel
License: GPL (>= 2)
MD5sum: bed6d3c659beb6d8df1867bb60ad741d
NeedsCompilation: yes
Title: GaGa hierarchical model for high-throughput data analysis
Description: Implements the GaGa model for high-throughput data
analysis, including differential expression analysis,
supervised gene clustering and classification. Additionally, it
performs sequential sample size calculations using the GaGa and
LNNGV models (the latter from EBarrays package).
biocViews: ImmunoOncology, OneChannel, MassSpectrometry,
MultipleComparison, DifferentialExpression, Classification
Author: David Rossell <rosselldavid@gmail.com>.
Maintainer: David Rossell <rosselldavid@gmail.com>
git_url: https://git.bioconductor.org/packages/gaga
git_branch: devel
git_last_commit: 0c4c578
git_last_commit_date: 2025-02-26
Date/Publication: 2025-02-26
source.ver: src/contrib/gaga_2.53.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gaga_2.53.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gaga_2.53.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gaga_2.53.1.tgz
vignettes: vignettes/gaga/inst/doc/gagamanual.pdf
vignetteTitles: Manual for the gaga library
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gaga/inst/doc/gagamanual.R
importsMe: casper
dependencyCount: 17
Package: gage
Version: 2.57.0
Depends: R (>= 3.5.0)
Imports: graph, KEGGREST, AnnotationDbi, GO.db
Suggests: pathview, gageData, org.Hs.eg.db, hgu133a.db, GSEABase,
Rsamtools, GenomicAlignments,
TxDb.Hsapiens.UCSC.hg19.knownGene, DESeq2, edgeR, limma
License: GPL (>=2.0)
MD5sum: 93290fa7281f2064318a5f6d6b6ec35f
NeedsCompilation: no
Title: Generally Applicable Gene-set Enrichment for Pathway Analysis
Description: GAGE is a published method for gene set (enrichment or
GSEA) or pathway analysis. GAGE is generally applicable
independent of microarray or RNA-Seq data attributes including
sample sizes, experimental designs, assay platforms, and other
types of heterogeneity, and consistently achieves superior
performance over other frequently used methods. In gage
package, we provide functions for basic GAGE analysis, result
processing and presentation. We have also built pipeline
routines for of multiple GAGE analyses in a batch, comparison
between parallel analyses, and combined analysis of
heterogeneous data from different sources/studies. In addition,
we provide demo microarray data and commonly used gene set data
based on KEGG pathways and GO terms. These funtions and data
are also useful for gene set analysis using other methods.
biocViews: Pathways, GO, DifferentialExpression, Microarray,
OneChannel, TwoChannel, RNASeq, Genetics, MultipleComparison,
GeneSetEnrichment, GeneExpression, SystemsBiology, Sequencing
Author: Weijun Luo
Maintainer: Weijun Luo <luo_weijun@yahoo.com>
URL: https://github.com/datapplab/gage,
http://www.biomedcentral.com/1471-2105/10/161
git_url: https://git.bioconductor.org/packages/gage
git_branch: devel
git_last_commit: 9f732fe
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/gage_2.57.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gage_2.57.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gage_2.57.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gage_2.57.0.tgz
vignettes: vignettes/gage/inst/doc/dataPrep.pdf,
vignettes/gage/inst/doc/gage.pdf,
vignettes/gage/inst/doc/RNA-seqWorkflow.pdf
vignetteTitles: Gene set and data preparation, Generally Applicable
Gene-set/Pathway Analysis, RNA-Seq Data Pathway and Gene-set
Analysis Workflows
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gage/inst/doc/dataPrep.R,
vignettes/gage/inst/doc/gage.R,
vignettes/gage/inst/doc/RNA-seqWorkflow.R
dependsOnMe: EGSEA
suggestsMe: FGNet, pathview, SBGNview, gageData
dependencyCount: 47
Package: GAprediction
Version: 1.33.0
Depends: R (>= 3.3)
Imports: glmnet, stats, utils, Matrix
Suggests: knitr, rmarkdown
License: GPL (>=2)
Archs: x64
MD5sum: 918003f283068c5e67a7988827538a47
NeedsCompilation: no
Title: Prediction of gestational age with Illumina HumanMethylation450
data
Description: [GAprediction] predicts gestational age using Illumina
HumanMethylation450 CpG data.
biocViews: ImmunoOncology, DNAMethylation, Epigenetics, Regression,
BiomedicalInformatics
Author: Jon Bohlin
Maintainer: Jon Bohlin <jon.bohlin@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GAprediction
git_branch: devel
git_last_commit: e77d435
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GAprediction_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GAprediction_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GAprediction_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GAprediction_1.33.0.tgz
vignettes: vignettes/GAprediction/inst/doc/GAprediction.html
vignetteTitles: GAprediction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GAprediction/inst/doc/GAprediction.R
dependencyCount: 17
Package: garfield
Version: 1.35.0
Suggests: knitr
License: GPL-3
MD5sum: 8dc5320e1c3b24a97960d8339671429d
NeedsCompilation: yes
Title: GWAS Analysis of Regulatory or Functional Information Enrichment
with LD correction
Description: GARFIELD is a non-parametric functional enrichment
analysis approach described in the paper GARFIELD: GWAS
analysis of regulatory or functional information enrichment
with LD correction. Briefly, it is a method that leverages GWAS
findings with regulatory or functional annotations (primarily
from ENCODE and Roadmap epigenomics data) to find features
relevant to a phenotype of interest. It performs greedy pruning
of GWAS SNPs (LD r2 > 0.1) and then annotates them based on
functional information overlap. Next, it quantifies Fold
Enrichment (FE) at various GWAS significance cutoffs and
assesses them by permutation testing, while matching for minor
allele frequency, distance to nearest transcription start site
and number of LD proxies (r2 > 0.8).
biocViews: Software, StatisticalMethod, Annotation,
FunctionalPrediction, GenomeAnnotation
Author: Sandro Morganella <sm22@sanger.ac.uk>
Maintainer: Valentina Iotchkova <vi1@sanger.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/garfield
git_branch: devel
git_last_commit: ff65122
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/garfield_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/garfield_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/garfield_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/garfield_1.35.0.tgz
vignettes: vignettes/garfield/inst/doc/vignette.pdf
vignetteTitles: garfield Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 0
Package: GARS
Version: 1.27.0
Depends: R (>= 3.5), ggplot2, cluster
Imports: DaMiRseq, MLSeq, stats, methods, SummarizedExperiment
Suggests: BiocStyle, knitr, testthat
License: GPL (>= 2)
MD5sum: 194a80609ef8afa4b82a532611438178
NeedsCompilation: no
Title: GARS: Genetic Algorithm for the identification of Robust Subsets
of variables in high-dimensional and challenging datasets
Description: Feature selection aims to identify and remove redundant,
irrelevant and noisy variables from high-dimensional datasets.
Selecting informative features affects the subsequent
classification and regression analyses by improving their
overall performances. Several methods have been proposed to
perform feature selection: most of them relies on univariate
statistics, correlation, entropy measurements or the usage of
backward/forward regressions. Herein, we propose an efficient,
robust and fast method that adopts stochastic optimization
approaches for high-dimensional. GARS is an innovative
implementation of a genetic algorithm that selects robust
features in high-dimensional and challenging datasets.
biocViews: Classification, FeatureExtraction, Clustering
Author: Mattia Chiesa <mattia.chiesa@hotmail.it>, Luca Piacentini
<luca.piacentini@cardiologicomonzino.it>
Maintainer: Mattia Chiesa <mattia.chiesa@hotmail.it>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GARS
git_branch: devel
git_last_commit: d0acef7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GARS_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GARS_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GARS_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GARS_1.27.0.tgz
vignettes: vignettes/GARS/inst/doc/GARS.pdf
vignetteTitles: GARS: a Genetic Algorithm for the identification of
Robust Subsets of variables in high-dimensional and challenging
datasets
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GARS/inst/doc/GARS.R
dependencyCount: 272
Package: GateFinder
Version: 1.27.0
Imports: splancs, mvoutlier, methods, stats, diptest, flowCore, flowFP
Suggests: RUnit, BiocGenerics
License: Artistic-2.0
Archs: x64
MD5sum: 24fcd82c35d0c4d05fecdd7114163b3f
NeedsCompilation: no
Title: Projection-based Gating Strategy Optimization for Flow and Mass
Cytometry
Description: Given a vector of cluster memberships for a cell
population, identifies a sequence of gates (polygon filters on
2D scatter plots) for isolation of that cell type.
biocViews: ImmunoOncology, FlowCytometry, CellBiology, Clustering
Author: Nima Aghaeepour <naghaeep@gmail.com>, Erin F. Simonds
<erin.simonds@gmail.com>
Maintainer: Nima Aghaeepour <naghaeep@gmail.com>
git_url: https://git.bioconductor.org/packages/GateFinder
git_branch: devel
git_last_commit: 77959e6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GateFinder_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GateFinder_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GateFinder_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GateFinder_1.27.0.tgz
vignettes: vignettes/GateFinder/inst/doc/GateFinder.pdf
vignetteTitles: GateFinder
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GateFinder/inst/doc/GateFinder.R
dependencyCount: 40
Package: gatom
Version: 1.5.0
Depends: R (>= 4.3.0)
Imports: data.table, igraph, BioNet, plyr, methods, XML, sna,
intergraph, network, GGally, grid, ggplot2, mwcsr, pryr,
htmlwidgets, htmltools, shinyCyJS (>= 1.0.0)
Suggests: testthat, knitr, rmarkdown, KEGGREST, AnnotationDbi,
org.Mm.eg.db, reactome.db, fgsea, readr, BiocStyle, R.utils
License: MIT + file LICENCE
MD5sum: fc6aedcba23e0bf7010f028ab4402810
NeedsCompilation: no
Title: Finding an Active Metabolic Module in Atom Transition Network
Description: This package implements a metabolic network analysis
pipeline to identify an active metabolic module based on high
throughput data. The pipeline takes as input transcriptional
and/or metabolic data and finds a metabolic subnetwork (module)
most regulated between the two conditions of interest. The
package further provides functions for module post-processing,
annotation and visualization.
biocViews: GeneExpression, DifferentialExpression, Pathways, Network
Author: Anastasiia Gainullina [aut], Mariia Emelianova [aut], Alexey
Sergushichev [aut, cre]
Maintainer: Alexey Sergushichev <alsergbox@gmail.com>
URL: https://github.com/ctlab/gatom/
VignetteBuilder: knitr
BugReports: https://github.com/ctlab/gatom/issues
git_url: https://git.bioconductor.org/packages/gatom
git_branch: devel
git_last_commit: 97bd9cf
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/gatom_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gatom_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gatom_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gatom_1.5.0.tgz
vignettes: vignettes/gatom/inst/doc/gatom-tutorial.html
vignetteTitles: Using gatom package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gatom/inst/doc/gatom-tutorial.R
dependencyCount: 118
Package: GBScleanR
Version: 2.1.4
Depends: SeqArray
Imports: stats, utils, methods, ggplot2, tidyr, expm, Rcpp,
RcppParallel, gdsfmt
LinkingTo: Rcpp, RcppParallel
Suggests: BiocStyle, testthat (>= 3.0.0), knitr, rmarkdown
License: GPL-3 + file LICENSE
MD5sum: d24cd611ac56dd427760087fa3150d94
NeedsCompilation: yes
Title: Error correction tool for noisy genotyping by sequencing (GBS)
data
Description: GBScleanR is a package for quality check, filtering, and
error correction of genotype data derived from next generation
sequcener (NGS) based genotyping platforms. GBScleanR takes
Variant Call Format (VCF) file as input. The main function of
this package is `estGeno()` which estimates the true genotypes
of samples from given read counts for genotype markers using a
hidden Markov model with incorporating uneven observation ratio
of allelic reads. This implementation gives robust genotype
estimation even in noisy genotype data usually observed in
Genotyping-By-Sequnencing (GBS) and similar methods, e.g.
RADseq. The current implementation accepts genotype data of a
diploid population at any generation of multi-parental cross,
e.g. biparental F2 from inbred parents, biparental F2 from
outbred parents, and 8-way recombinant inbred lines (8-way
RILs) which can be refered to as MAGIC population.
biocViews: GeneticVariability, SNP, Genetics, HiddenMarkovModel,
Sequencing, QualityControl
Author: Tomoyuki Furuta [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0869-6626>)
Maintainer: Tomoyuki Furuta <f.tomoyuki@okayama-u.ac.jp>
URL: https://github.com/tomoyukif/GBScleanR
SystemRequirements: GNU make, C++11
VignetteBuilder: knitr
BugReports: https://github.com/tomoyukif/GBScleanR/issues
git_url: https://git.bioconductor.org/packages/GBScleanR
git_branch: devel
git_last_commit: 685365b
git_last_commit_date: 2025-02-25
Date/Publication: 2025-02-25
source.ver: src/contrib/GBScleanR_2.1.4.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GBScleanR_2.1.5.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GBScleanR_2.1.5.tgz
vignettes: vignettes/GBScleanR/inst/doc/BasicUsageOfGBScleanR.html
vignetteTitles: BasicUsageOfGBScleanR.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GBScleanR/inst/doc/BasicUsageOfGBScleanR.R
dependencyCount: 69
Package: gcapc
Version: 1.31.0
Depends: R (>= 3.4)
Imports: BiocGenerics, GenomeInfoDb, S4Vectors, IRanges, Biostrings,
BSgenome, GenomicRanges, Rsamtools, GenomicAlignments,
matrixStats, MASS, splines, grDevices, graphics, stats, methods
Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Mmusculus.UCSC.mm10
License: GPL-3
MD5sum: 5b0b88ac974944f87451269bbac643bf
NeedsCompilation: no
Title: GC Aware Peak Caller
Description: Peak calling for ChIP-seq data with consideration of
potential GC bias in sequencing reads. GC bias is first
estimated with generalized linear mixture models using
effective GC strategy, then applied into peak significance
estimation.
biocViews: Sequencing, ChIPSeq, BatchEffect, PeakDetection
Author: Mingxiang Teng and Rafael A. Irizarry
Maintainer: Mingxiang Teng <tengmx@gmail.com>
URL: https://github.com/tengmx/gcapc
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/gcapc
git_branch: devel
git_last_commit: 35dd473
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/gcapc_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gcapc_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gcapc_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gcapc_1.31.0.tgz
vignettes: vignettes/gcapc/inst/doc/gcapc.html
vignetteTitles: The gcapc user's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gcapc/inst/doc/gcapc.R
suggestsMe: epigraHMM
dependencyCount: 61
Package: gcatest
Version: 2.7.0
Depends: R (>= 4.0)
Imports: methods, lfa
Suggests: knitr, ggplot2, testthat, BEDMatrix, genio
License: GPL (>= 3)
MD5sum: a2d99c00c123b21999aba948039b5ef6
NeedsCompilation: no
Title: Genotype Conditional Association TEST
Description: GCAT is an association test for genome wide association
studies that controls for population structure under a general
class of trait models. This test conditions on the trait,
which makes it immune to confounding by unmodeled environmental
factors. Population structure is modeled via logistic factors,
which are estimated using the `lfa` package.
biocViews: SNP, DimensionReduction, PrincipalComponent,
GenomeWideAssociation
Author: Wei Hao [aut], Minsun Song [aut], Alejandro Ochoa [aut, cre]
(ORCID: <https://orcid.org/0000-0003-4928-3403>), John D.
Storey [aut] (ORCID: <https://orcid.org/0000-0001-5992-402X>)
Maintainer: Alejandro Ochoa <alejandro.ochoa@duke.edu>
URL: https://github.com/StoreyLab/gcatest
VignetteBuilder: knitr
BugReports: https://github.com/StoreyLab/gcatest/issues
git_url: https://git.bioconductor.org/packages/gcatest
git_branch: devel
git_last_commit: 98df64e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/gcatest_2.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gcatest_2.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gcatest_2.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gcatest_2.7.0.tgz
vignettes: vignettes/gcatest/inst/doc/gcatest.pdf
vignetteTitles: gcat Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gcatest/inst/doc/gcatest.R
suggestsMe: jackstraw
dependencyCount: 13
Package: gCrisprTools
Version: 2.13.0
Depends: R (>= 4.1)
Imports: Biobase, limma, RobustRankAggreg, ggplot2,
SummarizedExperiment, grid, rmarkdown, grDevices, graphics,
methods, ComplexHeatmap, stats, utils, parallel
Suggests: edgeR, knitr, AnnotationDbi, org.Mm.eg.db, org.Hs.eg.db,
BiocGenerics, markdown, RUnit, sparrow, msigdbr, fgsea
License: Artistic-2.0
MD5sum: 63e6b3d7cfdcad01c1d7370b76ee3b8d
NeedsCompilation: no
Title: Suite of Functions for Pooled Crispr Screen QC and Analysis
Description: Set of tools for evaluating pooled high-throughput
screening experiments, typically employing CRISPR/Cas9 or shRNA
expression cassettes. Contains methods for interrogating
library and cassette behavior within an experiment, identifying
differentially abundant cassettes, aggregating signals to
identify candidate targets for empirical validation, hypothesis
testing, and comprehensive reporting. Version 2.0 extends these
applications to include a variety of tools for contextualizing
and integrating signals across many experiments, incorporates
extended signal enrichment methodologies via the "sparrow"
package, and streamlines many formal requirements to aid in
interpretablity.
biocViews: ImmunoOncology, CRISPR, PooledScreens, ExperimentalDesign,
BiomedicalInformatics, CellBiology, FunctionalGenomics,
Pharmacogenomics, Pharmacogenetics, SystemsBiology,
DifferentialExpression, GeneSetEnrichment, Genetics,
MultipleComparison, Normalization, Preprocessing,
QualityControl, RNASeq, Regression, Software, Visualization
Author: Russell Bainer, Dariusz Ratman, Steve Lianoglou, Peter Haverty
Maintainer: Russell Bainer <russ.bainer@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/gCrisprTools
git_branch: devel
git_last_commit: 1bb7f87
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/gCrisprTools_2.13.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gCrisprTools_2.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gCrisprTools_2.13.0.tgz
vignettes: vignettes/gCrisprTools/inst/doc/Contrast_Comparisons.html,
vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.html,
vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.html
vignetteTitles: Contrast_Comparisons_gCrisprTools,
Example_Workflow_gCrisprTools, gCrisprTools_Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gCrisprTools/inst/doc/Contrast_Comparisons.R,
vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.R,
vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.R
dependencyCount: 98
Package: gcrma
Version: 2.79.0
Depends: R (>= 2.6.0), affy (>= 1.23.2), graphics, methods, stats,
utils
Imports: Biobase, affy (>= 1.23.2), affyio (>= 1.13.3), XVector,
Biostrings (>= 2.11.32), splines, BiocManager
Suggests: affydata, tools, splines, hgu95av2cdf, hgu95av2probe
License: LGPL
MD5sum: b9ec3e607db211efa9ff3efd57092a60
NeedsCompilation: yes
Title: Background Adjustment Using Sequence Information
Description: Background adjustment using sequence information
biocViews: Microarray, OneChannel, Preprocessing
Author: Jean(ZHIJIN) Wu, Rafael Irizarry with contributions from James
MacDonald <jmacdon@med.umich.edu> Jeff Gentry
Maintainer: Z. Wu <zwu@stat.brown.edu>
git_url: https://git.bioconductor.org/packages/gcrma
git_branch: devel
git_last_commit: 7375785
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/gcrma_2.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gcrma_2.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gcrma_2.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gcrma_2.79.0.tgz
vignettes: vignettes/gcrma/inst/doc/gcrma2.0.pdf
vignetteTitles: gcrma1.2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: affyILM, affyPLM, maskBAD, webbioc
importsMe: affycoretools, affylmGUI
suggestsMe: panp, aroma.affymetrix
dependencyCount: 31
Package: GDCRNATools
Version: 1.27.0
Depends: R (>= 3.5.0)
Imports: shiny, jsonlite, rjson, XML, limma, edgeR, DESeq2,
clusterProfiler, DOSE, org.Hs.eg.db, biomaRt, survival,
survminer, pathview, ggplot2, gplots, DT, GenomicDataCommons,
BiocParallel
Suggests: knitr, testthat, prettydoc, rmarkdown
License: Artistic-2.0
MD5sum: 329645369f7a6e679006263f8d98c63f
NeedsCompilation: no
Title: GDCRNATools: an R/Bioconductor package for integrative analysis
of lncRNA, mRNA, and miRNA data in GDC
Description: This is an easy-to-use package for downloading,
organizing, and integrative analyzing RNA expression data in
GDC with an emphasis on deciphering the lncRNA-mRNA related
ceRNA regulatory network in cancer. Three databases of
lncRNA-miRNA interactions including spongeScan, starBase, and
miRcode, as well as three databases of mRNA-miRNA interactions
including miRTarBase, starBase, and miRcode are incorporated
into the package for ceRNAs network construction. limma, edgeR,
and DESeq2 can be used to identify differentially expressed
genes/miRNAs. Functional enrichment analyses including GO,
KEGG, and DO can be performed based on the clusterProfiler and
DO packages. Both univariate CoxPH and KM survival analyses of
multiple genes can be implemented in the package. Besides some
routine visualization functions such as volcano plot, bar plot,
and KM plot, a few simply shiny apps are developed to
facilitate visualization of results on a local webpage.
biocViews: ImmunoOncology, GeneExpression, DifferentialExpression,
GeneRegulation, GeneTarget, NetworkInference, Survival,
Visualization, GeneSetEnrichment, NetworkEnrichment, Network,
RNASeq, GO, KEGG
Author: Ruidong Li, Han Qu, Shibo Wang, Julong Wei, Le Zhang, Renyuan
Ma, Jianming Lu, Jianguo Zhu, Wei-De Zhong, Zhenyu Jia
Maintainer: Ruidong Li <rli012@ucr.edu>, Han Qu <hqu002@ucr.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GDCRNATools
git_branch: devel
git_last_commit: e6c5e2e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GDCRNATools_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GDCRNATools_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GDCRNATools_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GDCRNATools_1.27.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
dependencyCount: 231
Package: gDNAx
Version: 1.5.1
Depends: R (>= 4.3)
Imports: methods, BiocGenerics, BiocParallel, matrixStats, Biostrings,
S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, GenomicFiles,
GenomicAlignments, GenomicFeatures, Rsamtools, AnnotationHub,
RColorBrewer, AnnotationDbi, bitops, plotrix,
SummarizedExperiment, grDevices, graphics, stats, utils, cli
Suggests: BiocStyle, knitr, rmarkdown, RUnit,
TxDb.Hsapiens.UCSC.hg38.knownGene, gDNAinRNAseqData
License: Artistic-2.0
MD5sum: ebb6d4bdb2d28f00bc163a97ac703a98
NeedsCompilation: no
Title: Diagnostics for assessing genomic DNA contamination in RNA-seq
data
Description: Provides diagnostics for assessing genomic DNA
contamination in RNA-seq data, as well as plots representing
these diagnostics. Moreover, the package can be used to get an
insight into the strand library protocol used and, in case of
strand-specific libraries, the strandedness of the data.
Furthermore, it provides functionality to filter out reads of
potential gDNA origin.
biocViews: Transcription, Transcriptomics, RNASeq, Sequencing,
Preprocessing, Software, GeneExpression, Coverage,
DifferentialExpression, FunctionalGenomics, SplicedAlignment,
Alignment
Author: Beatriz Calvo-Serra [aut], Robert Castelo [aut, cre]
Maintainer: Robert Castelo <robert.castelo@upf.edu>
URL: https://github.com/functionalgenomics/gDNAx
VignetteBuilder: knitr
BugReports: https://github.com/functionalgenomics/gDNAx/issues
git_url: https://git.bioconductor.org/packages/gDNAx
git_branch: devel
git_last_commit: 02b891e
git_last_commit_date: 2025-01-12
Date/Publication: 2025-01-12
source.ver: src/contrib/gDNAx_1.5.1.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gDNAx_1.5.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gDNAx_1.5.1.tgz
vignettes: vignettes/gDNAx/inst/doc/gDNAx.html
vignetteTitles: The gDNAx package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gDNAx/inst/doc/gDNAx.R
dependencyCount: 101
Package: gDR
Version: 1.5.1
Depends: R (>= 4.2), gDRcore (>= 1.1.19), gDRimport (>= 1.1.9),
gDRutils (>= 1.1.12)
Suggests: BiocStyle, BumpyMatrix, futile.logger, gDRstyle (>= 1.1.5),
gDRtestData (>= 1.1.10), kableExtra, knitr, markdown, purrr,
rmarkdown, SummarizedExperiment, testthat, yaml
License: Artistic-2.0
Archs: x64
MD5sum: 22a6fbb1edd5b8b95cfbf82808a30f84
NeedsCompilation: no
Title: Umbrella package for R packages in the gDR suite
Description: Package is a part of the gDR suite. It reexports functions
from other packages in the gDR suite that contain critical
processing functions and utilities. The vignette walks through
the full processing pipeline for drug response analyses that
the gDR suite offers.
biocViews: Software, DataImport, ShinyApps
Author: Allison Vuong [aut], Bartosz Czech [aut] (ORCID:
<https://orcid.org/0000-0002-9908-3007>), Arkadiusz Gladki
[cre, aut] (ORCID: <https://orcid.org/0000-0002-7059-6378>),
Marc Hafner [aut] (ORCID:
<https://orcid.org/0000-0003-1337-7598>), Dariusz Scigocki
[aut], Janina Smola [aut], Sergiu Mocanu [aut]
Maintainer: Arkadiusz Gladki <gladki.arkadiusz@gmail.com>
URL: https://github.com/gdrplatform/gDR,
https://gdrplatform.github.io/gDR/
VignetteBuilder: knitr
BugReports: https://github.com/gdrplatform/gDR/issues
git_url: https://git.bioconductor.org/packages/gDR
git_branch: devel
git_last_commit: 8e0f13d
git_last_commit_date: 2024-11-06
Date/Publication: 2024-12-18
source.ver: src/contrib/gDR_1.5.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gDR_1.5.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gDR_1.5.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gDR_1.5.1.tgz
vignettes: vignettes/gDR/inst/doc/gDR.html
vignetteTitles: Running the drug response processing pipeline
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gDR/inst/doc/gDR.R
dependencyCount: 212
Package: gDRcore
Version: 1.5.5
Depends: R (>= 4.2)
Imports: BumpyMatrix, BiocParallel, checkmate, futile.logger, gDRutils
(>= 1.5.6), MultiAssayExperiment, purrr, stringr, S4Vectors,
SummarizedExperiment, data.table
Suggests: BiocStyle, gDRstyle (>= 1.1.5), gDRimport (>= 1.1.9),
gDRtestData (>= 1.1.10), IRanges, knitr, pkgbuild, qs,
testthat, yaml
License: Artistic-2.0
MD5sum: 5d5481b27a796e038050b1ffffed829f
NeedsCompilation: yes
Title: Processing functions and interface to process and analyze drug
dose-response data
Description: This package contains core functions to process and
analyze drug response data. The package provides tools for
normalizing, averaging, and calculation of gDR metrics data.
All core functions are wrapped into the pipeline function
allowing analyzing the data in a straightforward way.
biocViews: Software, ShinyApps
Author: Bartosz Czech [aut] (ORCID:
<https://orcid.org/0000-0002-9908-3007>), Arkadiusz Gladki
[cre, aut] (ORCID: <https://orcid.org/0000-0002-7059-6378>),
Marc Hafner [aut] (ORCID:
<https://orcid.org/0000-0003-1337-7598>), Pawel Piatkowski
[aut], Natalia Potocka [aut], Dariusz Scigocki [aut], Janina
Smola [aut], Sergiu Mocanu [aut], Marcin Kamianowski [aut],
Allison Vuong [aut]
Maintainer: Arkadiusz Gladki <gladki.arkadiusz@gmail.com>
URL: https://github.com/gdrplatform/gDRcore,
https://gdrplatform.github.io/gDRcore/
VignetteBuilder: knitr
BugReports: https://github.com/gdrplatform/gDRcore/issues
git_url: https://git.bioconductor.org/packages/gDRcore
git_branch: devel
git_last_commit: 49f2415
git_last_commit_date: 2025-03-07
Date/Publication: 2025-03-07
source.ver: src/contrib/gDRcore_1.5.5.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gDRcore_1.5.5.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gDRcore_1.5.5.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gDRcore_1.5.5.tgz
vignettes: vignettes/gDRcore/inst/doc/gDR-annotation.html,
vignettes/gDRcore/inst/doc/gDR-data-model.html,
vignettes/gDRcore/inst/doc/gDRcore.html
vignetteTitles: gDRcore, Vignette Title, gDRcore
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gDRcore/inst/doc/gDR-annotation.R,
vignettes/gDRcore/inst/doc/gDR-data-model.R,
vignettes/gDRcore/inst/doc/gDRcore.R
dependsOnMe: gDR
dependencyCount: 120
Package: gDRimport
Version: 1.5.10
Depends: R (>= 4.2)
Imports: assertthat, BumpyMatrix, checkmate, CoreGx, PharmacoGx,
data.table, futile.logger, gDRutils (>= 1.3.17), magrittr,
methods, MultiAssayExperiment, readxl, rio, S4Vectors, stats,
stringi, SummarizedExperiment, tibble, tools, utils, XML, yaml,
openxlsx
Suggests: BiocStyle, gDRtestData (>= 1.3.3), gDRstyle (>= 1.3.3),
knitr, purrr, qs, testthat
License: Artistic-2.0
MD5sum: 79c7cf133711b45354e9c74bcff9175f
NeedsCompilation: no
Title: Package for handling the import of dose-response data
Description: The package is a part of the gDR suite. It helps to
prepare raw drug response data for downstream processing. It
mainly contains helper functions for
importing/loading/validating dose-response data provided in
different file formats.
biocViews: Software, Infrastructure, DataImport
Author: Arkadiusz Gladki [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7059-6378>), Bartosz Czech [aut]
(ORCID: <https://orcid.org/0000-0002-9908-3007>), Marc Hafner
[aut] (ORCID: <https://orcid.org/0000-0003-1337-7598>), Sergiu
Mocanu [aut], Dariusz Scigocki [aut], Allison Vuong [aut], Luca
Gerosa [aut] (ORCID: <https://orcid.org/0000-0001-6805-9410>),
Janina Smola [aut]
Maintainer: Arkadiusz Gladki <gladki.arkadiusz@gmail.com>
URL: https://github.com/gdrplatform/gDRimport,
https://gdrplatform.github.io/gDRimport/
VignetteBuilder: knitr
BugReports: https://github.com/gdrplatform/gDRimport/issues
git_url: https://git.bioconductor.org/packages/gDRimport
git_branch: devel
git_last_commit: ec3a733
git_last_commit_date: 2025-03-27
Date/Publication: 2025-03-27
source.ver: src/contrib/gDRimport_1.5.10.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gDRimport_1.5.10.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gDRimport_1.5.10.tgz
vignettes:
vignettes/gDRimport/inst/doc/ConvertingMAEtoPharmacoSet.html,
vignettes/gDRimport/inst/doc/ConvertingPharmacoSetToGDR.html,
vignettes/gDRimport/inst/doc/gDRimport.html
vignetteTitles: Converting a gDR-generated MultiAssayExperiment object
into a PharmacoSet, Converting PharmacoSet Drug Response Data
into gDR object, gDRimport
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gDRimport/inst/doc/ConvertingMAEtoPharmacoSet.R,
vignettes/gDRimport/inst/doc/ConvertingPharmacoSetToGDR.R,
vignettes/gDRimport/inst/doc/gDRimport.R
dependsOnMe: gDR
suggestsMe: gDRcore
dependencyCount: 210
Package: gDRstyle
Version: 1.5.3
Depends: R (>= 4.2)
Imports: BiocCheck, BiocManager, BiocStyle, checkmate, desc, git2r,
lintr (>= 3.0.0), rcmdcheck, remotes, yaml, rjson, pkgbuild,
withr
Suggests: knitr, testthat (>= 3.0.0)
License: Artistic-2.0
MD5sum: 3bafd8961ee319a0fcce28d9ff8c135b
NeedsCompilation: no
Title: A package with style requirements for the gDR suite
Description: Package fills a helper package role for whole gDR suite.
It helps to support good development practices by keeping style
requirements and style tests for other packages. It also
contains build helpers to make all package requirements met.
biocViews: Software, Infrastructure
Author: Allison Vuong [aut], Dariusz Scigocki [aut], Marcin Kamianowski
[aut], Aleksander Chlebowski [ctb], Janina Smola [aut],
Arkadiusz Gladki [cre, aut] (ORCID:
<https://orcid.org/0000-0002-7059-6378>), Bartosz Czech [aut]
(ORCID: <https://orcid.org/0000-0002-9908-3007>)
Maintainer: Arkadiusz Gladki <gladki.arkadiusz@gmail.com>
URL: https://github.com/gdrplatform/gDRstyle,
https://gdrplatform.github.io/gDRstyle/
VignetteBuilder: knitr
BugReports: https://github.com/gdrplatform/gDRstyle/issues
git_url: https://git.bioconductor.org/packages/gDRstyle
git_branch: devel
git_last_commit: 37eff6b
git_last_commit_date: 2025-02-04
Date/Publication: 2025-02-05
source.ver: src/contrib/gDRstyle_1.5.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gDRstyle_1.5.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gDRstyle_1.5.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gDRstyle_1.5.3.tgz
vignettes: vignettes/gDRstyle/inst/doc/gDRstyle.html,
vignettes/gDRstyle/inst/doc/style_guide.html
vignetteTitles: gDRstyle-package, gDR-style-guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gDRstyle/inst/doc/gDRstyle.R,
vignettes/gDRstyle/inst/doc/style_guide.R
suggestsMe: gDR, gDRcore, gDRimport, gDRutils, gDRtestData
dependencyCount: 104
Package: gDRutils
Version: 1.5.17
Depends: R (>= 4.2)
Imports: BiocParallel, BumpyMatrix, checkmate, data.table, drc,
jsonlite, jsonvalidate, methods, MultiAssayExperiment,
S4Vectors, stats, stringr, SummarizedExperiment, utils
Suggests: BiocManager, BiocStyle, futile.logger, gDRstyle (>= 1.1.5),
gDRtestData (>= 1.1.10), IRanges, knitr, lintr, mockery, purrr,
qs, rcmdcheck, rmarkdown, scales, testthat, tools, yaml
License: Artistic-2.0
Archs: x64
MD5sum: 6f9154be9f572c02eb0d4254dc91d373
NeedsCompilation: no
Title: A package with helper functions for processing drug response
data
Description: This package contains utility functions used throughout
the gDR platform to fit data, manipulate data, and convert and
validate data structures. This package also has the necessary
default constants for gDR platform. Many of the functions are
utilized by the gDRcore package.
biocViews: Software, Infrastructure
Author: Bartosz Czech [aut] (ORCID:
<https://orcid.org/0000-0002-9908-3007>), Arkadiusz Gladki
[cre, aut] (ORCID: <https://orcid.org/0000-0002-7059-6378>),
Aleksander Chlebowski [aut], Marc Hafner [aut] (ORCID:
<https://orcid.org/0000-0003-1337-7598>), Pawel Piatkowski
[aut], Dariusz Scigocki [aut], Janina Smola [aut], Sergiu
Mocanu [aut], Allison Vuong [aut]
Maintainer: Arkadiusz Gladki <gladki.arkadiusz@gmail.com>
URL: https://github.com/gdrplatform/gDRutils,
https://gdrplatform.github.io/gDRutils/
VignetteBuilder: knitr
BugReports: https://github.com/gdrplatform/gDRutils/issues
git_url: https://git.bioconductor.org/packages/gDRutils
git_branch: devel
git_last_commit: d8be146
git_last_commit_date: 2025-03-27
Date/Publication: 2025-03-28
source.ver: src/contrib/gDRutils_1.5.17.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gDRutils_1.5.17.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gDRutils_1.5.17.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gDRutils_1.5.17.tgz
vignettes: vignettes/gDRutils/inst/doc/gDRutils.html
vignetteTitles: gDRutils
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gDRutils/inst/doc/gDRutils.R
dependsOnMe: gDR
importsMe: gDRcore, gDRimport
dependencyCount: 119
Package: GDSArray
Version: 1.27.2
Depends: R (>= 3.5), gdsfmt, methods, BiocGenerics, DelayedArray (>=
0.5.32)
Imports: tools, S4Vectors (>= 0.17.34), SNPRelate, SeqArray
Suggests: testthat, knitr, markdown, rmarkdown, BiocStyle, BiocManager
License: GPL-3
MD5sum: b9edbdd2ea9457702a69a3bf2d09ff29
NeedsCompilation: no
Title: Representing GDS files as array-like objects
Description: GDS files are widely used to represent genotyping or
sequence data. The GDSArray package implements the `GDSArray`
class to represent nodes in GDS files in a matrix-like
representation that allows easy manipulation (e.g., subsetting,
mathematical transformation) in _R_. The data remains on disk
until needed, so that very large files can be processed.
biocViews: Infrastructure, DataRepresentation, Sequencing,
GenotypingArray
Author: Qian Liu [aut], Martin Morgan [aut], Hervé Pagès [aut], Xiuwen
Zheng [aut, cre]
Maintainer: Xiuwen Zheng <zhengx@u.washington.edu>
URL: https://github.com/Bioconductor/GDSArray
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/GDSArray/issues
git_url: https://git.bioconductor.org/packages/GDSArray
git_branch: devel
git_last_commit: d3b0346
git_last_commit_date: 2025-03-18
Date/Publication: 2025-03-19
source.ver: src/contrib/GDSArray_1.27.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GDSArray_1.27.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GDSArray_1.27.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GDSArray_1.27.2.tgz
vignettes: vignettes/GDSArray/inst/doc/GDSArray.html
vignetteTitles: GDSArray: Representing GDS files as array-like objects
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GDSArray/inst/doc/GDSArray.R
importsMe: CNVRanger, VariantExperiment
suggestsMe: DelayedDataFrame
dependencyCount: 40
Package: gdsfmt
Version: 1.43.3
Depends: R (>= 2.15.0), methods
Suggests: parallel, digest, Matrix, crayon, RUnit, knitr, markdown,
rmarkdown, BiocGenerics
License: LGPL-3
MD5sum: 86c5b6f0b953d1550c192ad58ddb6680
NeedsCompilation: yes
Title: R Interface to CoreArray Genomic Data Structure (GDS) Files
Description: Provides a high-level R interface to CoreArray Genomic
Data Structure (GDS) data files. GDS is portable across
platforms with hierarchical structure to store multiple
scalable array-oriented data sets with metadata information. It
is suited for large-scale datasets, especially for data which
are much larger than the available random-access memory. The
gdsfmt package offers the efficient operations specifically
designed for integers of less than 8 bits, since a diploid
genotype, like single-nucleotide polymorphism (SNP), usually
occupies fewer bits than a byte. Data compression and
decompression are available with relatively efficient random
access. It is also allowed to read a GDS file in parallel with
multiple R processes supported by the package parallel.
biocViews: Infrastructure, DataImport
Author: Xiuwen Zheng [aut, cre]
(<https://orcid.org/0000-0002-1390-0708>), Stephanie Gogarten
[ctb], Jean-loup Gailly and Mark Adler [ctb] (for the included
zlib sources), Yann Collet [ctb] (for the included LZ4
sources), xz contributors [ctb] (for the included liblzma
sources)
Maintainer: Xiuwen Zheng <zhengx@u.washington.edu>
URL: https://github.com/zhengxwen/gdsfmt
VignetteBuilder: knitr
BugReports: https://github.com/zhengxwen/gdsfmt/issues
git_url: https://git.bioconductor.org/packages/gdsfmt
git_branch: devel
git_last_commit: 6443a12
git_last_commit_date: 2025-03-14
Date/Publication: 2025-03-17
source.ver: src/contrib/gdsfmt_1.43.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gdsfmt_1.43.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gdsfmt_1.43.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gdsfmt_1.43.3.tgz
vignettes: vignettes/gdsfmt/inst/doc/gdsfmt.html
vignetteTitles: Introduction to GDS Format
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gdsfmt/inst/doc/gdsfmt.R
dependsOnMe: bigmelon, GDSArray, RAIDS, SAIGEgds, SCArray, SeqArray,
SNPRelate, Mega2R
importsMe: CNVRanger, GBScleanR, GENESIS, ggmanh, GWASTools,
SCArray.sat, SeqSQC, SeqVarTools, VariantExperiment, EthSEQ,
gwid, simplePHENOTYPES, snplinkage
suggestsMe: AnnotationHub, HIBAG
linksToMe: SeqArray, SNPRelate
dependencyCount: 1
Package: GeDi
Version: 1.3.0
Depends: R (>= 4.4.0)
Imports: GOSemSim, Matrix, shiny, shinyWidgets, bs4Dash, rintrojs,
utils, DT, dplyr, shinyBS, STRINGdb, igraph, visNetwork,
shinycssloaders, fontawesome, grDevices, parallel, stats,
ggplot2, plotly, GeneTonic, RColorBrewer, scales, readxl,
ggdendro, ComplexHeatmap, BiocNeighbors, tm, wordcloud2, tools,
BiocParallel, BiocFileCache, cluster, circlize
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), DESeq2, htmltools,
pcaExplorer, AnnotationDbi, macrophage, topGO, biomaRt,
ReactomePA, clusterProfiler, BiocStyle, org.Hs.eg.db
License: MIT + file LICENSE
MD5sum: 5cc01f63020533b4e6eccee1d88b7a2f
NeedsCompilation: no
Title: Defining and visualizing the distances between different
genesets
Description: The package provides different distances measurements to
calculate the difference between genesets. Based on these
scores the genesets are clustered and visualized as graph. This
is all presented in an interactive Shiny application for easy
usage.
biocViews: GUI, GeneSetEnrichment, Software, Transcription, RNASeq,
Visualization, Clustering, Pathways, ReportWriting, GO, KEGG,
Reactome, ShinyApps
Author: Annekathrin Nedwed [aut, cre] (ORCID:
<https://orcid.org/0000-0002-2475-4945>), Federico Marini [aut]
(ORCID: <https://orcid.org/0000-0003-3252-7758>)
Maintainer: Annekathrin Nedwed <anneludt@uni-mainz.de>
URL: https://github.com/AnnekathrinSilvia/GeDi
VignetteBuilder: knitr
BugReports: https://github.com/AnnekathrinSilvia/GeDi/issues
git_url: https://git.bioconductor.org/packages/GeDi
git_branch: devel
git_last_commit: 8157757
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GeDi_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GeDi_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GeDi_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GeDi_1.3.0.tgz
vignettes: vignettes/GeDi/inst/doc/GeDi_manual.html
vignetteTitles: The GeDi User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GeDi/inst/doc/GeDi_manual.R
dependencyCount: 242
Package: GEM
Version: 1.33.0
Depends: R (>= 3.3)
Imports: tcltk, ggplot2, methods, stats, grDevices, graphics, utils
Suggests: knitr, RUnit, testthat, BiocGenerics, rmarkdown, markdown
License: Artistic-2.0
Archs: x64
MD5sum: f27b9a0b5fd77003d0af5e4943a957ae
NeedsCompilation: no
Title: GEM: fast association study for the interplay of Gene,
Environment and Methylation
Description: Tools for analyzing EWAS, methQTL and GxE genome widely.
biocViews: MethylSeq, MethylationArray, GenomeWideAssociation,
Regression, DNAMethylation, SNP, GeneExpression, GUI
Author: Hong Pan, Joanna D Holbrook, Neerja Karnani, Chee-Keong Kwoh
Maintainer: Hong Pan <pan_hong@sics.a-star.edu.sg>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GEM
git_branch: devel
git_last_commit: 8a904e0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GEM_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GEM_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GEM_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GEM_1.33.0.tgz
vignettes: vignettes/GEM/inst/doc/user_guide.html
vignetteTitles: The GEM User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GEM/inst/doc/user_guide.R
dependencyCount: 36
Package: gemini
Version: 1.21.0
Depends: R (>= 4.1.0)
Imports: dplyr, grDevices, ggplot2, magrittr, mixtools, scales,
pbmcapply, parallel, stats, utils
Suggests: knitr, rmarkdown, testthat
License: BSD_3_clause + file LICENSE
MD5sum: 330252ffa777c92b6e0295d5d3df544a
NeedsCompilation: no
Title: GEMINI: Variational inference approach to infer genetic
interactions from pairwise CRISPR screens
Description: GEMINI uses log-fold changes to model sample-dependent and
independent effects, and uses a variational Bayes approach to
infer these effects. The inferred effects are used to score and
identify genetic interactions, such as lethality and recovery.
More details can be found in Zamanighomi et al. 2019 (in
press).
biocViews: Software, CRISPR, Bayesian, DataImport
Author: Mahdi Zamanighomi [aut], Sidharth Jain [aut, cre]
Maintainer: Sidharth Jain <sidharthsjain@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/sellerslab/gemini/issues
git_url: https://git.bioconductor.org/packages/gemini
git_branch: devel
git_last_commit: 22ea9ce
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/gemini_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gemini_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gemini_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gemini_1.21.0.tgz
vignettes: vignettes/gemini/inst/doc/gemini-quickstart.html
vignetteTitles: QuickStart
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/gemini/inst/doc/gemini-quickstart.R
dependencyCount: 84
Package: gemma.R
Version: 3.3.0
Imports: magrittr, glue, memoise, jsonlite, data.table, rlang,
lubridate, utils, stringr, SummarizedExperiment, Biobase,
tibble, tidyr, S4Vectors, httr, rappdirs, bit64, assertthat,
digest, R.utils, base64enc
Suggests: testthat (>= 2.0.0), rmarkdown, knitr, dplyr, covr, ggplot2,
ggrepel, BiocStyle, microbenchmark, magick, purrr, pheatmap,
viridis, poolr, kableExtra, listviewer, shiny
License: Apache License (>= 2)
MD5sum: c86375579111b2bf5767268472222a66
NeedsCompilation: no
Title: A wrapper for Gemma's Restful API to access curated gene
expression data and differential expression analyses
Description: Low- and high-level wrappers for Gemma's RESTful API. They
enable access to curated expression and differential expression
data from over 10,000 published studies. Gemma is a web site,
database and a set of tools for the meta-analysis, re-use and
sharing of genomics data, currently primarily targeted at the
analysis of gene expression profiles.
biocViews: Software, DataImport, Microarray, SingleCell,
ThirdPartyClient, DifferentialExpression, GeneExpression,
Bayesian, Annotation, ExperimentalDesign, Normalization,
BatchEffect, Preprocessing
Author: Javier Castillo-Arnemann [aut] (ORCID:
<https://orcid.org/0000-0002-5626-9004>), Jordan Sicherman
[aut] (ORCID: <https://orcid.org/0000-0001-8160-4567>), Ogan
Mancarci [cre, aut] (ORCID:
<https://orcid.org/0000-0002-1452-0889>), Guillaume
Poirier-Morency [aut] (ORCID:
<https://orcid.org/0000-0002-6554-0441>)
Maintainer: Ogan Mancarci <ogan.mancarci@gmail.com>
URL: https://pavlidislab.github.io/gemma.R/,
https://github.com/PavlidisLab/gemma.R
VignetteBuilder: knitr
BugReports: https://github.com/PavlidisLab/gemma.R/issues
git_url: https://git.bioconductor.org/packages/gemma.R
git_branch: devel
git_last_commit: 04df853
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/gemma.R_3.3.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gemma.R_3.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gemma.R_3.3.0.tgz
vignettes: vignettes/gemma.R/inst/doc/gemma.R.html,
vignettes/gemma.R/inst/doc/metadata.html,
vignettes/gemma.R/inst/doc/metanalysis.html
vignetteTitles: Accessing curated gene expression data with gemma.R, A
guide to metadata for samples and differential expression
analyses, A meta analysis on effects of Parkinson's Disease
using Gemma.R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gemma.R/inst/doc/gemma.R.R,
vignettes/gemma.R/inst/doc/metadata.R,
vignettes/gemma.R/inst/doc/metanalysis.R
dependencyCount: 70
Package: genArise
Version: 1.83.0
Depends: R (>= 1.7.1), locfit, tkrplot, methods
Imports: graphics, grDevices, methods, stats, tcltk, utils, xtable
License: file LICENSE
License_restricts_use: yes
MD5sum: 453cbd6a5b9e5f1411844da5d3eb2bca
NeedsCompilation: no
Title: Microarray Analysis tool
Description: genArise is an easy to use tool for dual color microarray
data. Its GUI-Tk based environment let any non-experienced user
performs a basic, but not simple, data analysis just following
a wizard. In addition it provides some tools for the developer.
biocViews: Microarray, TwoChannel, Preprocessing
Author: Ana Patricia Gomez Mayen <pgomez@ifc.unam.mx>,\\ Gustavo Corral
Guille <gcorral@ifc.unam.mx>, \\ Lina Riego Ruiz
<lriego@ifc.unam.mx>,\\ Gerardo Coello Coutino
<gcoello@ifc.unam.mx>
Maintainer: IFC Development Team <info-genarise@ifc.unam.mx>
URL: http://www.ifc.unam.mx/genarise
git_url: https://git.bioconductor.org/packages/genArise
git_branch: devel
git_last_commit: 26bc1fd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/genArise_1.83.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/genArise_1.83.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/genArise_1.83.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/genArise_1.83.0.tgz
vignettes: vignettes/genArise/inst/doc/genArise.pdf
vignetteTitles: genAriseGUI Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/genArise/inst/doc/genArise.R
dependencyCount: 11
Package: geneAttribution
Version: 1.33.0
Imports: utils, GenomicRanges, org.Hs.eg.db, BiocGenerics,
GenomeInfoDb, GenomicFeatures, IRanges, rtracklayer
Suggests: TxDb.Hsapiens.UCSC.hg38.knownGene,
TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, rmarkdown, testthat
License: Artistic-2.0
MD5sum: f1ea9e6c0bf1546579c01e0dfcdf3fa1
NeedsCompilation: no
Title: Identification of candidate genes associated with genetic
variation
Description: Identification of the most likely gene or genes through
which variation at a given genomic locus in the human genome
acts. The most basic functionality assumes that the closer gene
is to the input locus, the more likely the gene is to be
causative. Additionally, any empirical data that links genomic
regions to genes (e.g. eQTL or genome conformation data) can be
used if it is supplied in the UCSC .BED file format.
biocViews: SNP, GenePrediction, GenomeWideAssociation,
VariantAnnotation, GenomicVariation
Author: Arthur Wuster
Maintainer: Arthur Wuster <wustera@gene.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/geneAttribution
git_branch: devel
git_last_commit: feca5bb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/geneAttribution_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/geneAttribution_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/geneAttribution_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/geneAttribution_1.33.0.tgz
vignettes: vignettes/geneAttribution/inst/doc/geneAttribution.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 78
Package: GeneBreak
Version: 1.37.0
Depends: R(>= 3.2), QDNAseq, CGHcall, CGHbase, GenomicRanges
Imports: graphics, methods
License: GPL-2
MD5sum: ed08e1367fe12a67c536532cae31a8c1
NeedsCompilation: no
Title: Gene Break Detection
Description: Recurrent breakpoint gene detection on copy number
aberration profiles.
biocViews: aCGH, CopyNumberVariation, DNASeq, Genetics, Sequencing,
WholeGenome, Visualization
Author: Evert van den Broek, Stef van Lieshout
Maintainer: Evert van den Broek <vandenbroek.evert@gmail.com>
URL: https://github.com/stefvanlieshout/GeneBreak
git_url: https://git.bioconductor.org/packages/GeneBreak
git_branch: devel
git_last_commit: 3793a7c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GeneBreak_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GeneBreak_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GeneBreak_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GeneBreak_1.37.0.tgz
vignettes: vignettes/GeneBreak/inst/doc/GeneBreak.pdf
vignetteTitles: GeneBreak
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GeneBreak/inst/doc/GeneBreak.R
dependencyCount: 59
Package: geneClassifiers
Version: 1.31.0
Depends: R (>= 3.6.0)
Imports: utils, methods, stats, Biobase, BiocGenerics
Suggests: testthat
License: GPL-2
MD5sum: b3f5b0f806bf0467c2240e66284e538c
NeedsCompilation: no
Title: Application of gene classifiers
Description: This packages aims for easy accessible application of
classifiers which have been published in literature using an
ExpressionSet as input.
biocViews: GeneExpression, BiomedicalInformatics, Classification,
Survival, Microarray
Author: R Kuiper [cre, aut] (ORCID:
<https://orcid.org/0000-0002-3703-1762>)
Maintainer: R Kuiper <r.kuiper.emc@gmail.com>
URL: https://doi.org/doi:10.18129/B9.bioc.geneClassifiers
BugReports: https://github.com/rkuiper/geneClassifiers/issues
git_url: https://git.bioconductor.org/packages/geneClassifiers
git_branch: devel
git_last_commit: ef8632b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/geneClassifiers_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/geneClassifiers_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/geneClassifiers_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/geneClassifiers_1.31.0.tgz
vignettes: vignettes/geneClassifiers/inst/doc/geneClassifiers.pdf,
vignettes/geneClassifiers/inst/doc/MissingCovariates.pdf
vignetteTitles: geneClassifiers introduction, geneClassifiers and
missing probesets
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/geneClassifiers/inst/doc/geneClassifiers.R
dependencyCount: 7
Package: GeneExpressionSignature
Version: 1.53.0
Depends: R (>= 4.0)
Imports: Biobase, stats, methods
Suggests: apcluster, GEOquery, knitr, rmarkdown, BiocStyle
License: GPL-2
MD5sum: adb44f8d0655c4d19e0c2163172313da
NeedsCompilation: no
Title: Gene Expression Signature based Similarity Metric
Description: This package gives the implementations of the gene
expression signature and its distance to each. Gene expression
signature is represented as a list of genes whose expression is
correlated with a biological state of interest. And its
distance is defined using a nonparametric, rank-based
pattern-matching strategy based on the Kolmogorov-Smirnov
statistic. Gene expression signature and its distance can be
used to detect similarities among the signatures of drugs,
diseases, and biological states of interest.
biocViews: GeneExpression
Author: Yang Cao [aut, cre], Fei Li [ctb], Lu Han [ctb]
Maintainer: Yang Cao <yiluheihei@gmail.com>
URL: https://github.com/yiluheihei/GeneExpressionSignature
VignetteBuilder: knitr
BugReports:
https://github.com/yiluheihei/GeneExpressionSignature/issues/
git_url: https://git.bioconductor.org/packages/GeneExpressionSignature
git_branch: devel
git_last_commit: fe30f47
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GeneExpressionSignature_1.53.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.5/GeneExpressionSignature_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GeneExpressionSignature_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GeneExpressionSignature_1.53.0.tgz
vignettes:
vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.html
vignetteTitles: GeneExpressionSignature
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.R
dependencyCount: 7
Package: genefilter
Version: 1.89.0
Imports: MatrixGenerics (>= 1.11.1), AnnotationDbi, annotate, Biobase,
graphics, methods, stats, survival, grDevices
Suggests: class, hgu95av2.db, tkWidgets, ALL, ROC, RColorBrewer,
BiocStyle, knitr
License: Artistic-2.0
MD5sum: c0a7aa5a34dd32fd0e5fd88261c2d009
NeedsCompilation: yes
Title: genefilter: methods for filtering genes from high-throughput
experiments
Description: Some basic functions for filtering genes.
biocViews: Microarray
Author: Robert Gentleman [aut], Vincent J. Carey [aut], Wolfgang Huber
[aut], Florian Hahne [aut], Emmanuel Taiwo [ctb]
('howtogenefinder' vignette translation from Sweave to
RMarkdown / HTML.), Khadijah Amusat [ctb] (Converted genefilter
vignette from Sweave to RMarkdown / HTML.), Bioconductor
Package Maintainer [cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/genefilter
git_branch: devel
git_last_commit: 46934e9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/genefilter_1.89.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/genefilter_1.89.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/genefilter_1.89.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/genefilter_1.89.0.tgz
vignettes:
vignettes/genefilter/inst/doc/independent_filtering_plots.pdf,
vignettes/genefilter/inst/doc/howtogenefilter.html,
vignettes/genefilter/inst/doc/howtogenefinder.html
vignetteTitles: 03 - Additional plots for: Independent filtering
increases power for detecting differentially expressed genes,,
Bourgon et al.,, PNAS (2010), Using the genefilter function to
filter genes from a microarray, How to find genes whose
expression profile is similar to that of specified genes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/genefilter/inst/doc/howtogenefilter.R,
vignettes/genefilter/inst/doc/howtogenefinder.R,
vignettes/genefilter/inst/doc/independent_filtering_plots.R
dependsOnMe: CNTools, GeneMeta, sva, maEndToEnd, rnaseqGene, lmQCM
importsMe: a4Base, annmap, arrayQualityMetrics, broadSeq, Category,
cbaf, ClassifyR, countsimQC, covRNA, DEXSeq, GSRI, metaseqR2,
methylCC, methylumi, minfi, MLInterfaces, mogsa, NBAMSeq,
pcaExplorer, PECA, phenoTest, protGear, SGCP, spatialHeatmap,
SpliceWiz, tilingArray, XDE, zinbwave, FlowSorted.Blood.EPIC,
IHWpaper, causalBatch, CoNI, dGAselID, MiDA, netgsa
suggestsMe: annotate, BioNet, categoryCompare, clusterStab, codelink,
cola, compcodeR, DelayedArray, EnrichedHeatmap, factDesign,
ffpe, GenomicFiles, GOstats, GSAR, GSEAlm, GSVA, HDF5Array,
logicFS, lumi, MMUPHin, npGSEA, oligo, phyloseq, pvac, qpgraph,
rtracklayer, siggenes, simplifyEnrichment, TCGAbiolinks, topGO,
BloodCancerMultiOmics2017, curatedBladderData,
curatedOvarianData, estrogen, ffpeExampleData, gageData,
MAQCsubset, RforProteomics, rheumaticConditionWOLLBOLD,
Single.mTEC.Transcriptomes, maGUI, oncoPredict, SuperLearner
dependencyCount: 55
Package: genefu
Version: 2.39.1
Depends: R (>= 4.1), survcomp, biomaRt, iC10, AIMS
Imports: amap, impute, mclust, limma, graphics, stats, utils,
iC10TrainingData
Suggests: GeneMeta, breastCancerVDX, breastCancerMAINZ,
breastCancerTRANSBIG, breastCancerUPP, breastCancerUNT,
breastCancerNKI, rmeta, Biobase, xtable, knitr, caret,
survival, BiocStyle, magick, rmarkdown
License: Artistic-2.0
MD5sum: 084b70ba6d34ccabc01decb6414e8403
NeedsCompilation: no
Title: Computation of Gene Expression-Based Signatures in Breast Cancer
Description: This package contains functions implementing various tasks
usually required by gene expression analysis, especially in
breast cancer studies: gene mapping between different
microarray platforms, identification of molecular subtypes,
implementation of published gene signatures, gene selection,
and survival analysis.
biocViews: DifferentialExpression, GeneExpression, Visualization,
Clustering, Classification
Author: Deena M.A. Gendoo [aut], Natchar Ratanasirigulchai [aut],
Markus S. Schroeder [aut], Laia Pare [aut], Joel S Parker
[aut], Aleix Prat [aut], Nikta Feizi [ctb], Christopher Eeles
[ctb], Jermiah Joseph [ctb], Benjamin Haibe-Kains [aut, cre]
Maintainer: Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>
URL: http://www.pmgenomics.ca/bhklab/software/genefu
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/genefu
git_branch: devel
git_last_commit: 1ef27ec
git_last_commit_date: 2024-11-27
Date/Publication: 2024-11-27
source.ver: src/contrib/genefu_2.39.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/genefu_2.39.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/genefu_2.39.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/genefu_2.39.1.tgz
vignettes: vignettes/genefu/inst/doc/genefu.html
vignetteTitles: genefu: A Package For Breast Cancer Gene Expression
Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/genefu/inst/doc/genefu.R
importsMe: consensusOV, PDATK
suggestsMe: GSgalgoR, breastCancerMAINZ, breastCancerNKI,
breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP,
breastCancerVDX
dependencyCount: 112
Package: GeneGA
Version: 1.57.0
Depends: seqinr, hash, methods
License: GPL version 2
MD5sum: 4f83d6ab6ade79efb21d1570af95ec49
NeedsCompilation: no
Title: Design gene based on both mRNA secondary structure and codon
usage bias using Genetic algorithm
Description: R based Genetic algorithm for gene expression optimization
by considering both mRNA secondary structure and codon usage
bias, GeneGA includes the information of highly expressed genes
of almost 200 genomes. Meanwhile, Vienna RNA Package is needed
to ensure GeneGA to function properly.
biocViews: GeneExpression
Author: Zhenpeng Li and Haixiu Huang
Maintainer: Zhenpeng Li <zpli21@gmail.com>
URL: http://www.tbi.univie.ac.at/~ivo/RNA/
git_url: https://git.bioconductor.org/packages/GeneGA
git_branch: devel
git_last_commit: 29ef0d4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GeneGA_1.57.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GeneGA_1.57.0.tgz
vignettes: vignettes/GeneGA/inst/doc/GeneGA.pdf
vignetteTitles: GeneGA
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GeneGA/inst/doc/GeneGA.R
dependencyCount: 17
Package: GeneGeneInteR
Version: 1.33.0
Depends: R (>= 4.0)
Imports: snpStats, mvtnorm, Rsamtools, igraph, kernlab, FactoMineR,
IRanges, GenomicRanges, data.table,grDevices, graphics,stats,
utils, methods
License: GPL (>= 2)
MD5sum: ce52e64c41a3fb0331b7e4338cd27081
NeedsCompilation: yes
Title: Tools for Testing Gene-Gene Interaction at the Gene Level
Description: The aim of this package is to propose several methods for
testing gene-gene interaction in case-control association
studies. Such a test can be done by aggregating SNP-SNP
interaction tests performed at the SNP level (SSI) or by using
gene-gene multidimensionnal methods (GGI) methods. The package
also proposes tools for a graphic display of the results.
<doi:10.18637/jss.v095.i12>.
biocViews: GenomeWideAssociation, SNP, Genetics, GeneticVariability
Author: Mathieu Emily [aut, cre], Nicolas Sounac [ctb], Florian Kroell
[ctb], Magalie Houee-Bigot [aut]
Maintainer: Mathieu Emily <mathieu.emily@agrocampus-ouest.fr>
git_url: https://git.bioconductor.org/packages/GeneGeneInteR
git_branch: devel
git_last_commit: bbad5d1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GeneGeneInteR_1.33.0.tar.gz
vignettes: vignettes/GeneGeneInteR/inst/doc/GenePair.pdf,
vignettes/GeneGeneInteR/inst/doc/VignetteGeneGeneInteR_Introduction.pdf
vignetteTitles: Pairwise interaction tests, GeneGeneInteR Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GeneGeneInteR/inst/doc/GenePair.R,
vignettes/GeneGeneInteR/inst/doc/VignetteGeneGeneInteR_Introduction.R
dependencyCount: 143
Package: GeneMeta
Version: 1.79.0
Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), genefilter
Imports: methods, Biobase (>= 2.5.5)
Suggests: RColorBrewer
License: Artistic-2.0
Archs: x64
MD5sum: 533275774692525b0f6d8b69a0ea281e
NeedsCompilation: no
Title: MetaAnalysis for High Throughput Experiments
Description: A collection of meta-analysis tools for analysing high
throughput experimental data
biocViews: Sequencing, GeneExpression, Microarray
Author: Lara Lusa <lusa@ifom-firc.it>, R. Gentleman, M. Ruschhaupt
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/GeneMeta
git_branch: devel
git_last_commit: 7798817
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GeneMeta_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GeneMeta_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GeneMeta_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GeneMeta_1.79.0.tgz
vignettes: vignettes/GeneMeta/inst/doc/GeneMeta.pdf
vignetteTitles: GeneMeta Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GeneMeta/inst/doc/GeneMeta.R
importsMe: XDE
suggestsMe: genefu
dependencyCount: 56
Package: GeneNetworkBuilder
Version: 1.49.2
Depends: R (>= 2.15.1), Rcpp (>= 0.9.13)
Imports: plyr, graph, htmlwidgets, Rgraphviz, RCy3, rjson, XML,
methods, grDevices, stats, graphics
LinkingTo: Rcpp
Suggests: RUnit, BiocGenerics, RBGL, knitr, shiny, STRINGdb, BiocStyle,
magick, rmarkdown, org.Hs.eg.db
License: GPL (>= 2)
MD5sum: d9592df92718dd452a3466bd256376f3
NeedsCompilation: yes
Title: GeneNetworkBuilder: a bioconductor package for building
regulatory network using ChIP-chip/ChIP-seq data and Gene
Expression Data
Description: Appliation for discovering direct or indirect targets of
transcription factors using ChIP-chip or ChIP-seq, and
microarray or RNA-seq gene expression data. Inputting a list of
genes of potential targets of one TF from ChIP-chip or
ChIP-seq, and the gene expression results, GeneNetworkBuilder
generates a regulatory network of the TF.
biocViews: Sequencing, Microarray, GraphAndNetwork
Author: Jianhong Ou, Haibo Liu, Heidi A Tissenbaum and Lihua Julie Zhu
Maintainer: Jianhong Ou <jou@morgridge.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GeneNetworkBuilder
git_branch: devel
git_last_commit: 5750686
git_last_commit_date: 2025-03-07
Date/Publication: 2025-03-25
source.ver: src/contrib/GeneNetworkBuilder_1.49.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GeneNetworkBuilder_1.49.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GeneNetworkBuilder_1.49.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GeneNetworkBuilder_1.49.2.tgz
vignettes:
vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.html,
vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkFromGenes.html
vignetteTitles: GeneNetworkBuilder Vignette, Generate Network from a
list of gene
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.R,
vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkFromGenes.R
dependencyCount: 69
Package: GeneOverlap
Version: 1.43.0
Imports: stats, RColorBrewer, gplots, methods
Suggests: RUnit, BiocGenerics, BiocStyle
License: GPL-3
MD5sum: 86d88b9f8468f7baff086509fac46253
NeedsCompilation: no
Title: Test and visualize gene overlaps
Description: Test two sets of gene lists and visualize the results.
biocViews: MultipleComparison, Visualization
Author: Li Shen, Icahn School of Medicine at Mount Sinai
<shenli.sam@gmail.com>
Maintainer: Ant<c3><b3>nio Miguel de Jesus Domingues, Max-Planck
Institute for Cell Biology and Genetics
<amjdomingues@gmail.com>
URL: http://shenlab-sinai.github.io/shenlab-sinai/
git_url: https://git.bioconductor.org/packages/GeneOverlap
git_branch: devel
git_last_commit: 30085d3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GeneOverlap_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GeneOverlap_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GeneOverlap_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GeneOverlap_1.43.0.tgz
vignettes: vignettes/GeneOverlap/inst/doc/GeneOverlap.pdf
vignetteTitles: Testing and visualizing gene overlaps with the
"GeneOverlap" package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GeneOverlap/inst/doc/GeneOverlap.R
dependencyCount: 9
Package: geneplast
Version: 1.33.0
Depends: R (>= 4.0), methods
Imports: igraph, snow, ape, grDevices, graphics, stats, utils,
data.table
Suggests: RTN, RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown,
Fletcher2013b, geneplast.data, geneplast.data.string.v91,
ggplot2, ggpubr, plyr
License: GPL (>= 2)
Archs: x64
MD5sum: 2bc0bf20bcc0c9ad776c9a12a041e52a
NeedsCompilation: no
Title: Evolutionary and plasticity analysis of orthologous groups
Description: Geneplast is designed for evolutionary and plasticity
analysis based on orthologous groups distribution in a given
species tree. It uses Shannon information theory and orthologs
abundance to estimate the Evolutionary Plasticity Index.
Additionally, it implements the Bridge algorithm to determine
the evolutionary root of a given gene based on its orthologs
distribution.
biocViews: Genetics, GeneRegulation, SystemsBiology
Author: Rodrigo Dalmolin, Mauro Castro
Maintainer: Mauro Castro <mauro.a.castro@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/geneplast
git_branch: devel
git_last_commit: e60530e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/geneplast_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/geneplast_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/geneplast_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/geneplast_1.33.0.tgz
vignettes: vignettes/geneplast/inst/doc/geneplast_Trefflich2019.html,
vignettes/geneplast/inst/doc/geneplast.html
vignetteTitles: "Supporting Material for Trefflich2019.", "Geneplast:
evolutionary analysis of orthologous groups."
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/geneplast/inst/doc/geneplast_Trefflich2019.R,
vignettes/geneplast/inst/doc/geneplast.R
importsMe: geneplast.data
suggestsMe: TreeAndLeaf, geneplast.data
dependencyCount: 24
Package: geneplotter
Version: 1.85.0
Depends: R (>= 2.10), methods, Biobase, BiocGenerics, lattice, annotate
Imports: AnnotationDbi, graphics, grDevices, grid, RColorBrewer, stats,
utils
Suggests: Rgraphviz, fibroEset, hgu95av2.db, hu6800.db, hgu133a.db,
BiocStyle, knitr
License: Artistic-2.0
MD5sum: 5a71dc891376a76babff0a5497d96310
NeedsCompilation: no
Title: Graphics related functions for Bioconductor
Description: Functions for plotting genomic data
biocViews: Visualization
Author: Robert Gentleman [aut], Rohit Satyam [ctb] (Converted
geneplotter vignette from Sweave to RMarkdown / HTML.),
Bioconductor Package Maintainer [cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/geneplotter
git_branch: devel
git_last_commit: fac451a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/geneplotter_1.85.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/geneplotter_1.85.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/geneplotter_1.85.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/geneplotter_1.85.0.tgz
vignettes: vignettes/geneplotter/inst/doc/visualize.pdf,
vignettes/geneplotter/inst/doc/byChroms.html
vignetteTitles: Visualization of Microarray Data, How to Assemble a
chromLocation Object
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/geneplotter/inst/doc/byChroms.R,
vignettes/geneplotter/inst/doc/visualize.R
dependsOnMe: HD2013SGI, maEndToEnd
importsMe: biocGraph, DEXSeq, MethylSeekR
suggestsMe: biocGraph, Category, EnrichmentBrowser, GOstats,
Single.mTEC.Transcriptomes
dependencyCount: 51
Package: geneRecommender
Version: 1.79.0
Depends: R (>= 1.8.0), Biobase (>= 1.4.22), methods
Imports: Biobase, methods, stats
License: GPL (>= 2)
MD5sum: ece1387300be55ebea823259bbdaffc7
NeedsCompilation: no
Title: A gene recommender algorithm to identify genes coexpressed with
a query set of genes
Description: This package contains a targeted clustering algorithm for
the analysis of microarray data. The algorithm can aid in the
discovery of new genes with similar functions to a given list
of genes already known to have closely related functions.
biocViews: Microarray, Clustering
Author: Gregory J. Hather <ghather@gmail.com>, with contributions from
Art B. Owen <art@stat.stanford.edu> and Terence P. Speed
<terry@stat.berkeley.edu>
Maintainer: Greg Hather <ghather@gmail.com>
git_url: https://git.bioconductor.org/packages/geneRecommender
git_branch: devel
git_last_commit: 4ad7f25
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/geneRecommender_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/geneRecommender_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/geneRecommender_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/geneRecommender_1.79.0.tgz
vignettes: vignettes/geneRecommender/inst/doc/geneRecommender.pdf
vignetteTitles: Using the geneRecommender Package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/geneRecommender/inst/doc/geneRecommender.R
dependencyCount: 7
Package: GeneRegionScan
Version: 1.63.0
Depends: methods, Biobase (>= 2.5.5), Biostrings
Imports: S4Vectors (>= 0.9.25), Biobase (>= 2.5.5), affxparser,
RColorBrewer, Biostrings
Suggests: BSgenome, affy, AnnotationDbi
License: GPL (>= 2)
MD5sum: 9e57cfac88df55e5aac12dde4186af4a
NeedsCompilation: no
Title: GeneRegionScan
Description: A package with focus on analysis of discrete regions of
the genome. This package is useful for investigation of one or
a few genes using Affymetrix data, since it will extract probe
level data using the Affymetrix Power Tools application and
wrap these data into a ProbeLevelSet. A ProbeLevelSet directly
extends the expressionSet, but includes additional information
about the sequence of each probe and the probe set it is
derived from. The package includes a number of functions used
for plotting these probe level data as a function of location
along sequences of mRNA-strands. This can be used for analysis
of variable splicing, and is especially well suited for use
with exon-array data.
biocViews: Microarray, DataImport, SNP, OneChannel, Visualization
Author: Lasse Folkersen, Diego Diez
Maintainer: Lasse Folkersen <lasfol@cbs.dtu.dk>
git_url: https://git.bioconductor.org/packages/GeneRegionScan
git_branch: devel
git_last_commit: 1cf7743
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GeneRegionScan_1.63.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GeneRegionScan_1.63.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GeneRegionScan_1.63.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GeneRegionScan_1.63.0.tgz
vignettes: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.pdf
vignetteTitles: GeneRegionScan
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.R
dependencyCount: 28
Package: geneRxCluster
Version: 1.43.0
Depends: GenomicRanges,IRanges
Suggests: RUnit, BiocGenerics
License: GPL (>= 2)
MD5sum: 95737029fb44b5c816de39f6de1660a0
NeedsCompilation: yes
Title: gRx Differential Clustering
Description: Detect Differential Clustering of Genomic Sites such as
gene therapy integrations. The package provides some functions
for exploring genomic insertion sites originating from two
different sources. Possibly, the two sources are two different
gene therapy vectors. Vectors are preferred that target
sensitive regions less frequently, motivating the search for
localized clusters of insertions and comparison of the clusters
formed by integration of different vectors. Scan statistics
allow the discovery of spatial differences in clustering and
calculation of False Discovery Rates (FDRs) providing
statistical methods for comparing retroviral vectors. A scan
statistic for comparing two vectors using multiple window
widths to detect clustering differentials and compute FDRs is
implemented here.
biocViews: Sequencing, Clustering, Genetics
Author: Charles Berry
Maintainer: Charles Berry <ccberry@ucsd.edu>
git_url: https://git.bioconductor.org/packages/geneRxCluster
git_branch: devel
git_last_commit: 9ad2c2b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/geneRxCluster_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/geneRxCluster_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/geneRxCluster_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/geneRxCluster_1.43.0.tgz
vignettes: vignettes/geneRxCluster/inst/doc/tutorial.pdf
vignetteTitles: Using geneRxCluster
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/geneRxCluster/inst/doc/tutorial.R
dependencyCount: 23
Package: GeneSelectMMD
Version: 2.51.0
Depends: R (>= 2.13.2), Biobase
Imports: MASS, graphics, stats, limma
Suggests: ALL
License: GPL (>= 2)
MD5sum: 62e4a9091a54608ce93ab740c6bb32b6
NeedsCompilation: yes
Title: Gene selection based on the marginal distributions of gene
profiles that characterized by a mixture of three-component
multivariate distributions
Description: Gene selection based on a mixture of marginal
distributions.
biocViews: DifferentialExpression
Author: Jarrett Morrow <remdj@channing.harvard.edu>, Weiliang Qiu
<weiliang.qiu@gmail.com>, Wenqing He <whe@stats.uwo.ca>,
Xiaogang Wang <stevenw@mathstat.yorku.ca>, Ross Lazarus
<ross.lazarus@channing.harvard.edu>.
Maintainer: Weiliang Qiu <weiliang.qiu@gmail.com>
git_url: https://git.bioconductor.org/packages/GeneSelectMMD
git_branch: devel
git_last_commit: 618717e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GeneSelectMMD_2.51.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GeneSelectMMD_2.51.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GeneSelectMMD_2.51.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GeneSelectMMD_2.51.0.tgz
vignettes: vignettes/GeneSelectMMD/inst/doc/gsMMD.pdf
vignetteTitles: Gene Selection based on a mixture of marginal
distributions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GeneSelectMMD/inst/doc/gsMMD.R
importsMe: iCheck
dependencyCount: 11
Package: GENESIS
Version: 2.37.1
Imports: Biobase, BiocGenerics, BiocParallel, GWASTools, gdsfmt,
GenomicRanges, IRanges, S4Vectors, SeqArray, SeqVarTools,
SNPRelate, data.table, graphics, grDevices, igraph, Matrix,
methods, reshape2, stats, utils
Suggests: CompQuadForm, COMPoissonReg, poibin, SPAtest, survey,
testthat, BiocStyle, knitr, rmarkdown, GWASdata, dplyr,
ggplot2, GGally, RColorBrewer,
TxDb.Hsapiens.UCSC.hg19.knownGene
License: GPL-3
Archs: x64
MD5sum: 3a97511271640efb7d6476ac4ac44958
NeedsCompilation: yes
Title: GENetic EStimation and Inference in Structured samples
(GENESIS): Statistical methods for analyzing genetic data from
samples with population structure and/or relatedness
Description: The GENESIS package provides methodology for estimating,
inferring, and accounting for population and pedigree structure
in genetic analyses. The current implementation provides
functions to perform PC-AiR (Conomos et al., 2015, Gen Epi) and
PC-Relate (Conomos et al., 2016, AJHG). PC-AiR performs a
Principal Components Analysis on genome-wide SNP data for the
detection of population structure in a sample that may contain
known or cryptic relatedness. Unlike standard PCA, PC-AiR
accounts for relatedness in the sample to provide accurate
ancestry inference that is not confounded by family structure.
PC-Relate uses ancestry representative principal components to
adjust for population structure/ancestry and accurately
estimate measures of recent genetic relatedness such as kinship
coefficients, IBD sharing probabilities, and inbreeding
coefficients. Additionally, functions are provided to perform
efficient variance component estimation and mixed model
association testing for both quantitative and binary
phenotypes.
biocViews: SNP, GeneticVariability, Genetics, StatisticalMethod,
DimensionReduction, PrincipalComponent, GenomeWideAssociation,
QualityControl, BiocViews
Author: Matthew P. Conomos, Stephanie M. Gogarten, Lisa Brown, Han
Chen, Thomas Lumley, Kenneth Rice, Tamar Sofer, Adrienne Stilp,
Timothy Thornton, Chaoyu Yu
Maintainer: Stephanie M. Gogarten <sdmorris@uw.edu>
URL: https://github.com/UW-GAC/GENESIS
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GENESIS
git_branch: devel
git_last_commit: 7655885
git_last_commit_date: 2025-01-31
Date/Publication: 2025-02-02
source.ver: src/contrib/GENESIS_2.37.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GENESIS_2.37.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GENESIS_2.37.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GENESIS_2.37.1.tgz
vignettes: vignettes/GENESIS/inst/doc/assoc_test_seq.html,
vignettes/GENESIS/inst/doc/assoc_test.html,
vignettes/GENESIS/inst/doc/pcair.html
vignetteTitles: Analyzing Sequence Data using the GENESIS Package,
Genetic Association Testing using the GENESIS Package,
Population Structure and Relatedness Inference using the
GENESIS Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GENESIS/inst/doc/assoc_test_seq.R,
vignettes/GENESIS/inst/doc/assoc_test.R,
vignettes/GENESIS/inst/doc/pcair.R
dependsOnMe: RAIDS
dependencyCount: 127
Package: GeneStructureTools
Version: 1.27.0
Imports:
Biostrings,GenomicRanges,IRanges,data.table,plyr,stringdist,stringr,S4Vectors,BSgenome.Mmusculus.UCSC.mm10,stats,utils,Gviz,rtracklayer,methods
Suggests: BiocStyle, knitr, rmarkdown
License: BSD_3_clause + file LICENSE
Archs: x64
MD5sum: 2dc8927a7c3358f42c847d48cfedf8b6
NeedsCompilation: no
Title: Tools for spliced gene structure manipulation and analysis
Description: GeneStructureTools can be used to create in silico
alternative splicing events, and analyse potential effects this
has on functional gene products.
biocViews: ImmunoOncology, Software, DifferentialSplicing,
FunctionalPrediction, Transcriptomics, AlternativeSplicing,
RNASeq
Author: Beth Signal
Maintainer: Beth Signal <b.signal@garvan.org.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GeneStructureTools
git_branch: devel
git_last_commit: 001986b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GeneStructureTools_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GeneStructureTools_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GeneStructureTools_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GeneStructureTools_1.27.0.tgz
vignettes: vignettes/GeneStructureTools/inst/doc/Vignette.html
vignetteTitles: Introduction to GeneStructureTools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GeneStructureTools/inst/doc/Vignette.R
dependencyCount: 159
Package: geNetClassifier
Version: 1.47.0
Depends: R (>= 2.10.1), Biobase (>= 2.5.5), EBarrays, minet, methods
Imports: e1071, graphics, grDevices
Suggests: leukemiasEset, RUnit, BiocGenerics
Enhances: RColorBrewer, igraph, infotheo
License: GPL (>= 2)
Archs: x64
MD5sum: dc1abac4f7ed1509a9d4bc0a62024e3b
NeedsCompilation: no
Title: Classify diseases and build associated gene networks using gene
expression profiles
Description: Comprehensive package to automatically train and validate
a multi-class SVM classifier based on gene expression data.
Provides transparent selection of gene markers, their
coexpression networks, and an interface to query the
classifier.
biocViews: Classification, DifferentialExpression, Microarray
Author: Sara Aibar, Celia Fontanillo and Javier De Las Rivas.
Bioinformatics and Functional Genomics Group. Cancer Research
Center (CiC-IBMCC, CSIC/USAL). Salamanca. Spain.
Maintainer: Sara Aibar <saibar@usal.es>
URL: http://www.cicancer.org
git_url: https://git.bioconductor.org/packages/geNetClassifier
git_branch: devel
git_last_commit: fc4565d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/geNetClassifier_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/geNetClassifier_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/geNetClassifier_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/geNetClassifier_1.47.0.tgz
vignettes:
vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.pdf
vignetteTitles: geNetClassifier-vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.R
importsMe: bioCancer, canceR
dependencyCount: 18
Package: GeneticsPed
Version: 1.69.0
Depends: R (>= 2.4.0), MASS
Imports: gdata, genetics
Suggests: RUnit, gtools
License: LGPL (>= 2.1) | file LICENSE
MD5sum: e6abe1c9f8c6713677b635d23d6ee3b8
NeedsCompilation: yes
Title: Pedigree and genetic relationship functions
Description: Classes and methods for handling pedigree data. It also
includes functions to calculate genetic relationship measures
as relationship and inbreeding coefficients and other
utilities. Note that package is not yet stable. Use it with
care!
biocViews: Genetics
Author: Gregor Gorjanc and David A. Henderson <DNADavenator@GMail.Com>,
with code contributions by Brian Kinghorn and Andrew Percy (see
file COPYING)
Maintainer: David Henderson <DNADavenator@GMail.Com>
URL: http://rgenetics.org
git_url: https://git.bioconductor.org/packages/GeneticsPed
git_branch: devel
git_last_commit: 48e7cab
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GeneticsPed_1.69.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GeneticsPed_1.69.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GeneticsPed_1.69.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GeneticsPed_1.69.0.tgz
vignettes: vignettes/GeneticsPed/inst/doc/geneticRelatedness.pdf,
vignettes/GeneticsPed/inst/doc/pedigreeHandling.pdf,
vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.pdf
vignetteTitles: Calculation of genetic relatedness/relationship between
individuals in the pedigree, Pedigree handling, Quantitative
genetic (animal) model example in R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GeneticsPed/inst/doc/geneticRelatedness.R,
vignettes/GeneticsPed/inst/doc/pedigreeHandling.R,
vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.R
dependencyCount: 11
Package: GeneTonic
Version: 3.1.2
Depends: R (>= 4.0.0)
Imports: AnnotationDbi, backbone, bs4Dash (>= 2.0.0), circlize,
colorspace, colourpicker, ComplexHeatmap, ComplexUpset,
dendextend, DESeq2, dplyr, DT, dynamicTreeCut, expm, ggforce,
ggplot2 (>= 3.5.0), ggrepel, ggridges, GO.db, graphics,
grDevices, grid, igraph, matrixStats, methods, mosdef (>=
1.1.0), plotly, RColorBrewer, rintrojs, rlang, rmarkdown,
S4Vectors, scales, shiny, shinyAce, shinycssloaders,
shinyWidgets, stats, SummarizedExperiment, tidyr, tippy, tools,
utils, viridis, visNetwork
Suggests: knitr, BiocStyle, htmltools, clusterProfiler, macrophage,
org.Hs.eg.db, magrittr, testthat (>= 2.1.0)
License: MIT + file LICENSE
MD5sum: 668a8a7ecf12b2fb6f856c1a0e5c4ada
NeedsCompilation: no
Title: Enjoy Analyzing And Integrating The Results From Differential
Expression Analysis And Functional Enrichment Analysis
Description: This package provides functionality to combine the
existing pieces of the transcriptome data and results, making
it easier to generate insightful observations and hypothesis.
Its usage is made easy with a Shiny application, combining the
benefits of interactivity and reproducibility e.g. by capturing
the features and gene sets of interest highlighted during the
live session, and creating an HTML report as an artifact where
text, code, and output coexist. Using the GeneTonicList as a
standardized container for all the required components, it is
possible to simplify the generation of multiple visualizations
and summaries.
biocViews: GUI, GeneExpression, Software, Transcription,
Transcriptomics, Visualization, DifferentialExpression,
Pathways, ReportWriting, GeneSetEnrichment, Annotation, GO,
ShinyApps
Author: Federico Marini [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3252-7758>), Annekathrin Ludt
[aut] (ORCID: <https://orcid.org/0000-0002-2475-4945>)
Maintainer: Federico Marini <marinif@uni-mainz.de>
URL: https://github.com/federicomarini/GeneTonic
VignetteBuilder: knitr
BugReports: https://github.com/federicomarini/GeneTonic/issues
git_url: https://git.bioconductor.org/packages/GeneTonic
git_branch: devel
git_last_commit: f1af392
git_last_commit_date: 2025-01-09
Date/Publication: 2025-01-10
source.ver: src/contrib/GeneTonic_3.1.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GeneTonic_3.1.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GeneTonic_3.1.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GeneTonic_3.1.2.tgz
vignettes: vignettes/GeneTonic/inst/doc/GeneTonic_manual.html
vignetteTitles: The GeneTonic User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GeneTonic/inst/doc/GeneTonic_manual.R
importsMe: GeDi
suggestsMe: mosdef
dependencyCount: 219
Package: geneXtendeR
Version: 1.33.0
Depends: rtracklayer, GO.db, R (>= 3.5.0)
Imports: data.table, dplyr, graphics, networkD3, RColorBrewer,
SnowballC, tm, utils, wordcloud, AnnotationDbi, BiocStyle,
org.Rn.eg.db
Suggests: knitr, rmarkdown, testthat, org.Ag.eg.db, org.Bt.eg.db,
org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db,
org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Pt.eg.db,
org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, rtracklayer
License: GPL (>= 3)
Archs: x64
MD5sum: 9626a7a4d239aa4c3dce8fd3864e7432
NeedsCompilation: yes
Title: Optimized Functional Annotation Of ChIP-seq Data
Description: geneXtendeR optimizes the functional annotation of
ChIP-seq peaks by exploring relative differences in annotating
ChIP-seq peak sets to variable-length gene bodies. In contrast
to prior techniques, geneXtendeR considers peak annotations
beyond just the closest gene, allowing users to see peak
summary statistics for the first-closest gene, second-closest
gene, ..., n-closest gene whilst ranking the output according
to biologically relevant events and iteratively comparing the
fidelity of peak-to-gene overlap across a user-defined range of
upstream and downstream extensions on the original boundaries
of each gene's coordinates. Since different ChIP-seq peak
callers produce different differentially enriched peaks with a
large variance in peak length distribution and total peak
count, annotating peak lists with their nearest genes can often
be a noisy process. As such, the goal of geneXtendeR is to
robustly link differentially enriched peaks with their
respective genes, thereby aiding experimental follow-up and
validation in designing primers for a set of prospective gene
candidates during qPCR.
biocViews: ChIPSeq, Genetics, Annotation, GenomeAnnotation,
DifferentialPeakCalling, Coverage, PeakDetection, ChipOnChip,
HistoneModification, DataImport, NaturalLanguageProcessing,
Visualization, GO, Software
Author: Bohdan Khomtchouk [aut, cre], William Koehler [aut]
Maintainer: Bohdan Khomtchouk <khomtchoukmed@gmail.com>
URL: https://github.com/Bohdan-Khomtchouk/geneXtendeR
VignetteBuilder: knitr
BugReports: https://github.com/Bohdan-Khomtchouk/geneXtendeR/issues
git_url: https://git.bioconductor.org/packages/geneXtendeR
git_branch: devel
git_last_commit: 05a7f4c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/geneXtendeR_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/geneXtendeR_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/geneXtendeR_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/geneXtendeR_1.33.0.tgz
vignettes: vignettes/geneXtendeR/inst/doc/geneXtendeR.pdf
vignetteTitles: geneXtendeR.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 116
Package: GENIE3
Version: 1.29.0
Imports: stats, reshape2, dplyr
Suggests: knitr, rmarkdown, foreach, doRNG, doParallel, Biobase,
SummarizedExperiment, testthat, methods, BiocStyle
License: GPL (>= 2)
MD5sum: ba7b397291d20d31a10cabdf3aa7cf86
NeedsCompilation: yes
Title: GEne Network Inference with Ensemble of trees
Description: This package implements the GENIE3 algorithm for inferring
gene regulatory networks from expression data.
biocViews: NetworkInference, SystemsBiology, DecisionTree, Regression,
Network, GraphAndNetwork, GeneExpression
Author: Van Anh Huynh-Thu, Sara Aibar, Pierre Geurts
Maintainer: Van Anh Huynh-Thu <vahuynh@uliege.be>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GENIE3
git_branch: devel
git_last_commit: 709fdc0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GENIE3_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GENIE3_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GENIE3_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GENIE3_1.29.0.tgz
vignettes: vignettes/GENIE3/inst/doc/GENIE3.html
vignetteTitles: GENIE3
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GENIE3/inst/doc/GENIE3.R
importsMe: BioNERO, MetNet, bulkAnalyseR
suggestsMe: dnapath
dependencyCount: 27
Package: genoCN
Version: 1.59.0
Imports: graphics, stats, utils
License: GPL (>=2)
MD5sum: e45dd1bb1825e2484e2d676a26dcd3a1
NeedsCompilation: yes
Title: genotyping and copy number study tools
Description: Simultaneous identification of copy number states and
genotype calls for regions of either copy number variations or
copy number aberrations
biocViews: Microarray, Genetics
Author: Wei Sun and ZhengZheng Tang
Maintainer: Wei Sun <wsun@bios.unc.edu>
git_url: https://git.bioconductor.org/packages/genoCN
git_branch: devel
git_last_commit: 32e05e9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/genoCN_1.59.0.tar.gz
vignettes: vignettes/genoCN/inst/doc/genoCN.pdf
vignetteTitles: add stuff
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/genoCN/inst/doc/genoCN.R
dependencyCount: 3
Package: genomation
Version: 1.39.0
Depends: R (>= 3.5.0), grid
Imports: Biostrings (>= 2.47.6), BSgenome (>= 1.47.3), data.table,
GenomeInfoDb, GenomicRanges (>= 1.31.8), GenomicAlignments (>=
1.15.6), S4Vectors (>= 0.17.25), ggplot2, gridBase, impute,
IRanges (>= 2.13.12), matrixStats, methods, parallel, plotrix,
plyr, readr, reshape2, Rsamtools (>= 1.31.2), seqPattern,
rtracklayer (>= 1.39.7), Rcpp (>= 0.12.14)
LinkingTo: Rcpp
Suggests: BiocGenerics, genomationData, knitr, RColorBrewer, rmarkdown,
RUnit
License: Artistic-2.0
Archs: x64
MD5sum: ddc73d57b46d70e9280b353d6d9c8fa5
NeedsCompilation: yes
Title: Summary, annotation and visualization of genomic data
Description: A package for summary and annotation of genomic intervals.
Users can visualize and quantify genomic intervals over
pre-defined functional regions, such as promoters, exons,
introns, etc. The genomic intervals represent regions with a
defined chromosome position, which may be associated with a
score, such as aligned reads from HT-seq experiments, TF
binding sites, methylation scores, etc. The package can use any
tabular genomic feature data as long as it has minimal
information on the locations of genomic intervals. In addition,
It can use BAM or BigWig files as input.
biocViews: Annotation, Sequencing, Visualization, CpGIsland
Author: Altuna Akalin [aut, cre], Vedran Franke [aut, cre], Katarzyna
Wreczycka [aut], Alexander Gosdschan [ctb], Liz Ing-Simmons
[ctb], Bozena Mika-Gospodorz [ctb]
Maintainer: Altuna Akalin <aakalin@gmail.com>, Vedran Franke
<vedran.franke@gmail.com>, Katarzyna Wreczycka
<katwre@gmail.com>
URL: http://bioinformatics.mdc-berlin.de/genomation/
VignetteBuilder: knitr
BugReports: https://github.com/BIMSBbioinfo/genomation/issues
git_url: https://git.bioconductor.org/packages/genomation
git_branch: devel
git_last_commit: 9a99fa2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/genomation_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/genomation_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/genomation_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/genomation_1.39.0.tgz
vignettes: vignettes/genomation/inst/doc/GenomationManual.html
vignetteTitles: genomation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/genomation/inst/doc/GenomationManual.R
importsMe: CexoR, EpiCompare, fCCAC, GenomicPlot, RCAS
suggestsMe: methylKit
dependencyCount: 106
Package: GenomAutomorphism
Version: 1.9.1
Depends: R (>= 4.4.0),
Imports: Biostrings, BiocGenerics, BiocParallel, GenomeInfoDb,
GenomicRanges, IRanges, matrixStats, XVector, dplyr,
data.table, parallel, doParallel, foreach, methods, S4Vectors,
stats, numbers, utils
Suggests: spelling, rmarkdown, BiocStyle, testthat (>= 3.0.0), knitr
License: Artistic-2.0
Archs: x64
MD5sum: 18de8e3c230ccd3fd5a540d9b2ebd7a0
NeedsCompilation: no
Title: Compute the automorphisms between DNA's Abelian group
representations
Description: This is a R package to compute the automorphisms between
pairwise aligned DNA sequences represented as elements from a
Genomic Abelian group. In a general scenario, from genomic
regions till the whole genomes from a given population (from
any species or close related species) can be algebraically
represented as a direct sum of cyclic groups or more
specifically Abelian p-groups. Basically, we propose the
representation of multiple sequence alignments of length N bp
as element of a finite Abelian group created by the direct sum
of homocyclic Abelian group of prime-power order.
biocViews: MathematicalBiology, ComparativeGenomics,
FunctionalGenomics, MultipleSequenceAlignment, WholeGenome
Author: Robersy Sanchez [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5246-1453>)
Maintainer: Robersy Sanchez <genomicmath@gmail.com>
URL: https://github.com/genomaths/GenomAutomorphism
VignetteBuilder: knitr
BugReports: https://github.com/genomaths/GenomAutomorphism/issues
git_url: https://git.bioconductor.org/packages/GenomAutomorphism
git_branch: devel
git_last_commit: cd1ebdf
git_last_commit_date: 2024-12-29
Date/Publication: 2024-12-30
source.ver: src/contrib/GenomAutomorphism_1.9.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GenomAutomorphism_1.9.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GenomAutomorphism_1.9.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GenomAutomorphism_1.9.1.tgz
vignettes: vignettes/GenomAutomorphism/inst/doc/GenomAutomorphism.html
vignetteTitles: Get started-with GenomAutomorphism
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GenomAutomorphism/inst/doc/GenomAutomorphism.R
dependencyCount: 56
Package: GenomeInfoDb
Version: 1.43.4
Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.53.2), S4Vectors (>=
0.45.2), IRanges (>= 2.41.1)
Imports: stats, stats4, utils, UCSC.utils, GenomeInfoDbData
Suggests: R.utils, data.table, GenomicRanges, Rsamtools,
GenomicAlignments, GenomicFeatures, BSgenome,
TxDb.Dmelanogaster.UCSC.dm3.ensGene,
BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Celegans.UCSC.ce2,
BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocStyle, knitr
License: Artistic-2.0
Archs: x64
MD5sum: dcd7cd466b6154741512a746287e3469
NeedsCompilation: no
Title: Utilities for manipulating chromosome names, including modifying
them to follow a particular naming style
Description: Contains data and functions that define and allow
translation between different chromosome sequence naming
conventions (e.g., "chr1" versus "1"), including a function
that attempts to place sequence names in their natural, rather
than lexicographic, order.
biocViews: Genetics, DataRepresentation, Annotation, GenomeAnnotation
Author: Sonali Arora [aut], Martin Morgan [aut], Marc Carlson [aut],
Hervé Pagès [aut, cre], Prisca Chidimma Maduka [ctb], Atuhurira
Kirabo Kakopo [ctb], Haleema Khan [ctb] (vignette translation
from Sweave to Rmarkdown / HTML), Emmanuel Chigozie Elendu
[ctb]
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/GenomeInfoDb
VignetteBuilder: knitr
Video: http://youtu.be/wdEjCYSXa7w
BugReports: https://github.com/Bioconductor/GenomeInfoDb/issues
git_url: https://git.bioconductor.org/packages/GenomeInfoDb
git_branch: devel
git_last_commit: a7d190e
git_last_commit_date: 2025-01-23
Date/Publication: 2025-01-24
source.ver: src/contrib/GenomeInfoDb_1.43.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GenomeInfoDb_1.43.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GenomeInfoDb_1.43.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GenomeInfoDb_1.43.4.tgz
vignettes: vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.pdf,
vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.html
vignetteTitles: GenomeInfoDb: Introduction to GenomeInfoDb, Submitting
your organism to GenomeInfoDb
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.R,
vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.R
dependsOnMe: Biostrings, BSgenome, BSgenomeForge, bumphunter, CODEX,
CSAR, GenomicAlignments, GenomicFeatures, GenomicRanges,
GenomicTuples, gmapR, HelloRanges, IdeoViz, Rsamtools, SCOPE,
txdbmaker, VariantAnnotation, BSgenome.Hsapiens.UCSC.hg38,
BSgenome.Hsapiens.UCSC.hg38.masked, UCSCRepeatMasker, RTIGER
importsMe: alabaster.ranges, AllelicImbalance, amplican, AneuFinder,
AnnotationHubData, annotatr, ATACseqQC, ATACseqTFEA, atena,
BaalChIP, ballgown, bambu, bedbaser, BindingSiteFinder,
biovizBase, biscuiteer, BiSeq, bnbc, branchpointer,
breakpointR, bsseq, BUSpaRse, CAGEfightR, cageminer, CAGEr,
cardelino, casper, cBioPortalData, CexoR, cfdnakit, cfDNAPro,
chimeraviz, chipenrich, ChIPexoQual, ChIPpeakAnno, ChIPseeker,
chromVAR, circRNAprofiler, cleanUpdTSeq, CleanUpRNAseq,
cn.mops, CNEr, CNVfilteR, CNVPanelizer, CNVRanger, Cogito,
comapr, compEpiTools, consensusSeekeR, conumee,
CopyNumberPlots, crisprBowtie, crisprBwa, crisprDesign,
CRISPRseek, crisprShiny, CrispRVariants, crisprViz, csaw,
customProDB, DAMEfinder, Damsel, decompTumor2Sig, DegCre,
demuxSNP, derfinder, derfinderPlot, DEScan2, DEWSeq, diffHic,
diffUTR, DMRcate, DMRScan, dmrseq, DominoEffect, easylift,
easyRNASeq, ELMER, enhancerHomologSearch, ensembldb,
EpiCompare, epigenomix, epigraHMM, EpiMix, epimutacions,
epiregulon, EpiTxDb, epivizr, epivizrData, epivizrStandalone,
erma, esATAC, EventPointer, extraChIPs, factR, FindIT2, FLAMES,
FRASER, funtooNorm, G4SNVHunter, GA4GHclient, GA4GHshiny,
gcapc, gDNAx, geneAttribution, genomation, GenomAutomorphism,
genomeIntervals, GenomicDistributions, GenomicFiles,
GenomicInteractionNodes, GenomicInteractions, GenomicOZone,
GenomicPlot, GenomicScores, GenVisR, geomeTriD, ggbio, gmoviz,
goseq, GOTHiC, GRaNIE, GreyListChIP, GUIDEseq, Gviz, gwascat,
h5vc, heatmaps, HicAggR, HiCBricks, HiCDOC, HiCExperiment,
HiContacts, HiCParser, hicVennDiagram, HiTC, idr2d, IMAS,
INSPEcT, InteractionSet, IsoformSwitchAnalyzeR, IVAS,
karyoploteR, katdetectr, MADSEQ, mariner, maser, metagene2,
metaseqR2, methimpute, methInheritSim, methodical, methylKit,
methylPipe, methylSig, methylumi, minfi, MinimumDistance,
mobileRNA, monaLisa, mosaics, Motif2Site, motifbreakR,
motifmatchr, MotifPeeker, motifTestR, MouseFM, msgbsR,
multicrispr, multiHiCcompare, MungeSumstats, musicatk,
MutationalPatterns, myvariant, NADfinder, nearBynding, normr,
nucleR, nullranges, OGRE, OMICsPCA, ORFik, Organism.dplyr,
panelcn.mops, periodicDNA, PICB, pipeFrame, plotgardener,
plyinteractions, plyranges, podkat, pram, prebs, proActiv,
profileplyr, ProteoDisco, PureCN, qpgraph, qsea, QuasR, R3CPET,
r3Cseq, raer, RaggedExperiment, ramr, RareVariantVis, RCAS,
RcisTarget, recount, recoup, regioneR, regionReport, REMP,
Repitools, RESOLVE, rfPred, RgnTX, rGREAT, RiboCrypt,
RiboProfiling, riboSeqR, ribosomeProfilingQC, rigvf,
RJMCMCNucleosomes, rnaEditr, RNAmodR, roar, RTCGAToolbox,
rtracklayer, scanMiR, scanMiRApp, scDblFinder, scmeth,
scRNAseqApp, scruff, segmentSeq, seqArchRplus, SeqArray,
seqCAT, seqsetvis, sesame, sevenC, SGSeq, ShortRead, signeR,
SigsPack, SingleMoleculeFootprinting, sitadela, SNPhood, soGGi,
SomaticSignatures, SOMNiBUS, SparseSignatures, spatzie, spiky,
SpliceWiz, SplicingGraphs, SPLINTER, srnadiff, strandCheckR,
SummarizedExperiment, svaNUMT, svaRetro, tadar, TAPseq,
TCGAutils, TEKRABber, TENxIO, TFBSTools, tidyCoverage,
TitanCNA, TnT, trackViewer, transcriptR, transmogR,
tRNAscanImport, TVTB, tximeta, Ularcirc, UMI4Cats, VanillaICE,
VariantFiltering, VariantTools, VaSP, VplotR, wiggleplotr,
YAPSA, fitCons.UCSC.hg19, GenomicState, grasp2db,
MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5,
MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5,
MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5,
MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5,
MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5,
MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5,
MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38,
MafH5.gnomAD.v4.0.GRCh38, phastCons100way.UCSC.hg19,
phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38,
SNPlocs.Hsapiens.dbSNP144.GRCh37,
SNPlocs.Hsapiens.dbSNP144.GRCh38,
SNPlocs.Hsapiens.dbSNP149.GRCh38,
SNPlocs.Hsapiens.dbSNP150.GRCh38,
SNPlocs.Hsapiens.dbSNP155.GRCh37,
SNPlocs.Hsapiens.dbSNP155.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh37,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, BioPlex, chipenrich.data,
GenomicDistributionsData, MethylSeqData, sesameData,
crispRdesignR, driveR, GRIN2, ICAMS, locuszoomr, MAAPER,
Mega2R, SeedMatchR, Signac, simMP
suggestsMe: AlphaMissenseR, AnnotationForge, AnnotationHub, DFplyr,
DiffBind, epialleleR, ExperimentHubData, fishpond, ldblock,
megadepth, methrix, OUTRIDER, parglms, QDNAseq, RAIDS,
regioneReloaded, scTreeViz, splatter, systemPipeR, TFutils,
UCSC.utils, BioMartGOGeneSets, xcoredata, gkmSVM, polyRAD,
Seurat
dependencyCount: 20
Package: genomeIntervals
Version: 1.63.0
Depends: R (>= 2.15.0), methods, intervals (>= 0.14.0), BiocGenerics
(>= 0.15.2)
Imports: GenomeInfoDb (>= 1.5.8), GenomicRanges (>= 1.21.16),
IRanges(>= 2.3.14), S4Vectors (>= 0.7.10)
License: Artistic-2.0
MD5sum: 77f0a207a62d086208aedf25d78d93b9
NeedsCompilation: no
Title: Operations on genomic intervals
Description: This package defines classes for representing genomic
intervals and provides functions and methods for working with
these. Note: The package provides the basic infrastructure for
and is enhanced by the package 'girafe'.
biocViews: DataImport, Infrastructure, Genetics
Author: Julien Gagneur <gagneur@in.tum.de>, Joern Toedling, Richard
Bourgon, Nicolas Delhomme
Maintainer: Julien Gagneur <gagneur@in.tum.de>
git_url: https://git.bioconductor.org/packages/genomeIntervals
git_branch: devel
git_last_commit: ebab913
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/genomeIntervals_1.63.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/genomeIntervals_1.63.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/genomeIntervals_1.63.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/genomeIntervals_1.63.0.tgz
vignettes: vignettes/genomeIntervals/inst/doc/genomeIntervals.pdf
vignetteTitles: Overview of the genomeIntervals package.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/genomeIntervals/inst/doc/genomeIntervals.R
dependsOnMe: girafe
importsMe: easyRNASeq
dependencyCount: 24
Package: genomes
Version: 3.37.0
Depends: readr, curl
License: GPL-3
MD5sum: dae05ba81e818f034fac20dbcec8f55e
NeedsCompilation: no
Title: Genome sequencing project metadata
Description: Download genome and assembly reports from NCBI
biocViews: Annotation, Genetics
Author: Chris Stubben
Maintainer: Chris Stubben <stubben@lanl.gov>
git_url: https://git.bioconductor.org/packages/genomes
git_branch: devel
git_last_commit: 4bdaa9c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/genomes_3.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/genomes_3.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/genomes_3.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/genomes_3.37.0.tgz
vignettes: vignettes/genomes/inst/doc/genomes.pdf
vignetteTitles: Genome metadata
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/genomes/inst/doc/genomes.R
dependencyCount: 31
Package: GenomicAlignments
Version: 1.43.0
Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>=
0.27.12), IRanges (>= 2.23.9), GenomeInfoDb (>= 1.13.1),
GenomicRanges (>= 1.55.3), SummarizedExperiment (>= 1.9.13),
Biostrings (>= 2.55.7), Rsamtools (>= 1.31.2)
Imports: methods, utils, stats, BiocGenerics, S4Vectors, IRanges,
GenomicRanges, Biostrings, Rsamtools, BiocParallel
LinkingTo: S4Vectors, IRanges
Suggests: ShortRead, rtracklayer, BSgenome, GenomicFeatures,
RNAseqData.HNRNPC.bam.chr14, pasillaBamSubset,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Dmelanogaster.UCSC.dm3.ensGene,
BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Hsapiens.UCSC.hg19,
DESeq2, edgeR, RUnit, knitr, BiocStyle
License: Artistic-2.0
MD5sum: 2e03f7ccaecb69c76382bcac5d4e3849
NeedsCompilation: yes
Title: Representation and manipulation of short genomic alignments
Description: Provides efficient containers for storing and manipulating
short genomic alignments (typically obtained by aligning short
reads to a reference genome). This includes read counting,
computing the coverage, junction detection, and working with
the nucleotide content of the alignments.
biocViews: Infrastructure, DataImport, Genetics, Sequencing, RNASeq,
SNP, Coverage, Alignment, ImmunoOncology
Author: Hervé Pagès [aut, cre], Valerie Obenchain [aut], Martin Morgan
[aut], Fedor Bezrukov [ctb], Robert Castelo [ctb], Halimat C.
Atanda [ctb] (Translated 'WorkingWithAlignedNucleotides'
vignette from Sweave to RMarkdown / HTML.)
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/GenomicAlignments
VignetteBuilder: knitr
Video: https://www.youtube.com/watch?v=2KqBSbkfhRo ,
https://www.youtube.com/watch?v=3PK_jx44QTs
BugReports: https://github.com/Bioconductor/GenomicAlignments/issues
git_url: https://git.bioconductor.org/packages/GenomicAlignments
git_branch: devel
git_last_commit: db308fe
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GenomicAlignments_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GenomicAlignments_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GenomicAlignments_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GenomicAlignments_1.43.0.tgz
vignettes:
vignettes/GenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.pdf,
vignettes/GenomicAlignments/inst/doc/OverlapEncodings.pdf,
vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.pdf,
vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.html
vignetteTitles: An Introduction to the GenomicAlignments Package,
Overlap encodings, Counting reads with summarizeOverlaps,
Working with aligned nucleotides (WORK-IN-PROGRESS!)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/GenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.R,
vignettes/GenomicAlignments/inst/doc/OverlapEncodings.R,
vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.R,
vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.R
dependsOnMe: AllelicImbalance, Basic4Cseq, ChIPexoQual, HelloRanges,
hiReadsProcessor, igvR, ORFik, prebs, recoup, RiboDiPA,
ShortRead, SplicingGraphs, sequencing
importsMe: AneuFinder, APAlyzer, ASpli, ATACseqQC, ATACseqTFEA, atena,
BaalChIP, bambu, biovizBase, breakpointR, CAGEfightR, CAGEr,
cfDNAPro, chimeraviz, ChIPpeakAnno, ChIPQC, CNEr, consensusDE,
CoverageView, CrispRVariants, CSSQ, customProDB, DAMEfinder,
DegNorm, derfinder, DEScan2, DiffBind, DNAfusion,
DuplexDiscovereR, easyRNASeq, esATAC, FLAMES, FRASER, gcapc,
gDNAx, genomation, GenomicFiles, GenomicPlot, ggbio, gmapR,
gmoviz, GreyListChIP, GUIDEseq, Gviz, icetea, IMAS, INSPEcT,
IntEREst, MADSEQ, MDTS, metagene2, metaseqR2, methylPipe,
mosaics, Motif2Site, MotifPeeker, msgbsR, NADfinder, PICB,
PICS, plyranges, pram, proActiv, raer, ramwas, Repitools,
RiboProfiling, ribosomeProfilingQC, RNAmodR, roar, Rqc,
rtracklayer, saseR, scPipe, scruff, seqsetvis, SGSeq, soGGi,
spiky, SPLINTER, srnadiff, strandCheckR, TAPseq, TCseq,
trackViewer, transcriptR, Ularcirc, UMI4Cats, VaSP, VplotR,
ZygosityPredictor, leeBamViews, alakazam, iimi, MAAPER, PACVr,
VALERIE
suggestsMe: amplican, BindingSiteFinder, BiocParallel, csaw, DEXSeq,
EpiCompare, ExperimentHub, extraChIPs, gage, GenomeInfoDb,
GenomicDataCommons, GenomicFeatures, GenomicRanges,
GenomicTuples, igvShiny, IRanges, QuasR, Rsamtools, SARC,
similaRpeak, Streamer, systemPipeR, NanoporeRNASeq,
parathyroidSE, RNAseqData.HNRNPC.bam.chr14, seqmagick
dependencyCount: 50
Package: GenomicDataCommons
Version: 1.31.1
Depends: R (>= 4.1.0)
Imports: stats, httr, xml2, jsonlite, utils, rlang, readr,
GenomicRanges, IRanges, dplyr, rappdirs, tibble, tidyr
Suggests: BiocStyle, knitr, rmarkdown, DT, testthat, listviewer,
ggplot2, GenomicAlignments, Rsamtools, BiocParallel,
TxDb.Hsapiens.UCSC.hg38.knownGene, VariantAnnotation, maftools,
R.utils, data.table
License: Artistic-2.0
MD5sum: 8d59f62431e6db366376b85cdaaf3d05
NeedsCompilation: no
Title: NIH / NCI Genomic Data Commons Access
Description: Programmatically access the NIH / NCI Genomic Data Commons
RESTful service.
biocViews: DataImport, Sequencing
Author: Martin Morgan [aut], Sean Davis [aut, cre], Marcel Ramos [ctb]
Maintainer: Sean Davis <seandavi@gmail.com>
URL: https://bioconductor.org/packages/GenomicDataCommons,
http://github.com/Bioconductor/GenomicDataCommons,
http://bioconductor.github.io/GenomicDataCommons/
VignetteBuilder: knitr
BugReports:
https://github.com/Bioconductor/GenomicDataCommons/issues/new
git_url: https://git.bioconductor.org/packages/GenomicDataCommons
git_branch: devel
git_last_commit: eb3ac69
git_last_commit_date: 2025-02-03
Date/Publication: 2025-02-04
source.ver: src/contrib/GenomicDataCommons_1.31.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GenomicDataCommons_1.31.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GenomicDataCommons_1.31.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GenomicDataCommons_1.31.1.tgz
vignettes: vignettes/GenomicDataCommons/inst/doc/overview.html,
vignettes/GenomicDataCommons/inst/doc/questions-and-answers.html,
vignettes/GenomicDataCommons/inst/doc/somatic_mutations.html
vignetteTitles: Introduction to Accessing the NCI Genomic Data Commons,
Questions and answers from over the years, Somatic Mutation
Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GenomicDataCommons/inst/doc/overview.R,
vignettes/GenomicDataCommons/inst/doc/questions-and-answers.R,
vignettes/GenomicDataCommons/inst/doc/somatic_mutations.R
importsMe: GDCRNATools, TCGAutils
suggestsMe: autonomics
dependencyCount: 55
Package: GenomicDistributions
Version: 1.15.0
Depends: R (>= 4.0), IRanges, GenomicRanges
Imports: data.table, ggplot2, reshape2, methods, utils, Biostrings,
plyr, dplyr, scales, broom, GenomeInfoDb, stats
Suggests: AnnotationFilter, rtracklayer, testthat, knitr, BiocStyle,
rmarkdown, GenomicDistributionsData
Enhances: BSgenome, extrafont, ensembldb, GenomicFeatures
License: BSD_2_clause + file LICENSE
Archs: x64
MD5sum: a6a9291416dc08b35c7a31187da9240c
NeedsCompilation: no
Title: GenomicDistributions: fast analysis of genomic intervals with
Bioconductor
Description: If you have a set of genomic ranges, this package can help
you with visualization and comparison. It produces several
kinds of plots, for example: Chromosome distribution plots,
which visualize how your regions are distributed over
chromosomes; feature distance distribution plots, which
visualizes how your regions are distributed relative to a
feature of interest, like Transcription Start Sites (TSSs);
genomic partition plots, which visualize how your regions
overlap given genomic features such as promoters, introns,
exons, or intergenic regions. It also makes it easy to compare
one set of ranges to another.
biocViews: Software, GenomeAnnotation, GenomeAssembly,
DataRepresentation, Sequencing, Coverage, FunctionalGenomics,
Visualization
Author: Kristyna Kupkova [aut, cre], Jose Verdezoto [aut], Tessa Danehy
[aut], John Lawson [aut], Jose Verdezoto [aut], Michal
Stolarczyk [aut], Jason Smith [aut], Bingjie Xue [aut], Sophia
Rogers [aut], John Stubbs [aut], Nathan C. Sheffield [aut]
Maintainer: Kristyna Kupkova <kristynakupkova@gmail.com>
URL: http://code.databio.org/GenomicDistributions
VignetteBuilder: knitr
BugReports: http://github.com/databio/GenomicDistributions
git_url: https://git.bioconductor.org/packages/GenomicDistributions
git_branch: devel
git_last_commit: 9720508
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GenomicDistributions_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GenomicDistributions_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GenomicDistributions_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GenomicDistributions_1.15.0.tgz
vignettes: vignettes/GenomicDistributions/inst/doc/full-power.html,
vignettes/GenomicDistributions/inst/doc/intro.html
vignetteTitles: 2. Full power GenomicDistributions, 1. Getting started
with GenomicDistributions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GenomicDistributions/inst/doc/intro.R
dependencyCount: 68
Package: GenomicFeatures
Version: 1.59.1
Depends: R (>= 3.5.0), BiocGenerics (>= 0.51.2), S4Vectors (>=
0.17.29), IRanges (>= 2.37.1), GenomeInfoDb (>= 1.35.8),
GenomicRanges (>= 1.55.2), AnnotationDbi (>= 1.41.4)
Imports: methods, utils, stats, DBI, XVector, Biostrings, rtracklayer
Suggests: txdbmaker, org.Mm.eg.db, org.Hs.eg.db, BSgenome,
BSgenome.Hsapiens.UCSC.hg19 (>= 1.3.17),
BSgenome.Celegans.UCSC.ce11, BSgenome.Dmelanogaster.UCSC.dm3
(>= 1.3.17), mirbase.db, FDb.UCSC.tRNAs,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Celegans.UCSC.ce11.ensGene,
TxDb.Dmelanogaster.UCSC.dm3.ensGene (>= 2.7.1),
TxDb.Mmusculus.UCSC.mm10.knownGene (>= 3.4.7),
TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts,
TxDb.Hsapiens.UCSC.hg38.knownGene (>= 3.4.6),
SNPlocs.Hsapiens.dbSNP144.GRCh38, Rsamtools, pasillaBamSubset
(>= 0.0.5), GenomicAlignments (>= 1.15.7), ensembldb,
AnnotationFilter, RUnit, BiocStyle, knitr, markdown
License: Artistic-2.0
MD5sum: 7b12f556e66b1cc86f47c3b8fba885f5
NeedsCompilation: no
Title: Query the gene models of a given organism/assembly
Description: Extract the genomic locations of genes, transcripts,
exons, introns, and CDS, for the gene models stored in a TxDb
object. A TxDb object is a small database that contains the
gene models of a given organism/assembly. Bioconductor provides
a small collection of TxDb objects in the form of
ready-to-install TxDb packages for the most commonly studied
organisms. Additionally, the user can easily make a TxDb object
(or package) for the organism/assembly of their choice by using
the tools from the txdbmaker package.
biocViews: Genetics, Infrastructure, Annotation, Sequencing,
GenomeAnnotation
Author: M. Carlson [aut], H. Pagès [aut, cre], P. Aboyoun [aut], S.
Falcon [aut], M. Morgan [aut], D. Sarkar [aut], M. Lawrence
[aut], V. Obenchain [aut], S. Arora [ctb], J. MacDonald [ctb],
M. Ramos [ctb], S. Saini [ctb], P. Shannon [ctb], L. Shepherd
[ctb], D. Tenenbaum [ctb], D. Van Twisk [ctb]
Maintainer: H. Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/GenomicFeatures
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/GenomicFeatures/issues
git_url: https://git.bioconductor.org/packages/GenomicFeatures
git_branch: devel
git_last_commit: 2d2ddd6
git_last_commit_date: 2024-11-06
Date/Publication: 2024-11-07
source.ver: src/contrib/GenomicFeatures_1.59.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GenomicFeatures_1.59.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GenomicFeatures_1.59.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GenomicFeatures_1.59.1.tgz
vignettes: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.html
vignetteTitles: Obtaining and Utilizing TxDb Objects
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.R
dependsOnMe: Cogito, cpvSNP, CRISPRseek, ensembldb, GSReg, Guitar,
HelloRanges, mygene, OrganismDbi, OUTRIDER, RareVariantVis,
RiboDiPA, SplicingGraphs, txdbmaker,
FDb.FANTOM4.promoters.hg19, FDb.InfiniumMethylation.hg18,
FDb.InfiniumMethylation.hg19, FDb.UCSC.snp135common.hg19,
FDb.UCSC.snp137common.hg19, FDb.UCSC.tRNAs, Homo.sapiens,
Mus.musculus, Rattus.norvegicus,
TxDb.Athaliana.BioMart.plantsmart22,
TxDb.Athaliana.BioMart.plantsmart25,
TxDb.Athaliana.BioMart.plantsmart28,
TxDb.Athaliana.BioMart.plantsmart51,
TxDb.Btaurus.UCSC.bosTau8.refGene,
TxDb.Btaurus.UCSC.bosTau9.refGene,
TxDb.Celegans.UCSC.ce11.ensGene,
TxDb.Celegans.UCSC.ce11.refGene,
TxDb.Celegans.UCSC.ce6.ensGene,
TxDb.Cfamiliaris.UCSC.canFam3.refGene,
TxDb.Cfamiliaris.UCSC.canFam4.refGene,
TxDb.Cfamiliaris.UCSC.canFam5.refGene,
TxDb.Cfamiliaris.UCSC.canFam6.refGene,
TxDb.Dmelanogaster.UCSC.dm3.ensGene,
TxDb.Dmelanogaster.UCSC.dm6.ensGene,
TxDb.Drerio.UCSC.danRer10.refGene,
TxDb.Drerio.UCSC.danRer11.refGene,
TxDb.Ggallus.UCSC.galGal4.refGene,
TxDb.Ggallus.UCSC.galGal5.refGene,
TxDb.Ggallus.UCSC.galGal6.refGene, TxDb.Hsapiens.BioMart.igis,
TxDb.Hsapiens.UCSC.hg18.knownGene,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts,
TxDb.Hsapiens.UCSC.hg38.knownGene,
TxDb.Hsapiens.UCSC.hg38.refGene,
TxDb.Mmulatta.UCSC.rheMac10.refGene,
TxDb.Mmulatta.UCSC.rheMac3.refGene,
TxDb.Mmulatta.UCSC.rheMac8.refGene,
TxDb.Mmusculus.UCSC.mm10.ensGene,
TxDb.Mmusculus.UCSC.mm10.knownGene,
TxDb.Mmusculus.UCSC.mm39.knownGene,
TxDb.Mmusculus.UCSC.mm39.refGene,
TxDb.Mmusculus.UCSC.mm9.knownGene,
TxDb.Ptroglodytes.UCSC.panTro4.refGene,
TxDb.Ptroglodytes.UCSC.panTro5.refGene,
TxDb.Ptroglodytes.UCSC.panTro6.refGene,
TxDb.Rnorvegicus.BioMart.igis,
TxDb.Rnorvegicus.UCSC.rn4.ensGene,
TxDb.Rnorvegicus.UCSC.rn5.refGene,
TxDb.Rnorvegicus.UCSC.rn6.ncbiRefSeq,
TxDb.Rnorvegicus.UCSC.rn6.refGene,
TxDb.Rnorvegicus.UCSC.rn7.refGene,
TxDb.Scerevisiae.UCSC.sacCer2.sgdGene,
TxDb.Scerevisiae.UCSC.sacCer3.sgdGene,
TxDb.Sscrofa.UCSC.susScr11.refGene,
TxDb.Sscrofa.UCSC.susScr3.refGene, generegulation
importsMe: AllelicImbalance, AnnotationHubData, annotatr, APAlyzer,
appreci8R, ASpli, atena, bambu, BgeeCall, BindingSiteFinder,
biovizBase, bumphunter, BUSpaRse, CAGEfightR, CAGEr, casper,
chevreulProcess, ChIPpeakAnno, ChIPQC, ChIPseeker,
compEpiTools, consensusDE, crisprDesign, crisprViz, CSSQ,
customProDB, Damsel, decompTumor2Sig, derfinder, derfinderPlot,
DNAfusion, doubletrouble, EDASeq, ELMER, ELViS, EpiMix,
epimutacions, EpiTxDb, epivizrData, epivizrStandalone, esATAC,
EventPointer, factR, FindIT2, FLAMES, FRASER, GA4GHshiny,
gDNAx, geneAttribution, GenomicInteractionNodes, GenomicPlot,
GenVisR, ggbio, gINTomics, gmapR, gmoviz, goseq, GUIDEseq,
Gviz, gwascat, HiLDA, icetea, InPAS, INSPEcT, IntEREst,
karyoploteR, lumi, magpie, mCSEA, metaseqR2, methylumi, msgbsR,
multicrispr, musicatk, ORFik, Organism.dplyr, OutSplice,
proActiv, proBAMr, ProteoDisco, PureCN, qpgraph, QuasR, raer,
RCAS, recoup, RgnTX, rGREAT, RiboCrypt, RiboProfiling,
ribosomeProfilingQC, RITAN, RNAmodR, SARC, saseR, scanMiRApp,
scruff, SGSeq, sitadela, spatzie, SPLINTER, srnadiff,
StructuralVariantAnnotation, svaNUMT, svaRetro, TAPseq,
TCGAutils, TFEA.ChIP, trackViewer, transcriptR, transmogR,
TRESS, txcutr, tximeta, Ularcirc, UMI4Cats, VariantAnnotation,
VariantFiltering, VariantTools, wavClusteR,
FDb.FANTOM4.promoters.hg19, FDb.InfiniumMethylation.hg18,
FDb.InfiniumMethylation.hg19, FDb.UCSC.snp135common.hg19,
FDb.UCSC.snp137common.hg19, FDb.UCSC.tRNAs, GenomicState,
Homo.sapiens, Mus.musculus, Rattus.norvegicus,
TxDb.Athaliana.BioMart.plantsmart22,
TxDb.Athaliana.BioMart.plantsmart25,
TxDb.Hsapiens.BioMart.igis, TxDb.Rnorvegicus.BioMart.igis,
DMRcatedata, geneLenDataBase, GenomicDistributionsData,
scRNAseq, ExpHunterSuite, CSeQTL, driveR, MAAPER, oncoPredict,
SeedMatchR
suggestsMe: BANDITS, biomvRCNS, Biostrings, BSgenomeForge, chipseq,
chromPlot, CrispRVariants, csaw, cummeRbund, DEXSeq, eisaR,
fishpond, GenomeInfoDb, GenomicAlignments, GenomicRanges,
HDF5Array, HiContacts, InteractiveComplexHeatmap, IRanges,
MiRaGE, MutationalPatterns, pageRank, plotgardener, recount,
RNAmodR.ML, Rsamtools, rtracklayer, scPipe, ShortRead,
SummarizedExperiment, systemPipeR, TFutils, tidyCoverage, TnT,
VplotR, wiggleplotr, BSgenome.Btaurus.UCSC.bosTau3,
BSgenome.Btaurus.UCSC.bosTau4, BSgenome.Btaurus.UCSC.bosTau6,
BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9,
BSgenome.Celegans.UCSC.ce10, BSgenome.Celegans.UCSC.ce11,
BSgenome.Celegans.UCSC.ce2, BSgenome.Cfamiliaris.UCSC.canFam2,
BSgenome.Cfamiliaris.UCSC.canFam3,
BSgenome.Dmelanogaster.UCSC.dm2,
BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10,
BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5,
BSgenome.Drerio.UCSC.danRer6, BSgenome.Drerio.UCSC.danRer7,
BSgenome.Gaculeatus.UCSC.gasAcu1,
BSgenome.Ggallus.UCSC.galGal3, BSgenome.Ggallus.UCSC.galGal4,
BSgenome.Hsapiens.UCSC.hg17, BSgenome.Mmulatta.UCSC.rheMac2,
BSgenome.Mmulatta.UCSC.rheMac3, BSgenome.Mmusculus.UCSC.mm8,
BSgenome.Ptroglodytes.UCSC.panTro2,
BSgenome.Ptroglodytes.UCSC.panTro3,
BSgenome.Rnorvegicus.UCSC.rn6, BioPlex, curatedAdipoChIP,
ObMiTi, parathyroidSE, Single.mTEC.Transcriptomes,
systemPipeRdata, CAGEWorkflow, polyRAD
dependencyCount: 76
Package: GenomicFiles
Version: 1.43.0
Depends: R (>= 3.1.0), methods, BiocGenerics (>= 0.11.2),
MatrixGenerics, GenomicRanges (>= 1.31.16),
SummarizedExperiment, BiocParallel (>= 1.1.0), Rsamtools (>=
1.17.29), rtracklayer (>= 1.25.3)
Imports: GenomicAlignments (>= 1.7.7), IRanges, S4Vectors (>= 0.9.25),
VariantAnnotation (>= 1.27.9), GenomeInfoDb
Suggests: BiocStyle, RUnit, genefilter, deepSNV, snpStats, knitr,
RNAseqData.HNRNPC.bam.chr14, Biostrings, Homo.sapiens
License: Artistic-2.0
MD5sum: cfea074c49b3ec19fc7ed49c6fa11d48
NeedsCompilation: no
Title: Distributed computing by file or by range
Description: This package provides infrastructure for parallel
computations distributed 'by file' or 'by range'. User defined
MAPPER and REDUCER functions provide added flexibility for data
combination and manipulation.
biocViews: Genetics, Infrastructure, DataImport, Sequencing, Coverage
Author: Bioconductor Package Maintainer [aut, cre], Valerie Obenchain
[aut], Michael Love [aut], Lori Shepherd [aut], Martin Morgan
[aut], Sonali Kumari [ctb] (Converted 'GenomicFiles' vignettes
from Sweave to RMarkdown / HTML.)
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
Video: https://www.youtube.com/watch?v=3PK_jx44QTs
git_url: https://git.bioconductor.org/packages/GenomicFiles
git_branch: devel
git_last_commit: dce61b1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GenomicFiles_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GenomicFiles_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GenomicFiles_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GenomicFiles_1.43.0.tgz
vignettes: vignettes/GenomicFiles/inst/doc/GenomicFiles.html
vignetteTitles: Introduction to GenomicFiles
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GenomicFiles/inst/doc/GenomicFiles.R
dependsOnMe: erma, IntEREst
importsMe: CAGEfightR, derfinder, gDNAx, QuasR, Rqc, TFutils, VCFArray
suggestsMe: ldblock, MungeSumstats
dependencyCount: 79
Package: genomicInstability
Version: 1.13.0
Depends: R (>= 4.1.0), checkmate
Imports: mixtools, SummarizedExperiment
Suggests: SingleCellExperiment, ExperimentHub, pROC
License: file LICENSE
MD5sum: a36fa1245e429b6b8f227f21577537f0
NeedsCompilation: no
Title: Genomic Instability estimation for scRNA-Seq
Description: This package contain functions to run genomic instability
analysis (GIA) from scRNA-Seq data. GIA estimates the
association between gene expression and genomic location of the
coding genes. It uses the aREA algorithm to quantify the
enrichment of sets of contiguous genes (loci-blocks) on the
gene expression profiles and estimates the Genomic Instability
Score (GIS) for each analyzed cell.
biocViews: SystemsBiology, GeneExpression, SingleCell
Author: Mariano Alvarez [aut, cre], Pasquale Laise [aut], DarwinHealth
[cph]
Maintainer: Mariano Alvarez <reef103@gmail.com>
URL: https://github.com/DarwinHealth/genomicInstability
BugReports: https://github.com/DarwinHealth/genomicInstability
git_url: https://git.bioconductor.org/packages/genomicInstability
git_branch: devel
git_last_commit: a8d0388
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/genomicInstability_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/genomicInstability_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/genomicInstability_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/genomicInstability_1.13.0.tgz
vignettes: vignettes/genomicInstability/inst/doc/genomicInstability.pdf
vignetteTitles: Using genomicInstability
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/genomicInstability/inst/doc/genomicInstability.R
dependencyCount: 102
Package: GenomicInteractionNodes
Version: 1.11.3
Depends: R (>= 4.2.0), stats
Imports: AnnotationDbi, graph, GO.db, GenomicRanges, GenomicFeatures,
GenomeInfoDb, methods, IRanges, RBGL, S4Vectors
Suggests: RUnit, BiocStyle, knitr, rmarkdown, rtracklayer, testthat,
TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db
License: file LICENSE
MD5sum: 3ee56f736c5a352bf97cddbad499919d
NeedsCompilation: no
Title: A R/Bioconductor package to detect the interaction nodes from
HiC/HiChIP/HiCAR data
Description: The GenomicInteractionNodes package can import
interactions from bedpe file and define the interaction nodes,
the genomic interaction sites with multiple interaction loops.
The interaction nodes is a binding platform regulates one or
multiple genes. The detected interaction nodes will be
annotated for downstream validation.
biocViews: HiC, Sequencing, Software
Author: Jianhong Ou [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8652-2488>), Yarui Diao [fnd]
Maintainer: Jianhong Ou <jou@morgridge.org>
URL: https://github.com/jianhong/GenomicInteractionNodes
VignetteBuilder: knitr
BugReports: https://github.com/jianhong/GenomicInteractionNodes/issues
git_url: https://git.bioconductor.org/packages/GenomicInteractionNodes
git_branch: devel
git_last_commit: 628aad6
git_last_commit_date: 2025-02-12
Date/Publication: 2025-02-13
source.ver: src/contrib/GenomicInteractionNodes_1.11.3.tar.gz
win.binary.ver:
bin/windows/contrib/4.5/GenomicInteractionNodes_1.11.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GenomicInteractionNodes_1.11.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GenomicInteractionNodes_1.11.3.tgz
vignettes:
vignettes/GenomicInteractionNodes/inst/doc/GenomicInteractionNodes_vignettes.html
vignetteTitles: GenomicInteractionNodes Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles:
vignettes/GenomicInteractionNodes/inst/doc/GenomicInteractionNodes_vignettes.R
dependencyCount: 80
Package: GenomicInteractions
Version: 1.41.0
Depends: R (>= 3.5), InteractionSet
Imports: Rsamtools, rtracklayer, GenomicRanges (>= 1.29.6), IRanges,
BiocGenerics (>= 0.15.3), data.table, stringr, GenomeInfoDb,
ggplot2, grid, gridExtra, methods, igraph, S4Vectors (>=
0.13.13), dplyr, Gviz, Biobase, graphics, stats, utils,
grDevices
Suggests: knitr, rmarkdown, BiocStyle, testthat
License: GPL-3
MD5sum: 72890628f2db2d7333bb6a9188d09fce
NeedsCompilation: no
Title: Utilities for handling genomic interaction data
Description: Utilities for handling genomic interaction data such as
ChIA-PET or Hi-C, annotating genomic features with interaction
information, and producing plots and summary statistics.
biocViews: Software,Infrastructure,DataImport,DataRepresentation,HiC
Author: Harmston, N., Ing-Simmons, E., Perry, M., Baresic, A., Lenhard,
B.
Maintainer: Liz Ing-Simmons <liz.ingsimmons@gmail.com>
URL:
https://github.com/ComputationalRegulatoryGenomicsICL/GenomicInteractions/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GenomicInteractions
git_branch: devel
git_last_commit: c7d2904
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GenomicInteractions_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GenomicInteractions_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GenomicInteractions_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GenomicInteractions_1.41.0.tgz
vignettes:
vignettes/GenomicInteractions/inst/doc/chiapet_vignette.html,
vignettes/GenomicInteractions/inst/doc/hic_vignette.html
vignetteTitles: chiapet_vignette.html, GenomicInteractions-HiC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.R,
vignettes/GenomicInteractions/inst/doc/hic_vignette.R
importsMe: CAGEfightR, spatzie, OHCA
suggestsMe: Chicago, ELMER, extraChIPs, sevenC, chicane
dependencyCount: 158
Package: GenomicOZone
Version: 1.21.0
Depends: R (>= 4.0.0), Ckmeans.1d.dp (>= 4.3.0), GenomicRanges,
biomaRt, ggplot2
Imports: grDevices, stats, utils, plyr, gridExtra, lsr, parallel,
ggbio, S4Vectors, IRanges, GenomeInfoDb, Rdpack
Suggests: readxl, GEOquery, knitr, rmarkdown
License: LGPL (>=3)
MD5sum: 9719f701ee02bb9fefad6e649f8d086c
NeedsCompilation: no
Title: Delineate outstanding genomic zones of differential gene
activity
Description: The package clusters gene activity along chromosome into
zones, detects differential zones as outstanding, and
visualizes maps of outstanding zones across the genome. It
enables characterization of effects on multiple genes within
adaptive genomic neighborhoods, which could arise from genome
reorganization, structural variation, or epigenome alteration.
It guarantees cluster optimality, linear runtime to sample
size, and reproducibility. One can apply it on genome-wide
activity measurements such as copy number, transcriptomic,
proteomic, and methylation data.
biocViews: Software, GeneExpression, Transcription,
DifferentialExpression, FunctionalPrediction, GeneRegulation,
BiomedicalInformatics, CellBiology, FunctionalGenomics,
Genetics, SystemsBiology, Transcriptomics, Clustering,
Regression, RNASeq, Annotation, Visualization, Sequencing,
Coverage, DifferentialMethylation, GenomicVariation,
StructuralVariation, CopyNumberVariation
Author: Hua Zhong, Mingzhou Song
Maintainer: Hua Zhong<zh9118@gmail.com>, Mingzhou Song
<joemsong@cs.nmsu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GenomicOZone
git_branch: devel
git_last_commit: 423a96e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GenomicOZone_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GenomicOZone_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GenomicOZone_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GenomicOZone_1.21.0.tgz
vignettes: vignettes/GenomicOZone/inst/doc/GenomicOZone.html
vignetteTitles: GenomicOZone
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GenomicOZone/inst/doc/GenomicOZone.R
dependencyCount: 166
Package: GenomicPlot
Version: 1.5.3
Depends: R (>= 4.4.0), GenomicRanges (>= 1.46.1)
Imports: methods, Rsamtools, parallel, tidyr, rtracklayer (>= 1.54.0),
plyranges (>= 1.14.0), cowplot (>= 1.1.1), VennDiagram,
ggplotify, GenomeInfoDb, IRanges, ComplexHeatmap, RCAS (>=
1.20.0), scales (>= 1.2.0), GenomicAlignments (>= 1.30.0),
edgeR, circlize, viridis, ggsignif (>= 0.6.3), ggsci (>= 2.9),
ggpubr, grDevices, graphics, stats, utils, GenomicFeatures,
genomation (>= 1.36.0), txdbmaker, ggplot2 (>= 3.3.5),
BiocGenerics, dplyr, grid
Suggests: knitr, rmarkdown, R.utils, Biobase, BiocStyle, testthat,
AnnotationDbi
License: GPL-2
MD5sum: b38bbb8c849f7df71894518cb1dd3a49
NeedsCompilation: no
Title: Plot profiles of next generation sequencing data in genomic
features
Description: Visualization of next generation sequencing (NGS) data is
essential for interpreting high-throughput genomics experiment
results. 'GenomicPlot' facilitates plotting of NGS data in
various formats (bam, bed, wig and bigwig); both coverage and
enrichment over input can be computed and displayed with
respect to genomic features (such as UTR, CDS, enhancer), and
user defined genomic loci or regions. Statistical tests on
signal intensity within user defined regions of interest can be
performed and represented as boxplots or bar graphs. Parallel
processing is used to speed up computation on multicore
platforms. In addition to genomic plots which is suitable for
displaying of coverage of genomic DNA (such as ChIPseq data),
metagenomic (without introns) plots can also be made for RNAseq
or CLIPseq data as well.
biocViews: AlternativeSplicing, ChIPSeq, Coverage, GeneExpression,
RNASeq, Sequencing, Software, Transcription, Visualization,
Annotation
Author: Shuye Pu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6664-8438>)
Maintainer: Shuye Pu <shuye2009@gmail.com>
URL: https://github.com/shuye2009/GenomicPlot
VignetteBuilder: knitr
BugReports: https://github.com/shuye2009/GenomicPlot/issues
git_url: https://git.bioconductor.org/packages/GenomicPlot
git_branch: devel
git_last_commit: efbf81a
git_last_commit_date: 2025-01-30
Date/Publication: 2025-01-31
source.ver: src/contrib/GenomicPlot_1.5.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GenomicPlot_1.5.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GenomicPlot_1.5.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GenomicPlot_1.5.3.tgz
vignettes: vignettes/GenomicPlot/inst/doc/GenomicPlot_vignettes.html
vignetteTitles: GenomicPlot_vignettes.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GenomicPlot/inst/doc/GenomicPlot_vignettes.R
dependencyCount: 212
Package: GenomicRanges
Version: 1.59.1
Depends: R (>= 4.0.0), methods, stats4, BiocGenerics (>= 0.53.2),
S4Vectors (>= 0.45.2), IRanges (>= 2.41.1), GenomeInfoDb (>=
1.43.1)
Imports: utils, stats, XVector (>= 0.29.2)
LinkingTo: S4Vectors, IRanges
Suggests: Matrix, Biobase, AnnotationDbi, annotate, Biostrings (>=
2.25.3), SummarizedExperiment (>= 0.1.5), Rsamtools (>=
1.13.53), GenomicAlignments, rtracklayer, BSgenome,
GenomicFeatures, txdbmaker, Gviz, VariantAnnotation,
AnnotationHub, DESeq2, DEXSeq, edgeR, KEGGgraph,
RNAseqData.HNRNPC.bam.chr14, pasillaBamSubset, KEGGREST,
hgu95av2.db, hgu95av2probe, BSgenome.Scerevisiae.UCSC.sacCer2,
BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10,
TxDb.Athaliana.BioMart.plantsmart22,
TxDb.Dmelanogaster.UCSC.dm3.ensGene,
TxDb.Hsapiens.UCSC.hg38.knownGene,
TxDb.Mmusculus.UCSC.mm10.knownGene, RUnit, digest, knitr,
rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: 15318a339da11d8cbaa476ae63137912
NeedsCompilation: yes
Title: Representation and manipulation of genomic intervals
Description: The ability to efficiently represent and manipulate
genomic annotations and alignments is playing a central role
when it comes to analyzing high-throughput sequencing data
(a.k.a. NGS data). The GenomicRanges package defines general
purpose containers for storing and manipulating genomic
intervals and variables defined along a genome. More
specialized containers for representing and manipulating short
alignments against a reference genome, or a matrix-like
summarization of an experiment, are defined in the
GenomicAlignments and SummarizedExperiment packages,
respectively. Both packages build on top of the GenomicRanges
infrastructure.
biocViews: Genetics, Infrastructure, DataRepresentation, Sequencing,
Annotation, GenomeAnnotation, Coverage
Author: Patrick Aboyoun [aut], Hervé Pagès [aut, cre], Michael Lawrence
[aut], Sonali Arora [ctb], Martin Morgan [ctb], Kayla Morrell
[ctb], Valerie Obenchain [ctb], Marcel Ramos [ctb], Lori
Shepherd [ctb], Dan Tenenbaum [ctb], Daniel van Twisk [ctb]
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/GenomicRanges
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/GenomicRanges/issues
git_url: https://git.bioconductor.org/packages/GenomicRanges
git_branch: devel
git_last_commit: efa80fa
git_last_commit_date: 2024-11-15
Date/Publication: 2024-11-15
source.ver: src/contrib/GenomicRanges_1.59.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GenomicRanges_1.59.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GenomicRanges_1.59.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GenomicRanges_1.59.1.tgz
vignettes: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.pdf,
vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.pdf,
vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.pdf,
vignettes/GenomicRanges/inst/doc/Ten_things_slides.pdf,
vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.html
vignetteTitles: 5. Extending GenomicRanges, 2. GenomicRanges HOWTOs, 3.
A quick introduction to GRanges and GRangesList objects
(slides), 4. Ten Things You Didn't Know (slides from BioC
2016), 1. An Introduction to the GenomicRanges Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.R,
vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.R,
vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.R,
vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.R,
vignettes/GenomicRanges/inst/doc/Ten_things_slides.R
dependsOnMe: alabaster.ranges, AllelicImbalance, AneuFinder, annmap,
AnnotationHubData, BaalChIP, Basic4Cseq, betaHMM,
BindingSiteFinder, biomvRCNS, BiSeq, bnbc, BPRMeth,
breakpointR, BSgenome, bsseq, BubbleTree, bumphunter, CAFE,
CAGEfightR, casper, chimeraviz, ChIPpeakAnno, ChIPQC, chipseq,
chromPlot, CINdex, cn.mops, cnvGSA, CNVPanelizer, CNVRanger,
COCOA, Cogito, compEpiTools, consensusSeekeR, CSAR, csaw, CSSQ,
deepSNV, DEScan2, DESeq2, DEXSeq, DiffBind, diffHic, DMCFB,
DMCHMM, DMRcaller, DNAshapeR, easylift, EnrichedHeatmap,
ensembldb, epigenomix, esATAC, ExCluster, extraChIPs, fastseg,
fCCAC, FindIT2, GeneBreak, GenomicAlignments,
GenomicDistributions, GenomicFeatures, GenomicFiles,
GenomicOZone, GenomicPlot, GenomicScores, GenomicTuples, gmapR,
gmoviz, GOTHiC, GreyListChIP, gtrellis, GUIDEseq, Guitar, Gviz,
HelloRanges, HERON, hiAnnotator, HiCDOC, HiTC, IdeoViz, igvR,
igvShiny, InTAD, intansv, InteractionSet, IntEREst, IWTomics,
karyoploteR, m6Aboost, maser, MBASED, Melissa, metagene2,
methimpute, methodical, methylKit, methylPipe, minfi, MotifDb,
motifTestR, msgbsR, MutationalPatterns, NADfinder, oncoscanR,
ORFik, periodicDNA, plyranges, podkat, QuasR, r3Cseq,
RaggedExperiment, recoup, regioneR, RepViz, rGREAT, riboSeqR,
ribosomeProfilingQC, RJMCMCNucleosomes, RNAmodR, RnBeads,
Rsamtools, RSVSim, rtracklayer, SARC, Scale4C, SCOPE,
segmentSeq, seqArchRplus, seqCAT, SeqGate, SGSeq, SICtools,
SigFuge, SMITE, SNPhood, SomaticSignatures, spiky,
StructuralVariantAnnotation, SummarizedExperiment, svaNUMT,
svaRetro, tadar, TnT, trackViewer, transmogR, traseR, tRNA,
tRNAdbImport, tRNAscanImport, txdbmaker, VanillaICE, VarCon,
VariantAnnotation, VariantTools, VplotR, vtpnet, vulcan,
wavClusteR, YAPSA, EuPathDB, excluderanges, ChAMPdata,
EatonEtAlChIPseq, nullrangesData, RnBeads.hg19, RnBeads.hg38,
RnBeads.mm10, RnBeads.mm9, RnBeads.rn5, WGSmapp, liftOver,
sequencing, PlasmaMutationDetector, rnaCrosslinkOO, RTIGER
importsMe: ACE, alabaster.se, ALDEx2, amplican, AnnotationFilter,
annotatr, APAlyzer, apeglm, appreci8R, ASpli, AssessORF,
ATACseqQC, ATACseqTFEA, atena, BadRegionFinder, ballgown,
bambu, bamsignals, baySeq, BBCAnalyzer, beadarray, BEAT,
bedbaser, BiFET, BioTIP, biovizBase, biscuiteer, BiSeq,
BOBaFIT, borealis, branchpointer, BREW3R.r, BSgenomeForge,
BUSpaRse, cageminer, CAGEr, cardelino, cBioPortalData, CexoR,
cfdnakit, cfDNAPro, cfTools, chipenrich, ChIPexoQual,
ChIPseeker, chipseq, ChIPseqR, chromDraw, ChromHeatMap,
ChromSCape, chromVAR, cicero, circRNAprofiler, cleanUpdTSeq,
CleanUpRNAseq, cliProfiler, CNEr, CNVfilteR, CNViz, CNVMetrics,
comapr, coMethDMR, conumee, CopyNumberPlots, CoverageView,
crisprBase, crisprBowtie, crisprDesign, CRISPRseek,
CrispRVariants, crisprViz, CTexploreR, customProDB, DAMEfinder,
Damsel, debrowser, decompTumor2Sig, deconvR, DEFormats, DegCre,
DegNorm, deltaCaptureC, derfinder, derfinderPlot, DEWSeq,
diffUTR, dinoR, DMRcate, dmrseq, DNAfusion, DominoEffect,
doubletrouble, DRIMSeq, DropletUtils, DuplexDiscovereR,
easyRNASeq, EDASeq, EDIRquery, eisaR, ELMER, ELViS,
enhancerHomologSearch, epialleleR, EpiCompare, epidecodeR,
epigraHMM, EpiMix, epimutacions, epiregulon, epistack, EpiTxDb,
epivizr, epivizrData, erma, EventPointer, factR, fcScan,
FilterFFPE, fishpond, FLAMES, FRASER, G4SNVHunter, GA4GHclient,
gcapc, gDNAx, geneAttribution, GeneGeneInteR, GENESIS,
genomation, GenomAutomorphism, genomeIntervals,
GenomicAlignments, GenomicDataCommons, GenomicInteractionNodes,
GenomicInteractions, GenVisR, geomeTriD, ggbio, gINTomics,
GOfuncR, GrafGen, GRaNIE, gwascat, h5vc, heatmaps, hermes,
HicAggR, HiCBricks, HiCcompare, HiCExperiment, HiContacts,
HiCool, HiCParser, hicVennDiagram, HilbertCurve, HiLDA,
hiReadsProcessor, hummingbird, icetea, ideal, idr2d, IMAS,
iNETgrate, INSPEcT, ipdDb, IsoformSwitchAnalyzeR, isomiRs,
IVAS, karyoploteR, katdetectr, knowYourCG, loci2path, LOLA,
LoomExperiment, lumi, MADSEQ, magpie, mariner, mCSEA, MDTS,
MEAL, MEDIPS, megadepth, memes, metaseqR2, methInheritSim,
MethReg, methrix, methylCC, methylInheritance, MethylSeekR,
methylSig, methylumi, MinimumDistance, MIRA, missMethyl,
mitoClone2, MMDiff2, mobileRNA, Modstrings, monaLisa,
Moonlight2R, mosaics, Motif2Site, motifbreakR, motifmatchr,
MotifPeeker, MouseFM, MSA2dist, MultiAssayExperiment,
multicrispr, MultiDataSet, multiHiCcompare, MungeSumstats,
musicatk, NanoMethViz, ncRNAtools, nearBynding, normr, nucleR,
nullranges, OGRE, oligoClasses, OmaDB, openPrimeR,
Organism.dplyr, OrganismDbi, OUTRIDER, OutSplice, packFinder,
pageRank, panelcn.mops, partCNV, PAST, pcaExplorer, pepStat,
pgxRpi, PhIPData, PICB, PICS, PING, PIPETS, plotgardener,
plyinteractions, pqsfinder, pram, prebs, preciseTAD, primirTSS,
proActiv, proBAMr, profileplyr, ProteoDisco, PureCN, Pviz,
QDNAseq, qpgraph, qsea, Qtlizer, R3CPET, R453Plus1Toolbox,
raer, RAIDS, ramr, RareVariantVis, RCAS, RcisTarget, recount,
recount3, regionalpcs, regioneR, regionReport, regutools, REMP,
Repitools, RESOLVE, rfPred, rGADEM, RgnTX, Rhisat2, RiboCrypt,
RiboDiPA, RiboProfiling, rigvf, Rmmquant, rmspc, rnaEditr,
RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, roar,
RTCGAToolbox, saseR, scanMiR, scanMiRApp, scDblFinder, scmeth,
scoreInvHap, scPipe, scRNAseqApp, scruff, scuttle, segmenter,
seq2pathway, SeqArray, seqPattern, seqsetvis, SeqSQC,
SeqVarTools, sesame, sevenC, shinyepico, ShortRead, signeR,
SigsPack, SimFFPE, SingleCellExperiment,
SingleMoleculeFootprinting, sitadela, snapcount, soGGi,
SOMNiBUS, SparseSignatures, spatzie, SpectralTAD, SpliceWiz,
SplicingGraphs, SPLINTER, srnadiff, strandCheckR, syntenet,
systemPipeR, TAPseq, target, TCGAbiolinks, TCGAutils, TCseq,
TDbasedUFE, TDbasedUFEadv, TENET, TENxIO, terraTCGAdata, TFARM,
TFBSTools, TFEA.ChIP, TFHAZ, tidybulk, tidyCoverage, TitanCNA,
tLOH, tracktables, transcriptR, transite, TRESS, tricycle,
triplex, TVTB, txcutr, tximeta, Ularcirc, UMI4Cats,
uncoverappLib, Uniquorn, UPDhmm, VariantFiltering, VaSP,
VCFArray, wiggleplotr, xcore, XNAString, ZygosityPredictor,
BioMartGOGeneSets, fitCons.UCSC.hg19,
MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5,
MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5,
MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5,
MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5,
MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5,
MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5,
MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38,
MafH5.gnomAD.v4.0.GRCh38, phastCons100way.UCSC.hg19,
phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38,
SNPlocs.Hsapiens.dbSNP144.GRCh37,
SNPlocs.Hsapiens.dbSNP144.GRCh38,
SNPlocs.Hsapiens.dbSNP149.GRCh38,
SNPlocs.Hsapiens.dbSNP150.GRCh38,
SNPlocs.Hsapiens.dbSNP155.GRCh37,
SNPlocs.Hsapiens.dbSNP155.GRCh38, TENET.AnnotationHub,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh37,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, BioPlex, biscuiteerData,
chipenrich.data, COSMIC.67, ELMER.data, fourDNData,
GenomicDistributionsData, leeBamViews, mCSEAdata,
MethylSeqData, pepDat, scMultiome, scRNAseq, sesameData,
SomaticCancerAlterations, spatialLIBD, TENET.ExperimentHub,
TumourMethData, VariantToolsData, ExpHunterSuite,
recountWorkflow, cinaR, cpp11bigwig, crispRdesignR, driveR,
geneHapR, geno2proteo, GenoPop, hahmmr, hoardeR, ICAMS,
karyotapR, locuszoomr, lolliplot, LoopRig, MAAPER, MitoHEAR,
noisyr, numbat, oncoPredict, PACVr, RapidoPGS, revert,
scPloidy, Signac, simMP, VALERIE
suggestsMe: AlphaMissenseR, AnnotationHub, autonomics, biobroom,
BiocGenerics, BiocParallel, Chicago, ComplexHeatmap,
cummeRbund, DFplyr, epivizrChart, GenomeInfoDb, ggmanh, Glimma,
GSReg, GWASTools, HDF5Array, InteractiveComplexHeatmap,
interactiveDisplay, IRanges, iSEE, lute, maftools, MiRaGE,
MIRit, omicsPrint, parglms, recountmethylation, RTCGA,
S4Vectors, SeqGSEA, shiny.gosling, splatter, TFutils,
universalmotif, updateObject, alternativeSplicingEvents.hg19,
alternativeSplicingEvents.hg38, CTCF, GenomicState,
BeadArrayUseCases, GeuvadisTranscriptExpr, MetaScope,
nanotubes, RNAmodR.Data, Single.mTEC.Transcriptomes,
systemPipeRdata, xcoredata, CAGEWorkflow, chicane, DGEobj,
gkmSVM, MARVEL, polyRAD, Rgff, rliger, seqmagick, Seurat,
sigminer, smer, SNPassoc, updog, valr
dependencyCount: 22
Package: GenomicScores
Version: 2.19.1
Depends: R (>= 3.5), S4Vectors (>= 0.7.21), GenomicRanges, methods,
BiocGenerics (>= 0.13.8)
Imports: stats, utils, XML, httr, Biobase, BiocManager, BiocFileCache,
IRanges (>= 2.3.23), Biostrings, GenomeInfoDb, AnnotationHub,
rhdf5, DelayedArray, HDF5Array
Suggests: RUnit, BiocStyle, knitr, rmarkdown, VariantAnnotation,
gwascat, RColorBrewer, shiny, shinyjs, shinycustomloader,
data.table, DT, magrittr, shinydashboard,
BSgenome.Hsapiens.UCSC.hg38, phastCons100way.UCSC.hg38,
MafDb.1Kgenomes.phase1.hs37d5, MafH5.gnomAD.v4.0.GRCh38,
SNPlocs.Hsapiens.dbSNP144.GRCh37,
TxDb.Hsapiens.UCSC.hg38.knownGene
License: Artistic-2.0
MD5sum: c1e6fb3927e6707b226f0f071509fce3
NeedsCompilation: no
Title: Infrastructure to work with genomewide position-specific scores
Description: Provide infrastructure to store and access genomewide
position-specific scores within R and Bioconductor.
biocViews: Infrastructure, Genetics, Annotation, Sequencing, Coverage,
AnnotationHubSoftware
Author: Robert Castelo [aut, cre], Pau Puigdevall [ctb], Pablo
RodrÃguez [ctb]
Maintainer: Robert Castelo <robert.castelo@upf.edu>
URL: https://github.com/rcastelo/GenomicScores
VignetteBuilder: knitr
BugReports: https://github.com/rcastelo/GenomicScores/issues
git_url: https://git.bioconductor.org/packages/GenomicScores
git_branch: devel
git_last_commit: e3ad739
git_last_commit_date: 2025-02-01
Date/Publication: 2025-02-02
source.ver: src/contrib/GenomicScores_2.19.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GenomicScores_2.19.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GenomicScores_2.19.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GenomicScores_2.19.1.tgz
vignettes: vignettes/GenomicScores/inst/doc/GenomicScores.html
vignetteTitles: An introduction to the GenomicScores package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GenomicScores/inst/doc/GenomicScores.R
dependsOnMe: AlphaMissense.v2023.hg19, AlphaMissense.v2023.hg38,
cadd.v1.6.hg19, cadd.v1.6.hg38, fitCons.UCSC.hg19,
MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5,
MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5,
MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5,
MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5,
MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5,
MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5,
MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38,
MafH5.gnomAD.v4.0.GRCh38, phastCons100way.UCSC.hg19,
phastCons100way.UCSC.hg38, phastCons30way.UCSC.hg38,
phastCons35way.UCSC.mm39, phastCons7way.UCSC.hg38,
phyloP35way.UCSC.mm39
importsMe: appreci8R, ATACseqQC, primirTSS, RareVariantVis,
VariantFiltering
suggestsMe: methrix
dependencyCount: 81
Package: GenomicSuperSignature
Version: 1.15.0
Depends: R (>= 4.1.0), SummarizedExperiment
Imports: ComplexHeatmap, ggplot2, methods, S4Vectors, Biobase, ggpubr,
dplyr, plotly, BiocFileCache, grid, flextable, irlba
Suggests: knitr, rmarkdown, devtools, roxygen2, pkgdown, usethis,
BiocStyle, testthat, forcats, stats, wordcloud, circlize,
EnrichmentBrowser, clusterProfiler, msigdbr, cluster,
RColorBrewer, reshape2, tibble, BiocManager, bcellViper, readr,
utils
License: Artistic-2.0
Archs: x64
MD5sum: 6d24d51cac0fbd8a135e53044f02d556
NeedsCompilation: no
Title: Interpretation of RNA-seq experiments through robust, efficient
comparison to public databases
Description: This package provides a novel method for interpreting new
transcriptomic datasets through near-instantaneous comparison
to public archives without high-performance computing
requirements. Through the pre-computed index, users can
identify public resources associated with their dataset such as
gene sets, MeSH term, and publication. Functions to identify
interpretable annotations and intuitive visualization options
are implemented in this package.
biocViews: Transcriptomics, SystemsBiology, PrincipalComponent, RNASeq,
Sequencing, Pathways, Clustering
Author: Sehyun Oh [aut, cre], Levi Waldron [aut], Sean Davis [aut]
Maintainer: Sehyun Oh <shbrief@gmail.com>
URL: https://github.com/shbrief/GenomicSuperSignature
VignetteBuilder: knitr
BugReports: https://github.com/shbrief/GenomicSuperSignature/issues
git_url: https://git.bioconductor.org/packages/GenomicSuperSignature
git_branch: devel
git_last_commit: 675b807
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GenomicSuperSignature_1.15.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.5/GenomicSuperSignature_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GenomicSuperSignature_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GenomicSuperSignature_1.15.0.tgz
vignettes: vignettes/GenomicSuperSignature/inst/doc/Contents.html,
vignettes/GenomicSuperSignature/inst/doc/Quickstart.html
vignetteTitles: Introduction on RAVmodel, Quickstart
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GenomicSuperSignature/inst/doc/Contents.R,
vignettes/GenomicSuperSignature/inst/doc/Quickstart.R
dependencyCount: 165
Package: GenomicTuples
Version: 1.41.1
Depends: R (>= 4.0), GenomicRanges (>= 1.37.4), GenomeInfoDb (>=
1.15.2), S4Vectors (>= 0.17.25)
Imports: methods, BiocGenerics (>= 0.21.2), Rcpp (>= 0.11.2), IRanges
(>= 2.19.13), data.table, stats4, stats, utils
LinkingTo: Rcpp
Suggests: testthat, knitr, BiocStyle, rmarkdown, covr,
GenomicAlignments, Biostrings
License: Artistic-2.0
MD5sum: 0a3008d71c7f4f003dead5a1a8aae444
NeedsCompilation: yes
Title: Representation and Manipulation of Genomic Tuples
Description: GenomicTuples defines general purpose containers for
storing genomic tuples. It aims to provide functionality for
tuples of genomic co-ordinates that are analogous to those
available for genomic ranges in the GenomicRanges Bioconductor
package.
biocViews: Infrastructure, DataRepresentation, Sequencing
Author: Peter Hickey [aut, cre], Marcin Cieslik [ctb], Hervé Pagès
[ctb]
Maintainer: Peter Hickey <peter.hickey@gmail.com>
URL: www.github.com/PeteHaitch/GenomicTuples
VignetteBuilder: knitr
BugReports: https://github.com/PeteHaitch/GenomicTuples/issues
git_url: https://git.bioconductor.org/packages/GenomicTuples
git_branch: devel
git_last_commit: ff46eeb
git_last_commit_date: 2024-11-12
Date/Publication: 2024-11-13
source.ver: src/contrib/GenomicTuples_1.41.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GenomicTuples_1.41.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GenomicTuples_1.41.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GenomicTuples_1.41.1.tgz
vignettes:
vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.html
vignetteTitles: GenomicTuplesIntroduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.R
dependencyCount: 25
Package: GenProSeq
Version: 1.11.0
Depends: keras, mclust, R (>= 4.2)
Imports: tensorflow, word2vec, DeepPINCS, ttgsea, CatEncoders,
reticulate, stats
Suggests: VAExprs, stringdist, knitr, testthat, rmarkdown
License: Artistic-2.0
MD5sum: b885a9b2d5d2edfe10e03c117825d698
NeedsCompilation: no
Title: Generating Protein Sequences with Deep Generative Models
Description: Generative modeling for protein engineering is key to
solving fundamental problems in synthetic biology, medicine,
and material science. Machine learning has enabled us to
generate useful protein sequences on a variety of scales.
Generative models are machine learning methods which seek to
model the distribution underlying the data, allowing for the
generation of novel samples with similar properties to those on
which the model was trained. Generative models of proteins can
learn biologically meaningful representations helpful for a
variety of downstream tasks. Furthermore, they can learn to
generate protein sequences that have not been observed before
and to assign higher probability to protein sequences that
satisfy desired criteria. In this package, common deep
generative models for protein sequences, such as variational
autoencoder (VAE), generative adversarial networks (GAN), and
autoregressive models are available. In the VAE and GAN, the
Word2vec is used for embedding. The transformer encoder is
applied to protein sequences for the autoregressive model.
biocViews: Software, Proteomics
Author: Dongmin Jung [cre, aut] (ORCID:
<https://orcid.org/0000-0001-7499-8422>)
Maintainer: Dongmin Jung <dmdmjung@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GenProSeq
git_branch: devel
git_last_commit: 45b803f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GenProSeq_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GenProSeq_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GenProSeq_1.11.0.tgz
vignettes: vignettes/GenProSeq/inst/doc/GenProSeq.html
vignetteTitles: GenProSeq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GenProSeq/inst/doc/GenProSeq.R
dependencyCount: 149
Package: GenVisR
Version: 1.39.0
Depends: R (>= 3.3.0), methods
Imports: AnnotationDbi, biomaRt (>= 2.45.8), BiocGenerics, Biostrings,
DBI, GenomicFeatures, GenomicRanges (>= 1.25.4), ggplot2 (>=
2.1.0), gridExtra (>= 2.0.0), gtable, gtools, IRanges (>=
2.7.5), plyr (>= 1.8.3), reshape2, Rsamtools, scales, viridis,
data.table, BSgenome, GenomeInfoDb, VariantAnnotation
Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, knitr, RMySQL,
roxygen2, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene,
rmarkdown, vdiffr, formatR, TxDb.Hsapiens.UCSC.hg38.knownGene,
BSgenome.Hsapiens.UCSC.hg38
License: GPL-3 + file LICENSE
MD5sum: 87f8f68a1e14b7c50d048ac388449224
NeedsCompilation: no
Title: Genomic Visualizations in R
Description: Produce highly customizable publication quality graphics
for genomic data primarily at the cohort level.
biocViews: Infrastructure, DataRepresentation, Classification, DNASeq
Author: Zachary Skidmore [aut, cre], Alex Wagner [aut], Robert Lesurf
[aut], Katie Campbell [aut], Jason Kunisaki [aut], Obi Griffith
[aut], Malachi Griffith [aut]
Maintainer: Zachary Skidmore <zlskidmore@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/griffithlab/GenVisR/issues
git_url: https://git.bioconductor.org/packages/GenVisR
git_branch: devel
git_last_commit: cf208dc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GenVisR_1.39.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GenVisR_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GenVisR_1.39.0.tgz
vignettes: vignettes/GenVisR/inst/doc/Intro.html,
vignettes/GenVisR/inst/doc/waterfall_introduction.html
vignetteTitles: GenVisR: An introduction, waterfall: function
introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GenVisR/inst/doc/Intro.R,
vignettes/GenVisR/inst/doc/waterfall_introduction.R
dependencyCount: 123
Package: GeoDiff
Version: 1.13.0
Depends: R (>= 4.1.0), Biobase
Imports: Matrix, robust, plyr, lme4, Rcpp (>= 1.0.4.6), withr, methods,
graphics, stats, testthat, GeomxTools, NanoStringNCTools
LinkingTo: Rcpp, RcppArmadillo, roptim
Suggests: knitr, rmarkdown, dplyr
License: MIT + file LICENSE
Archs: x64
MD5sum: c38af61909fd4e14c12f89bf090acca6
NeedsCompilation: yes
Title: Count model based differential expression and normalization on
GeoMx RNA data
Description: A series of statistical models using count generating
distributions for background modelling, feature and sample QC,
normalization and differential expression analysis on GeoMx RNA
data. The application of these methods are demonstrated by
example data analysis vignette.
biocViews: GeneExpression, DifferentialExpression, Normalization
Author: Nicole Ortogero [cre], Lei Yang [aut], Zhi Yang [aut]
Maintainer: Nicole Ortogero <nortogero@nanostring.com>
URL: https://github.com/Nanostring-Biostats/GeoDiff
VignetteBuilder: knitr
BugReports: https://github.com/Nanostring-Biostats/GeoDiff
git_url: https://git.bioconductor.org/packages/GeoDiff
git_branch: devel
git_last_commit: e81e283
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GeoDiff_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GeoDiff_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GeoDiff_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GeoDiff_1.13.0.tgz
vignettes: vignettes/GeoDiff/inst/doc/Workflow_WTA_kidney.html
vignetteTitles: Workflow_WTA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GeoDiff/inst/doc/Workflow_WTA_kidney.R
dependencyCount: 152
Package: GEOexplorer
Version: 1.13.0
Depends: shiny, limma, Biobase, plotly, enrichR, R (>= 4.1.0)
Imports: DT, XML, httr, sva, xfun, edgeR, htmltools, factoextra,
heatmaply, pheatmap, scales, shinyHeatmaply, shinybusy,
ggplot2, stringr, umap, GEOquery, impute, grDevices, stats,
graphics, markdown, knitr, utils, xml2, R.utils, readxl,
shinycssloaders, car
Suggests: rmarkdown, usethis, testthat (>= 3.0.0)
License: GPL-3
MD5sum: d958e826cb3f82d45016c524578a280e
NeedsCompilation: no
Title: GEOexplorer: a webserver for gene expression analysis and
visualisation
Description: GEOexplorer is a webserver and R/Bioconductor package and
web application that enables users to perform gene expression
analysis. The development of GEOexplorer was made possible
because of the excellent code provided by GEO2R (https:
//www.ncbi.nlm.nih.gov/geo/geo2r/).
biocViews: Software, GeneExpression, mRNAMicroarray,
DifferentialExpression, Microarray, MicroRNAArray,
Transcriptomics, RNASeq
Author: Guy Hunt [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5217-2678>), Rafael Henkin [ctb,
ths] (ORCID: <https://orcid.org/0000-0002-5511-5230>), Alfredo
Iacoangeli [ctb, ths] (ORCID:
<https://orcid.org/0000-0002-5280-5017>), Fabrizio Smeraldi
[ctb, ths] (ORCID: <https://orcid.org/0000-0002-0057-8940>),
Michael Barnes [ctb, ths] (ORCID:
<https://orcid.org/0000-0001-9097-7381>)
Maintainer: Guy Hunt <guy.hunt@kcl.ac.uk>
URL: https://github.com/guypwhunt/GEOexplorer/
VignetteBuilder: knitr
BugReports: https://github.com/guypwhunt/GEOexplorer/issues
git_url: https://git.bioconductor.org/packages/GEOexplorer
git_branch: devel
git_last_commit: 6e19a44
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GEOexplorer_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GEOexplorer_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GEOexplorer_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GEOexplorer_1.13.0.tgz
vignettes: vignettes/GEOexplorer/inst/doc/GEOexplorer.html
vignetteTitles: GEOexplorer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GEOexplorer/inst/doc/GEOexplorer.R
dependencyCount: 230
Package: GEOfastq
Version: 1.15.0
Imports: xml2, rvest, stringr, RCurl, doParallel, foreach, plyr
Suggests: BiocCheck, roxygen2, knitr, rmarkdown, testthat
License: MIT + file LICENSE
Archs: x64
MD5sum: 44f73f06eeda9fba0e833cf40e82335c
NeedsCompilation: no
Title: Downloads ENA Fastqs With GEO Accessions
Description: GEOfastq is used to download fastq files from the European
Nucleotide Archive (ENA) starting with an accession from the
Gene Expression Omnibus (GEO). To do this, sample metadata is
retrieved from GEO and the Sequence Read Archive (SRA). SRA run
accessions are then used to construct FTP and aspera download
links for fastq files generated by the ENA.
biocViews: RNASeq, DataImport
Author: Alex Pickering [cre, aut] (ORCID:
<https://orcid.org/0000-0002-0002-6759>)
Maintainer: Alex Pickering <alexvpickering@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/alexvpickering/GEOfastq/issues
git_url: https://git.bioconductor.org/packages/GEOfastq
git_branch: devel
git_last_commit: 7ac929a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/GEOfastq_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GEOfastq_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GEOfastq_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GEOfastq_1.15.0.tgz
vignettes: vignettes/GEOfastq/inst/doc/GEOfastq.html
vignetteTitles: Using the GEOfastq Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GEOfastq/inst/doc/GEOfastq.R
dependencyCount: 38
Package: GEOmetadb
Version: 1.69.2
Depends: R.utils,RSQLite
Suggests: knitr, rmarkdown, dplyr, dbplyr, tm, wordcloud
License: Artistic-2.0
MD5sum: 2159a90b33aa4b133e4f3c8c8964331f
NeedsCompilation: no
Title: A compilation of metadata from NCBI GEO
Description: The NCBI Gene Expression Omnibus (GEO) represents the
largest public repository of microarray data. However, finding
data of interest can be challenging using current tools.
GEOmetadb is an attempt to make access to the metadata
associated with samples, platforms, and datasets much more
feasible. This is accomplished by parsing all the NCBI GEO
metadata into a SQLite database that can be stored and queried
locally. GEOmetadb is simply a thin wrapper around the SQLite
database along with associated documentation. Finally, the
SQLite database is updated regularly as new data is added to
GEO and can be downloaded at will for the most up-to-date
metadata. GEOmetadb paper:
http://bioinformatics.oxfordjournals.org/cgi/content/short/24/23/2798
.
biocViews: Infrastructure
Author: Jack Zhu and Sean Davis
Maintainer: Jack Zhu <zhujack@mail.nih.gov>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GEOmetadb
git_branch: devel
git_last_commit: 9a07aa7
git_last_commit_date: 2024-12-10
Date/Publication: 2024-12-10
source.ver: src/contrib/GEOmetadb_1.69.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GEOmetadb_1.69.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GEOmetadb_1.69.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GEOmetadb_1.69.2.tgz
vignettes: vignettes/GEOmetadb/inst/doc/GEOmetadb.html
vignetteTitles: GEOmetadb
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GEOmetadb/inst/doc/GEOmetadb.R
suggestsMe: antiProfilesData, maGUI
dependencyCount: 24
Package: geomeTriD
Version: 1.1.9
Depends: R (>= 4.4.0)
Imports: BiocGenerics, GenomeInfoDb, GenomicRanges, graphics,
grDevices, grid, htmlwidgets, igraph, InteractionSet, IRanges,
MASS, Matrix, methods, plotrix, rgl, rjson, S4Vectors, scales,
stats, trackViewer
Suggests: RUnit, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene,
manipulateWidget, shiny, BiocStyle, knitr, rmarkdown, testthat
License: MIT + file LICENSE
MD5sum: dcd55e615c4a2f74f3b45113dac0a088
NeedsCompilation: no
Title: A R/Bioconductor package for interactive 3D plot of epigenetic
data or single cell data
Description: geomeTriD (Three Dimensional Geometry Package) create
interactive 3D plots using the GL library with the 'three.js'
visualization library (https://threejs.org) or the rgl library.
In addition to creating interactive 3D plots, the application
also generates simplified models in 2D. These 2D models provide
a more straightforward visual representation, making it easier
to analyze and interpret the data quickly. This functionality
ensures that users have access to both detailed
three-dimensional visualizations and more accessible
two-dimensional views, catering to various analytical needs.
biocViews: Visualization
Author: Jianhong Ou [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8652-2488>)
Maintainer: Jianhong Ou <jou@morgridge.org>
URL: https://github.com/jianhong/geomeTriD
VignetteBuilder: knitr
BugReports: https://github.com/jianhong/geomeTriD/issues
git_url: https://git.bioconductor.org/packages/geomeTriD
git_branch: devel
git_last_commit: f140e46
git_last_commit_date: 2025-02-12
Date/Publication: 2025-02-13
source.ver: src/contrib/geomeTriD_1.1.9.tar.gz
win.binary.ver: bin/windows/contrib/4.5/geomeTriD_1.1.9.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/geomeTriD_1.1.9.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/geomeTriD_1.1.9.tgz
vignettes: vignettes/geomeTriD/inst/doc/geomeTriD.html
vignetteTitles: geomeTriD Vignette: Plot data in 3D
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/geomeTriD/inst/doc/geomeTriD.R
dependencyCount: 167
Package: GeomxTools
Version: 3.11.0
Depends: R (>= 3.6), Biobase, NanoStringNCTools, S4Vectors
Imports: BiocGenerics, rjson, readxl, EnvStats, reshape2, methods,
utils, stats, data.table, lmerTest, dplyr, stringr, grDevices,
graphics, GGally, rlang, ggplot2, SeuratObject
Suggests: rmarkdown, knitr, testthat (>= 3.0.0), parallel, ggiraph,
Seurat, SpatialExperiment (>= 1.4.0), SpatialDecon, patchwork
License: MIT
MD5sum: fee07998ac5baedd5b7d8afe3df55efe
NeedsCompilation: no
Title: NanoString GeoMx Tools
Description: Tools for NanoString Technologies GeoMx Technology.
Package provides functions for reading in DCC and PKC files
based on an ExpressionSet derived object. Normalization and QC
functions are also included.
biocViews: GeneExpression, Transcription, CellBasedAssays, DataImport,
Transcriptomics, Proteomics, mRNAMicroarray,
ProprietaryPlatforms, RNASeq, Sequencing, ExperimentalDesign,
Normalization, Spatial
Author: Maddy Griswold [cre, aut], Nicole Ortogero [aut], Zhi Yang
[aut], Ronalyn Vitancol [aut], David Henderson [aut]
Maintainer: Maddy Griswold <mgriswold@nanostring.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GeomxTools
git_branch: devel
git_last_commit: d685805
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GeomxTools_3.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GeomxTools_3.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GeomxTools_3.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GeomxTools_3.11.0.tgz
vignettes:
vignettes/GeomxTools/inst/doc/Developer_Introduction_to_the_NanoStringGeoMxSet.html,
vignettes/GeomxTools/inst/doc/GeomxSet_coercions.html,
vignettes/GeomxTools/inst/doc/Protein_in_GeomxTools.html
vignetteTitles: Developer Introduction to the NanoStringGeoMxSet,
Coercion of GeoMxSet to Seurat and SpatialExperiment Objects,
Protein data using GeomxTools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles:
vignettes/GeomxTools/inst/doc/Developer_Introduction_to_the_NanoStringGeoMxSet.R,
vignettes/GeomxTools/inst/doc/GeomxSet_coercions.R,
vignettes/GeomxTools/inst/doc/Protein_in_GeomxTools.R
dependsOnMe: GeoMxWorkflows
importsMe: GeoDiff, SpatialDecon, SpatialOmicsOverlay
dependencyCount: 130
Package: GEOquery
Version: 2.75.0
Depends: methods, Biobase
Imports: readr (>= 1.3.1), xml2, dplyr, data.table, tidyr, magrittr,
limma, curl, rentrez, R.utils, stringr, SummarizedExperiment,
S4Vectors, rvest, httr2
Suggests: knitr, rmarkdown, BiocGenerics, testthat, covr, markdown
License: MIT + file LICENSE
MD5sum: c2fe537f5a4f392721ca06b02d72a3fb
NeedsCompilation: no
Title: Get data from NCBI Gene Expression Omnibus (GEO)
Description: The NCBI Gene Expression Omnibus (GEO) is a public
repository of microarray data. Given the rich and varied
nature of this resource, it is only natural to want to apply
BioConductor tools to these data. GEOquery is the bridge
between GEO and BioConductor.
biocViews: Microarray, DataImport, OneChannel, TwoChannel, SAGE
Author: Sean Davis [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8991-6458>)
Maintainer: Sean Davis <seandavi@gmail.com>
URL: https://github.com/seandavi/GEOquery,
http://seandavi.github.io/GEOquery,
http://seandavi.github.io/GEOquery/
VignetteBuilder: knitr
BugReports: https://github.com/seandavi/GEOquery/issues/new
git_url: https://git.bioconductor.org/packages/GEOquery
git_branch: devel
git_last_commit: 2a416df
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GEOquery_2.75.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GEOquery_2.75.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GEOquery_2.75.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GEOquery_2.75.0.tgz
vignettes: vignettes/GEOquery/inst/doc/GEOquery.html
vignetteTitles: Using GEOquery
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GEOquery/inst/doc/GEOquery.R
dependsOnMe: DrugVsDisease, SCAN.UPC, dyebiasexamples, GSE103322,
GSE13015, GSE62944
importsMe: bigmelon, ChIPXpress, DExMA, EGAD, GEOexplorer, minfi,
Moonlight2R, MoonlightR, phantasus, recount, BeadArrayUseCases,
BioPlex, GSE13015, healthyControlsPresenceChecker,
geneExpressionFromGEO, RCPA
suggestsMe: AUCell, autonomics, COTAN, ctsGE, dearseq, debCAM,
diffcoexp, dyebias, EpiDISH, EpiMix, fgsea, FLAMES,
GeneExpressionSignature, GenomicOZone, GeoTcgaData,
methylclock, multiClust, MultiDataSet, omicsPrint, PCAtools,
phantasusLite, quantiseqr, RegEnrich, RGSEA, Rnits, runibic,
skewr, spatialHeatmap, TargetScore, zFPKM, ath1121501frmavecs,
airway, antiProfilesData, muscData, parathyroidSE,
prostateCancerCamcap, prostateCancerGrasso,
prostateCancerStockholm, prostateCancerTaylor,
prostateCancerVarambally, RegParallel, AnnoProbe, BED, easybio,
fdrci, maGUI, metaMA, MLML2R, NACHO, TcGSA, tinyarray
dependencyCount: 77
Package: GEOsubmission
Version: 1.59.0
Imports: affy, Biobase, utils
License: GPL (>= 2)
MD5sum: 71681b7ff7748e885a7d2bf4526dca72
NeedsCompilation: no
Title: Prepares microarray data for submission to GEO
Description: Helps to easily submit a microarray dataset and the
associated sample information to GEO by preparing a single file
for upload (direct deposit).
biocViews: Microarray
Author: Alexandre Kuhn <alexandre.m.kuhn@gmail.com>
Maintainer: Alexandre Kuhn <alexandre.m.kuhn@gmail.com>
git_url: https://git.bioconductor.org/packages/GEOsubmission
git_branch: devel
git_last_commit: d655fdc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GEOsubmission_1.59.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GEOsubmission_1.59.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GEOsubmission_1.59.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GEOsubmission_1.59.0.tgz
vignettes: vignettes/GEOsubmission/inst/doc/GEOsubmission.pdf
vignetteTitles: GEOsubmission Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GEOsubmission/inst/doc/GEOsubmission.R
dependencyCount: 12
Package: GeoTcgaData
Version: 2.7.0
Depends: R (>= 4.2.0)
Imports: utils, data.table, plyr, cqn, topconfects, stats,
SummarizedExperiment, methods
Suggests: knitr, rmarkdown, DESeq2, S4Vectors, ChAMP, impute, tidyr,
clusterProfiler, org.Hs.eg.db, edgeR, limma, quantreg, minfi,
IlluminaHumanMethylation450kanno.ilmn12.hg19, dearseq, NOISeq,
testthat (>= 3.0.0), CATT, TCGAbiolinks, enrichplot, GEOquery,
BiocGenerics
License: Artistic-2.0
MD5sum: 41861e6be3d1fb356ec3be8bacf916b8
NeedsCompilation: no
Title: Processing Various Types of Data on GEO and TCGA
Description: Gene Expression Omnibus(GEO) and The Cancer Genome Atlas
(TCGA) provide us with a wealth of data, such as RNA-seq, DNA
Methylation, SNP and Copy number variation data. It's easy to
download data from TCGA using the gdc tool, but processing
these data into a format suitable for bioinformatics analysis
requires more work. This R package was developed to handle
these data.
biocViews: GeneExpression, DifferentialExpression, RNASeq,
CopyNumberVariation, Microarray, Software, DNAMethylation,
DifferentialMethylation, SNP, ATACSeq, MethylationArray
Author: Erqiang Hu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1798-7513>)
Maintainer: Erqiang Hu <13766876214@163.com>
URL: https://github.com/YuLab-SMU/GeoTcgaData
VignetteBuilder: knitr
BugReports: https://github.com/YuLab-SMU/GeoTcgaData/issues
git_url: https://git.bioconductor.org/packages/GeoTcgaData
git_branch: devel
git_last_commit: 49ac154
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GeoTcgaData_2.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GeoTcgaData_2.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GeoTcgaData_2.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GeoTcgaData_2.7.0.tgz
vignettes: vignettes/GeoTcgaData/inst/doc/GeoTcgaData.html
vignetteTitles: GeoTcgaData
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GeoTcgaData/inst/doc/GeoTcgaData.R
dependencyCount: 74
Package: gep2pep
Version: 1.27.0
Imports: repo (>= 2.1.1), foreach, stats, utils, GSEABase, methods,
Biobase, XML, rhdf5, digest, iterators
Suggests: WriteXLS, testthat, knitr, rmarkdown
License: GPL-3
MD5sum: b410ffe8297f221ea3c80ed9ccf1ac8d
NeedsCompilation: no
Title: Creation and Analysis of Pathway Expression Profiles (PEPs)
Description: Pathway Expression Profiles (PEPs) are based on the
expression of pathways (defined as sets of genes) as opposed to
individual genes. This package converts gene expression
profiles to PEPs and performs enrichment analysis of both
pathways and experimental conditions, such as "drug set
enrichment analysis" and "gene2drug" drug discovery analysis
respectively.
biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment,
DimensionReduction, Pathways, GO
Author: Francesco Napolitano <franapoli@gmail.com>
Maintainer: Francesco Napolitano <franapoli@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/gep2pep
git_branch: devel
git_last_commit: 98fa3a0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/gep2pep_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gep2pep_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gep2pep_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gep2pep_1.27.0.tgz
vignettes: vignettes/gep2pep/inst/doc/vignette.html
vignetteTitles: Introduction to gep2pep
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gep2pep/inst/doc/vignette.R
dependencyCount: 58
Package: getDEE2
Version: 1.17.3
Depends: R (>= 4.0)
Imports: stats, utils, SummarizedExperiment, htm2txt
Suggests: knitr, testthat, rmarkdown
License: GPL-3
MD5sum: f458c750fe44445dd38374cd987a8011
NeedsCompilation: no
Title: Programmatic access to the DEE2 RNA expression dataset
Description: Digital Expression Explorer 2 (or DEE2 for short) is a
repository of processed RNA-seq data in the form of counts. It
was designed so that researchers could undertake re-analysis
and meta-analysis of published RNA-seq studies quickly and
easily. As of April 2020, over 1 million SRA datasets have been
processed. This package provides an R interface to access these
expression data. More information about the DEE2 project can be
found at the project homepage (http://dee2.io) and main
publication (https://doi.org/10.1093/gigascience/giz022).
biocViews: GeneExpression, Transcriptomics, Sequencing
Author: Mark Ziemann [aut, cre], Antony Kaspi [aut]
Maintainer: Mark Ziemann <mark.ziemann@gmail.com>
URL: https://github.com/markziemann/getDEE2
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/getDEE2
git_branch: devel
git_last_commit: 1ea1249
git_last_commit_date: 2025-01-13
Date/Publication: 2025-01-14
source.ver: src/contrib/getDEE2_1.17.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/getDEE2_1.17.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/getDEE2_1.17.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/getDEE2_1.17.3.tgz
vignettes: vignettes/getDEE2/inst/doc/getDEE2.html
vignetteTitles: getDEE2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/getDEE2/inst/doc/getDEE2.R
importsMe: homosapienDEE2CellScore
dependencyCount: 37
Package: geva
Version: 1.15.0
Depends: R (>= 4.1)
Imports: grDevices, graphics, methods, stats, utils, dbscan,
fastcluster, matrixStats
Suggests: devtools, knitr, rmarkdown, roxygen2, limma, topGO, testthat
(>= 3.0.0)
License: LGPL-3
MD5sum: 94faaabc99a34bdbe92afda8d61f2b83
NeedsCompilation: no
Title: Gene Expression Variation Analysis (GEVA)
Description: Statistic methods to evaluate variations of differential
expression (DE) between multiple biological conditions. It
takes into account the fold-changes and p-values from previous
differential expression (DE) results that use large-scale data
(*e.g.*, microarray and RNA-seq) and evaluates which genes
would react in response to the distinct experiments. This
evaluation involves an unique pipeline of statistical methods,
including weighted summarization, quantile detection, cluster
analysis, and ANOVA tests, in order to classify a subset of
relevant genes whose DE is similar or dependent to certain
biological factors.
biocViews: Classification, DifferentialExpression, GeneExpression,
Microarray, MultipleComparison, RNASeq, SystemsBiology,
Transcriptomics
Author: Itamar José Guimarães Nunes [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6246-4658>), Murilo Zanini David
[ctb], Bruno César Feltes [ctb] (ORCID:
<https://orcid.org/0000-0002-2825-8295>), Marcio Dorn [ctb]
(ORCID: <https://orcid.org/0000-0001-8534-3480>)
Maintainer: Itamar José Guimarães Nunes <nunesijg@gmail.com>
URL: https://github.com/sbcblab/geva
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/geva
git_branch: devel
git_last_commit: d89cefb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/geva_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/geva_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/geva_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/geva_1.15.0.tgz
vignettes: vignettes/geva/inst/doc/geva.pdf
vignetteTitles: GEVA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/geva/inst/doc/geva.R
dependencyCount: 10
Package: GEWIST
Version: 1.51.0
Depends: R (>= 2.10), car
License: GPL-2
MD5sum: a5923199545cab4f38f2b32125172ec4
NeedsCompilation: no
Title: Gene Environment Wide Interaction Search Threshold
Description: This 'GEWIST' package provides statistical tools to
efficiently optimize SNP prioritization for gene-gene and
gene-environment interactions.
biocViews: MultipleComparison, Genetics
Author: Wei Q. Deng, Guillaume Pare
Maintainer: Wei Q. Deng <dengwq@mcmaster.ca>
git_url: https://git.bioconductor.org/packages/GEWIST
git_branch: devel
git_last_commit: f80228c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GEWIST_1.51.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GEWIST_1.51.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GEWIST_1.51.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GEWIST_1.51.0.tgz
vignettes: vignettes/GEWIST/inst/doc/GEWIST.pdf
vignetteTitles: GEWIST.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GEWIST/inst/doc/GEWIST.R
dependencyCount: 72
Package: geyser
Version: 0.99.8
Depends: R (>= 3.5.0)
Imports: bslib (>= 0.6.0), BiocStyle, ComplexHeatmap, dplyr, DT,
ggbeeswarm, ggplot2, htmltools, magrittr, shiny,
SummarizedExperiment, tibble, tidyselect, tidyr
Suggests: airway, knitr, DESeq2, recount3, rmarkdown, stringr, testthat
(>= 3.0.0)
License: CC0
MD5sum: 77440e43bdbefe03fc7401d774bea6df
NeedsCompilation: no
Title: Gene Expression displaYer of SummarizedExperiment in R
Description: Lightweight Expression displaYer (plotter / viewer) of
SummarizedExperiment object in R. This package provides a quick
and easy Shiny-based GUI to empower a user to use a
SummarizedExperiment object to view (gene) expression grouped
from the sample metadata columns (in the `colData` slot).
Feature expression can either be viewed with a box plot or a
heatmap.
biocViews: Software, ShinyApps, GUI, GeneExpression
Author: David McGaughey [aut, cre] (ORCID:
<https://orcid.org/0000-0002-9224-2888>)
Maintainer: David McGaughey <mcgaughey@gmail.com>
URL: https://github.com/davemcg/geyser
VignetteBuilder: knitr
BugReports: https://github.com/davemcg/geyser/issues
git_url: https://git.bioconductor.org/packages/geyser
git_branch: devel
git_last_commit: 437a52d
git_last_commit_date: 2025-01-14
Date/Publication: 2025-01-14
source.ver: src/contrib/geyser_0.99.8.tar.gz
win.binary.ver: bin/windows/contrib/4.5/geyser_0.99.8.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/geyser_0.99.8.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/geyser_0.99.8.tgz
vignettes: vignettes/geyser/inst/doc/Gene_Expression_Plotting_GUI.html
vignetteTitles: Gene_Expression_Plotting_GUI
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/geyser/inst/doc/Gene_Expression_Plotting_GUI.R
dependencyCount: 120
Package: gg4way
Version: 1.5.0
Depends: R (>= 4.3.0), ggplot2
Imports: DESeq2, dplyr, edgeR, ggrepel, glue, janitor, limma, magrittr,
methods, purrr, rlang, scales, stats, stringr, tibble, tidyr
Suggests: airway, BiocStyle, knitr, org.Hs.eg.db, rmarkdown, testthat,
vdiffr
License: MIT + file LICENSE
MD5sum: f01db7c0264c1f61bb3ef2fe23fba14e
NeedsCompilation: no
Title: 4way Plots of Differential Expression
Description: 4way plots enable a comparison of the logFC values from
two contrasts of differential gene expression. The gg4way
package creates 4way plots using the ggplot2 framework and
supports popular Bioconductor objects. The package also
provides information about the correlation between contrasts
and significant genes of interest.
biocViews: Software, Visualization, DifferentialExpression,
GeneExpression, Transcription, RNASeq, SingleCell, Sequencing
Author: Benjamin I Laufer [aut, cre], Brad A Friedman [aut]
Maintainer: Benjamin I Laufer <blaufer@gmail.com>
URL: https://github.com/ben-laufer/gg4way
VignetteBuilder: knitr
BugReports: https://github.com/ben-laufer/gg4way/issues
git_url: https://git.bioconductor.org/packages/gg4way
git_branch: devel
git_last_commit: b67ad3a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/gg4way_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gg4way_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gg4way_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gg4way_1.5.0.tgz
vignettes: vignettes/gg4way/inst/doc/gg4way.html
vignetteTitles: gg4way
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/gg4way/inst/doc/gg4way.R
dependencyCount: 91
Package: ggbio
Version: 1.55.0
Depends: methods, BiocGenerics, ggplot2 (>= 1.0.0)
Imports: grid, grDevices, graphics, stats, utils, gridExtra, scales,
reshape2, gtable, Hmisc, biovizBase (>= 1.29.2), Biobase,
S4Vectors (>= 0.13.13), IRanges (>= 2.11.16), GenomeInfoDb (>=
1.1.3), GenomicRanges (>= 1.29.14), SummarizedExperiment,
Biostrings, Rsamtools (>= 1.17.28), GenomicAlignments (>=
1.1.16), BSgenome, VariantAnnotation (>= 1.11.4), rtracklayer
(>= 1.25.16), GenomicFeatures (>= 1.29.11), OrganismDbi,
GGally, ensembldb (>= 1.99.13), AnnotationDbi,
AnnotationFilter, rlang
Suggests: vsn, BSgenome.Hsapiens.UCSC.hg19, Homo.sapiens,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Mmusculus.UCSC.mm9.knownGene, knitr, BiocStyle, testthat,
EnsDb.Hsapiens.v75, tinytex
License: Artistic-2.0
MD5sum: 5bd9bce4f66ee9564c6071d78bde3303
NeedsCompilation: no
Title: Visualization tools for genomic data
Description: The ggbio package extends and specializes the grammar of
graphics for biological data. The graphics are designed to
answer common scientific questions, in particular those often
asked of high throughput genomics data. All core Bioconductor
data structures are supported, where appropriate. The package
supports detailed views of particular genomic regions, as well
as genome-wide overviews. Supported overviews include ideograms
and grand linear views. High-level plots include sequence
fragment length, edge-linked interval to data view, mismatch
pileup, and several splicing summaries.
biocViews: Infrastructure, Visualization
Author: Tengfei Yin [aut], Michael Lawrence [aut, ths, cre], Dianne
Cook [aut, ths], Johannes Rainer [ctb]
Maintainer: Michael Lawrence <michafla@gene.com>
URL: https://lawremi.github.io/ggbio/
VignetteBuilder: knitr
BugReports: https://github.com/lawremi/ggbio/issues
git_url: https://git.bioconductor.org/packages/ggbio
git_branch: devel
git_last_commit: 289a36c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ggbio_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ggbio_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ggbio_1.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ggbio_1.55.0.tgz
vignettes: vignettes/ggbio/inst/doc/ggbio.pdf
vignetteTitles: Part 0: Introduction and quick start
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: CAFE, intansv
importsMe: BOBaFIT, cageminer, Damsel, derfinderPlot, FLAMES,
GenomicOZone, msgbsR, R3CPET, ReportingTools, RiboProfiling,
scruff, SomaticSignatures, OHCA
suggestsMe: bambu, beadarray, ensembldb, FRASER, gwascat,
interactiveDisplay, NanoStringNCTools, OUTRIDER, regionReport,
RnBeads, shiny.gosling, StructuralVariantAnnotation,
universalmotif, NanoporeRNASeq, Single.mTEC.Transcriptomes,
SomaticCancerAlterations
dependencyCount: 161
Package: ggcyto
Version: 1.35.0
Depends: methods, ggplot2(>= 3.5.0), flowCore(>= 1.41.5), ncdfFlow(>=
2.17.1), flowWorkspace(>= 4.3.1)
Imports: plyr, scales, hexbin, data.table, RColorBrewer, gridExtra,
rlang
Suggests: testthat, flowWorkspaceData, knitr, rmarkdown, flowStats,
openCyto, flowViz, ggridges, vdiffr
License: file LICENSE
MD5sum: e7bbdb05371ed796964828177de690fe
NeedsCompilation: no
Title: Visualize Cytometry data with ggplot
Description: With the dedicated fortify method implemented for flowSet,
ncdfFlowSet and GatingSet classes, both raw and gated flow
cytometry data can be plotted directly with ggplot. ggcyto
wrapper and some customed layers also make it easy to add gates
and population statistics to the plot.
biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays,
Infrastructure, Visualization
Author: Mike Jiang
Maintainer: Mike Jiang <mike@ozette.com>
URL: https://github.com/RGLab/ggcyto/issues
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ggcyto
git_branch: devel
git_last_commit: 21e5576
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ggcyto_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ggcyto_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ggcyto_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ggcyto_1.35.0.tgz
vignettes: vignettes/ggcyto/inst/doc/autoplot.html,
vignettes/ggcyto/inst/doc/ggcyto.flowSet.html,
vignettes/ggcyto/inst/doc/ggcyto.GatingSet.html,
vignettes/ggcyto/inst/doc/Top_features_of_ggcyto.html
vignetteTitles: Quick plot for cytometry data, Visualize flowSet with
ggcyto, Visualize GatingSet with ggcyto, Feature summary of
ggcyto
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ggcyto/inst/doc/autoplot.R,
vignettes/ggcyto/inst/doc/ggcyto.flowSet.R,
vignettes/ggcyto/inst/doc/ggcyto.GatingSet.R,
vignettes/ggcyto/inst/doc/Top_features_of_ggcyto.R
dependsOnMe: flowGate
importsMe: CytoML, CytoPipeline
suggestsMe: CATALYST, flowCore, flowStats, flowTime, flowWorkspace,
openCyto
dependencyCount: 69
Package: ggkegg
Version: 1.5.1
Depends: R (>= 4.3.0), ggplot2, ggraph, XML, igraph, tidygraph
Imports: BiocFileCache, data.table, dplyr, magick, patchwork,
shadowtext, stringr, tibble, methods, utils, stats, grDevices,
gtable
Suggests: knitr, clusterProfiler, bnlearn, rmarkdown, BiocStyle,
AnnotationDbi, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: 7330f478c7a6297903774514c50a66ef
NeedsCompilation: no
Title: Analyzing and visualizing KEGG information using the grammar of
graphics
Description: This package aims to import, parse, and analyze KEGG data
such as KEGG PATHWAY and KEGG MODULE. The package supports
visualizing KEGG information using ggplot2 and ggraph through
using the grammar of graphics. The package enables the direct
visualization of the results from various omics analysis
packages.
biocViews: Pathways, DataImport, KEGG
Author: Noriaki Sato [cre, aut]
Maintainer: Noriaki Sato <nori@hgc.jp>
URL: https://github.com/noriakis/ggkegg
VignetteBuilder: knitr
BugReports: https://github.com/noriakis/ggkegg/issues
git_url: https://git.bioconductor.org/packages/ggkegg
git_branch: devel
git_last_commit: a1cea0f
git_last_commit_date: 2025-01-29
Date/Publication: 2025-01-29
source.ver: src/contrib/ggkegg_1.5.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ggkegg_1.5.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ggkegg_1.5.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ggkegg_1.5.1.tgz
vignettes: vignettes/ggkegg/inst/doc/usage_of_ggkegg.html
vignetteTitles: ggkegg
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ggkegg/inst/doc/usage_of_ggkegg.R
importsMe: pathfindR
dependencyCount: 82
Package: ggmanh
Version: 1.11.0
Depends: methods, ggplot2
Imports: gdsfmt, ggrepel, grDevices, paletteer, RColorBrewer, rlang,
scales, SeqArray (>= 1.32.0), stats, tidyr, dplyr, pals,
magrittr
Suggests: BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0),
GenomicRanges
License: MIT + file LICENSE
MD5sum: 96897f0e2e4f5a8b4a9011a200a04030
NeedsCompilation: no
Title: Visualization Tool for GWAS Result
Description: Manhattan plot and QQ Plot are commonly used to visualize
the end result of Genome Wide Association Study. The "ggmanh"
package aims to keep the generation of these plots simple while
maintaining customizability. Main functions include
manhattan_plot, qqunif, and thinPoints.
biocViews: Visualization, GenomeWideAssociation, Genetics
Author: John Lee [aut, cre], John Lee [aut] (AbbVie), Xiuwen Zheng
[ctb, dtc]
Maintainer: John Lee <swannyy.stat@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ggmanh
git_branch: devel
git_last_commit: c8acd63
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ggmanh_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ggmanh_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ggmanh_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ggmanh_1.11.0.tgz
vignettes: vignettes/ggmanh/inst/doc/ggmanh.html
vignetteTitles: Guide to ggmanh Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ggmanh/inst/doc/ggmanh.R
suggestsMe: SAIGEgds
dependencyCount: 76
Package: ggmsa
Version: 1.13.1
Depends: R (>= 4.1.0)
Imports: Biostrings, ggplot2, magrittr, tidyr, utils, stats, aplot,
RColorBrewer, ggalt, ggforce, dplyr, R4RNA, grDevices,
seqmagick, grid, methods, statebins, ggtree (>= 1.17.1)
Suggests: ggtreeExtra, ape, cowplot, knitr, BiocStyle, rmarkdown,
readxl, ggnewscale, kableExtra, gggenes, testthat (>= 3.0.0)
License: Artistic-2.0
MD5sum: 3897916d10f6c061d8e0a776a62ae56b
NeedsCompilation: no
Title: Plot Multiple Sequence Alignment using 'ggplot2'
Description: A visual exploration tool for multiple sequence alignment
and associated data. Supports MSA of DNA, RNA, and protein
sequences using 'ggplot2'. Multiple sequence alignment can
easily be combined with other 'ggplot2' plots, such as
phylogenetic tree Visualized by 'ggtree', boxplot, genome map
and so on. More features: visualization of sequence logos,
sequence bundles, RNA secondary structures and detection of
sequence recombinations.
biocViews: Software, Visualization, Alignment, Annotation,
MultipleSequenceAlignment
Author: Guangchuang Yu [aut, cre, ths] (ORCID:
<https://orcid.org/0000-0002-6485-8781>), Lang Zhou [aut],
Shuangbin Xu [ctb], Huina Huang [ctb]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
URL: https://doi.org/10.1093/bib/bbac222(paper),
https://www.amazon.com/Integration-Manipulation-Visualization-Phylogenetic-Computational-ebook/dp/B0B5NLZR1Z/
(book)
VignetteBuilder: knitr
BugReports: https://github.com/YuLab-SMU/ggmsa/issues
git_url: https://git.bioconductor.org/packages/ggmsa
git_branch: devel
git_last_commit: b90d4b9
git_last_commit_date: 2024-12-19
Date/Publication: 2024-12-20
source.ver: src/contrib/ggmsa_1.13.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ggmsa_1.13.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ggmsa_1.13.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ggmsa_1.13.1.tgz
vignettes: vignettes/ggmsa/inst/doc/ggmsa.html
vignetteTitles: ggmsa
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ggmsa/inst/doc/ggmsa.R
importsMe: ggaligner, SeedMatchR
dependencyCount: 115
Package: GGPA
Version: 1.19.0
Depends: R (>= 4.0.0), stats, methods, graphics, GGally, network, sna,
scales, matrixStats
Imports: Rcpp (>= 0.11.3)
LinkingTo: Rcpp, RcppArmadillo
Suggests: BiocStyle
License: GPL (>= 2)
MD5sum: fb6053eb870f7dbd3e5a79709ab02d6c
NeedsCompilation: yes
Title: graph-GPA: A graphical model for prioritizing GWAS results and
investigating pleiotropic architecture
Description: Genome-wide association studies (GWAS) is a widely used
tool for identification of genetic variants associated with
phenotypes and diseases, though complex diseases featuring many
genetic variants with small effects present difficulties for
traditional these studies. By leveraging pleiotropy, the
statistical power of a single GWAS can be increased. This
package provides functions for fitting graph-GPA, a statistical
framework to prioritize GWAS results by integrating pleiotropy.
'GGPA' package provides user-friendly interface to fit
graph-GPA models, implement association mapping, and generate a
phenotype graph.
biocViews: Software, StatisticalMethod, Classification,
GenomeWideAssociation, SNP, Genetics, Clustering,
MultipleComparison, Preprocessing, GeneExpression,
DifferentialExpression
Author: Dongjun Chung, Hang J. Kim, Carter Allen
Maintainer: Dongjun Chung <dongjun.chung@gmail.com>
URL: https://github.com/dongjunchung/GGPA/
SystemRequirements: GNU make
git_url: https://git.bioconductor.org/packages/GGPA
git_branch: devel
git_last_commit: 872cd05
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GGPA_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GGPA_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GGPA_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GGPA_1.19.0.tgz
vignettes: vignettes/GGPA/inst/doc/GGPA-example.pdf
vignetteTitles: GGPA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GGPA/inst/doc/GGPA-example.R
dependencyCount: 61
Package: ggsc
Version: 1.5.0
Imports: Rcpp, RcppParallel, cli, dplyr, ggfun (>= 0.1.5), ggplot2,
grDevices, grid, methods, rlang, scattermore, stats, Seurat,
SingleCellExperiment, SummarizedExperiment, tidydr, tidyr,
tibble, utils, RColorBrewer, yulab.utils, scales
LinkingTo: Rcpp, RcppArmadillo, RcppParallel
Suggests: aplot, BiocParallel, forcats, ggforce, ggnewscale, igraph,
knitr, ks, Matrix, prettydoc, rmarkdown, scran, scater,
scatterpie (>= 0.2.4), scuttle, shadowtext, sf, SeuratObject,
SpatialExperiment, STexampleData, testthat (>= 3.0.0), MASS
License: Artistic-2.0
MD5sum: 1faa602e3dbcc94518b181cb5c3d35d0
NeedsCompilation: yes
Title: Visualizing Single Cell and Spatial Transcriptomics
Description: Useful functions to visualize single cell and spatial
data. It supports visualizing 'Seurat', 'SingleCellExperiment'
and 'SpatialExperiment' objects through grammar of graphics
syntax implemented in 'ggplot2'.
biocViews: DimensionReduction, GeneExpression, SingleCell, Software,
Spatial, Transcriptomics,Visualization
Author: Guangchuang Yu [aut, cre, cph] (ORCID:
<https://orcid.org/0000-0002-6485-8781>), Shuangbin Xu [aut]
(ORCID: <https://orcid.org/0000-0003-3513-5362>), Noriaki Sato
[ctb]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
URL: https://github.com/YuLab-SMU/ggsc (devel),
https://yulab-smu.top/ggsc/ (docs)
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/YuLab-SMU/ggsc/issues
git_url: https://git.bioconductor.org/packages/ggsc
git_branch: devel
git_last_commit: 95664cd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ggsc_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ggsc_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ggsc_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ggsc_1.5.0.tgz
vignettes: vignettes/ggsc/inst/doc/ggsc.html
vignetteTitles: Visualizing single cell data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ggsc/inst/doc/ggsc.R
suggestsMe: SVP
dependencyCount: 175
Package: ggseqalign
Version: 1.1.2
Depends: R (>= 4.4.0)
Imports: pwalign, dplyr, ggplot2
Suggests: Biostrings, BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0)
License: Artistic-2.0
Archs: x64
MD5sum: 8e85e24e52cab7fdc629dc560a7b9524
NeedsCompilation: no
Title: Minimal Visualization of Sequence Alignments
Description: Simple visualizations of alignments of DNA or AA sequences
as well as arbitrary strings. Compatible with Biostrings and
ggplot2. The plots are fully customizable using ggplot2
modifiers such as theme().
biocViews: Alignment, MultipleSequenceAlignment, Software,
Visualization
Author: Simeon Lim Rossmann [aut, cre] (ORCID:
<https://orcid.org/0000-0003-0435-8221>)
Maintainer: Simeon Lim Rossmann <simeon.rossmann@nmbu.no>
URL: https://github.com/simeross/ggseqalign
VignetteBuilder: knitr
BugReports: https://github.com/simeross/ggseqalign/issues
git_url: https://git.bioconductor.org/packages/ggseqalign
git_branch: devel
git_last_commit: 8d3190c
git_last_commit_date: 2025-03-03
Date/Publication: 2025-03-03
source.ver: src/contrib/ggseqalign_1.1.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ggseqalign_1.1.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ggseqalign_1.1.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ggseqalign_1.1.2.tgz
vignettes: vignettes/ggseqalign/inst/doc/ggseqalign.html
vignetteTitles: Quickstart Guide to ggseqalign
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ggseqalign/inst/doc/ggseqalign.R
dependencyCount: 57
Package: ggspavis
Version: 1.13.0
Depends: ggplot2
Imports: SpatialExperiment, SingleCellExperiment, SummarizedExperiment,
ggside, grid, ggrepel, RColorBrewer, scales, grDevices,
methods, stats
Suggests: BiocStyle, rmarkdown, knitr, STexampleData, BumpyMatrix,
scater, scran, uwot, testthat, patchwork
License: MIT + file LICENSE
Archs: x64
MD5sum: 6a55a885cccd2399e9c02fdd8dc1ee02
NeedsCompilation: no
Title: Visualization functions for spatial transcriptomics data
Description: Visualization functions for spatial transcriptomics data.
Includes functions to generate several types of plots,
including spot plots, feature (molecule) plots, reduced
dimension plots, spot-level quality control (QC) plots, and
feature-level QC plots, for datasets from the 10x Genomics
Visium and other technological platforms. Datasets are assumed
to be in either SpatialExperiment or SingleCellExperiment
format.
biocViews: Spatial, SingleCell, Transcriptomics, GeneExpression,
QualityControl, DimensionReduction
Author: Lukas M. Weber [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3282-1730>), Helena L. Crowell
[aut] (ORCID: <https://orcid.org/0000-0002-4801-1767>), Yixing
E. Dong [aut] (ORCID: <https://orcid.org/0009-0003-5115-5686>)
Maintainer: Lukas M. Weber <lmweberedu@gmail.com>
URL: https://github.com/lmweber/ggspavis
VignetteBuilder: knitr
BugReports: https://github.com/lmweber/ggspavis/issues
git_url: https://git.bioconductor.org/packages/ggspavis
git_branch: devel
git_last_commit: 479bf59
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ggspavis_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ggspavis_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ggspavis_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ggspavis_1.13.0.tgz
vignettes: vignettes/ggspavis/inst/doc/ggspavis_overview.html
vignetteTitles: ggspavis overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ggspavis/inst/doc/ggspavis_overview.R
suggestsMe: smoothclust, HCATonsilData
dependencyCount: 88
Package: ggtree
Version: 3.15.0
Depends: R (>= 3.5.0)
Imports: ape, aplot, dplyr, ggplot2 (> 3.3.6), grid, magrittr, methods,
purrr, rlang, ggfun (>= 0.1.7), yulab.utils (>= 0.1.6), tidyr,
tidytree (>= 0.4.5), treeio (>= 1.8.0), utils, scales, stats,
cli
Suggests: emojifont, ggimage, ggplotify, shadowtext, grDevices, knitr,
prettydoc, rmarkdown, testthat, tibble, glue
License: Artistic-2.0
MD5sum: e6e370294b42f85c77689f426ea6caa6
NeedsCompilation: no
Title: an R package for visualization of tree and annotation data
Description: 'ggtree' extends the 'ggplot2' plotting system which
implemented the grammar of graphics. 'ggtree' is designed for
visualization and annotation of phylogenetic trees and other
tree-like structures with their annotation data.
biocViews: Alignment, Annotation, Clustering, DataImport,
MultipleSequenceAlignment, Phylogenetics, ReproducibleResearch,
Software, Visualization
Author: Guangchuang Yu [aut, cre, cph] (ORCID:
<https://orcid.org/0000-0002-6485-8781>), Tommy Tsan-Yuk Lam
[aut, ths], Shuangbin Xu [aut] (ORCID:
<https://orcid.org/0000-0003-3513-5362>), Lin Li [ctb], Bradley
Jones [ctb], Justin Silverman [ctb], Watal M. Iwasaki [ctb],
Yonghe Xia [ctb], Ruizhu Huang [ctb]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
URL:
https://www.amazon.com/Integration-Manipulation-Visualization-Phylogenetic-Computational-ebook/dp/B0B5NLZR1Z/
(book),
http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12628
(paper)
VignetteBuilder: knitr
BugReports: https://github.com/YuLab-SMU/ggtree/issues
git_url: https://git.bioconductor.org/packages/ggtree
git_branch: devel
git_last_commit: ac91900
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ggtree_3.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ggtree_3.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ggtree_3.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ggtree_3.15.0.tgz
vignettes: vignettes/ggtree/inst/doc/ggtree.html
vignetteTitles: ggtree
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ggtree/inst/doc/ggtree.R
dependsOnMe: ggtreeDendro, tanggle
importsMe: cardelino, cogeqc, enrichplot, ggmsa, ggtreeExtra,
ggtreeSpace, gINTomics, iSEEtree, lefser, LinTInd, LymphoSeq,
miaViz, MicrobiotaProcess, orthogene, philr, scBubbletree,
scDotPlot, singleCellTK, sitePath, SVP, systemPipeTools,
treeclimbR, treekoR, BioVizSeq, DAISIEprep, ddtlcm, dowser,
EvoPhylo, FossilSim, genBaRcode, harrietr, numbat, Platypus,
RevGadgets, scistreer, shinyTempSignal, STraTUS, Sysrecon,
TransProR
suggestsMe: compcodeR, syntenet, TreeAndLeaf, treeio,
TreeSummarizedExperiment, universalmotif, aplot, aplotExtra,
CoOL, DAISIE, deeptime, gggenomes, ggimage, ggtangle,
idiogramFISH, MetaNet, nosoi, oppr, PCMBase, pctax, RAINBOWR,
rhierbaps, rphylopic
dependencyCount: 59
Package: ggtreeDendro
Version: 1.9.0
Depends: ggtree (>= 3.5.3)
Imports: ggplot2, stats, tidytree, utils
Suggests: aplot, cluster, knitr, MASS, mdendro, prettydoc, pvclust,
rmarkdown, testthat (>= 3.0.0), treeio, yulab.utils
License: Artistic-2.0
MD5sum: df5a67737c1d7da95c7a70710108023c
NeedsCompilation: no
Title: Drawing 'dendrogram' using 'ggtree'
Description: Offers a set of 'autoplot' methods to visualize tree-like
structures (e.g., hierarchical clustering and
classification/regression trees) using 'ggtree'. You can adjust
graphical parameters using grammar of graphic syntax and
integrate external data to the tree.
biocViews: Clustering, Classification, DecisionTree, Phylogenetics,
Visualization
Author: Guangchuang Yu [aut, cre, cph] (ORCID:
<https://orcid.org/0000-0002-6485-8781>), Shuangbin Xu [ctb]
(ORCID: <https://orcid.org/0000-0003-3513-5362>), Chuanjie
Zhang [ctb]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ggtreeDendro
git_branch: devel
git_last_commit: ecbc889
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ggtreeDendro_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ggtreeDendro_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ggtreeDendro_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ggtreeDendro_1.9.0.tgz
vignettes: vignettes/ggtreeDendro/inst/doc/ggtreeDendro.html
vignetteTitles: Visualizing Dendrogram using ggtree
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ggtreeDendro/inst/doc/ggtreeDendro.R
dependencyCount: 60
Package: ggtreeExtra
Version: 1.17.0
Imports: ggplot2, utils, rlang, ggnewscale, stats, ggtree, tidytree (>=
0.3.9), cli, magrittr
Suggests: treeio, ggstar, patchwork, knitr, rmarkdown, prettydoc,
markdown, testthat (>= 3.0.0), pillar
License: GPL (>= 3)
MD5sum: 52ec85f98a6658c924536ffefaffc7d0
NeedsCompilation: no
Title: An R Package To Add Geometric Layers On Circular Or Other Layout
Tree Of "ggtree"
Description: 'ggtreeExtra' extends the method for mapping and
visualizing associated data on phylogenetic tree using
'ggtree'. These associated data can be presented on the
external panels to circular layout, fan layout, or other
rectangular layout tree built by 'ggtree' with the grammar of
'ggplot2'.
biocViews: Software, Visualization, Phylogenetics, Annotation
Author: Shuangbin Xu [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3513-5362>), Guangchuang Yu [aut,
ctb] (ORCID: <https://orcid.org/0000-0002-6485-8781>)
Maintainer: Shuangbin Xu <xshuangbin@163.com>
URL: https://github.com/YuLab-SMU/ggtreeExtra/
VignetteBuilder: knitr
BugReports: https://github.com/YuLab-SMU/ggtreeExtra/issues
git_url: https://git.bioconductor.org/packages/ggtreeExtra
git_branch: devel
git_last_commit: 5620083
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ggtreeExtra_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ggtreeExtra_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ggtreeExtra_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ggtreeExtra_1.17.0.tgz
vignettes: vignettes/ggtreeExtra/inst/doc/ggtreeExtra.html
vignetteTitles: ggtreeExtra
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ggtreeExtra/inst/doc/ggtreeExtra.R
importsMe: MicrobiotaProcess
suggestsMe: enrichplot, ggmsa, pctax, TransProR
dependencyCount: 61
Package: ggtreeSpace
Version: 1.3.0
Imports: interp, ape, dplyr, GGally, ggplot2, grid, ggtree, phytools,
rlang, tibble, tidyr, tidyselect, stats
Suggests: knitr, prettydoc, rmarkdown, BiocStyle, testthat (>= 3.0.0)
License: Artistic-2.0
MD5sum: 07cb1d853ce0917e0e70df9beb38c82a
NeedsCompilation: no
Title: Visualizing Phylomorphospaces using 'ggtree'
Description: This package is a comprehensive visualization tool
specifically designed for exploring phylomorphospace. It not
only simplifies the process of generating phylomorphospace, but
also enhances it with the capability to add graphic layers to
the plot with grammar of graphics to create fully annotated
phylomorphospaces. It also provide some utilities to help
interpret evolutionary patterns.
biocViews: Annotation, Visualization, Phylogenetics, Software
Author: Guangchuang Yu [aut, cre, ths, cph] (ORCID:
<https://orcid.org/0000-0002-6485-8781>), Li Lin [ctb]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
URL: https://github.com/YuLab-SMU/ggtreeSpace
VignetteBuilder: knitr
BugReports: https://github.com/YuLab-SMU/ggtreeSpace/issues
git_url: https://git.bioconductor.org/packages/ggtreeSpace
git_branch: devel
git_last_commit: 4e39526
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ggtreeSpace_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ggtreeSpace_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ggtreeSpace_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ggtreeSpace_1.3.0.tgz
vignettes: vignettes/ggtreeSpace/inst/doc/ggtreeSpace.html
vignetteTitles: Introduction to ggtreeSpace
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ggtreeSpace/inst/doc/ggtreeSpace.R
dependencyCount: 90
Package: GIGSEA
Version: 1.25.0
Depends: R (>= 3.5), Matrix, MASS, locfdr, stats, utils
Suggests: knitr, rmarkdown
License: LGPL-3
MD5sum: 970533d0cc85ca2b6827c4147bf69dfd
NeedsCompilation: no
Title: Genotype Imputed Gene Set Enrichment Analysis
Description: We presented the Genotype-imputed Gene Set Enrichment
Analysis (GIGSEA), a novel method that uses
GWAS-and-eQTL-imputed trait-associated differential gene
expression to interrogate gene set enrichment for the
trait-associated SNPs. By incorporating eQTL from large gene
expression studies, e.g. GTEx, GIGSEA appropriately addresses
such challenges for SNP enrichment as gene size, gene boundary,
SNP distal regulation, and multiple-marker regulation. The
weighted linear regression model, taking as weights both
imputation accuracy and model completeness, was used to perform
the enrichment test, properly adjusting the bias due to
redundancy in different gene sets. The permutation test,
furthermore, is used to evaluate the significance of
enrichment, whose efficiency can be largely elevated by
expressing the computational intensive part in terms of large
matrix operation. We have shown the appropriate type I error
rates for GIGSEA (<5%), and the preliminary results also
demonstrate its good performance to uncover the real signal.
biocViews:
GeneSetEnrichment,SNP,VariantAnnotation,GeneExpression,GeneRegulation,Regression,DifferentialExpression
Author: Shijia Zhu
Maintainer: Shijia Zhu <shijia.zhu@mssm.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GIGSEA
git_branch: devel
git_last_commit: af5e89c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GIGSEA_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GIGSEA_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GIGSEA_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GIGSEA_1.25.0.tgz
vignettes: vignettes/GIGSEA/inst/doc/GIGSEA_tutorial.pdf
vignetteTitles: GIGSEA: Genotype Imputed Gene Set Enrichment Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GIGSEA/inst/doc/GIGSEA_tutorial.R
suggestsMe: GIGSEAdata
dependencyCount: 11
Package: ginmappeR
Version: 1.3.3
Imports: KEGGREST, UniProt.ws, XML, rentrez, httr, utils, memoise,
cachem, jsonlite, rvest
Suggests: RUnit, BiocGenerics, markdown, knitr
License: GPL-3 + file LICENSE
MD5sum: de5acaf2c36e873785a151442ea210f3
NeedsCompilation: no
Title: Gene Identifier Mapper
Description: Provides functionalities to translate gene or protein
identifiers between state-of-art biological databases: CARD
(<https://card.mcmaster.ca/>), NCBI Protein, Nucleotide and
Gene (<https://www.ncbi.nlm.nih.gov/>), UniProt
(<https://www.uniprot.org/>) and KEGG (<https://www.kegg.jp>).
Also offers complementary functionality like NCBI identical
proteins or UniProt similar genes clusters retrieval.
biocViews: Annotation, KEGG, Genetics, ThirdPartyClient, Software
Author: Fernando Sola [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1685-3270>), Daniel Ayala [aut]
(ORCID: <https://orcid.org/0000-0003-2095-1009>), Marina Pulido
[aut] (ORCID: <https://orcid.org/0000-0003-1633-4294>), Rafael
Ayala [aut] (ORCID: <https://orcid.org/0000-0002-9332-4623>),
Lorena López-Cerero [aut] (ORCID:
<https://orcid.org/0000-0001-8950-4384>), Inma Hernández [aut]
(ORCID: <https://orcid.org/0000-0001-8920-6635>), David Ruiz
[aut] (ORCID: <https://orcid.org/0000-0003-4460-5493>)
Maintainer: Fernando Sola <fsola@us.es>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ginmappeR
git_branch: devel
git_last_commit: 2c546ae
git_last_commit_date: 2024-12-02
Date/Publication: 2024-12-02
source.ver: src/contrib/ginmappeR_1.3.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ginmappeR_1.3.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ginmappeR_1.3.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ginmappeR_1.3.3.tgz
vignettes: vignettes/ginmappeR/inst/doc/ginmappeR.html
vignetteTitles: ginmappeR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ginmappeR/inst/doc/ginmappeR.R
dependencyCount: 74
Package: gINTomics
Version: 1.3.0
Depends: R (>= 4.4.0)
Imports: BiocParallel, biomaRt, OmnipathR, edgeR, ggplot2, ggridges,
gtools, MultiAssayExperiment, plyr, stringi, stringr,
SummarizedExperiment, methods, stats, reshape2, randomForest,
limma, org.Hs.eg.db, org.Mm.eg.db, BiocGenerics,
GenomicFeatures, ReactomePA, clusterProfiler, dplyr,
AnnotationDbi, TxDb.Hsapiens.UCSC.hg38.knownGene,
TxDb.Mmusculus.UCSC.mm10.knownGene, shiny, GenomicRanges,
ggtree, shinydashboard, plotly, DT, MASS,
InteractiveComplexHeatmap, ComplexHeatmap, visNetwork,
shiny.gosling, ggvenn, RColorBrewer, utils, grDevices, callr,
circlize
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0)
License: AGPL-3
MD5sum: f44ab279a1201e3e292d834875f95320
NeedsCompilation: no
Title: Multi-Omics data integration
Description: gINTomics is an R package for Multi-Omics data integration
and visualization. gINTomics is designed to detect the
association between the expression of a target and of its
regulators, taking into account also their genomics
modifications such as Copy Number Variations (CNV) and
methylation. What is more, gINTomics allows integration results
visualization via a Shiny-based interactive app.
biocViews: GeneExpression, RNASeq, Microarray, Visualization,
CopyNumberVariation, GeneTarget
Author: Angelo Velle [cre, aut] (ORCID:
<https://orcid.org/0000-0002-4010-6390>), Francesco Patane'
[aut] (ORCID: <https://orcid.org/0009-0001-8619-447X>), Chiara
Romualdi [aut] (ORCID: <https://orcid.org/0000-0003-4792-9047>)
Maintainer: Angelo Velle <angelo.velle@unipd.it>
URL: https://github.com/angelovelle96/gINTomics
VignetteBuilder: knitr
BugReports: https://github.com/angelovelle96/gINTomics/issues
git_url: https://git.bioconductor.org/packages/gINTomics
git_branch: devel
git_last_commit: 3ba788e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/gINTomics_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gINTomics_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gINTomics_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gINTomics_1.3.0.tgz
vignettes: vignettes/gINTomics/inst/doc/gINTomics.html
vignetteTitles: gINTomics vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gINTomics/inst/doc/gINTomics.R
dependencyCount: 238
Package: girafe
Version: 1.59.1
Depends: R (>= 2.10.0), methods, BiocGenerics (>= 0.13.8), S4Vectors
(>= 0.17.25), Rsamtools (>= 1.31.2), intervals (>= 0.13.1),
ShortRead (>= 1.37.1), genomeIntervals (>= 1.25.1), grid
Imports: methods, Biobase, Biostrings (>= 2.47.6), pwalign, graphics,
grDevices, stats, utils, IRanges (>= 2.13.12)
Suggests: MASS, org.Mm.eg.db, RColorBrewer
Enhances: genomeIntervals
License: Artistic-2.0
MD5sum: bc21bdb3cb980df68f983905695335bb
NeedsCompilation: yes
Title: Genome Intervals and Read Alignments for Functional Exploration
Description: The package 'girafe' deals with the genome-level
representation of aligned reads from next-generation sequencing
data. It contains an object class for enabling a detailed
description of genome intervals with aligned reads and
functions for comparing, visualising, exporting and working
with such intervals and the aligned reads. As such, the package
interacts with and provides a link between the packages
ShortRead, IRanges and genomeIntervals.
biocViews: Sequencing
Author: Joern Toedling, with contributions from Constance Ciaudo,
Olivier Voinnet, Edith Heard, Emmanuel Barillot, and Wolfgang
Huber
Maintainer: J. Toedling <jtoedling@yahoo.de>
PackageStatus: Deprecated
git_url: https://git.bioconductor.org/packages/girafe
git_branch: devel
git_last_commit: 8e02237
git_last_commit_date: 2024-11-25
Date/Publication: 2025-02-20
source.ver: src/contrib/girafe_1.59.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/girafe_1.59.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/girafe_1.59.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/girafe_1.59.1.tgz
vignettes: vignettes/girafe/inst/doc/girafe.pdf
vignetteTitles: Genome intervals and read alignments for functional
exploration
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/girafe/inst/doc/girafe.R
dependencyCount: 65
Package: GLAD
Version: 2.71.0
Depends: R (>= 2.10)
Imports: aws
License: GPL-2
MD5sum: 8f7b39144bdaad24d240809cb1dcb3ce
NeedsCompilation: yes
Title: Gain and Loss Analysis of DNA
Description: Analysis of array CGH data : detection of breakpoints in
genomic profiles and assignment of a status (gain, normal or
loss) to each chromosomal regions identified.
biocViews: Microarray, CopyNumberVariation
Author: Philippe Hupe
Maintainer: Philippe Hupe <glad@curie.fr>
URL: http://bioinfo.curie.fr
SystemRequirements: gsl. Note: users should have GSL installed. Windows
users: 'consult the README file available in the inst directory
of the source distribution for necessary configuration
instructions'.
git_url: https://git.bioconductor.org/packages/GLAD
git_branch: devel
git_last_commit: 1c16508
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GLAD_2.71.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GLAD_2.71.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GLAD_2.71.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GLAD_2.71.0.tgz
vignettes: vignettes/GLAD/inst/doc/GLAD.pdf
vignetteTitles: GLAD
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GLAD/inst/doc/GLAD.R
dependsOnMe: ITALICS
importsMe: ITALICS, MANOR
suggestsMe: aroma.cn, aroma.core
dependencyCount: 4
Package: GladiaTOX
Version: 1.23.0
Depends: R (>= 3.6.0), data.table (>= 1.9.4)
Imports: DBI, RMariaDB, RSQLite, numDeriv, RColorBrewer, parallel,
stats, methods, graphics, grDevices, xtable, tools, brew,
stringr, RJSONIO, ggplot2, ggrepel, tidyr, utils, RCurl, XML
Suggests: roxygen2, knitr, rmarkdown, testthat, BiocStyle
License: GPL-2
Archs: x64
MD5sum: 9fa799d4bc2d1ed96b918962bf1a374c
NeedsCompilation: no
Title: R Package for Processing High Content Screening data
Description: GladiaTOX R package is an open-source, flexible solution
to high-content screening data processing and reporting in
biomedical research. GladiaTOX takes advantage of the tcpl core
functionalities and provides a number of extensions: it
provides a web-service solution to fetch raw data; it computes
severity scores and exports ToxPi formatted files; furthermore
it contains a suite of functionalities to generate pdf reports
for quality control and data processing.
biocViews: Software, WorkflowStep, Normalization, Preprocessing,
QualityControl
Author: Vincenzo Belcastro [aut, cre], Dayne L Filer [aut], Stephane
Cano [aut]
Maintainer: PMP S.A. R Support <DL.RSupport@pmi.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GladiaTOX
git_branch: devel
git_last_commit: c12ce38
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GladiaTOX_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GladiaTOX_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GladiaTOX_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GladiaTOX_1.23.0.tgz
vignettes: vignettes/GladiaTOX/inst/doc/GladiaTOX.html
vignetteTitles: GladiaTOX
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GladiaTOX/inst/doc/GladiaTOX.R
dependencyCount: 68
Package: Glimma
Version: 2.17.3
Depends: R (>= 4.0.0)
Imports: htmlwidgets, edgeR, DESeq2, limma, SummarizedExperiment,
stats, jsonlite, methods, S4Vectors
Suggests: testthat, knitr, rmarkdown, BiocStyle, IRanges,
GenomicRanges, pryr, AnnotationHub, scRNAseq, scater, scran,
scRNAseq,
License: GPL-3
Archs: x64
MD5sum: 3db95f0a63a0d2fa24112467bb722543
NeedsCompilation: no
Title: Interactive visualizations for gene expression analysis
Description: This package produces interactive visualizations for
RNA-seq data analysis, utilizing output from limma, edgeR, or
DESeq2. It produces interactive htmlwidgets versions of popular
RNA-seq analysis plots to enhance the exploration of analysis
results by overlaying interactive features. The plots can be
viewed in a web browser or embedded in notebook documents.
biocViews: DifferentialExpression, GeneExpression, Microarray,
ReportWriting, RNASeq, Sequencing, Visualization
Author: Shian Su [aut, cre], Hasaru Kariyawasam [aut], Oliver Voogd
[aut], Matthew Ritchie [aut], Charity Law [aut], Stuart Lee
[ctb], Isaac Virshup [ctb]
Maintainer: Shian Su <su.s@wehi.edu.au>
URL: https://github.com/hasaru-k/GlimmaV2
VignetteBuilder: knitr
BugReports: https://github.com/hasaru-k/GlimmaV2/issues
git_url: https://git.bioconductor.org/packages/Glimma
git_branch: devel
git_last_commit: 9847590
git_last_commit_date: 2025-02-07
Date/Publication: 2025-02-09
source.ver: src/contrib/Glimma_2.17.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Glimma_2.17.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Glimma_2.17.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Glimma_2.17.3.tgz
vignettes: vignettes/Glimma/inst/doc/DESeq2.html,
vignettes/Glimma/inst/doc/limma_edger.html,
vignettes/Glimma/inst/doc/single_cell_edger.html
vignetteTitles: DESeq2, Introduction using limma or edgeR, Single Cells
with edgeR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Glimma/inst/doc/DESeq2.R,
vignettes/Glimma/inst/doc/limma_edger.R,
vignettes/Glimma/inst/doc/single_cell_edger.R
dependsOnMe: RNAseq123
importsMe: affycoretools
dependencyCount: 99
Package: glmGamPoi
Version: 1.19.5
Depends: R (>= 4.1.0)
Imports: Rcpp, beachmat, DelayedMatrixStats, matrixStats,
MatrixGenerics, SparseArray (>= 1.5.21), S4Vectors,
DelayedArray, HDF5Array, Matrix, SummarizedExperiment,
SingleCellExperiment, BiocGenerics, methods, stats, utils,
splines, rlang, vctrs
LinkingTo: Rcpp, RcppArmadillo, beachmat, assorthead
Suggests: testthat (>= 2.1.0), zoo, DESeq2, edgeR, limma, MASS,
statmod, ggplot2, bench, BiocParallel, knitr, rmarkdown,
BiocStyle, TENxPBMCData, muscData, scran, dplyr
License: GPL-3
MD5sum: 946beacc247ba45dcf04eef9c51ba49b
NeedsCompilation: yes
Title: Fit a Gamma-Poisson Generalized Linear Model
Description: Fit linear models to overdispersed count data. The package
can estimate the overdispersion and fit repeated models for
matrix input. It is designed to handle large input datasets as
they typically occur in single cell RNA-seq experiments.
biocViews: Regression, RNASeq, Software, SingleCell
Author: Constantin Ahlmann-Eltze [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3762-068X>), Nathan Lubock [ctb]
(ORCID: <https://orcid.org/0000-0001-8064-2465>), Michael Love
[ctb]
Maintainer: Constantin Ahlmann-Eltze <artjom31415@googlemail.com>
URL: https://github.com/const-ae/glmGamPoi
SystemRequirements: C++17
VignetteBuilder: knitr
BugReports: https://github.com/const-ae/glmGamPoi/issues
git_url: https://git.bioconductor.org/packages/glmGamPoi
git_branch: devel
git_last_commit: a645b9e
git_last_commit_date: 2025-03-17
Date/Publication: 2025-03-17
source.ver: src/contrib/glmGamPoi_1.19.5.tar.gz
win.binary.ver: bin/windows/contrib/4.5/glmGamPoi_1.19.5.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/glmGamPoi_1.19.5.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/glmGamPoi_1.19.5.tgz
vignettes: vignettes/glmGamPoi/inst/doc/glmGamPoi.html,
vignettes/glmGamPoi/inst/doc/pseudobulk.html
vignetteTitles: glmGamPoi Quickstart, Pseudobulk and differential
expression
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/glmGamPoi/inst/doc/glmGamPoi.R,
vignettes/glmGamPoi/inst/doc/pseudobulk.R
importsMe: BASiCStan, lemur, transformGamPoi, SCdeconR
suggestsMe: DESeq2, DEXSeq, scregclust, Seurat
dependencyCount: 54
Package: glmSparseNet
Version: 1.25.0
Depends: R (>= 4.3.0)
Imports: biomaRt, checkmate, dplyr, forcats, futile.logger, ggplot2,
glue, httr, lifecycle, methods, parallel, readr, rlang, glmnet,
Matrix, MultiAssayExperiment, SummarizedExperiment, survminer,
TCGAutils, utils
Suggests: BiocStyle, curatedTCGAData, knitr, magrittr, reshape2, pROC,
rmarkdown, survival, testthat, VennDiagram, withr
License: GPL-3
MD5sum: 1674da34f781b5a36088759e54528683
NeedsCompilation: no
Title: Network Centrality Metrics for Elastic-Net Regularized Models
Description: glmSparseNet is an R-package that generalizes sparse
regression models when the features (e.g. genes) have a graph
structure (e.g. protein-protein interactions), by including
network-based regularizers. glmSparseNet uses the glmnet
R-package, by including centrality measures of the network as
penalty weights in the regularization. The current version
implements regularization based on node degree, i.e. the
strength and/or number of its associated edges, either by
promoting hubs in the solution or orphan genes in the solution.
All the glmnet distribution families are supported, namely
"gaussian", "poisson", "binomial", "multinomial", "cox", and
"mgaussian".
biocViews: Software, StatisticalMethod, DimensionReduction, Regression,
Classification, Survival, Network, GraphAndNetwork
Author: André VerÃssimo [aut, cre] (ORCID:
<https://orcid.org/0000-0002-2212-339X>), Susana Vinga [aut],
Eunice Carrasquinha [ctb], Marta Lopes [ctb]
Maintainer: André VerÃssimo <andre.verissimo@tecnico.ulisboa.pt>
URL: https://www.github.com/sysbiomed/glmSparseNet
VignetteBuilder: knitr
BugReports: https://www.github.com/sysbiomed/glmSparseNet/issues
git_url: https://git.bioconductor.org/packages/glmSparseNet
git_branch: devel
git_last_commit: 91851c6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/glmSparseNet_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/glmSparseNet_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/glmSparseNet_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/glmSparseNet_1.25.0.tgz
vignettes: vignettes/glmSparseNet/inst/doc/example_brca_logistic.html,
vignettes/glmSparseNet/inst/doc/example_brca_protein-protein-interactions_survival.html,
vignettes/glmSparseNet/inst/doc/example_brca_survival.html,
vignettes/glmSparseNet/inst/doc/example_prad_survival.html,
vignettes/glmSparseNet/inst/doc/example_skcm_survival.html,
vignettes/glmSparseNet/inst/doc/separate2GroupsCox.html
vignetteTitles: Example for Classification -- Breast Invasive
Carcinoma, Breast survival dataset using network from STRING
DB, Example for Survival Data -- Breast Invasive Carcinoma,
Example for Survival Data -- Prostate Adenocarcinoma, Example
for Survival Data -- Skin Melanoma, Separate 2 groups in Cox
regression
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/glmSparseNet/inst/doc/example_brca_logistic.R,
vignettes/glmSparseNet/inst/doc/example_brca_protein-protein-interactions_survival.R,
vignettes/glmSparseNet/inst/doc/example_brca_survival.R,
vignettes/glmSparseNet/inst/doc/example_prad_survival.R,
vignettes/glmSparseNet/inst/doc/example_skcm_survival.R,
vignettes/glmSparseNet/inst/doc/separate2GroupsCox.R
importsMe: priorityelasticnet
dependencyCount: 185
Package: GlobalAncova
Version: 4.25.0
Depends: methods, corpcor, globaltest
Imports: annotate, AnnotationDbi, Biobase, dendextend, GSEABase, VGAM
Suggests: GO.db, golubEsets, hu6800.db, vsn, Rgraphviz
License: GPL (>= 2)
Archs: x64
MD5sum: 95f934dc780d868a0edfa9a535b44cff
NeedsCompilation: yes
Title: Global test for groups of variables via model comparisons
Description: The association between a variable of interest (e.g. two
groups) and the global pattern of a group of variables (e.g. a
gene set) is tested via a global F-test. We give the following
arguments in support of the GlobalAncova approach: After
appropriate normalisation, gene-expression-data appear rather
symmetrical and outliers are no real problem, so least squares
should be rather robust. ANCOVA with interaction yields
saturated data modelling e.g. different means per group and
gene. Covariate adjustment can help to correct for possible
selection bias. Variance homogeneity and uncorrelated residuals
cannot be expected. Application of ordinary least squares gives
unbiased, but no longer optimal estimates
(Gauss-Markov-Aitken). Therefore, using the classical F-test is
inappropriate, due to correlation. The test statistic however
mirrors deviations from the null hypothesis. In combination
with a permutation approach, empirical significance levels can
be approximated. Alternatively, an approximation yields
asymptotic p-values. The framework is generalized to groups of
categorical variables or even mixed data by a likelihood ratio
approach. Closed and hierarchical testing procedures are
supported. This work was supported by the NGFN grant 01 GR
0459, BMBF, Germany and BMBF grant 01ZX1309B, Germany.
biocViews: Microarray, OneChannel, DifferentialExpression, Pathways,
Regression
Author: U. Mansmann, R. Meister, M. Hummel, R. Scheufele, with
contributions from S. Knueppel
Maintainer: Manuela Hummel <manuela.hummel@web.de>
git_url: https://git.bioconductor.org/packages/GlobalAncova
git_branch: devel
git_last_commit: 5090aa6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GlobalAncova_4.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GlobalAncova_4.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GlobalAncova_4.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GlobalAncova_4.25.0.tgz
vignettes: vignettes/GlobalAncova/inst/doc/GlobalAncova.pdf,
vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.pdf
vignetteTitles: GlobalAncova.pdf, GlobalAncovaDecomp.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GlobalAncova/inst/doc/GlobalAncova.R,
vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.R
importsMe: miRtest
suggestsMe: GiANT
dependencyCount: 81
Package: globalSeq
Version: 1.35.0
Depends: R (>= 3.0.0)
Suggests: knitr, testthat, SummarizedExperiment, S4Vectors
License: GPL-3
MD5sum: 82503f8353883178ee5503ae12c8b245
NeedsCompilation: no
Title: Global Test for Counts
Description: The method may be conceptualised as a test of overall
significance in regression analysis, where the response
variable is overdispersed and the number of explanatory
variables exceeds the sample size. Useful for testing for
association between RNA-Seq and high-dimensional data.
biocViews: GeneExpression, ExonArray, DifferentialExpression,
GenomeWideAssociation, Transcriptomics, DimensionReduction,
Regression, Sequencing, WholeGenome, RNASeq, ExomeSeq, miRNA,
MultipleComparison
Author: Armin Rauschenberger [aut, cre]
Maintainer: Armin Rauschenberger <armin.rauschenberger@uni.lu>
URL: https://github.com/rauschenberger/globalSeq
VignetteBuilder: knitr
BugReports: https://github.com/rauschenberger/globalSeq/issues
git_url: https://git.bioconductor.org/packages/globalSeq
git_branch: devel
git_last_commit: 68e448d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/globalSeq_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/globalSeq_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/globalSeq_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/globalSeq_1.35.0.tgz
vignettes: vignettes/globalSeq/inst/doc/globalSeq.pdf,
vignettes/globalSeq/inst/doc/article.html,
vignettes/globalSeq/inst/doc/vignette.html
vignetteTitles: vignette source, article frame, vignette frame
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/globalSeq/inst/doc/globalSeq.R
dependencyCount: 0
Package: globaltest
Version: 5.61.0
Depends: methods, survival
Imports: Biobase, AnnotationDbi, annotate, graphics
Suggests: vsn, golubEsets, KEGGREST, hu6800.db, Rgraphviz, GO.db,
lungExpression, org.Hs.eg.db, GSEABase, penalized, gss, MASS,
boot, rpart, mstate
License: GPL (>= 2)
Archs: x64
MD5sum: 1b16186bde528acc0ce0766249f8984f
NeedsCompilation: no
Title: Testing Groups of Covariates/Features for Association with a
Response Variable, with Applications to Gene Set Testing
Description: The global test tests groups of covariates (or features)
for association with a response variable. This package
implements the test with diagnostic plots and multiple testing
utilities, along with several functions to facilitate the use
of this test for gene set testing of GO and KEGG terms.
biocViews: Microarray, OneChannel, Bioinformatics,
DifferentialExpression, GO, Pathways
Author: Jelle Goeman and Jan Oosting, with contributions by Livio
Finos, Aldo Solari, Dominic Edelmann
Maintainer: Jelle Goeman <j.j.goeman@lumc.nl>
git_url: https://git.bioconductor.org/packages/globaltest
git_branch: devel
git_last_commit: d812738
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/globaltest_5.61.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/globaltest_5.61.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/globaltest_5.61.0.tgz
vignettes: vignettes/globaltest/inst/doc/GlobalTest.pdf
vignetteTitles: Global Test
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/globaltest/inst/doc/GlobalTest.R
dependsOnMe: GlobalAncova
importsMe: BiSeq, EGSEA, SIM, miRtest
suggestsMe: topGO, GiANT, penalized
dependencyCount: 53
Package: GloScope
Version: 1.5.0
Depends: R (>= 4.4.0)
Imports: utils, stats, MASS, mclust, ggplot2, RANN, FNN, BiocParallel,
mvnfast, SingleCellExperiment, rlang
Suggests: BiocStyle, testthat (>= 3.0.0), knitr, rmarkdown,
zellkonverter
License: Artistic-2.0
MD5sum: 9fded23ee3a3e47a413f0632ca535a59
NeedsCompilation: no
Title: Population-level Representation on scRNA-Seq data
Description: This package aims at representing and summarizing the
entire single-cell profile of a sample. It allows researchers
to perform important bioinformatic analyses at the sample-level
such as visualization and quality control. The main functions
Estimate sample distribution and calculate statistical
divergence among samples, and visualize the distance matrix
through MDS plots.
biocViews: DataRepresentation, QualityControl, RNASeq, Sequencing,
Software, SingleCell
Author: William Torous [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5668-5510>), Hao Wang [aut]
(ORCID: <https://orcid.org/0000-0002-0749-474X>), Elizabeth
Purdom [aut], Boying Gong [aut]
Maintainer: William Torous <wtorous@berkeley.edu>
VignetteBuilder: knitr
BugReports: https://github.com/epurdom/GloScope/issues
git_url: https://git.bioconductor.org/packages/GloScope
git_branch: devel
git_last_commit: 62fe7a1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GloScope_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GloScope_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GloScope_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GloScope_1.5.0.tgz
vignettes: vignettes/GloScope/inst/doc/GloScopeTutorial.html
vignetteTitles: GloScope
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GloScope/inst/doc/GloScopeTutorial.R
dependencyCount: 79
Package: gmapR
Version: 1.49.0
Depends: R (>= 2.15.0), methods, GenomeInfoDb (>= 1.1.3), GenomicRanges
(>= 1.31.8), Rsamtools (>= 1.31.2)
Imports: S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), BiocGenerics (>=
0.25.1), rtracklayer (>= 1.39.7), GenomicFeatures (>= 1.31.3),
Biostrings, VariantAnnotation (>= 1.25.11), tools, Biobase,
BSgenome, GenomicAlignments (>= 1.15.6), BiocParallel
Suggests: RUnit, BSgenome.Dmelanogaster.UCSC.dm3,
BSgenome.Scerevisiae.UCSC.sacCer3, org.Hs.eg.db,
TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19,
LungCancerLines
License: Artistic-2.0
MD5sum: b42d9c94e51a80c60b0aac79a8bf42f3
NeedsCompilation: yes
Title: An R interface to the GMAP/GSNAP/GSTRUCT suite
Description: GSNAP and GMAP are a pair of tools to align short-read
data written by Tom Wu. This package provides convenience
methods to work with GMAP and GSNAP from within R. In addition,
it provides methods to tally alignment results on a
per-nucleotide basis using the bam_tally tool.
biocViews: Alignment
Author: Cory Barr, Thomas Wu, Michael Lawrence
Maintainer: Michael Lawrence <michafla@gene.com>
git_url: https://git.bioconductor.org/packages/gmapR
git_branch: devel
git_last_commit: fbb42ae
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/gmapR_1.49.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gmapR_1.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gmapR_1.49.0.tgz
vignettes: vignettes/gmapR/inst/doc/gmapR.pdf
vignetteTitles: gmapR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gmapR/inst/doc/gmapR.R
suggestsMe: VariantTools, VariantToolsData
dependencyCount: 79
Package: GmicR
Version: 1.21.0
Imports: AnnotationDbi, ape, bnlearn, Category, DT, doParallel,
foreach, gRbase, GSEABase, gRain, GOstats, org.Hs.eg.db,
org.Mm.eg.db, shiny, WGCNA, data.table, grDevices, graphics,
reshape2, stats, utils
Suggests: knitr, rmarkdown, testthat
License: GPL-2 + file LICENSE
MD5sum: 319ed322a3c55d2a74d8f64a59eba19c
NeedsCompilation: no
Title: Combines WGCNA and xCell readouts with bayesian network
learrning to generate a Gene-Module Immune-Cell network (GMIC)
Description: This package uses bayesian network learning to detect
relationships between Gene Modules detected by WGCNA and immune
cell signatures defined by xCell. It is a hypothesis generating
tool.
biocViews: Software, SystemsBiology, GraphAndNetwork, Network,
NetworkInference, GUI, ImmunoOncology, GeneExpression,
QualityControl, Bayesian, Clustering
Author: Richard Virgen-Slane
Maintainer: Richard Virgen-Slane <RVS.BioTools@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GmicR
git_branch: devel
git_last_commit: 84bfd05
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GmicR_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GmicR_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GmicR_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GmicR_1.21.0.tgz
vignettes: vignettes/GmicR/inst/doc/GmicR_vignette.html
vignetteTitles: GmicR_vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GmicR/inst/doc/GmicR_vignette.R
dependencyCount: 154
Package: gmoviz
Version: 1.19.0
Depends: circlize, GenomicRanges, graphics, R (>= 4.0)
Imports: grid, gridBase, Rsamtools, ComplexHeatmap, BiocGenerics,
Biostrings, GenomeInfoDb, methods, GenomicAlignments,
GenomicFeatures, IRanges, rtracklayer, pracma, colorspace,
S4Vectors
Suggests: testthat, knitr, rmarkdown, pasillaBamSubset, BiocStyle,
BiocManager
License: GPL-3
MD5sum: c4a8540b1a0fa3fd8183ebf07dd150a5
NeedsCompilation: no
Title: Seamless visualization of complex genomic variations in GMOs and
edited cell lines
Description: Genetically modified organisms (GMOs) and cell lines are
widely used models in all kinds of biological research. As part
of characterising these models, DNA sequencing technology and
bioinformatics analyses are used systematically to study their
genomes. Therefore, large volumes of data are generated and
various algorithms are applied to analyse this data, which
introduces a challenge on representing all findings in an
informative and concise manner. `gmoviz` provides users with an
easy way to visualise and facilitate the explanation of complex
genomic editing events on a larger, biologically-relevant
scale.
biocViews: Visualization, Sequencing, GeneticVariability,
GenomicVariation, Coverage
Author: Kathleen Zeglinski [cre, aut], Arthur Hsu [aut], Monther
Alhamdoosh [aut] (ORCID:
<https://orcid.org/0000-0002-2411-1325>), Constantinos
Koutsakis [aut]
Maintainer: Kathleen Zeglinski <kathleen.zeglinski@csl.com.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/gmoviz
git_branch: devel
git_last_commit: f73a067
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/gmoviz_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gmoviz_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gmoviz_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gmoviz_1.19.0.tgz
vignettes: vignettes/gmoviz/inst/doc/gmoviz_advanced.html,
vignettes/gmoviz/inst/doc/gmoviz_overview.html
vignetteTitles: Advanced usage of gmoviz, Introduction to gmoviz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gmoviz/inst/doc/gmoviz_advanced.R,
vignettes/gmoviz/inst/doc/gmoviz_overview.R
dependencyCount: 92
Package: GMRP
Version: 1.35.0
Depends: R(>= 3.3.0),stats,utils,graphics, grDevices, diagram, plotrix,
base,GenomicRanges
Suggests: BiocStyle, BiocGenerics
License: GPL (>= 2)
MD5sum: b83e883e39526efa3aacdf3d25c6af25
NeedsCompilation: no
Title: GWAS-based Mendelian Randomization and Path Analyses
Description: Perform Mendelian randomization analysis of multiple SNPs
to determine risk factors causing disease of study and to
exclude confounding variabels and perform path analysis to
construct path of risk factors to the disease.
biocViews: Sequencing, Regression, SNP
Author: Yuan-De Tan
Maintainer: Yuan-De Tan <tanyuande@gmail.com>
git_url: https://git.bioconductor.org/packages/GMRP
git_branch: devel
git_last_commit: 261aa5d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GMRP_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GMRP_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GMRP_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GMRP_1.35.0.tgz
vignettes: vignettes/GMRP/inst/doc/GMRP-manual.pdf,
vignettes/GMRP/inst/doc/GMRP.pdf
vignetteTitles: GMRP-manual.pdf, Causal Effect Analysis of Risk Factors
for Disease with the "GMRP" package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GMRP/inst/doc/GMRP.R
dependencyCount: 28
Package: GNET2
Version: 1.23.0
Depends: R (>= 3.6)
Imports:
ggplot2,xgboost,Rcpp,reshape2,grid,DiagrammeR,methods,stats,matrixStats,graphics,SummarizedExperiment,dplyr,igraph,
grDevices, utils
LinkingTo: Rcpp
Suggests: knitr, rmarkdown
License: Apache License 2.0
MD5sum: 75259c0f570ea6ae1e968f8fe552f392
NeedsCompilation: yes
Title: Constructing gene regulatory networks from expression data
through functional module inference
Description: Cluster genes to functional groups with E-M process.
Iteratively perform TF assigning and Gene assigning, until the
assignment of genes did not change, or max number of iterations
is reached.
biocViews: GeneExpression, Regression, Network, NetworkInference,
Software
Author: Chen Chen, Jie Hou and Jianlin Cheng
Maintainer: Chen Chen <ccm3x@mail.missouri.edu>
URL: https://github.com/chrischen1/GNET2
VignetteBuilder: knitr
BugReports: https://github.com/chrischen1/GNET2/issues
git_url: https://git.bioconductor.org/packages/GNET2
git_branch: devel
git_last_commit: fb558f7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GNET2_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GNET2_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GNET2_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GNET2_1.23.0.tgz
vignettes: vignettes/GNET2/inst/doc/run_gnet2.html
vignetteTitles: Build functional gene modules with GNET2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GNET2/inst/doc/run_gnet2.R
dependencyCount: 107
Package: GNOSIS
Version: 1.5.0
Depends: R (>= 4.3.0), shiny, shinydashboard, shinydashboardPlus,
dashboardthemes, shinyWidgets, shinymeta, tidyverse,
operator.tools, maftools
Imports: DT, fontawesome, shinycssloaders, cBioPortalData, shinyjs,
reshape2, RColorBrewer, survival, survminer, stats,
compareGroups, rpart, partykit, DescTools, car, rstatix,
fabricatr, shinylogs, magrittr
Suggests: BiocStyle, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 8122dc73fb552a75eccf80fb0d28b747
NeedsCompilation: no
Title: Genomics explorer using statistical and survival analysis in R
Description: GNOSIS incorporates a range of R packages enabling users
to efficiently explore and visualise clinical and genomic data
obtained from cBioPortal. GNOSIS uses an intuitive GUI and
multiple tab panels supporting a range of functionalities.
These include data upload and initial exploration, data
recoding and subsetting, multiple visualisations, survival
analysis, statistical analysis and mutation analysis, in
addition to facilitating reproducible research.
biocViews: Software, ShinyApps, Survival, GUI
Author: Lydia King [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0696-9811>), Marcel Ramos [ctb]
Maintainer: Lydia King <l.king18@universityofgalway.ie>
URL: https://github.com/Lydia-King/GNOSIS/
VignetteBuilder: knitr
Video: https://doi.org/10.5281/zenodo.5788544
BugReports: https://github.com/Lydia-King/GNOSIS/issues
git_url: https://git.bioconductor.org/packages/GNOSIS
git_branch: devel
git_last_commit: 45d7820
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GNOSIS_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GNOSIS_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GNOSIS_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GNOSIS_1.5.0.tgz
vignettes: vignettes/GNOSIS/inst/doc/GNOSIS.html
vignetteTitles: GNOSIS Overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GNOSIS/inst/doc/GNOSIS.R
dependencyCount: 301
Package: GOexpress
Version: 1.41.0
Depends: R (>= 3.4), grid, stats, graphics, Biobase (>= 2.22.0)
Imports: biomaRt (>= 2.18.0), stringr (>= 0.6.2), ggplot2 (>= 0.9.0),
RColorBrewer (>= 1.0), gplots (>= 2.13.0), randomForest (>=
4.6), RCurl (>= 1.95)
Suggests: BiocStyle
License: GPL (>= 3)
Archs: x64
MD5sum: 3b17ab255807ac9aa93b5591162c1fd7
NeedsCompilation: no
Title: Visualise microarray and RNAseq data using gene ontology
annotations
Description: The package contains methods to visualise the expression
profile of genes from a microarray or RNA-seq experiment, and
offers a supervised clustering approach to identify GO terms
containing genes with expression levels that best classify two
or more predefined groups of samples. Annotations for the genes
present in the expression dataset may be obtained from Ensembl
through the biomaRt package, if not provided by the user. The
default random forest framework is used to evaluate the
capacity of each gene to cluster samples according to the
factor of interest. Finally, GO terms are scored by averaging
the rank (alternatively, score) of their respective gene sets
to cluster the samples. P-values may be computed to assess the
significance of GO term ranking. Visualisation function include
gene expression profile, gene ontology-based heatmaps, and
hierarchical clustering of experimental samples using gene
expression data.
biocViews: Software, GeneExpression, Transcription,
DifferentialExpression, GeneSetEnrichment, DataRepresentation,
Clustering, TimeCourse, Microarray, Sequencing, RNASeq,
Annotation, MultipleComparison, Pathways, GO, Visualization,
ImmunoOncology
Author: Kevin Rue-Albrecht [aut, cre], Tharvesh M.L. Ali [ctb], Paul A.
McGettigan [ctb], Belinda Hernandez [ctb], David A. Magee
[ctb], Nicolas C. Nalpas [ctb], Andrew Parnell [ctb], Stephen
V. Gordon [ths], David E. MacHugh [ths]
Maintainer: Kevin Rue-Albrecht <kevinrue67@gmail.com>
URL: https://github.com/kevinrue/GOexpress
git_url: https://git.bioconductor.org/packages/GOexpress
git_branch: devel
git_last_commit: 015022e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GOexpress_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GOexpress_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GOexpress_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GOexpress_1.41.0.tgz
vignettes: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.pdf
vignetteTitles: UsersGuide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.R
suggestsMe: InteractiveComplexHeatmap
dependencyCount: 92
Package: GOfuncR
Version: 1.27.0
Depends: R (>= 3.4), vioplot (>= 0.2),
Imports: Rcpp (>= 0.11.5), mapplots (>= 1.5), gtools (>= 3.5.0),
GenomicRanges (>= 1.28.4), IRanges, AnnotationDbi, utils,
grDevices, graphics, stats,
LinkingTo: Rcpp
Suggests: Homo.sapiens, BiocStyle, knitr, markdown, rmarkdown, testthat
License: GPL (>= 2)
MD5sum: 4c14aa224d718feb0cb350454fd12723
NeedsCompilation: yes
Title: Gene ontology enrichment using FUNC
Description: GOfuncR performs a gene ontology enrichment analysis based
on the ontology enrichment software FUNC. GO-annotations are
obtained from OrganismDb or OrgDb packages ('Homo.sapiens' by
default); the GO-graph is included in the package and updated
regularly (01-May-2021). GOfuncR provides the standard
candidate vs. background enrichment analysis using the
hypergeometric test, as well as three additional tests: (i) the
Wilcoxon rank-sum test that is used when genes are ranked, (ii)
a binomial test that is used when genes are associated with two
counts and (iii) a Chi-square or Fisher's exact test that is
used in cases when genes are associated with four counts. To
correct for multiple testing and interdependency of the tests,
family-wise error rates are computed based on random
permutations of the gene-associated variables. GOfuncR also
provides tools for exploring the ontology graph and the
annotations, and options to take gene-length or spatial
clustering of genes into account. It is also possible to
provide custom gene coordinates, annotations and ontologies.
biocViews: GeneSetEnrichment, GO
Author: Steffi Grote
Maintainer: Steffi Grote <grote.steffi@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GOfuncR
git_branch: devel
git_last_commit: 872b8cd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GOfuncR_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GOfuncR_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GOfuncR_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GOfuncR_1.27.0.tgz
vignettes: vignettes/GOfuncR/inst/doc/GOfuncR.html
vignetteTitles: Introduction to GOfuncR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GOfuncR/inst/doc/GOfuncR.R
dependencyCount: 54
Package: GOpro
Version: 1.33.0
Depends: R (>= 3.5.0)
Imports: AnnotationDbi, dendextend, doParallel, foreach, parallel,
org.Hs.eg.db, GO.db, Rcpp, stats, graphics,
MultiAssayExperiment, IRanges, S4Vectors
LinkingTo: Rcpp, BH
Suggests: knitr, rmarkdown, RTCGA.PANCAN12, BiocStyle, testthat
License: GPL-3
MD5sum: 4d5cb94a8683f861d5a3d43f1834ad58
NeedsCompilation: yes
Title: Find the most characteristic gene ontology terms for groups of
human genes
Description: Find the most characteristic gene ontology terms for
groups of human genes. This package was created as a part of
the thesis which was developed under the auspices of MI^2 Group
(http://mi2.mini.pw.edu.pl/,
https://github.com/geneticsMiNIng).
biocViews: Annotation, Clustering, GO, GeneExpression,
GeneSetEnrichment, MultipleComparison
Author: Lidia Chrabaszcz
Maintainer: Lidia Chrabaszcz <chrabaszcz.lidia@gmail.com>
URL: https://github.com/mi2-warsaw/GOpro
VignetteBuilder: knitr
BugReports: https://github.com/mi2-warsaw/GOpro/issues
git_url: https://git.bioconductor.org/packages/GOpro
git_branch: devel
git_last_commit: 5a8d050
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GOpro_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GOpro_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GOpro_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GOpro_1.33.0.tgz
vignettes: vignettes/GOpro/inst/doc/GOpro_vignette.html
vignetteTitles: GOpro: Determine groups of genes and find their
characteristic GO term
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GOpro/inst/doc/GOpro_vignette.R
dependencyCount: 97
Package: goProfiles
Version: 1.69.0
Depends: Biobase, AnnotationDbi, GO.db, CompQuadForm, stringr
Suggests: org.Hs.eg.db
License: GPL-2
Archs: x64
MD5sum: 9ce163968fed115b0476a65dae740117
NeedsCompilation: no
Title: goProfiles: an R package for the statistical analysis of
functional profiles
Description: The package implements methods to compare lists of genes
based on comparing the corresponding 'functional profiles'.
biocViews: Annotation, GO, GeneExpression, GeneSetEnrichment,
GraphAndNetwork, Microarray, MultipleComparison, Pathways,
Software
Author: Alex Sanchez, Jordi Ocana and Miquel Salicru
Maintainer: Alex Sanchez <asanchez@ub.edu>
git_url: https://git.bioconductor.org/packages/goProfiles
git_branch: devel
git_last_commit: 9437379
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/goProfiles_1.69.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/goProfiles_1.69.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/goProfiles_1.69.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/goProfiles_1.69.0.tgz
vignettes: vignettes/goProfiles/inst/doc/goProfiles-comparevisual.pdf,
vignettes/goProfiles/inst/doc/goProfiles-plotProfileMF.pdf,
vignettes/goProfiles/inst/doc/goProfiles.pdf
vignetteTitles: goProfiles-comparevisual.pdf,
goProfiles-plotProfileMF.pdf, goProfiles Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/goProfiles/inst/doc/goProfiles.R
importsMe: goSorensen
dependencyCount: 50
Package: GOSemSim
Version: 2.33.0
Depends: R (>= 3.5.0)
Imports: AnnotationDbi, DBI, digest, GO.db, methods, rlang, R.utils,
stats, utils, yulab.utils (>= 0.1.6)
LinkingTo: Rcpp
Suggests: AnnotationHub, BiocManager, clusterProfiler, DOSE, knitr,
org.Hs.eg.db, prettydoc, rappdirs, readr, rmarkdown, testthat,
tidyr, tidyselect, ROCR
License: Artistic-2.0
Archs: x64
MD5sum: 413ad21f835b8b411ccf3cef1f8c7158
NeedsCompilation: yes
Title: GO-terms Semantic Similarity Measures
Description: The semantic comparisons of Gene Ontology (GO) annotations
provide quantitative ways to compute similarities between genes
and gene groups, and have became important basis for many
bioinformatics analysis approaches. GOSemSim is an R package
for semantic similarity computation among GO terms, sets of GO
terms, gene products and gene clusters. GOSemSim implemented
five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang
respectively.
biocViews: Annotation, GO, Clustering, Pathways, Network, Software
Author: Guangchuang Yu [aut, cre], Alexey Stukalov [ctb], Pingfan Guo
[ctb], Chuanle Xiao [ctb], LluÃs Revilla Sancho [ctb]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
URL: https://yulab-smu.top/biomedical-knowledge-mining-book/
VignetteBuilder: knitr
BugReports: https://github.com/YuLab-SMU/GOSemSim/issues
git_url: https://git.bioconductor.org/packages/GOSemSim
git_branch: devel
git_last_commit: 5f25326
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GOSemSim_2.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GOSemSim_2.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GOSemSim_2.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GOSemSim_2.33.0.tgz
vignettes: vignettes/GOSemSim/inst/doc/GOSemSim.html
vignetteTitles: GOSemSim
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GOSemSim/inst/doc/GOSemSim.R
dependsOnMe: tRanslatome
importsMe: clusterProfiler, DOSE, enrichplot, GeDi, meshes, Rcpi,
rrvgo, scQTLtools, ViSEAGO, BiSEp
suggestsMe: BioCor, epiNEM, FELLA, SemDist, genekitr, protr, scDiffCom
dependencyCount: 53
Package: goseq
Version: 1.59.0
Depends: R (>= 2.11.0), BiasedUrn, geneLenDataBase (>= 1.9.2)
Imports: mgcv, graphics, stats, utils, AnnotationDbi, GO.db,
BiocGenerics, methods, rtracklayer, GenomicFeatures,
GenomeInfoDb
Suggests: edgeR, org.Hs.eg.db
License: LGPL (>= 2)
MD5sum: dd8bb1ce8a3a2ad7b5d72b5b498a1249
NeedsCompilation: no
Title: Gene Ontology analyser for RNA-seq and other length biased data
Description: Detects Gene Ontology and/or other user defined categories
which are over/under represented in RNA-seq data.
biocViews: ImmunoOncology, Sequencing, GO, GeneExpression,
Transcription, RNASeq, DifferentialExpression, Annotation,
GeneSetEnrichment, KEGG, Pathways, Software
Author: Matthew Young [aut], Nadia Davidson [aut], Federico Marini
[ctb, cre] (ORCID: <https://orcid.org/0000-0003-3252-7758>)
Maintainer: Federico Marini <marinif@uni-mainz.de>
URL: https://github.com/federicomarini/goseq
BugReports: https://github.com/federicomarini/goseq/issues
git_url: https://git.bioconductor.org/packages/goseq
git_branch: devel
git_last_commit: 87e9f39
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-05
source.ver: src/contrib/goseq_1.59.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/goseq_1.59.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/goseq_1.59.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/goseq_1.59.0.tgz
vignettes: vignettes/goseq/inst/doc/goseq.pdf
vignetteTitles: goseq User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/goseq/inst/doc/goseq.R
dependsOnMe: rgsepd
importsMe: Damsel, ideal, mosdef, SMITE
suggestsMe: sparrow
dependencyCount: 107
Package: goSorensen
Version: 1.9.0
Depends: R (>= 4.3)
Imports: clusterProfiler, goProfiles, org.Hs.eg.db, parallel, stats,
stringr
Suggests: BiocManager, BiocStyle, knitr, rmarkdown, org.At.tair.db,
org.Ag.eg.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db,
org.Dm.eg.db, org.Dr.eg.db, org.EcSakai.eg.db, org.EcK12.eg.db,
org.Gg.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Rn.eg.db,
org.Sc.sgd.db, org.Ss.eg.db, org.Pt.eg.db, org.Xl.eg.db
License: GPL-3
Archs: x64
MD5sum: 26d4c4e929aca5f1f4d36f78b1c9c434
NeedsCompilation: no
Title: Statistical inference based on the Sorensen-Dice dissimilarity
and the Gene Ontology (GO)
Description: This package implements inferential methods to compare
gene lists in terms of their biological meaning as expressed in
the GO. The compared gene lists are characterized by
cross-tabulation frequency tables of enriched GO items.
Dissimilarity between gene lists is evaluated using the
Sorensen-Dice index. The fundamental guiding principle is that
two gene lists are taken as similar if they share a great
proportion of common enriched GO items.
biocViews: Annotation, GO, GeneSetEnrichment, Software, Microarray,
Pathways, GeneExpression, MultipleComparison, GraphAndNetwork,
Reactome, Clustering, KEGG
Author: Pablo Flores [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7156-8547>), Jordi Ocana [aut,
ctb] (ORCID: 0000-0002-4736-699), Alexandre Sanchez-Pla [ctb]
(ORCID: <https://orcid.org/0000-0002-8673-7737>), Miquel
Salicru [ctb] (ORCID: <https://orcid.org/0000-0001-9644-5626>)
Maintainer: Pablo Flores <p_flores@espoch.edu.ec>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/goSorensen
git_branch: devel
git_last_commit: 88ed371
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/goSorensen_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/goSorensen_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/goSorensen_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/goSorensen_1.9.0.tgz
vignettes: vignettes/goSorensen/inst/doc/goSorensen_Introduction.html,
vignettes/goSorensen/inst/doc/irrelevance-threshold_Matrix_Dissimilarities.html,
vignettes/goSorensen/inst/doc/README.html
vignetteTitles: An introduction to equivalence test between feature
lists using goSorensen., An Irrelevance Threshold Matrix of
Dissimilarities., README
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/goSorensen/inst/doc/goSorensen_Introduction.R,
vignettes/goSorensen/inst/doc/irrelevance-threshold_Matrix_Dissimilarities.R,
vignettes/goSorensen/inst/doc/README.R
dependencyCount: 126
Package: goSTAG
Version: 1.31.0
Depends: R (>= 3.4)
Imports: AnnotationDbi, biomaRt, GO.db, graphics, memoise, stats, utils
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: GPL-3
MD5sum: a07925ede5f939382476258e53ad1444
NeedsCompilation: no
Title: A tool to use GO Subtrees to Tag and Annotate Genes within a set
Description: Gene lists derived from the results of genomic analyses
are rich in biological information. For instance,
differentially expressed genes (DEGs) from a microarray or
RNA-Seq analysis are related functionally in terms of their
response to a treatment or condition. Gene lists can vary in
size, up to several thousand genes, depending on the robustness
of the perturbations or how widely different the conditions are
biologically. Having a way to associate biological relatedness
between hundreds and thousands of genes systematically is
impractical by manually curating the annotation and function of
each gene. Over-representation analysis (ORA) of genes was
developed to identify biological themes. Given a Gene Ontology
(GO) and an annotation of genes that indicate the categories
each one fits into, significance of the over-representation of
the genes within the ontological categories is determined by a
Fisher's exact test or modeling according to a hypergeometric
distribution. Comparing a small number of enriched biological
categories for a few samples is manageable using Venn diagrams
or other means for assessing overlaps. However, with hundreds
of enriched categories and many samples, the comparisons are
laborious. Furthermore, if there are enriched categories that
are shared between samples, trying to represent a common theme
across them is highly subjective. goSTAG uses GO subtrees to
tag and annotate genes within a set. goSTAG visualizes the
similarities between the over-representation of DEGs by
clustering the p-values from the enrichment statistical tests
and labels clusters with the GO term that has the most paths to
the root within the subtree generated from all the GO terms in
the cluster.
biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment,
Clustering, Microarray, mRNAMicroarray, RNASeq, Visualization,
GO, ImmunoOncology
Author: Brian D. Bennett and Pierre R. Bushel
Maintainer: Brian D. Bennett <brian.bennett@nih.gov>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/goSTAG
git_branch: devel
git_last_commit: fd8ca2d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/goSTAG_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/goSTAG_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/goSTAG_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/goSTAG_1.31.0.tgz
vignettes: vignettes/goSTAG/inst/doc/goSTAG.html
vignetteTitles: The goSTAG User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/goSTAG/inst/doc/goSTAG.R
dependencyCount: 69
Package: GOstats
Version: 2.73.0
Depends: R (>= 2.10), Biobase (>= 1.15.29), Category (>= 2.43.2), graph
Imports: methods, stats, stats4, AnnotationDbi (>= 0.0.89), GO.db (>=
1.13.0), RBGL, annotate (>= 1.13.2), AnnotationForge, Rgraphviz
Suggests: hgu95av2.db (>= 1.13.0), ALL, multtest, genefilter,
RColorBrewer, xtable, SparseM, GSEABase, geneplotter,
org.Hs.eg.db, RUnit, BiocGenerics, BiocStyle, knitr
License: Artistic-2.0
MD5sum: 615bb6e4c02a678912df97e8d9a43d92
NeedsCompilation: no
Title: Tools for manipulating GO and microarrays
Description: A set of tools for interacting with GO and microarray
data. A variety of basic manipulation tools for graphs,
hypothesis testing and other simple calculations.
biocViews: Annotation, GO, MultipleComparison, GeneExpression,
Microarray, Pathways, GeneSetEnrichment, GraphAndNetwork
Author: Robert Gentleman [aut], Seth Falcon [ctb], Robert Castelo
[ctb], Sonali Kumari [ctb] (Converted vignettes from Sweave to
R Markdown / HTML.), Dennis Ndubi [ctb] (Converted
GOstatsHyperG vignette from Sweave to R Markdown / HTML.),
Bioconductor Package Maintainer [cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GOstats
git_branch: devel
git_last_commit: a8f7238
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GOstats_2.73.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GOstats_2.73.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GOstats_2.73.0.tgz
vignettes:
vignettes/GOstats/inst/doc/GOstatsForUnsupportedOrganisms.html,
vignettes/GOstats/inst/doc/GOstatsHyperG.html,
vignettes/GOstats/inst/doc/GOvis.html
vignetteTitles: How To Use GOstats and Category to do Hypergeometric
testing with unsupported model organisms, Hypergeometric Tests
Using GOstats, Visualizing and Distances Using GO
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GOstats/inst/doc/GOstatsForUnsupportedOrganisms.R,
vignettes/GOstats/inst/doc/GOstatsHyperG.R,
vignettes/GOstats/inst/doc/GOvis.R
dependsOnMe: MineICA
importsMe: affycoretools, attract, categoryCompare, GmicR, ideal,
miRLAB, pcaExplorer, scTensor, SGCP
suggestsMe: a4, Category, fastLiquidAssociation, fgga, GSEAlm,
interactiveDisplay, MineICA, MLP, qpgraph, RnBeads, safe,
maGUI, sand
dependencyCount: 66
Package: GOTHiC
Version: 1.43.0
Depends: R (>= 3.5.0), methods, GenomicRanges, Biostrings, BSgenome,
data.table
Imports: BiocGenerics, S4Vectors (>= 0.9.38), IRanges, Rsamtools,
ShortRead, rtracklayer, ggplot2, BiocManager, grDevices, utils,
stats, GenomeInfoDb
Suggests: HiCDataLymphoblast
Enhances: parallel
License: GPL-3
MD5sum: c9f44f8f6298ef87708580cab72baea1
NeedsCompilation: no
Title: Binomial test for Hi-C data analysis
Description: This is a Hi-C analysis package using a cumulative
binomial test to detect interactions between distal genomic
loci that have significantly more reads than expected by chance
in Hi-C experiments. It takes mapped paired NGS reads as input
and gives back the list of significant interactions for a given
bin size in the genome.
biocViews: ImmunoOncology, Sequencing, Preprocessing, Epigenetics, HiC
Author: Borbala Mifsud and Robert Sugar
Maintainer: Borbala Mifsud <b.mifsud@qmul.ac.uk>
git_url: https://git.bioconductor.org/packages/GOTHiC
git_branch: devel
git_last_commit: fdd7c2b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GOTHiC_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GOTHiC_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GOTHiC_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GOTHiC_1.43.0.tgz
vignettes: vignettes/GOTHiC/inst/doc/package_vignettes.pdf
vignetteTitles: package_vignettes.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GOTHiC/inst/doc/package_vignettes.R
importsMe: OHCA
dependencyCount: 97
Package: goTools
Version: 1.81.0
Depends: GO.db
Imports: AnnotationDbi, GO.db, graphics, grDevices
Suggests: hgu133a.db
License: GPL-2
MD5sum: 3caefa49b01ca79d0242a4e9cdce7533
NeedsCompilation: no
Title: Functions for Gene Ontology database
Description: Wraper functions for description/comparison of oligo ID
list using Gene Ontology database
biocViews: Microarray,GO,Visualization
Author: Yee Hwa (Jean) Yang <jean@biostat.ucsf.edu>, Agnes Paquet
<paquetagnes@yahoo.com>
Maintainer: Agnes Paquet <paquetagnes@yahoo.com>
git_url: https://git.bioconductor.org/packages/goTools
git_branch: devel
git_last_commit: 6659a1b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/goTools_1.81.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/goTools_1.81.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/goTools_1.81.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/goTools_1.81.0.tgz
vignettes: vignettes/goTools/inst/doc/goTools.pdf
vignetteTitles: goTools overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/goTools/inst/doc/goTools.R
dependencyCount: 46
Package: GPA
Version: 1.19.0
Depends: R (>= 4.0.0), methods, graphics, Rcpp
Imports: parallel, ggplot2, ggrepel, plyr, vegan, DT, shiny, shinyBS,
stats, utils, grDevices
LinkingTo: Rcpp
Suggests: gpaExample
License: GPL (>= 2)
MD5sum: 5580d226b5700c1a5955c7f67b9f1617
NeedsCompilation: yes
Title: GPA (Genetic analysis incorporating Pleiotropy and Annotation)
Description: This package provides functions for fitting GPA, a
statistical framework to prioritize GWAS results by integrating
pleiotropy information and annotation data. In addition, it
also includes ShinyGPA, an interactive visualization toolkit to
investigate pleiotropic architecture.
biocViews: Software, StatisticalMethod, Classification,
GenomeWideAssociation, SNP, Genetics, Clustering,
MultipleComparison, Preprocessing, GeneExpression,
DifferentialExpression
Author: Dongjun Chung, Emma Kortemeier, Carter Allen
Maintainer: Dongjun Chung <dongjun.chung@gmail.com>
URL: http://dongjunchung.github.io/GPA/
SystemRequirements: GNU make
BugReports: https://github.com/dongjunchung/GPA/issues
git_url: https://git.bioconductor.org/packages/GPA
git_branch: devel
git_last_commit: adab3a9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GPA_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GPA_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GPA_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GPA_1.19.0.tgz
vignettes: vignettes/GPA/inst/doc/GPA-example.pdf
vignetteTitles: GPA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GPA/inst/doc/GPA-example.R
dependencyCount: 77
Package: gpls
Version: 1.79.0
Imports: stats
Suggests: MASS
License: Artistic-2.0
MD5sum: 0d7fd8d90ec0d098e47a44f74d32750d
NeedsCompilation: no
Title: Classification using generalized partial least squares
Description: Classification using generalized partial least squares for
two-group and multi-group (more than 2 group) classification.
biocViews: Classification, Microarray, Regression
Author: Beiying Ding
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/gpls
git_branch: devel
git_last_commit: eb0ca4c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/gpls_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gpls_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gpls_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gpls_1.79.0.tgz
vignettes: vignettes/gpls/inst/doc/gpls.pdf
vignetteTitles: gpls Tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gpls/inst/doc/gpls.R
suggestsMe: MLInterfaces
dependencyCount: 1
Package: gpuMagic
Version: 1.23.0
Depends: R (>= 3.6.0), methods, utils
Imports: Deriv, DescTools, digest, pryr, stringr, BiocGenerics
LinkingTo: Rcpp
Suggests: testthat, knitr, rmarkdown, BiocStyle
License: GPL-3
Archs: x64
MD5sum: 2406c611a2c17a7834959ed57aea298f
NeedsCompilation: yes
Title: An openCL compiler with the capacity to compile R functions and
run the code on GPU
Description: The package aims to help users write openCL code with
little or no effort. It is able to compile an user-defined R
function and run it on a device such as a CPU or a GPU. The
user can also write and run their openCL code directly by
calling .kernel function.
biocViews: Infrastructure
Author: Jiefei Wang [aut, cre], Martin Morgan [aut]
Maintainer: Jiefei Wang <szwjf08@gmail.com>
SystemRequirements: 1. C++11, 2. a graphic driver or a CPU SDK. 3. ICD
loader For Windows user, an ICD loader is required at
C:/windows/system32/OpenCL.dll (Usually it is installed by the
graphic driver). For Linux user (Except mac):
ocl-icd-opencl-dev package is required. For Mac user, no action
is needed for the system has installed the dependency. 4. GNU
make
VignetteBuilder: knitr
BugReports: https://github.com/Jiefei-Wang/gpuMagic/issues
git_url: https://git.bioconductor.org/packages/gpuMagic
git_branch: devel
git_last_commit: 06a3340
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/gpuMagic_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gpuMagic_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gpuMagic_1.23.0.tgz
vignettes: vignettes/gpuMagic/inst/doc/Customized-openCL-code.html,
vignettes/gpuMagic/inst/doc/Quick_start_guide.html
vignetteTitles: Customized_opencl_code, quickStart
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gpuMagic/inst/doc/Customized-openCL-code.R,
vignettes/gpuMagic/inst/doc/Quick_start_guide.R
dependencyCount: 72
Package: GrafGen
Version: 1.3.0
Depends: R (>= 4.3.0)
Imports: stats, utils, graphics, ggplot2, plotly, zlibbioc, scales,
RColorBrewer, dplyr, grDevices, GenomicRanges, shiny, cowplot,
ggpubr, stringr, rlang
Suggests: knitr, rmarkdown, RUnit, BiocManager, BiocGenerics,
BiocStyle, devtools
License: GPL-2
MD5sum: 2a4cb7a3d73f1589c211e73f8dd97278
NeedsCompilation: yes
Title: Classification of Helicobacter Pylori Genomes
Description: To classify Helicobacter pylori genomes according to
genetic distance from nine reference populations. The nine
reference populations are hpgpAfrica, hpgpAfrica-distant,
hpgpAfroamerica, hpgpEuroamerica, hpgpMediterranea, hpgpEurope,
hpgpEurasia, hpgpAsia, and hpgpAklavik86-like. The vertex
populations are Africa, Europe and Asia.
biocViews: Genetics, Software, GenomeAnnotation, Classification
Author: William Wheeler [aut, cre], Difei Wang [aut], Isaac Zhao [aut],
Yumi Jin [aut], Charles Rabkin [aut]
Maintainer: William Wheeler <wheelerb@imsweb.com>
VignetteBuilder: knitr
BugReports: https://github.com/wheelerb/GrafGen/issues
git_url: https://git.bioconductor.org/packages/GrafGen
git_branch: devel
git_last_commit: bcc109a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GrafGen_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GrafGen_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GrafGen_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GrafGen_1.3.0.tgz
vignettes: vignettes/GrafGen/inst/doc/vignette.html
vignetteTitles: GrafGen: Classifying H. pylori genomes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GrafGen/inst/doc/vignette.R
dependencyCount: 129
Package: GRaNIE
Version: 1.11.0
Depends: R (>= 4.2.0)
Imports: futile.logger, checkmate, patchwork (>= 1.2.0), reshape2,
data.table, matrixStats, Matrix, GenomicRanges, RColorBrewer,
ComplexHeatmap, DESeq2, circlize, progress, utils, methods,
stringr, tools, scales, igraph, S4Vectors, ggplot2, rlang,
Biostrings, GenomeInfoDb (>= 1.34.8), SummarizedExperiment,
forcats, gridExtra, limma, tidyselect, readr, grid, tidyr (>=
1.3.0), dplyr, stats, grDevices, graphics, magrittr, tibble,
viridis, colorspace, biomaRt, topGO, AnnotationHub, ensembldb
Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm39,
BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm9,
BSgenome.Rnorvegicus.UCSC.rn6, BSgenome.Rnorvegicus.UCSC.rn7,
BSgenome.Dmelanogaster.UCSC.dm6,
BSgenome.Mmulatta.UCSC.rheMac10,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Hsapiens.UCSC.hg38.knownGene,
TxDb.Mmusculus.UCSC.mm39.knownGene,
TxDb.Mmusculus.UCSC.mm10.knownGene,
TxDb.Mmusculus.UCSC.mm9.knownGene,
TxDb.Rnorvegicus.UCSC.rn6.refGene,
TxDb.Rnorvegicus.UCSC.rn7.refGene,
TxDb.Dmelanogaster.UCSC.dm6.ensGene,
TxDb.Mmulatta.UCSC.rheMac10.refGene, org.Hs.eg.db,
org.Mm.eg.db, org.Rn.eg.db, org.Dm.eg.db, org.Mmu.eg.db, IHW,
clusterProfiler, ReactomePA, DOSE, BiocFileCache, ChIPseeker,
testthat (>= 3.0.0), BiocStyle, csaw, BiocParallel, WGCNA,
variancePartition, purrr, EDASeq, JASPAR2022, JASPAR2024,
RSQLite, TFBSTools, motifmatchr, rbioapi, LDlinkR
License: Artistic-2.0
Archs: x64
MD5sum: ecde9ce0b2c0fe073791d85cd8ff06dd
NeedsCompilation: no
Title: GRaNIE: Reconstruction cell type specific gene regulatory
networks including enhancers using single-cell or bulk
chromatin accessibility and RNA-seq data
Description: Genetic variants associated with diseases often affect
non-coding regions, thus likely having a regulatory role. To
understand the effects of genetic variants in these regulatory
regions, identifying genes that are modulated by specific
regulatory elements (REs) is crucial. The effect of gene
regulatory elements, such as enhancers, is often cell-type
specific, likely because the combinations of transcription
factors (TFs) that are regulating a given enhancer have
cell-type specific activity. This TF activity can be quantified
with existing tools such as diffTF and captures differences in
binding of a TF in open chromatin regions. Collectively, this
forms a gene regulatory network (GRN) with cell-type and
data-specific TF-RE and RE-gene links. Here, we reconstruct
such a GRN using single-cell or bulk RNAseq and open chromatin
(e.g., using ATACseq or ChIPseq for open chromatin marks) and
optionally (Capture) Hi-C data. Our network contains different
types of links, connecting TFs to regulatory elements, the
latter of which is connected to genes in the vicinity or within
the same chromatin domain (TAD). We use a statistical framework
to assign empirical FDRs and weights to all links using a
permutation-based approach.
biocViews: Software, GeneExpression, GeneRegulation, NetworkInference,
GeneSetEnrichment, BiomedicalInformatics, Genetics,
Transcriptomics, ATACSeq, RNASeq, GraphAndNetwork, Regression,
Transcription, ChIPSeq
Author: Christian Arnold [cre, aut], Judith Zaugg [aut], Rim Moussa
[aut], Armando Reyes-Palomares [ctb], Giovanni Palla [ctb],
Maksim Kholmatov [ctb]
Maintainer: Christian Arnold <chrarnold@web.de>
URL: https://grp-zaugg.embl-community.io/GRaNIE
VignetteBuilder: knitr
BugReports: https://git.embl.de/grp-zaugg/GRaNIE/issues
git_url: https://git.bioconductor.org/packages/GRaNIE
git_branch: devel
git_last_commit: 7fdc850
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GRaNIE_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GRaNIE_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GRaNIE_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GRaNIE_1.11.0.tgz
vignettes: vignettes/GRaNIE/inst/doc/GRaNIE_packageDetails.html,
vignettes/GRaNIE/inst/doc/GRaNIE_singleCell_eGRNs.html,
vignettes/GRaNIE/inst/doc/GRaNIE_workflow.html
vignetteTitles: Package Details, Single-cell eGRN inference, Workflow
example
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GRaNIE/inst/doc/GRaNIE_packageDetails.R,
vignettes/GRaNIE/inst/doc/GRaNIE_singleCell_eGRNs.R,
vignettes/GRaNIE/inst/doc/GRaNIE_workflow.R
dependencyCount: 155
Package: granulator
Version: 1.15.0
Depends: R (>= 4.1)
Imports: cowplot, e1071, epiR, dplyr, dtangle, ggplot2, ggplotify,
grDevices, limSolve, magrittr, MASS, nnls, parallel, pheatmap,
purrr, rlang, stats, tibble, tidyr, utils
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: GPL-3
Archs: x64
MD5sum: 8280e2deaba8dd3a239cdb08b6e3e3b3
NeedsCompilation: no
Title: Rapid benchmarking of methods for *in silico* deconvolution of
bulk RNA-seq data
Description: granulator is an R package for the cell type deconvolution
of heterogeneous tissues based on bulk RNA-seq data or single
cell RNA-seq expression profiles. The package provides a
unified testing interface to rapidly run and benchmark multiple
state-of-the-art deconvolution methods. Data for the
deconvolution of peripheral blood mononuclear cells (PBMCs)
into individual immune cell types is provided as well.
biocViews: RNASeq, GeneExpression, DifferentialExpression,
Transcriptomics, SingleCell, StatisticalMethod, Regression
Author: Sabina Pfister [aut, cre], Vincent Kuettel [aut], Enrico
Ferrero [aut]
Maintainer: Sabina Pfister <sabina.pfister@novartis.com>
URL: https://github.com/xanibas/granulator
VignetteBuilder: knitr
BugReports: https://github.com/xanibas/granulator/issues
git_url: https://git.bioconductor.org/packages/granulator
git_branch: devel
git_last_commit: 61e04a0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/granulator_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/granulator_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/granulator_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/granulator_1.15.0.tgz
vignettes: vignettes/granulator/inst/doc/granulator.html
vignetteTitles: Deconvoluting bulk RNA-seq data with granulator
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/granulator/inst/doc/granulator.R
suggestsMe: deconvR
dependencyCount: 111
Package: graper
Version: 1.23.0
Depends: R (>= 3.6)
Imports: Matrix, Rcpp, stats, ggplot2, methods, cowplot, matrixStats
LinkingTo: Rcpp, RcppArmadillo, BH
Suggests: knitr, rmarkdown, BiocStyle, testthat
License: GPL (>= 2)
MD5sum: e141f1412fcae49e4dcdaa67872f5349
NeedsCompilation: yes
Title: Adaptive penalization in high-dimensional regression and
classification with external covariates using variational Bayes
Description: This package enables regression and classification on
high-dimensional data with different relative strengths of
penalization for different feature groups, such as different
assays or omic types. The optimal relative strengths are chosen
adaptively. Optimisation is performed using a variational Bayes
approach.
biocViews: Regression, Bayesian, Classification
Author: Britta Velten [aut, cre], Wolfgang Huber [aut]
Maintainer: Britta Velten <britta.velten@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/graper
git_branch: devel
git_last_commit: 91803f0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/graper_1.23.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/graper_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/graper_1.23.0.tgz
vignettes: vignettes/graper/inst/doc/example_linear.html,
vignettes/graper/inst/doc/example_logistic.html
vignetteTitles: example_linear, example_logistic
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/graper/inst/doc/example_linear.R,
vignettes/graper/inst/doc/example_logistic.R
dependencyCount: 40
Package: graph
Version: 1.85.3
Depends: R (>= 2.10), methods, BiocGenerics (>= 0.13.11)
Imports: stats, stats4, utils
Suggests: SparseM (>= 0.36), XML, RBGL, RUnit, cluster, BiocStyle,
knitr
Enhances: Rgraphviz
License: Artistic-2.0
MD5sum: c473d74a5a9743c7f54b339b7b5da054
NeedsCompilation: yes
Title: graph: A package to handle graph data structures
Description: A package that implements some simple graph handling
capabilities.
biocViews: GraphAndNetwork
Author: R Gentleman [aut], Elizabeth Whalen [aut], W Huber [aut], S
Falcon [aut], Jeff Gentry [aut], Paul Shannon [aut], Halimat C.
Atanda [ctb] (Converted 'MultiGraphClass' and 'GraphClass'
vignettes from Sweave to RMarkdown / HTML.), Paul Villafuerte
[ctb] (Converted vignettes from Sweave to RMarkdown / HTML.),
Aliyu Atiku Mustapha [ctb] (Converted 'Graph' vignette from
Sweave to RMarkdown / HTML.), Bioconductor Package Maintainer
[cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/graph
git_branch: devel
git_last_commit: 5a3babc
git_last_commit_date: 2025-03-19
Date/Publication: 2025-03-25
source.ver: src/contrib/graph_1.85.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/graph_1.85.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/graph_1.85.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/graph_1.85.3.tgz
vignettes: vignettes/graph/inst/doc/clusterGraph.html,
vignettes/graph/inst/doc/graph.html,
vignettes/graph/inst/doc/graphAttributes.html,
vignettes/graph/inst/doc/GraphClass.html,
vignettes/graph/inst/doc/MultiGraphClass.html
vignetteTitles: clusterGraph and distGraph, How to use the graph
package, Attributes for Graph Objects, Graph Design, graphBAM
and MultiGraph Classes
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/graph/inst/doc/clusterGraph.R,
vignettes/graph/inst/doc/graph.R,
vignettes/graph/inst/doc/graphAttributes.R,
vignettes/graph/inst/doc/GraphClass.R,
vignettes/graph/inst/doc/MultiGraphClass.R
dependsOnMe: apComplex, biocGraph, BioMVCClass, BioNet, BLMA,
CellNOptR, clipper, CNORfeeder, EnrichmentBrowser, GOstats,
GraphAT, GSEABase, hypergraph, keggorthology, MineICA,
pathRender, Pigengene, RbcBook1, RBGL, RBioinf, RCyjs,
Rgraphviz, ROntoTools, SRAdb, topGO, vtpnet, DLBCL, SNAData,
yeastExpData, cyjShiny, dlsem, gridGraphviz, GUIProfiler,
PairViz, PerfMeas, SubpathwayLNCE
importsMe: AnnotationHubData, BgeeDB, BiocCheck, BiocFHIR, biocGraph,
BiocPkgTools, biocViews, bnem, CAMERA, Category,
categoryCompare, chimeraviz, ChIPpeakAnno, CHRONOS, consICA,
CytoML, dce, DEGraph, DEsubs, EnrichDO, epiNEM, EventPointer,
fgga, flowClust, flowWorkspace, gage, GeneNetworkBuilder,
GenomicInteractionNodes, GraphAT, graphite, hyperdraw,
KEGGgraph, MIRit, mnem, MOSClip, NCIgraph, netresponse,
OncoSimulR, ontoProc, openCyto, oposSOM, OrganismDbi, pathview,
PhenStat, qpgraph, RCy3, RGraph2js, rsbml, Rtreemix, SGCP,
SplicingGraphs, Streamer, VariantFiltering, BioPlex, abn,
BayesNetBP, BCDAG, BiDAG, BNrich, ceg, CePa, classGraph,
clustNet, CodeDepends, cogmapr, ggm, gridDebug, HEMDAG, net4pg,
netgsa, NetPreProc, pcalg, pcgen, rags2ridges, RANKS, RCPA,
rsolr, rSpectral, SEMgraph, stablespec, topologyGSA, tpc,
unifDAG, zenplots
suggestsMe: AnnotationDbi, DAPAR, DEGraph, EBcoexpress, ecolitk,
gwascat, KEGGlincs, MLP, NetPathMiner, omXplore, rBiopaxParser,
RCX, rTRM, S4Vectors, SPIA, VariantTools, arulesViz, bnlearn,
bnstruct, bsub, ChoR, gbutils, GeneNet, gMCP, lava, loon,
maGUI, netmeta, psych, rEMM, rPref, sisal, textplot, tidygraph
dependencyCount: 7
Package: GraphAlignment
Version: 1.71.0
License: file LICENSE
License_restricts_use: yes
MD5sum: 9f0915a96c528eb4186f11b2a0235aeb
NeedsCompilation: yes
Title: GraphAlignment
Description: Graph alignment is an extension package for the R
programming environment which provides functions for finding an
alignment between two networks based on link and node
similarity scores. (J. Berg and M. Laessig, "Cross-species
analysis of biological networks by Bayesian alignment", PNAS
103 (29), 10967-10972 (2006))
biocViews: GraphAndNetwork, Network
Author: Joern P. Meier <mail@ionflux.org>, Michal Kolar, Ville
Mustonen, Michael Laessig, and Johannes Berg.
Maintainer: Joern P. Meier <mail@ionflux.org>
URL: http://www.thp.uni-koeln.de/~berg/GraphAlignment/
git_url: https://git.bioconductor.org/packages/GraphAlignment
git_branch: devel
git_last_commit: 3faa9a6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GraphAlignment_1.71.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GraphAlignment_1.71.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GraphAlignment_1.71.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GraphAlignment_1.71.0.tgz
vignettes: vignettes/GraphAlignment/inst/doc/GraphAlignment.pdf
vignetteTitles: GraphAlignment
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GraphAlignment/inst/doc/GraphAlignment.R
dependencyCount: 0
Package: GraphAT
Version: 1.79.0
Depends: R (>= 2.10), graph, methods
Imports: graph, MCMCpack, methods, stats
License: LGPL
Archs: x64
MD5sum: d5d435e9756b6b39c7375563fa535290
NeedsCompilation: no
Title: Graph Theoretic Association Tests
Description: Functions and data used in Balasubramanian, et al. (2004)
biocViews: Network, GraphAndNetwork
Author: R. Balasubramanian, T. LaFramboise, D. Scholtens
Maintainer: Thomas LaFramboise <tlaframb@hsph.harvard.edu>
git_url: https://git.bioconductor.org/packages/GraphAT
git_branch: devel
git_last_commit: 87c3fcb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GraphAT_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GraphAT_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GraphAT_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GraphAT_1.79.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 21
Package: graphite
Version: 1.53.0
Depends: R (>= 4.2), methods
Imports: AnnotationDbi, graph (>= 1.67.1), httr, rappdirs, stats,
utils, graphics, rlang, purrr
Suggests: checkmate, a4Preproc, ALL, BiocStyle, codetools,
hgu133plus2.db, hgu95av2.db, impute, knitr, org.Hs.eg.db,
parallel, R.rsp, RCy3, rmarkdown, SPIA (>= 2.2), testthat,
topologyGSA (>= 1.4.0)
License: AGPL-3
Archs: x64
MD5sum: 3cc81f42b9b9fcedb30731b9bd3a8c01
NeedsCompilation: no
Title: GRAPH Interaction from pathway Topological Environment
Description: Graph objects from pathway topology derived from KEGG,
Panther, PathBank, PharmGKB, Reactome SMPDB and WikiPathways
databases.
biocViews: Pathways, ThirdPartyClient, GraphAndNetwork, Network,
Reactome, KEGG, Metabolomics
Author: Gabriele Sales [cre], Enrica Calura [aut], Chiara Romualdi
[aut]
Maintainer: Gabriele Sales <gabriele.sales@unipd.it>
URL: https://github.com/sales-lab/graphite
VignetteBuilder: R.rsp
BugReports: https://github.com/sales-lab/graphite/issues
git_url: https://git.bioconductor.org/packages/graphite
git_branch: devel
git_last_commit: b1e8a35
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/graphite_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/graphite_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/graphite_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/graphite_1.53.0.tgz
vignettes: vignettes/graphite/inst/doc/graphite.pdf,
vignettes/graphite/inst/doc/metabolites.pdf
vignetteTitles: GRAPH Interaction from pathway Topological Environment,
metabolites.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/graphite/inst/doc/graphite.R
importsMe: CBNplot, dce, EnrichmentBrowser, MIRit, mogsa, MOSClip,
multiGSEA, ReactomePA, sSNAPPY, ICDS, netgsa
suggestsMe: clipper, InterCellar, metaboliteIDmapping
dependencyCount: 49
Package: GRENITS
Version: 1.59.0
Depends: R (>= 2.12.0), Rcpp (>= 0.8.6), RcppArmadillo (>= 0.2.8),
ggplot2 (>= 0.9.0)
Imports: graphics, grDevices, reshape2, stats, utils
LinkingTo: Rcpp, RcppArmadillo
Suggests: network
License: GPL (>= 2)
MD5sum: a6c0acb85a8e5bd4fd86ee5c593eed48
NeedsCompilation: yes
Title: Gene Regulatory Network Inference Using Time Series
Description: The package offers four network inference statistical
models using Dynamic Bayesian Networks and Gibbs Variable
Selection: a linear interaction model, two linear interaction
models with added experimental noise (Gaussian and Student
distributed) for the case where replicates are available and a
non-linear interaction model.
biocViews: NetworkInference, GeneRegulation, TimeCourse,
GraphAndNetwork, GeneExpression, Network, Bayesian
Author: Edward Morrissey
Maintainer: Edward Morrissey <edward.morrissey@gmail.com>
git_url: https://git.bioconductor.org/packages/GRENITS
git_branch: devel
git_last_commit: 021f1b5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GRENITS_1.59.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GRENITS_1.59.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GRENITS_1.59.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GRENITS_1.59.0.tgz
vignettes: vignettes/GRENITS/inst/doc/GRENITS_package.pdf
vignetteTitles: GRENITS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GRENITS/inst/doc/GRENITS_package.R
dependencyCount: 42
Package: GreyListChIP
Version: 1.39.0
Depends: R (>= 4.0), methods, GenomicRanges
Imports: GenomicAlignments, BSgenome, Rsamtools, rtracklayer, MASS,
parallel, GenomeInfoDb, SummarizedExperiment, stats, utils
Suggests: BiocStyle, BiocGenerics, RUnit, BSgenome.Hsapiens.UCSC.hg19
License: Artistic-2.0
MD5sum: 59d1f6e897dc1fad22568790039a7726
NeedsCompilation: no
Title: Grey Lists -- Mask Artefact Regions Based on ChIP Inputs
Description: Identify regions of ChIP experiments with high signal in
the input, that lead to spurious peaks during peak calling.
Remove reads aligning to these regions prior to peak calling,
for cleaner ChIP analysis.
biocViews: ChIPSeq, Alignment, Preprocessing, DifferentialPeakCalling,
Sequencing, GenomeAnnotation, Coverage
Author: Matt Eldridge [cre], Gord Brown [aut]
Maintainer: Matt Eldridge <matthew.eldridge@cruk.cam.ac.uk>
git_url: https://git.bioconductor.org/packages/GreyListChIP
git_branch: devel
git_last_commit: daa5333
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GreyListChIP_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GreyListChIP_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GreyListChIP_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GreyListChIP_1.39.0.tgz
vignettes: vignettes/GreyListChIP/inst/doc/GreyList-demo.pdf
vignetteTitles: Generating Grey Lists from Input Libraries
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GreyListChIP/inst/doc/GreyList-demo.R
importsMe: DiffBind, epigraHMM
dependencyCount: 60
Package: GRmetrics
Version: 1.33.0
Depends: R (>= 4.0), SummarizedExperiment
Imports: drc, plotly, ggplot2, S4Vectors, stats
Suggests: knitr, rmarkdown, BiocStyle, tinytex
License: GPL-3
MD5sum: 5475419908cb6438495231ab376f2c06
NeedsCompilation: no
Title: Calculate growth-rate inhibition (GR) metrics
Description: Functions for calculating and visualizing growth-rate
inhibition (GR) metrics.
biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Software,
TimeCourse, Visualization
Author: Nicholas Clark
Maintainer: Nicholas Clark <nicholas.clark00@gmail.com>, Mario
Medvedovic <medvedm@ucmail.uc.edu>
URL: https://github.com/uc-bd2k/GRmetrics
VignetteBuilder: knitr
BugReports: https://github.com/uc-bd2k/GRmetrics/issues
git_url: https://git.bioconductor.org/packages/GRmetrics
git_branch: devel
git_last_commit: cdb20a0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GRmetrics_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GRmetrics_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GRmetrics_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GRmetrics_1.33.0.tgz
vignettes: vignettes/GRmetrics/inst/doc/GRmetrics-vignette.html
vignetteTitles: GRmetrics: an R package for calculation and
visualization of dose-response metrics based on growth rate
inhibition
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GRmetrics/inst/doc/GRmetrics-vignette.R
dependencyCount: 131
Package: groHMM
Version: 1.41.2
Depends: R (>= 4.1.0), MASS, parallel, S4Vectors (>= 0.17.25), IRanges
(>= 2.13.12), GenomeInfoDb, GenomicRanges (>= 1.31.8),
GenomicAlignments (>= 1.15.6), rtracklayer (>= 1.39.7)
Suggests: BiocStyle, GenomicFeatures, edgeR, org.Hs.eg.db,
TxDb.Hsapiens.UCSC.hg19.knownGene
License: GPL-3
Archs: x64
MD5sum: 64e2bdf6a47558c67751eb08de132bad
NeedsCompilation: yes
Title: GRO-seq Analysis Pipeline
Description: A pipeline for the analysis of GRO-seq data.
biocViews: Sequencing, Software
Author: Charles G. Danko [aut], Minho Chae [aut], Andre Martins [ctb],
W. Lee Kraus [aut, fnd], Anusha Nagari [ctb], Tulip Nandu [cre,
ctb], Pariksheet Nanda [ctb] (ORCID:
<https://orcid.org/0000-0001-9726-4552>)
Maintainer: Tulip Nandu <tulip.nandu@utsouthwestern.edu>
URL: https://github.com/Kraus-Lab/groHMM
BugReports: https://github.com/Kraus-Lab/groHMM/issues
git_url: https://git.bioconductor.org/packages/groHMM
git_branch: devel
git_last_commit: b15cead
git_last_commit_date: 2025-03-27
Date/Publication: 2025-03-28
source.ver: src/contrib/groHMM_1.41.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/groHMM_1.41.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/groHMM_1.41.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/groHMM_1.41.2.tgz
vignettes: vignettes/groHMM/inst/doc/groHMM.pdf
vignetteTitles: groHMM tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/groHMM/inst/doc/groHMM.R
Package: GSALightning
Version: 1.35.0
Depends: R (>= 3.3.0)
Imports: Matrix, data.table, stats
Suggests: knitr, rmarkdown
License: GPL (>=2)
MD5sum: 0119a7f58c43b4863dce710c42126208
NeedsCompilation: no
Title: Fast Permutation-based Gene Set Analysis
Description: GSALightning provides a fast implementation of
permutation-based gene set analysis for two-sample problem.
This package is particularly useful when testing simultaneously
a large number of gene sets, or when a large number of
permutations is necessary for more accurate p-values
estimation.
biocViews: Software, BiologicalQuestion, GeneSetEnrichment,
DifferentialExpression, GeneExpression, Transcription
Author: Billy Heung Wing Chang
Maintainer: Billy Heung Wing Chang <billyheungwing@gmail.com>
URL: https://github.com/billyhw/GSALightning
VignetteBuilder: knitr
BugReports: https://github.com/billyhw/GSALightning/issues
git_url: https://git.bioconductor.org/packages/GSALightning
git_branch: devel
git_last_commit: 7414410
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GSALightning_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GSALightning_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GSALightning_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GSALightning_1.35.0.tgz
vignettes: vignettes/GSALightning/inst/doc/vignette.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GSALightning/inst/doc/vignette.R
dependencyCount: 9
Package: GSAR
Version: 1.41.0
Depends: R (>= 3.0.1), igraph (>= 0.7.1)
Imports: stats, graphics
Suggests: MASS, GSVAdata, ALL, tweeDEseqCountData, GSEABase, annotate,
org.Hs.eg.db, Biobase, genefilter, hgu95av2.db, edgeR,
BiocStyle
License: GPL (>=2)
MD5sum: 4d989333329a423a1ea3de72a0748942
NeedsCompilation: no
Title: Gene Set Analysis in R
Description: Gene set analysis using specific alternative hypotheses.
Tests for differential expression, scale and net correlation
structure.
biocViews: Software, StatisticalMethod, DifferentialExpression
Author: Yasir Rahmatallah <yrahmatallah@uams.edu>, Galina Glazko
<gvglazko@uams.edu>
Maintainer: Yasir Rahmatallah <yrahmatallah@uams.edu>, Galina Glazko
<gvglazko@uams.edu>
git_url: https://git.bioconductor.org/packages/GSAR
git_branch: devel
git_last_commit: 04f1246
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GSAR_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GSAR_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GSAR_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GSAR_1.41.0.tgz
vignettes: vignettes/GSAR/inst/doc/GSAR.pdf
vignetteTitles: Gene Set Analysis in R -- the GSAR Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GSAR/inst/doc/GSAR.R
dependencyCount: 17
Package: GSCA
Version: 2.37.0
Depends: shiny, sp, gplots, ggplot2, reshape2, RColorBrewer, rhdf5,
R(>= 2.10.0)
Imports: graphics
Suggests: Affyhgu133aExpr, Affymoe4302Expr, Affyhgu133A2Expr,
Affyhgu133Plus2Expr
License: GPL(>=2)
Archs: x64
MD5sum: 638626be275c7ce865b2c384608d6a37
NeedsCompilation: no
Title: GSCA: Gene Set Context Analysis
Description: GSCA takes as input several lists of activated and
repressed genes. GSCA then searches through a compendium of
publicly available gene expression profiles for biological
contexts that are enriched with a specified pattern of gene
expression. GSCA provides both traditional R functions and
interactive, user-friendly user interface.
biocViews: GeneExpression, Visualization, GUI
Author: Zhicheng Ji, Hongkai Ji
Maintainer: Zhicheng Ji <zji4@jhu.edu>
git_url: https://git.bioconductor.org/packages/GSCA
git_branch: devel
git_last_commit: b2008f7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GSCA_2.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GSCA_2.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GSCA_2.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GSCA_2.37.0.tgz
vignettes: vignettes/GSCA/inst/doc/GSCA.pdf
vignetteTitles: GSCA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GSCA/inst/doc/GSCA.R
dependencyCount: 72
Package: gscreend
Version: 1.21.0
Depends: R (>= 3.6)
Imports: SummarizedExperiment, nloptr, fGarch, methods, BiocParallel,
graphics
Suggests: knitr, testthat, rmarkdown, BiocStyle
License: GPL-3
MD5sum: 840e3d21b837d7a97c068ea2fa2ccd61
NeedsCompilation: no
Title: Analysis of pooled genetic screens
Description: Package for the analysis of pooled genetic screens (e.g.
CRISPR-KO). The analysis of such screens is based on the
comparison of gRNA abundances before and after a cell
proliferation phase. The gscreend packages takes gRNA counts as
input and allows detection of genes whose knockout decreases or
increases cell proliferation.
biocViews: Software, StatisticalMethod, PooledScreens, CRISPR
Author: Katharina Imkeller [cre, aut], Wolfgang Huber [aut]
Maintainer: Katharina Imkeller <k.imkeller@dkfz.de>
URL: https://github.com/imkeller/gscreend
VignetteBuilder: knitr
BugReports: https://github.com/imkeller/gscreend/issues
git_url: https://git.bioconductor.org/packages/gscreend
git_branch: devel
git_last_commit: 12b0796
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/gscreend_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gscreend_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gscreend_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gscreend_1.21.0.tgz
vignettes: vignettes/gscreend/inst/doc/gscreend_simulated_data.html
vignetteTitles: Example_simulated
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gscreend/inst/doc/gscreend_simulated_data.R
dependencyCount: 60
Package: GSEABase
Version: 1.69.1
Depends: R (>= 2.6.0), BiocGenerics (>= 0.13.8), Biobase (>= 2.17.8),
annotate (>= 1.45.3), methods, graph (>= 1.37.2)
Imports: AnnotationDbi, XML
Suggests: hgu95av2.db, GO.db, org.Hs.eg.db, Rgraphviz, ReportingTools,
testthat, BiocStyle, knitr, RUnit
License: Artistic-2.0
MD5sum: b750ab09b776a22dff8b1722530e4798
NeedsCompilation: no
Title: Gene set enrichment data structures and methods
Description: This package provides classes and methods to support Gene
Set Enrichment Analysis (GSEA).
biocViews: GeneExpression, GeneSetEnrichment, GraphAndNetwork, GO, KEGG
Author: Martin Morgan [aut], Seth Falcon [aut], Robert Gentleman [aut],
Paul Villafuerte [ctb] ('GSEABase' vignette translation from
Sweave to Rmarkdown / HTML), Bioconductor Package Maintainer
[cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GSEABase
git_branch: devel
git_last_commit: 269c029
git_last_commit_date: 2025-02-10
Date/Publication: 2025-02-10
source.ver: src/contrib/GSEABase_1.69.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GSEABase_1.69.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GSEABase_1.69.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GSEABase_1.69.1.tgz
vignettes: vignettes/GSEABase/inst/doc/GSEABase.html
vignetteTitles: An introduction to GSEABase
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GSEABase/inst/doc/GSEABase.R
dependsOnMe: AGDEX, BicARE, CCPROMISE, Cepo, cpvSNP, npGSEA, PROMISE,
splineTimeR, TissueEnrich, GSVAdata, OSCA.basic
importsMe: AUCell, BioCor, canceR, Category, categoryCompare, cosmosR,
dreamlet, EnrichmentBrowser, escape, gep2pep, GlobalAncova,
GmicR, GSRI, GSVA, mastR, miRSM, mogsa, oppar, PanomiR,
phenoTest, PROMISE, RcisTarget, ReportingTools, scTGIF,
signatureSearch, singleCellTK, singscore, slalom, sparrow,
TFutils, TMSig, vissE, zenith, msigdb, SingscoreAMLMutations,
clustermole, RVA
suggestsMe: BiocSet, epiregulon.extra, gage, globaltest, GOstats, GSAR,
MAST, phenoTest, BaseSet
dependencyCount: 49
Package: GSEABenchmarkeR
Version: 1.27.0
Depends: R (>= 3.5.0), Biobase, SummarizedExperiment
Imports: AnnotationDbi, AnnotationHub, BiocFileCache, BiocParallel,
edgeR, EnrichmentBrowser, ExperimentHub, grDevices, graphics,
KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO, methods,
S4Vectors, stats, utils
Suggests: BiocStyle, GSE62944, knitr, rappdirs, rmarkdown
License: Artistic-2.0
MD5sum: 8d4bbd34303acf44f0614054cdd1aa23
NeedsCompilation: no
Title: Reproducible GSEA Benchmarking
Description: The GSEABenchmarkeR package implements an extendable
framework for reproducible evaluation of set- and network-based
methods for enrichment analysis of gene expression data. This
includes support for the efficient execution of these methods
on comprehensive real data compendia (microarray and RNA-seq)
using parallel computation on standard workstations and
institutional computer grids. Methods can then be assessed with
respect to runtime, statistical significance, and relevance of
the results for the phenotypes investigated.
biocViews: ImmunoOncology, Microarray, RNASeq, GeneExpression,
DifferentialExpression, Pathways, GraphAndNetwork, Network,
GeneSetEnrichment, NetworkEnrichment, Visualization,
ReportWriting
Author: Ludwig Geistlinger [aut, cre], Gergely Csaba [aut], Mara
Santarelli [ctb], Lucas Schiffer [ctb], Marcel Ramos [ctb],
Ralf Zimmer [aut], Levi Waldron [aut]
Maintainer: Ludwig Geistlinger <ludwig.geistlinger@gmail.com>
URL: https://github.com/waldronlab/GSEABenchmarkeR
VignetteBuilder: knitr
BugReports: https://github.com/waldronlab/GSEABenchmarkeR/issues
git_url: https://git.bioconductor.org/packages/GSEABenchmarkeR
git_branch: devel
git_last_commit: 7fc44fc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GSEABenchmarkeR_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GSEABenchmarkeR_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GSEABenchmarkeR_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GSEABenchmarkeR_1.27.0.tgz
vignettes: vignettes/GSEABenchmarkeR/inst/doc/GSEABenchmarkeR.html
vignetteTitles: Reproducible GSEA Benchmarking
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GSEABenchmarkeR/inst/doc/GSEABenchmarkeR.R
suggestsMe: roastgsa
dependencyCount: 110
Package: GSEAlm
Version: 1.67.0
Depends: Biobase
Suggests: GSEABase,Category, multtest, ALL, annotate, hgu95av2.db,
genefilter, GOstats, RColorBrewer
License: Artistic-2.0
MD5sum: 2486c505d9f23474c702e814cef676cf
NeedsCompilation: no
Title: Linear Model Toolset for Gene Set Enrichment Analysis
Description: Models and methods for fitting linear models to gene
expression data, together with tools for computing and using
various regression diagnostics.
biocViews: Microarray
Author: Assaf Oron, Robert Gentleman (with contributions from S. Falcon
and Z. Jiang)
Maintainer: Assaf Oron <assaf@uw.edu>
git_url: https://git.bioconductor.org/packages/GSEAlm
git_branch: devel
git_last_commit: dc2aaa7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GSEAlm_1.67.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GSEAlm_1.67.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GSEAlm_1.67.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GSEAlm_1.67.0.tgz
vignettes: vignettes/GSEAlm/inst/doc/GSEAlm.pdf
vignetteTitles: Linear models in GSEA
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GSEAlm/inst/doc/GSEAlm.R
dependencyCount: 7
Package: GSEAmining
Version: 1.17.0
Depends: R (>= 4.0)
Imports: dplyr, tidytext, dendextend, tibble, ggplot2, ggwordcloud,
stringr, gridExtra, rlang, grDevices, graphics, stats, methods
Suggests: knitr, rmarkdown, BiocStyle, clusterProfiler, testthat, tm
License: GPL-3 | file LICENSE
MD5sum: 687e214c785f18de67516123de3206ea
NeedsCompilation: no
Title: Make Biological Sense of Gene Set Enrichment Analysis Outputs
Description: Gene Set Enrichment Analysis is a very powerful and
interesting computational method that allows an easy
correlation between differential expressed genes and biological
processes. Unfortunately, although it was designed to help
researchers to interpret gene expression data it can generate
huge amounts of results whose biological meaning can be
difficult to interpret. Many available tools rely on the
hierarchically structured Gene Ontology (GO) classification to
reduce reundandcy in the results. However, due to the
popularity of GSEA many more gene set collections, such as
those in the Molecular Signatures Database are emerging. Since
these collections are not organized as those in GO, their usage
for GSEA do not always give a straightforward answer or, in
other words, getting all the meaninful information can be
challenging with the currently available tools. For these
reasons, GSEAmining was born to be an easy tool to create
reproducible reports to help researchers make biological sense
of GSEA outputs. Given the results of GSEA, GSEAmining clusters
the different gene sets collections based on the presence of
the same genes in the leadind edge (core) subset. Leading edge
subsets are those genes that contribute most to the enrichment
score of each collection of genes or gene sets. For this
reason, gene sets that participate in similar biological
processes should share genes in common and in turn cluster
together. After that, GSEAmining is able to identify and
represent for each cluster: - The most enriched terms in the
names of gene sets (as wordclouds) - The most enriched genes in
the leading edge subsets (as bar plots). In each case, positive
and negative enrichments are shown in different colors so it is
easy to distinguish biological processes or genes that may be
of interest in that particular study.
biocViews: GeneSetEnrichment, Clustering, Visualization
Author: Oriol Arqués [aut, cre]
Maintainer: Oriol Arqués <oriol.arques@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GSEAmining
git_branch: devel
git_last_commit: 775d0d7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GSEAmining_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GSEAmining_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GSEAmining_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GSEAmining_1.17.0.tgz
vignettes: vignettes/GSEAmining/inst/doc/GSEAmining.html
vignetteTitles: GSEAmining
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GSEAmining/inst/doc/GSEAmining.R
dependencyCount: 61
Package: gsean
Version: 1.27.0
Depends: R (>= 3.5), fgsea, PPInfer
Suggests: SummarizedExperiment, pasilla, org.Dm.eg.db, AnnotationDbi,
knitr, plotly, WGCNA, rmarkdown
License: Artistic-2.0
MD5sum: e04286a690b56f7042c21ae4c08c183d
NeedsCompilation: yes
Title: Gene Set Enrichment Analysis with Networks
Description: Biological molecules in a living organism seldom work
individually. They usually interact each other in a cooperative
way. Biological process is too complicated to understand
without considering such interactions. Thus, network-based
procedures can be seen as powerful methods for studying complex
process. However, many methods are devised for analyzing
individual genes. It is said that techniques based on
biological networks such as gene co-expression are more precise
ways to represent information than those using lists of genes
only. This package is aimed to integrate the gene expression
and biological network. A biological network is constructed
from gene expression data and it is used for Gene Set
Enrichment Analysis.
biocViews: Software, StatisticalMethod, Network, GraphAndNetwork,
GeneSetEnrichment, GeneExpression, NetworkEnrichment, Pathways,
DifferentialExpression
Author: Dongmin Jung
Maintainer: Dongmin Jung <dmdmjung@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/gsean
git_branch: devel
git_last_commit: 2515344
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/gsean_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gsean_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gsean_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gsean_1.27.0.tgz
vignettes: vignettes/gsean/inst/doc/gsean.html
vignetteTitles: gsean
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gsean/inst/doc/gsean.R
dependencyCount: 117
Package: GSgalgoR
Version: 1.17.0
Imports: cluster, doParallel, foreach, matchingR, nsga2R, survival,
proxy, stats, methods,
Suggests: knitr, rmarkdown, ggplot2, BiocStyle, genefu, survcomp,
Biobase, survminer, breastCancerTRANSBIG, breastCancerUPP,
iC10TrainingData, pamr, testthat
License: MIT + file LICENSE
MD5sum: 32f43d5754160bf6fb5b6352e92a314c
NeedsCompilation: no
Title: An Evolutionary Framework for the Identification and Study of
Prognostic Gene Expression Signatures in Cancer
Description: A multi-objective optimization algorithm for disease
sub-type discovery based on a non-dominated sorting genetic
algorithm. The 'Galgo' framework combines the advantages of
clustering algorithms for grouping heterogeneous 'omics' data
and the searching properties of genetic algorithms for feature
selection. The algorithm search for the optimal number of
clusters determination considering the features that maximize
the survival difference between sub-types while keeping cluster
consistency high.
biocViews: GeneExpression, Transcription, Clustering, Classification,
Survival
Author: Martin Guerrero [aut], Carlos Catania [cre]
Maintainer: Carlos Catania <harpomaxx@gmail.com>
URL: https://github.com/harpomaxx/GSgalgoR
VignetteBuilder: knitr
BugReports: https://github.com/harpomaxx/GSgalgoR/issues
git_url: https://git.bioconductor.org/packages/GSgalgoR
git_branch: devel
git_last_commit: 5111e23
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GSgalgoR_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GSgalgoR_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GSgalgoR_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GSgalgoR_1.17.0.tgz
vignettes: vignettes/GSgalgoR/inst/doc/GSgalgoR_callbacks.html,
vignettes/GSgalgoR/inst/doc/GSgalgoR.html
vignetteTitles: GSgalgoR_callbacks.html, GSgalgoR.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GSgalgoR/inst/doc/GSgalgoR_callbacks.R,
vignettes/GSgalgoR/inst/doc/GSgalgoR.R
dependencyCount: 22
Package: GSReg
Version: 1.41.0
Depends: R (>= 2.13.1), Homo.sapiens, org.Hs.eg.db, GenomicFeatures,
AnnotationDbi
Suggests: GenomicRanges, GSBenchMark
License: GPL-2
MD5sum: f29c09b7f03d4bda2b4e1eff2ac7a0a3
NeedsCompilation: yes
Title: Gene Set Regulation (GS-Reg)
Description: A package for gene set analysis based on the variability
of expressions as well as a method to detect Alternative
Splicing Events . It implements DIfferential RAnk Conservation
(DIRAC) and gene set Expression Variation Analysis (EVA)
methods. For detecting Differentially Spliced genes, it
provides an implementation of the Spliced-EVA (SEVA).
biocViews: GeneRegulation, Pathways, GeneExpression,
GeneticVariability, GeneSetEnrichment, AlternativeSplicing
Author: Bahman Afsari <bahman@jhu.edu>, Elana J. Fertig
<ejfertig@jhmi.edu>
Maintainer: Bahman Afsari <bahman@jhu.edu>, Elana J. Fertig
<ejfertig@jhmi.edu>
git_url: https://git.bioconductor.org/packages/GSReg
git_branch: devel
git_last_commit: 2723c81
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GSReg_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GSReg_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GSReg_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GSReg_1.41.0.tgz
vignettes: vignettes/GSReg/inst/doc/GSReg.pdf
vignetteTitles: Working with the GSReg package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GSReg/inst/doc/GSReg.R
dependencyCount: 109
Package: GSRI
Version: 2.55.0
Depends: R (>= 2.14.2), fdrtool
Imports: methods, graphics, stats, utils, genefilter, Biobase,
GSEABase, les (>= 1.1.6)
Suggests: limma, hgu95av2.db
Enhances: parallel
License: GPL-3
Archs: x64
MD5sum: d0c5140cb37bdbfa082741f95884dd5a
NeedsCompilation: no
Title: Gene Set Regulation Index
Description: The GSRI package estimates the number of differentially
expressed genes in gene sets, utilizing the concept of the Gene
Set Regulation Index (GSRI).
biocViews: Microarray, Transcription, DifferentialExpression,
GeneSetEnrichment, GeneRegulation
Author: Julian Gehring, Kilian Bartholome, Clemens Kreutz, Jens Timmer
Maintainer: Julian Gehring <jg-bioc@gmx.com>
git_url: https://git.bioconductor.org/packages/GSRI
git_branch: devel
git_last_commit: 5b1a284
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GSRI_2.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GSRI_2.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GSRI_2.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GSRI_2.55.0.tgz
vignettes: vignettes/GSRI/inst/doc/gsri.pdf
vignetteTitles: Introduction to the GSRI package: Estimating Regulatory
Effects utilizing the Gene Set Regulation Index
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GSRI/inst/doc/gsri.R
dependencyCount: 67
Package: GSVA
Version: 2.1.10
Depends: R (>= 3.5.0)
Imports: methods, stats, utils, graphics, S4Vectors, IRanges, Biobase,
SummarizedExperiment, GSEABase, Matrix (>= 1.5-0), parallel,
BiocParallel, SingleCellExperiment, SpatialExperiment,
sparseMatrixStats, DelayedArray, DelayedMatrixStats, HDF5Array,
BiocSingular, cli
LinkingTo: cli
Suggests: BiocGenerics, RUnit, BiocStyle, knitr, rmarkdown, limma,
RColorBrewer, org.Hs.eg.db, genefilter, edgeR, GSVAdata, shiny,
shinydashboard, ggplot2, data.table, plotly, future, promises,
shinybusy, shinyjs
License: GPL (>= 2)
MD5sum: 0803c4c5e9aba1fdf64295cba0961e8d
NeedsCompilation: yes
Title: Gene Set Variation Analysis for Microarray and RNA-Seq Data
Description: Gene Set Variation Analysis (GSVA) is a non-parametric,
unsupervised method for estimating variation of gene set
enrichment through the samples of a expression data set. GSVA
performs a change in coordinate systems, transforming the data
from a gene by sample matrix to a gene-set by sample matrix,
thereby allowing the evaluation of pathway enrichment for each
sample. This new matrix of GSVA enrichment scores facilitates
applying standard analytical methods like functional
enrichment, survival analysis, clustering, CNV-pathway analysis
or cross-tissue pathway analysis, in a pathway-centric manner.
biocViews: FunctionalGenomics, Microarray, RNASeq, Pathways,
GeneSetEnrichment
Author: Robert Castelo [aut, cre], Justin Guinney [aut], Alexey
Sergushichev [ctb], Pablo Sebastian Rodriguez [ctb], Axel Klenk
[ctb]
Maintainer: Robert Castelo <robert.castelo@upf.edu>
URL: https://github.com/rcastelo/GSVA
VignetteBuilder: knitr
BugReports: https://github.com/rcastelo/GSVA/issues
git_url: https://git.bioconductor.org/packages/GSVA
git_branch: devel
git_last_commit: a815a20
git_last_commit_date: 2025-03-21
Date/Publication: 2025-03-21
source.ver: src/contrib/GSVA_2.1.10.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GSVA_2.1.10.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GSVA_2.1.10.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GSVA_2.1.10.tgz
vignettes: vignettes/GSVA/inst/doc/GSVA.html
vignetteTitles: Gene set variation analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GSVA/inst/doc/GSVA.R
dependsOnMe: SMDIC
importsMe: consensusOV, EGSEA, escape, octad, oppar, scFeatures,
signifinder, singleCellTK, TBSignatureProfiler, autoGO,
clustermole, DRviaSPCN, GSEMA, psSubpathway, scMappR, SIGN,
sigQC, ssdGSA
suggestsMe: decoupleR, MCbiclust, sparrow, SPONGE, ReporterScore
dependencyCount: 103
Package: gtrellis
Version: 1.39.0
Depends: R (>= 3.1.2), grid, IRanges, GenomicRanges
Imports: circlize (>= 0.4.8), GetoptLong, grDevices, utils
Suggests: testthat (>= 1.0.0), knitr, RColorBrewer, markdown,
rmarkdown, ComplexHeatmap (>= 1.99.0), Cairo, png, jpeg, tiff
License: MIT + file LICENSE
Archs: x64
MD5sum: b9045b6bb4acfbed12419baa8768681d
NeedsCompilation: no
Title: Genome Level Trellis Layout
Description: Genome level Trellis graph visualizes genomic data
conditioned by genomic categories (e.g. chromosomes). For each
genomic category, multiple dimensional data which are
represented as tracks describe different features from
different aspects. This package provides high flexibility to
arrange genomic categories and to add self-defined graphics in
the plot.
biocViews: Software, Visualization, Sequencing
Author: Zuguang Gu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7395-8709>)
Maintainer: Zuguang Gu <z.gu@dkfz.de>
URL: https://github.com/jokergoo/gtrellis
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/gtrellis
git_branch: devel
git_last_commit: d40aa01
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/gtrellis_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gtrellis_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gtrellis_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gtrellis_1.39.0.tgz
vignettes: vignettes/gtrellis/inst/doc/gtrellis.html
vignetteTitles: Make Genome-level Trellis Graph
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/gtrellis/inst/doc/gtrellis.R
importsMe: YAPSA
dependencyCount: 32
Package: GUIDEseq
Version: 1.37.0
Depends: R (>= 3.5.0), GenomicRanges, BiocGenerics
Imports: Biostrings, pwalign, CRISPRseek, ChIPpeakAnno, data.table,
matrixStats, BSgenome, parallel, IRanges (>= 2.5.5), S4Vectors
(>= 0.9.6), stringr, multtest, GenomicAlignments (>= 1.7.3),
GenomeInfoDb, Rsamtools, hash, limma,dplyr, GenomicFeatures,
rio, tidyr, tools, methods, purrr, ggplot2, openxlsx,
patchwork, rlang
Suggests: knitr, RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Hsapiens.UCSC.hg38, TxDb.Hsapiens.UCSC.hg19.knownGene,
org.Hs.eg.db, testthat (>= 3.0.0)
License: GPL (>= 2)
MD5sum: 7ea8ae85c95700b0244a6bdc547235d6
NeedsCompilation: no
Title: GUIDE-seq and PEtag-seq analysis pipeline
Description: The package implements GUIDE-seq and PEtag-seq analysis
workflow including functions for filtering UMI and reads with
low coverage, obtaining unique insertion sites (proxy of
cleavage sites), estimating the locations of the insertion
sites, aka, peaks, merging estimated insertion sites from plus
and minus strand, and performing off target search of the
extended regions around insertion sites with mismatches and
indels.
biocViews: ImmunoOncology, GeneRegulation, Sequencing, WorkflowStep,
CRISPR
Author: Lihua Julie Zhu, Michael Lawrence, Ankit Gupta, Hervé Pagès ,
Alper Kucukural, Manuel Garber, Scot A. Wolfe
Maintainer: Lihua Julie Zhu <julie.zhu@umassmed.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GUIDEseq
git_branch: devel
git_last_commit: f947d90
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GUIDEseq_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GUIDEseq_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GUIDEseq_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GUIDEseq_1.37.0.tgz
vignettes: vignettes/GUIDEseq/inst/doc/GUIDEseq.pdf
vignetteTitles: GUIDEseq Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GUIDEseq/inst/doc/GUIDEseq.R
dependencyCount: 181
Package: Guitar
Version: 2.23.0
Depends: GenomicFeatures, rtracklayer,AnnotationDbi, GenomicRanges,
magrittr, ggplot2, methods, stats,utils ,knitr,dplyr
License: GPL-2
Archs: x64
MD5sum: 8885c8ad55a7639303addac39369712e
NeedsCompilation: no
Title: Guitar
Description: The package is designed for visualization of RNA-related
genomic features with respect to the landmarks of RNA
transcripts, i.e., transcription starting site, start codon,
stop codon and transcription ending site.
biocViews: Sequencing, SplicedAlignment, Alignment, DataImport, RNASeq,
MethylSeq, QualityControl, Transcription
Author: Xiao Du, Hui Liu, Lin Zhang, Jia Meng
Maintainer: Jia Meng <jia.meng@xjtlu.edu.cn>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Guitar
git_branch: devel
git_last_commit: 4f9853b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Guitar_2.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Guitar_2.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Guitar_2.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Guitar_2.23.0.tgz
vignettes: vignettes/Guitar/inst/doc/Guitar-Overview.pdf
vignetteTitles: Guitar
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Guitar/inst/doc/Guitar-Overview.R
dependencyCount: 103
Package: Gviz
Version: 1.51.0
Depends: R (>= 4.3), methods, S4Vectors (>= 0.9.25), IRanges (>=
1.99.18), GenomicRanges (>= 1.17.20), grid
Imports: XVector (>= 0.5.7), rtracklayer (>= 1.25.13), lattice,
RColorBrewer, biomaRt (>= 2.11.0), AnnotationDbi (>= 1.27.5),
Biobase (>= 2.15.3), GenomicFeatures (>= 1.17.22), ensembldb
(>= 2.11.3), BSgenome (>= 1.33.1), Biostrings (>= 2.33.11),
biovizBase (>= 1.13.8), Rsamtools (>= 1.17.28), latticeExtra
(>= 0.6-26), matrixStats (>= 0.8.14), GenomicAlignments (>=
1.1.16), GenomeInfoDb (>= 1.1.3), BiocGenerics (>= 0.11.3),
digest(>= 0.6.8), graphics, grDevices, stats, utils
Suggests: BSgenome.Hsapiens.UCSC.hg19, xml2, BiocStyle, knitr,
rmarkdown, testthat
License: Artistic-2.0
MD5sum: 7d5393d2029b0e09f2ad52adb60abc4e
NeedsCompilation: no
Title: Plotting data and annotation information along genomic
coordinates
Description: Genomic data analyses requires integrated visualization of
known genomic information and new experimental data. Gviz uses
the biomaRt and the rtracklayer packages to perform live
annotation queries to Ensembl and UCSC and translates this to
e.g. gene/transcript structures in viewports of the grid
graphics package. This results in genomic information plotted
together with your data.
biocViews: Visualization, Microarray, Sequencing
Author: Florian Hahne [aut], Steffen Durinck [aut], Robert Ivanek [aut,
cre] (ORCID: <https://orcid.org/0000-0002-8403-056X>), Arne
Mueller [aut], Steve Lianoglou [aut], Ge Tan [aut], Lance
Parsons [aut], Shraddha Pai [aut], Thomas McCarthy [ctb], Felix
Ernst [ctb], Mike Smith [ctb]
Maintainer: Robert Ivanek <robert.ivanek@unibas.ch>
URL: https://github.com/ivanek/Gviz
VignetteBuilder: knitr
BugReports: https://github.com/ivanek/Gviz/issues
git_url: https://git.bioconductor.org/packages/Gviz
git_branch: devel
git_last_commit: 60a0f4a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Gviz_1.51.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Gviz_1.51.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Gviz_1.51.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Gviz_1.51.0.tgz
vignettes: vignettes/Gviz/inst/doc/Gviz.html
vignetteTitles: The Gviz User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Gviz/inst/doc/Gviz.R
dependsOnMe: biomvRCNS, chimeraviz, cicero, cummeRbund, Pviz,
rnaseqGene, csawBook
importsMe: AllelicImbalance, ASpli, CAGEfightR, comapr, crisprViz,
DMRcate, DuplexDiscovereR, ELMER, epimutacions,
GenomicInteractions, maser, mCSEA, MEAL, methylPipe,
motifbreakR, OGRE, PING, primirTSS, regutools, RNAmodR,
RNAmodR.AlkAnilineSeq, RNAmodR.RiboMethSeq, SPLINTER, srnadiff,
tadar, trackViewer, TVTB, uncoverappLib, VariantFiltering,
DMRcatedata, GRIN2
suggestsMe: annmap, BindingSiteFinder, cellbaseR, CNEr, CNVRanger,
ensembldb, extraChIPs, fishpond, GenomicRanges, gwascat,
interactiveDisplay, MIRit, pqsfinder, QuasR, RnBeads,
segmenter, SplicingGraphs, TFutils, Single.mTEC.Transcriptomes,
CAGEWorkflow, chipseqDB, chicane, RTIGER
dependencyCount: 155
Package: GWAS.BAYES
Version: 1.17.0
Depends: R (>= 4.3.0)
Imports: GA (>= 3.2), caret (>= 6.0-86), memoise (>= 1.1.0), Matrix (>=
1.2-18), limma (>= 3.54.0), stats (>= 4.2.2), MASS (>=
7.3-58.1)
Suggests: BiocStyle, knitr, rmarkdown, formatR, rrBLUP
License: GPL-3 + file LICENSE
MD5sum: 8240beed7ffee9b583e661db57e4b2c1
NeedsCompilation: no
Title: Bayesian analysis of Gaussian GWAS data
Description: This package is built to perform GWAS analysis using
Bayesian techniques. Currently, GWAS.BAYES has functionality
for the implementation of BICOSS (Williams, J., Ferreira, M.
A., and Ji, T. (2022). BICOSS: Bayesian iterative conditional
stochastic search for GWAS. BMC Bioinformatics), BGWAS
(Williams, J., Xu, S., Ferreira, M. A.. (2023) "BGWAS: Bayesian
variable selection in linear mixed models with nonlocal priors
for genome-wide association studies." BMC Bioinformatics), and
GINA. All methods currently are for the analysis of Gaussian
phenotypes The research related to this package was supported
in part by National Science Foundation awards DMS 1853549, DMS
1853556, and DMS 2054173.
biocViews: Bayesian, AssayDomain, SNP, GenomeWideAssociation
Author: Jacob Williams [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6425-1365>), Marco Ferreira [aut]
(ORCID: <https://orcid.org/0000-0002-4705-5661>), Tieming Ji
[aut]
Maintainer: Jacob Williams <jwilliams@vt.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GWAS.BAYES
git_branch: devel
git_last_commit: 645ae38
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GWAS.BAYES_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GWAS.BAYES_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GWAS.BAYES_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GWAS.BAYES_1.17.0.tgz
vignettes: vignettes/GWAS.BAYES/inst/doc/Vignette_BICOSS.html,
vignettes/GWAS.BAYES/inst/doc/Vignette_GINA.html
vignetteTitles: BICOSS, GINA
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/GWAS.BAYES/inst/doc/Vignette_BICOSS.R,
vignettes/GWAS.BAYES/inst/doc/Vignette_GINA.R
dependencyCount: 94
Package: gwascat
Version: 2.39.2
Depends: R (>= 4.1.0), methods
Imports: S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges
(>= 1.29.6), GenomicFeatures, readr, Biostrings, AnnotationDbi,
BiocFileCache, snpStats, VariantAnnotation, AnnotationHub,
data.table, tibble
Suggests: DO.db, DT, knitr, RBGL, testthat, rmarkdown, dplyr, Gviz,
Rsamtools, rtracklayer, graph, ggbio, DelayedArray,
TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, BiocStyle
Enhances: SNPlocs.Hsapiens.dbSNP144.GRCh37
License: Artistic-2.0
MD5sum: 4e3d3070066a41a526726c1ee5cd9a6b
NeedsCompilation: no
Title: representing and modeling data in the EMBL-EBI GWAS catalog
Description: Represent and model data in the EMBL-EBI GWAS catalog.
biocViews: Genetics
Author: VJ Carey <stvjc@channing.harvard.edu>
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/gwascat
git_branch: devel
git_last_commit: 8252998
git_last_commit_date: 2025-03-27
Date/Publication: 2025-03-27
source.ver: src/contrib/gwascat_2.39.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gwascat_2.39.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gwascat_2.39.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gwascat_2.39.2.tgz
vignettes: vignettes/gwascat/inst/doc/gwascat.html,
vignettes/gwascat/inst/doc/gwascatOnt.html
vignetteTitles: gwascat: structuring and querying the NHGRI GWAS
catalog, gwascat -- GRanges for GWAS hits in EBI catalog
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gwascat/inst/doc/gwascat.R,
vignettes/gwascat/inst/doc/gwascatOnt.R
dependsOnMe: vtpnet, liftOver
importsMe: circRNAprofiler
suggestsMe: GenomicScores, hmdbQuery, ldblock, parglms, TFutils,
grasp2db
dependencyCount: 110
Package: GWASTools
Version: 1.53.1
Depends: Biobase
Imports: graphics, stats, utils, methods, gdsfmt, DBI, RSQLite,
GWASExactHW, DNAcopy, survival, sandwich, lmtest, logistf,
quantsmooth, data.table
Suggests: ncdf4, GWASdata, BiocGenerics, RUnit, Biostrings,
GenomicRanges, IRanges, SNPRelate, snpStats, S4Vectors,
VariantAnnotation, parallel, BiocStyle, knitr
License: Artistic-2.0
MD5sum: 29df1c6aca680810722b4449e5d4e243
NeedsCompilation: no
Title: Tools for Genome Wide Association Studies
Description: Classes for storing very large GWAS data sets and
annotation, and functions for GWAS data cleaning and analysis.
biocViews: SNP, GeneticVariability, QualityControl, Microarray
Author: Stephanie M. Gogarten [aut], Cathy Laurie [aut], Tushar
Bhangale [aut], Matthew P. Conomos [aut], Cecelia Laurie [aut],
Michael Lawrence [aut], Caitlin McHugh [aut], Ian Painter
[aut], Xiuwen Zheng [aut], Jess Shen [aut], Rohit Swarnkar
[aut], Adrienne Stilp [aut], Sarah Nelson [aut], David Levine
[aut], Sonali Kumari [ctb] (Converted vignettes from Sweave to
RMarkdown / HTML.), Stephanie M. Gogarten [cre]
Maintainer: Stephanie M. Gogarten <sdmorris@uw.edu>
URL: https://github.com/smgogarten/GWASTools
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/GWASTools
git_branch: devel
git_last_commit: 45e582c
git_last_commit_date: 2025-03-18
Date/Publication: 2025-03-18
source.ver: src/contrib/GWASTools_1.53.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GWASTools_1.53.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GWASTools_1.53.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GWASTools_1.53.1.tgz
vignettes: vignettes/GWASTools/inst/doc/DataCleaning.pdf,
vignettes/GWASTools/inst/doc/Formats.pdf,
vignettes/GWASTools/inst/doc/Affymetrix.html
vignetteTitles: GWAS Data Cleaning, Data formats in GWASTools,
Preparing Affymetrix Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GWASTools/inst/doc/Affymetrix.R,
vignettes/GWASTools/inst/doc/DataCleaning.R,
vignettes/GWASTools/inst/doc/Formats.R
dependsOnMe: mBPCR, GWASdata, snplinkage
importsMe: GENESIS, gwasurvivr
suggestsMe: podkat
dependencyCount: 96
Package: gwasurvivr
Version: 1.25.0
Depends: R (>= 3.4.0)
Imports: GWASTools, survival, VariantAnnotation, parallel, matrixStats,
SummarizedExperiment, stats, utils, SNPRelate
Suggests: BiocStyle, knitr, rmarkdown
License: Artistic-2.0
MD5sum: 1d5f28130207c5065e692cc7545e2201
NeedsCompilation: no
Title: gwasurvivr: an R package for genome wide survival analysis
Description: gwasurvivr is a package to perform survival analysis using
Cox proportional hazard models on imputed genetic data.
biocViews: GenomeWideAssociation, Survival, Regression, Genetics, SNP,
GeneticVariability, Pharmacogenomics, BiomedicalInformatics
Author: Abbas Rizvi, Ezgi Karaesmen, Martin Morgan, Lara
Sucheston-Campbell
Maintainer: Abbas Rizvi <aarizv@gmail.com>
URL: https://github.com/suchestoncampbelllab/gwasurvivr
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/gwasurvivr
git_branch: devel
git_last_commit: dd7257d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/gwasurvivr_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gwasurvivr_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gwasurvivr_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gwasurvivr_1.25.0.tgz
vignettes: vignettes/gwasurvivr/inst/doc/gwasurvivr_Introduction.html
vignetteTitles: gwasurvivr Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/gwasurvivr/inst/doc/gwasurvivr_Introduction.R
dependencyCount: 145
Package: GWENA
Version: 1.17.0
Depends: R (>= 4.1)
Imports: WGCNA (>= 1.67), dplyr (>= 0.8.3), dynamicTreeCut (>= 1.63-1),
ggplot2 (>= 3.1.1), gprofiler2 (>= 0.1.6), magrittr (>= 1.5),
tibble (>= 2.1.1), tidyr (>= 1.0.0), NetRep (>= 1.2.1), igraph
(>= 1.2.4.1), RColorBrewer (>= 1.1-2), purrr (>= 0.3.3), rlist
(>= 0.4.6.1), matrixStats (>= 0.55.0), SummarizedExperiment (>=
1.14.1), stringr (>= 1.4.0), cluster (>= 2.1.0), grDevices (>=
4.0.4), methods, graphics, stats, utils
Suggests: testthat (>= 2.1.0), knitr (>= 1.25), rmarkdown (>= 1.16),
prettydoc (>= 0.3.0), httr (>= 1.4.1), S4Vectors (>= 0.22.1),
BiocStyle (>= 2.15.8)
License: GPL-3
MD5sum: 9b214ac4abefdd1b5bd7f7223358d60b
NeedsCompilation: no
Title: Pipeline for augmented co-expression analysis
Description: The development of high-throughput sequencing led to
increased use of co-expression analysis to go beyong single
feature (i.e. gene) focus. We propose GWENA (Gene Whole
co-Expression Network Analysis) , a tool designed to perform
gene co-expression network analysis and explore the results in
a single pipeline. It includes functional enrichment of modules
of co-expressed genes, phenotypcal association, topological
analysis and comparison of networks configuration between
conditions.
biocViews: Software, GeneExpression, Network, Clustering,
GraphAndNetwork, GeneSetEnrichment, Pathways, Visualization,
RNASeq, Transcriptomics, mRNAMicroarray, Microarray,
NetworkEnrichment, Sequencing, GO
Author: Gwenaëlle Lemoine [aut, cre] (ORCID:
<https://orcid.org/0000-0003-4747-1937>), Marie-Pier
Scott-Boyer [ths], Arnaud Droit [fnd]
Maintainer: Gwenaëlle Lemoine <lemoine.gwenaelle@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/Kumquatum/GWENA/issues
git_url: https://git.bioconductor.org/packages/GWENA
git_branch: devel
git_last_commit: 19342a3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/GWENA_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/GWENA_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/GWENA_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GWENA_1.17.0.tgz
vignettes: vignettes/GWENA/inst/doc/GWENA_guide.html
vignetteTitles: GWENA-vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/GWENA/inst/doc/GWENA_guide.R
dependencyCount: 141
Package: gypsum
Version: 1.3.0
Imports: utils, httr2, jsonlite, parallel, filelock, rappdirs
Suggests: knitr, rmarkdown, testthat, BiocStyle, digest, jsonvalidate,
DBI, RSQLite, S4Vectors, methods
License: MIT + file LICENSE
MD5sum: e21eed74adb2850fa6a06ec9576a9ed9
NeedsCompilation: no
Title: Interface to the gypsum REST API
Description: Client for the gypsum REST API
(https://gypsum.artifactdb.com), a cloud-based file store in
the ArtifactDB ecosystem. This package provides functions for
uploads, downloads, and various adminstrative and management
tasks. Check out the documentation at
https://github.com/ArtifactDB/gypsum-worker for more details.
biocViews: DataImport
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://github.com/ArtifactDB/gypsum-R
VignetteBuilder: knitr
BugReports: https://github.com/ArtifactDB/gypsum-R/issues
git_url: https://git.bioconductor.org/packages/gypsum
git_branch: devel
git_last_commit: c2d990c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/gypsum_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/gypsum_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/gypsum_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/gypsum_1.3.0.tgz
vignettes: vignettes/gypsum/inst/doc/userguide.html
vignetteTitles: Hitting the gypsum API
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/gypsum/inst/doc/userguide.R
importsMe: celldex, scRNAseq
dependencyCount: 21
Package: h5mread
Version: 0.99.4
Depends: R (>= 4.5), methods, rhdf5, BiocGenerics, SparseArray
Imports: stats, tools, rhdf5filters, S4Vectors, IRanges, S4Arrays
LinkingTo: Rhdf5lib, S4Vectors
Suggests: BiocParallel, ExperimentHub, TENxBrainData, HDF5Array,
testthat, knitr, rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: 29ca18c0acd0f287e843b67868f89eb6
NeedsCompilation: yes
Title: A fast HDF5 reader
Description: The main function in the h5mread package is h5mread(),
which allows reading arbitrary data from an HDF5 dataset into
R, similarly to what the h5read() function from the rhdf5
package does. In the case of h5mread(), the implementation has
been optimized to make it as fast and memory-efficient as
possible.
biocViews: Infrastructure, DataRepresentation, DataImport
Author: Hervé Pagès [aut, cre] (ORCID:
<https://orcid.org/0009-0002-8272-4522>)
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/h5mread
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/hpages/h5mread/issues
git_url: https://git.bioconductor.org/packages/h5mread
git_branch: devel
git_last_commit: 8aced10
git_last_commit_date: 2025-01-21
Date/Publication: 2025-01-22
source.ver: src/contrib/h5mread_0.99.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/h5mread_0.99.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/h5mread_0.99.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/h5mread_0.99.4.tgz
vignettes: vignettes/h5mread/inst/doc/h5mread.html
vignetteTitles: The h5mread package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/h5mread/inst/doc/h5mread.R
dependsOnMe: HDF5Array
importsMe: SpliceWiz
suggestsMe: MultiAssayExperiment
dependencyCount: 24
Package: h5vc
Version: 2.41.1
Depends: grid, gridExtra, ggplot2
Imports: rhdf5, reshape, S4Vectors, IRanges, Biostrings, Rsamtools (>=
2.13.1), methods, GenomicRanges, abind, BiocParallel,
BatchJobs, h5vcData, GenomeInfoDb
LinkingTo: Rhtslib (>= 1.99.1)
Suggests: knitr, locfit, BSgenome.Hsapiens.UCSC.hg19, biomaRt,
BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocGenerics, rmarkdown
License: GPL (>= 3)
MD5sum: fadd44158573bd602a0e8e5b3412f2c5
NeedsCompilation: yes
Title: Managing alignment tallies using a hdf5 backend
Description: This package contains functions to interact with tally
data from NGS experiments that is stored in HDF5 files.
Author: Paul Theodor Pyl
Maintainer: Paul Theodor Pyl <paul.theodor.pyl@gmail.com>
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/h5vc
git_branch: devel
git_last_commit: 2312231
git_last_commit_date: 2025-01-22
Date/Publication: 2025-01-22
source.ver: src/contrib/h5vc_2.41.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/h5vc_2.41.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/h5vc_2.41.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/h5vc_2.41.1.tgz
vignettes: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.html,
vignettes/h5vc/inst/doc/h5vc.tour.html
vignetteTitles: Building a minimal genome browser with h5vc and shiny,
h5vc -- Tour
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.R,
vignettes/h5vc/inst/doc/h5vc.tour.R
suggestsMe: h5vcData
dependencyCount: 96
Package: hapFabia
Version: 1.49.0
Depends: R (>= 3.6.0), Biobase, fabia (>= 2.3.1)
Imports: methods, graphics, grDevices, stats, utils
License: LGPL (>= 2.1)
MD5sum: 43558a996af670e27850187db87c8b5f
NeedsCompilation: yes
Title: hapFabia: Identification of very short segments of identity by
descent (IBD) characterized by rare variants in large
sequencing data
Description: A package to identify very short IBD segments in large
sequencing data by FABIA biclustering. Two haplotypes are
identical by descent (IBD) if they share a segment that both
inherited from a common ancestor. Current IBD methods reliably
detect long IBD segments because many minor alleles in the
segment are concordant between the two haplotypes. However,
many cohort studies contain unrelated individuals which share
only short IBD segments. This package provides software to
identify short IBD segments in sequencing data. Knowledge of
short IBD segments are relevant for phasing of genotyping data,
association studies, and for population genetics, where they
shed light on the evolutionary history of humans. The package
supports VCF formats, is based on sparse matrix operations, and
provides visualization of haplotype clusters in different
formats.
biocViews: Genetics, GeneticVariability, SNP, Sequencing, Sequencing,
Visualization, Clustering, SequenceMatching, Software
Author: Sepp Hochreiter <hochreit@bioinf.jku.at>
Maintainer: Andreas Mitterecker <mitterecker@bioinf.jku.at>
URL: http://www.bioinf.jku.at/software/hapFabia/hapFabia.html
git_url: https://git.bioconductor.org/packages/hapFabia
git_branch: devel
git_last_commit: 469b361
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/hapFabia_1.49.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/hapFabia_1.49.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/hapFabia_1.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/hapFabia_1.49.0.tgz
vignettes: vignettes/hapFabia/inst/doc/hapfabia.pdf
vignetteTitles: hapFabia: Manual for the R package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/hapFabia/inst/doc/hapfabia.R
dependencyCount: 9
Package: Harman
Version: 1.35.0
Depends: R (>= 3.6)
Imports: Rcpp (>= 0.11.2), graphics, stats, Ckmeans.1d.dp, parallel,
methods, matrixStats
LinkingTo: Rcpp
Suggests: HarmanData, BiocGenerics, BiocStyle, knitr, rmarkdown, RUnit,
RColorBrewer, bladderbatch, limma, minfi, lumi, msmsEDA,
affydata, minfiData, sva
License: GPL-3 + file LICENCE
Archs: x64
MD5sum: a70de6b8a59093b02e4b45f06cec0369
NeedsCompilation: yes
Title: The removal of batch effects from datasets using a PCA and
constrained optimisation based technique
Description: Harman is a PCA and constrained optimisation based
technique that maximises the removal of batch effects from
datasets, with the constraint that the probability of
overcorrection (i.e. removing genuine biological signal along
with batch noise) is kept to a fraction which is set by the
end-user.
biocViews: BatchEffect, Microarray, MultipleComparison,
PrincipalComponent, Normalization, Preprocessing,
DNAMethylation, Transcription, Software, StatisticalMethod
Author: Yalchin Oytam [aut], Josh Bowden [aut], Jason Ross [aut, cre]
Maintainer: Jason Ross <jason.ross@csiro.au>
URL: http://www.bioinformatics.csiro.au/harman/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Harman
git_branch: devel
git_last_commit: e1dee17
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Harman_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Harman_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Harman_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Harman_1.35.0.tgz
vignettes: vignettes/Harman/inst/doc/IntroductionToHarman.html
vignetteTitles: IntroductionToHarman
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Harman/inst/doc/IntroductionToHarman.R
importsMe: debrowser
suggestsMe: HarmanData
dependencyCount: 11
Package: HarmonizR
Version: 1.5.0
Depends: R (>= 4.2.0)
Imports: doParallel (>= 1.0.16), foreach (>= 1.5.1), janitor (>=
2.1.0), plyr (>= 1.8.6), sva (>= 3.36.0), seriation (>= 1.3.5),
limma (>= 3.46.0), SummarizedExperiment
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
License: GPL-3
Archs: x64
MD5sum: 9bd022c25b3fd617ca6d04246cd2603d
NeedsCompilation: no
Title: Handles missing values and makes more data available
Description: An implementation, which takes input data and makes it
available for proper batch effect removal by ComBat or Limma.
The implementation appropriately handles missing values by
dissecting the input matrix into smaller matrices with
sufficient data to feed the ComBat or limma algorithm. The
adjusted data is returned to the user as a rebuild matrix. The
implementation is meant to make as much data available as
possible with minimal data loss.
biocViews: BatchEffect
Author: Simon Schlumbohm [aut, cre], Julia Neumann [aut], Philipp
Neumann [aut]
Maintainer: Simon Schlumbohm <schlumbohm@hsu-hh.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/HarmonizR
git_branch: devel
git_last_commit: 024ca72
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HarmonizR_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HarmonizR_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HarmonizR_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HarmonizR_1.5.0.tgz
vignettes: vignettes/HarmonizR/inst/doc/HarmonizR_Vignette.html
vignetteTitles: HarmonizR_Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HarmonizR/inst/doc/HarmonizR_Vignette.R
dependencyCount: 111
Package: Harshlight
Version: 1.79.0
Depends: R (>= 2.10)
Imports: affy, altcdfenvs, Biobase, stats, utils
License: GPL (>= 2)
MD5sum: 6ef2f45f8f9ccedcff290c7641f37f9c
NeedsCompilation: yes
Title: A "corrective make-up" program for microarray chips
Description: The package is used to detect extended, diffuse and
compact blemishes on microarray chips. Harshlight automatically
marks the areas in a collection of chips (affybatch objects)
and a corrected AffyBatch object is returned, in which the
defected areas are substituted with NAs or the median of the
values of the same probe in the other chips in the collection.
The new version handle the substitute value as whole matrix to
solve the memory problem.
biocViews: Microarray, QualityControl, Preprocessing, OneChannel,
ReportWriting
Author: Mayte Suarez-Farinas, Maurizio Pellegrino, Knut M. Wittkowski,
Marcelo O. Magnasco
Maintainer: Maurizio Pellegrino <mpellegri@berkeley.edu>
URL: http://asterion.rockefeller.edu/Harshlight/
git_url: https://git.bioconductor.org/packages/Harshlight
git_branch: devel
git_last_commit: ed62604
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Harshlight_1.79.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Harshlight_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Harshlight_1.79.0.tgz
vignettes: vignettes/Harshlight/inst/doc/Harshlight.pdf
vignetteTitles: Harshlight
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Harshlight/inst/doc/Harshlight.R
dependencyCount: 34
Package: hca
Version: 1.15.1
Depends: R (>= 4.1)
Imports: httr, jsonlite, dplyr, tibble, tidyr, readr, BiocFileCache,
tools, utils, digest, shiny, miniUI, DT
Suggests: LoomExperiment, SummarizedExperiment, SingleCellExperiment,
S4Vectors, methods, testthat (>= 3.0.0), knitr, rmarkdown,
BiocStyle
License: MIT + file LICENSE
Archs: x64
MD5sum: 91a8c50dffe66726c440093dbc1a429b
NeedsCompilation: no
Title: Exploring the Human Cell Atlas Data Coordinating Platform
Description: This package provides users with the ability to query the
Human Cell Atlas data repository for single-cell experiment
data. The `projects()`, `files()`, `samples()` and `bundles()`
functions retrieve summary information on each of these
indexes; corresponding `*_details()` are available for
individual entries of each index. File-based resources can be
downloaded using `files_download()`. Advanced use of the
package allows the user to page through large result sets, and
to flexibly query the 'list-of-lists' structure representing
query responses.
biocViews: Software, SingleCell
Author: Maya McDaniel [aut], Martin Morgan [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5874-8148>), Kayla Interdonato
[ctb]
Maintainer: Martin Morgan <martin.morgan@roswellpark.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/hca
git_branch: devel
git_last_commit: a5dd033
git_last_commit_date: 2025-03-23
Date/Publication: 2025-03-23
source.ver: src/contrib/hca_1.15.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/hca_1.15.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/hca_1.15.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/hca_1.15.1.tgz
vignettes: vignettes/hca/inst/doc/hca_manifest_vignette.html,
vignettes/hca/inst/doc/hca_vignette.html
vignetteTitles: Working With Human Cell Atlas Manifests, Accessing
Human Cell Atlas Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/hca/inst/doc/hca_manifest_vignette.R,
vignettes/hca/inst/doc/hca_vignette.R
dependencyCount: 83
Package: HDF5Array
Version: 1.35.15
Depends: R (>= 3.4), methods, SparseArray (>= 1.7.5), DelayedArray (>=
0.33.5), h5mread (>= 0.99.4)
Imports: utils, stats, tools, Matrix, BiocGenerics (>= 0.51.2),
S4Vectors, IRanges, S4Arrays (>= 1.1.1), rhdf5
Suggests: BiocParallel, GenomicRanges, SummarizedExperiment (>=
1.15.1), h5vcData, ExperimentHub, TENxBrainData, zellkonverter,
GenomicFeatures, SingleCellExperiment, DelayedMatrixStats,
genefilter, RSpectra, RUnit, knitr, rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: 33b7ae7f1c88c12b64144663ced7ac75
NeedsCompilation: no
Title: HDF5 datasets as array-like objects in R
Description: The HDF5Array package is an HDF5 backend for DelayedArray
objects. It implements the HDF5Array, H5SparseMatrix,
H5ADMatrix, and TENxMatrix classes, 4 convenient and
memory-efficient array-like containers for representing and
manipulating either: (1) a conventional (a.k.a. dense) HDF5
dataset, (2) an HDF5 sparse matrix (stored in CSR/CSC/Yale
format), (3) the central matrix of an h5ad file (or any matrix
in the /layers group), or (4) a 10x Genomics sparse matrix. All
these containers are DelayedArray extensions and thus support
all operations (delayed or block-processed) supported by
DelayedArray objects.
biocViews: Infrastructure, DataRepresentation, DataImport, Sequencing,
RNASeq, Coverage, Annotation, GenomeAnnotation, SingleCell,
ImmunoOncology
Author: Hervé Pagès [aut, cre] (ORCID:
<https://orcid.org/0009-0002-8272-4522>)
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/HDF5Array
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/HDF5Array/issues
git_url: https://git.bioconductor.org/packages/HDF5Array
git_branch: devel
git_last_commit: 1bd0eaf
git_last_commit_date: 2025-02-18
Date/Publication: 2025-02-19
source.ver: src/contrib/HDF5Array_1.35.15.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HDF5Array_1.35.15.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HDF5Array_1.35.15.tgz
vignettes: vignettes/HDF5Array/inst/doc/HDF5Array_performance.html
vignetteTitles: HDF5Array performance
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HDF5Array/inst/doc/HDF5Array_performance.R
dependsOnMe: MAGAR, TENxBrainData, TENxPBMCData
importsMe: alabaster.matrix, beachmat.hdf5, BgeeDB, biscuiteer, bsseq,
Cepo, chihaya, clusterExperiment, CuratedAtlasQueryR,
cytomapper, DelayedTensor, DropletUtils, FRASER, GenomicScores,
glmGamPoi, GSVA, lemur, LoomExperiment, mariner, methodical,
methrix, minfi, MOFA2, netSmooth, orthos, recountmethylation,
scmeth, signatureSearch, SpliceWiz, TENxIO, transformGamPoi,
xenLite, MafH5.gnomAD.v4.0.GRCh38, curatedTCGAData, HCAData,
HCATonsilData, imcdatasets, MerfishData, MethylSeqData,
orthosData, scMultiome, SingleCellMultiModal,
TabulaMurisSenisData, TumourMethData, ebvcube
suggestsMe: beachmat, BiocGenerics, BiocSklearn, cellxgenedp,
DelayedArray, DelayedMatrixStats, h5mread, iSEE, MAST,
mbkmeans, metabolomicsWorkbenchR, MuData, MultiAssayExperiment,
PDATK, QFeatures, S4Arrays, SCArray, scMerge, scran, scry,
SparseArray, spatialHeatmap, SummarizedExperiment,
zellkonverter, STexampleData, SeuratObject, SpatialDDLS
dependencyCount: 26
Package: HDTD
Version: 1.41.0
Depends: R (>= 4.1)
Imports: stats, Rcpp (>= 1.0.1)
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown
License: GPL-3
Archs: x64
MD5sum: 217f915f9f0d33aa9042cae66100d4dd
NeedsCompilation: yes
Title: Statistical Inference about the Mean Matrix and the Covariance
Matrices in High-Dimensional Transposable Data (HDTD)
Description: Characterization of intra-individual variability using
physiologically relevant measurements provides important
insights into fundamental biological questions ranging from
cell type identity to tumor development. For each individual,
the data measurements can be written as a matrix with the
different subsamples of the individual recorded in the columns
and the different phenotypic units recorded in the rows.
Datasets of this type are called high-dimensional transposable
data. The HDTD package provides functions for conducting
statistical inference for the mean relationship between the row
and column variables and for the covariance structure within
and between the row and column variables.
biocViews: DifferentialExpression, Genetics, GeneExpression,
Microarray, Sequencing, StatisticalMethod, Software
Author: Anestis Touloumis [cre, aut] (ORCID:
<https://orcid.org/0000-0002-5965-1639>), John C. Marioni [aut]
(ORCID: <https://orcid.org/0000-0001-9092-0852>), Simon
Tavar\'{e} [aut] (ORCID:
<https://orcid.org/0000-0002-3716-4952>)
Maintainer: Anestis Touloumis <A.Touloumis@brighton.ac.uk>
URL: http://github.com/AnestisTouloumis/HDTD
VignetteBuilder: knitr
BugReports: http://github.com/AnestisTouloumis/HDTD/issues
git_url: https://git.bioconductor.org/packages/HDTD
git_branch: devel
git_last_commit: f05e068
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HDTD_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HDTD_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HDTD_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HDTD_1.41.0.tgz
vignettes: vignettes/HDTD/inst/doc/HDTD.html
vignetteTitles: HDTD to Analyze High-Dimensional Transposable Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HDTD/inst/doc/HDTD.R
dependencyCount: 5
Package: hdxmsqc
Version: 1.3.0
Depends: R(>= 4.3), QFeatures, S4Vectors, Spectra
Imports: dplyr, tidyr, ggplot2, BiocStyle, knitr, methods, grDevices,
stats, MsCoreUtils
Suggests: RColorBrewer, pheatmap, MASS, patchwork, testthat
License: file LICENSE
MD5sum: d3435039c6516e3674560cebe524fc18
NeedsCompilation: no
Title: An R package for quality Control for hydrogen deuterium exchange
mass spectrometry experiments
Description: The hdxmsqc package enables us to analyse and visualise
the quality of HDX-MS experiments. Either as a final quality
check before downstream analysis and publication or as part of
a interative procedure to determine the quality of the data.
The package builds on the QFeatures and Spectra packages to
integrate with other mass-spectrometry data.
biocViews: QualityControl,DataImport, Proteomics, MassSpectrometry,
Metabolomics
Author: Oliver M. Crook [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5669-8506>)
Maintainer: Oliver M. Crook <oliver.crook@stats.ox.ac.uk>
URL: http://github.com/ococrook/hdxmsqc
VignetteBuilder: knitr
BugReports: https://github.com/ococrook/hdxmsqc/issues
git_url: https://git.bioconductor.org/packages/hdxmsqc
git_branch: devel
git_last_commit: 75e2815
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/hdxmsqc_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/hdxmsqc_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/hdxmsqc_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/hdxmsqc_1.3.0.tgz
vignettes: vignettes/hdxmsqc/inst/doc/qc-streamlined.html
vignetteTitles: Qualityt control for differential hydrogen deuterium
exchange mass spectrometry data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/hdxmsqc/inst/doc/qc-streamlined.R
dependencyCount: 121
Package: heatmaps
Version: 1.31.0
Depends: R (>= 3.5.0)
Imports: methods, grDevices, graphics, stats, Biostrings,
GenomicRanges, IRanges, KernSmooth, plotrix, Matrix, EBImage,
RColorBrewer, BiocGenerics, GenomeInfoDb
Suggests: BSgenome.Drerio.UCSC.danRer7, knitr, rmarkdown, testthat
License: Artistic-2.0
MD5sum: 64872a90e60068f12c49744a64153dfc
NeedsCompilation: no
Title: Flexible Heatmaps for Functional Genomics and Sequence Features
Description: This package provides functions for plotting heatmaps of
genome-wide data across genomic intervals, such as ChIP-seq
signals at peaks or across promoters. Many functions are also
provided for investigating sequence features.
biocViews: Visualization, SequenceMatching, FunctionalGenomics
Author: Malcolm Perry <mgperry32@gmail.com>
Maintainer: Malcolm Perry <mgperry32@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/heatmaps
git_branch: devel
git_last_commit: 2ebde80
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/heatmaps_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/heatmaps_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/heatmaps_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/heatmaps_1.31.0.tgz
vignettes: vignettes/heatmaps/inst/doc/heatmaps.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/heatmaps/inst/doc/heatmaps.R
importsMe: seqArchRplus
dependencyCount: 65
Package: Heatplus
Version: 3.15.0
Imports: graphics, grDevices, stats, RColorBrewer
Suggests: Biobase, hgu95av2.db, limma
License: GPL (>= 2)
Archs: x64
MD5sum: d63b8115d5202e58f7c7d0d74c96ec0e
NeedsCompilation: no
Title: Heatmaps with row and/or column covariates and colored clusters
Description: Display a rectangular heatmap (intensity plot) of a data
matrix. By default, both samples (columns) and features (row)
of the matrix are sorted according to a hierarchical
clustering, and the corresponding dendrogram is plotted.
Optionally, panels with additional information about samples
and features can be added to the plot.
biocViews: Microarray, Visualization
Author: Alexander Ploner <Alexander.Ploner@ki.se>
Maintainer: Alexander Ploner <Alexander.Ploner@ki.se>
URL: https://github.com/alexploner/Heatplus
BugReports: https://github.com/alexploner/Heatplus/issues
git_url: https://git.bioconductor.org/packages/Heatplus
git_branch: devel
git_last_commit: dc4ccf1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Heatplus_3.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Heatplus_3.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Heatplus_3.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Heatplus_3.15.0.tgz
vignettes: vignettes/Heatplus/inst/doc/annHeatmap.pdf,
vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.pdf,
vignettes/Heatplus/inst/doc/oldHeatplus.pdf
vignetteTitles: Annotated and regular heatmaps, Commented package
source, Old functions (deprecated)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Heatplus/inst/doc/annHeatmap.R,
vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.R,
vignettes/Heatplus/inst/doc/oldHeatplus.R
dependsOnMe: phenoTest, tRanslatome, heatmapFlex
suggestsMe: mtbls2, RforProteomics
dependencyCount: 4
Package: HelloRanges
Version: 1.33.0
Depends: methods, BiocGenerics, S4Vectors (>= 0.17.39), IRanges (>=
2.13.12), GenomicRanges (>= 1.31.10), Biostrings (>= 2.41.3),
BSgenome, GenomicFeatures (>= 1.31.5), VariantAnnotation (>=
1.19.3), Rsamtools, GenomicAlignments (>= 1.15.7), rtracklayer
(>= 1.33.8), GenomeInfoDb, SummarizedExperiment, BiocIO
Imports: docopt, stats, tools, utils
Suggests: HelloRangesData, BiocStyle, RUnit,
TxDb.Hsapiens.UCSC.hg19.knownGene
License: GPL (>= 2)
MD5sum: 0505cfd72189a08447387c4f2344edef
NeedsCompilation: no
Title: Introduce *Ranges to bedtools users
Description: Translates bedtools command-line invocations to R code
calling functions from the Bioconductor *Ranges infrastructure.
This is intended to educate novice Bioconductor users and to
compare the syntax and semantics of the two frameworks.
biocViews: Sequencing, Annotation, Coverage, GenomeAnnotation,
DataImport, SequenceMatching, VariantAnnotation
Author: Michael Lawrence
Maintainer: Michael Lawrence <michafla@gene.com>
git_url: https://git.bioconductor.org/packages/HelloRanges
git_branch: devel
git_last_commit: ee9547e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HelloRanges_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HelloRanges_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HelloRanges_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HelloRanges_1.33.0.tgz
vignettes: vignettes/HelloRanges/inst/doc/tutorial.pdf
vignetteTitles: HelloRanges Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HelloRanges/inst/doc/tutorial.R
importsMe: OMICsPCA
suggestsMe: plyranges
dependencyCount: 80
Package: HELP
Version: 1.65.0
Depends: R (>= 2.8.0), stats, graphics, grDevices, Biobase, methods
License: GPL (>= 2)
MD5sum: d6ac978c90b9ccda8940d9083b42c8d5
NeedsCompilation: no
Title: Tools for HELP data analysis
Description: The package contains a modular pipeline for analysis of
HELP microarray data, and includes graphical and mathematical
tools with more general applications.
biocViews: CpGIsland, DNAMethylation, Microarray, TwoChannel,
DataImport, QualityControl, Preprocessing, Visualization
Author: Reid F. Thompson <reid.thompson@gmail.com>, John M. Greally
<john.greally@einstein.yu.edu>, with contributions from Mark
Reimers <mreimers@vcu.edu>
Maintainer: Reid F. Thompson <reid.thompson@gmail.com>
git_url: https://git.bioconductor.org/packages/HELP
git_branch: devel
git_last_commit: 1379b76
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HELP_1.65.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HELP_1.65.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HELP_1.65.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HELP_1.65.0.tgz
vignettes: vignettes/HELP/inst/doc/HELP.pdf
vignetteTitles: 1. Primer
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HELP/inst/doc/HELP.R
dependencyCount: 8
Package: HEM
Version: 1.79.0
Depends: R (>= 2.1.0)
Imports: Biobase, grDevices, stats, utils
License: GPL (>= 2)
MD5sum: c2dca411ab25432c54b3b57d5d3c26c3
NeedsCompilation: yes
Title: Heterogeneous error model for identification of differentially
expressed genes under multiple conditions
Description: This package fits heterogeneous error models for analysis
of microarray data
biocViews: Microarray, DifferentialExpression
Author: HyungJun Cho <hcho@virginia.edu> and Jae K. Lee
<jaeklee@virginia.edu>
Maintainer: HyungJun Cho <hcho@virginia.edu>
URL:
http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/
git_url: https://git.bioconductor.org/packages/HEM
git_branch: devel
git_last_commit: 47fa135
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HEM_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HEM_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HEM_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HEM_1.79.0.tgz
vignettes: vignettes/HEM/inst/doc/HEM.pdf
vignetteTitles: HEM Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 8
Package: hermes
Version: 1.11.0
Depends: ggfortify, R (>= 4.1), SummarizedExperiment (>= 1.16)
Imports: assertthat, Biobase, BiocGenerics, biomaRt, checkmate (>=
2.1), circlize, ComplexHeatmap, DESeq2, dplyr, edgeR, EnvStats,
forcats (>= 1.0.0), GenomicRanges, ggplot2, ggrepel (>= 0.9),
IRanges, lifecycle, limma, magrittr, matrixStats, methods,
MultiAssayExperiment, purrr, R6, Rdpack, rlang, S4Vectors,
stats, tidyr, utils
Suggests: BiocStyle, DelayedArray, DT, grid, httr, knitr, rmarkdown,
statmod, testthat (>= 2.0), vdiffr
License: Apache License 2.0
MD5sum: c8f8b92195ef5810566a0ae77a0d3237
NeedsCompilation: no
Title: Preprocessing, analyzing, and reporting of RNA-seq data
Description: Provides classes and functions for quality control,
filtering, normalization and differential expression analysis
of pre-processed `RNA-seq` data. Data can be imported from
`SummarizedExperiment` as well as `matrix` objects and can be
annotated from `BioMart`. Filtering for genes without too low
expression or containing required annotations, as well as
filtering for samples with sufficient correlation to other
samples or total number of reads is supported. The standard
normalization methods including cpm, rpkm and tpm can be used,
and 'DESeq2` as well as voom differential expression analyses
are available.
biocViews: RNASeq, DifferentialExpression, Normalization,
Preprocessing, QualityControl
Author: Daniel Sabanés Bové [aut, cre], Namrata Bhatia [aut], Stefanie
Bienert [aut], Benoit Falquet [aut], Haocheng Li [aut], Jeff
Luong [aut], Lyndsee Midori Zhang [aut], Alex Richardson [aut],
Simona Rossomanno [aut], Tim Treis [aut], Mark Yan [aut], Naomi
Chang [aut], Chendi Liao [aut], Carolyn Zhang [aut], Joseph N.
Paulson [aut], F. Hoffmann-La Roche AG [cph, fnd]
Maintainer: Daniel Sabanés Bové <daniel.sabanes_bove@rconis.com>
URL: https://github.com/insightsengineering/hermes/
VignetteBuilder: knitr
BugReports: https://github.com/insightsengineering/hermes/issues
git_url: https://git.bioconductor.org/packages/hermes
git_branch: devel
git_last_commit: 2f1b4a7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/hermes_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/hermes_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/hermes_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/hermes_1.11.0.tgz
vignettes: vignettes/hermes/inst/doc/hermes.html
vignetteTitles: Introduction to `hermes`
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/hermes/inst/doc/hermes.R
dependencyCount: 133
Package: HERON
Version: 1.5.0
Depends: R (>= 4.4.0), SummarizedExperiment (>= 1.1.6), GenomicRanges,
IRanges, S4Vectors
Imports: matrixStats, stats, data.table, harmonicmeanp, metap, cluster,
spdep, Matrix, limma, methods
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
License: GPL (>= 3)
MD5sum: ae204cb642f6466762c668d62efd237c
NeedsCompilation: no
Title: Hierarchical Epitope pROtein biNding
Description: HERON is a software package for analyzing peptide binding
array data. In addition to identifying significant binding
probes, HERON also provides functions for finding epitopes
(string of consecutive peptides within a protein). HERON also
calculates significance on the probe, epitope, and protein
level by employing meta p-value methods. HERON is designed for
obtaining calls on the sample level and calculates fractions of
hits for different conditions.
biocViews: Microarray, Software
Author: Sean McIlwain [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3820-8400>), Irene Ong [aut]
(ORCID: <https://orcid.org/0000-0002-9353-6941>)
Maintainer: Sean McIlwain <sean.mcilwain@wisc.edu>
URL: https://github.com/Ong-Research/HERON
VignetteBuilder: knitr
BugReports: https://github.com/Ong-Research/HERON/issues
git_url: https://git.bioconductor.org/packages/HERON
git_branch: devel
git_last_commit: a355e34
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HERON_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HERON_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HERON_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HERON_1.5.0.tgz
vignettes: vignettes/HERON/inst/doc/full_analysis.html
vignetteTitles: Analyzing High Density Peptide Array Data using HERON
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HERON/inst/doc/full_analysis.R
dependencyCount: 85
Package: Herper
Version: 1.17.0
Depends: R (>= 4.0), reticulate
Imports: utils, rjson, withr, stats
Suggests: BiocStyle, testthat, knitr, rmarkdown
License: GPL-3
MD5sum: 02eff3bb7ec941338edb44b7643560f1
NeedsCompilation: no
Title: The Herper package is a simple toolset to install and manage
conda packages and environments from R
Description: Many tools for data analysis are not available in R, but
are present in public repositories like conda. The Herper
package provides a comprehensive set of functions to interact
with the conda package managament system. With Herper users can
install, manage and run conda packages from the comfort of
their R session. Herper also provides an ad-hoc approach to
handling external system requirements for R packages. For
people developing packages with python conda dependencies we
recommend using basilisk
(https://bioconductor.org/packages/release/bioc/html/basilisk.html)
to internally support these system requirments pre-hoc.
biocViews: Infrastructure, Software
Author: Matt Paul [aut] (ORCID:
<https://orcid.org/0000-0002-3020-7729>), Thomas Carroll [aut,
cre] (ORCID: <https://orcid.org/0000-0002-0073-1714>), Doug
Barrows [aut], Kathryn Rozen-Gagnon [ctb]
Maintainer: Thomas Carroll <tc.infomatics@gmail.com>
URL: https://github.com/RockefellerUniversity/Herper
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Herper
git_branch: devel
git_last_commit: a708ac5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Herper_1.17.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Herper_1.17.0.tgz
vignettes: vignettes/Herper/inst/doc/QuickStart.html
vignetteTitles: Quick Start
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Herper/inst/doc/QuickStart.R
dependencyCount: 19
Package: HGC
Version: 1.15.0
Depends: R (>= 4.1.0)
Imports: Rcpp (>= 1.0.0), RcppEigen(>= 0.3.2.0), Matrix, RANN, ape,
dendextend, ggplot2, mclust, patchwork, dplyr, grDevices,
methods, stats
LinkingTo: Rcpp, RcppEigen
Suggests: BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0)
License: GPL-3
Archs: x64
MD5sum: be69dfb4c0e1d29aae8a203a3be549f5
NeedsCompilation: yes
Title: A fast hierarchical graph-based clustering method
Description: HGC (short for Hierarchical Graph-based Clustering) is an
R package for conducting hierarchical clustering on large-scale
single-cell RNA-seq (scRNA-seq) data. The key idea is to
construct a dendrogram of cells on their shared nearest
neighbor (SNN) graph. HGC provides functions for building
graphs and for conducting hierarchical clustering on the graph.
The users with old R version could visit
https://github.com/XuegongLab/HGC/tree/HGC4oldRVersion to get
HGC package built for R 3.6.
biocViews: SingleCell, Software, Clustering, RNASeq, GraphAndNetwork,
DNASeq
Author: Zou Ziheng [aut], Hua Kui [aut], XGlab [cre, cph]
Maintainer: XGlab <xglab@mail.tsinghua.edu.cn>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/HGC
git_branch: devel
git_last_commit: c8f04e4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HGC_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HGC_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HGC_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HGC_1.15.0.tgz
vignettes: vignettes/HGC/inst/doc/HGC.html
vignetteTitles: HGC package manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HGC/inst/doc/HGC.R
dependencyCount: 50
Package: hiAnnotator
Version: 1.41.0
Depends: GenomicRanges, R (>= 2.10)
Imports: foreach, iterators, rtracklayer, dplyr, BSgenome, ggplot2,
scales, methods
Suggests: knitr, doParallel, testthat, BiocGenerics, markdown
License: GPL (>= 2)
MD5sum: f4c84ccc8e20355a1d22040fc9b47650
NeedsCompilation: no
Title: Functions for annotating GRanges objects
Description: hiAnnotator contains set of functions which allow users to
annotate a GRanges object with custom set of annotations. The
basic philosophy of this package is to take two GRanges objects
(query & subject) with common set of seqnames (i.e.
chromosomes) and return associated annotation per seqnames and
rows from the query matching seqnames and rows from the subject
(i.e. genes or cpg islands). The package comes with three types
of annotation functions which calculates if a position from
query is: within a feature, near a feature, or count features
in defined window sizes. Moreover, each function is equipped
with parallel backend to utilize the foreach package. In
addition, the package is equipped with wrapper functions, which
finds appropriate columns needed to make a GRanges object from
a common data frame.
biocViews: Software, Annotation
Author: Nirav V Malani <malnirav@gmail.com>
Maintainer: Nirav V Malani <malnirav@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/hiAnnotator
git_branch: devel
git_last_commit: e4def22
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/hiAnnotator_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/hiAnnotator_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/hiAnnotator_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/hiAnnotator_1.41.0.tgz
vignettes: vignettes/hiAnnotator/inst/doc/Intro.html
vignetteTitles: Using hiAnnotator
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/hiAnnotator/inst/doc/Intro.R
dependsOnMe: hiReadsProcessor
dependencyCount: 89
Package: HIBAG
Version: 1.43.1
Depends: R (>= 3.2.0)
Imports: methods, RcppParallel
LinkingTo: RcppParallel (>= 5.0.0)
Suggests: parallel, ggplot2, reshape2, gdsfmt, SNPRelate, SeqArray,
knitr, markdown, rmarkdown, Rsamtools
License: GPL-3
MD5sum: 7bab737223a3cced04a11debeb95d0ed
NeedsCompilation: yes
Title: HLA Genotype Imputation with Attribute Bagging
Description: Imputes HLA classical alleles using GWAS SNP data, and it
relies on a training set of HLA and SNP genotypes. HIBAG can be
used by researchers with published parameter estimates instead
of requiring access to large training sample datasets. It
combines the concepts of attribute bagging, an ensemble
classifier method, with haplotype inference for SNPs and HLA
types. Attribute bagging is a technique which improves the
accuracy and stability of classifier ensembles using bootstrap
aggregating and random variable selection.
biocViews: Genetics, StatisticalMethod
Author: Xiuwen Zheng [aut, cre, cph] (ORCID:
<https://orcid.org/0000-0002-1390-0708>), Bruce Weir [ctb, ths]
(ORCID: <https://orcid.org/0000-0002-4883-1247>)
Maintainer: Xiuwen Zheng <zhengx@u.washington.edu>
URL: https://github.com/zhengxwen/HIBAG,
https://hibag.s3.amazonaws.com/index.html
SystemRequirements: C++11, GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/HIBAG
git_branch: devel
git_last_commit: 4d1767d
git_last_commit_date: 2024-11-19
Date/Publication: 2024-11-20
source.ver: src/contrib/HIBAG_1.43.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HIBAG_1.43.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HIBAG_1.43.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HIBAG_1.43.1.tgz
vignettes: vignettes/HIBAG/inst/doc/HIBAG.html,
vignettes/HIBAG/inst/doc/HLA_Association.html,
vignettes/HIBAG/inst/doc/Implementation.html
vignetteTitles: HIBAG vignette html, HLA association vignette html,
HIBAG algorithm implementation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HIBAG/inst/doc/HIBAG.R,
vignettes/HIBAG/inst/doc/HLA_Association.R,
vignettes/HIBAG/inst/doc/Implementation.R
dependencyCount: 2
Package: HicAggR
Version: 1.3.0
Depends: R (>= 4.2.0)
Imports: InteractionSet, BiocGenerics, BiocParallel, dplyr,
GenomeInfoDb, GenomicRanges, ggplot2, grDevices, IRanges,
Matrix, methods, rhdf5, rlang, rtracklayer, S4Vectors, stats,
utils, strawr, tibble, stringr, tidyr, gridExtra, data.table,
reshape, checkmate, purrr, withr
Suggests: covr, tools, kableExtra (>= 1.3.4), knitr (>= 1.45),
rmarkdown, testthat (>= 3.0.0), BiocFileCache (>= 2.6.1)
License: MIT + file LICENSE
Archs: x64
MD5sum: c7aff2e8cc6294b28fdb05f649e4835a
NeedsCompilation: no
Title: Set of 3D genomic interaction analysis tools
Description: This package provides a set of functions useful in the
analysis of 3D genomic interactions. It includes the import of
standard HiC data formats into R and HiC normalisation
procedures. The main objective of this package is to improve
the visualization and quantification of the analysis of HiC
contacts through aggregation. The package allows to import 1D
genomics data, such as peaks from ATACSeq, ChIPSeq, to create
potential couples between features of interest under
user-defined parameters such as distance between pairs of
features of interest. It allows then the extraction of contact
values from the HiC data for these couples and to perform
Aggregated Peak Analysis (APA) for visualization, but also to
compare normalized contact values between conditions. Overall
the package allows to integrate 1D genomics data with 3D
genomics data, providing an easy access to HiC contact values.
biocViews: Software, HiC, DataImport, DataRepresentation,
Normalization, Visualization, DNA3DStructure, ATACSeq, ChIPSeq,
DNaseSeq, RNASeq
Author: Robel Tesfaye [aut, ctb] (ORCID:
<https://orcid.org/0000-0003-2358-219X>), David Depierre [aut],
Naomi Schickele [ctb], Nicolas Chanard [aut], Refka Askri
[ctb], Stéphane Schaak [aut, ctb], Pascal Martin [ctb], Olivier
Cuvier [cre, ctb] (ORCID:
<https://orcid.org/0000-0003-0644-2734>)
Maintainer: Olivier Cuvier <olivier.cuvier@univ-tlse3.fr>
URL: https://bioconductor.org/packages/HicAggR,
https://cuvierlab.github.io/HicAggR/,
https://github.com/CuvierLab/HicAggR
VignetteBuilder: knitr
BugReports: https://github.com/CuvierLab/HicAggR/issues
git_url: https://git.bioconductor.org/packages/HicAggR
git_branch: devel
git_last_commit: 1905b6c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HicAggR_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HicAggR_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HicAggR_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HicAggR_1.3.0.tgz
vignettes: vignettes/HicAggR/inst/doc/HicAggR.html
vignetteTitles: HicAggR - In depth tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/HicAggR/inst/doc/HicAggR.R
dependencyCount: 102
Package: HiCBricks
Version: 1.25.0
Depends: R (>= 3.6), utils, curl, rhdf5, R6, grid
Imports: ggplot2, viridis, RColorBrewer, scales, reshape2, stringr,
data.table, GenomeInfoDb, GenomicRanges, stats, IRanges,
grDevices, S4Vectors, digest, tibble, jsonlite, BiocParallel,
R.utils, readr, methods
Suggests: BiocStyle, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: b60ff97b743516c921e2951ac87b5649
NeedsCompilation: no
Title: Framework for Storing and Accessing Hi-C Data Through HDF Files
Description: HiCBricks is a library designed for handling large
high-resolution Hi-C datasets. Over the years, the Hi-C field
has experienced a rapid increase in the size and complexity of
datasets. HiCBricks is meant to overcome the challenges related
to the analysis of such large datasets within the R
environment. HiCBricks offers user-friendly and efficient
solutions for handling large high-resolution Hi-C datasets. The
package provides an R/Bioconductor framework with the bricks to
build more complex data analysis pipelines and algorithms.
HiCBricks already incorporates example algorithms for calling
domain boundaries and functions for high quality data
visualization.
biocViews: DataImport, Infrastructure, Software, Technology,
Sequencing, HiC
Author: Koustav Pal [aut, cre], Carmen Livi [ctb], Ilario Tagliaferri
[ctb]
Maintainer: Koustav Pal <koustav.pal@ifom.eu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/HiCBricks
git_branch: devel
git_last_commit: 51b1cc8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HiCBricks_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HiCBricks_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HiCBricks_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HiCBricks_1.25.0.tgz
vignettes: vignettes/HiCBricks/inst/doc/IntroductionToHiCBricks.html
vignetteTitles: IntroductionToHiCBricks.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/HiCBricks/inst/doc/IntroductionToHiCBricks.R
importsMe: bnbc
dependencyCount: 89
Package: HiCcompare
Version: 1.29.0
Depends: R (>= 3.5.0), dplyr
Imports: data.table, ggplot2, gridExtra, mgcv, stats, InteractionSet,
GenomicRanges, IRanges, S4Vectors, BiocParallel, KernSmooth,
methods, utils, graphics, pheatmap, gtools, rhdf5
Suggests: knitr, rmarkdown, testthat, multiHiCcompare
License: MIT + file LICENSE
Archs: x64
MD5sum: e0630fb56464ecd9ca04fdbfc2d2fd76
NeedsCompilation: no
Title: HiCcompare: Joint normalization and comparative analysis of
multiple Hi-C datasets
Description: HiCcompare provides functions for joint normalization and
difference detection in multiple Hi-C datasets. HiCcompare
operates on processed Hi-C data in the form of
chromosome-specific chromatin interaction matrices. It accepts
three-column tab-separated text files storing chromatin
interaction matrices in a sparse matrix format which are
available from several sources. HiCcompare is designed to give
the user the ability to perform a comparative analysis on the
3-Dimensional structure of the genomes of cells in different
biological states.`HiCcompare` differs from other packages that
attempt to compare Hi-C data in that it works on processed data
in chromatin interaction matrix format instead of pre-processed
sequencing data. In addition, `HiCcompare` provides a
non-parametric method for the joint normalization and removal
of biases between two Hi-C datasets for the purpose of
comparative analysis. `HiCcompare` also provides a simple yet
robust method for detecting differences between Hi-C datasets.
biocViews: Software, HiC, Sequencing, Normalization
Author: Mikhail Dozmorov [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0086-8358>), Kellen Cresswell
[aut], John Stansfield [aut]
Maintainer: Mikhail Dozmorov <mikhail.dozmorov@gmail.com>
URL: https://github.com/dozmorovlab/HiCcompare
VignetteBuilder: knitr
BugReports: https://github.com/dozmorovlab/HiCcompare/issues
git_url: https://git.bioconductor.org/packages/HiCcompare
git_branch: devel
git_last_commit: 07988eb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HiCcompare_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HiCcompare_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HiCcompare_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HiCcompare_1.29.0.tgz
vignettes: vignettes/HiCcompare/inst/doc/HiCcompare-vignette.html
vignetteTitles: HiCcompare Usage Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/HiCcompare/inst/doc/HiCcompare-vignette.R
importsMe: multiHiCcompare, SpectralTAD, TADCompare
dependencyCount: 84
Package: HiCDCPlus
Version: 1.15.0
Imports:
Rcpp,InteractionSet,GenomicInteractions,bbmle,pscl,BSgenome,data.table,dplyr,tidyr,GenomeInfoDb,rlang,splines,MASS,GenomicRanges,IRanges,tibble,R.utils,Biostrings,rtracklayer,methods,S4Vectors
LinkingTo: Rcpp
Suggests: BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm10,
BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38,
RUnit, BiocGenerics, knitr, rmarkdown, HiTC, DESeq2, Matrix,
BiocFileCache, rappdirs
Enhances: parallel
License: GPL-3
MD5sum: 2003eaa7ca546056ee12e1723c122f44
NeedsCompilation: yes
Title: Hi-C Direct Caller Plus
Description: Systematic 3D interaction calls and differential analysis
for Hi-C and HiChIP. The HiC-DC+ (Hi-C/HiChIP direct caller
plus) package enables principled statistical analysis of Hi-C
and HiChIP data sets – including calling significant
interactions within a single experiment and performing
differential analysis between conditions given replicate
experiments – to facilitate global integrative studies. HiC-DC+
estimates significant interactions in a Hi-C or HiChIP
experiment directly from the raw contact matrix for each
chromosome up to a specified genomic distance, binned by
uniform genomic intervals or restriction enzyme fragments, by
training a background model to account for random polymer
ligation and systematic sources of read count variation.
biocViews: HiC, DNA3DStructure, Software, Normalization
Author: Merve Sahin [cre, aut] (ORCID:
<https://orcid.org/0000-0003-3858-8332>)
Maintainer: Merve Sahin <merve.sahn@gmail.com>
SystemRequirements: JRE 8+
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/HiCDCPlus
git_branch: devel
git_last_commit: cd71b78
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HiCDCPlus_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HiCDCPlus_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HiCDCPlus_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HiCDCPlus_1.15.0.tgz
vignettes: vignettes/HiCDCPlus/inst/doc/HiCDCPlus.html
vignetteTitles: Analyzing Hi-C and HiChIP data with HiCDCPlus
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HiCDCPlus/inst/doc/HiCDCPlus.R
dependencyCount: 168
Package: HiCDOC
Version: 1.9.2
Depends: InteractionSet, GenomicRanges, SummarizedExperiment, R (>=
4.1.0)
Imports: methods, ggplot2, Rcpp (>= 0.12.8), stats, S4Vectors, gtools,
pbapply, BiocParallel, BiocGenerics, grid, cowplot, gridExtra,
data.table, multiHiCcompare, GenomeInfoDb
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, testthat, BiocStyle, BiocManager, rhdf5
License: LGPL-3
MD5sum: 3791e422ff3875616f1914a8b69a0960
NeedsCompilation: yes
Title: A/B compartment detection and differential analysis
Description: HiCDOC normalizes intrachromosomal Hi-C matrices, uses
unsupervised learning to predict A/B compartments from multiple
replicates, and detects significant compartment changes between
experiment conditions. It provides a collection of functions
assembled into a pipeline to filter and normalize the data,
predict the compartments and visualize the results. It accepts
several type of data: tabular `.tsv` files, Cooler `.cool` or
`.mcool` files, Juicer `.hic` files or HiC-Pro `.matrix` and
`.bed` files.
biocViews: HiC, DNA3DStructure, Normalization, Sequencing, Software,
Clustering
Author: Kurylo Cyril [aut], Zytnicki Matthias [aut], Foissac Sylvain
[aut], Maigné Élise [aut, cre]
Maintainer: Maigné Élise <elise.maigne@inrae.fr>
URL: https://github.com/mzytnicki/HiCDOC
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/mzytnicki/HiCDOC/issues
git_url: https://git.bioconductor.org/packages/HiCDOC
git_branch: devel
git_last_commit: 43dadba
git_last_commit_date: 2024-12-12
Date/Publication: 2024-12-12
source.ver: src/contrib/HiCDOC_1.9.2.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HiCDOC_1.9.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HiCDOC_1.9.2.tgz
vignettes: vignettes/HiCDOC/inst/doc/HiCDOC.html
vignetteTitles: HiCDOC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HiCDOC/inst/doc/HiCDOC.R
importsMe: treediff
dependencyCount: 95
Package: HiCExperiment
Version: 1.7.1
Depends: R (>= 4.2)
Imports: InteractionSet, strawr, GenomeInfoDb, GenomicRanges, IRanges,
S4Vectors, BiocGenerics, BiocIO, BiocParallel, methods, rhdf5,
Matrix, vroom, dplyr, stats
Suggests: HiContacts, HiContactsData, BiocFileCache, rtracklayer,
testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 97cec416aae2f0749312afd8a5f6db2e
NeedsCompilation: no
Title: Bioconductor class for interacting with Hi-C files in R
Description: R generic interface to Hi-C contact matrices in
`.(m)cool`, `.hic` or HiC-Pro derived formats, as well as other
Hi-C processed file formats. Contact matrices can be partially
parsed using a random access method, allowing a
memory-efficient representation of Hi-C data in R. The
`HiCExperiment` class stores the Hi-C contacts parsed from
local contact matrix files. `HiCExperiment` instances can be
further investigated in R using the `HiContacts` analysis
package.
biocViews: HiC, DNA3DStructure, DataImport
Author: Jacques Serizay [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4295-0624>)
Maintainer: Jacques Serizay <jacquesserizay@gmail.com>
URL: https://github.com/js2264/HiCExperiment
VignetteBuilder: knitr
BugReports: https://github.com/js2264/HiCExperiment/issues
git_url: https://git.bioconductor.org/packages/HiCExperiment
git_branch: devel
git_last_commit: ecd1da9
git_last_commit_date: 2025-03-18
Date/Publication: 2025-03-18
source.ver: src/contrib/HiCExperiment_1.7.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HiCExperiment_1.7.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HiCExperiment_1.7.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HiCExperiment_1.7.1.tgz
vignettes: vignettes/HiCExperiment/inst/doc/HiCExperiment.html
vignetteTitles: Introduction to HiCExperiment
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/HiCExperiment/inst/doc/HiCExperiment.R
dependsOnMe: HiContacts, HiCool, DNAZooData
importsMe: fourDNData, OHCA
dependencyCount: 74
Package: HiContacts
Version: 1.9.1
Depends: R (>= 4.2), HiCExperiment
Imports: InteractionSet, SummarizedExperiment, GenomicRanges, IRanges,
GenomeInfoDb, S4Vectors, methods, BiocGenerics, BiocIO,
BiocParallel, RSpectra, Matrix, tibble, tidyr, dplyr, readr,
stringr, ggplot2, ggrastr, scales, stats, utils
Suggests: HiContactsData, rtracklayer, GenomicFeatures, Biostrings,
BSgenome.Scerevisiae.UCSC.sacCer3, WGCNA, Rfast, terra,
patchwork, testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 8b1c5b69e455c99917479e2135f59fd3
NeedsCompilation: no
Title: Analysing cool files in R with HiContacts
Description: HiContacts provides a collection of tools to analyse and
visualize Hi-C datasets imported in R by HiCExperiment.
biocViews: HiC, DNA3DStructure
Author: Jacques Serizay [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4295-0624>)
Maintainer: Jacques Serizay <jacquesserizay@gmail.com>
URL: https://github.com/js2264/HiContacts
VignetteBuilder: knitr
BugReports: https://github.com/js2264/HiContacts/issues
git_url: https://git.bioconductor.org/packages/HiContacts
git_branch: devel
git_last_commit: 146485f
git_last_commit_date: 2025-03-18
Date/Publication: 2025-03-18
source.ver: src/contrib/HiContacts_1.9.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HiContacts_1.9.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HiContacts_1.9.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HiContacts_1.9.1.tgz
vignettes: vignettes/HiContacts/inst/doc/HiContacts.html
vignetteTitles: Introduction to HiContacts
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/HiContacts/inst/doc/HiContacts.R
importsMe: OHCA
suggestsMe: HiCExperiment, HiCool
dependencyCount: 106
Package: HiCool
Version: 1.7.1
Depends: R (>= 4.2), HiCExperiment
Imports: BiocIO, S4Vectors, GenomicRanges, IRanges, InteractionSet,
vroom, basilisk, reticulate, rmarkdown, rmdformats, plotly,
dplyr, stringr, sessioninfo, utils
Suggests: HiContacts, HiContactsData, AnnotationHub, BiocFileCache,
BiocStyle, testthat, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 0b85343bfd4344b64f2ff6a5d6e682ea
NeedsCompilation: no
Title: HiCool
Description: HiCool provides an R interface to process and normalize
Hi-C paired-end fastq reads into .(m)cool files. .(m)cool is a
compact, indexed HDF5 file format specifically tailored for
efficiently storing HiC-based data. On top of processing fastq
reads, HiCool provides a convenient reporting function to
generate shareable reports summarizing Hi-C experiments and
including quality controls.
biocViews: HiC, DNA3DStructure, DataImport
Author: Jacques Serizay [aut, cre]
Maintainer: Jacques Serizay <jacquesserizay@gmail.com>
URL: https://github.com/js2264/HiCool
VignetteBuilder: knitr
BugReports: https://github.com/js2264/HiCool/issues
git_url: https://git.bioconductor.org/packages/HiCool
git_branch: devel
git_last_commit: b69d494
git_last_commit_date: 2025-03-18
Date/Publication: 2025-03-18
source.ver: src/contrib/HiCool_1.7.1.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HiCool_1.7.1.tgz
vignettes: vignettes/HiCool/inst/doc/HiCool.html
vignetteTitles: HiCool
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/HiCool/inst/doc/HiCool.R
importsMe: OHCA
dependencyCount: 131
Package: HiCParser
Version: 0.99.7
Imports: data.table, InteractionSet, GenomicRanges,
SummarizedExperiment, Rcpp (>= 1.0.12), S4Vectors, gtools,
pbapply, BiocGenerics, GenomeInfoDb
LinkingTo: Rcpp
Suggests: rhdf5, BiocStyle, knitr, sessioninfo, testthat (>= 3.0.0)
License: LGPL
MD5sum: 95314a879e910f2466ba845aaa5cbaa7
NeedsCompilation: yes
Title: Parser for HiC data in R
Description: This package is a parser to import HiC data into R. It
accepts several type of data: tabular files, Cooler `.cool` or
`.mcool` files, Juicer `.hic` files or HiC-Pro `.matrix` and
`.bed` files. The HiC data can be several files, for several
replicates and conditions. The data is formated in an
InteractionSet object.
biocViews: Software, HiC, DataImport
Author: Zytnicki Matthias [aut], Maigné Élise [aut, cre]
Maintainer: Maigné Élise <elise.maigne@inrae.fr>
URL: https://github.com/emaigne/HiCParser
VignetteBuilder: knitr
BugReports: https://github.com/emaigne/HiCParser/issues
git_url: https://git.bioconductor.org/packages/HiCParser
git_branch: devel
git_last_commit: 0961075
git_last_commit_date: 2025-01-06
Date/Publication: 2025-02-19
source.ver: src/contrib/HiCParser_0.99.7.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HiCParser_0.99.7.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HiCParser_0.99.7.tgz
vignettes: vignettes/HiCParser/inst/doc/HiCParser.html
vignetteTitles: Introduction to HiCParser
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HiCParser/inst/doc/HiCParser.R
dependencyCount: 42
Package: hicVennDiagram
Version: 1.5.2
Depends: R (>= 4.3.0)
Imports: GenomeInfoDb, GenomicRanges, IRanges, InteractionSet,
rtracklayer, ggplot2, ComplexUpset, reshape2, eulerr,
S4Vectors, methods, utils, htmlwidgets, svglite
Suggests: BiocStyle, knitr, rmarkdown, testthat, ChIPpeakAnno, grid,
TxDb.Hsapiens.UCSC.hg38.knownGene
License: GPL-3
Archs: x64
MD5sum: 247558f98e81e4b7b07c163dbba020d5
NeedsCompilation: no
Title: Venn Diagram for genomic interaction data
Description: A package to generate high-resolution Venn and Upset plots
for genomic interaction data from HiC, ChIA-PET, HiChIP,
PLAC-Seq, Hi-TrAC, HiCAR and etc. The package generates plots
specifically crafted to eliminate the deceptive visual
representation caused by the counts method.
biocViews: DNA3DStructure, HiC, Visualization
Author: Jianhong Ou [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8652-2488>)
Maintainer: Jianhong Ou <jou@morgridge.org>
URL: https://github.com/jianhong/hicVennDiagram
VignetteBuilder: knitr
BugReports: https://github.com/jianhong/hicVennDiagram/issues
git_url: https://git.bioconductor.org/packages/hicVennDiagram
git_branch: devel
git_last_commit: 3e3d7f2
git_last_commit_date: 2025-02-12
Date/Publication: 2025-02-12
source.ver: src/contrib/hicVennDiagram_1.5.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/hicVennDiagram_1.5.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/hicVennDiagram_1.5.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/hicVennDiagram_1.5.2.tgz
vignettes: vignettes/hicVennDiagram/inst/doc/hicVennDiagram.html
vignetteTitles: hicVennDiagram Vignette: overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/hicVennDiagram/inst/doc/hicVennDiagram.R
dependencyCount: 118
Package: hierGWAS
Version: 1.37.0
Depends: R (>= 3.2.0)
Imports: fastcluster,glmnet, fmsb
Suggests: BiocGenerics, RUnit, MASS
License: GPL-3
MD5sum: f34e1edb03571790caa113a908f2ec28
NeedsCompilation: no
Title: Asessing statistical significance in predictive GWA studies
Description: Testing individual SNPs, as well as arbitrarily large
groups of SNPs in GWA studies, using a joint model of all SNPs.
The method controls the FWER, and provides an automatic,
data-driven refinement of the SNP clusters to smaller groups or
single markers.
biocViews: SNP, LinkageDisequilibrium, Clustering
Author: Laura Buzdugan
Maintainer: Laura Buzdugan <buzdugan@stat.math.ethz.ch>
git_url: https://git.bioconductor.org/packages/hierGWAS
git_branch: devel
git_last_commit: 06911bf
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/hierGWAS_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/hierGWAS_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/hierGWAS_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/hierGWAS_1.37.0.tgz
vignettes: vignettes/hierGWAS/inst/doc/hierGWAS.pdf
vignetteTitles: User manual for R-Package hierGWAS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/hierGWAS/inst/doc/hierGWAS.R
dependencyCount: 19
Package: hierinf
Version: 1.25.0
Depends: R (>= 3.6.0)
Imports: fmsb, glmnet, methods, parallel, stats
Suggests: knitr, MASS, testthat
License: GPL-3 | file LICENSE
MD5sum: b698b6f920b3cc2e9d23e8df219e1dff
NeedsCompilation: no
Title: Hierarchical Inference
Description: Tools to perform hierarchical inference for one or
multiple studies / data sets based on high-dimensional
multivariate (generalised) linear models. A possible
application is to perform hierarchical inference for GWA
studies to find significant groups or single SNPs (if the
signal is strong) in a data-driven and automated procedure. The
method is based on an efficient hierarchical multiple testing
correction and controls the FWER. The functions can easily be
run in parallel.
biocViews: Clustering, GenomeWideAssociation, LinkageDisequilibrium,
Regression, SNP
Author: Claude Renaux [aut, cre], Laura Buzdugan [aut], Markus Kalisch
[aut], Peter Bühlmann [aut]
Maintainer: Claude Renaux <renaux@stat.math.ethz.ch>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/hierinf
git_branch: devel
git_last_commit: 0124639
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/hierinf_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/hierinf_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/hierinf_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/hierinf_1.25.0.tgz
vignettes: vignettes/hierinf/inst/doc/vignette-hierinf.pdf
vignetteTitles: vignette-hierinf.Rnw
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/hierinf/inst/doc/vignette-hierinf.R
dependencyCount: 19
Package: HilbertCurve
Version: 2.1.0
Depends: R (>= 4.0.0), grid
Imports: methods, utils, png, grDevices, circlize (>= 0.3.3), IRanges,
GenomicRanges, polylabelr, Rcpp
LinkingTo: Rcpp
Suggests: knitr, testthat (>= 1.0.0), ComplexHeatmap (>= 1.99.0),
markdown, RColorBrewer, RCurl, GetoptLong, rmarkdown
License: MIT + file LICENSE
MD5sum: f74cd989521a322b403c568cf4c9caed
NeedsCompilation: yes
Title: Making 2D Hilbert Curve
Description: Hilbert curve is a type of space-filling curves that fold
one dimensional axis into a two dimensional space, but with
still preserves the locality. This package aims to provide an
easy and flexible way to visualize data through Hilbert curve.
biocViews: Software, Visualization, Sequencing, Coverage,
GenomeAnnotation
Author: Zuguang Gu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7395-8709>)
Maintainer: Zuguang Gu <z.gu@dkfz.de>
URL: https://github.com/jokergoo/HilbertCurve,
https://jokergoo.github.io/HilbertCurve/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/HilbertCurve
git_branch: devel
git_last_commit: 32b4f09
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HilbertCurve_2.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HilbertCurve_2.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HilbertCurve_2.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HilbertCurve_2.1.0.tgz
vignettes: vignettes/HilbertCurve/inst/doc/HilbertCurve.html
vignetteTitles: The HilbertCurve package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
suggestsMe: InteractiveComplexHeatmap
dependencyCount: 32
Package: HilbertVis
Version: 1.65.0
Depends: R (>= 2.6.0), grid, lattice
Suggests: IRanges, EBImage
License: GPL (>= 3)
MD5sum: 344126afb55ca55662be7c7d40c14ab7
NeedsCompilation: yes
Title: Hilbert curve visualization
Description: Functions to visualize long vectors of integer data by
means of Hilbert curves
biocViews: Visualization
Author: Simon Anders <sanders@fs.tum.de>
Maintainer: Simon Anders <sanders@fs.tum.de>
URL: http://www.ebi.ac.uk/~anders/hilbert
git_url: https://git.bioconductor.org/packages/HilbertVis
git_branch: devel
git_last_commit: 4c32450
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HilbertVis_1.65.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HilbertVis_1.65.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HilbertVis_1.65.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HilbertVis_1.65.0.tgz
vignettes: vignettes/HilbertVis/inst/doc/HilbertVis.pdf
vignetteTitles: Visualising very long data vectors with the Hilbert
curve
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HilbertVis/inst/doc/HilbertVis.R
dependsOnMe: HilbertVisGUI
importsMe: ChIPseqR
dependencyCount: 6
Package: HilbertVisGUI
Version: 1.65.0
Depends: R (>= 2.6.0), HilbertVis (>= 1.1.6)
Suggests: lattice, IRanges
License: GPL (>= 3)
MD5sum: 20533513836de7a80b49aefaa62c01e8
NeedsCompilation: yes
Title: HilbertVisGUI
Description: An interactive tool to visualize long vectors of integer
data by means of Hilbert curves
biocViews: Visualization
Author: Simon Anders <sanders@fs.tum.de>
Maintainer: Simon Anders <sanders@fs.tum.de>
URL: http://www.ebi.ac.uk/~anders/hilbert
SystemRequirements: gtkmm-2.4, GNU make
git_url: https://git.bioconductor.org/packages/HilbertVisGUI
git_branch: devel
git_last_commit: 42a8573
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HilbertVisGUI_1.65.0.tar.gz
vignettes: vignettes/HilbertVisGUI/inst/doc/HilbertVisGUI.pdf
vignetteTitles: See vignette in package HilbertVis
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: TRUE
hasLICENSE: FALSE
dependencyCount: 7
Package: HiLDA
Version: 1.21.0
Depends: R(>= 4.1), ggplot2
Imports: R2jags, abind, cowplot, grid, forcats, stringr, GenomicRanges,
S4Vectors, XVector, Biostrings, GenomicFeatures,
BSgenome.Hsapiens.UCSC.hg19, BiocGenerics, tidyr, grDevices,
stats, TxDb.Hsapiens.UCSC.hg19.knownGene, utils, methods, Rcpp
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, testthat, BiocStyle
License: GPL-3
MD5sum: df84f2f52c9d85330421b431794f29e1
NeedsCompilation: yes
Title: Conducting statistical inference on comparing the mutational
exposures of mutational signatures by using hierarchical latent
Dirichlet allocation
Description: A package built under the Bayesian framework of applying
hierarchical latent Dirichlet allocation. It statistically
tests whether the mutational exposures of mutational signatures
(Shiraishi-model signatures) are different between two groups.
The package also provides inference and visualization.
biocViews: Software, SomaticMutation, Sequencing, StatisticalMethod,
Bayesian
Author: Zhi Yang [aut, cre], Yuichi Shiraishi [ctb]
Maintainer: Zhi Yang <zyang895@gmail.com>
URL: https://github.com/USCbiostats/HiLDA,
https://doi.org/10.1101/577452
SystemRequirements: JAGS 4.0.0
VignetteBuilder: knitr
BugReports: https://github.com/USCbiostats/HiLDA/issues
git_url: https://git.bioconductor.org/packages/HiLDA
git_branch: devel
git_last_commit: 09c4796
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HiLDA_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HiLDA_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HiLDA_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HiLDA_1.21.0.tgz
vignettes: vignettes/HiLDA/inst/doc/HiLDA.html
vignetteTitles: An introduction to HiLDA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: FALSE
Rfiles: vignettes/HiLDA/inst/doc/HiLDA.R
importsMe: selectKSigs
dependencyCount: 114
Package: hipathia
Version: 3.7.0
Depends: R (>= 4.1), igraph (>= 1.0.1), AnnotationHub(>= 2.6.5),
MultiAssayExperiment(>= 1.4.9), SummarizedExperiment(>= 1.8.1)
Imports: coin, stats, limma, grDevices, utils, graphics,
preprocessCore, servr, DelayedArray, matrixStats, methods,
S4Vectors, ggplot2, ggpubr, dplyr, tibble, visNetwork,
reshape2, MetBrewer
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: GPL-2
Archs: x64
MD5sum: 07d352e397f623fe73e6a6c7e4f2951d
NeedsCompilation: no
Title: HiPathia: High-throughput Pathway Analysis
Description: Hipathia is a method for the computation of signal
transduction along signaling pathways from transcriptomic data.
The method is based on an iterative algorithm which is able to
compute the signal intensity passing through the nodes of a
network by taking into account the level of expression of each
gene and the intensity of the signal arriving to it. It also
provides a new approach to functional analysis allowing to
compute the signal arriving to the functions annotated to each
pathway.
biocViews: Pathways, GraphAndNetwork, GeneExpression, GeneSignaling, GO
Author: Marta R. Hidalgo [aut, cre], José Carbonell-Caballero [ctb],
Francisco Salavert [ctb], Alicia Amadoz [ctb], Çankut Cubuk
[ctb], Joaquin Dopazo [ctb]
Maintainer: Marta R. Hidalgo <marta.hidalgo@outlook.es>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/hipathia
git_branch: devel
git_last_commit: eda829a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/hipathia_3.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/hipathia_3.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/hipathia_3.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/hipathia_3.7.0.tgz
vignettes: vignettes/hipathia/inst/doc/hipathia-vignette.pdf
vignetteTitles: Hipathia Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/hipathia/inst/doc/hipathia-vignette.R
dependencyCount: 163
Package: HIPPO
Version: 1.19.0
Depends: R (>= 3.6.0)
Imports: ggplot2, graphics, stats, reshape2, gridExtra, Rtsne, umap,
dplyr, rlang, magrittr, irlba, Matrix, SingleCellExperiment,
ggrepel
Suggests: knitr, rmarkdown
License: GPL (>=2)
Archs: x64
MD5sum: 0e70e9fd11abdc4e3828e969e64c2a69
NeedsCompilation: no
Title: Heterogeneity-Induced Pre-Processing tOol
Description: For scRNA-seq data, it selects features and clusters the
cells simultaneously for single-cell UMI data. It has a novel
feature selection method using the zero inflation instead of
gene variance, and computationally faster than other existing
methods since it only relies on PCA+Kmeans rather than
graph-clustering or consensus clustering.
biocViews: Sequencing, SingleCell, GeneExpression,
DifferentialExpression, Clustering
Author: Tae Kim [aut, cre], Mengjie Chen [aut]
Maintainer: Tae Kim <tk382@uchicago.edu>
URL: https://github.com/tk382/HIPPO
VignetteBuilder: knitr
BugReports: https://github.com/tk382/HIPPO/issues
git_url: https://git.bioconductor.org/packages/HIPPO
git_branch: devel
git_last_commit: 3720af6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HIPPO_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HIPPO_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HIPPO_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HIPPO_1.19.0.tgz
vignettes: vignettes/HIPPO/inst/doc/example.html
vignetteTitles: Example analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HIPPO/inst/doc/example.R
dependencyCount: 83
Package: hiReadsProcessor
Version: 1.43.0
Depends: R (>= 3.5.0), Biostrings, pwalign, GenomicAlignments,
BiocParallel, hiAnnotator
Imports: sonicLength, dplyr, BiocGenerics, GenomicRanges, readxl,
methods
Suggests: knitr, testthat, markdown
License: GPL-3
MD5sum: b80d2d7b95e06d07082c8b9f448a340c
NeedsCompilation: no
Title: Functions to process LM-PCR reads from 454/Illumina data
Description: hiReadsProcessor contains set of functions which allow
users to process LM-PCR products sequenced using any platform.
Given an excel/txt file containing parameters for
demultiplexing and sample metadata, the functions automate
trimming of adaptors and identification of the genomic product.
Genomic products are further processed for QC and abundance
quantification.
biocViews: Sequencing, Preprocessing
Author: Nirav V Malani <malnirav@gmail.com>
Maintainer: Nirav V Malani <malnirav@gmail.com>
SystemRequirements: BLAT, UCSC hg18 in 2bit format for BLAT
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/hiReadsProcessor
git_branch: devel
git_last_commit: b964203
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/hiReadsProcessor_1.43.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/hiReadsProcessor_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/hiReadsProcessor_1.43.0.tgz
vignettes: vignettes/hiReadsProcessor/inst/doc/Tutorial.html
vignetteTitles: Using hiReadsProcessor
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/hiReadsProcessor/inst/doc/Tutorial.R
dependencyCount: 98
Package: HIREewas
Version: 1.25.0
Depends: R (>= 3.5.0)
Imports: quadprog, gplots, grDevices, stats
Suggests: BiocStyle, knitr, BiocGenerics
License: GPL (>= 2)
Archs: x64
MD5sum: 4aabd8e5e4fb80bd69508c2a7faac4c5
NeedsCompilation: yes
Title: Detection of cell-type-specific risk-CpG sites in epigenome-wide
association studies
Description: In epigenome-wide association studies, the measured
signals for each sample are a mixture of methylation profiles
from different cell types. The current approaches to the
association detection only claim whether a
cytosine-phosphate-guanine (CpG) site is associated with the
phenotype or not, but they cannot determine the cell type in
which the risk-CpG site is affected by the phenotype. We
propose a solid statistical method, HIgh REsolution (HIRE),
which not only substantially improves the power of association
detection at the aggregated level as compared to the existing
methods but also enables the detection of risk-CpG sites for
individual cell types. The "HIREewas" R package is to implement
HIRE model in R.
biocViews: DNAMethylation, DifferentialMethylation, FeatureExtraction
Author: Xiangyu Luo <xyluo1991@gmail.com>, Can Yang <macyang@ust.hk>,
Yingying Wei <yweicuhk@gmail.com>
Maintainer: Xiangyu Luo <xyluo1991@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/HIREewas
git_branch: devel
git_last_commit: bf30fc5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HIREewas_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HIREewas_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HIREewas_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HIREewas_1.25.0.tgz
vignettes: vignettes/HIREewas/inst/doc/HIREewas.pdf
vignetteTitles: HIREewas
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HIREewas/inst/doc/HIREewas.R
dependencyCount: 10
Package: HiTC
Version: 1.51.0
Depends: R (>= 2.15.0), methods, IRanges, GenomicRanges
Imports: Biostrings, graphics, grDevices, rtracklayer, RColorBrewer,
Matrix, parallel, GenomeInfoDb
Suggests: BiocStyle, HiCDataHumanIMR90, BSgenome.Hsapiens.UCSC.hg18
License: Artistic-2.0
MD5sum: a1ca38bd3c9534d76f952528e4dec185
NeedsCompilation: no
Title: High Throughput Chromosome Conformation Capture analysis
Description: The HiTC package was developed to explore high-throughput
'C' data such as 5C or Hi-C. Dedicated R classes as well as
standard methods for quality controls, normalization,
visualization, and further analysis are also provided.
biocViews: Sequencing, HighThroughputSequencing, HiC
Author: Nicolas Servant
Maintainer: Nicolas Servant <nicolas.servant@curie.fr>
git_url: https://git.bioconductor.org/packages/HiTC
git_branch: devel
git_last_commit: 1718634
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HiTC_1.51.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HiTC_1.51.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HiTC_1.51.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HiTC_1.51.0.tgz
vignettes: vignettes/HiTC/inst/doc/HiC_analysis.pdf,
vignettes/HiTC/inst/doc/HiTC.pdf
vignetteTitles: Hi-C data analysis using HiTC, Introduction to HiTC
package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HiTC/inst/doc/HiC_analysis.R,
vignettes/HiTC/inst/doc/HiTC.R
suggestsMe: HiCDCPlus, HiCDataHumanIMR90, adjclust
dependencyCount: 59
Package: hmdbQuery
Version: 1.27.0
Depends: R (>= 3.5), XML
Imports: S4Vectors, methods, utils
Suggests: knitr, annotate, gwascat, testthat, rmarkdown
License: Artistic-2.0
MD5sum: 79a222938f6a8b2587d58a0f7c6df2be
NeedsCompilation: no
Title: utilities for exploration of human metabolome database
Description: Define utilities for exploration of human metabolome
database, including functions to retrieve specific metabolite
entries and data snapshots with pairwise associations
(metabolite-gene,-protein,-disease).
biocViews: Metabolomics, Infrastructure
Author: Vince Carey <stvjc@channing.harvard.edu>
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/hmdbQuery
git_branch: devel
git_last_commit: cca0a20
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/hmdbQuery_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/hmdbQuery_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/hmdbQuery_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/hmdbQuery_1.27.0.tgz
vignettes: vignettes/hmdbQuery/inst/doc/hmdbQuery.html
vignetteTitles: hmdbQuery: working with Human Metabolome Database
(hmdb.ca)
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/hmdbQuery/inst/doc/hmdbQuery.R
dependencyCount: 9
Package: HMMcopy
Version: 1.49.0
Depends: R (>= 2.10.0), data.table (>= 1.11.8)
License: GPL-3
Archs: x64
MD5sum: 1e18b2621b069075ea77d334344211c0
NeedsCompilation: yes
Title: Copy number prediction with correction for GC and mappability
bias for HTS data
Description: Corrects GC and mappability biases for readcounts (i.e.
coverage) in non-overlapping windows of fixed length for single
whole genome samples, yielding a rough estimate of copy number
for furthur analysis. Designed for rapid correction of high
coverage whole genome tumour and normal samples.
biocViews: Sequencing, Preprocessing, Visualization,
CopyNumberVariation, Microarray
Author: Daniel Lai, Gavin Ha, Sohrab Shah
Maintainer: Daniel Lai <dalai@bccrc.ca>
git_url: https://git.bioconductor.org/packages/HMMcopy
git_branch: devel
git_last_commit: 9dbb51b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HMMcopy_1.49.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HMMcopy_1.49.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HMMcopy_1.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HMMcopy_1.49.0.tgz
vignettes: vignettes/HMMcopy/inst/doc/HMMcopy.pdf
vignetteTitles: HMMcopy
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HMMcopy/inst/doc/HMMcopy.R
importsMe: qsea
dependencyCount: 2
Package: HoloFoodR
Version: 1.1.1
Depends: R(>= 4.4.0), MultiAssayExperiment, TreeSummarizedExperiment
Imports: dplyr, httr2, jsonlite, S4Vectors, stats, utils
Suggests: BiocStyle, DT, ggh4x, ggsignif, knitr, MGnifyR, mia, miaViz,
MOFA2, patchwork, reticulate, rmarkdown, scater, shadowtext,
testthat, UpSetR
License: Artistic-2.0 | file LICENSE
MD5sum: 47ba161999e3267f633be5d64b04e372
NeedsCompilation: no
Title: R interface to EBI HoloFood resource
Description: Utility package to facilitate integration and analysis of
EBI HoloFood data in R. This package streamlines access to the
resource, allowing for direct loading of data into formats
optimized for downstream analytics.
biocViews: Software, Infrastructure, DataImport, Microbiome,
MicrobiomeData
Author: Tuomas Borman [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8563-8884>), Artur Sannikov [aut]
(ORCID: <https://orcid.org/0000-0001-7765-123X>), Leo Lahti
[aut] (ORCID: <https://orcid.org/0000-0001-5537-637X>)
Maintainer: Tuomas Borman <tuomas.v.borman@utu.fi>
URL: https://github.com/EBI-Metagenomics/HoloFoodR
VignetteBuilder: knitr
BugReports: https://github.com/EBI-Metagenomics/HoloFoodR/issues
git_url: https://git.bioconductor.org/packages/HoloFoodR
git_branch: devel
git_last_commit: 1f9b3fb
git_last_commit_date: 2025-02-24
Date/Publication: 2025-03-27
source.ver: src/contrib/HoloFoodR_1.1.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HoloFoodR_1.1.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HoloFoodR_1.1.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HoloFoodR_1.1.1.tgz
vignettes: vignettes/HoloFoodR/inst/doc/HoloFoodR.html
vignetteTitles: HoloFoodR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/HoloFoodR/inst/doc/HoloFoodR.R
dependencyCount: 80
Package: hoodscanR
Version: 1.5.2
Depends: R (>= 4.3)
Imports: knitr, rmarkdown, SpatialExperiment, SummarizedExperiment,
circlize, ComplexHeatmap, scico, rlang, utils, ggplot2, grid,
methods, stats, RANN, Rcpp (>= 1.0.9)
LinkingTo: Rcpp
Suggests: testthat (>= 3.0.0), BiocStyle
License: GPL-3 + file LICENSE
MD5sum: 1d99caaa547508cfb51560f587c81ad8
NeedsCompilation: yes
Title: Spatial cellular neighbourhood scanning in R
Description: hoodscanR is an user-friendly R package providing
functions to assist cellular neighborhood analysis of any
spatial transcriptomics data with single-cell resolution. All
functions in the package are built based on the
SpatialExperiment object, allowing integration into various
spatial transcriptomics-related packages from Bioconductor. The
package can result in cell-level neighborhood annotation
output, along with funtions to perform neighborhood
colocalization analysis and neighborhood-based cell clustering.
biocViews: Spatial, Transcriptomics, SingleCell, Clustering
Author: Ning Liu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-9487-9305>), Jarryd Martin [aut]
Maintainer: Ning Liu <ning.liu@adelaide.edu.au>
URL: https://github.com/DavisLaboratory/hoodscanR,
https://davislaboratory.github.io/hoodscanR/
VignetteBuilder: knitr
BugReports: https://github.com/DavisLaboratory/hoodscanR/issues
git_url: https://git.bioconductor.org/packages/hoodscanR
git_branch: devel
git_last_commit: 83a157c
git_last_commit_date: 2025-02-09
Date/Publication: 2025-02-09
source.ver: src/contrib/hoodscanR_1.5.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/hoodscanR_1.5.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/hoodscanR_1.5.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/hoodscanR_1.5.2.tgz
vignettes: vignettes/hoodscanR/inst/doc/Quick_start.html
vignetteTitles: hoodscanR_introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/hoodscanR/inst/doc/Quick_start.R
dependencyCount: 117
Package: hopach
Version: 2.67.0
Depends: R (>= 2.11.0), cluster, Biobase, methods
Imports: graphics, grDevices, stats, utils, BiocGenerics
License: GPL (>= 2)
Archs: x64
MD5sum: 55279eba3e6c6e7b9819a5dbd7926105
NeedsCompilation: yes
Title: Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH)
Description: The HOPACH clustering algorithm builds a hierarchical tree
of clusters by recursively partitioning a data set, while
ordering and possibly collapsing clusters at each level. The
algorithm uses the Mean/Median Split Silhouette (MSS) criteria
to identify the level of the tree with maximally homogeneous
clusters. It also runs the tree down to produce a final ordered
list of the elements. The non-parametric bootstrap allows one
to estimate the probability that each element belongs to each
cluster (fuzzy clustering).
biocViews: Clustering
Author: Katherine S. Pollard, with Mark J. van der Laan
<laan@stat.berkeley.edu> and Greg Wall
Maintainer: Katherine S. Pollard <katherine.pollard@gladstone.ucsf.edu>
URL: http://www.stat.berkeley.edu/~laan/, http://docpollard.org/
git_url: https://git.bioconductor.org/packages/hopach
git_branch: devel
git_last_commit: 1e51949
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/hopach_2.67.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/hopach_2.67.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/hopach_2.67.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/hopach_2.67.0.tgz
vignettes: vignettes/hopach/inst/doc/hopach.pdf
vignetteTitles: hopach
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/hopach/inst/doc/hopach.R
importsMe: phenoTest, scClassify, treekoR
suggestsMe: MicrobiotaProcess, seqArchR
dependencyCount: 9
Package: HPAanalyze
Version: 1.25.0
Depends: R (>= 3.5.0)
Imports: dplyr, openxlsx, ggplot2, tibble, xml2, stats, utils,
gridExtra
Suggests: knitr, rmarkdown, markdown, devtools, BiocStyle
License: GPL-3 + file LICENSE
MD5sum: 2876647d9806621ec1e5b0f030a9c921
NeedsCompilation: no
Title: Retrieve and analyze data from the Human Protein Atlas
Description: Provide functions for retrieving, exploratory analyzing
and visualizing the Human Protein Atlas data.
biocViews: Proteomics, CellBiology, Visualization, Software
Author: Anh Nhat Tran [aut, cre]
Maintainer: Anh Nhat Tran <trannhatanh89@gmail.com>
URL: https://github.com/anhtr/HPAanalyze
VignetteBuilder: knitr
BugReports: https://github.com/anhtr/HPAanalyze/issues
git_url: https://git.bioconductor.org/packages/HPAanalyze
git_branch: devel
git_last_commit: d90d2c8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HPAanalyze_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HPAanalyze_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HPAanalyze_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HPAanalyze_1.25.0.tgz
vignettes: vignettes/HPAanalyze/inst/doc/a_HPAanalyze_quick_start.html,
vignettes/HPAanalyze/inst/doc/b_HPAanalyze_indepth.html,
vignettes/HPAanalyze/inst/doc/c_HPAanalyze_case_query.html,
vignettes/HPAanalyze/inst/doc/d_HPAanalyze_case_offline_xml.html,
vignettes/HPAanalyze/inst/doc/e_HPAanalyze_case_json.html,
vignettes/HPAanalyze/inst/doc/f_HPAanalyze_case_images.html,
vignettes/HPAanalyze/inst/doc/z_HPAanalyze_paper_figures.html
vignetteTitles: "1. Quick-start guide: Acquire and visualize the Human
Protein Atlas (HPA) data in one function with HPAanalyze", "2.
In-depth: Working with Human Protein Atlas (HPA) data in R with
HPAanalyze", "3. Tutorial: Combine HPAanalyze with your Human
Protein Atlas (HPA) queries", "4. Tutorial: Working with Human
Protein Atlas (HPA) xml files offline", "5. Tutorial: Export
Human Protein Atlas (HPA) data as JSON", "6. Tutorial: Download
histology images from the Human Protein Atlas", "99. Code for
figures from HPAanalyze paper"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/HPAanalyze/inst/doc/a_HPAanalyze_quick_start.R,
vignettes/HPAanalyze/inst/doc/b_HPAanalyze_indepth.R,
vignettes/HPAanalyze/inst/doc/c_HPAanalyze_case_query.R,
vignettes/HPAanalyze/inst/doc/d_HPAanalyze_case_offline_xml.R,
vignettes/HPAanalyze/inst/doc/e_HPAanalyze_case_json.R,
vignettes/HPAanalyze/inst/doc/f_HPAanalyze_case_images.R,
vignettes/HPAanalyze/inst/doc/z_HPAanalyze_paper_figures.R
dependencyCount: 45
Package: hpar
Version: 1.49.0
Depends: R (>= 3.5.0)
Imports: utils, ExperimentHub
Suggests: org.Hs.eg.db, GO.db, AnnotationDbi, knitr, BiocStyle,
testthat, rmarkdown, dplyr, DT
License: Artistic-2.0
Archs: x64
MD5sum: a36c8779696ea3bc0b5ee98c3cc39cc6
NeedsCompilation: no
Title: Human Protein Atlas in R
Description: The hpar package provides a simple R interface to and data
from the Human Protein Atlas project.
biocViews: Proteomics, CellBiology, DataImport, FunctionalGenomics,
SystemsBiology, ExperimentHubSoftware
Author: Laurent Gatto [cre, aut] (ORCID:
<https://orcid.org/0000-0002-1520-2268>), Manon Martin [aut]
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/hpar
git_branch: devel
git_last_commit: 1c9e5d1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/hpar_1.49.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/hpar_1.49.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/hpar_1.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/hpar_1.49.0.tgz
vignettes: vignettes/hpar/inst/doc/hpar.html
vignetteTitles: Human Protein Atlas in R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/hpar/inst/doc/hpar.R
importsMe: MetaboSignal
suggestsMe: pRoloc, RforProteomics
dependencyCount: 66
Package: HPiP
Version: 1.13.0
Depends: R (>= 4.1)
Imports: dplyr (>= 1.0.6), httr (>= 1.4.2), readr, tidyr, tibble,
utils, stringr, magrittr, caret, corrplot, ggplot2, pROC,
PRROC, igraph, graphics, stats, purrr, grDevices, protr, MCL
Suggests: rmarkdown, colorspace, e1071, kernlab, ranger,
SummarizedExperiment, Biostrings, randomForest, gprofiler2,
gridExtra, ggthemes, BiocStyle, BiocGenerics, RUnit, tools,
knitr
License: MIT + file LICENSE
Archs: x64
MD5sum: 6b72504483f6b537a67f4f2373feefd2
NeedsCompilation: no
Title: Host-Pathogen Interaction Prediction
Description: HPiP (Host-Pathogen Interaction Prediction) uses an
ensemble learning algorithm for prediction of host-pathogen
protein-protein interactions (HP-PPIs) using structural and
physicochemical descriptors computed from amino
acid-composition of host and pathogen proteins.The proposed
package can effectively address data shortages and data
unavailability for HP-PPI network reconstructions. Moreover,
establishing computational frameworks in that regard will
reveal mechanistic insights into infectious diseases and
suggest potential HP-PPI targets, thus narrowing down the range
of possible candidates for subsequent wet-lab experimental
validations.
biocViews: Proteomics, SystemsBiology, NetworkInference,
StructuralPrediction, GenePrediction, Network
Author: Matineh Rahmatbakhsh [aut, trl, cre], Mohan Babu [led]
Maintainer: Matineh Rahmatbakhsh <matinerb.94@gmail.com>
URL: https://github.com/mrbakhsh/HPiP
VignetteBuilder: knitr
BugReports: https://github.com/mrbakhsh/HPiP/issues
git_url: https://git.bioconductor.org/packages/HPiP
git_branch: devel
git_last_commit: 65e718f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HPiP_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HPiP_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HPiP_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HPiP_1.13.0.tgz
vignettes: vignettes/HPiP/inst/doc/HPiP_tutorial.html
vignetteTitles: Introduction to HPiP
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/HPiP/inst/doc/HPiP_tutorial.R
dependencyCount: 108
Package: HTSFilter
Version: 1.47.0
Depends: R (>= 4.0.0)
Imports: edgeR, DESeq2, BiocParallel, Biobase, utils, stats, grDevices,
graphics, methods
Suggests: EDASeq, testthat, knitr, rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: 0500aad07d4ed2ec5fc62eaa4341fca3
NeedsCompilation: no
Title: Filter replicated high-throughput transcriptome sequencing data
Description: This package implements a filtering procedure for
replicated transcriptome sequencing data based on a global
Jaccard similarity index in order to identify genes with low,
constant levels of expression across one or more experimental
conditions.
biocViews: Sequencing, RNASeq, Preprocessing, DifferentialExpression,
GeneExpression, Normalization, ImmunoOncology
Author: Andrea Rau [cre, aut] (ORCID:
<https://orcid.org/0000-0001-6469-488X>), Melina Gallopin
[ctb], Gilles Celeux [ctb], Florence Jaffrézic [ctb]
Maintainer: Andrea Rau <andrea.rau@inrae.fr>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/HTSFilter
git_branch: devel
git_last_commit: e83b4d0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HTSFilter_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HTSFilter_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HTSFilter_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HTSFilter_1.47.0.tgz
vignettes: vignettes/HTSFilter/inst/doc/HTSFilter.html
vignetteTitles: HTSFilter
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HTSFilter/inst/doc/HTSFilter.R
importsMe: coseq
suggestsMe: HTSCluster
dependencyCount: 79
Package: HuBMAPR
Version: 1.1.8
Depends: R (>= 4.4.0)
Imports: httr2, dplyr, tidyr, tibble, rjsoncons, rlang, utils, stringr,
whisker, purrr
Suggests: testthat (>= 3.0.0), knitr, ggplot2, rmarkdown, BiocStyle,
pryr
License: Artistic-2.0
MD5sum: 4f9803f7d981f1170078ae72b6a25852
NeedsCompilation: no
Title: Interface to 'HuBMAP'
Description: 'HuBMAP' provides an open, global bio-molecular atlas of
the human body at the cellular level. The `datasets()`,
`samples()`, `donors()`, `publications()`, and `collections()`
functions retrieves the information for each of these entity
types. `*_details()` are available for individual entries of
each entity type. `*_derived()` are available for retrieving
derived datasets or samples for individual entries of each
entity type. Data files can be accessed using
`bulk_data_transfer()`.
biocViews: Software, SingleCell, DataImport, ThirdPartyClient, Spatial,
Infrastructure
Author: Christine Hou [aut, cre] (ORCID:
<https://orcid.org/0009-0001-5350-0629>), Martin Morgan [aut]
(ORCID: <https://orcid.org/0000-0002-5874-8148>), Federico
Marini [aut] (ORCID: <https://orcid.org/0000-0003-3252-7758>)
Maintainer: Christine Hou <chris2018hou@gmail.com>
URL: https://christinehou11.github.io/HuBMAPR/,
https://github.com/christinehou11/HuBMAPR
VignetteBuilder: knitr
BugReports: https://github.com/christinehou11/HuBMAPR/issues
git_url: https://git.bioconductor.org/packages/HuBMAPR
git_branch: devel
git_last_commit: 9f529c2
git_last_commit_date: 2025-02-12
Date/Publication: 2025-02-13
source.ver: src/contrib/HuBMAPR_1.1.8.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HuBMAPR_1.1.8.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HuBMAPR_1.1.8.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HuBMAPR_1.1.8.tgz
vignettes: vignettes/HuBMAPR/inst/doc/hubmapr_vignettes.html
vignetteTitles: Accessing Human Cell Atlas Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HuBMAPR/inst/doc/hubmapr_vignettes.R
dependencyCount: 35
Package: HubPub
Version: 1.15.4
Imports: available, usethis, biocthis, dplyr, aws.s3, fs, BiocManager,
utils
Suggests: AnnotationHubData, ExperimentHubData, testthat, knitr,
rmarkdown, BiocStyle,
License: Artistic-2.0
MD5sum: 812b090b7eecb753862642b3df84010f
NeedsCompilation: no
Title: Utilities to create and use Bioconductor Hubs
Description: HubPub provides users with functionality to help with the
Bioconductor Hub structures. The package provides the ability
to create a skeleton of a Hub style package that the user can
then populate with the necessary information. There are also
functions to help add resources to the Hub package metadata
files as well as publish data to the Bioconductor S3 bucket.
biocViews: DataImport, Infrastructure, Software, ThirdPartyClient
Author: Kayla Interdonato [aut, cre], Martin Morgan [aut], Lori
Shepherd [ctb]
Maintainer: Kayla Interdonato <kayla.morrell16@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/HubPub/issues
git_url: https://git.bioconductor.org/packages/HubPub
git_branch: devel
git_last_commit: c0787a1
git_last_commit_date: 2025-03-14
Date/Publication: 2025-03-17
source.ver: src/contrib/HubPub_1.15.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HubPub_1.15.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HubPub_1.15.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HubPub_1.15.4.tgz
vignettes: vignettes/HubPub/inst/doc/CreateAHubPackage.html,
vignettes/HubPub/inst/doc/HubPub.html
vignetteTitles: Creating A Hub Package: ExperimentHub or AnnotationHub,
HubPub: Help with publication of Hub packages
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HubPub/inst/doc/CreateAHubPackage.R,
vignettes/HubPub/inst/doc/HubPub.R
suggestsMe: AnnotationHub, AnnotationHubData, ExperimentHub,
ExperimentHubData
dependencyCount: 77
Package: hummingbird
Version: 1.17.0
Depends: R (>= 4.0)
Imports: Rcpp, graphics, GenomicRanges, SummarizedExperiment, IRanges
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, BiocStyle
License: GPL (>=2)
MD5sum: 3f1a24c568f16a0400df56ce42ba123b
NeedsCompilation: yes
Title: Bayesian Hidden Markov Model for the detection of differentially
methylated regions
Description: A package for detecting differential methylation. It
exploits a Bayesian hidden Markov model that incorporates
location dependence among genomic loci, unlike most existing
methods that assume independence among observations. Bayesian
priors are applied to permit information sharing across an
entire chromosome for improved power of detection. The direct
output of our software package is the best sequence of
methylation states, eliminating the use of a subjective, and
most of the time an arbitrary, threshold of p-value for
determining significance. At last, our methodology does not
require replication in either or both of the two comparison
groups.
biocViews: HiddenMarkovModel, Bayesian, DNAMethylation,
BiomedicalInformatics, Sequencing, GeneExpression,
DifferentialExpression, DifferentialMethylation
Author: Eleni Adam [aut, cre], Tieming Ji [aut], Desh Ranjan [aut]
Maintainer: Eleni Adam <eadam002@odu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/hummingbird
git_branch: devel
git_last_commit: 8a0488f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/hummingbird_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/hummingbird_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/hummingbird_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/hummingbird_1.17.0.tgz
vignettes: vignettes/hummingbird/inst/doc/hummingbird.html
vignetteTitles: hummingbird
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/hummingbird/inst/doc/hummingbird.R
dependencyCount: 37
Package: HybridExpress
Version: 1.3.0
Depends: R (>= 4.3.0)
Imports: ggplot2, patchwork, rlang, DESeq2, SummarizedExperiment,
stats, methods, RColorBrewer, ComplexHeatmap, grDevices,
BiocParallel
Suggests: BiocStyle, knitr, sessioninfo, testthat (>= 3.0.0)
License: GPL-3
MD5sum: 10597b654d2f90371966642513caec12
NeedsCompilation: no
Title: Comparative analysis of RNA-seq data for hybrids and their
progenitors
Description: HybridExpress can be used to perform comparative
transcriptomics analysis of hybrids (or allopolyploids)
relative to their progenitor species. The package features
functions to perform exploratory analyses of sample grouping,
identify differentially expressed genes in hybrids relative to
their progenitors, classify genes in expression categories (N =
12) and classes (N = 5), and perform functional analyses. We
also provide users with graphical functions for the seamless
creation of publication-ready figures that are commonly used in
the literature.
biocViews: Software, FunctionalGenomics, GeneExpression,
Transcriptomics, RNASeq, Classification, DifferentialExpression
Author: Fabricio Almeida-Silva [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5314-2964>), Lucas Prost-Boxoen
[aut] (ORCID: <https://orcid.org/0000-0003-2779-9097>), Yves
Van de Peer [aut] (ORCID:
<https://orcid.org/0000-0003-4327-3730>)
Maintainer: Fabricio Almeida-Silva <fabricio_almeidasilva@hotmail.com>
URL: https://github.com/almeidasilvaf/HybridExpress
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/tag/HybridExpress
git_url: https://git.bioconductor.org/packages/HybridExpress
git_branch: devel
git_last_commit: 7c5952c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HybridExpress_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HybridExpress_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HybridExpress_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HybridExpress_1.3.0.tgz
vignettes: vignettes/HybridExpress/inst/doc/HybridExpress.html
vignetteTitles: Comparative transcriptomic analysis of hybrids and
their progenitors
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HybridExpress/inst/doc/HybridExpress.R
dependencyCount: 90
Package: HybridMTest
Version: 1.51.0
Depends: R (>= 2.9.0), Biobase, fdrtool, MASS, survival
Imports: stats
License: GPL Version 2 or later
MD5sum: 5d39e4e60a51d08bd4d258c9c985f504
NeedsCompilation: no
Title: Hybrid Multiple Testing
Description: Performs hybrid multiple testing that incorporates method
selection and assumption evaluations into the analysis using
empirical Bayes probability (EBP) estimates obtained by
Grenander density estimation. For instance, for 3-group
comparison analysis, Hybrid Multiple testing considers EBPs as
weighted EBPs between F-test and H-test with EBPs from Shapiro
Wilk test of normality as weigth. Instead of just using EBPs
from F-test only or using H-test only, this methodology
combines both types of EBPs through EBPs from Shapiro Wilk test
of normality. This methodology uses then the law of total EBPs.
biocViews: GeneExpression, Genetics, Microarray
Author: Stan Pounds <stanley.pounds@stjude.org>, Demba Fofana
<demba.fofana@stjude.org>
Maintainer: Demba Fofana <demba.fofana@stjude.org>
git_url: https://git.bioconductor.org/packages/HybridMTest
git_branch: devel
git_last_commit: a4de83e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/HybridMTest_1.51.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/HybridMTest_1.51.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/HybridMTest_1.51.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/HybridMTest_1.51.0.tgz
vignettes: vignettes/HybridMTest/inst/doc/HybridMTest.pdf
vignetteTitles: Hybrid Multiple Testing
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/HybridMTest/inst/doc/HybridMTest.R
importsMe: APAlyzer
dependencyCount: 15
Package: hypeR
Version: 2.5.0
Depends: R (>= 3.6.0)
Imports: ggplot2, ggforce, R6, magrittr, dplyr, purrr, stats, stringr,
scales, rlang, httr, openxlsx, htmltools, reshape2, reactable,
msigdbr, kableExtra, rmarkdown, igraph, visNetwork, shiny,
BiocStyle
Suggests: tidyverse, devtools, testthat, knitr
License: GPL-3 + file LICENSE
MD5sum: 9dc7205d966b3e13d0125b904fd54253
NeedsCompilation: no
Title: An R Package For Geneset Enrichment Workflows
Description: An R Package for Geneset Enrichment Workflows.
biocViews: GeneSetEnrichment, Annotation, Pathways
Author: Anthony Federico [aut, cre], Andrew Chen [aut], Stefano Monti
[aut]
Maintainer: Anthony Federico <anfed@bu.edu>
URL: https://github.com/montilab/hypeR
VignetteBuilder: knitr
BugReports: https://github.com/montilab/hypeR/issues
git_url: https://git.bioconductor.org/packages/hypeR
git_branch: devel
git_last_commit: 2152271
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/hypeR_2.5.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/hypeR_2.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/hypeR_2.5.0.tgz
vignettes: vignettes/hypeR/inst/doc/hypeR.html
vignetteTitles: hypeR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/hypeR/inst/doc/hypeR.R
dependencyCount: 102
Package: hyperdraw
Version: 1.59.0
Depends: R (>= 2.9.0)
Imports: methods, grid, graph, hypergraph, Rgraphviz, stats4
License: GPL (>= 2)
Archs: x64
MD5sum: e0d43591b53642a3702a9378e10bd2fe
NeedsCompilation: no
Title: Visualizing Hypergaphs
Description: Functions for visualizing hypergraphs.
biocViews: Visualization, GraphAndNetwork
Author: Paul Murrell
Maintainer: Paul Murrell <p.murrell@auckland.ac.nz>
SystemRequirements: graphviz
git_url: https://git.bioconductor.org/packages/hyperdraw
git_branch: devel
git_last_commit: b1686fd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/hyperdraw_1.59.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/hyperdraw_1.59.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/hyperdraw_1.59.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/hyperdraw_1.59.0.tgz
vignettes: vignettes/hyperdraw/inst/doc/hyperdraw.pdf
vignetteTitles: Hyperdraw
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/hyperdraw/inst/doc/hyperdraw.R
dependsOnMe: BiGGR
dependencyCount: 12
Package: hypergraph
Version: 1.79.0
Depends: R (>= 2.1.0), methods, utils, graph
Suggests: BiocGenerics, RUnit
License: Artistic-2.0
MD5sum: ee7fd25509e00cb6d04ba93fd9f4b220
NeedsCompilation: no
Title: A package providing hypergraph data structures
Description: A package that implements some simple capabilities for
representing and manipulating hypergraphs.
biocViews: GraphAndNetwork
Author: Seth Falcon, Robert Gentleman
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/hypergraph
git_branch: devel
git_last_commit: d88b9b3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/hypergraph_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/hypergraph_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/hypergraph_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/hypergraph_1.79.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: altcdfenvs
importsMe: BiGGR, hyperdraw
dependencyCount: 8
Package: iASeq
Version: 1.51.0
Depends: R (>= 2.14.1)
Imports: graphics, grDevices
License: GPL-2
MD5sum: 983607606a0d80f499f84f43d72e6a92
NeedsCompilation: no
Title: iASeq: integrating multiple sequencing datasets for detecting
allele-specific events
Description: It fits correlation motif model to multiple RNAseq or
ChIPseq studies to improve detection of allele-specific events
and describe correlation patterns across studies.
biocViews: ImmunoOncology, SNP, RNASeq, ChIPSeq
Author: Yingying Wei, Hongkai Ji
Maintainer: Yingying Wei <ywei@jhsph.edu>
git_url: https://git.bioconductor.org/packages/iASeq
git_branch: devel
git_last_commit: 5d875d8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/iASeq_1.51.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iASeq_1.51.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iASeq_1.51.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iASeq_1.51.0.tgz
vignettes: vignettes/iASeq/inst/doc/iASeqVignette.pdf
vignetteTitles: iASeq Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iASeq/inst/doc/iASeqVignette.R
dependencyCount: 2
Package: iasva
Version: 1.25.0
Depends: R (>= 3.5),
Imports: irlba, stats, cluster, graphics, SummarizedExperiment,
BiocParallel
Suggests: knitr, testthat, rmarkdown, sva, Rtsne, pheatmap, corrplot,
DescTools, RColorBrewer
License: GPL-2
MD5sum: 9b3019881850ade3b2193b62c2552e73
NeedsCompilation: no
Title: Iteratively Adjusted Surrogate Variable Analysis
Description: Iteratively Adjusted Surrogate Variable Analysis (IA-SVA)
is a statistical framework to uncover hidden sources of
variation even when these sources are correlated. IA-SVA
provides a flexible methodology to i) identify a hidden factor
for unwanted heterogeneity while adjusting for all known
factors; ii) test the significance of the putative hidden
factor for explaining the unmodeled variation in the data; and
iii), if significant, use the estimated factor as an additional
known factor in the next iteration to uncover further hidden
factors.
biocViews: Preprocessing, QualityControl, BatchEffect, RNASeq,
Software, StatisticalMethod, FeatureExtraction, ImmunoOncology
Author: Donghyung Lee [aut, cre], Anthony Cheng [aut], Nathan Lawlor
[aut], Duygu Ucar [aut]
Maintainer: Donghyung Lee <Donghyung.Lee@jax.org>, Anthony Cheng
<Anthony.Cheng@jax.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/iasva
git_branch: devel
git_last_commit: f02ee47
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/iasva_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iasva_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iasva_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iasva_1.25.0.tgz
vignettes:
vignettes/iasva/inst/doc/detecting_hidden_heterogeneity_iasvaV0.95.html
vignetteTitles: "Detecting hidden heterogeneity in single cell RNA-Seq
data"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/iasva/inst/doc/detecting_hidden_heterogeneity_iasvaV0.95.R
dependencyCount: 48
Package: iBBiG
Version: 1.51.0
Depends: biclust
Imports: stats4,xtable,ade4
Suggests: methods
License: Artistic-2.0
MD5sum: 0905aee1dcd57fe89f9283c868b10e79
NeedsCompilation: yes
Title: Iterative Binary Biclustering of Genesets
Description: iBBiG is a bi-clustering algorithm which is optimizes for
binary data analysis. We apply it to meta-gene set analysis of
large numbers of gene expression datasets. The iterative
algorithm extracts groups of phenotypes from multiple studies
that are associated with similar gene sets. iBBiG does not
require prior knowledge of the number or scale of clusters and
allows discovery of clusters with diverse sizes
biocViews: Clustering, Annotation, GeneSetEnrichment
Author: Daniel Gusenleitner, Aedin Culhane
Maintainer: Aedin Culhane <aedin@jimmy.harvard.edu>
URL: http://bcb.dfci.harvard.edu/~aedin/publications/
git_url: https://git.bioconductor.org/packages/iBBiG
git_branch: devel
git_last_commit: 370a007
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/iBBiG_1.51.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iBBiG_1.51.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iBBiG_1.51.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iBBiG_1.51.0.tgz
vignettes: vignettes/iBBiG/inst/doc/tutorial.pdf
vignetteTitles: iBBiG User Manual
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iBBiG/inst/doc/tutorial.R
importsMe: miRSM
dependencyCount: 57
Package: ibh
Version: 1.55.0
Depends: simpIntLists
Suggests: yeastCC, stats
License: GPL (>= 2)
MD5sum: 3a81381c6440acf97eadf13924a4aed4
NeedsCompilation: no
Title: Interaction Based Homogeneity for Evaluating Gene Lists
Description: This package contains methods for calculating Interaction
Based Homogeneity to evaluate fitness of gene lists to an
interaction network which is useful for evaluation of
clustering results and gene list analysis. BioGRID interactions
are used in the calculation. The user can also provide their
own interactions.
biocViews: QualityControl, DataImport, GraphAndNetwork,
NetworkEnrichment
Author: Kircicegi Korkmaz, Volkan Atalay, Rengul Cetin Atalay.
Maintainer: Kircicegi Korkmaz <e102771@ceng.metu.edu.tr>
git_url: https://git.bioconductor.org/packages/ibh
git_branch: devel
git_last_commit: 946a7be
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ibh_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ibh_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ibh_1.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ibh_1.55.0.tgz
vignettes: vignettes/ibh/inst/doc/ibh.pdf
vignetteTitles: ibh
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ibh/inst/doc/ibh.R
dependencyCount: 1
Package: iBMQ
Version: 1.47.0
Depends: R(>= 2.15.0),Biobase (>= 2.16.0), ggplot2 (>= 0.9.2)
License: Artistic-2.0
MD5sum: a545e1d14e55fa3853dff76aa871b347
NeedsCompilation: yes
Title: integrated Bayesian Modeling of eQTL data
Description: integrated Bayesian Modeling of eQTL data
biocViews: Microarray, Preprocessing, GeneExpression, SNP
Author: Marie-Pier Scott-Boyer and Greg Imholte
Maintainer: Greg Imholte <gimholte@uw.edu>
URL: http://www.rglab.org
SystemRequirements: GSL and OpenMP
git_url: https://git.bioconductor.org/packages/iBMQ
git_branch: devel
git_last_commit: f5a261f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/iBMQ_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iBMQ_1.47.0.zip
vignettes: vignettes/iBMQ/inst/doc/iBMQ.pdf
vignetteTitles: iBMQ: An Integrated Hierarchical Bayesian Model for
Multivariate eQTL Mapping
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iBMQ/inst/doc/iBMQ.R
dependencyCount: 38
Package: iCARE
Version: 1.35.0
Depends: R (>= 3.3.0), plotrix, gtools, Hmisc
Suggests: RUnit, BiocGenerics
License: GPL-3 + file LICENSE
Archs: x64
MD5sum: 0e9725db4a47d7c5fefb5677c55b6266
NeedsCompilation: yes
Title: Individualized Coherent Absolute Risk Estimation (iCARE)
Description: An R package to build, validate and apply absolute risk
models
biocViews: Software, StatisticalMethod, GenomeWideAssociation
Author: Parichoy Pal Choudhury, Paige Maas, William Wheeler, Nilanjan
Chatterjee
Maintainer: Parichoy Pal Choudhury <Parichoy.PalChoudhury@cancer.org>
git_url: https://git.bioconductor.org/packages/iCARE
git_branch: devel
git_last_commit: 6ae8860
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/iCARE_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iCARE_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iCARE_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iCARE_1.35.0.tgz
vignettes: vignettes/iCARE/inst/doc/vignette_model_validation.pdf,
vignettes/iCARE/inst/doc/vignette.pdf
vignetteTitles: iCARE Vignette Model Validation, iCARE Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/iCARE/inst/doc/vignette_model_validation.R,
vignettes/iCARE/inst/doc/vignette.R
dependencyCount: 75
Package: Icens
Version: 1.79.0
Depends: survival
Imports: graphics
License: Artistic-2.0
MD5sum: b83efacf90773a1b038a5cee3524cfdd
NeedsCompilation: no
Title: NPMLE for Censored and Truncated Data
Description: Many functions for computing the NPMLE for censored and
truncated data.
biocViews: Infrastructure
Author: R. Gentleman and Alain Vandal
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
git_url: https://git.bioconductor.org/packages/Icens
git_branch: devel
git_last_commit: 19e9542
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Icens_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Icens_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Icens_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Icens_1.79.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: PROcess, icensBKL, interval
importsMe: PROcess, LTRCtrees
suggestsMe: ReIns
dependencyCount: 10
Package: icetea
Version: 1.25.0
Depends: R (>= 4.0)
Imports: stats, utils, methods, graphics, grDevices, ggplot2,
GenomicFeatures, ShortRead, BiocParallel, Biostrings,
S4Vectors, Rsamtools, BiocGenerics, IRanges, GenomicAlignments,
GenomicRanges, rtracklayer, SummarizedExperiment,
VariantAnnotation, limma, edgeR, csaw, DESeq2,
TxDb.Dmelanogaster.UCSC.dm6.ensGene
Suggests: knitr, rmarkdown, Rsubread (>= 1.29.0), testthat
License: GPL-3 + file LICENSE
Archs: x64
MD5sum: 7817515f9ddaf10ead9fb7ca79cddb78
NeedsCompilation: no
Title: Integrating Cap Enrichment with Transcript Expression Analysis
Description: icetea (Integrating Cap Enrichment with Transcript
Expression Analysis) provides functions for end-to-end analysis
of multiple 5'-profiling methods such as CAGE, RAMPAGE and
MAPCap, beginning from raw reads to detection of transcription
start sites using replicates. It also allows performing
differential TSS detection between group of samples, therefore,
integrating the mRNA cap enrichment information with transcript
expression analysis.
biocViews: ImmunoOncology, Transcription, GeneExpression, Sequencing,
RNASeq, Transcriptomics, DifferentialExpression
Author: Vivek Bhardwaj [aut, cre]
Maintainer: Vivek Bhardwaj <v.bhardwaj@hubrecht.eu>
URL: https://github.com/vivekbhr/icetea
VignetteBuilder: knitr
BugReports: https://github.com/vivekbhr/icetea/issues
git_url: https://git.bioconductor.org/packages/icetea
git_branch: devel
git_last_commit: 795af55
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/icetea_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/icetea_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/icetea_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/icetea_1.25.0.tgz
vignettes: vignettes/icetea/inst/doc/mapcap_analysis.html
vignetteTitles: Analysing transcript 5'-profiling data using icetea
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/icetea/inst/doc/mapcap_analysis.R
dependencyCount: 117
Package: iCheck
Version: 1.37.0
Depends: R (>= 3.2.0), Biobase, lumi, gplots
Imports: stats, graphics, preprocessCore, grDevices, randomForest,
affy, limma, parallel, GeneSelectMMD, rgl, MASS, lmtest,
scatterplot3d, utils
License: GPL (>= 2)
Archs: x64
MD5sum: b0e307c80db1be2d39dcad2d96c8221c
NeedsCompilation: no
Title: QC Pipeline and Data Analysis Tools for High-Dimensional
Illumina mRNA Expression Data
Description: QC pipeline and data analysis tools for high-dimensional
Illumina mRNA expression data.
biocViews: GeneExpression, DifferentialExpression, Microarray,
Preprocessing, DNAMethylation, OneChannel, TwoChannel,
QualityControl
Author: Weiliang Qiu [aut, cre], Brandon Guo [aut, ctb], Christopher
Anderson [aut, ctb], Barbara Klanderman [aut, ctb], Vincent
Carey [aut, ctb], Benjamin Raby [aut, ctb]
Maintainer: Weiliang Qiu <stwxq@channing.harvard.edu>
git_url: https://git.bioconductor.org/packages/iCheck
git_branch: devel
git_last_commit: 3a391bf
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/iCheck_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iCheck_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iCheck_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iCheck_1.37.0.tgz
vignettes: vignettes/iCheck/inst/doc/iCheck.pdf
vignetteTitles: iCheck
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iCheck/inst/doc/iCheck.R
dependencyCount: 189
Package: iChip
Version: 1.61.0
Depends: R (>= 2.10.0)
Imports: limma
License: GPL (>= 2)
MD5sum: 602222977534c27e9e2f41cbc8cb2853
NeedsCompilation: yes
Title: Bayesian Modeling of ChIP-chip Data Through Hidden Ising Models
Description: Hidden Ising models are implemented to identify enriched
genomic regions in ChIP-chip data. They can be used to analyze
the data from multiple platforms (e.g., Affymetrix, Agilent,
and NimbleGen), and the data with single to multiple
replicates.
biocViews: ChIPchip, OneChannel, AgilentChip, Microarray
Author: Qianxing Mo
Maintainer: Qianxing Mo <qianxing.mo@moffitt.org>
git_url: https://git.bioconductor.org/packages/iChip
git_branch: devel
git_last_commit: c3b6d48
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/iChip_1.61.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iChip_1.61.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iChip_1.61.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iChip_1.61.0.tgz
vignettes: vignettes/iChip/inst/doc/iChip.pdf
vignetteTitles: iChip
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iChip/inst/doc/iChip.R
dependencyCount: 7
Package: iClusterPlus
Version: 1.43.1
Depends: R (>= 4.1.0), parallel
Suggests: RUnit, BiocGenerics
License: GPL (>= 2)
MD5sum: ccf67c3c2c26187a450952c38516f268
NeedsCompilation: yes
Title: Integrative clustering of multi-type genomic data
Description: Integrative clustering of multiple genomic data using a
joint latent variable model.
biocViews: Multi-omics, Clustering
Author: Qianxing Mo, Ronglai Shen
Maintainer: Qianxing Mo <qianxing.mo@moffitt.org>, Ronglai Shen
<shenr@mskcc.org>
git_url: https://git.bioconductor.org/packages/iClusterPlus
git_branch: devel
git_last_commit: 7a3601f
git_last_commit_date: 2024-11-22
Date/Publication: 2024-11-24
source.ver: src/contrib/iClusterPlus_1.43.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iClusterPlus_1.43.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iClusterPlus_1.43.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iClusterPlus_1.43.1.tgz
vignettes: vignettes/iClusterPlus/inst/doc/iClusterPlus.pdf,
vignettes/iClusterPlus/inst/doc/iManual.pdf
vignetteTitles: iClusterPlus, iManual.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
suggestsMe: MultiDataSet
dependencyCount: 1
Package: iCNV
Version: 1.27.0
Depends: R (>= 3.3.1), CODEX
Imports: fields, ggplot2, truncnorm, tidyr, data.table, dplyr,
grDevices, graphics, stats, utils, rlang
Suggests: knitr, rmarkdown, WES.1KG.WUGSC
License: GPL-2
MD5sum: b18c0c5d596240fb921148bfb6a0cf5f
NeedsCompilation: no
Title: Integrated Copy Number Variation detection
Description: Integrative copy number variation (CNV) detection from
multiple platform and experimental design.
biocViews: ImmunoOncology, ExomeSeq, WholeGenome, SNP,
CopyNumberVariation, HiddenMarkovModel
Author: Zilu Zhou, Nancy Zhang
Maintainer: Zilu Zhou <zhouzilu@pennmedicine.upenn.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/iCNV
git_branch: devel
git_last_commit: 3beaddb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/iCNV_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iCNV_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iCNV_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iCNV_1.27.0.tgz
vignettes: vignettes/iCNV/inst/doc/iCNV-vignette.html
vignetteTitles: iCNV Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iCNV/inst/doc/iCNV-vignette.R
dependencyCount: 100
Package: iCOBRA
Version: 1.35.1
Depends: R (>= 4.4.0)
Imports: shiny (>= 0.9.1.9008), shinydashboard, shinyBS, reshape2,
ggplot2 (>= 3.4.0), scales, ROCR, dplyr, DT, limma, methods,
UpSetR, markdown, utils, rlang
Suggests: knitr, rmarkdown, testthat
License: GPL (>=2)
Archs: x64
MD5sum: 272f5794e5589af0d664d264edf562ec
NeedsCompilation: no
Title: Comparison and Visualization of Ranking and Assignment Methods
Description: This package provides functions for calculation and
visualization of performance metrics for evaluation of ranking
and binary classification (assignment) methods. Various types
of performance plots can be generated programmatically. The
package also contains a shiny application for interactive
exploration of results.
biocViews: Classification, Visualization
Author: Charlotte Soneson [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3833-2169>)
Maintainer: Charlotte Soneson <charlottesoneson@gmail.com>
URL: https://github.com/csoneson/iCOBRA
VignetteBuilder: knitr
BugReports: https://github.com/csoneson/iCOBRA/issues
git_url: https://git.bioconductor.org/packages/iCOBRA
git_branch: devel
git_last_commit: 29e512f
git_last_commit_date: 2024-12-31
Date/Publication: 2024-12-31
source.ver: src/contrib/iCOBRA_1.35.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iCOBRA_1.35.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iCOBRA_1.35.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iCOBRA_1.35.1.tgz
vignettes: vignettes/iCOBRA/inst/doc/iCOBRA.html
vignetteTitles: iCOBRA User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iCOBRA/inst/doc/iCOBRA.R
suggestsMe: muscat
dependencyCount: 91
Package: ideal
Version: 2.1.1
Depends: topGO
Imports: DESeq2, SummarizedExperiment, mosdef (>= 1.1.0),
GenomicRanges, IRanges, S4Vectors, ggplot2 (>= 2.0.0),
heatmaply, plotly, pheatmap, IHW, gplots, UpSetR, goseq,
stringr, dplyr, limma, GOstats, GO.db, AnnotationDbi, shiny (>=
0.12.0), shinydashboard, shinyBS, DT, rentrez, rintrojs, rlang,
ggrepel, knitr, rmarkdown, shinyAce, BiocParallel, grDevices,
graphics, base64enc, methods, utils, stats
Suggests: testthat, BiocStyle, markdown, airway, org.Hs.eg.db,
TxDb.Hsapiens.UCSC.hg38.knownGene, DEFormats, htmltools, edgeR
License: MIT + file LICENSE
Archs: x64
MD5sum: 2f10c9b0e75410951802b60c3b078c4a
NeedsCompilation: no
Title: Interactive Differential Expression AnaLysis
Description: This package provides functions for an Interactive
Differential Expression AnaLysis of RNA-sequencing datasets, to
extract quickly and effectively information downstream the step
of differential expression. A Shiny application encapsulates
the whole package. Support for reproducibility of the whole
analysis is provided by means of a template report which gets
automatically compiled and can be stored/shared.
biocViews: ImmunoOncology, GeneExpression, DifferentialExpression,
RNASeq, Sequencing, Visualization, QualityControl, GUI,
GeneSetEnrichment, ReportWriting, ShinyApps
Author: Federico Marini [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3252-7758>)
Maintainer: Federico Marini <marinif@uni-mainz.de>
URL: https://github.com/federicomarini/ideal,
https://federicomarini.github.io/ideal/
VignetteBuilder: knitr
BugReports: https://github.com/federicomarini/ideal/issues
git_url: https://git.bioconductor.org/packages/ideal
git_branch: devel
git_last_commit: 38ec392
git_last_commit_date: 2024-12-19
Date/Publication: 2024-12-20
source.ver: src/contrib/ideal_2.1.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ideal_2.1.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ideal_2.1.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ideal_2.1.1.tgz
vignettes: vignettes/ideal/inst/doc/ideal-usersguide.html
vignetteTitles: ideal User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ideal/inst/doc/ideal-usersguide.R
dependencyCount: 235
Package: IdeoViz
Version: 1.43.0
Depends: R (>= 3.5.0), Biobase, IRanges, GenomicRanges, RColorBrewer,
rtracklayer, graphics, GenomeInfoDb
License: GPL-2
MD5sum: a1c336c595cdf718138ed8ee5ea0265d
NeedsCompilation: no
Title: Plots data (continuous/discrete) along chromosomal ideogram
Description: Plots data associated with arbitrary genomic intervals
along chromosomal ideogram.
biocViews: Visualization,Microarray
Author: Shraddha Pai <shraddha.pai@utoronto.ca>, Jingliang Ren
Maintainer: Shraddha Pai <shraddha.pai@utoronto.ca>
git_url: https://git.bioconductor.org/packages/IdeoViz
git_branch: devel
git_last_commit: b8543cd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/IdeoViz_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/IdeoViz_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/IdeoViz_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/IdeoViz_1.43.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
dependencyCount: 59
Package: idiogram
Version: 1.83.0
Depends: R (>= 2.10), methods, Biobase, annotate, plotrix
Suggests: hu6800.db, hgu95av2.db, golubEsets
License: GPL-2
MD5sum: ea355153a09177abbc5c02212cd0ca46
NeedsCompilation: no
Title: idiogram
Description: A package for plotting genomic data by chromosomal
location
biocViews: Visualization
Author: Karl J. Dykema <karl.dykema@vai.org>
Maintainer: Karl J. Dykema <karl.dykema@vai.org>
git_url: https://git.bioconductor.org/packages/idiogram
git_branch: devel
git_last_commit: fca554d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/idiogram_1.83.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/idiogram_1.83.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/idiogram_1.83.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/idiogram_1.83.0.tgz
vignettes: vignettes/idiogram/inst/doc/idiogram.pdf
vignetteTitles: HOWTO: idiogram
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/idiogram/inst/doc/idiogram.R
dependencyCount: 49
Package: idpr
Version: 1.17.0
Depends: R (>= 4.1.0)
Imports: ggplot2 (>= 3.3.0), magrittr (>= 1.5), dplyr (>= 0.8.5), plyr
(>= 1.8.6), jsonlite (>= 1.6.1), rlang (>= 0.4.6), Biostrings
(>= 2.56.0), methods (>= 4.0.0)
Suggests: knitr, rmarkdown, pwalign, msa, ape, testthat, seqinr
License: LGPL (>= 3)
MD5sum: 631de9c69b10fafc0cacbe655cc451a1
NeedsCompilation: no
Title: Profiling and Analyzing Intrinsically Disordered Proteins in R
Description: ‘idpr’ aims to integrate tools for the computational
analysis of intrinsically disordered proteins (IDPs) within R.
This package is used to identify known characteristics of IDPs
for a sequence of interest with easily reported and dynamic
results. Additionally, this package includes tools for
IDP-based sequence analysis to be used in conjunction with
other R packages. Described in McFadden WM & Yanowitz JL
(2022). "idpr: A package for profiling and analyzing
Intrinsically Disordered Proteins in R." PloS one, 17(4),
e0266929. <https://doi.org/10.1371/journal.pone.0266929>.
biocViews: StructuralPrediction, Proteomics, CellBiology
Author: William M. McFadden [cre, aut], Judith L. Yanowitz [aut, fnd],
Michael Buszczak [ctb, fnd]
Maintainer: William M. McFadden <wmm27@pitt.edu>
VignetteBuilder: knitr
BugReports: https://github.com/wmm27/idpr/issues
git_url: https://git.bioconductor.org/packages/idpr
git_branch: devel
git_last_commit: 91e32e9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/idpr_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/idpr_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/idpr_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/idpr_1.17.0.tgz
vignettes: vignettes/idpr/inst/doc/chargeHydropathy-vignette.html,
vignettes/idpr/inst/doc/disorderedMatrices-vignette.html,
vignettes/idpr/inst/doc/idpr-vignette.html,
vignettes/idpr/inst/doc/iupred-vignette.html,
vignettes/idpr/inst/doc/sequenceMAP-vignette.html,
vignettes/idpr/inst/doc/structuralTendency-vignette.html
vignetteTitles: Charge and Hydropathy Vignette, Disordered Matrices
Vignette, idpr Package Overview Vignette, IUPred Vignette,
Sequence Map Vignette, Structural Tendency Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/idpr/inst/doc/chargeHydropathy-vignette.R,
vignettes/idpr/inst/doc/disorderedMatrices-vignette.R,
vignettes/idpr/inst/doc/idpr-vignette.R,
vignettes/idpr/inst/doc/iupred-vignette.R,
vignettes/idpr/inst/doc/sequenceMAP-vignette.R,
vignettes/idpr/inst/doc/structuralTendency-vignette.R
dependencyCount: 58
Package: idr2d
Version: 1.21.0
Depends: R (>= 3.6)
Imports: dplyr (>= 0.7.6), futile.logger (>= 1.4.3), GenomeInfoDb (>=
1.14.0), GenomicRanges (>= 1.30), ggplot2 (>= 3.1.1),
grDevices, grid, idr (>= 1.2), IRanges (>= 2.18.0), magrittr
(>= 1.5), methods, reticulate (>= 1.13), scales (>= 1.0.0),
stats, stringr (>= 1.3.1), utils
Suggests: DT (>= 0.4), htmltools (>= 0.3.6), knitr (>= 1.20), rmarkdown
(>= 1.10), roxygen2 (>= 6.1.0), testthat (>= 2.1.0)
License: MIT + file LICENSE
MD5sum: 488ed81955108ab84b743ce346960849
NeedsCompilation: no
Title: Irreproducible Discovery Rate for Genomic Interactions Data
Description: A tool to measure reproducibility between genomic
experiments that produce two-dimensional peaks (interactions
between peaks), such as ChIA-PET, HiChIP, and HiC. idr2d is an
extension of the original idr package, which is intended for
(one-dimensional) ChIP-seq peaks.
biocViews: DNA3DStructure, GeneRegulation, PeakDetection, Epigenetics,
FunctionalGenomics, Classification, HiC
Author: Konstantin Krismer [aut, cre, cph] (ORCID:
<https://orcid.org/0000-0001-8994-3416>), David Gifford [ths,
cph] (ORCID: <https://orcid.org/0000-0003-1709-4034>)
Maintainer: Konstantin Krismer <krismer@mit.edu>
URL: https://idr2d.mit.edu
SystemRequirements: Python (>= 3.5.0), hic-straw
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/idr2d
git_branch: devel
git_last_commit: 43c8e8a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/idr2d_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/idr2d_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/idr2d_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/idr2d_1.21.0.tgz
vignettes: vignettes/idr2d/inst/doc/idr1d.html,
vignettes/idr2d/inst/doc/idr2d.html
vignetteTitles: Identify reproducible genomic peaks from replicate
ChIP-seq experiments, Identify reproducible genomic
interactions from replicate ChIA-PET experiments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/idr2d/inst/doc/idr1d.R,
vignettes/idr2d/inst/doc/idr2d.R
dependencyCount: 69
Package: IFAA
Version: 1.9.0
Depends: R (>= 4.2.0),
Imports: mathjaxr, doRNG, foreach (>= 1.4.3), Matrix (>= 1.4-0), HDCI
(>= 1.0-2), parallel (>= 3.3.0), doParallel (>= 1.0.11),
parallelly , glmnet, stats, utils, SummarizedExperiment,
stringr, S4Vectors, DescTools, MatrixExtra, methods
Suggests: knitr, rmarkdown, RUnit, BiocGenerics, BiocStyle
License: GPL-2
MD5sum: bdf772320600f2cad0e79b084be9d9c9
NeedsCompilation: no
Title: Robust Inference for Absolute Abundance in Microbiome Analysis
Description: This package offers a robust approach to make inference on
the association of covariates with the absolute abundance (AA)
of microbiome in an ecosystem. It can be also directly applied
to relative abundance (RA) data to make inference on AA because
the ratio of two RA is equal to the ratio of their AA. This
algorithm can estimate and test the associations of interest
while adjusting for potential confounders. The estimates of
this method have easy interpretation like a typical regression
analysis. High-dimensional covariates are handled with
regularization and it is implemented by parallel computing.
False discovery rate is automatically controlled by this
approach. Zeros do not need to be imputed by a positive value
for the analysis. The IFAA package also offers the 'MZILN'
function for estimating and testing associations of abundance
ratios with covariates.
biocViews: Software, Technology, Sequencing, Microbiome, Regression
Author: Quran Wu [aut], Zhigang Li [aut, cre]
Maintainer: Zhigang Li <lzg2151@gmail.com>
URL: https://pubmed.ncbi.nlm.nih.gov/35241863/,
https://pubmed.ncbi.nlm.nih.gov/30923584/,
https://github.com/quranwu/IFAA
VignetteBuilder: knitr
BugReports: https://github.com/quranwu/IFAA/issues
git_url: https://git.bioconductor.org/packages/IFAA
git_branch: devel
git_last_commit: aaff157
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/IFAA_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/IFAA_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/IFAA_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/IFAA_1.9.0.tgz
vignettes: vignettes/IFAA/inst/doc/IFAA.pdf
vignetteTitles: IFAA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/IFAA/inst/doc/IFAA.R
dependencyCount: 103
Package: iGC
Version: 1.37.0
Depends: R (>= 3.2.0)
Imports: plyr, data.table
Suggests: BiocStyle, knitr, rmarkdown
Enhances: doMC
License: GPL-2
MD5sum: 02a13bebc0c5ff6d64c10304cd96b2d6
NeedsCompilation: no
Title: An integrated analysis package of Gene expression and Copy
number alteration
Description: This package is intended to identify differentially
expressed genes driven by Copy Number Alterations from samples
with both gene expression and CNA data.
biocViews: Software, Biological Question, DifferentialExpression,
GenomicVariation, AssayDomain, CopyNumberVariation,
GeneExpression, ResearchField, Genetics, Technology,
Microarray, Sequencing, WorkflowStep, MultipleComparison
Author: Yi-Pin Lai [aut], Liang-Bo Wang [aut, cre], Tzu-Pin Lu [aut],
Eric Y. Chuang [aut]
Maintainer: Liang-Bo Wang <r02945054@ntu.edu.tw>
URL: http://github.com/ccwang002/iGC
VignetteBuilder: knitr
BugReports: http://github.com/ccwang002/iGC/issues
git_url: https://git.bioconductor.org/packages/iGC
git_branch: devel
git_last_commit: 9bbfe28
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/iGC_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iGC_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iGC_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iGC_1.37.0.tgz
vignettes: vignettes/iGC/inst/doc/Introduction.html
vignetteTitles: Introduction to iGC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iGC/inst/doc/Introduction.R
dependencyCount: 5
Package: IgGeneUsage
Version: 1.21.0
Depends: R (>= 4.2.0)
Imports: methods, reshape2 (>= 1.4.3), Rcpp (>= 0.12.0), RcppParallel
(>= 5.0.1), rstan (>= 2.18.1), rstantools (>= 2.2.0),
SummarizedExperiment, tidyr
LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0),
RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>=
2.18.0)
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0), ggplot2,
ggforce, ggrepel, patchwork
License: MIT + file LICENSE
MD5sum: eabf6a2295c976be0bce0973e18b99fe
NeedsCompilation: yes
Title: Differential gene usage in immune repertoires
Description: Detection of biases in the usage of immunoglobulin (Ig)
genes is an important task in immune repertoire profiling.
IgGeneUsage detects aberrant Ig gene usage between biological
conditions using a probabilistic model which is analyzed
computationally by Bayes inference. With this IgGeneUsage also
avoids some common problems related to the current practice of
null-hypothesis significance testing.
biocViews: DifferentialExpression, Regression, Genetics, Bayesian,
BiomedicalInformatics, ImmunoOncology, MathematicalBiology
Author: Simo Kitanovski [aut, cre]
Maintainer: Simo Kitanovski <simo.kitanovski@uni-due.de>
URL: https://github.com/snaketron/IgGeneUsage
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/snaketron/IgGeneUsage/issues
git_url: https://git.bioconductor.org/packages/IgGeneUsage
git_branch: devel
git_last_commit: 63e09a3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/IgGeneUsage_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/IgGeneUsage_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/IgGeneUsage_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/IgGeneUsage_1.21.0.tgz
vignettes: vignettes/IgGeneUsage/inst/doc/User_Manual.html
vignetteTitles: User Manual: IgGeneUsage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/IgGeneUsage/inst/doc/User_Manual.R
dependencyCount: 94
Package: igvR
Version: 1.27.0
Depends: R (>= 3.5.0), GenomicRanges, GenomicAlignments, BrowserViz (>=
2.17.1)
Imports: methods, BiocGenerics, httpuv, utils, rtracklayer,
VariantAnnotation, RColorBrewer, httr
Suggests: RUnit, BiocStyle, knitr, rmarkdown, MotifDb, seqLogo
License: MIT + file LICENSE
MD5sum: 2b627e18c27e4966e97c358d1919b115
NeedsCompilation: no
Title: igvR: integrative genomics viewer
Description: Access to igv.js, the Integrative Genomics Viewer running
in a web browser.
biocViews: Visualization, ThirdPartyClient, GenomeBrowsers
Author: Paul Shannon
Maintainer: Arkadiusz Gladki <gladki.arkadiusz@gmail.com>
URL: https://gladkia.github.io/igvR/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/igvR
git_branch: devel
git_last_commit: e490c15
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/igvR_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/igvR_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/igvR_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/igvR_1.27.0.tgz
vignettes: vignettes/igvR/inst/doc/v00.basicIntro.html,
vignettes/igvR/inst/doc/v01.stockGenome.html,
vignettes/igvR/inst/doc/v02.customGenome.html,
vignettes/igvR/inst/doc/v03.ctcfChIP.html,
vignettes/igvR/inst/doc/v04.pairedEnd.html,
vignettes/igvR/inst/doc/v05.ucscTableBrowser.html,
vignettes/igvR/inst/doc/v06.annotationHub.html,
vignettes/igvR/inst/doc/v07.gwas.html
vignetteTitles: "Introduction: a simple demo", "Use a Stock Genome",
"Use a Custom Genome", "Explore CTCF ChIP-seq alignments,,
MACS2 narrowPeaks,, Motif Matching and H3K4me3 methylation",
"Paired-end Interaction Tracks", "Obtain and Display H3K4Me3
K562 track from UCSC table browser", "Obtain and Display
H3K27ac K562 track from the AnnotationHub", "GWAS Tracks"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/igvR/inst/doc/v00.basicIntro.R,
vignettes/igvR/inst/doc/v01.stockGenome.R,
vignettes/igvR/inst/doc/v02.customGenome.R,
vignettes/igvR/inst/doc/v03.ctcfChIP.R,
vignettes/igvR/inst/doc/v04.pairedEnd.R,
vignettes/igvR/inst/doc/v05.ucscTableBrowser.R,
vignettes/igvR/inst/doc/v06.annotationHub.R,
vignettes/igvR/inst/doc/v07.gwas.R
dependencyCount: 86
Package: igvShiny
Version: 1.3.0
Depends: R (>= 3.5.0), GenomicRanges, methods, shiny
Imports: BiocGenerics, checkmate, futile.logger, GenomeInfoDbData,
htmlwidgets, httr, jsonlite, randomcoloR, utils
Suggests: BiocStyle, GenomicAlignments, knitr, Rsamtools, rtracklayer,
RUnit, shinytest2, VariantAnnotation
License: MIT + file LICENSE
MD5sum: 59482c9300e3063e708675e921b0e7b2
NeedsCompilation: no
Title: igvShiny: a wrapper of Integrative Genomics Viewer (IGV - an
interactive tool for visualization and exploration integrated
genomic data)
Description: This package is a wrapper of Integrative Genomics Viewer
(IGV). It comprises an htmlwidget version of IGV. It can be
used as a module in Shiny apps.
biocViews: Software, ShinyApps, Sequencing, Coverage
Author: Paul Shannon [aut], Arkadiusz Gladki [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7059-6378>), Karolina Scigocka
[aut]
Maintainer: Arkadiusz Gladki <gladki.arkadiusz@gmail.com>
URL: https://github.com/gladkia/igvShiny,
https://gladkia.github.io/igvShiny/
VignetteBuilder: knitr
BugReports: https://github.com/gladkia/igvShiny/issues
git_url: https://git.bioconductor.org/packages/igvShiny
git_branch: devel
git_last_commit: 9a89fc1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/igvShiny_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/igvShiny_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/igvShiny_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/igvShiny_1.3.0.tgz
vignettes: vignettes/igvShiny/inst/doc/igvShiny.html
vignetteTitles: igvShiny Overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/igvShiny/inst/doc/igvShiny.R
dependencyCount: 79
Package: IHW
Version: 1.35.0
Depends: R (>= 3.3.0)
Imports: methods, slam, lpsymphony, fdrtool, BiocGenerics
Suggests: ggplot2, dplyr, gridExtra, scales, DESeq2, airway, testthat,
Matrix, BiocStyle, knitr, rmarkdown, devtools
License: Artistic-2.0
Archs: x64
MD5sum: 057a314663cb054ed6a6bc49ffe59f4c
NeedsCompilation: no
Title: Independent Hypothesis Weighting
Description: Independent hypothesis weighting (IHW) is a multiple
testing procedure that increases power compared to the method
of Benjamini and Hochberg by assigning data-driven weights to
each hypothesis. The input to IHW is a two-column table of
p-values and covariates. The covariate can be any
continuous-valued or categorical variable that is thought to be
informative on the statistical properties of each hypothesis
test, while it is independent of the p-value under the null
hypothesis.
biocViews: ImmunoOncology, MultipleComparison, RNASeq
Author: Nikos Ignatiadis [aut, cre], Wolfgang Huber [aut]
Maintainer: Nikos Ignatiadis <nikos.ignatiadis01@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/IHW
git_branch: devel
git_last_commit: a1c2d79
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/IHW_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/IHW_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/IHW_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/IHW_1.35.0.tgz
vignettes: vignettes/IHW/inst/doc/introduction_to_ihw.html
vignetteTitles: "Introduction to IHW"
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/IHW/inst/doc/introduction_to_ihw.R
dependsOnMe: IHWpaper
importsMe: ideal, scp
suggestsMe: DEWSeq, GRaNIE, metagenomeSeq, BloodCancerMultiOmics2017,
BisRNA, DGEobj.utils
dependencyCount: 10
Package: illuminaio
Version: 0.49.0
Imports: base64
Suggests: RUnit, BiocGenerics, IlluminaDataTestFiles (>= 1.0.2),
BiocStyle
License: GPL-2
MD5sum: 4fc6b066a9ab10c6d1250aaecbee2bba
NeedsCompilation: yes
Title: Parsing Illumina Microarray Output Files
Description: Tools for parsing Illumina's microarray output files,
including IDAT.
biocViews: Infrastructure, DataImport, Microarray, ProprietaryPlatforms
Author: Keith Baggerly [aut], Henrik Bengtsson [aut], Kasper Daniel
Hansen [aut, cre], Matt Ritchie [aut], Mike L. Smith [aut], Tim
Triche Jr. [ctb]
Maintainer: Kasper Daniel Hansen <kasperdanielhansen@gmail.com>
URL: https://github.com/HenrikBengtsson/illuminaio
BugReports: https://github.com/HenrikBengtsson/illuminaio/issues
git_url: https://git.bioconductor.org/packages/illuminaio
git_branch: devel
git_last_commit: 6a685ff
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/illuminaio_0.49.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/illuminaio_0.49.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/illuminaio_0.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/illuminaio_0.49.0.tgz
vignettes: vignettes/illuminaio/inst/doc/EncryptedFormat.pdf,
vignettes/illuminaio/inst/doc/illuminaio.pdf
vignetteTitles: Description of Encrypted IDAT Format, Introduction to
illuminaio
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/illuminaio/inst/doc/illuminaio.R
dependsOnMe: normalize450K, RnBeads, wateRmelon, EGSEA123
importsMe: beadarray, bigmelon, crlmm, methylumi, minfi
suggestsMe: limma
dependencyCount: 4
Package: ILoReg
Version: 1.17.0
Depends: R (>= 4.0.0)
Imports: Matrix, parallel, foreach, aricode, LiblineaR, SparseM,
ggplot2, cowplot, RSpectra, umap, Rtsne, fastcluster,
parallelDist, cluster, dendextend, DescTools, plyr, scales,
pheatmap, reshape2, dplyr, doRNG, SingleCellExperiment,
SummarizedExperiment, S4Vectors, methods, stats, doSNOW, utils
Suggests: knitr, rmarkdown, BiocStyle
License: GPL-3
MD5sum: 222c77945ffae592bc514f9d41f64e8e
NeedsCompilation: no
Title: ILoReg: a tool for high-resolution cell population
identification from scRNA-Seq data
Description: ILoReg is a tool for identification of cell populations
from scRNA-seq data. In particular, ILoReg is useful for
finding cell populations with subtle transcriptomic
differences. The method utilizes a self-supervised learning
method, called Iteratitive Clustering Projection (ICP), to find
cluster probabilities, which are used in noise reduction prior
to PCA and the subsequent hierarchical clustering and t-SNE
steps. Additionally, functions for differential expression
analysis to find gene markers for the populations and gene
expression visualization are provided.
biocViews: SingleCell, Software, Clustering, DimensionReduction,
RNASeq, Visualization, Transcriptomics, DataRepresentation,
DifferentialExpression, Transcription, GeneExpression
Author: Johannes Smolander [cre, aut], Sini Junttila [aut], Mikko S
Venäläinen [aut], Laura L Elo [aut]
Maintainer: Johannes Smolander <johannes.smolander@gmail.com>
URL: https://github.com/elolab/ILoReg
VignetteBuilder: knitr
BugReports: https://github.com/elolab/ILoReg/issues
git_url: https://git.bioconductor.org/packages/ILoReg
git_branch: devel
git_last_commit: 8522e63
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ILoReg_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ILoReg_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ILoReg_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ILoReg_1.17.0.tgz
vignettes: vignettes/ILoReg/inst/doc/ILoReg.html
vignetteTitles: ILoReg package manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ILoReg/inst/doc/ILoReg.R
dependencyCount: 133
Package: IMAS
Version: 1.31.0
Depends: R (> 3.0.0),GenomicFeatures, ggplot2, IVAS
Imports: doParallel, lme4, BiocGenerics, GenomicRanges, IRanges,
foreach, AnnotationDbi, S4Vectors, GenomeInfoDb, stats,
ggfortify, grDevices, methods, Matrix, utils, graphics,
gridExtra, grid, lattice, Rsamtools, survival, BiocParallel,
GenomicAlignments, parallel
Suggests: BiocStyle, RUnit
License: GPL-2
MD5sum: befcd5c3a470d97ab382e5e75823553a
NeedsCompilation: no
Title: Integrative analysis of Multi-omics data for Alternative
Splicing
Description: Integrative analysis of Multi-omics data for Alternative
splicing.
biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression,
DifferentialSplicing, GeneExpression, GeneRegulation,
Regression, RNASeq, Sequencing, SNP, Software, Transcription
Author: Seonggyun Han, Younghee Lee
Maintainer: Seonggyun Han <hangost@ssu.ac.kr>
git_url: https://git.bioconductor.org/packages/IMAS
git_branch: devel
git_last_commit: 807fef2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/IMAS_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/IMAS_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/IMAS_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/IMAS_1.31.0.tgz
vignettes: vignettes/IMAS/inst/doc/IMAS.pdf
vignetteTitles: IMAS : Integrative analysis of Multi-omics data for
Alternative Splicing
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/IMAS/inst/doc/IMAS.R
dependencyCount: 119
Package: imcRtools
Version: 1.13.0
Depends: R (>= 4.1), SpatialExperiment
Imports: S4Vectors, stats, utils, SummarizedExperiment, methods,
pheatmap, scuttle, stringr, readr, EBImage, cytomapper, abind,
BiocParallel, viridis, dplyr, magrittr, DT, igraph,
SingleCellExperiment, vroom, BiocNeighbors, RTriangle, ggraph,
tidygraph, ggplot2, data.table, sf, concaveman, tidyselect,
distances, MatrixGenerics, rlang, grDevices
Suggests: CATALYST, grid, tidyr, BiocStyle, knitr, rmarkdown, markdown,
testthat
License: GPL-3
Archs: x64
MD5sum: ec87981b33f4860013871cea3d6eee7f
NeedsCompilation: no
Title: Methods for imaging mass cytometry data analysis
Description: This R package supports the handling and analysis of
imaging mass cytometry and other highly multiplexed imaging
data. The main functionality includes reading in single-cell
data after image segmentation and measurement, data formatting
to perform channel spillover correction and a number of spatial
analysis approaches. First, cell-cell interactions are detected
via spatial graph construction; these graphs can be visualized
with cells representing nodes and interactions representing
edges. Furthermore, per cell, its direct neighbours are
summarized to allow spatial clustering. Per image/grouping
level, interactions between types of cells are counted,
averaged and compared against random permutations. In that way,
types of cells that interact more (attraction) or less
(avoidance) frequently than expected by chance are detected.
biocViews: ImmunoOncology, SingleCell, Spatial, DataImport, Clustering
Author: Nils Eling [aut], Tobias Hoch [ctb], Vito Zanotelli [ctb], Jana
Fischer [ctb], Daniel Schulz [ctb, cre] (ORCID:
<https://orcid.org/0000-0002-0913-1678>), Lasse Meyer [ctb]
Maintainer: Daniel Schulz <daniel.schulz@uzh.ch>
URL: https://github.com/BodenmillerGroup/imcRtools
VignetteBuilder: knitr
BugReports: https://github.com/BodenmillerGroup/imcRtools/issues
git_url: https://git.bioconductor.org/packages/imcRtools
git_branch: devel
git_last_commit: 19f5a4b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/imcRtools_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/imcRtools_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/imcRtools_1.13.0.tgz
vignettes: vignettes/imcRtools/inst/doc/imcRtools.html
vignetteTitles: "Tools for IMC data analysis"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/imcRtools/inst/doc/imcRtools.R
suggestsMe: spicyR
dependencyCount: 185
Package: IMMAN
Version: 1.27.0
Imports: STRINGdb, pwalign, igraph, graphics, utils, seqinr
Suggests: knitr, rmarkdown, testthat
License: Artistic-2.0
MD5sum: 9e11c73ae21aa245f81bfbdfe39c15ed
NeedsCompilation: no
Title: Interlog protein network reconstruction by Mapping and Mining
ANalysis
Description: Reconstructing Interlog Protein Network (IPN) integrated
from several Protein protein Interaction Networks (PPINs).
Using this package, overlaying different PPINs to mine
conserved common networks between diverse species will be
applicable.
biocViews: SequenceMatching, Alignment, SystemsBiology,
GraphAndNetwork, Network, Proteomics
Author: Minoo Ashtiani, Payman Nickchi, Abdollah Safari, Mehdi Mirzaie,
Mohieddin Jafari
Maintainer: Minoo Ashtiani <ashtiani.minoo@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/IMMAN
git_branch: devel
git_last_commit: b80f1c2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/IMMAN_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/IMMAN_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/IMMAN_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/IMMAN_1.27.0.tgz
vignettes: vignettes/IMMAN/inst/doc/IMMAN.html
vignetteTitles: IMMAN
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/IMMAN/inst/doc/IMMAN.R
dependencyCount: 71
Package: immApex
Version: 1.1.0
Depends: R (>= 4.3.0)
Imports: hash, httr, keras3, magrittr, matrixStats, methods,
reticulate, rvest, SingleCellExperiment, stats, stringi,
stringr, tensorflow, utils
Suggests: BiocStyle, ggplot2, knitr, markdown, rmarkdown, scRepertoire,
spelling, testthat, viridis
License: MIT + file LICENSE
MD5sum: 6b8e4e07f633f247f647af2a31f0dee1
NeedsCompilation: no
Title: Tools for Adaptive Immune Receptor Sequence-Based Machine and
Deep Learning
Description: A set of tools to build tensorflow/keras3-based models in
R from amino acid and nucleotide sequences focusing on adaptive
immune receptors. The package includes pre-processing of
sequences, unifying gene nomenclature usage, encoding
sequences, and combining models. This package will serve as the
basis of future immune receptor sequence
functions/packages/models compatible with the scRepertoire
ecosystem.
biocViews: Software, ImmunoOncology, SingleCell, Classification,
Annotation, Sequencing, MotifAnnotation
Author: Nick Borcherding [aut, cre]
Maintainer: Nick Borcherding <ncborch@gmail.com>
URL: https://github.com/BorchLab/immApex/
VignetteBuilder: knitr
BugReports: https://github.com/BorchLab/immApex/issues
git_url: https://git.bioconductor.org/packages/immApex
git_branch: devel
git_last_commit: 4ae4eb1
git_last_commit_date: 2025-02-20
Date/Publication: 2025-02-20
source.ver: src/contrib/immApex_1.1.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/immApex_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/immApex_1.1.0.tgz
vignettes: vignettes/immApex/inst/doc/immApex.html
vignetteTitles: Making Deep Learning Models with immApex
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/immApex/inst/doc/immApex.R
dependencyCount: 78
Package: immunoClust
Version: 1.39.4
Depends: R(>= 3.6), flowCore
Imports: methods, stats, graphics, grid, lattice, grDevices
Suggests: BiocStyle, utils, testthat
License: Artistic-2.0
MD5sum: a300400739854fedacde3ed9eb9a42ee
NeedsCompilation: yes
Title: immunoClust - Automated Pipeline for Population Detection in
Flow Cytometry
Description: immunoClust is a model based clustering approach for Flow
Cytometry samples. The cell-events of single Flow Cytometry
samples are modelled by a mixture of multinominal normal- or
t-distributions. The cell-event clusters of several samples are
modelled by a mixture of multinominal normal-distributions
aiming stable co-clusters across these samples.
biocViews: Clustering, FlowCytometry, SingleCell, CellBasedAssays,
ImmunoOncology
Author: Till Soerensen [aut, cre]
Maintainer: Till Soerensen <till.soerensen@bioretis.com>
git_url: https://git.bioconductor.org/packages/immunoClust
git_branch: devel
git_last_commit: 2ebcfe7
git_last_commit_date: 2025-03-20
Date/Publication: 2025-03-20
source.ver: src/contrib/immunoClust_1.39.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/immunoClust_1.39.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/immunoClust_1.39.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/immunoClust_1.39.4.tgz
vignettes: vignettes/immunoClust/inst/doc/immunoClust.pdf
vignetteTitles: immunoClust package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/immunoClust/inst/doc/immunoClust.R
dependencyCount: 20
Package: immunogenViewer
Version: 1.1.1
Depends: R (>= 4.0)
Imports: ggplot2, httr, jsonlite, patchwork, UniProt.ws
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), DT
License: Apache License (>= 2)
MD5sum: 69593666291e87e5fb5d4a816bcd22b4
NeedsCompilation: no
Title: Visualization and evaluation of protein immunogens
Description: Plots protein properties and visualizes position of
peptide immunogens within protein sequence. Allows evaluation
of immunogens based on structural and functional annotations to
infer suitability for antibody-based methods aiming to detect
native proteins.
biocViews: FeatureExtraction, Proteomics, Software, Visualization
Author: Katharina Waury [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8570-7640>)
Maintainer: Katharina Waury <kathiwaury@gmail.com>
URL: https://github.com/kathiwaury/immunogenViewer
VignetteBuilder: knitr
BugReports: https://github.com/kathiwaury/immunogenViewer/issues
git_url: https://git.bioconductor.org/packages/immunogenViewer
git_branch: devel
git_last_commit: b48a51e
git_last_commit_date: 2025-02-08
Date/Publication: 2025-02-09
source.ver: src/contrib/immunogenViewer_1.1.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/immunogenViewer_1.1.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/immunogenViewer_1.1.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/immunogenViewer_1.1.1.tgz
vignettes:
vignettes/immunogenViewer/inst/doc/immunogenViewer_vignette.html
vignetteTitles: Using immunogenViewer to evaluate and choose antibodies
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/immunogenViewer/inst/doc/immunogenViewer_vignette.R
dependencyCount: 87
Package: immunotation
Version: 1.15.0
Depends: R (>= 4.1)
Imports: stringr, ontologyIndex, curl, ggplot2, readr, rvest, tidyr,
xml2, maps, rlang
Suggests: BiocGenerics, rmarkdown, BiocStyle, knitr, testthat, DT
License: GPL-3
MD5sum: 7761beb3d3f07f2747930b2cf3717e78
NeedsCompilation: no
Title: Tools for working with diverse immune genes
Description: MHC (major histocompatibility complex) molecules are cell
surface complexes that present antigens to T cells. The
repertoire of antigens presented in a given genetic background
largely depends on the sequence of the encoded MHC molecules,
and thus, in humans, on the highly variable HLA (human
leukocyte antigen) genes of the hyperpolymorphic HLA locus.
More than 28,000 different HLA alleles have been reported, with
significant differences in allele frequencies between human
populations worldwide. Reproducible and consistent annotation
of HLA alleles in large-scale bioinformatics workflows remains
challenging, because the available reference databases and
software tools often use different HLA naming schemes. The
package immunotation provides tools for consistent annotation
of HLA genes in typical immunoinformatics workflows such as for
example the prediction of MHC-presented peptides in different
human donors. Converter functions that provide mappings between
different HLA naming schemes are based on the MHC restriction
ontology (MRO). The package also provides automated access to
HLA alleles frequencies in worldwide human reference
populations stored in the Allele Frequency Net Database.
biocViews: Software, ImmunoOncology, BiomedicalInformatics, Genetics,
Annotation
Author: Katharina Imkeller [cre, aut]
Maintainer: Katharina Imkeller <k.imkeller@dkfz.de>
VignetteBuilder: knitr
BugReports: https://github.com/imkeller/immunotation/issues
git_url: https://git.bioconductor.org/packages/immunotation
git_branch: devel
git_last_commit: 6821974
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/immunotation_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/immunotation_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/immunotation_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/immunotation_1.15.0.tgz
vignettes: vignettes/immunotation/inst/doc/immunotation_vignette.html
vignetteTitles: User guide immunotation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/immunotation/inst/doc/immunotation_vignette.R
dependencyCount: 66
Package: IMPCdata
Version: 1.43.0
Depends: R (>= 2.3.0)
Imports: rjson
License: file LICENSE
Archs: x64
MD5sum: 9f67c6aaef588f549f4c176f254b8088
NeedsCompilation: no
Title: Retrieves data from IMPC database
Description: Package contains methods for data retrieval from IMPC
Database.
biocViews: ExperimentData
Author: Natalja Kurbatova, Jeremy Mason
Maintainer: Jeremy Mason <jmason@ebi.ac.uk>
git_url: https://git.bioconductor.org/packages/IMPCdata
git_branch: devel
git_last_commit: 85978f5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/IMPCdata_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/IMPCdata_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/IMPCdata_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/IMPCdata_1.43.0.tgz
vignettes: vignettes/IMPCdata/inst/doc/IMPCdata.pdf
vignetteTitles: IMPCdata Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/IMPCdata/inst/doc/IMPCdata.R
dependencyCount: 1
Package: impute
Version: 1.81.0
Depends: R (>= 2.10)
License: GPL-2
MD5sum: 6c0c74800705cf0a04f7500770f571c4
NeedsCompilation: yes
Title: impute: Imputation for microarray data
Description: Imputation for microarray data (currently KNN only)
biocViews: Microarray
Author: Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan,
Gilbert Chu
Maintainer: Balasubramanian Narasimhan <naras@stat.Stanford.EDU>
git_url: https://git.bioconductor.org/packages/impute
git_branch: devel
git_last_commit: bd0db5e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/impute_1.81.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/impute_1.81.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/impute_1.81.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/impute_1.81.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: AMARETTO, CGHcall, TIN, curatedBreastData, imputeLCMD,
moduleColor, snpReady, swamp
importsMe: biscuiteer, cola, DExMA, doppelgangR, EGAD, EpiMix,
fastLiquidAssociation, genefu, genomation, GEOexplorer, MAGAR,
MatrixQCvis, MEAT, methylclock, MethylMix, miRLAB, MSnbase,
netboost, Pigengene, pmp, POMA, REMP, RNAAgeCalc, Rnits,
MetaGxBreast, MetaGxOvarian, MetaGxPancreas, DIscBIO, ePCR,
FAMT, GSEMA, iC10, lilikoi, mi4p, PCAPAM50, PINSPlus, Rnmr1D,
samr, speaq, WGCNA
suggestsMe: BioNet, DAPAR, GeoTcgaData, graphite, MethPed, MsCoreUtils,
QFeatures, qmtools, RnBeads, scp, TBSignatureProfiler,
TCGAutils, DDPNA, GSA, maGUI, MetChem, romic
dependencyCount: 0
Package: INDEED
Version: 2.21.0
Depends: glasso (>= 1.8), R (>= 3.5)
Imports: devtools (>= 1.13.0), graphics (>= 3.3.1), stats (>= 3.3.1),
utils (>= 3.3.1), igraph (>= 1.2.4), visNetwork(>= 2.0.6)
Suggests: knitr (>= 1.19), rmarkdown (>= 1.8), testthat (>= 2.0.0)
License: Artistic-2.0
MD5sum: 1682548f1f12fb64757be840192ecd6b
NeedsCompilation: no
Title: Interactive Visualization of Integrated Differential Expression
and Differential Network Analysis for Biomarker Candidate
Selection Package
Description: An R package for integrated differential expression and
differential network analysis based on omic data for cancer
biomarker discovery. Both correlation and partial correlation
can be used to generate differential network to aid the
traditional differential expression analysis to identify
changes between biomolecules on both their expression and
pairwise association levels. A detailed description of the
methodology has been published in Methods journal (PMID:
27592383). An interactive visualization feature allows for the
exploration and selection of candidate biomarkers.
biocViews: ImmunoOncology, Software, ResearchField, BiologicalQuestion,
StatisticalMethod, DifferentialExpression, MassSpectrometry,
Metabolomics
Author: Yiming Zuo <yimingzuo@gmail.com>, Kian Ghaffari
<kg.ghaffari@gmail.com>, Zhenzhi Li <zzrickli@gmail.com>
Maintainer: Ressom group <hwr@georgetown.edu>, Yiming Zuo
<yimingzuo@gmail.com>
URL: http://github.com/ressomlab/INDEED
VignetteBuilder: knitr
BugReports: http://github.com/ressomlab/INDEED/issues
git_url: https://git.bioconductor.org/packages/INDEED
git_branch: devel
git_last_commit: c505f83
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/INDEED_2.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/INDEED_2.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/INDEED_2.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/INDEED_2.21.0.tgz
vignettes: vignettes/INDEED/inst/doc/Introduction_to_INDEED.pdf
vignetteTitles: INDEED R package for cancer biomarker discovery
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/INDEED/inst/doc/Introduction_to_INDEED.R
dependencyCount: 106
Package: iNETgrate
Version: 1.5.0
Depends: R (>= 4.3.0), BiocStyle (>= 2.18.1)
Imports: SummarizedExperiment, GenomicRanges (>= 1.24.1), stats, WGCNA,
grDevices, graphics, survival, igraph, Pigengene (>= 1.19.26),
Homo.sapiens, glmnet, caret, gplots, minfi, matrixStats, Rfast,
tidyr, tidyselect, utils
Suggests: knitr, org.Hs.eg.db, org.Mm.eg.db,
IlluminaHumanMethylation450kanno.ilmn12.hg19, AnnotationDbi,
sesameData, TCGAbiolinks (>= 2.29.4)
License: GPL-3
MD5sum: 7fa52af0838f5006b9d03b80bd11c75e
NeedsCompilation: no
Title: Integrates DNA methylation data with gene expression in a single
gene network
Description: The iNETgrate package provides functions to build a
correlation network in which nodes are genes. DNA methylation
and gene expression data are integrated to define the
connections between genes. This network is used to identify
modules (clusters) of genes. The biological information in each
of the resulting modules is represented by an eigengene. These
biological signatures can be used as features e.g., for
classification of patients into risk categories. The resulting
biological signatures are very robust and give a holistic view
of the underlying molecular changes.
biocViews: GeneExpression, RNASeq, DNAMethylation, NetworkInference,
Network, GraphAndNetwork, BiomedicalInformatics,
SystemsBiology, Transcriptomics, Classification, Clustering,
DimensionReduction, PrincipalComponent, mRNAMicroarray,
Normalization, GenePrediction, KEGG, Survival
Author: Isha Mehta [aut] (<https://orcid.org/0000-0002-6009-0787>),
Ghazal Ebrahimi [aut], Hanie Samimi [aut], Habil Zare [aut,
cre] (<https://orcid.org/0000-0001-5902-6238>)
Maintainer: Habil Zare <zare@u.washington.edu>
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/BiocManager/issues
git_url: https://git.bioconductor.org/packages/iNETgrate
git_branch: devel
git_last_commit: 4216302
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/iNETgrate_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iNETgrate_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iNETgrate_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iNETgrate_1.5.0.tgz
vignettes: vignettes/iNETgrate/inst/doc/iNETgrate_inference.pdf
vignetteTitles: iNETgrate: Integrating gene expression and DNA
methylation data in a gene network
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iNETgrate/inst/doc/iNETgrate_inference.R
dependencyCount: 287
Package: infercnv
Version: 1.23.0
Depends: R(>= 4.0)
Imports: graphics, grDevices, RColorBrewer, gplots, futile.logger,
stats, utils, methods, ape, phyclust, Matrix, fastcluster,
parallelDist, dplyr, HiddenMarkov, ggplot2, edgeR, coin,
caTools, digest, RANN, igraph, reshape2, rjags, fitdistrplus,
future, foreach, doParallel, Seurat, BiocGenerics,
SummarizedExperiment, SingleCellExperiment, tidyr, parallel,
coda, gridExtra, argparse
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: BSD_3_clause + file LICENSE
MD5sum: df24c96b78e40c1ead33e83fe98a65d6
NeedsCompilation: no
Title: Infer Copy Number Variation from Single-Cell RNA-Seq Data
Description: Using single-cell RNA-Seq expression to visualize CNV in
cells.
biocViews: Software, CopyNumberVariation, VariantDetection,
StructuralVariation, GenomicVariation, Genetics,
Transcriptomics, StatisticalMethod, Bayesian,
HiddenMarkovModel, SingleCell
Author: Timothy Tickle [aut], Itay Tirosh [aut], Christophe Georgescu
[aut, cre], Maxwell Brown [aut], Brian Haas [aut]
Maintainer: Christophe Georgescu <cgeorges@broadinstitute.org>
URL: https://github.com/broadinstitute/inferCNV/wiki
SystemRequirements: JAGS 4.x.y
VignetteBuilder: knitr
BugReports: https://github.com/broadinstitute/inferCNV/issues
git_url: https://git.bioconductor.org/packages/infercnv
git_branch: devel
git_last_commit: ec059e2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/infercnv_1.23.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/infercnv_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/infercnv_1.23.0.tgz
vignettes: vignettes/infercnv/inst/doc/inferCNV.html
vignetteTitles: Visualizing Large-scale Copy Number Variation in
Single-Cell RNA-Seq Expression Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/infercnv/inst/doc/inferCNV.R
suggestsMe: SCpubr
dependencyCount: 199
Package: infinityFlow
Version: 1.17.0
Depends: R (>= 4.0.0), flowCore
Imports: stats, grDevices, utils, graphics, pbapply, matlab, png,
raster, grid, uwot, gtools, Biobase, generics, parallel,
methods, xgboost
Suggests: knitr, rmarkdown, keras, tensorflow, glmnetUtils, e1071
License: GPL-3
Archs: x64
MD5sum: 0d8e8d1de4ce1552077863bf0d149d0e
NeedsCompilation: no
Title: Augmenting Massively Parallel Cytometry Experiments Using
Multivariate Non-Linear Regressions
Description: Pipeline to analyze and merge data files produced by
BioLegend's LEGENDScreen or BD Human Cell Surface Marker
Screening Panel (BD Lyoplates).
biocViews: Software, FlowCytometry, CellBasedAssays, SingleCell,
Proteomics
Author: Etienne Becht [cre, aut]
Maintainer: Etienne Becht <etienne.becht@protonmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/infinityFlow
git_branch: devel
git_last_commit: ac4a2be
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/infinityFlow_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/infinityFlow_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/infinityFlow_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/infinityFlow_1.17.0.tgz
vignettes: vignettes/infinityFlow/inst/doc/basic_usage.html,
vignettes/infinityFlow/inst/doc/training_non_default_regression_models.html
vignetteTitles: Basic usage of the infinityFlow package, Training non
default regression models
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/infinityFlow/inst/doc/basic_usage.R,
vignettes/infinityFlow/inst/doc/training_non_default_regression_models.R
dependencyCount: 41
Package: Informeasure
Version: 1.17.0
Depends: R (>= 4.0)
Imports: entropy
Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0),
SummarizedExperiment
License: Artistic-2.0
MD5sum: 184a35c135751226cec3341714e599bb
NeedsCompilation: no
Title: R implementation of information measures
Description: This package consolidates a comprehensive set of
information measurements, encompassing mutual information,
conditional mutual information, interaction information,
partial information decomposition, and part mutual information.
biocViews: GeneExpression, NetworkInference, Network, Software
Author: Chu Pan [aut, cre]
Maintainer: Chu Pan <chu.pan@hnu.edu.cn>
URL: https://github.com/chupan1218/Informeasure
VignetteBuilder: knitr
BugReports: https://github.com/chupan1218/Informeasure/issues
git_url: https://git.bioconductor.org/packages/Informeasure
git_branch: devel
git_last_commit: b93e002
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Informeasure_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Informeasure_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Informeasure_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Informeasure_1.17.0.tgz
vignettes: vignettes/Informeasure/inst/doc/Informeasure.html
vignetteTitles: Informeasure: a tool to quantify nonlinear dependence
between variables in biological regulatory networks from an
information theory perspective
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Informeasure/inst/doc/Informeasure.R
dependencyCount: 1
Package: InPAS
Version: 2.15.2
Depends: R (>= 3.5.0)
Imports:
AnnotationDbi,batchtools,Biobase,Biostrings,BSgenome,cleanUpdTSeq,
depmixS4,dplyr,flock,future,future.apply,GenomeInfoDb,GenomicRanges,
GenomicFeatures, ggplot2, IRanges, limma,
magrittr,methods,parallelly, plyranges, preprocessCore,
readr,reshape2, RSQLite, stats,S4Vectors, utils
Suggests: BiocGenerics,BiocManager, BiocStyle,
BSgenome.Mmusculus.UCSC.mm10, BSgenome.Hsapiens.UCSC.hg19,
EnsDb.Hsapiens.v86, EnsDb.Mmusculus.v79, knitr, markdown,
rmarkdown, rtracklayer, RUnit, grDevices,
TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Mmusculus.UCSC.mm10.knownGene
License: GPL (>= 2)
MD5sum: cb5c33be188c36e9dfb84ee74bb88790
NeedsCompilation: no
Title: Identify Novel Alternative PolyAdenylation Sites (PAS) from
RNA-seq data
Description: Alternative polyadenylation (APA) is one of the important
post- transcriptional regulation mechanisms which occurs in
most human genes. InPAS facilitates the discovery of novel APA
sites and the differential usage of APA sites from RNA-Seq
data. It leverages cleanUpdTSeq to fine tune identified APA
sites by removing false sites.
biocViews: Alternative Polyadenylation, Differential Polyadenylation
Site Usage, RNA-seq, Gene Regulation, Transcription
Author: Jianhong Ou [aut, cre], Haibo Liu [aut], Lihua Julie Zhu [aut],
Sungmi M. Park [aut], Michael R. Green [aut]
Maintainer: Jianhong Ou <jou@morgridge.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/InPAS
git_branch: devel
git_last_commit: deae9ac
git_last_commit_date: 2025-01-03
Date/Publication: 2025-01-03
source.ver: src/contrib/InPAS_2.15.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/InPAS_2.15.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/InPAS_2.15.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/InPAS_2.15.2.tgz
vignettes: vignettes/InPAS/inst/doc/InPAS.html
vignetteTitles: InPAS Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/InPAS/inst/doc/InPAS.R
dependencyCount: 146
Package: INPower
Version: 1.43.0
Depends: R (>= 3.1.0), mvtnorm
Suggests: RUnit, BiocGenerics
License: GPL-2 + file LICENSE
MD5sum: b2f4eff27b4ae59e30ae717033d44855
NeedsCompilation: no
Title: An R package for computing the number of susceptibility SNPs
Description: An R package for computing the number of susceptibility
SNPs and power of future studies
biocViews: SNP
Author: Ju-Hyun Park
Maintainer: Bill Wheeler <wheelerb@imsweb.com>
git_url: https://git.bioconductor.org/packages/INPower
git_branch: devel
git_last_commit: bf23655
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/INPower_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/INPower_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/INPower_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/INPower_1.43.0.tgz
vignettes: vignettes/INPower/inst/doc/vignette.pdf
vignetteTitles: INPower Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/INPower/inst/doc/vignette.R
dependencyCount: 2
Package: INSPEcT
Version: 1.37.0
Depends: R (>= 3.6), methods, Biobase, BiocParallel
Imports: pROC, deSolve, rootSolve, KernSmooth, readxl, GenomicFeatures,
GenomicRanges, IRanges, BiocGenerics, GenomicAlignments,
Rsamtools, S4Vectors, GenomeInfoDb, DESeq2, plgem, rtracklayer,
SummarizedExperiment, TxDb.Mmusculus.UCSC.mm9.knownGene, shiny
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-2
MD5sum: 3beb5f79b402e70a485ee2a59d46b376
NeedsCompilation: no
Title: Modeling RNA synthesis, processing and degradation with RNA-seq
data
Description: INSPEcT (INference of Synthesis, Processing and
dEgradation rates from Transcriptomic data) RNA-seq data in
time-course experiments or steady-state conditions, with or
without the support of nascent RNA data.
biocViews: Sequencing, RNASeq, GeneRegulation, TimeCourse,
SystemsBiology
Author: Stefano de Pretis
Maintainer: Stefano de Pretis <ste.depo@gmail.com>, Mattia Furlan
<Mattia.Furlan@iit.it>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/INSPEcT
git_branch: devel
git_last_commit: 7d70bac
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/INSPEcT_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/INSPEcT_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/INSPEcT_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/INSPEcT_1.37.0.tgz
vignettes: vignettes/INSPEcT/inst/doc/INSPEcT_GUI.html,
vignettes/INSPEcT/inst/doc/INSPEcT.html
vignetteTitles: INSPEcT_GUI.html, INSPEcT - INference of Synthesis,,
Processing and dEgradation rates from Transcriptomic data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/INSPEcT/inst/doc/INSPEcT_GUI.R,
vignettes/INSPEcT/inst/doc/INSPEcT.R
dependencyCount: 130
Package: INTACT
Version: 1.7.0
Depends: R (>= 4.3.0)
Imports: SQUAREM, bdsmatrix, numDeriv, stats, tidyr, ggplot2
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: GPL-3 + file LICENSE
MD5sum: 59928e5db440a961a83ee923b038a4e2
NeedsCompilation: no
Title: Integrate TWAS and Colocalization Analysis for Gene Set
Enrichment Analysis
Description: This package integrates colocalization probabilities from
colocalization analysis with transcriptome-wide association
study (TWAS) scan summary statistics to implicate genes that
may be biologically relevant to a complex trait. The
probabilistic framework implemented in this package constrains
the TWAS scan z-score-based likelihood using a gene-level
colocalization probability. Given gene set annotations, this
package can estimate gene set enrichment using posterior
probabilities from the TWAS-colocalization integration step.
biocViews: Bayesian, GeneSetEnrichment
Author: Jeffrey Okamoto [aut, cre] (ORCID:
<https://orcid.org/0000-0001-9988-1618>), Xiaoquan Wen [aut]
(ORCID: <https://orcid.org/0000-0001-8990-2737>)
Maintainer: Jeffrey Okamoto <jokamoto@umich.edu>
URL: https://github.com/jokamoto97/INTACT
VignetteBuilder: knitr
BugReports: https://github.com/jokamoto97/INTACT/issues
git_url: https://git.bioconductor.org/packages/INTACT
git_branch: devel
git_last_commit: 597e401
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/INTACT_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/INTACT_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/INTACT_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/INTACT_1.7.0.tgz
vignettes: vignettes/INTACT/inst/doc/INTACT.html
vignetteTitles: INTACT: Integrate TWAS and Colocalization Analysis for
Gene Set Enrichment
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/INTACT/inst/doc/INTACT.R
dependencyCount: 47
Package: InTAD
Version: 1.27.0
Depends: R (>= 3.5), methods, S4Vectors, IRanges, GenomicRanges,
MultiAssayExperiment, SummarizedExperiment,stats
Imports:
BiocGenerics,Biobase,rtracklayer,parallel,graphics,mclust,qvalue,
ggplot2,utils,ggpubr
Suggests: testthat, BiocStyle, knitr, rmarkdown
License: GPL (>=2)
MD5sum: 8f7867dcde0a211838cbb540d217dba0
NeedsCompilation: no
Title: Search for correlation between epigenetic signals and gene
expression in TADs
Description: The package is focused on the detection of correlation
between expressed genes and selected epigenomic signals (i.e.
enhancers obtained from ChIP-seq data) either within
topologically associated domains (TADs) or between chromatin
contact loop anchors. Various parameters can be controlled to
investigate the influence of external factors and visualization
plots are available for each analysis step.
biocViews: Epigenetics, Sequencing, ChIPSeq, RNASeq, HiC,
GeneExpression,ImmunoOncology
Author: Konstantin Okonechnikov, Serap Erkek, Lukas Chavez
Maintainer: Konstantin Okonechnikov <k.okonechnikov@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/InTAD
git_branch: devel
git_last_commit: 4f6fc18
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/InTAD_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/InTAD_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/InTAD_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/InTAD_1.27.0.tgz
vignettes: vignettes/InTAD/inst/doc/InTAD.html
vignetteTitles: Correlation of epigenetic signals and genes in TADs
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/InTAD/inst/doc/InTAD.R
dependencyCount: 130
Package: intansv
Version: 1.47.0
Depends: R (>= 2.14.0), plyr, ggbio, GenomicRanges
Imports: BiocGenerics, IRanges
License: MIT + file LICENSE
MD5sum: d427bf514a9afc046119b89042930038
NeedsCompilation: no
Title: Integrative analysis of structural variations
Description: This package provides efficient tools to read and
integrate structural variations predicted by popular softwares.
Annotation and visulation of structural variations are also
implemented in the package.
biocViews: Genetics, Annotation, Sequencing, Software
Author: Wen Yao <ywhzau@gmail.com>
Maintainer: Wen Yao <ywhzau@gmail.com>
git_url: https://git.bioconductor.org/packages/intansv
git_branch: devel
git_last_commit: 69fa786
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/intansv_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/intansv_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/intansv_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/intansv_1.47.0.tgz
vignettes: vignettes/intansv/inst/doc/intansvOverview.pdf
vignetteTitles: An Introduction to intansv
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/intansv/inst/doc/intansvOverview.R
dependencyCount: 162
Package: interacCircos
Version: 1.17.0
Depends: R (>= 4.1)
Imports: RColorBrewer, htmlwidgets, plyr, methods
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: 2dfa499eaccdf6ee4d62fc3e4e83db72
NeedsCompilation: no
Title: The Generation of Interactive Circos Plot
Description: Implement in an efficient approach to display the genomic
data, relationship, information in an interactive circular
genome(Circos) plot. 'interacCircos' are inspired by
'circosJS', 'BioCircos.js' and 'NG-Circos' and we integrate the
modules of 'circosJS', 'BioCircos.js' and 'NG-Circos' into this
R package, based on 'htmlwidgets' framework.
biocViews: Visualization
Author: Zhe Cui [aut, cre]
Maintainer: Zhe Cui <mrcuizhe@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/interacCircos
git_branch: devel
git_last_commit: 147c9a6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/interacCircos_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/interacCircos_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/interacCircos_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/interacCircos_1.17.0.tgz
vignettes: vignettes/interacCircos/inst/doc/interacCircos.html
vignetteTitles: interacCircos
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/interacCircos/inst/doc/interacCircos.R
dependencyCount: 35
Package: InteractionSet
Version: 1.35.0
Depends: GenomicRanges, SummarizedExperiment
Imports: methods, Matrix, Rcpp, BiocGenerics, S4Vectors (>= 0.27.12),
IRanges, GenomeInfoDb
LinkingTo: Rcpp
Suggests: testthat, knitr, rmarkdown, BiocStyle
License: GPL-3
MD5sum: 64888b2e7bb30e1b4f22a5d6ae664eea
NeedsCompilation: yes
Title: Base Classes for Storing Genomic Interaction Data
Description: Provides the GInteractions, InteractionSet and
ContactMatrix objects and associated methods for storing and
manipulating genomic interaction data from Hi-C and ChIA-PET
experiments.
biocViews: Infrastructure, DataRepresentation, Software, HiC
Author: Aaron Lun [aut, cre], Malcolm Perry [aut], Elizabeth
Ing-Simmons [aut]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/InteractionSet
git_branch: devel
git_last_commit: 8c47d4b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/InteractionSet_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/InteractionSet_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/InteractionSet_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/InteractionSet_1.35.0.tgz
vignettes: vignettes/InteractionSet/inst/doc/interactions.html
vignetteTitles: Genomic interaction classes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/InteractionSet/inst/doc/interactions.R
dependsOnMe: diffHic, DuplexDiscovereR, GenomicInteractions, HiCDOC,
sevenC, nullrangesData
importsMe: CAGEfightR, ChIPpeakAnno, DegCre, EDIRquery, extraChIPs,
geomeTriD, HicAggR, HiCcompare, HiCExperiment, HiContacts,
HiCool, HiCParser, hicVennDiagram, mariner, nullranges,
plyinteractions, trackViewer, treediff
suggestsMe: plotgardener, transmogR, updateObject, CAGEWorkflow
dependencyCount: 37
Package: InteractiveComplexHeatmap
Version: 1.15.0
Depends: R (>= 4.0.0), ComplexHeatmap (>= 2.11.0)
Imports: grDevices, stats, shiny, grid, GetoptLong, S4Vectors (>=
0.26.1), digest, IRanges, kableExtra (>= 1.3.1), utils,
svglite, htmltools, clisymbols, jsonlite, RColorBrewer,
fontawesome
Suggests: knitr, rmarkdown, testthat, EnrichedHeatmap, GenomicRanges,
data.table, circlize, GenomicFeatures, tidyverse, tidyHeatmap,
cluster, org.Hs.eg.db, simplifyEnrichment, GO.db, SC3,
GOexpress, SingleCellExperiment, scater, gplots, pheatmap,
airway, DESeq2, DT, cola, BiocManager, gridtext, HilbertCurve
(>= 1.21.1), shinydashboard, SummarizedExperiment, pkgndep, ks
License: MIT + file LICENSE
MD5sum: 61cd9b90186c941db580101bb03093d8
NeedsCompilation: no
Title: Make Interactive Complex Heatmaps
Description: This package can easily make heatmaps which are produced
by the ComplexHeatmap package into interactive applications. It
provides two types of interactivities: 1. on the interactive
graphics device, and 2. on a Shiny app. It also provides
functions for integrating the interactive heatmap widgets for
more complex Shiny app development.
biocViews: Software, Visualization, Sequencing
Author: Zuguang Gu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7395-8709>)
Maintainer: Zuguang Gu <z.gu@dkfz.de>
URL: https://github.com/jokergoo/InteractiveComplexHeatmap
VignetteBuilder: knitr
BugReports:
https://github.com/jokergoo/InteractiveComplexHeatmap/issues
git_url:
https://git.bioconductor.org/packages/InteractiveComplexHeatmap
git_branch: devel
git_last_commit: 4548478
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/InteractiveComplexHeatmap_1.15.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.5/InteractiveComplexHeatmap_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/InteractiveComplexHeatmap_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/InteractiveComplexHeatmap_1.15.0.tgz
vignettes:
vignettes/InteractiveComplexHeatmap/inst/doc/decoration.html,
vignettes/InteractiveComplexHeatmap/inst/doc/deseq2_app.html,
vignettes/InteractiveComplexHeatmap/inst/doc/from_scratch.html,
vignettes/InteractiveComplexHeatmap/inst/doc/implementation.html,
vignettes/InteractiveComplexHeatmap/inst/doc/interactivate_indirect.html,
vignettes/InteractiveComplexHeatmap/inst/doc/InteractiveComplexHeatmap.html,
vignettes/InteractiveComplexHeatmap/inst/doc/share.html,
vignettes/InteractiveComplexHeatmap/inst/doc/shiny_dev.html
vignetteTitles: 4. Decorations on heatmaps, 6. A Shiny app for
visualizing DESeq2 results, 7. Implement interactive heatmap
from scratch, 2. How interactive complex heatmap is
implemented, 5. Interactivate heatmaps indirectly generated by
pheatmap(),, heatmap.2() and heatmap(), 1. How to visualize
heatmaps interactively, 8. Share interactive heatmaps to
collaborators, 3. Functions for Shiny app development
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/InteractiveComplexHeatmap/inst/doc/decoration.R,
vignettes/InteractiveComplexHeatmap/inst/doc/deseq2_app.R,
vignettes/InteractiveComplexHeatmap/inst/doc/from_scratch.R,
vignettes/InteractiveComplexHeatmap/inst/doc/implementation.R,
vignettes/InteractiveComplexHeatmap/inst/doc/interactivate_indirect.R,
vignettes/InteractiveComplexHeatmap/inst/doc/InteractiveComplexHeatmap.R,
vignettes/InteractiveComplexHeatmap/inst/doc/share.R,
vignettes/InteractiveComplexHeatmap/inst/doc/shiny_dev.R
importsMe: CRISPRball, gINTomics, mineSweepR
suggestsMe: CTexploreR, simona, simplifyEnrichment, metasnf
dependencyCount: 81
Package: interactiveDisplay
Version: 1.45.1
Depends: R (>= 3.5.0), methods, BiocGenerics, grid
Imports: interactiveDisplayBase (>= 1.7.3), shiny, RColorBrewer,
ggplot2, reshape2, plyr, gridSVG, XML, Category, AnnotationDbi
Suggests: RUnit, hgu95av2.db, knitr, GenomicRanges,
SummarizedExperiment, GOstats, ggbio, GO.db, Gviz, rtracklayer,
metagenomeSeq, gplots, vegan, Biobase
Enhances: rstudio
License: Artistic-2.0
MD5sum: 1e61e523f704c37ecda456de75b0bb67
NeedsCompilation: no
Title: Package for enabling powerful shiny web displays of Bioconductor
objects
Description: The interactiveDisplay package contains the methods needed
to generate interactive Shiny based display methods for
Bioconductor objects.
biocViews: GO, GeneExpression, Microarray, Sequencing, Classification,
Network, QualityControl, Visualization, Visualization,
Genetics, DataRepresentation, GUI, AnnotationData, ShinyApps
Author: Bioconductor Package Maintainer [cre], Shawn Balcome [aut],
Marc Carlson [ctb]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/interactiveDisplay
git_branch: devel
git_last_commit: d6784e2
git_last_commit_date: 2024-12-17
Date/Publication: 2024-12-18
source.ver: src/contrib/interactiveDisplay_1.45.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/interactiveDisplay_1.45.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/interactiveDisplay_1.45.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/interactiveDisplay_1.45.1.tgz
vignettes: vignettes/interactiveDisplay/inst/doc/interactiveDisplay.pdf
vignetteTitles: interactiveDisplay: A package for enabling interactive
visualization of Bioconductor objects
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/interactiveDisplay/inst/doc/interactiveDisplay.R
suggestsMe: metagenomeSeq
dependencyCount: 113
Package: interactiveDisplayBase
Version: 1.45.0
Depends: R (>= 2.10), methods, BiocGenerics
Imports: shiny, DT
Suggests: knitr, markdown
Enhances: rstudioapi
License: Artistic-2.0
Archs: x64
MD5sum: 0013248584852a215739490f3432a998
NeedsCompilation: no
Title: Base package for enabling powerful shiny web displays of
Bioconductor objects
Description: The interactiveDisplayBase package contains the the basic
methods needed to generate interactive Shiny based display
methods for Bioconductor objects.
biocViews: GO, GeneExpression, Microarray, Sequencing, Classification,
Network, QualityControl, Visualization, Visualization,
Genetics, DataRepresentation, GUI, AnnotationData, ShinyApps
Author: Bioconductor Package Maintainer [cre], Shawn Balcome [aut],
Marc Carlson [ctb], Marcel Ramos [ctb]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/interactiveDisplayBase
git_branch: devel
git_last_commit: 5218b82
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/interactiveDisplayBase_1.45.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.5/interactiveDisplayBase_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/interactiveDisplayBase_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/interactiveDisplayBase_1.45.0.tgz
vignettes:
vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.html
vignetteTitles: Using interactiveDisplayBase for Bioconductor object
visualization and modification
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.R
importsMe: interactiveDisplay
suggestsMe: recount3
dependencyCount: 49
Package: InterCellar
Version: 2.13.0
Depends: R (>= 4.1)
Imports: config, golem, shiny, DT, shinydashboard, shinyFiles,
shinycssloaders, data.table, fs, dplyr, tidyr, circlize,
colourpicker, dendextend, factoextra, ggplot2, plotly, plyr,
shinyFeedback, shinyalert, tibble, umap, visNetwork,
wordcloud2, readxl, htmlwidgets, colorspace, signal, scales,
htmltools, ComplexHeatmap, grDevices, stats, tools, utils,
biomaRt, rlang, fmsb, igraph
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, glue, graphite,
processx, attempt, BiocStyle, httr
License: MIT + file LICENSE
MD5sum: d1b9955aa5cf68a7918d4e0c98fc98b5
NeedsCompilation: no
Title: InterCellar: an R-Shiny app for interactive analysis and
exploration of cell-cell communication in single-cell
transcriptomics
Description: InterCellar is implemented as an R/Bioconductor Package
containing a Shiny app that allows users to interactively
analyze cell-cell communication from scRNA-seq data. Starting
from precomputed ligand-receptor interactions, InterCellar
provides filtering options, annotations and multiple
visualizations to explore clusters, genes and functions.
Finally, based on functional annotation from Gene Ontology and
pathway databases, InterCellar implements data-driven analyses
to investigate cell-cell communication in one or multiple
conditions.
biocViews: Software, SingleCell, Visualization, GO, Transcriptomics
Author: Marta Interlandi [cre, aut] (ORCID:
<https://orcid.org/0000-0002-6863-2552>)
Maintainer: Marta Interlandi <marta.interlandi01@gmail.com>
URL: https://github.com/martaint/InterCellar
VignetteBuilder: knitr
BugReports: https://github.com/martaint/InterCellar/issues
git_url: https://git.bioconductor.org/packages/InterCellar
git_branch: devel
git_last_commit: e86c244
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/InterCellar_2.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/InterCellar_2.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/InterCellar_2.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/InterCellar_2.13.0.tgz
vignettes: vignettes/InterCellar/inst/doc/user_guide.html
vignetteTitles: InterCellar User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/InterCellar/inst/doc/user_guide.R
dependencyCount: 202
Package: IntEREst
Version: 1.31.4
Depends: R (>= 3.5.0), GenomicRanges, Rsamtools, SummarizedExperiment,
edgeR, S4Vectors, GenomicFiles
Imports: seqLogo, Biostrings, GenomicFeatures (>= 1.39.4), txdbmaker,
IRanges, seqinr, graphics, grDevices, stats, utils, grid,
methods, DBI, RMariaDB, GenomicAlignments, BiocParallel,
BiocGenerics, DEXSeq, DESeq2
Suggests: clinfun, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19
License: GPL-2
MD5sum: 52522c01ae934d7d2163fdffd43b6912
NeedsCompilation: no
Title: Intron-Exon Retention Estimator
Description: This package performs Intron-Exon Retention analysis on
RNA-seq data (.bam files).
biocViews: Software, AlternativeSplicing, Coverage,
DifferentialSplicing, Sequencing, RNASeq, Alignment,
Normalization, DifferentialExpression, ImmunoOncology
Author: Ali Oghabian <Ali.Oghabian@Helsinki.Fi>, Dario Greco
<dario.greco@helsinki.fi>, Mikko Frilander
<Mikko.Frilander@helsinki.fi>
Maintainer: Ali Oghabian <Ali.Oghabian@Helsinki.Fi>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/IntEREst
git_branch: devel
git_last_commit: d1e5d01
git_last_commit_date: 2025-03-28
Date/Publication: 2025-03-28
source.ver: src/contrib/IntEREst_1.31.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/IntEREst_1.31.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/IntEREst_1.31.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/IntEREst_1.31.4.tgz
vignettes: vignettes/IntEREst/inst/doc/IntEREst.html
vignetteTitles: IntEREst,, Intron Exon Retention Estimator
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/IntEREst/inst/doc/IntEREst.R
dependencyCount: 141
Package: IntramiRExploreR
Version: 1.29.0
Depends: R (>= 3.4)
Imports: igraph (>= 1.0.1), FGNet (>= 3.0.7), knitr (>= 1.12.3), stats,
utils, grDevices, graphics
Suggests: gProfileR, topGO, org.Dm.eg.db, rmarkdown, testthat
License: GPL-2
MD5sum: fac728779bc94f8f14de8e647d0a6ca9
NeedsCompilation: no
Title: Predicting Targets for Drosophila Intragenic miRNAs
Description: Intra-miR-ExploreR, an integrative miRNA target prediction
bioinformatics tool, identifies targets combining expression
and biophysical interactions of a given microRNA (miR). Using
the tool, we have identified targets for 92 intragenic miRs in
D. melanogaster, using available microarray expression data,
from Affymetrix 1 and Affymetrix2 microarray array platforms,
providing a global perspective of intragenic miR targets in
Drosophila. Predicted targets are grouped according to
biological functions using the DAVID Gene Ontology tool and are
ranked based on a biologically relevant scoring system,
enabling the user to identify functionally relevant targets for
a given miR.
biocViews: Software, Microarray, GeneTarget, StatisticalMethod,
GeneExpression, GenePrediction
Author: Surajit Bhattacharya and Daniel Cox
Maintainer: Surajit Bhattacharya <sbhattach2@childrensnational.org>
URL: https://github.com/VilainLab/IntramiRExploreR
VignetteBuilder: knitr
BugReports: https://github.com/VilainLab/IntramiRExploreR
git_url: https://git.bioconductor.org/packages/IntramiRExploreR
git_branch: devel
git_last_commit: a02de99
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/IntramiRExploreR_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/IntramiRExploreR_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/IntramiRExploreR_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/IntramiRExploreR_1.29.0.tgz
vignettes: vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR.pdf,
vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR_vignettes.html
vignetteTitles: IntramiRExploreR.pdf, IntramiRExploreR
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR_vignettes.R
dependencyCount: 37
Package: IONiseR
Version: 2.31.0
Depends: R (>= 3.4)
Imports: rhdf5, dplyr, magrittr, tidyr, ShortRead, Biostrings, ggplot2,
methods, BiocGenerics, XVector, tibble, stats, BiocParallel,
bit64, stringr, utils
Suggests: BiocStyle, knitr, rmarkdown, gridExtra, testthat,
minionSummaryData
License: MIT + file LICENSE
MD5sum: c30f2f6420db22002e52c8f8693f4c47
NeedsCompilation: no
Title: Quality Assessment Tools for Oxford Nanopore MinION data
Description: IONiseR provides tools for the quality assessment of
Oxford Nanopore MinION data. It extracts summary statistics
from a set of fast5 files and can be used either before or
after base calling. In addition to standard summaries of the
read-types produced, it provides a number of plots for
visualising metrics relative to experiment run time or
spatially over the surface of a flowcell.
biocViews: QualityControl, DataImport, Sequencing
Author: Mike Smith [aut, cre]
Maintainer: Mike Smith <grimbough@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/IONiseR
git_branch: devel
git_last_commit: 5d8cf1e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/IONiseR_2.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/IONiseR_2.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/IONiseR_2.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/IONiseR_2.31.0.tgz
vignettes: vignettes/IONiseR/inst/doc/IONiseR.html
vignetteTitles: Quality assessment tools for nanopore data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/IONiseR/inst/doc/IONiseR.R
dependencyCount: 98
Package: iPath
Version: 1.13.0
Depends: R (>= 4.1), mclust, BiocParallel, survival
Imports: Rcpp (>= 1.0.5), matrixStats, ggpubr, ggplot2, survminer,
stats
LinkingTo: Rcpp, RcppArmadillo
Suggests: rmarkdown, BiocStyle, knitr
License: GPL-2
MD5sum: f48067f9f3bb44ccbf7156c60d61d205
NeedsCompilation: yes
Title: iPath pipeline for detecting perturbed pathways at individual
level
Description: iPath is the Bioconductor package used for calculating
personalized pathway score and test the association with
survival outcomes. Abundant single-gene biomarkers have been
identified and used in the clinics. However, hundreds of
oncogenes or tumor-suppressor genes are involved during the
process of tumorigenesis. We believe individual-level
expression patterns of pre-defined pathways or gene sets are
better biomarkers than single genes. In this study, we devised
a computational method named iPath to identify prognostic
biomarker pathways, one sample at a time. To test its utility,
we conducted a pan-cancer analysis across 14 cancer types from
The Cancer Genome Atlas and demonstrated that iPath is capable
of identifying highly predictive biomarkers for clinical
outcomes, including overall survival, tumor subtypes, and tumor
stage classifications. We found that pathway-based biomarkers
are more robust and effective than single genes.
biocViews: Pathways, Software, GeneExpression, Survival
Author: Kenong Su [aut, cre], Zhaohui Qin [aut]
Maintainer: Kenong Su <kenong.su@emory.edu>
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/suke18/iPath/issues
git_url: https://git.bioconductor.org/packages/iPath
git_branch: devel
git_last_commit: 8c1cca1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/iPath_1.13.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iPath_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iPath_1.13.0.tgz
vignettes: vignettes/iPath/inst/doc/iPath.html
vignetteTitles: The iPath User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iPath/inst/doc/iPath.R
dependencyCount: 115
Package: ipdDb
Version: 1.25.0
Depends: R (>= 3.5.0), methods, AnnotationDbi (>= 1.43.1),
AnnotationHub
Imports: Biostrings, GenomicRanges, RSQLite, DBI, IRanges, stats,
assertthat
Suggests: knitr, rmarkdown, testthat
License: Artistic-2.0
MD5sum: 946179ec16dd2e6f25e63a45b078c3c9
NeedsCompilation: no
Title: IPD IMGT/HLA and IPD KIR database for Homo sapiens
Description: All alleles from the IPD IMGT/HLA
<https://www.ebi.ac.uk/ipd/imgt/hla/> and IPD KIR
<https://www.ebi.ac.uk/ipd/kir/> database for Homo sapiens.
Reference: Robinson J, Maccari G, Marsh SGE, Walter L, Blokhuis
J, Bimber B, Parham P, De Groot NG, Bontrop RE, Guethlein LA,
and Hammond JA KIR Nomenclature in non-human species
Immunogenetics (2018), in preparation.
biocViews: GenomicVariation, SequenceMatching, VariantAnnotation,
DataRepresentation,AnnotationHubSoftware
Author: Steffen Klasberg
Maintainer: Steffen Klasberg <klasberg@dkms-lab.de>
URL: https://github.com/DKMS-LSL/ipdDb
organism: Homo sapiens
VignetteBuilder: knitr
BugReports: https://github.com/DKMS-LSL/ipdDb/issues/new
git_url: https://git.bioconductor.org/packages/ipdDb
git_branch: devel
git_last_commit: db4516e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ipdDb_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ipdDb_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ipdDb_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ipdDb_1.25.0.tgz
vignettes: vignettes/ipdDb/inst/doc/Readme.html
vignetteTitles: ipdDb
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ipdDb/inst/doc/Readme.R
dependencyCount: 67
Package: IPO
Version: 1.33.0
Depends: xcms (>= 1.50.0), rsm, CAMERA, grDevices, graphics, stats,
utils
Imports: BiocParallel
Suggests: RUnit, BiocGenerics, msdata, mtbls2, faahKO, knitr
Enhances: parallel
License: GPL (>= 2) + file LICENSE
MD5sum: c0744f28c11a64976a9d8e3823430abb
NeedsCompilation: no
Title: Automated Optimization of XCMS Data Processing parameters
Description: The outcome of XCMS data processing strongly depends on
the parameter settings. IPO (`Isotopologue Parameter
Optimization`) is a parameter optimization tool that is
applicable for different kinds of samples and liquid
chromatography coupled to high resolution mass spectrometry
devices, fast and free of labeling steps. IPO uses natural,
stable 13C isotopes to calculate a peak picking score.
Retention time correction is optimized by minimizing the
relative retention time differences within features and
grouping parameters are optimized by maximizing the number of
features showing exactly one peak from each injection of a
pooled sample. The different parameter settings are achieved by
design of experiment. The resulting scores are evaluated using
response surface models.
biocViews: ImmunoOncology, Metabolomics, MassSpectrometry
Author: Gunnar Libiseller <Gunnar.Libiseller@joanneum.at>, Christoph
Magnes <christoph.magnes@joanneum.at>, Thomas Lieb
<thomas.r.lieb@gmail.com>
Maintainer: Thomas Lieb <thomas.r.lieb@gmail.com>
URL: https://github.com/rietho/IPO
VignetteBuilder: knitr
BugReports: https://github.com/rietho/IPO/issues/new
git_url: https://git.bioconductor.org/packages/IPO
git_branch: devel
git_last_commit: 6da5785
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/IPO_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/IPO_1.33.0.zip
vignettes: vignettes/IPO/inst/doc/IPO.html
vignetteTitles: XCMS Parameter Optimization with IPO
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/IPO/inst/doc/IPO.R
dependencyCount: 162
Package: IRanges
Version: 2.41.3
Depends: R (>= 4.0.0), methods, utils, stats, BiocGenerics (>= 0.53.2),
S4Vectors (>= 0.45.4)
Imports: stats4
LinkingTo: S4Vectors
Suggests: XVector, GenomicRanges, Rsamtools, GenomicAlignments,
GenomicFeatures, BSgenome.Celegans.UCSC.ce2, pasillaBamSubset,
RUnit, BiocStyle
License: Artistic-2.0
MD5sum: eebb835fdb62ad48fb54995dca93a177
NeedsCompilation: yes
Title: Foundation of integer range manipulation in Bioconductor
Description: Provides efficient low-level and highly reusable S4
classes for storing, manipulating and aggregating over
annotated ranges of integers. Implements an algebra of range
operations, including efficient algorithms for finding overlaps
and nearest neighbors. Defines efficient list-like classes for
storing, transforming and aggregating large grouped data, i.e.,
collections of atomic vectors and DataFrames.
biocViews: Infrastructure, DataRepresentation
Author: Hervé Pagès [aut, cre], Patrick Aboyoun [aut], Michael Lawrence
[aut]
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/IRanges
BugReports: https://github.com/Bioconductor/IRanges/issues
git_url: https://git.bioconductor.org/packages/IRanges
git_branch: devel
git_last_commit: d7cd5d4
git_last_commit_date: 2025-02-11
Date/Publication: 2025-02-12
source.ver: src/contrib/IRanges_2.41.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/IRanges_2.41.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/IRanges_2.41.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/IRanges_2.41.3.tgz
vignettes: vignettes/IRanges/inst/doc/IRangesOverview.pdf
vignetteTitles: An Overview of the IRanges package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/IRanges/inst/doc/IRangesOverview.R
dependsOnMe: AnnotationDbi, AnnotationHubData, BaalChIP, bambu,
biomvRCNS, Biostrings, BiSeq, BSgenome, BSgenomeForge,
BubbleTree, bumphunter, CAFE, casper, CexoR, chimeraviz,
ChIPpeakAnno, chipseq, CODEX, consensusSeekeR, CSAR, CSSQ,
customProDB, deepSNV, DelayedArray, DESeq2, DEXSeq,
DirichletMultinomial, DMCFB, DMCHMM, DMRcaller, epigenomix,
ExCluster, fCCAC, GenomeInfoDb, GenomicAlignments,
GenomicDistributions, GenomicFeatures, GenomicRanges, gtrellis,
Gviz, HelloRanges, HERON, HiTC, IdeoViz, InTAD, MotifDb,
MultimodalExperiment, NADfinder, oncoscanR, ORFik, OTUbase,
pepStat, periodicDNA, plyranges, proBAMr, pwalign, RepViz,
rGADEM, rGREAT, RJMCMCNucleosomes, RNAmodR, S4Arrays, Scale4C,
SCOPE, seqArchRplus, SGSeq, SICtools, Structstrings, TEQC,
traseR, triplex, VariantTools, VplotR, XVector, pd.ag,
pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501,
pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine,
pd.bsubtilis, pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine,
pd.canine.2, pd.celegans, pd.chicken, pd.chigene.1.0.st,
pd.chigene.1.1.st, pd.chogene.2.0.st, pd.chogene.2.1.st,
pd.citrus, pd.clariom.d.human, pd.clariom.s.human,
pd.clariom.s.human.ht, pd.clariom.s.mouse,
pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht,
pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st,
pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array,
pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1,
pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2,
pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st,
pd.equgene.1.1.st, pd.felgene.1.0.st, pd.felgene.1.1.st,
pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5,
pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st,
pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a,
pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219,
pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d,
pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm,
pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800,
pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1,
pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize,
pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240,
pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st,
pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a,
pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c,
pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0,
pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1,
pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st,
pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0,
pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180,
pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st,
pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine,
pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st,
pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1,
pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st,
pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st,
pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c,
pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice,
pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0,
pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean,
pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato,
pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2,
pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98,
pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, harbChIP,
LiebermanAidenHiC2009
importsMe: alabaster.bumpy, alabaster.ranges, alabaster.se, ALDEx2,
AllelicImbalance, amplican, AneuFinder, annmap, annotatr,
appreci8R, ASpli, AssessORF, ATACseqQC, ATACseqTFEA, atena,
ballgown, bamsignals, BBCAnalyzer, beadarray,
BindingSiteFinder, biovizBase, biscuiteer, BiSeq, bnbc,
BPRMeth, branchpointer, breakpointR, bsseq, BUMHMM,
BumpyMatrix, BUSpaRse, CAGEfightR, cageminer, CAGEr,
cBioPortalData, cfdnakit, cfDNAPro, ChIPanalyser, chipenrich,
ChIPexoQual, ChIPQC, ChIPseeker, chipseq, ChIPseqR, ChIPsim,
ChromHeatMap, ChromSCape, chromVAR, cicero, CINdex,
circRNAprofiler, CircSeqAlignTk, cleanUpdTSeq, cleaver,
cn.mops, CNEr, CNVfilteR, CNVMetrics, CNVPanelizer, CNVRanger,
CNVrd2, COCOA, comapr, coMethDMR, compEpiTools, ComplexHeatmap,
CompoundDb, conumee, CopyNumberPlots, CoverageView, crisprBase,
crisprBowtie, crisprDesign, crisprScore, CRISPRseek,
CrispRVariants, crisprViz, csaw, CTexploreR, dada2, DAMEfinder,
debrowser, DECIPHER, deconvR, DegCre, DegNorm,
DelayedMatrixStats, deltaCaptureC, demuxSNP, derfinder,
derfinderHelper, derfinderPlot, DEScan2, DiffBind, diffHic,
diffUTR, DMRcate, DMRScan, dmrseq, DNAfusion, DominoEffect,
dreamlet, DRIMSeq, DropletUtils, dStruct, easyRNASeq, EDASeq,
eisaR, ELMER, ELViS, enhancerHomologSearch, EnrichedHeatmap,
ensembldb, EpiCompare, epidecodeR, epigraHMM, EpiMix,
epimutacions, epiregulon, epistack, EpiTxDb, epivizr,
epivizrData, erma, esATAC, EventPointer, extraChIPs, factR,
FastqCleaner, fastseg, fcScan, FilterFFPE, FindIT2, fishpond,
FLAMES, FRASER, G4SNVHunter, GA4GHclient, gcapc, gDNAx,
geneAttribution, GeneGeneInteR, GENESIS, genomation,
GenomAutomorphism, genomeIntervals, GenomicAlignments,
GenomicDataCommons, GenomicFiles, GenomicInteractionNodes,
GenomicInteractions, GenomicOZone, GenomicPlot, GenomicScores,
GenomicTuples, GenVisR, geomeTriD, ggbio, girafe, gmapR,
gmoviz, GOfuncR, GOpro, GOTHiC, GSVA, GUIDEseq, gwascat,
h5mread, h5vc, HDF5Array, heatmaps, hermes, HicAggR, HiCBricks,
HiCcompare, HiCExperiment, HiContacts, HiCool, hicVennDiagram,
HilbertCurve, hummingbird, icetea, ideal, idr2d, IMAS, InPAS,
INSPEcT, intansv, InteractionSet, InteractiveComplexHeatmap,
IntEREst, ipdDb, iSEEu, IsoformSwitchAnalyzeR, isomiRs, IVAS,
karyoploteR, katdetectr, knowYourCG, LinTInd, LOLA, m6Aboost,
MADSEQ, magpie, mariner, maser, MatrixRider, mCSEA, MDTS, MEAL,
MEDIPS, MesKit, metagene2, metaseqR2, methimpute,
methInheritSim, methodical, MethReg, methrix, methylCC,
methylInheritance, methylKit, methylPipe, MethylSeekR,
methylSig, methylumi, mia, minfi, MinimumDistance, MIRA,
missMethyl, mobileRNA, Modstrings, monaLisa, mosaics, MOSim,
Motif2Site, motifbreakR, motifmatchr, MotifPeeker, motifTestR,
MouseFM, msa, MSA2dist, MsBackendMassbank, MsBackendMgf,
MsBackendMsp, MsBackendRawFileReader, MsBackendSql,
MsExperiment, msgbsR, MSnbase, MultiAssayExperiment,
MultiDataSet, mumosa, MungeSumstats, musicatk,
MutationalPatterns, NanoMethViz, NanoStringNCTools, ncRNAtools,
normr, nucleoSim, nucleR, nullranges, OGRE, oligoClasses,
OmaDB, OMICsPCA, openPrimeR, Organism.dplyr, OrganismDbi,
OUTRIDER, OutSplice, packFinder, panelcn.mops, pcaExplorer,
pdInfoBuilder, PhIPData, PICB, PICS, PING, plotgardener,
plyinteractions, podkat, pqsfinder, pram, prebs, preciseTAD,
primirTSS, proActiv, profileplyr, ProteoDisco, PureCN, Pviz,
QDNAseq, QFeatures, qpgraph, qPLEXanalyzer, qsea, QuasR,
R3CPET, r3Cseq, R453Plus1Toolbox, raer, RaggedExperiment,
RAIDS, ramr, RareVariantVis, RCAS, recount, recoup, REDseq,
regioneR, regutools, REMP, Repitools, ReportingTools, RESOLVE,
rfaRm, rfPred, RgnTX, RiboCrypt, RiboDiPA, RiboProfiling,
riboSeqR, ribosomeProfilingQC, rigvf, rnaEditr,
RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq,
RnBeads, roar, rprimer, Rqc, Rsamtools, RSVSim, RTN,
rtracklayer, SARC, sarks, saseR, SCAN.UPC, scanMiR, scanMiRApp,
scDblFinder, scHOT, scPipe, scRNAseqApp, segmenter, segmentSeq,
SeqArray, seqCAT, seqPattern, seqsetvis, SeqSQC, SeqVarTools,
sesame, sevenC, ShortRead, signeR, signifinder, SimFFPE,
SingleMoleculeFootprinting, sitadela, SMITE, snapcount,
SNPhood, soGGi, SomaticSignatures, SOMNiBUS, SparseArray,
SparseSignatures, spatzie, Spectra, spiky, SpliceWiz,
SplicingGraphs, SPLINTER, srnadiff, strandCheckR,
StructuralVariantAnnotation, SummarizedExperiment, SynExtend,
tadar, TAPseq, target, TCGAbiolinks, TCGAutils, TCseq, TENET,
TFBSTools, TFEA.ChIP, TFHAZ, tidyCoverage, TitanCNA, TnT,
tracktables, trackViewer, transcriptR, transmogR,
TreeSummarizedExperiment, TRESS, tricycle, tRNA, tRNAdbImport,
tRNAscanImport, TVTB, txcutr, txdbmaker, tximeta, UMI4Cats,
Uniquorn, universalmotif, UPDhmm, VanillaICE, VarCon,
VariantAnnotation, VariantExperiment, VariantFiltering, VaSP,
VDJdive, wavClusteR, wiggleplotr, xcms, xcore, XNAString,
XVector, yamss, ZygosityPredictor, fitCons.UCSC.hg19,
GenomicState, MafDb.1Kgenomes.phase1.GRCh38,
MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38,
MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38,
MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38,
MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38,
MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38,
MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19,
MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v4.0.GRCh38,
pd.081229.hg18.promoter.medip.hx1,
pd.2006.07.18.hg18.refseq.promoter,
pd.2006.07.18.mm8.refseq.promoter,
pd.2006.10.31.rn34.refseq.promoter, pd.charm.hg18.example,
pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.mirna.3.1,
phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38,
phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP144.GRCh37,
SNPlocs.Hsapiens.dbSNP144.GRCh38,
SNPlocs.Hsapiens.dbSNP149.GRCh38,
SNPlocs.Hsapiens.dbSNP150.GRCh38,
SNPlocs.Hsapiens.dbSNP155.GRCh37,
SNPlocs.Hsapiens.dbSNP155.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh37,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, chipenrich.data,
fourDNData, leeBamViews, MethylSeqData, pd.atdschip.tiling,
sesameData, SomaticCancerAlterations, spatialLIBD, alakazam,
cpp11bigwig, crispRdesignR, cubar, geneHapR, geno2proteo,
GenoPop, hahmmr, hoardeR, ICAMS, iimi, karyotapR, locuszoomr,
lolliplot, longreadvqs, LoopRig, MAAPER, MitoHEAR, noisyr,
numbat, oncoPredict, PACVr, RapidoPGS, refseqR, revert,
rnaCrosslinkOO, RTIGER, Signac, simMP, tidygenomics, VALERIE
suggestsMe: annotate, AnnotationHub, BaseSpaceR, BiocGenerics,
BREW3R.r, Chicago, ClassifyR, DFplyr, easylift, epivizrChart,
gDRcore, gDRutils, Glimma, GWASTools, HilbertVis,
HilbertVisGUI, maftools, martini, MiRaGE, multicrispr, partCNV,
plyxp, regionalpcs, regionReport, RTCGA, S4Vectors, SigsPack,
splatter, svaNUMT, svaRetro, systemPipeR, TFutils, tidybulk,
MetaScope, scMultiome, systemPipeRdata, xcoredata, yeastRNASeq,
fuzzyjoin, gkmSVM, MARVEL, MiscMetabar, polyRAD, pQTLdata,
rliger, scPloidy, seqmagick, Seurat, sigminer, SNPassoc, updog,
valr
linksToMe: Biostrings, CNEr, DECIPHER, GenomicAlignments,
GenomicRanges, kebabs, MatrixRider, pwalign, Rsamtools,
rtracklayer, ShortRead, SparseArray, Structstrings, triplex,
VariantAnnotation, VariantFiltering, XVector
dependencyCount: 8
Package: ISAnalytics
Version: 1.17.1
Depends: R (>= 4.5)
Imports: utils, dplyr, readr, tidyr, purrr, rlang, tibble, stringr, fs,
lubridate, lifecycle, ggplot2, ggrepel, stats, readxl, tools,
grDevices, forcats, glue, shiny, shinyWidgets, datamods, bslib,
DT
Suggests: testthat, covr, knitr, BiocStyle, sessioninfo, rmarkdown,
roxygen2, vegan, withr, extraDistr, ggalluvial, scales,
gridExtra, R.utils, RefManageR, flexdashboard, circlize,
plotly, gtools, eulerr, openxlsx, jsonlite, pheatmap,
BiocParallel, progressr, future, doFuture, foreach, psych,
data.table, Rcapture
License: CC BY 4.0
MD5sum: f2ea0fe3781775d78651e5dfa321a745
NeedsCompilation: no
Title: Analyze gene therapy vector insertion sites data identified from
genomics next generation sequencing reads for clonal tracking
studies
Description: In gene therapy, stem cells are modified using viral
vectors to deliver the therapeutic transgene and replace
functional properties since the genetic modification is stable
and inherited in all cell progeny. The retrieval and mapping of
the sequences flanking the virus-host DNA junctions allows the
identification of insertion sites (IS), essential for
monitoring the evolution of genetically modified cells in vivo.
A comprehensive toolkit for the analysis of IS is required to
foster clonal trackign studies and supporting the assessment of
safety and long term efficacy in vivo. This package is aimed at
(1) supporting automation of IS workflow, (2) performing base
and advance analysis for IS tracking (clonal abundance, clonal
expansions and statistics for insertional mutagenesis, etc.),
(3) providing basic biology insights of transduced stem cells
in vivo.
biocViews: BiomedicalInformatics, Sequencing, SingleCell
Author: Francesco Gazzo [cre], Giulia Pais [aut] (ORCID:
<https://orcid.org/0009-0005-5621-4803>), Andrea Calabria
[aut], Giulio Spinozzi [aut]
Maintainer: Francesco Gazzo <gazzo.francesco@hsr.it>
URL: https://calabrialab.github.io/ISAnalytics,
https://github.com//calabrialab/isanalytics,
https://calabrialab.github.io/ISAnalytics/
VignetteBuilder: knitr
BugReports: https://github.com/calabrialab/ISAnalytics/issues
git_url: https://git.bioconductor.org/packages/ISAnalytics
git_branch: devel
git_last_commit: 3b0f63a
git_last_commit_date: 2024-12-05
Date/Publication: 2024-12-05
source.ver: src/contrib/ISAnalytics_1.17.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ISAnalytics_1.17.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ISAnalytics_1.17.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ISAnalytics_1.17.1.tgz
vignettes: vignettes/ISAnalytics/inst/doc/ISAnalytics.html,
vignettes/ISAnalytics/inst/doc/workflow_start.html
vignetteTitles: ISAnalytics, workflow_start
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ISAnalytics/inst/doc/ISAnalytics.R,
vignettes/ISAnalytics/inst/doc/workflow_start.R
dependencyCount: 115
Package: iSEE
Version: 2.19.3
Depends: SummarizedExperiment, SingleCellExperiment
Imports: methods, BiocGenerics, S4Vectors, utils, stats, shiny,
shinydashboard, shinyAce, shinyjs, DT, rintrojs, ggplot2,
ggrepel, colourpicker, igraph, vipor, mgcv, graphics,
grDevices, viridisLite, shinyWidgets, listviewer,
ComplexHeatmap, circlize, grid
Suggests: testthat, covr, BiocStyle, knitr, rmarkdown, scRNAseq,
TENxPBMCData, scater, DelayedArray, HDF5Array, RColorBrewer,
viridis, htmltools, GenomicRanges
License: MIT + file LICENSE
MD5sum: 5edec6a25593f60e4c35293af91559f3
NeedsCompilation: no
Title: Interactive SummarizedExperiment Explorer
Description: Create an interactive Shiny-based graphical user interface
for exploring data stored in SummarizedExperiment objects,
including row- and column-level metadata. The interface
supports transmission of selections between plots and tables,
code tracking, interactive tours, interactive or programmatic
initialization, preservation of app state, and extensibility to
new panel types via S4 classes. Special attention is given to
single-cell data in a SingleCellExperiment object with
visualization of dimensionality reduction results.
biocViews: CellBasedAssays, Clustering, DimensionReduction,
FeatureExtraction, GeneExpression, GUI, ImmunoOncology,
ShinyApps, SingleCell, Transcription, Transcriptomics,
Visualization
Author: Kevin Rue-Albrecht [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3899-3872>), Federico Marini [aut]
(ORCID: <https://orcid.org/0000-0003-3252-7758>), Charlotte
Soneson [aut] (ORCID: <https://orcid.org/0000-0003-3833-2169>),
Aaron Lun [aut] (ORCID:
<https://orcid.org/0000-0002-3564-4813>)
Maintainer: Kevin Rue-Albrecht <kevinrue67@gmail.com>
URL: https://isee.github.io/iSEE/
VignetteBuilder: knitr
BugReports: https://github.com/iSEE/iSEE/issues
git_url: https://git.bioconductor.org/packages/iSEE
git_branch: devel
git_last_commit: 55c1d18
git_last_commit_date: 2025-03-06
Date/Publication: 2025-03-06
source.ver: src/contrib/iSEE_2.19.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iSEE_2.19.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iSEE_2.19.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iSEE_2.19.3.tgz
vignettes: vignettes/iSEE/inst/doc/basic.html,
vignettes/iSEE/inst/doc/bigdata.html,
vignettes/iSEE/inst/doc/configure.html,
vignettes/iSEE/inst/doc/custom.html,
vignettes/iSEE/inst/doc/ecm.html,
vignettes/iSEE/inst/doc/links.html,
vignettes/iSEE/inst/doc/voice.html
vignetteTitles: 1. The iSEE User's Guide, 6. Using iSEE with big data,
3. Configuring iSEE apps, 5. Deploying custom panels, 4. The
ExperimentColorMap Class, 2. Sharing information across panels,
7. Speech recognition
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/iSEE/inst/doc/basic.R,
vignettes/iSEE/inst/doc/bigdata.R,
vignettes/iSEE/inst/doc/configure.R,
vignettes/iSEE/inst/doc/custom.R,
vignettes/iSEE/inst/doc/ecm.R, vignettes/iSEE/inst/doc/links.R,
vignettes/iSEE/inst/doc/voice.R
dependsOnMe: iSEEde, iSEEhex, iSEEpathways, iSEEtree, iSEEu
importsMe: iSEEfier, iSEEhub, iSEEindex
suggestsMe: schex, DuoClustering2018, HCAData, HCATonsilData,
TabulaMurisData, TabulaMurisSenisData
dependencyCount: 120
Package: iSEEde
Version: 1.5.0
Depends: iSEE
Imports: DESeq2, edgeR, methods, S4Vectors, shiny, SummarizedExperiment
Suggests: airway, BiocStyle, covr, knitr, limma, org.Hs.eg.db,
RefManageR, rmarkdown, scuttle, sessioninfo, statmod, testthat
(>= 3.0.0)
License: Artistic-2.0
MD5sum: 66cee6294aaafe1527e971b0c9232239
NeedsCompilation: no
Title: iSEE extension for panels related to differential expression
analysis
Description: This package contains diverse functionality to extend the
usage of the iSEE package, including additional classes for the
panels or modes facilitating the analysis of differential
expression results. This package does not perform differential
expression. Instead, it provides methods to embed precomputed
differential expression results in a SummarizedExperiment
object, in a manner that is compatible with interactive
visualisation in iSEE applications.
biocViews: Software, Infrastructure, DifferentialExpression
Author: Kevin Rue-Albrecht [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3899-3872>), Thomas Sandmann [ctb]
(ORCID: <https://orcid.org/0000-0002-6601-8890>), Denali
Therapeutics [fnd]
Maintainer: Kevin Rue-Albrecht <kevinrue67@gmail.com>
URL: https://github.com/iSEE/iSEEde
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/iSEEde
git_url: https://git.bioconductor.org/packages/iSEEde
git_branch: devel
git_last_commit: 3899f44
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/iSEEde_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iSEEde_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iSEEde_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iSEEde_1.5.0.tgz
vignettes: vignettes/iSEEde/inst/doc/annotations.html,
vignettes/iSEEde/inst/doc/iSEEde.html,
vignettes/iSEEde/inst/doc/methods.html,
vignettes/iSEEde/inst/doc/rounding.html
vignetteTitles: Using annotations to facilitate interactive
exploration, Introduction to iSEEde, Supported differential
expression methods, Rounding numeric values
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iSEEde/inst/doc/annotations.R,
vignettes/iSEEde/inst/doc/iSEEde.R,
vignettes/iSEEde/inst/doc/methods.R,
vignettes/iSEEde/inst/doc/rounding.R
suggestsMe: iSEEpathways
dependencyCount: 134
Package: iSEEfier
Version: 1.3.0
Imports: iSEE, iSEEu, methods, ggplot2, igraph, rlang, stats,
SummarizedExperiment, SingleCellExperiment, visNetwork,
BiocBaseUtils
Suggests: knitr, rmarkdown, scater, scRNAseq, org.Mm.eg.db, scuttle,
BiocStyle, testthat (>= 3.0.0)
License: MIT + file LICENSE
Archs: x64
MD5sum: 55dbead1277f722f21985e5a08459c95
NeedsCompilation: no
Title: Streamlining the creation of initial states for starting an iSEE
instance
Description: iSEEfier provides a set of functionality to quickly and
intuitively create, inspect, and combine initial configuration
objects. These can be conveniently passed in a straightforward
manner to the function call to launch iSEE() with the specified
configuration. This package currently works seamlessly with the
sets of panels provided by the iSEE and iSEEu packages, but can
be extended to accommodate the usage of any custom panel (e.g.
from iSEEde, iSEEpathways, or any panel developed independently
by the user).
biocViews: CellBasedAssays, Clustering, DimensionReduction,
FeatureExtraction, GUI, GeneExpression, ImmunoOncology,
ShinyApps, SingleCell, Software, Transcription,
Transcriptomics, Visualization
Author: Najla Abassi [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8357-0938>), Federico Marini [aut]
(ORCID: <https://orcid.org/0000-0003-3252-7758>)
Maintainer: Najla Abassi <abassi.nejla96@gmail.com>
URL: https://github.com/NajlaAbassi/iSEEfier
VignetteBuilder: knitr
BugReports: https://github.com/NajlaAbassi/iSEEfier/issues
git_url: https://git.bioconductor.org/packages/iSEEfier
git_branch: devel
git_last_commit: 94647b3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/iSEEfier_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iSEEfier_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iSEEfier_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iSEEfier_1.3.0.tgz
vignettes: vignettes/iSEEfier/inst/doc/iSEEfier_userguide.html
vignetteTitles: iSEEfier_userguide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/iSEEfier/inst/doc/iSEEfier_userguide.R
dependencyCount: 126
Package: iSEEhex
Version: 1.9.0
Depends: SummarizedExperiment, iSEE
Imports: ggplot2, hexbin, methods, shiny
Suggests: BiocStyle, covr, knitr, RefManageR, rmarkdown, sessioninfo,
testthat (>= 3.0.0), scRNAseq, scater
License: Artistic-2.0
MD5sum: 4b3c07bbe65dd5c8e15f35fb1731b025
NeedsCompilation: no
Title: iSEE extension for summarising data points in hexagonal bins
Description: This package provides panels summarising data points in
hexagonal bins for `iSEE`. It is part of `iSEEu`, the iSEE
universe of panels that extend the `iSEE` package.
biocViews: Software, Infrastructure
Author: Kevin Rue-Albrecht [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3899-3872>), Charlotte Soneson
[aut] (ORCID: <https://orcid.org/0000-0003-3833-2169>),
Federico Marini [aut] (ORCID:
<https://orcid.org/0000-0003-3252-7758>), Aaron Lun [aut]
(ORCID: <https://orcid.org/0000-0002-3564-4813>)
Maintainer: Kevin Rue-Albrecht <kevinrue67@gmail.com>
URL: https://github.com/iSEE/iSEEhex
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/iSEEhex
git_url: https://git.bioconductor.org/packages/iSEEhex
git_branch: devel
git_last_commit: 7dd9c2b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/iSEEhex_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iSEEhex_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iSEEhex_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iSEEhex_1.9.0.tgz
vignettes: vignettes/iSEEhex/inst/doc/iSEEhex.html
vignetteTitles: The iSEEhex package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iSEEhex/inst/doc/iSEEhex.R
dependsOnMe: iSEEu
dependencyCount: 122
Package: iSEEhub
Version: 1.9.0
Depends: SummarizedExperiment, SingleCellExperiment, ExperimentHub
Imports: AnnotationHub, BiocManager, DT, iSEE, methods, rintrojs,
S4Vectors, shiny, shinydashboard, shinyjs, utils
Suggests: BiocStyle, covr, knitr, RefManageR, rmarkdown, sessioninfo,
testthat (>= 3.0.0), nullrangesData
Enhances: BioPlex, biscuiteerData, bodymapRat, CLLmethylation,
CopyNeutralIMA, curatedAdipoArray, curatedAdipoChIP,
curatedMetagenomicData, curatedTCGAData, DMRcatedata,
DuoClustering2018, easierData, emtdata, epimutacionsData,
FieldEffectCrc, GenomicDistributionsData, GSE103322, GSE13015,
GSE62944, HDCytoData, HMP16SData, HumanAffyData, imcdatasets,
mcsurvdata, MetaGxBreast, MetaGxOvarian, MetaGxPancreas,
MethylSeqData, muscData, NxtIRFdata, ObMiTi, quantiseqr,
restfulSEData, RLHub, sesameData, SimBenchData,
SingleCellMultiModal, SingleMoleculeFootprintingData,
spatialDmelxsim, STexampleData, TabulaMurisData,
TabulaMurisSenisData, TENxVisiumData, tissueTreg,
VectraPolarisData, xcoredata
License: Artistic-2.0
MD5sum: c461ac4a2973209e404ef88a386011a3
NeedsCompilation: no
Title: iSEE for the Bioconductor ExperimentHub
Description: This package defines a custom landing page for an iSEE app
interfacing with the Bioconductor ExperimentHub. The landing
page allows users to browse the ExperimentHub, select a data
set, download and cache it, and import it directly into a
Bioconductor iSEE app.
biocViews: DataImport, ImmunoOncology Infrastructure, ShinyApps,
SingleCell, Software
Author: Kevin Rue-Albrecht [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3899-3872>)
Maintainer: Kevin Rue-Albrecht <kevinrue67@gmail.com>
URL: https://github.com/iSEE/iSEEhub
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/iSEEhub
git_url: https://git.bioconductor.org/packages/iSEEhub
git_branch: devel
git_last_commit: bd09a3b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/iSEEhub_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iSEEhub_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iSEEhub_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iSEEhub_1.9.0.tgz
vignettes: vignettes/iSEEhub/inst/doc/contributing.html,
vignettes/iSEEhub/inst/doc/iSEEhub.html
vignetteTitles: Contributing to iSEEhub, Introduction to iSEEhub
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iSEEhub/inst/doc/contributing.R,
vignettes/iSEEhub/inst/doc/iSEEhub.R
dependencyCount: 143
Package: iSEEindex
Version: 1.5.0
Depends: SummarizedExperiment, SingleCellExperiment
Imports: BiocFileCache, DT, iSEE, methods, paws.storage, rintrojs,
shiny, shinydashboard, shinyjs, stringr, urltools, utils
Suggests: BiocStyle, covr, knitr, RefManageR, rmarkdown, markdown,
scRNAseq, sessioninfo, testthat (>= 3.0.0), yaml
License: Artistic-2.0
MD5sum: e70a6d368fc3018695a5187490867821
NeedsCompilation: no
Title: iSEE extension for a landing page to a custom collection of data
sets
Description: This package provides an interface to any collection of
data sets within a single iSEE web-application. The main
functionality of this package is to define a custom landing
page allowing app maintainers to list a custom collection of
data sets that users can selected from and directly load
objects into an iSEE web-application.
biocViews: Software, Infrastructure
Author: Kevin Rue-Albrecht [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3899-3872>), Thomas Sandmann [ctb]
(ORCID: <https://orcid.org/0000-0002-6601-8890>), Federico
Marini [aut] (ORCID: <https://orcid.org/0000-0003-3252-7758>),
Denali Therapeutics [fnd]
Maintainer: Kevin Rue-Albrecht <kevinrue67@gmail.com>
URL: https://github.com/iSEE/iSEEindex
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/iSEEindex
git_url: https://git.bioconductor.org/packages/iSEEindex
git_branch: devel
git_last_commit: 7211e8a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/iSEEindex_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iSEEindex_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iSEEindex_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iSEEindex_1.5.0.tgz
vignettes: vignettes/iSEEindex/inst/doc/header.html,
vignettes/iSEEindex/inst/doc/iSEEindex.html,
vignettes/iSEEindex/inst/doc/resources.html
vignetteTitles: Adding custom header and footer to the landing page,
Introduction to iSEEindex, Implementing custom iSEEindex
resources
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iSEEindex/inst/doc/header.R,
vignettes/iSEEindex/inst/doc/iSEEindex.R,
vignettes/iSEEindex/inst/doc/resources.R
dependencyCount: 142
Package: iSEEpathways
Version: 1.5.0
Depends: iSEE
Imports: ggplot2, methods, S4Vectors, shiny, shinyWidgets, stats,
SummarizedExperiment
Suggests: airway, BiocStyle, covr, edgeR, fgsea, GO.db, iSEEde, knitr,
org.Hs.eg.db, RefManageR, rmarkdown, scater, scuttle,
sessioninfo, testthat (>= 3.0.0)
License: Artistic-2.0
MD5sum: 018a57ab767784c0d002743f4d50cb3b
NeedsCompilation: no
Title: iSEE extension for panels related to pathway analysis
Description: This package contains diverse functionality to extend the
usage of the iSEE package, including additional classes for the
panels or modes facilitating the analysis of pathway analysis
results. This package does not perform pathway analysis.
Instead, it provides methods to embed precomputed pathway
analysis results in a SummarizedExperiment object, in a manner
that is compatible with interactive visualisation in iSEE
applications.
biocViews: Software, Infrastructure, DifferentialExpression,
GeneExpression, GUI, Visualization, Pathways,
GeneSetEnrichment, GO, ShinyApps
Author: Kevin Rue-Albrecht [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3899-3872>), Thomas Sandmann [ctb]
(ORCID: <https://orcid.org/0000-0002-6601-8890>), Charlotte
Soneson [aut] (ORCID: <https://orcid.org/0000-0003-3833-2169>),
Federico Marini [ctb] (ORCID:
<https://orcid.org/0000-0003-3252-7758>), Denali Therapeutics
[fnd]
Maintainer: Kevin Rue-Albrecht <kevinrue67@gmail.com>
URL: https://github.com/iSEE/iSEEpathways
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/iSEEpathways
git_url: https://git.bioconductor.org/packages/iSEEpathways
git_branch: devel
git_last_commit: d58f5a5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/iSEEpathways_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iSEEpathways_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iSEEpathways_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iSEEpathways_1.5.0.tgz
vignettes: vignettes/iSEEpathways/inst/doc/gene-ontology.html,
vignettes/iSEEpathways/inst/doc/integration.html,
vignettes/iSEEpathways/inst/doc/iSEEpathways.html
vignetteTitles: Working with the Gene Ontology, Integration with other
panels, Introduction to iSEEpathways
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iSEEpathways/inst/doc/gene-ontology.R,
vignettes/iSEEpathways/inst/doc/integration.R,
vignettes/iSEEpathways/inst/doc/iSEEpathways.R
dependencyCount: 121
Package: iSEEtree
Version: 1.1.4
Depends: R (>= 4.4.0), iSEE (>= 2.19.2)
Imports: ape, ggplot2, ggtree, grDevices, methods, miaViz, purrr,
S4Vectors, shiny, mia, shinyWidgets, SingleCellExperiment,
SummarizedExperiment, tidygraph, TreeSummarizedExperiment,
utils
Suggests: biomformat, BiocStyle, knitr, RefManageR, remotes, rmarkdown,
scater, testthat (>= 3.0.0), vegan
License: Artistic-2.0
MD5sum: 4adf0795afb723a5adbe9f1468d43f2c
NeedsCompilation: no
Title: Interactive visualisation for microbiome data
Description: iSEEtree is an extension of iSEE for the
TreeSummarizedExperiment data container. It provides
interactive panel designs to explore hierarchical datasets,
such as the microbiome and cell lines.
biocViews: Software, Visualization, Microbiome, GUI, ShinyApps,
DataImport
Author: Giulio Benedetti [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8732-7692>), Ely Seraidarian [ctb]
(ORCID: <https://orcid.org/0009-0008-8602-093X>), Leo Lahti
[aut] (ORCID: <https://orcid.org/0000-0001-5537-637X>)
Maintainer: Giulio Benedetti <giulio.benedetti@utu.fi>
URL: https://github.com/microbiome/iSEEtree
VignetteBuilder: knitr
BugReports: https://github.com/microbiome/iSEEtree/issues
git_url: https://git.bioconductor.org/packages/iSEEtree
git_branch: devel
git_last_commit: be032f4
git_last_commit_date: 2025-03-19
Date/Publication: 2025-03-19
source.ver: src/contrib/iSEEtree_1.1.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iSEEtree_1.1.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iSEEtree_1.1.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iSEEtree_1.1.4.tgz
vignettes: vignettes/iSEEtree/inst/doc/iSEEtree.html,
vignettes/iSEEtree/inst/doc/panels.html
vignetteTitles: iSEEtree, iSEEtree
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iSEEtree/inst/doc/iSEEtree.R,
vignettes/iSEEtree/inst/doc/panels.R
dependencyCount: 228
Package: iSEEu
Version: 1.19.0
Depends: iSEE, iSEEhex
Imports: methods, S4Vectors, IRanges, shiny, SummarizedExperiment,
SingleCellExperiment, ggplot2 (>= 3.4.0), DT, stats,
colourpicker, shinyAce
Suggests: scRNAseq, scater, scran, airway, edgeR, AnnotationDbi,
org.Hs.eg.db, GO.db, KEGGREST, knitr, igraph, rmarkdown,
BiocStyle, htmltools, Rtsne, uwot, testthat (>= 2.1.0), covr
License: MIT + file LICENSE
MD5sum: d25d71ac493e83b19077ba094cda55fc
NeedsCompilation: no
Title: iSEE Universe
Description: iSEEu (the iSEE universe) contains diverse functionality
to extend the usage of the iSEE package, including additional
classes for the panels, or modes allowing easy configuration of
iSEE applications.
biocViews: ImmunoOncology, Visualization, GUI, DimensionReduction,
FeatureExtraction, Clustering, Transcription, GeneExpression,
Transcriptomics, SingleCell, CellBasedAssays
Author: Kevin Rue-Albrecht [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3899-3872>), Charlotte Soneson
[aut] (ORCID: <https://orcid.org/0000-0003-3833-2169>),
Federico Marini [aut] (ORCID:
<https://orcid.org/0000-0003-3252-7758>), Aaron Lun [aut]
(ORCID: <https://orcid.org/0000-0002-3564-4813>), Michael
Stadler [ctb]
Maintainer: Kevin Rue-Albrecht <kevinrue67@gmail.com>
URL: https://github.com/iSEE/iSEEu
VignetteBuilder: knitr
BugReports: https://github.com/iSEE/iSEEu/issues
git_url: https://git.bioconductor.org/packages/iSEEu
git_branch: devel
git_last_commit: 4869642
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/iSEEu_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iSEEu_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iSEEu_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iSEEu_1.19.0.tgz
vignettes: vignettes/iSEEu/inst/doc/iSEEu.html
vignetteTitles: Panel universe
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/iSEEu/inst/doc/iSEEu.R
importsMe: iSEEfier
dependencyCount: 123
Package: iSeq
Version: 1.59.0
Depends: R (>= 2.10.0)
License: GPL (>= 2)
MD5sum: 6382a55c713215430267e85be3d118ed
NeedsCompilation: yes
Title: Bayesian Hierarchical Modeling of ChIP-seq Data Through Hidden
Ising Models
Description: Bayesian hidden Ising models are implemented to identify
IP-enriched genomic regions from ChIP-seq data. They can be
used to analyze ChIP-seq data with and without controls and
replicates.
biocViews: ChIPSeq, Sequencing
Author: Qianxing Mo
Maintainer: Qianxing Mo <qianxing.mo@moffitt.org>
git_url: https://git.bioconductor.org/packages/iSeq
git_branch: devel
git_last_commit: f08fc7e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/iSeq_1.59.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iSeq_1.59.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iSeq_1.59.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iSeq_1.59.0.tgz
vignettes: vignettes/iSeq/inst/doc/iSeq.pdf
vignetteTitles: iSeq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iSeq/inst/doc/iSeq.R
dependencyCount: 0
Package: ISLET
Version: 1.9.0
Depends: R(>= 4.2.0), Matrix, parallel, BiocParallel,
SummarizedExperiment, BiocGenerics, lme4, nnls
Imports: stats, methods, purrr, abind
Suggests: BiocStyle, knitr, rmarkdown, htmltools, RUnit, dplyr
License: GPL-2
MD5sum: 24951acce6e5ca7450f04378f8cdeea7
NeedsCompilation: no
Title: Individual-Specific ceLl typE referencing Tool
Description: ISLET is a method to conduct signal deconvolution for
general -omics data. It can estimate the individual-specific
and cell-type-specific reference panels, when there are
multiple samples observed from each subject. It takes the input
of the observed mixture data (feature by sample matrix), and
the cell type mixture proportions (sample by cell type matrix),
and the sample-to-subject information. It can solve for the
reference panel on the individual-basis and conduct test to
identify cell-type-specific differential expression (csDE)
genes. It also improves estimated cell type mixture proportions
by integrating personalized reference panels.
biocViews: Software, RNASeq, Transcriptomics, Transcription,
Sequencing, GeneExpression, DifferentialExpression,
DifferentialMethylation
Author: Hao Feng [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2243-9949>), Qian Li [aut],
Guanqun Meng [aut]
Maintainer: Hao Feng <hxf155@case.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ISLET
git_branch: devel
git_last_commit: 7137eb9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ISLET_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ISLET_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ISLET_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ISLET_1.9.0.tgz
vignettes: vignettes/ISLET/inst/doc/ISLET.html
vignetteTitles: Individual-specific and cell-type-specific
deconvolution using ISLET
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ISLET/inst/doc/ISLET.R
dependencyCount: 66
Package: isobar
Version: 1.53.0
Depends: R (>= 2.10.0), Biobase, stats, methods
Imports: distr, plyr, biomaRt, ggplot2
Suggests: MSnbase, OrgMassSpecR, XML, RJSONIO, Hmisc, gplots,
RColorBrewer, gridExtra, limma, boot, DBI, MASS
License: LGPL-2
MD5sum: 189dac6af3815bd6ff377963ad60aaa1
NeedsCompilation: no
Title: Analysis and quantitation of isobarically tagged MSMS proteomics
data
Description: isobar provides methods for preprocessing, normalization,
and report generation for the analysis of quantitative mass
spectrometry proteomics data labeled with isobaric tags, such
as iTRAQ and TMT. Features modules for integrating and
validating PTM-centric datasets (isobar-PTM). More information
on http://www.ms-isobar.org.
biocViews: ImmunoOncology, Proteomics, MassSpectrometry,
Bioinformatics, MultipleComparisons, QualityControl
Author: Florian P Breitwieser <florian.bw@gmail.com> and Jacques
Colinge <jacques.colinge@inserm.fr>, with contributions from
Alexey Stukalov <stukalov@biochem.mpg.de>, Xavier Robin
<xavier.robin@unige.ch> and Florent Gluck
<florent.gluck@unige.ch>
Maintainer: Florian P Breitwieser <florian.bw@gmail.com>
URL: https://github.com/fbreitwieser/isobar
BugReports: https://github.com/fbreitwieser/isobar/issues
git_url: https://git.bioconductor.org/packages/isobar
git_branch: devel
git_last_commit: 2a92392
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/isobar_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/isobar_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/isobar_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/isobar_1.53.0.tgz
vignettes: vignettes/isobar/inst/doc/isobar-devel.pdf,
vignettes/isobar/inst/doc/isobar-ptm.pdf,
vignettes/isobar/inst/doc/isobar-usecases.pdf,
vignettes/isobar/inst/doc/isobar.pdf
vignetteTitles: isobar for developers, isobar for quantification of PTM
datasets, Usecases for isobar package, isobar package for iTRAQ
and TMT protein quantification
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/isobar/inst/doc/isobar-devel.R,
vignettes/isobar/inst/doc/isobar-ptm.R,
vignettes/isobar/inst/doc/isobar-usecases.R,
vignettes/isobar/inst/doc/isobar.R
dependencyCount: 90
Package: IsoBayes
Version: 1.5.0
Depends: R (>= 4.3.0)
Imports: methods, Rcpp, data.table, glue, stats, doParallel, parallel,
doRNG, foreach, iterators, ggplot2, HDInterval,
SummarizedExperiment, S4Vectors
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, testthat, BiocStyle
License: GPL-3
MD5sum: fad672779e9b38732bba0f108eafd75d
NeedsCompilation: yes
Title: IsoBayes: Single Isoform protein inference Method via Bayesian
Analyses
Description: IsoBayes is a Bayesian method to perform inference on
single protein isoforms. Our approach infers the
presence/absence of protein isoforms, and also estimates their
abundance; additionally, it provides a measure of the
uncertainty of these estimates, via: i) the posterior
probability that a protein isoform is present in the sample;
ii) a posterior credible interval of its abundance. IsoBayes
inputs liquid cromatography mass spectrometry (MS) data, and
can work with both PSM counts, and intensities. When available,
trascript isoform abundances (i.e., TPMs) are also
incorporated: TPMs are used to formulate an informative prior
for the respective protein isoform relative abundance. We
further identify isoforms where the relative abundance of
proteins and transcripts significantly differ. We use a
two-layer latent variable approach to model two sources of
uncertainty typical of MS data: i) peptides may be erroneously
detected (even when absent); ii) many peptides are compatible
with multiple protein isoforms. In the first layer, we sample
the presence/absence of each peptide based on its estimated
probability of being mistakenly detected, also known as PEP
(i.e., posterior error probability). In the second layer, for
peptides that were estimated as being present, we allocate
their abundance across the protein isoforms they map to. These
two steps allow us to recover the presence and abundance of
each protein isoform.
biocViews: StatisticalMethod, Bayesian, Proteomics, MassSpectrometry,
AlternativeSplicing, Sequencing, RNASeq, GeneExpression,
Genetics, Visualization, Software
Author: Jordy Bollon [aut], Simone Tiberi [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3054-9964>)
Maintainer: Simone Tiberi <simone.tiberi@unibo.it>
URL: https://github.com/SimoneTiberi/IsoBayes
SystemRequirements: C++17
VignetteBuilder: knitr
BugReports: https://github.com/SimoneTiberi/IsoBayes/issues
git_url: https://git.bioconductor.org/packages/IsoBayes
git_branch: devel
git_last_commit: 9b32421
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/IsoBayes_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/IsoBayes_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/IsoBayes_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/IsoBayes_1.5.0.tgz
vignettes: vignettes/IsoBayes/inst/doc/IsoBayes.html
vignetteTitles: IsoBayes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/IsoBayes/inst/doc/IsoBayes.R
dependencyCount: 74
Package: IsoCorrectoR
Version: 1.25.0
Depends: R (>= 3.5)
Imports: dplyr, magrittr, methods, quadprog, readr, readxl, stringr,
tibble, tools, utils, pracma, WriteXLS
Suggests: IsoCorrectoRGUI, knitr, rmarkdown, testthat, BiocStyle
License: GPL-3
Archs: x64
MD5sum: b29c72535e281d6f39e3950f3bcd9b9b
NeedsCompilation: no
Title: Correction for natural isotope abundance and tracer purity in MS
and MS/MS data from stable isotope labeling experiments
Description: IsoCorrectoR performs the correction of mass spectrometry
data from stable isotope labeling/tracing metabolomics
experiments with regard to natural isotope abundance and tracer
impurity. Data from both MS and MS/MS measurements can be
corrected (with any tracer isotope: 13C, 15N, 18O...), as well
as ultra-high resolution MS data from multiple-tracer
experiments (e.g. 13C and 15N used simultaneously). See the
Bioconductor package IsoCorrectoRGUI for a graphical user
interface to IsoCorrectoR. NOTE: With R version 4.0.0, writing
correction results to Excel files may currently not work on
Windows. However, writing results to csv works as before.
biocViews: Software, Metabolomics, MassSpectrometry, Preprocessing,
ImmunoOncology
Author: Christian Kohler [cre, aut], Paul Heinrich [aut]
Maintainer: Christian Kohler <christian.kohler@ur.de>
URL: https://genomics.ur.de/files/IsoCorrectoR/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/IsoCorrectoR
git_branch: devel
git_last_commit: b3c775a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/IsoCorrectoR_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/IsoCorrectoR_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/IsoCorrectoR_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/IsoCorrectoR_1.25.0.tgz
vignettes: vignettes/IsoCorrectoR/inst/doc/IsoCorrectoR.html
vignetteTitles: IsoCorrectoR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/IsoCorrectoR/inst/doc/IsoCorrectoR.R
importsMe: IsoCorrectoRGUI
dependencyCount: 41
Package: IsoCorrectoRGUI
Version: 1.23.0
Depends: R (>= 3.6)
Imports: IsoCorrectoR, readxl, tcltk2, tcltk, utils
Suggests: knitr, rmarkdown, testthat, BiocStyle
License: GPL-3
MD5sum: fb9907a431025b25bf919c456e446e31
NeedsCompilation: no
Title: Graphical User Interface for IsoCorrectoR
Description: IsoCorrectoRGUI is a Graphical User Interface for the
IsoCorrectoR package. IsoCorrectoR performs the correction of
mass spectrometry data from stable isotope labeling/tracing
metabolomics experiments with regard to natural isotope
abundance and tracer impurity. Data from both MS and MS/MS
measurements can be corrected (with any tracer isotope: 13C,
15N, 18O...), as well as high resolution MS data from
multiple-tracer experiments (e.g. 13C and 15N used
simultaneously).
biocViews: Software, Metabolomics, MassSpectrometry, Preprocessing,
GUI, ImmunoOncology
Author: Christian Kohler [cre, aut], Paul Kuerner [aut], Paul Heinrich
[aut]
Maintainer: Christian Kohler <christian.kohler@ur.de>
URL: https://genomics.ur.de/files/IsoCorrectoRGUI
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/IsoCorrectoRGUI
git_branch: devel
git_last_commit: 7bf3e50
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/IsoCorrectoRGUI_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/IsoCorrectoRGUI_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/IsoCorrectoRGUI_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/IsoCorrectoRGUI_1.23.0.tgz
vignettes: vignettes/IsoCorrectoRGUI/inst/doc/IsoCorrectoRGUI.html
vignetteTitles: IsoCorrectoR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/IsoCorrectoRGUI/inst/doc/IsoCorrectoRGUI.R
suggestsMe: IsoCorrectoR
dependencyCount: 44
Package: IsoformSwitchAnalyzeR
Version: 2.7.0
Depends: R (>= 4.2), limma, DEXSeq, satuRn (>= 1.7.0), sva, ggplot2 (>=
3.3.5), pfamAnalyzeR
Imports: methods, BSgenome, plyr, reshape2, gridExtra, Biostrings (>=
2.50.0), IRanges, GenomicRanges, RColorBrewer, rtracklayer,
VennDiagram, DBI, grDevices, graphics, stats, utils,
GenomeInfoDb, grid, tximport (>= 1.7.1), tximeta (>= 1.7.12),
edgeR, futile.logger, stringr, dplyr, magrittr, readr, tibble,
XVector, BiocGenerics, RCurl, Biobase, SummarizedExperiment,
tidyr, S4Vectors, BiocParallel, pwalign
Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, rmarkdown
License: GPL (>= 2)
MD5sum: 881096f03c6791f64eaa1d12d6971a55
NeedsCompilation: yes
Title: Identify, Annotate and Visualize Isoform Switches with
Functional Consequences from both short- and long-read RNA-seq
data
Description: Analysis of alternative splicing and isoform switches with
predicted functional consequences (e.g. gain/loss of protein
domains etc.) from quantification of all types of RNASeq by
tools such as Kallisto, Salmon, StringTie, Cufflinks/Cuffdiff
etc.
biocViews: GeneExpression, Transcription, AlternativeSplicing,
DifferentialExpression, DifferentialSplicing, Visualization,
StatisticalMethod, TranscriptomeVariant, BiomedicalInformatics,
FunctionalGenomics, SystemsBiology, Transcriptomics, RNASeq,
Annotation, FunctionalPrediction, GenePrediction, DataImport,
MultipleComparison, BatchEffect, ImmunoOncology
Author: Kristoffer Vitting-Seerup [cre, aut] (ORCID:
<https://orcid.org/0000-0002-6450-0608>), Jeroen Gilis [ctb]
(ORCID: <https://orcid.org/0000-0001-8415-0943>)
Maintainer: Kristoffer Vitting-Seerup <k.vitting.seerup@gmail.com>
URL: http://bioconductor.org/packages/IsoformSwitchAnalyzeR/
VignetteBuilder: knitr
BugReports:
https://github.com/kvittingseerup/IsoformSwitchAnalyzeR/issues
git_url: https://git.bioconductor.org/packages/IsoformSwitchAnalyzeR
git_branch: devel
git_last_commit: 15ecc4e
git_last_commit_date: 2024-10-29
Date/Publication: 2025-03-06
source.ver: src/contrib/IsoformSwitchAnalyzeR_2.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/IsoformSwitchAnalyzeR_2.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/IsoformSwitchAnalyzeR_2.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/IsoformSwitchAnalyzeR_2.7.0.tgz
vignettes:
vignettes/IsoformSwitchAnalyzeR/inst/doc/IsoformSwitchAnalyzeR.html
vignetteTitles: IsoformSwitchAnalyzeR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/IsoformSwitchAnalyzeR/inst/doc/IsoformSwitchAnalyzeR.R
dependencyCount: 154
Package: ISoLDE
Version: 1.35.0
Depends: R (>= 3.3.0),graphics,grDevices,stats,utils
License: GPL (>= 2.0)
Archs: x64
MD5sum: fe15a189ea6f2fe0cfe1c3b9b6c02d28
NeedsCompilation: yes
Title: Integrative Statistics of alleLe Dependent Expression
Description: This package provides ISoLDE a new method for identifying
imprinted genes. This method is dedicated to data arising from
RNA sequencing technologies. The ISoLDE package implements
original statistical methodology described in the publication
below.
biocViews: ImmunoOncology, GeneExpression, Transcription,
GeneSetEnrichment, Genetics, Sequencing, RNASeq,
MultipleComparison, SNP, GeneticVariability, Epigenetics,
MathematicalBiology, GeneRegulation
Author: Christelle Reynès [aut, cre], Marine Rohmer [aut], Guilhem
Kister [aut]
Maintainer: Christelle Reynès <christelle.reynes@igf.cnrs.fr>
URL: www.r-project.org
git_url: https://git.bioconductor.org/packages/ISoLDE
git_branch: devel
git_last_commit: e892789
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ISoLDE_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ISoLDE_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ISoLDE_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ISoLDE_1.35.0.tgz
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 4
Package: isomiRs
Version: 1.35.0
Depends: R (>= 4.4), SummarizedExperiment
Imports: AnnotationDbi, BiocGenerics, Biobase, broom, cluster, cowplot,
DEGreport, DESeq2, IRanges, dplyr, GenomicRanges, gplots,
ggplot2, gtools, gridExtra, grid, grDevices, graphics, GGally,
limma, methods, RColorBrewer, readr, reshape, rlang, stats,
stringr, S4Vectors, tidyr, tibble
Suggests: knitr, rmarkdown, org.Mm.eg.db, targetscan.Hs.eg.db,
pheatmap, BiocStyle, testthat
License: MIT + file LICENSE
MD5sum: ef3a3f886ccd5842943be4027c7159db
NeedsCompilation: no
Title: Analyze isomiRs and miRNAs from small RNA-seq
Description: Characterization of miRNAs and isomiRs, clustering and
differential expression.
biocViews: miRNA, RNASeq, DifferentialExpression, Clustering,
ImmunoOncology
Author: Lorena Pantano [aut, cre], Georgia Escaramis [aut] (CIBERESP -
CIBER Epidemiologia y Salud Publica)
Maintainer: Lorena Pantano <lorena.pantano@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/lpantano/isomiRs/issues
git_url: https://git.bioconductor.org/packages/isomiRs
git_branch: devel
git_last_commit: b036eff
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/isomiRs_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/isomiRs_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/isomiRs_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/isomiRs_1.35.0.tgz
vignettes: vignettes/isomiRs/inst/doc/isomiRs.html
vignetteTitles: miRNA and isomiR analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/isomiRs/inst/doc/isomiRs.R
dependencyCount: 149
Package: ITALICS
Version: 2.67.0
Depends: R (>= 2.0.0), GLAD, ITALICSData, oligo, affxparser,
pd.mapping50k.xba240
Imports: affxparser, DBI, GLAD, oligo, oligoClasses, stats
Suggests: pd.mapping50k.hind240, pd.mapping250k.sty, pd.mapping250k.nsp
License: GPL-2
MD5sum: 6da1e72b9ee3fcdb12b552fb277de3d6
NeedsCompilation: no
Title: ITALICS
Description: A Method to normalize of Affymetrix GeneChip Human Mapping
100K and 500K set
biocViews: Microarray, CopyNumberVariation
Author: Guillem Rigaill, Philippe Hupe
Maintainer: Guillem Rigaill <italics@curie.fr>
URL: http://bioinfo.curie.fr
git_url: https://git.bioconductor.org/packages/ITALICS
git_branch: devel
git_last_commit: ae94ff4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ITALICS_2.67.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ITALICS_2.67.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ITALICS_2.67.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ITALICS_2.67.0.tgz
vignettes: vignettes/ITALICS/inst/doc/ITALICS.pdf
vignetteTitles: ITALICS
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ITALICS/inst/doc/ITALICS.R
dependencyCount: 70
Package: iterativeBMA
Version: 1.65.0
Depends: BMA, leaps, Biobase (>= 2.5.5)
License: GPL (>= 2)
Archs: x64
MD5sum: a31b53ffecaf3451650f55a271457504
NeedsCompilation: no
Title: The Iterative Bayesian Model Averaging (BMA) algorithm
Description: The iterative Bayesian Model Averaging (BMA) algorithm is
a variable selection and classification algorithm with an
application of classifying 2-class microarray samples, as
described in Yeung, Bumgarner and Raftery (Bioinformatics 2005,
21: 2394-2402).
biocViews: Microarray, Classification
Author: Ka Yee Yeung, University of Washington, Seattle, WA, with
contributions from Adrian Raftery and Ian Painter
Maintainer: Ka Yee Yeung <kayee@u.washington.edu>
URL: http://faculty.washington.edu/kayee/research.html
git_url: https://git.bioconductor.org/packages/iterativeBMA
git_branch: devel
git_last_commit: 00a6f3f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/iterativeBMA_1.65.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iterativeBMA_1.65.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iterativeBMA_1.65.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iterativeBMA_1.65.0.tgz
vignettes: vignettes/iterativeBMA/inst/doc/iterativeBMA.pdf
vignetteTitles: The Iterative Bayesian Model Averaging Algorithm
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iterativeBMA/inst/doc/iterativeBMA.R
dependencyCount: 22
Package: iterativeBMAsurv
Version: 1.65.0
Depends: BMA, leaps, survival, splines
Imports: graphics, grDevices, stats, survival, utils
License: GPL (>= 2)
Archs: x64
MD5sum: 5f0db4b43ab1c2fdf2f85c00aeb29c18
NeedsCompilation: no
Title: The Iterative Bayesian Model Averaging (BMA) Algorithm For
Survival Analysis
Description: The iterative Bayesian Model Averaging (BMA) algorithm for
survival analysis is a variable selection method for applying
survival analysis to microarray data.
biocViews: Microarray
Author: Amalia Annest, University of Washington, Tacoma, WA Ka Yee
Yeung, University of Washington, Seattle, WA
Maintainer: Ka Yee Yeung <kayee@u.washington.edu>
URL: http://expression.washington.edu/ibmasurv/protected
git_url: https://git.bioconductor.org/packages/iterativeBMAsurv
git_branch: devel
git_last_commit: 18ae47d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/iterativeBMAsurv_1.65.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/iterativeBMAsurv_1.65.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/iterativeBMAsurv_1.65.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iterativeBMAsurv_1.65.0.tgz
vignettes: vignettes/iterativeBMAsurv/inst/doc/iterativeBMAsurv.pdf
vignetteTitles: The Iterative Bayesian Model Averaging Algorithm For
Survival Analysis
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/iterativeBMAsurv/inst/doc/iterativeBMAsurv.R
dependencyCount: 19
Package: IVAS
Version: 2.27.0
Depends: R (> 3.0.0),GenomicFeatures, ggplot2, Biobase
Imports: doParallel, lme4, BiocGenerics, GenomicRanges, IRanges,
foreach, AnnotationDbi, S4Vectors, GenomeInfoDb, ggfortify,
grDevices, methods, Matrix, BiocParallel,utils, stats
Suggests: BiocStyle
License: GPL-2
MD5sum: 7bdba9a5c95195792cc83e29fa991473
NeedsCompilation: no
Title: Identification of genetic Variants affecting Alternative
Splicing
Description: Identification of genetic variants affecting alternative
splicing.
biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression,
DifferentialSplicing, GeneExpression, GeneRegulation,
Regression, RNASeq, Sequencing, SNP, Software, Transcription
Author: Seonggyun Han, Sangsoo Kim
Maintainer: Seonggyun Han <hangost@ssu.ac.kr>
git_url: https://git.bioconductor.org/packages/IVAS
git_branch: devel
git_last_commit: 19b8da0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/IVAS_2.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/IVAS_2.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/IVAS_2.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/IVAS_2.27.0.tgz
vignettes: vignettes/IVAS/inst/doc/IVAS.pdf
vignetteTitles: IVAS : Identification of genetic Variants affecting
Alternative Splicing
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/IVAS/inst/doc/IVAS.R
dependsOnMe: IMAS
dependencyCount: 117
Package: ivygapSE
Version: 1.29.0
Depends: R (>= 3.5.0), SummarizedExperiment
Imports: shiny, survival, survminer, hwriter, plotly, ggplot2,
S4Vectors, graphics, stats, utils, UpSetR
Suggests: knitr, png, limma, grid, DT, randomForest, digest, testthat,
rmarkdown, BiocStyle, magick, statmod, codetools
License: Artistic-2.0
MD5sum: 4800b899c10d80f3c5458d2962c9965c
NeedsCompilation: no
Title: A SummarizedExperiment for Ivy-GAP data
Description: Define a SummarizedExperiment and exploratory app for
Ivy-GAP glioblastoma image, expression, and clinical data.
biocViews: Transcription, Software, Visualization, Survival,
GeneExpression, Sequencing
Author: Vince Carey
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ivygapSE
git_branch: devel
git_last_commit: 4df1812
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ivygapSE_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ivygapSE_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ivygapSE_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ivygapSE_1.29.0.tgz
vignettes: vignettes/ivygapSE/inst/doc/ivygapSE.html
vignetteTitles: ivygapSE -- SummarizedExperiment for Ivy-GAP
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ivygapSE/inst/doc/ivygapSE.R
dependencyCount: 153
Package: IWTomics
Version: 1.31.0
Depends: R (>= 3.5.0), GenomicRanges
Imports:
parallel,gtable,grid,graphics,methods,IRanges,KernSmooth,fda,S4Vectors,grDevices,stats,utils,tools
Suggests: knitr
License: GPL (>=2)
MD5sum: 54809a6925fc13ab8ec1a2ff26aef68c
NeedsCompilation: no
Title: Interval-Wise Testing for Omics Data
Description: Implementation of the Interval-Wise Testing (IWT) for
omics data. This inferential procedure tests for differences in
"Omics" data between two groups of genomic regions (or between
a group of genomic regions and a reference center of symmetry),
and does not require fixing location and scale at the outset.
biocViews: StatisticalMethod, MultipleComparison,
DifferentialExpression, DifferentialMethylation,
DifferentialPeakCalling, GenomeAnnotation, DataImport
Author: Marzia A Cremona, Alessia Pini, Francesca Chiaromonte, Simone
Vantini
Maintainer: Marzia A Cremona <mac78@psu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/IWTomics
git_branch: devel
git_last_commit: 87c60ab
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/IWTomics_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/IWTomics_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/IWTomics_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/IWTomics_1.31.0.tgz
vignettes: vignettes/IWTomics/inst/doc/IWTomics.pdf
vignetteTitles: Introduction to IWTomics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/IWTomics/inst/doc/IWTomics.R
dependencyCount: 74
Package: jazzPanda
Version: 0.99.6
Depends: R (>= 4.4.0)
Imports: spatstat.geom, dplyr, glmnet, caret, foreach, stats, magrittr,
doParallel, BiocParallel, methods,
BumpyMatrix,SpatialExperiment
Suggests: BiocStyle, knitr, rmarkdown, spatstat, Seurat, statmod,
corrplot, ggplot2, ggraph, ggrepel, gridExtra, reshape2,
igraph, jsonlite, vdiffr, patchwork, ggpubr, tidyr,
SpatialFeatureExperiment, ExperimentHub, TENxXeniumData,
SingleCellExperiment, SFEData, Matrix, data.table, scran,
scater, grid, testthat (>= 3.0.0)
License: GPL-3
MD5sum: 053e4c0034a0ca52126adaf177d7d082
NeedsCompilation: no
Title: Finding spatially relevant marker genes in image based spatial
transcriptomics data
Description: This package contains the function to find marker genes
for image-based spatial transcriptomics data. There are
functions to create spatial vectors from the cell and
transcript coordiantes, which are passed as inputs to find
marker genes. Marker genes are detected for every cluster by
two approaches. The first approach is by permtuation testing,
which is implmented in parallel for finding marker genes for
one sample study. The other approach is to build a linear model
for every gene. This approach can account for multiple samples
and backgound noise.
biocViews: Spatial, GeneExpression, DifferentialExpression,
StatisticalMethod, Transcriptomics
Author: Melody Jin [aut, cre] (ORCID:
<https://orcid.org/0000-0002-2222-0958>)
Maintainer: Melody Jin <jin.m@wehi.edu.au>
URL: https://github.com/phipsonlab/jazzPanda,
https://bhuvad.github.io/jazzPanda/
VignetteBuilder: knitr
BugReports: https://github.com/phipsonlab/jazzPanda/issues
git_url: https://git.bioconductor.org/packages/jazzPanda
git_branch: devel
git_last_commit: 7b643fe
git_last_commit_date: 2025-03-02
Date/Publication: 2025-03-10
source.ver: src/contrib/jazzPanda_0.99.6.tar.gz
win.binary.ver: bin/windows/contrib/4.5/jazzPanda_0.99.6.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/jazzPanda_0.99.6.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/jazzPanda_0.99.6.tgz
vignettes: vignettes/jazzPanda/inst/doc/jazzPanda.html
vignetteTitles: jazzPanda example
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/jazzPanda/inst/doc/jazzPanda.R
dependencyCount: 143
Package: karyoploteR
Version: 1.33.0
Depends: R (>= 3.4), regioneR, GenomicRanges, methods
Imports: regioneR, GenomicRanges, IRanges, Rsamtools, stats, graphics,
memoise, rtracklayer, GenomeInfoDb, S4Vectors, biovizBase,
digest, bezier, GenomicFeatures, bamsignals, AnnotationDbi,
grDevices, VariantAnnotation
Suggests: BiocStyle, knitr, rmarkdown, markdown, testthat, magrittr,
BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Hsapiens.UCSC.hg19.masked,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Mmusculus.UCSC.mm10.knownGene, org.Hs.eg.db, org.Mm.eg.db,
pasillaBamSubset
License: Artistic-2.0
MD5sum: 575febb46f71f107e192ad9f57350d52
NeedsCompilation: no
Title: Plot customizable linear genomes displaying arbitrary data
Description: karyoploteR creates karyotype plots of arbitrary genomes
and offers a complete set of functions to plot arbitrary data
on them. It mimicks many R base graphics functions coupling
them with a coordinate change function automatically mapping
the chromosome and data coordinates into the plot coordinates.
In addition to the provided data plotting functions, it is easy
to add new ones.
biocViews: Visualization, CopyNumberVariation, Sequencing, Coverage,
DNASeq, ChIPSeq, MethylSeq, DataImport, OneChannel
Author: Bernat Gel [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8878-349X>)
Maintainer: Bernat Gel <bgel@igtp.cat>
URL: https://github.com/bernatgel/karyoploteR
VignetteBuilder: knitr
BugReports: https://github.com/bernatgel/karyoploteR/issues
git_url: https://git.bioconductor.org/packages/karyoploteR
git_branch: devel
git_last_commit: 4e6d89c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/karyoploteR_1.33.0.tar.gz
vignettes: vignettes/karyoploteR/inst/doc/karyoploteR.html
vignetteTitles: karyoploteR vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/karyoploteR/inst/doc/karyoploteR.R
dependsOnMe: CopyNumberPlots
importsMe: CNVfilteR, CNViz, multicrispr
suggestsMe: Category, EpiMix, UPDhmm, MitoHEAR
dependencyCount: 141
Package: katdetectr
Version: 1.9.0
Depends: R (>= 4.2)
Imports: Biobase (>= 2.54.0), BiocParallel (>= 1.26.2), BSgenome (>=
1.62.0), BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.3),
BSgenome.Hsapiens.UCSC.hg38 (>= 1.4.4), changepoint (>= 2.2.3),
changepoint.np (>= 1.0.3), checkmate (>= 2.0.0), dplyr (>=
1.0.8), GenomeInfoDb (>= 1.28.4), GenomicRanges (>= 1.44.0),
ggplot2 (>= 3.3.5), ggtext (>= 0.1.1), IRanges (>= 2.26.0),
maftools (>= 2.10.5), methods (>= 4.1.3), plyranges (>=
1.17.0), Rdpack (>= 2.3.1), rlang (>= 1.0.2), S4Vectors (>=
0.30.2), scales (>= 1.2.0), tibble (>= 3.1.6), tidyr (>=
1.2.0), tools, utils, VariantAnnotation (>= 1.38.0)
Suggests: BiocStyle (>= 2.26.0), knitr (>= 1.37), rmarkdown (>= 2.13),
stats, testthat (>= 3.0.0)
License: GPL-3 + file LICENSE
MD5sum: a55f0455612abd5b40ddd5f7d3f9cde3
NeedsCompilation: no
Title: Detection, Characterization and Visualization of Kataegis in
Sequencing Data
Description: Kataegis refers to the occurrence of regional
hypermutation and is a phenomenon observed in a wide range of
malignancies. Using changepoint detection katdetectr aims to
identify putative kataegis foci from common data-formats
housing genomic variants. Katdetectr has shown to be a robust
package for the detection, characterization and visualization
of kataegis.
biocViews: WholeGenome, Software, SNP, Sequencing, Classification,
VariantAnnotation
Author: Daan Hazelaar [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7513-6813>), Job van Riet [aut]
(ORCID: <https://orcid.org/0000-0001-7767-7923>), Harmen van de
Werken [ths] (ORCID: <https://orcid.org/0000-0002-9794-1477>)
Maintainer: Daan Hazelaar <daanhazelaar@gmail.com>
URL: https://doi.org/doi:10.18129/B9.bioc.katdetectr
VignetteBuilder: knitr
BugReports: https://github.com/ErasmusMC-CCBC/katdetectr/issues
git_url: https://git.bioconductor.org/packages/katdetectr
git_branch: devel
git_last_commit: 641e734
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/katdetectr_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/katdetectr_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/katdetectr_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/katdetectr_1.9.0.tgz
vignettes: vignettes/katdetectr/inst/doc/General_overview.html
vignetteTitles: Overview_katdetectr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/katdetectr/inst/doc/General_overview.R
dependencyCount: 130
Package: KBoost
Version: 1.15.0
Depends: R (>= 4.1), stats, utils
Suggests: knitr, rmarkdown, testthat
License: GPL-2 | GPL-3
MD5sum: 0c664c8502b9f68eca6628199ef0302a
NeedsCompilation: no
Title: Inference of gene regulatory networks from gene expression data
Description: Reconstructing gene regulatory networks and transcription
factor activity is crucial to understand biological processes
and holds potential for developing personalized treatment. Yet,
it is still an open problem as state-of-art algorithm are often
not able to handle large amounts of data. Furthermore, many of
the present methods predict numerous false positives and are
unable to integrate other sources of information such as
previously known interactions. Here we introduce KBoost, an
algorithm that uses kernel PCA regression, boosting and
Bayesian model averaging for fast and accurate reconstruction
of gene regulatory networks. KBoost can also use a prior
network built on previously known transcription factor targets.
We have benchmarked KBoost using three different datasets
against other high performing algorithms. The results show that
our method compares favourably to other methods across
datasets.
biocViews: Network, GraphAndNetwork, Bayesian, NetworkInference,
GeneRegulation, Transcriptomics, SystemsBiology, Transcription,
GeneExpression, Regression, PrincipalComponent
Author: Luis F. Iglesias-Martinez [aut, cre] (ORCID:
<https://orcid.org/0000-0002-9110-2189>), Barbara de Kegel
[aut], Walter Kolch [aut]
Maintainer: Luis F. Iglesias-Martinez <luis.iglesiasmartinez@ucd.ie>
URL: https://github.com/Luisiglm/KBoost
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/KBoost
git_branch: devel
git_last_commit: b4d1509
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/KBoost_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/KBoost_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/KBoost_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/KBoost_1.15.0.tgz
vignettes: vignettes/KBoost/inst/doc/KBoost.html
vignetteTitles: KBoost
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/KBoost/inst/doc/KBoost.R
dependencyCount: 2
Package: KCsmart
Version: 2.65.0
Depends: siggenes, multtest, KernSmooth
Imports: methods, BiocGenerics
Enhances: Biobase, CGHbase
License: GPL-3
MD5sum: 5d4d18f1af1f0c74a494e7c04a161fae
NeedsCompilation: no
Title: Multi sample aCGH analysis package using kernel convolution
Description: Multi sample aCGH analysis package using kernel
convolution
biocViews: CopyNumberVariation, Visualization, aCGH, Microarray
Author: Jorma de Ronde, Christiaan Klijn, Arno Velds
Maintainer: Jorma de Ronde <j.d.ronde@nki.nl>
git_url: https://git.bioconductor.org/packages/KCsmart
git_branch: devel
git_last_commit: d71c2e0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/KCsmart_2.65.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/KCsmart_2.65.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/KCsmart_2.65.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/KCsmart_2.65.0.tgz
vignettes: vignettes/KCsmart/inst/doc/KCS.pdf
vignetteTitles: KCsmart example session
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/KCsmart/inst/doc/KCS.R
dependencyCount: 19
Package: kebabs
Version: 1.41.0
Depends: R (>= 3.3.0), Biostrings (>= 2.35.5), kernlab
Imports: methods, stats, Rcpp (>= 0.11.2), Matrix (>= 1.5-0), XVector
(>= 0.7.3), S4Vectors (>= 0.27.3), e1071, LiblineaR, graphics,
grDevices, utils, apcluster
LinkingTo: IRanges, XVector, Biostrings, Rcpp, S4Vectors
Suggests: SparseM, Biobase, BiocGenerics, knitr
License: GPL (>= 2.1)
MD5sum: 759859e1abad227e8c1b9976e4e1ef74
NeedsCompilation: yes
Title: Kernel-Based Analysis of Biological Sequences
Description: The package provides functionality for kernel-based
analysis of DNA, RNA, and amino acid sequences via SVM-based
methods. As core functionality, kebabs implements following
sequence kernels: spectrum kernel, mismatch kernel, gappy pair
kernel, and motif kernel. Apart from an efficient
implementation of standard position-independent functionality,
the kernels are extended in a novel way to take the position of
patterns into account for the similarity measure. Because of
the flexibility of the kernel formulation, other kernels like
the weighted degree kernel or the shifted weighted degree
kernel with constant weighting of positions are included as
special cases. An annotation-specific variant of the kernels
uses annotation information placed along the sequence together
with the patterns in the sequence. The package allows for the
generation of a kernel matrix or an explicit feature
representation in dense or sparse format for all available
kernels which can be used with methods implemented in other R
packages. With focus on SVM-based methods, kebabs provides a
framework which simplifies the usage of existing SVM
implementations in kernlab, e1071, and LiblineaR. Binary and
multi-class classification as well as regression tasks can be
used in a unified way without having to deal with the different
functions, parameters, and formats of the selected SVM. As
support for choosing hyperparameters, the package provides
cross validation - including grouped cross validation, grid
search and model selection functions. For easier biological
interpretation of the results, the package computes feature
weights for all SVMs and prediction profiles which show the
contribution of individual sequence positions to the prediction
result and indicate the relevance of sequence sections for the
learning result and the underlying biological functions.
biocViews: SupportVectorMachine, Classification, Clustering, Regression
Author: Johannes Palme [aut], Ulrich Bodenhofer [aut,cre]
Maintainer: Ulrich Bodenhofer <ulrich@bodenhofer.com>
URL: https://github.com/UBod/kebabs
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/kebabs
git_branch: devel
git_last_commit: 56e9334
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/kebabs_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/kebabs_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/kebabs_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/kebabs_1.41.0.tgz
vignettes: vignettes/kebabs/inst/doc/kebabs.pdf
vignetteTitles: KeBABS - An R Package for Kernel Based Analysis of
Biological Sequences
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/kebabs/inst/doc/kebabs.R
dependsOnMe: procoil
importsMe: odseq
dependencyCount: 36
Package: KEGGgraph
Version: 1.67.0
Depends: R (>= 3.5.0)
Imports: methods, XML (>= 2.3-0), graph, utils, RCurl, Rgraphviz
Suggests: RBGL, testthat, RColorBrewer, org.Hs.eg.db, hgu133plus2.db,
SPIA
License: GPL (>= 2)
MD5sum: 93ace09026b443db8cb39ae4d8e280b8
NeedsCompilation: no
Title: KEGGgraph: A graph approach to KEGG PATHWAY in R and
Bioconductor
Description: KEGGGraph is an interface between KEGG pathway and graph
object as well as a collection of tools to analyze, dissect and
visualize these graphs. It parses the regularly updated KGML
(KEGG XML) files into graph models maintaining all essential
pathway attributes. The package offers functionalities
including parsing, graph operation, visualization and etc.
biocViews: Pathways, GraphAndNetwork, Visualization, KEGG
Author: Jitao David Zhang, with inputs from Paul Shannon and Hervé
Pagès
Maintainer: Jitao David Zhang <jitao_david.zhang@roche.com>
URL: http://www.nextbiomotif.com
git_url: https://git.bioconductor.org/packages/KEGGgraph
git_branch: devel
git_last_commit: 522a8e8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/KEGGgraph_1.67.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/KEGGgraph_1.67.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/KEGGgraph_1.67.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/KEGGgraph_1.67.0.tgz
vignettes: vignettes/KEGGgraph/inst/doc/KEGGgraph.pdf,
vignettes/KEGGgraph/inst/doc/KEGGgraphApp.pdf
vignetteTitles: KEGGgraph: graph approach to KEGG PATHWAY, KEGGgraph:
Application Examples
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/KEGGgraph/inst/doc/KEGGgraph.R,
vignettes/KEGGgraph/inst/doc/KEGGgraphApp.R
dependsOnMe: lpNet, ROntoTools, SPIA
importsMe: clipper, DEGraph, EnrichmentBrowser, MetaboSignal,
MWASTools, NCIgraph, pathview, iCARH
suggestsMe: DEGraph, GenomicRanges, kangar00, maGUI, rags2ridges
dependencyCount: 14
Package: KEGGlincs
Version: 1.33.0
Depends: R (>= 3.3), KOdata, hgu133a.db, org.Hs.eg.db (>= 3.3.0)
Imports:
AnnotationDbi,KEGGgraph,igraph,plyr,gtools,httr,RJSONIO,KEGGREST,
methods,graphics,stats,utils, XML, grDevices
Suggests: BiocManager (>= 1.20.3), knitr, graph
License: GPL-3
MD5sum: b529e8ac2b959d99c697318472f4513a
NeedsCompilation: no
Title: Visualize all edges within a KEGG pathway and overlay LINCS data
Description: See what is going on 'under the hood' of KEGG pathways by
explicitly re-creating the pathway maps from information
obtained from KGML files.
biocViews: NetworkInference, GeneExpression, DataRepresentation,
ThirdPartyClient,CellBiology,GraphAndNetwork,Pathways,KEGG,Network
Author: Shana White
Maintainer: Shana White <vandersm@mail.uc.edu>, Mario Medvedovic
<medvedm@ucmail.uc.edu>
SystemRequirements: Cytoscape (>= 3.3.0), Java (>= 8)
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/KEGGlincs
git_branch: devel
git_last_commit: ead0a33
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/KEGGlincs_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/KEGGlincs_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/KEGGlincs_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/KEGGlincs_1.33.0.tgz
vignettes: vignettes/KEGGlincs/inst/doc/Example-workflow.html
vignetteTitles: KEGGlincs Workflows
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/KEGGlincs/inst/doc/Example-workflow.R
dependencyCount: 63
Package: keggorthology
Version: 2.59.0
Depends: R (>= 2.5.0), hgu95av2.db, graph
Imports: AnnotationDbi, DBI, grDevices, methods, tools, utils
Suggests: RBGL,ALL
License: Artistic-2.0
MD5sum: d22e505ac9ef77b13947307b04ca5e22
NeedsCompilation: no
Title: graph support for KO, KEGG Orthology
Description: graphical representation of the Feb 2010 KEGG Orthology.
The KEGG orthology is a set of pathway IDs that are not to be
confused with the KEGG ortholog IDs.
biocViews: Pathways, GraphAndNetwork, Visualization, KEGG
Author: VJ Carey <stvjc@channing.harvard.edu>
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
git_url: https://git.bioconductor.org/packages/keggorthology
git_branch: devel
git_last_commit: 1023f18
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/keggorthology_2.59.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/keggorthology_2.59.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/keggorthology_2.59.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/keggorthology_2.59.0.tgz
vignettes: vignettes/keggorthology/inst/doc/keggorth.pdf
vignetteTitles: keggorthology overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/keggorthology/inst/doc/keggorth.R
suggestsMe: MLInterfaces
dependencyCount: 48
Package: KEGGREST
Version: 1.47.0
Depends: R (>= 3.5.0)
Imports: methods, httr, png, Biostrings
Suggests: RUnit, BiocGenerics, BiocStyle, knitr, markdown
License: Artistic-2.0
Archs: x64
MD5sum: 81419475dde18ef4164e7c26f9d23262
NeedsCompilation: no
Title: Client-side REST access to the Kyoto Encyclopedia of Genes and
Genomes (KEGG)
Description: A package that provides a client interface to the Kyoto
Encyclopedia of Genes and Genomes (KEGG) REST API. Only for
academic use by academic users belonging to academic
institutions (see <https://www.kegg.jp/kegg/rest/>). Note that
KEGGREST is based on KEGGSOAP by J. Zhang, R. Gentleman, and
Marc Carlson, and KEGG (python package) by Aurelien Mazurie.
biocViews: Annotation, Pathways, ThirdPartyClient, KEGG
Author: Dan Tenenbaum [aut], Bioconductor Package Maintainer [aut,
cre], Martin Morgan [ctb], Kozo Nishida [ctb], Marcel Ramos
[ctb], Kristina Riemer [ctb], Lori Shepherd [ctb], Jeremy
Volkening [ctb]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://bioconductor.org/packages/KEGGREST
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/KEGGREST/issues
git_url: https://git.bioconductor.org/packages/KEGGREST
git_branch: devel
git_last_commit: 388585c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/KEGGREST_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/KEGGREST_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/KEGGREST_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/KEGGREST_1.47.0.tgz
vignettes: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.html
vignetteTitles: Accessing the KEGG REST API
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.R
dependsOnMe: ROntoTools
importsMe: ADAM, adSplit, AnnotationDbi, attract, BiocSet,
ChIPpeakAnno, CNEr, EnrichmentBrowser, famat, FELLA, funOmics,
gage, ginmappeR, MetaboDynamics, MetaboSignal, MWASTools,
PADOG, pairkat, pathview, SBGNview, SMITE, terapadog,
transomics2cytoscape, YAPSA
suggestsMe: Category, categoryCompare, gatom, GenomicRanges,
globaltest, iSEEu, MetMashR, MLP, padma, rGREAT, RTopper,
SomaScan.db, CALANGO, maGUI, phoenics, ReporterScore, scDiffCom
dependencyCount: 26
Package: KinSwingR
Version: 1.25.0
Depends: R (>= 3.5)
Imports: data.table, BiocParallel, sqldf, stats, grid, grDevices
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: 8e706ae61faad1d3f42a9777dd39244f
NeedsCompilation: no
Title: KinSwingR: network-based kinase activity prediction
Description: KinSwingR integrates phosphosite data derived from
mass-spectrometry data and kinase-substrate predictions to
predict kinase activity. Several functions allow the user to
build PWM models of kinase-subtrates, statistically infer
PWM:substrate matches, and integrate these data to infer kinase
activity.
biocViews: Proteomics, SequenceMatching, Network
Author: Ashley J. Waardenberg [aut, cre]
Maintainer: Ashley J. Waardenberg <a.waardenberg@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/KinSwingR
git_branch: devel
git_last_commit: ccf9fe9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/KinSwingR_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/KinSwingR_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/KinSwingR_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/KinSwingR_1.25.0.tgz
vignettes: vignettes/KinSwingR/inst/doc/KinSwingR.html
vignetteTitles: KinSwingR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/KinSwingR/inst/doc/KinSwingR.R
dependencyCount: 36
Package: kissDE
Version: 1.27.0
Imports: aods3, Biobase, DESeq2, DSS, ggplot2, gplots, graphics,
grDevices, matrixStats, stats, utils, foreach, doParallel,
parallel, shiny, shinycssloaders, ade4, factoextra, DT
Suggests: BiocStyle, testthat
License: GPL (>= 2)
MD5sum: 700c37ebc0b227bb3e757b76696c792e
NeedsCompilation: no
Title: Retrieves Condition-Specific Variants in RNA-Seq Data
Description: Retrieves condition-specific variants in RNA-seq data
(SNVs, alternative-splicings, indels). It has been developed as
a post-treatment of 'KisSplice' but can also be used with
user's own data.
biocViews: AlternativeSplicing, DifferentialSplicing,
ExperimentalDesign, GenomicVariation, RNASeq, Transcriptomics
Author: Clara Benoit-Pilven [aut], Camille Marchet [aut], Janice
Kielbassa [aut], Lilia Brinza [aut], Audric Cologne [aut],
Aurélie Siberchicot [aut, cre], Vincent Lacroix [aut], Frank
Picard [ctb], Laurent Jacob [ctb], Vincent Miele [ctb]
Maintainer: Aurélie Siberchicot <aurelie.siberchicot@univ-lyon1.fr>
URL: https://github.com/lbbe-software/kissDE
git_url: https://git.bioconductor.org/packages/kissDE
git_branch: devel
git_last_commit: f7cd154
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-06
source.ver: src/contrib/kissDE_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/kissDE_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/kissDE_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/kissDE_1.27.0.tgz
vignettes: vignettes/kissDE/inst/doc/kissDE.pdf
vignetteTitles: kissDE.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/kissDE/inst/doc/kissDE.R
dependencyCount: 203
Package: kmcut
Version: 1.1.0
Imports: survival, tools, methods, pracma, doParallel, foreach,
parallel, SummarizedExperiment, S4Vectors
Suggests: BiocStyle, knitr, rmarkdown,
License: Artistic-2.0
MD5sum: 8ea024181c4482d2390c5afb5c3a444e
NeedsCompilation: no
Title: Optimized Kaplan Meier analysis and identification and
validation of prognostic biomarkers
Description: The purpose of the package is to identify prognostic
biomarkers and an optimal numeric cutoff for each biomarker
that can be used to stratify a group of test subjects (samples)
into two sub-groups with significantly different survival
(better vs. worse). The package was developed for the analysis
of gene expression data, such as RNA-seq. However, it can be
used with any quantitative variable that has a sufficiently
large proportion of unique values.
biocViews: Software, StatisticalMethod, GeneExpression, Survival
Author: Igor Kuznetsov [aut, cre], Javed Khan [aut]
Maintainer: Igor Kuznetsov <ibkalb@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/kmcut
git_branch: devel
git_last_commit: da83cfd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/kmcut_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/kmcut_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/kmcut_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/kmcut_1.1.0.tgz
vignettes: vignettes/kmcut/inst/doc/kmcut_intro.html
vignetteTitles: kmcut_intro
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/kmcut/inst/doc/kmcut_intro.R
dependencyCount: 44
Package: KnowSeq
Version: 1.21.0
Depends: R (>= 4.0), cqn (>= 1.28.1)
Imports: stringr, methods, ggplot2 (>= 3.3.0), jsonlite, kernlab,
rlist, rmarkdown, reshape2, e1071, randomForest, caret, XML,
praznik, R.utils, httr, sva (>= 3.30.1), edgeR (>= 3.24.3),
limma (>= 3.38.3), grDevices, graphics, stats, utils, Hmisc (>=
4.4.0), gridExtra
Suggests: knitr
License: GPL (>=2)
MD5sum: 8ec747eefa20aef52a39f0aabd31d784
NeedsCompilation: no
Title: KnowSeq R/Bioc package: The Smart Transcriptomic Pipeline
Description: KnowSeq proposes a novel methodology that comprises the
most relevant steps in the Transcriptomic gene expression
analysis. KnowSeq expects to serve as an integrative tool that
allows to process and extract relevant biomarkers, as well as
to assess them through a Machine Learning approaches. Finally,
the last objective of KnowSeq is the biological knowledge
extraction from the biomarkers (Gene Ontology enrichment,
Pathway listing and Visualization and Evidences related to the
addressed disease). Although the package allows analyzing all
the data manually, the main strenght of KnowSeq is the
possibilty of carrying out an automatic and intelligent HTML
report that collect all the involved steps in one document. It
is important to highligh that the pipeline is totally modular
and flexible, hence it can be started from whichever of the
different steps. KnowSeq expects to serve as a novel tool to
help to the experts in the field to acquire robust knowledge
and conclusions for the data and diseases to study.
biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment,
DataImport, Classification, FeatureExtraction, Sequencing,
RNASeq, BatchEffect, Normalization, Preprocessing,
QualityControl, Genetics, Transcriptomics, Microarray,
Alignment, Pathways, SystemsBiology, GO, ImmunoOncology
Author: Daniel Castillo-Secilla [aut, cre], Juan Manuel Galvez [ctb],
Francisco Carrillo-Perez [ctb], Marta Verona-Almeida [ctb],
Daniel Redondo-Sanchez [ctb], Francisco Manuel Ortuno [ctb],
Luis Javier Herrera [ctb], Ignacio Rojas [ctb]
Maintainer: Daniel Castillo-Secilla <cased@ugr.es>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/KnowSeq
git_branch: devel
git_last_commit: 5a60694
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/KnowSeq_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/KnowSeq_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/KnowSeq_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/KnowSeq_1.21.0.tgz
vignettes: vignettes/KnowSeq/inst/doc/KnowSeq.html
vignetteTitles: The KnowSeq users guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/KnowSeq/inst/doc/KnowSeq.R
dependencyCount: 172
Package: knowYourCG
Version: 1.3.15
Depends: R (>= 4.4.0)
Imports: sesameData, dplyr, methods, rlang, GenomicRanges, IRanges,
reshape2, S4Vectors, stats, stringr, utils, ggplot2, ggrepel,
tibble, wheatmap, magrittr
Suggests: testthat (>= 3.0.0), SummarizedExperiment, rmarkdown, knitr,
sesame, gprofiler2, ggrastr
License: MIT + file LICENSE
MD5sum: 154488eb592ea2793f75180728a41536
NeedsCompilation: yes
Title: Functional analysis of DNA methylome datasets
Description: KnowYourCG (KYCG) is a supervised learning framework
designed for the functional analysis of DNA methylation data.
Unlike existing tools that focus on genes or genomic intervals,
KnowYourCG directly targets CpG dinucleotides, featuring
automated supervised screenings of diverse biological and
technical influences, including sequence motifs, transcription
factor binding, histone modifications, replication timing,
cell-type-specific methylation, and trait-epigenome
associations. KnowYourCG addresses the challenges of data
sparsity in various methylation datasets, including low-pass
Nanopore sequencing, single-cell DNA methylomes,
5-hydroxymethylation profiles, spatial DNA methylation maps,
and array-based datasets for epigenome-wide association studies
and epigenetic clocks.
biocViews: Epigenetics, DNAMethylation, Sequencing, SingleCell,
Spatial, MethylationArray
Author: Zhou Wanding [aut] (ORCID:
<https://orcid.org/0000-0001-9126-1932>), Goldberg David [aut,
cre] (ORCID: <https://orcid.org/0000-0002-9622-4708>), Fu
Hongxiang [ctb]
Maintainer: Goldberg David <golddc72@pennmedicine.upenn.edu>
URL: https://github.com/zhou-lab/knowYourCG
VignetteBuilder: knitr
BugReports: https://github.com/zhou-lab/knowYourCG/issues
git_url: https://git.bioconductor.org/packages/knowYourCG
git_branch: devel
git_last_commit: 48d831c
git_last_commit_date: 2025-01-04
Date/Publication: 2025-01-08
source.ver: src/contrib/knowYourCG_1.3.15.tar.gz
win.binary.ver: bin/windows/contrib/4.5/knowYourCG_1.3.15.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/knowYourCG_1.3.15.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/knowYourCG_1.3.15.tgz
vignettes: vignettes/knowYourCG/inst/doc/Array.html,
vignettes/knowYourCG/inst/doc/Continuous.html,
vignettes/knowYourCG/inst/doc/Sequencing.html
vignetteTitles: "2. Array Data Analysis", "3. Continuous Variable
Enrichment Analysis", "1. Sequencing Data Analysis"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/knowYourCG/inst/doc/Array.R,
vignettes/knowYourCG/inst/doc/Continuous.R,
vignettes/knowYourCG/inst/doc/Sequencing.R
dependencyCount: 97
Package: koinar
Version: 1.1.2
Depends: R (>= 4.3)
Imports: httr, jsonlite, methods, utils
Suggests: BiocManager, BiocStyle (>= 2.26), httptest, knitr, lattice,
msdata, OrgMassSpecR, protViz, S4Vectors, Spectra, testthat,
mzR
License: Apache License 2.0
MD5sum: 1d628ce5701599d48f13df22be7377c3
NeedsCompilation: no
Title: KoinaR - Remote machine learning inference using Koina
Description: A client to simplify fetching predictions from the Koina
web service. Koina is a model repository enabling the remote
execution of models. Predictions are generated as a response to
HTTP/S requests, the standard protocol used for nearly all web
traffic.
biocViews: MassSpectrometry, Proteomics, Infrastructure, Software
Author: Ludwig Lautenbacher [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1540-5911>), Christian Panse [aut]
(ORCID: <https://orcid.org/0000-0003-1975-3064>)
Maintainer: Ludwig Lautenbacher <ludwig.lautenbacher@tum.de>
URL: https://github.com/wilhelm-lab/koina
VignetteBuilder: knitr
BugReports: https://github.com/wilhelm-lab/koina/issues
git_url: https://git.bioconductor.org/packages/koinar
git_branch: devel
git_last_commit: ff3e895
git_last_commit_date: 2025-01-09
Date/Publication: 2025-01-09
source.ver: src/contrib/koinar_1.1.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/koinar_1.1.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/koinar_1.1.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/koinar_1.1.2.tgz
vignettes: vignettes/koinar/inst/doc/koina.html
vignetteTitles: On using the R lang client for koina
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/koinar/inst/doc/koina.R
dependencyCount: 11
Package: LACE
Version: 2.11.1
Depends: R (>= 4.2.0)
Imports: curl, igraph, foreach, doParallel, sortable, dplyr, forcats,
data.tree, graphics, grDevices, parallel, RColorBrewer, Rfast,
stats, SummarizedExperiment, utils, purrr, stringi, stringr,
Matrix, tidyr, jsonlite, readr, configr, DT, tools, fs,
data.table, htmltools, htmlwidgets, bsplus, shinyvalidate,
shiny, shinythemes, shinyFiles, shinyjs, shinyBS,
shinydashboard, biomaRt, callr, logr, ggplot2, svglite
Suggests: BiocGenerics, BiocStyle, testthat, knitr, rmarkdown
License: file LICENSE
MD5sum: 7d0616769a17569fd31b3c4e6176ff53
NeedsCompilation: no
Title: Longitudinal Analysis of Cancer Evolution (LACE)
Description: LACE is an algorithmic framework that processes
single-cell somatic mutation profiles from cancer samples
collected at different time points and in distinct experimental
settings, to produce longitudinal models of cancer evolution.
The approach solves a Boolean Matrix Factorization problem with
phylogenetic constraints, by maximizing a weighed likelihood
function computed on multiple time points.
biocViews: BiomedicalInformatics, SingleCell, SomaticMutation
Author: Daniele Ramazzotti [aut] (ORCID:
<https://orcid.org/0000-0002-6087-2666>), Fabrizio Angaroni
[aut], Davide Maspero [cre, aut], Alex Graudenzi [aut], Luca De
Sano [aut] (ORCID: <https://orcid.org/0000-0002-9618-3774>),
Gianluca Ascolani [aut]
Maintainer: Davide Maspero <d.maspero@campus.unimib.it>
URL: https://github.com/BIMIB-DISCo/LACE
VignetteBuilder: knitr
BugReports: https://github.com/BIMIB-DISCo/LACE
git_url: https://git.bioconductor.org/packages/LACE
git_branch: devel
git_last_commit: 2e92309
git_last_commit_date: 2025-03-15
Date/Publication: 2025-03-17
source.ver: src/contrib/LACE_2.11.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/LACE_2.11.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/LACE_2.11.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/LACE_2.11.1.tgz
vignettes: vignettes/LACE/inst/doc/v1_introduction.html,
vignettes/LACE/inst/doc/v2_running_LACE.html,
vignettes/LACE/inst/doc/v3_LACE_interface.html
vignetteTitles: Introduction, Running LACE, LACE-interface
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/LACE/inst/doc/v1_introduction.R,
vignettes/LACE/inst/doc/v2_running_LACE.R,
vignettes/LACE/inst/doc/v3_LACE_interface.R
dependencyCount: 165
Package: LBE
Version: 1.75.0
Depends: stats
Imports: graphics, stats, utils
Suggests: qvalue
License: GPL-2
MD5sum: e6cb772c9dee7d4cc767b52139dc5f2a
NeedsCompilation: no
Title: Estimation of the false discovery rate
Description: LBE is an efficient procedure for estimating the
proportion of true null hypotheses, the false discovery rate
(and so the q-values) in the framework of estimating procedures
based on the marginal distribution of the p-values without
assumption for the alternative hypothesis.
biocViews: MultipleComparison
Author: Cyril Dalmasso
Maintainer: Cyril Dalmasso <cyril.dalmasso@univ-evry.fr>
git_url: https://git.bioconductor.org/packages/LBE
git_branch: devel
git_last_commit: 4a18e0f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/LBE_1.75.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/LBE_1.75.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/LBE_1.75.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/LBE_1.75.0.tgz
vignettes: vignettes/LBE/inst/doc/LBE.pdf
vignetteTitles: LBE Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/LBE/inst/doc/LBE.R
dependencyCount: 3
Package: ldblock
Version: 1.37.0
Depends: R (>= 3.5), methods, rlang
Imports: BiocGenerics (>= 0.25.1), httr, Matrix
Suggests: RUnit, knitr, BiocStyle, gwascat, rmarkdown, snpStats,
VariantAnnotation, GenomeInfoDb, ensembldb, EnsDb.Hsapiens.v75,
Rsamtools, GenomicFiles (>= 1.13.6)
License: Artistic-2.0
Archs: x64
MD5sum: 8cf0d38bbf6d8891bad058896297f2ca
NeedsCompilation: no
Title: data structures for linkage disequilibrium measures in
populations
Description: Define data structures for linkage disequilibrium measures
in populations.
Author: VJ Carey <stvjc@channing.harvard.edu>
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ldblock
git_branch: devel
git_last_commit: 6555373
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ldblock_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ldblock_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ldblock_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ldblock_1.37.0.tgz
vignettes: vignettes/ldblock/inst/doc/ldblock.html
vignetteTitles: ldblock package: linkage disequilibrium data structures
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ldblock/inst/doc/ldblock.R
dependencyCount: 20
Package: LEA
Version: 3.19.8
Depends: R (>= 3.3.0), methods, stats, utils, graphics
Suggests: knitr
License: GPL-3
MD5sum: fbfcdcf42a36208d25525d7c6577e74e
NeedsCompilation: yes
Title: LEA: an R package for Landscape and Ecological Association
Studies
Description: LEA is an R package dedicated to population genomics,
landscape genomics and genotype-environment association tests.
LEA can run analyses of population structure and genome-wide
tests for local adaptation, and also performs imputation of
missing genotypes. The package includes statistical methods for
estimating ancestry coefficients from large genotypic matrices
and for evaluating the number of ancestral populations (snmf).
It performs statistical tests using latent factor mixed models
for identifying genetic polymorphisms that exhibit association
with environmental gradients or phenotypic traits (lfmm2). In
addition, LEA computes values of genetic offset statistics
based on new or predicted environments (genetic.gap,
genetic.offset). LEA is mainly based on optimized programs that
can scale with the dimensions of large data sets.
biocViews: Software, Statistical Method, Clustering, Regression
Author: Eric Frichot <eric.frichot@gmail.com>, Olivier Francois
<olivier.francois@grenoble-inp.fr>, Clement Gain
<clement.gain@univ-grenoble-alpes.fr>
Maintainer: Olivier Francois <olivier.francois@grenoble-inp.fr>
URL: http://membres-timc.imag.fr/Olivier.Francois/lea.html
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/LEA
git_branch: devel
git_last_commit: 2da796f
git_last_commit_date: 2025-03-11
Date/Publication: 2025-03-11
source.ver: src/contrib/LEA_3.19.8.tar.gz
win.binary.ver: bin/windows/contrib/4.5/LEA_3.19.8.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/LEA_3.19.8.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/LEA_3.19.8.tgz
vignettes: vignettes/LEA/inst/doc/LEA.pdf
vignetteTitles: LEA: An R Package for Landscape and Ecological
Association Studies
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/LEA/inst/doc/LEA.R
dependencyCount: 4
Package: LedPred
Version: 1.41.0
Depends: R (>= 3.2.0), e1071 (>= 1.6)
Imports: akima, ggplot2, irr, jsonlite, parallel, plot3D, plyr, RCurl,
ROCR, testthat
License: MIT | file LICENSE
MD5sum: 85f140e27d3223e7392b8cc324e33c98
NeedsCompilation: no
Title: Learning from DNA to Predict Enhancers
Description: This package aims at creating a predictive model of
regulatory sequences used to score unknown sequences based on
the content of DNA motifs, next-generation sequencing (NGS)
peaks and signals and other numerical scores of the sequences
using supervised classification. The package contains a
workflow based on the support vector machine (SVM) algorithm
that maps features to sequences, optimize SVM parameters and
feature number and creates a model that can be stored and used
to score the regulatory potential of unknown sequences.
biocViews: SupportVectorMachine, Software, MotifAnnotation, ChIPSeq,
Sequencing, Classification
Author: Elodie Darbo, Denis Seyres, Aitor Gonzalez
Maintainer: Aitor Gonzalez <aitor.gonzalez@univ-amu.fr>
BugReports: https://github.com/aitgon/LedPred/issues
git_url: https://git.bioconductor.org/packages/LedPred
git_branch: devel
git_last_commit: ae330cc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/LedPred_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/LedPred_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/LedPred_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/LedPred_1.41.0.tgz
vignettes: vignettes/LedPred/inst/doc/LedPred.pdf
vignetteTitles: LedPred Example
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/LedPred/inst/doc/LedPred.R
dependencyCount: 73
Package: lefser
Version: 1.17.6
Depends: SummarizedExperiment, R (>= 4.0.0)
Imports: coin, MASS, ggplot2, S4Vectors, stats, methods, utils, dplyr,
testthat, tibble, tidyr, forcats, stringr, ggtree,
BiocGenerics, ape, ggrepel, mia, purrr, tidyselect, treeio
Suggests: knitr, rmarkdown, curatedMetagenomicData, BiocStyle,
phyloseq, pkgdown, covr, withr
License: Artistic-2.0
MD5sum: 4b8857dbb291aaec840c71f0a4c689f8
NeedsCompilation: no
Title: R implementation of the LEfSE method for microbiome biomarker
discovery
Description: lefser is the R implementation of the popular microbiome
biomarker discovery too, LEfSe. It uses the Kruskal-Wallis
test, Wilcoxon-Rank Sum test, and Linear Discriminant Analysis
to find biomarkers from two-level classes (and optional
sub-classes).
biocViews: Software, Sequencing, DifferentialExpression, Microbiome,
StatisticalMethod, Classification
Author: Sehyun Oh [cre, ctb] (ORCID:
<https://orcid.org/0000-0002-9490-3061>), Asya Khleborodova
[aut], Samuel Gamboa-Tuz [ctb], Marcel Ramos [ctb] (ORCID:
<https://orcid.org/0000-0002-3242-0582>), Ludwig Geistlinger
[ctb] (ORCID: <https://orcid.org/0000-0002-2495-5464>), Levi
Waldron [ctb] (ORCID: <https://orcid.org/0000-0003-2725-0694>)
Maintainer: Sehyun Oh <shbrief@gmail.com>
URL: https://github.com/waldronlab/lefser
VignetteBuilder: knitr
BugReports: https://github.com/waldronlab/lefser/issues
git_url: https://git.bioconductor.org/packages/lefser
git_branch: devel
git_last_commit: 2138c66
git_last_commit_date: 2025-02-18
Date/Publication: 2025-02-19
source.ver: src/contrib/lefser_1.17.6.tar.gz
win.binary.ver: bin/windows/contrib/4.5/lefser_1.17.6.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/lefser_1.17.6.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/lefser_1.17.6.tgz
vignettes: vignettes/lefser/inst/doc/lefser.html
vignetteTitles: Quickstart
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/lefser/inst/doc/lefser.R
suggestsMe: dar
dependencyCount: 194
Package: lemur
Version: 1.5.0
Depends: R (>= 4.1)
Imports: stats, utils, irlba, methods, SingleCellExperiment,
SummarizedExperiment, rlang (>= 1.1.0), vctrs (>= 0.6.0),
glmGamPoi (>= 1.12.0), BiocGenerics, S4Vectors, Matrix,
DelayedMatrixStats, HDF5Array, MatrixGenerics, matrixStats,
Rcpp, harmony (>= 1.2.0), limma, BiocNeighbors
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat (>= 3.0.0), tidyverse, uwot, dplyr, edgeR, knitr,
rmarkdown, BiocStyle
License: MIT + file LICENSE
MD5sum: 5dcc004463111d8c134e2bb79c2e2465
NeedsCompilation: yes
Title: Latent Embedding Multivariate Regression
Description: Fit a latent embedding multivariate regression (LEMUR)
model to multi-condition single-cell data. The model provides a
parametric description of single-cell data measured with
treatment vs. control or more complex experimental designs. The
parametric model is used to (1) align conditions, (2) predict
log fold changes between conditions for all cells, and (3)
identify cell neighborhoods with consistent log fold changes.
For those neighborhoods, a pseudobulked differential expression
test is conducted to assess which genes are significantly
changed.
biocViews: Transcriptomics, DifferentialExpression, SingleCell,
DimensionReduction, Regression
Author: Constantin Ahlmann-Eltze [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3762-068X>)
Maintainer: Constantin Ahlmann-Eltze <artjom31415@googlemail.com>
URL: https://github.com/const-ae/lemur
VignetteBuilder: knitr
BugReports: https://github.com/const-ae/lemur/issues
git_url: https://git.bioconductor.org/packages/lemur
git_branch: devel
git_last_commit: 6e2e508
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/lemur_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/lemur_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/lemur_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/lemur_1.5.0.tgz
vignettes: vignettes/lemur/inst/doc/Introduction.html
vignetteTitles: Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/lemur/inst/doc/Introduction.R
dependencyCount: 85
Package: les
Version: 1.57.0
Depends: R (>= 2.13.2), methods, graphics, fdrtool
Imports: boot, gplots, RColorBrewer
Suggests: Biobase, limma
Enhances: parallel
License: GPL-3
Archs: x64
MD5sum: 6a64fbc99dd458a4ffd328adc5b5bb19
NeedsCompilation: no
Title: Identifying Differential Effects in Tiling Microarray Data
Description: The 'les' package estimates Loci of Enhanced Significance
(LES) in tiling microarray data. These are regions of
regulation such as found in differential transcription,
CHiP-chip, or DNA modification analysis. The package provides a
universal framework suitable for identifying differential
effects in tiling microarray data sets, and is independent of
the underlying statistics at the level of single probes.
biocViews: Microarray, DifferentialExpression, ChIPchip,
DNAMethylation, Transcription
Author: Julian Gehring, Clemens Kreutz, Jens Timmer
Maintainer: Julian Gehring <jg-bioc@gmx.com>
git_url: https://git.bioconductor.org/packages/les
git_branch: devel
git_last_commit: 9008cb7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/les_1.57.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/les_1.57.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/les_1.57.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/les_1.57.0.tgz
vignettes: vignettes/les/inst/doc/les.pdf
vignetteTitles: Introduction to the les package: Identifying
Differential Effects in Tiling Microarray Data with the Loci of
Enhanced Significance Framework
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/les/inst/doc/les.R
importsMe: GSRI
dependencyCount: 13
Package: levi
Version: 1.25.0
Imports: DT(>= 0.4), RColorBrewer(>= 1.1-2), colorspace(>= 1.3-2),
dplyr(>= 0.7.4), ggplot2(>= 2.2.1), httr(>= 1.3.1), igraph(>=
1.2.1), reshape2(>= 1.4.3), shiny(>= 1.0.5), shinydashboard(>=
0.7.0), shinyjs(>= 1.0), xml2(>= 1.2.0), knitr, Rcpp (>=
0.12.18), grid, grDevices, stats, utils, testthat, methods,
rmarkdown
LinkingTo: Rcpp
Suggests: rmarkdown, BiocStyle
License: GPL (>= 2)
MD5sum: 789edb284541cc686c1e617d3e17e1c2
NeedsCompilation: yes
Title: Landscape Expression Visualization Interface
Description: The tool integrates data from biological networks with
transcriptomes, displaying a heatmap with surface curves to
evidence the altered regions.
biocViews: GeneExpression, Sequencing, Network, Software
Author: Rafael Pilan [aut], Isabelle Silva [ctb], Agnes Takeda [ctb],
Jose Rybarczyk Filho [ctb, cre, ths]
Maintainer: Jose Luiz Rybarczyk Filho <jose.luiz@unesp.br>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/levi
git_branch: devel
git_last_commit: ccbbbd4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/levi_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/levi_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/levi_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/levi_1.25.0.tgz
vignettes: vignettes/levi/inst/doc/levi.html
vignetteTitles: "Using levi"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/levi/inst/doc/levi.R
dependencyCount: 99
Package: lfa
Version: 2.7.0
Depends: R (>= 4.0)
Imports: utils, methods, corpcor, RSpectra
Suggests: knitr, ggplot2, testthat, BEDMatrix, genio
License: GPL (>= 3)
MD5sum: bc372f60a806789e8ad9d370a00f2eaa
NeedsCompilation: yes
Title: Logistic Factor Analysis for Categorical Data
Description: Logistic Factor Analysis is a method for a PCA analogue on
Binomial data via estimation of latent structure in the natural
parameter. The main method estimates genetic population
structure from genotype data. There are also methods for
estimating individual-specific allele frequencies using the
population structure. Lastly, a structured Hardy-Weinberg
equilibrium (HWE) test is developed, which quantifies the
goodness of fit of the genotype data to the estimated
population structure, via the estimated individual-specific
allele frequencies (all of which generalizes traditional HWE
tests).
biocViews: SNP, DimensionReduction, PrincipalComponent, Regression
Author: Wei Hao [aut], Minsun Song [aut], Alejandro Ochoa [aut, cre]
(ORCID: <https://orcid.org/0000-0003-4928-3403>), John D.
Storey [aut] (ORCID: <https://orcid.org/0000-0001-5992-402X>)
Maintainer: Alejandro Ochoa <alejandro.ochoa@duke.edu>
URL: https://github.com/StoreyLab/lfa
VignetteBuilder: knitr
BugReports: https://github.com/StoreyLab/lfa/issues
git_url: https://git.bioconductor.org/packages/lfa
git_branch: devel
git_last_commit: bf41026
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/lfa_2.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/lfa_2.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/lfa_2.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/lfa_2.7.0.tgz
vignettes: vignettes/lfa/inst/doc/lfa.pdf
vignetteTitles: lfa Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/lfa/inst/doc/lfa.R
importsMe: gcatest
suggestsMe: jackstraw
dependencyCount: 12
Package: limma
Version: 3.63.10
Depends: R (>= 3.6.0)
Imports: grDevices, graphics, stats, utils, methods, statmod
Suggests: BiasedUrn, ellipse, gplots, knitr, locfit, MASS, splines,
affy, AnnotationDbi, Biobase, BiocStyle, GO.db, illuminaio,
org.Hs.eg.db, vsn
License: GPL (>=2)
Archs: x64
MD5sum: 83036ddb34b1d430d5124df85829fd93
NeedsCompilation: yes
Title: Linear Models for Microarray and Omics Data
Description: Data analysis, linear models and differential expression
for omics data.
biocViews: ExonArray, GeneExpression, Transcription,
AlternativeSplicing, DifferentialExpression,
DifferentialSplicing, GeneSetEnrichment, DataImport, Bayesian,
Clustering, Regression, TimeCourse, Microarray, MicroRNAArray,
mRNAMicroarray, OneChannel, ProprietaryPlatforms, TwoChannel,
Sequencing, RNASeq, BatchEffect, MultipleComparison,
Normalization, Preprocessing, QualityControl,
BiomedicalInformatics, CellBiology, Cheminformatics,
Epigenetics, FunctionalGenomics, Genetics, ImmunoOncology,
Metabolomics, Proteomics, SystemsBiology, Transcriptomics
Author: Gordon Smyth [cre,aut], Yifang Hu [ctb], Matthew Ritchie [ctb],
Jeremy Silver [ctb], James Wettenhall [ctb], Davis McCarthy
[ctb], Di Wu [ctb], Wei Shi [ctb], Belinda Phipson [ctb], Aaron
Lun [ctb], Natalie Thorne [ctb], Alicia Oshlack [ctb], Carolyn
de Graaf [ctb], Yunshun Chen [ctb], Goknur Giner [ctb], Mette
Langaas [ctb], Egil Ferkingstad [ctb], Marcus Davy [ctb],
Francois Pepin [ctb], Dongseok Choi [ctb], Charity Law [ctb],
Mengbo Li [ctb], Lizhong Chen [ctb]
Maintainer: Gordon Smyth <smyth@wehi.edu.au>
URL: https://bioinf.wehi.edu.au/limma/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/limma
git_branch: devel
git_last_commit: 80931c6
git_last_commit_date: 2025-03-19
Date/Publication: 2025-03-19
source.ver: src/contrib/limma_3.63.10.tar.gz
win.binary.ver: bin/windows/contrib/4.5/limma_3.63.10.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/limma_3.63.10.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/limma_3.63.10.tgz
vignettes: vignettes/limma/inst/doc/usersguide.pdf,
vignettes/limma/inst/doc/intro.html
vignetteTitles: limma User's Guide, A brief introduction to limma
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/limma/inst/doc/intro.R
dependsOnMe: ASpli, BLMA, cghMCR, codelink, convert, Cormotif,
DrugVsDisease, edgeR, ExiMiR, ExpressionAtlas, GEOexplorer,
IsoformSwitchAnalyzeR, limpa, marray, metagenomeSeq, metaseqR2,
mpra, NanoTube, octad, protGear, qpcrNorm, qusage, RBM,
RnBeads, Rnits, splineTimeR, TMSig, TOAST, tRanslatome,
TurboNorm, variancePartition, wateRmelon, zenith, CCl4,
Fletcher2013a, HD2013SGI, ReactomeGSA.data, EGSEA123,
maEndToEnd, RNAseq123, OSCA.basic, OSCA.workflows, BALLI,
BioInsight, CEDA, countTransformers, cp4p, DAAGbio, DRomics,
fmt, PerfMeas
importsMe: a4Base, ABSSeq, affycoretools, affylmGUI, AMARETTO,
animalcules, ArrayExpress, arrayQuality, arrayQualityMetrics,
artMS, ATACseqQC, ATACseqTFEA, attract, autonomics, AWFisher,
ballgown, BatchQC, beadarray, benchdamic, BERT, biotmle,
BloodGen3Module, bnem, bsseq, BubbleTree, bumphunter, casper,
ChAMP, CleanUpRNAseq, clusterExperiment, CNVRanger, combi,
compcodeR, consensusDE, consensusOV, crlmm, csaw, cTRAP, ctsGE,
DAMEfinder, DaMiRseq, debrowser, DELocal, DEP, derfinderPlot,
DESpace, DEsubs, DExMA, DiffBind, diffcyt, diffHic, diffUTR,
distinct, DMRcate, Doscheda, dreamlet, DRIMSeq, EGAD, EGSEA,
eisaR, EnrichmentBrowser, epigraHMM, EpiMix, erccdashboard,
EventPointer, EWCE, ExploreModelMatrix, flowBin, gCrisprTools,
GDCRNATools, genefu, GeneSelectMMD, GEOquery, gg4way,
gINTomics, Glimma, GRaNIE, GWAS.BAYES, HarmonizR, hermes,
HERON, hipathia, icetea, iCheck, iChip, iCOBRA, ideal, InPAS,
isomiRs, KnowSeq, lemur, limmaGUI, LimROTS, Linnorm, lipidr,
lmdme, mastR, MatrixQCvis, MBECS, MBQN, mCSEA, MEAL, methylKit,
MethylMix, microbiomeExplorer, miloR, minfi, MIRit, miRLAB,
missMethyl, MLSeq, moanin, monocle, MoonlightR, msImpute,
msqrob2, MSstats, MSstatsTMT, MultiDataSet, muscat, NADfinder,
NanoMethViz, nethet, NormalyzerDE, OLIN, omicRexposome, oppti,
OVESEG, PAA, PADOG, pairedGSEA, PanomiR, PathoStat,
pcaExplorer, PECA, PepSetTest, pepStat, phantasus, phenomis,
phenoTest, PhosR, PolySTest, POMA, POWSC, projectR, PRONE,
psichomics, qmtools, qPLEXanalyzer, qsea, RegEnrich, regsplice,
RNAseqCovarImpute, roastgsa, ROSeq, RTN, RTopper, saseR,
satuRn, scClassify, scone, scQTLtools, scran, ScreenR, scviR,
seqsetvis, shinyepico, SingleCellSignalR, singleCellTK,
sparrow, speckle, SPsimSeq, standR, STATegRa, Statial, sva,
timecourse, TOP, ToxicoGx, TPP, TPP2D, transcriptogramer, TVTB,
tweeDEseq, unifiedWMWqPCR, vsclust, vsn, weitrix, Wrench,
XAItest, yamss, yarn, BeadArrayUseCases, signatureSearchData,
spatialLIBD, ExpHunterSuite, ExpressionNormalizationWorkflow,
recountWorkflow, aliases2entrez, batchtma, BPM, Cascade, CIDER,
cinaR, DiPALM, dsb, eLNNpairedCov, Grouphmap, GSEMA,
GWASbyCluster, lfproQC, lilikoi, limorhyde2, lipidomeR,
MetAlyzer, metaMA, mi4p, MiDA, miRtest, MKmisc, MKomics,
MSclassifR, newIMVC, nlcv, OncoSubtype, Patterns, plfMA,
promor, ProtE, RANKS, RCPA, RPPanalyzer, scBio, scGOclust,
scRNAtools, scROSHI, ssizeRNA, statVisual, tinyarray,
TransProR, treediff, wrProteo
suggestsMe: ABarray, ADaCGH2, Biobase, biobroom, BiocSet, BioNet,
BioQC, broadSeq, Category, categoryCompare, celaref, CellBench,
CellMixS, ChIPpeakAnno, ClassifyR, CMA, coGPS, CONSTANd, cydar,
Damsel, DAPAR, dar, dearseq, DEGreport, derfinder, DEScan2,
dyebias, easyreporting, EnMCB, extraChIPs, fgsea, fishpond,
gage, GeoTcgaData, geva, glmGamPoi, GSRI, GSVA, Harman,
Heatplus, iSEEde, isobar, ivygapSE, les, lumi, lute, MAST,
methylumi, MLP, npGSEA, oligo, oppar, piano, PREDA, proDA,
puma, QFeatures, qsvaR, raer, randRotation, recountmethylation,
ribosomeProfilingQC, rtracklayer, Rvisdiff, signifinder,
spatialHeatmap, SpliceWiz, stageR, subSeq, systemPipeR, tadar,
TCGAbiolinks, tidybulk, topconfects, tximeta, tximport,
ViSEAGO, zFPKM, BloodCancerMultiOmics2017, bugphyzz,
GeuvadisTranscriptExpr, mammaPrintData, msigdb,
seventyGeneData, arrays, CAGEWorkflow, fluentGenomics,
simpleSingleCell, AnnoProbe, aroma.affymetrix, canvasXpress,
corncob, DGEobj.utils, easybio, GiANT, hexbin, limorhyde, LPS,
maGUI, NACHO, pctax, Platypus, pmartR, protti, RepeatedHighDim,
SCdeconR, seqgendiff, Seurat, simphony, st, volcano3D, wrGraph,
wrMisc, wrTopDownFrag
dependencyCount: 6
Package: limmaGUI
Version: 1.83.0
Imports: methods, grDevices, graphics, limma, R2HTML, tcltk, tkrplot,
xtable, utils
License: GPL (>=2)
MD5sum: d52b811ebb3b4565ecb47d7c79b387d7
NeedsCompilation: no
Title: GUI for limma Package With Two Color Microarrays
Description: A Graphical User Interface for differential expression
analysis of two-color microarray data using the limma package.
biocViews: GUI, GeneExpression, DifferentialExpression, DataImport,
Bayesian, Regression, TimeCourse, Microarray, mRNAMicroarray,
TwoChannel, BatchEffect, MultipleComparison, Normalization,
Preprocessing, QualityControl
Author: James Wettenhall [aut], Gordon Smyth [aut], Keith Satterley
[ctb]
Maintainer: Gordon Smyth <smyth@wehi.edu.au>
URL: http://bioinf.wehi.edu.au/limmaGUI/
git_url: https://git.bioconductor.org/packages/limmaGUI
git_branch: devel
git_last_commit: 39141a0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/limmaGUI_1.83.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/limmaGUI_1.83.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/limmaGUI_1.83.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/limmaGUI_1.83.0.tgz
vignettes: vignettes/limmaGUI/inst/doc/extract.pdf,
vignettes/limmaGUI/inst/doc/limmaGUI.pdf,
vignettes/limmaGUI/inst/doc/LinModIntro.pdf,
vignettes/limmaGUI/inst/doc/about.html,
vignettes/limmaGUI/inst/doc/CustMenu.html,
vignettes/limmaGUI/inst/doc/import.html,
vignettes/limmaGUI/inst/doc/index.html,
vignettes/limmaGUI/inst/doc/InputFiles.html,
vignettes/limmaGUI/inst/doc/lgDevel.html,
vignettes/limmaGUI/inst/doc/windowsFocus.html
vignetteTitles: Extracting limma objects from limmaGUI files, limmaGUI
Vignette, LinModIntro.pdf, about.html, CustMenu.html,
import.html, index.html, InputFiles.html, lgDevel.html,
windowsFocus.html
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/limmaGUI/inst/doc/limmaGUI.R
dependencyCount: 11
Package: limpa
Version: 0.99.10
Depends: limma
Imports: methods, stats, data.table, statmod
Suggests: arrow, knitr, BiocStyle
License: GPL (>=2)
Archs: x64
MD5sum: a6088fc1786c0c9a63974c8824a24333
NeedsCompilation: no
Title: Quantification and Differential Analysis of Proteomics Data
Description: Quantification and differential analysis of
mass-spectrometry proteomics data, with probabilistic recovery
of information from missing values. Estimates the detection
probability curve (DPC), which relates the probability of
successful detection to the underlying expression level of each
peptide, and uses it to incorporate peptide missing values into
protein quantification and into subsequent differential
expression analyses. The package produces objects suitable for
downstream analysis in limma. The package accepts peptide-level
data with missing values and produces complete protein
quantifications without missing values. The uncertainty
introduced by missing value imputation is propagated through to
the limma analyses using variance modeling and precision
weights. The package name "limpa" is an acronym for "Linear
Models for Proteomics Data".
biocViews: Bayesian, BiologicalQuestion, DataImport,
DifferentialExpression, GeneExpression, MassSpectrometry,
Preprocessing, Proteomics, Regression, Software
Author: Mengbo Li [aut] (ORCID:
<https://orcid.org/0000-0002-9666-5810>), Gordon Smyth [cre,
aut] (ORCID: <https://orcid.org/0000-0001-9221-2892>)
Maintainer: Gordon Smyth <smyth@wehi.edu.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/limpa
git_branch: devel
git_last_commit: daba68a
git_last_commit_date: 2025-03-25
Date/Publication: 2025-03-25
source.ver: src/contrib/limpa_0.99.10.tar.gz
win.binary.ver: bin/windows/contrib/4.5/limpa_0.99.10.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/limpa_0.99.10.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/limpa_0.99.10.tgz
vignettes: vignettes/limpa/inst/doc/limpa.html
vignetteTitles: Analyzing mass spectrometry data with limpa
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/limpa/inst/doc/limpa.R
dependencyCount: 8
Package: limpca
Version: 1.3.0
Depends: R (>= 3.5.0)
Imports: ggplot2, stringr, plyr, ggrepel, reshape2, grDevices,
graphics, doParallel, parallel, dplyr, tibble, tidyr, ggsci,
tidyverse, methods, stats, SummarizedExperiment, S4Vectors
Suggests: BiocStyle, pander, rmarkdown, car, gridExtra, knitr, testthat
(>= 3.0.0)
License: Artistic-2.0
Archs: x64
MD5sum: 89a075372214446c17a8c5368826f71d
NeedsCompilation: no
Title: An R package for the linear modeling of high-dimensional
designed data based on ASCA/APCA family of methods
Description: This package has for objectives to provide a method to
make Linear Models for high-dimensional designed data. limpca
applies a GLM (General Linear Model) version of ASCA and APCA
to analyse multivariate sample profiles generated by an
experimental design. ASCA/APCA provide powerful visualization
tools for multivariate structures in the space of each effect
of the statistical model linked to the experimental design and
contrarily to MANOVA, it can deal with mutlivariate datasets
having more variables than observations. This method can handle
unbalanced design.
biocViews: StatisticalMethod, PrincipalComponent, Regression,
Visualization, ExperimentalDesign, MultipleComparison,
GeneExpression, Metabolomics
Author: Bernadette Govaerts [aut, ths], Sebastien Franceschini [ctb],
Robin van Oirbeek [ctb], Michel Thiel [aut], Pascal de Tullio
[dtc], Manon Martin [aut, cre] (ORCID:
<https://orcid.org/0000-0003-4800-0942>), Nadia Benaiche [ctb]
Maintainer: Manon Martin <manon.martin@uclouvain.be>
URL: https://github.com/ManonMartin/limpca,
https://manonmartin.github.io/limpca/
VignetteBuilder: knitr
BugReports: https://github.com/ManonMartin/limpca/issues
git_url: https://git.bioconductor.org/packages/limpca
git_branch: devel
git_last_commit: 8e4f4ce
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/limpca_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/limpca_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/limpca_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/limpca_1.3.0.tgz
vignettes: vignettes/limpca/inst/doc/limpca.html,
vignettes/limpca/inst/doc/Trout.html,
vignettes/limpca/inst/doc/UCH.html
vignetteTitles: Get started with limpca, Analysis of the Trout dataset
with limpca, Analysis of the UCH dataset with limpca
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/limpca/inst/doc/limpca.R,
vignettes/limpca/inst/doc/Trout.R,
vignettes/limpca/inst/doc/UCH.R
dependencyCount: 141
Package: LimROTS
Version: 0.99.12
Depends: R (>= 4.5.0), SummarizedExperiment
Imports: limma, stringr, qvalue, utils, stats, BiocParallel,
basilisk.utils, S4Vectors, dplyr
Suggests: BiocStyle, ggplot2, magick, testthat (>= 3.0.0), knitr,
rmarkdown, caret, ROTS
License: Artistic-2.0
Archs: x64
MD5sum: c693cd89a4cd223be257d3fb5370d677
NeedsCompilation: no
Title: A Hybrid Method Integrating Empirical Bayes and
Reproducibility-Optimized Statistics for Robust Analysis of
Proteomics and Metabolomics Data
Description: Differential expression analysis is a prevalent method
utilised in the examination of diverse biological data. The
reproducibility-optimized test statistic (ROTS) modifies a
t-statistic based on the data's intrinsic characteristics and
ranks features according to their statistical significance for
differential expression between two or more groups
(f-statistic). Focussing on proteomics and metabolomics, the
current ROTS implementation cannot account for technical or
biological covariates such as MS batches or gender differences
among the samples. Consequently, we developed LimROTS, which
employs a reproducibility-optimized test statistic utilising
the limma methodology to simulate complex experimental designs.
LimROTS is a hybrid method integrating empirical bayes and
reproducibility-optimized statistics for robust analysis of
proteomics and metabolomics data.
biocViews: Software, GeneExpression, DifferentialExpression,
Microarray, RNASeq, Proteomics, ImmunoOncology, Metabolomics,
mRNAMicroarray
Author: Ali Mostafa Anwar [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5201-387X>), Leo Lahti [aut, ths]
(ORCID: <https://orcid.org/0000-0001-5537-637X>), Akewak Jeba
[aut, ctb] (ORCID: <https://orcid.org/0009-0007-1347-7552>),
Eleanor Coffey [aut, ths] (ORCID:
<https://orcid.org/0000-0002-9717-5610>)
Maintainer: Ali Mostafa Anwar <aliali.mostafa99@gmail.com>
URL: https://github.com/AliYoussef96/LimROTS,
https://aliyoussef96.github.io/LimROTS/
VignetteBuilder: knitr
BugReports: https://github.com/AliYoussef96/LimROTS/issues
git_url: https://git.bioconductor.org/packages/LimROTS
git_branch: devel
git_last_commit: aac524a
git_last_commit_date: 2024-12-19
Date/Publication: 2024-12-20
source.ver: src/contrib/LimROTS_0.99.12.tar.gz
win.binary.ver: bin/windows/contrib/4.5/LimROTS_0.99.12.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/LimROTS_0.99.12.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/LimROTS_0.99.12.tgz
vignettes: vignettes/LimROTS/inst/doc/LimROTS.html
vignetteTitles: LimROTS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/LimROTS/inst/doc/LimROTS.R
dependencyCount: 85
Package: lineagespot
Version: 1.11.0
Imports: VariantAnnotation, MatrixGenerics, SummarizedExperiment,
data.table, stringr, httr, utils
Suggests: BiocStyle, RefManageR, rmarkdown, knitr, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: 42c31ddd6813956bc966b40280df08d9
NeedsCompilation: no
Title: Detection of SARS-CoV-2 lineages in wastewater samples using
next-generation sequencing
Description: Lineagespot is a framework written in R, and aims to
identify SARS-CoV-2 related mutations based on a single (or a
list) of variant(s) file(s) (i.e., variant calling format). The
method can facilitate the detection of SARS-CoV-2 lineages in
wastewater samples using next generation sequencing, and
attempts to infer the potential distribution of the SARS-CoV-2
lineages.
biocViews: VariantDetection, VariantAnnotation, Sequencing
Author: Nikolaos Pechlivanis [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2502-612X>), Maria Tsagiopoulou
[aut], Maria Christina Maniou [aut], Anastasis Togkousidis
[aut], Evangelia Mouchtaropoulou [aut], Taxiarchis Chassalevris
[aut], Serafeim Chaintoutis [aut], Chrysostomos Dovas [aut],
Maria Petala [aut], Margaritis Kostoglou [aut], Thodoris
Karapantsios [aut], Stamatia Laidou [aut], Elisavet
Vlachonikola [aut], Aspasia Orfanou [aut], Styliani-Christina
Fragkouli [aut], Sofoklis Keisaris [aut], Anastasia
Chatzidimitriou [aut], Agis Papadopoulos [aut], Nikolaos
Papaioannou [aut], Anagnostis Argiriou [aut], Fotis E.
Psomopoulos [aut]
Maintainer: Nikolaos Pechlivanis <inab.bioinformatics@lists.certh.gr>
URL: https://github.com/BiodataAnalysisGroup/lineagespot
VignetteBuilder: knitr
BugReports: https://github.com/BiodataAnalysisGroup/lineagespot/issues
git_url: https://git.bioconductor.org/packages/lineagespot
git_branch: devel
git_last_commit: 9ae0440
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/lineagespot_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/lineagespot_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/lineagespot_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/lineagespot_1.11.0.tgz
vignettes: vignettes/lineagespot/inst/doc/lineagespot.html
vignetteTitles: lineagespot User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/lineagespot/inst/doc/lineagespot.R
dependencyCount: 83
Package: LinkHD
Version: 1.21.0
Depends: R(>= 3.6.0), methods, ggplot2, stats
Imports: scales, cluster, graphics, ggpubr, gridExtra, vegan, rio,
MultiAssayExperiment, emmeans, reshape2, data.table
Suggests: MASS (>= 7.3.0), knitr, rmarkdown, BiocStyle
License: GPL-3
MD5sum: e4a3547ac5521befec0bdaf0db8288d4
NeedsCompilation: no
Title: LinkHD: a versatile framework to explore and integrate
heterogeneous data
Description: Here we present Link-HD, an approach to integrate
heterogeneous datasets, as a generalization of STATIS-ACT
(“Structuration des Tableaux A Trois Indices de la
Statistique–Analyse Conjointe de Tableauxâ€), a family of
methods to join and compare information from multiple
subspaces. However, STATIS-ACT has some drawbacks since it only
allows continuous data and it is unable to establish
relationships between samples and features. In order to tackle
these constraints, we incorporate multiple distance options and
a linear regression based Biplot model in order to stablish
relationships between observations and variable and perform
variable selection.
biocViews: Classification,MultipleComparison,Regression,Software
Author: Laura M. Zingaretti [aut, cre]
Maintainer: "Laura M Zingaretti" <m.lau.zingaretti@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/LinkHD
git_branch: devel
git_last_commit: bdab22c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/LinkHD_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/LinkHD_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/LinkHD_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/LinkHD_1.21.0.tgz
vignettes: vignettes/LinkHD/inst/doc/LinkHD.html
vignetteTitles: Annotating Genomic Variants
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/LinkHD/inst/doc/LinkHD.R
dependencyCount: 135
Package: Linnorm
Version: 2.31.0
Depends: R(>= 4.1.0)
Imports: Rcpp (>= 0.12.2), RcppArmadillo (>= 0.8.100.1.0), fpc, vegan,
mclust, apcluster, ggplot2, ellipse, limma, utils, statmod,
MASS, igraph, grDevices, graphics, fastcluster, ggdendro, zoo,
stats, amap, Rtsne, gmodels
LinkingTo: Rcpp, RcppArmadillo
Suggests: BiocStyle, knitr, rmarkdown, markdown, gplots, RColorBrewer,
moments, testthat, matrixStats
License: MIT + file LICENSE
MD5sum: 24193fd4aef073ee40073d0b40b6f515
NeedsCompilation: yes
Title: Linear model and normality based normalization and
transformation method (Linnorm)
Description: Linnorm is an algorithm for normalizing and transforming
RNA-seq, single cell RNA-seq, ChIP-seq count data or any large
scale count data. It has been independently reviewed by Tian et
al. on Nature Methods
(https://doi.org/10.1038/s41592-019-0425-8). Linnorm can work
with raw count, CPM, RPKM, FPKM and TPM.
biocViews: ImmunoOncology, Sequencing, ChIPSeq, RNASeq,
DifferentialExpression, GeneExpression, Genetics,
Normalization, Software, Transcription, BatchEffect,
PeakDetection, Clustering, Network, SingleCell
Author: Shun Hang Yip <shunyip@bu.edu>
Maintainer: Shun Hang Yip <shunyip@bu.edu>
URL: https://doi.org/10.1093/nar/gkx828
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Linnorm
git_branch: devel
git_last_commit: 5c4a716
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Linnorm_2.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Linnorm_2.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Linnorm_2.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Linnorm_2.31.0.tgz
vignettes: vignettes/Linnorm/inst/doc/Linnorm_User_Manual.pdf
vignetteTitles: Linnorm User Manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Linnorm/inst/doc/Linnorm_User_Manual.R
importsMe: mnem
suggestsMe: SCdeconR
dependencyCount: 67
Package: LinTInd
Version: 1.11.0
Depends: R (>= 4.0), ggplot2, parallel, stats, S4Vectors
Imports: data.tree, reshape2, networkD3, stringdist, purrr, ape,
cowplot, ggnewscale, stringr, dplyr, rlist, pheatmap,
Biostrings, pwalign, IRanges, BiocGenerics(>= 0.36.1), ggtree
Suggests: knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: ae20498a4e1bee1d7f6a7f71e7e0159e
NeedsCompilation: no
Title: Lineage tracing by indels
Description: When we combine gene-editing technology and sequencing
technology, we need to reconstruct a lineage tree from alleles
generated and calculate the similarity between each pair of
groups. FindIndel() and IndelForm() function will help you
align each read to reference sequence and generate scar form
strings respectively. IndelIdents() function will help you to
define a scar form for each cell or read. IndelPlot() function
will help you to visualize the distribution of deletion and
insertion. TagProcess() function will help you to extract
indels for each cell or read. TagDist() function will help you
to calculate the similarity between each pair of groups across
the indwells they contain. BuildTree() function will help you
to reconstruct a tree. PlotTree() function will help you to
visualize the tree.
biocViews: SingleCell, CRISPR, Alignment
Author: Luyue Wang [aut, cre], Bin Xiang [ctb], Hengxin Liu [ctb], Wu
Wei [ths]
Maintainer: Luyue Wang <wly1995310@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/LinTInd
git_branch: devel
git_last_commit: 7c091a5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/LinTInd_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/LinTInd_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/LinTInd_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/LinTInd_1.11.0.tgz
vignettes: vignettes/LinTInd/inst/doc/tutorial.html
vignetteTitles: LinTInd - tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/LinTInd/inst/doc/tutorial.R
dependencyCount: 107
Package: lionessR
Version: 1.21.0
Depends: R (>= 3.6.0)
Imports: stats, SummarizedExperiment, S4Vectors
Suggests: knitr, rmarkdown, igraph, reshape2, limma,
License: MIT + file LICENSE
MD5sum: 9ff331891af088bca45c13ffb94715fb
NeedsCompilation: no
Title: Modeling networks for individual samples using LIONESS
Description: LIONESS, or Linear Interpolation to Obtain Network
Estimates for Single Samples, can be used to reconstruct
single-sample networks (https://arxiv.org/abs/1505.06440). This
code implements the LIONESS equation in the lioness function in
R to reconstruct single-sample networks. The default network
reconstruction method we use is based on Pearson correlation.
However, lionessR can run on any network reconstruction
algorithms that returns a complete, weighted adjacency matrix.
lionessR works for both unipartite and bipartite networks.
biocViews: Network, NetworkInference, GeneExpression
Author: Marieke Lydia Kuijjer [aut] (ORCID:
<https://orcid.org/0000-0001-6280-3130>), Ping-Han Hsieh [cre]
(ORCID: <https://orcid.org/0000-0003-3054-1409>)
Maintainer: Ping-Han Hsieh <dn070017@gmail.com>
URL: https://github.com/mararie/lionessR
VignetteBuilder: knitr
BugReports: https://github.com/mararie/lionessR/issues
git_url: https://git.bioconductor.org/packages/lionessR
git_branch: devel
git_last_commit: 26ea400
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/lionessR_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/lionessR_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/lionessR_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/lionessR_1.21.0.tgz
vignettes: vignettes/lionessR/inst/doc/lionessR.html
vignetteTitles: lionessR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/lionessR/inst/doc/lionessR.R
dependencyCount: 36
Package: lipidr
Version: 2.21.0
Depends: R (>= 3.6.0), SummarizedExperiment
Imports: methods, stats, utils, data.table, S4Vectors, rlang, dplyr,
tidyr, forcats, ggplot2, limma, fgsea, ropls, imputeLCMD,
magrittr
Suggests: knitr, rmarkdown, BiocStyle, ggrepel, plotly, spelling,
testthat
License: MIT + file LICENSE
MD5sum: 9caab0a213c881642f9881b0db1148e3
NeedsCompilation: no
Title: Data Mining and Analysis of Lipidomics Datasets
Description: lipidr an easy-to-use R package implementing a complete
workflow for downstream analysis of targeted and untargeted
lipidomics data. lipidomics results can be imported into lipidr
as a numerical matrix or a Skyline export, allowing integration
into current analysis frameworks. Data mining of lipidomics
datasets is enabled through integration with Metabolomics
Workbench API. lipidr allows data inspection, normalization,
univariate and multivariate analysis, displaying informative
visualizations. lipidr also implements a novel Lipid Set
Enrichment Analysis (LSEA), harnessing molecular information
such as lipid class, total chain length and unsaturation.
biocViews: Lipidomics, MassSpectrometry, Normalization, QualityControl,
Visualization
Author: Ahmed Mohamed [cre] (ORCID:
<https://orcid.org/0000-0001-6507-5300>), Ahmed Mohamed [aut],
Jeffrey Molendijk [aut]
Maintainer: Ahmed Mohamed <mohamed@kuicr.kyoto-u.ac.jp>
URL: https://github.com/ahmohamed/lipidr
VignetteBuilder: knitr
BugReports: https://github.com/ahmohamed/lipidr/issues/
git_url: https://git.bioconductor.org/packages/lipidr
git_branch: devel
git_last_commit: ce50d70
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/lipidr_2.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/lipidr_2.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/lipidr_2.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/lipidr_2.21.0.tgz
vignettes: vignettes/lipidr/inst/doc/workflow.html
vignetteTitles: lipidr_workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/lipidr/inst/doc/workflow.R
suggestsMe: rgoslin
dependencyCount: 127
Package: LiquidAssociation
Version: 1.61.0
Depends: geepack, methods, yeastCC, org.Sc.sgd.db
Imports: Biobase, graphics, grDevices, methods, stats
License: GPL (>=3)
Archs: x64
MD5sum: 31ae064cfd3b7fe7b81c7cdb6c1ec44b
NeedsCompilation: no
Title: LiquidAssociation
Description: The package contains functions for calculate direct and
model-based estimators for liquid association. It also provides
functions for testing the existence of liquid association given
a gene triplet data.
biocViews: Pathways, GeneExpression, CellBiology, Genetics, Network,
TimeCourse
Author: Yen-Yi Ho <yho@jhsph.edu>
Maintainer: Yen-Yi Ho <yho@jhsph.edu>
git_url: https://git.bioconductor.org/packages/LiquidAssociation
git_branch: devel
git_last_commit: 55fef47
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/LiquidAssociation_1.61.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/LiquidAssociation_1.61.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/LiquidAssociation_1.61.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/LiquidAssociation_1.61.0.tgz
vignettes: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.pdf
vignetteTitles: LiquidAssociation Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.R
dependsOnMe: fastLiquidAssociation
dependencyCount: 63
Package: lisaClust
Version: 1.15.6
Depends: R (>= 4.0)
Imports: ggplot2, class, concaveman, grid, BiocParallel,
spatstat.explore, spatstat.geom, BiocGenerics, S4Vectors,
methods, spicyR, purrr, stats, data.table, dplyr, tidyr,
SingleCellExperiment, SpatialExperiment, SummarizedExperiment,
pheatmap, spatstat.random, lifecycle, simpleSeg, rlang,
Suggests: SpatialDatasets, BiocStyle, knitr, rmarkdown, testthat (>=
3.0.0)
License: GPL (>=2)
MD5sum: 5d179b9a2588b550af1ba82492671969
NeedsCompilation: no
Title: lisaClust: Clustering of Local Indicators of Spatial Association
Description: lisaClust provides a series of functions to identify and
visualise regions of tissue where spatial associations between
cell-types is similar. This package can be used to provide a
high-level summary of cell-type colocalization in multiplexed
imaging data that has been segmented at a single-cell
resolution.
biocViews: SingleCell, CellBasedAssays, Spatial
Author: Ellis Patrick [aut, cre], Nicolas Canete [aut], Nicholas
Robertson [ctb], Alex Qin [ctb], Shreya
shreya.rajeshrao@sydney.edu.au Rao [ctb]
Maintainer: Ellis Patrick <ellis.patrick@sydney.edu.au>
URL: https://ellispatrick.github.io/lisaClust/,
https://github.com/ellispatrick/lisaClust
VignetteBuilder: knitr
BugReports: https://github.com/ellispatrick/lisaClust/issues
git_url: https://git.bioconductor.org/packages/lisaClust
git_branch: devel
git_last_commit: a5842b8
git_last_commit_date: 2024-11-19
Date/Publication: 2024-11-20
source.ver: src/contrib/lisaClust_1.15.6.tar.gz
win.binary.ver: bin/windows/contrib/4.5/lisaClust_1.15.6.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/lisaClust_1.15.6.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/lisaClust_1.15.6.tgz
vignettes: vignettes/lisaClust/inst/doc/lisaClust.html
vignetteTitles: "Inroduction to lisaClust"
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/lisaClust/inst/doc/lisaClust.R
suggestsMe: Statial
dependencyCount: 235
Package: lmdme
Version: 1.49.0
Depends: R (>= 2.14.1), pls, stemHypoxia
Imports: stats, methods, limma
Enhances: parallel
License: GPL (>=2)
MD5sum: 44aac4b2d23bd9bbc6c1515c6d011fa1
NeedsCompilation: no
Title: Linear Model decomposition for Designed Multivariate Experiments
Description: linear ANOVA decomposition of Multivariate Designed
Experiments implementation based on limma lmFit. Features:
i)Flexible formula type interface, ii) Fast limma based
implementation, iii) p-values for each estimated coefficient
levels in each factor, iv) F values for factor effects and v)
plotting functions for PCA and PLS.
biocViews: Microarray, OneChannel, TwoChannel, Visualization,
DifferentialExpression, ExperimentData, Cancer
Author: Cristobal Fresno and Elmer A. Fernandez
Maintainer: Cristobal Fresno <cfresno@bdmg.com.ar>
URL: http://www.bdmg.com.ar/?page_id=38
git_url: https://git.bioconductor.org/packages/lmdme
git_branch: devel
git_last_commit: 2d18be3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/lmdme_1.49.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/lmdme_1.49.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/lmdme_1.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/lmdme_1.49.0.tgz
vignettes: vignettes/lmdme/inst/doc/lmdme-vignette.pdf
vignetteTitles: lmdme: linear model framework for PCA/PLS analysis of
ANOVA decomposition on Designed Multivariate Experiments in R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/lmdme/inst/doc/lmdme-vignette.R
dependencyCount: 9
Package: LOBSTAHS
Version: 1.33.0
Depends: R (>= 3.4), xcms, CAMERA, methods
Imports: utils
Suggests: PtH2O2lipids, knitr, rmarkdown
License: GPL (>= 3) + file LICENSE
MD5sum: fd54d0828739d76f7b1932d52ac57282
NeedsCompilation: no
Title: Lipid and Oxylipin Biomarker Screening through Adduct Hierarchy
Sequences
Description: LOBSTAHS is a multifunction package for screening,
annotation, and putative identification of mass spectral
features in large, HPLC-MS lipid datasets. In silico data for a
wide range of lipids, oxidized lipids, and oxylipins can be
generated from user-supplied structural criteria with a
database generation function. LOBSTAHS then applies these
databases to assign putative compound identities to features in
any high-mass accuracy dataset that has been processed using
xcms and CAMERA. Users can then apply a series of orthogonal
screening criteria based on adduct ion formation patterns,
chromatographic retention time, and other properties, to
evaluate and assign confidence scores to this list of
preliminary assignments. During the screening routine, LOBSTAHS
rejects assignments that do not meet the specified criteria,
identifies potential isomers and isobars, and assigns a variety
of annotation codes to assist the user in evaluating the
accuracy of each assignment.
biocViews: ImmunoOncology, MassSpectrometry, Metabolomics, Lipidomics,
DataImport
Author: James Collins [aut, cre], Helen Fredricks [aut], Bethanie
Edwards [aut], Henry Holm [aut], Benjamin Van Mooy [aut],
Daniel Lowenstein [aut]
Maintainer: Henry Holm <hholm@whoi.edu>, Daniel Lowenstein
<dlowenstein@whoi.edu>, James Collins
<james.r.collins@aya.yale.edu>
URL: http://bioconductor.org/packages/LOBSTAHS
VignetteBuilder: knitr
BugReports: https://github.com/vanmooylipidomics/LOBSTAHS/issues/new
git_url: https://git.bioconductor.org/packages/LOBSTAHS
git_branch: devel
git_last_commit: 39dadf2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/LOBSTAHS_1.33.0.tar.gz
vignettes: vignettes/LOBSTAHS/inst/doc/LOBSTAHS.html
vignetteTitles: Discovery,, Identification,, and Screening of Lipids
and Oxylipins in HPLC-MS Datasets Using LOBSTAHS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/LOBSTAHS/inst/doc/LOBSTAHS.R
dependsOnMe: PtH2O2lipids
dependencyCount: 160
Package: loci2path
Version: 1.27.0
Depends: R (>= 3.5.0)
Imports: pheatmap, wordcloud, RColorBrewer, data.table, methods,
grDevices, stats, graphics, GenomicRanges, BiocParallel,
S4Vectors
Suggests: BiocStyle, knitr, rmarkdown
License: Artistic-2.0
MD5sum: e0dae695c17ecda22c76323f2b3f8c8a
NeedsCompilation: no
Title: Loci2path: regulatory annotation of genomic intervals based on
tissue-specific expression QTLs
Description: loci2path performs statistics-rigorous enrichment analysis
of eQTLs in genomic regions of interest. Using eQTL collections
provided by the Genotype-Tissue Expression (GTEx) project and
pathway collections from MSigDB.
biocViews: FunctionalGenomics, Genetics, GeneSetEnrichment, Software,
GeneExpression, Sequencing, Coverage, BioCarta
Author: Tianlei Xu
Maintainer: Tianlei Xu <tianlei.xu@emory.edu>
URL: https://github.com/StanleyXu/loci2path
VignetteBuilder: knitr
BugReports: https://github.com/StanleyXu/loci2path/issues
git_url: https://git.bioconductor.org/packages/loci2path
git_branch: devel
git_last_commit: 1995675
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/loci2path_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/loci2path_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/loci2path_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/loci2path_1.27.0.tgz
vignettes: vignettes/loci2path/inst/doc/loci2path-vignette.html
vignetteTitles: loci2path
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/loci2path/inst/doc/loci2path-vignette.R
dependencyCount: 51
Package: logicFS
Version: 2.27.0
Depends: LogicReg, mcbiopi, survival
Imports: graphics, methods, stats
Suggests: genefilter, siggenes
License: LGPL (>= 2)
MD5sum: 7623c570de1e33c11c74c9352947d5b0
NeedsCompilation: no
Title: Identification of SNP Interactions
Description: Identification of interactions between binary variables
using Logic Regression. Can, e.g., be used to find interesting
SNP interactions. Contains also a bagging version of logic
regression for classification.
biocViews: SNP, Classification, Genetics
Author: Holger Schwender, Tobias Tietz
Maintainer: Holger Schwender <holger.schw@gmx.de>
git_url: https://git.bioconductor.org/packages/logicFS
git_branch: devel
git_last_commit: 235b3ea
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/logicFS_2.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/logicFS_2.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/logicFS_2.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/logicFS_2.27.0.tgz
vignettes: vignettes/logicFS/inst/doc/logicFS.pdf
vignetteTitles: logicFS Manual
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/logicFS/inst/doc/logicFS.R
suggestsMe: trio
dependencyCount: 12
Package: LOLA
Version: 1.37.0
Depends: R (>= 3.5.0)
Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, data.table,
reshape2, utils, stats, methods
Suggests: parallel, testthat, knitr, BiocStyle, rmarkdown
Enhances: simpleCache, qvalue, ggplot2
License: GPL-3
MD5sum: 363de05d168ba6d92a1b8a609fbb6a05
NeedsCompilation: no
Title: Locus overlap analysis for enrichment of genomic ranges
Description: Provides functions for testing overlap of sets of genomic
regions with public and custom region set (genomic ranges)
databases. This makes it possible to do automated enrichment
analysis for genomic region sets, thus facilitating
interpretation of functional genomics and epigenomics data.
biocViews: GeneSetEnrichment, GeneRegulation, GenomeAnnotation,
SystemsBiology, FunctionalGenomics, ChIPSeq, MethylSeq,
Sequencing
Author: Nathan Sheffield <http://www.databio.org> [aut, cre], Christoph
Bock [ctb]
Maintainer: Nathan Sheffield <nathan@code.databio.org>
URL: http://code.databio.org/LOLA
VignetteBuilder: knitr
BugReports: http://github.com/nsheff/LOLA
git_url: https://git.bioconductor.org/packages/LOLA
git_branch: devel
git_last_commit: 9642694
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/LOLA_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/LOLA_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/LOLA_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/LOLA_1.37.0.tgz
vignettes: vignettes/LOLA/inst/doc/choosingUniverse.html,
vignettes/LOLA/inst/doc/gettingStarted.html,
vignettes/LOLA/inst/doc/usingLOLACore.html
vignetteTitles: 3. Choosing a Universe, 1. Getting Started with LOLA,
2. Using LOLA Core
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/LOLA/inst/doc/choosingUniverse.R,
vignettes/LOLA/inst/doc/gettingStarted.R,
vignettes/LOLA/inst/doc/usingLOLACore.R
suggestsMe: COCOA, MAGAR, MIRA, ramr
dependencyCount: 35
Package: LoomExperiment
Version: 1.25.0
Depends: R (>= 3.5.0), S4Vectors, SingleCellExperiment,
SummarizedExperiment, methods, rhdf5, BiocIO
Imports: DelayedArray, GenomicRanges, HDF5Array, Matrix, stats,
stringr, utils
Suggests: testthat, BiocStyle, knitr, rmarkdown, reticulate
License: Artistic-2.0
MD5sum: ba633778f94d20f7cfc04c6fde6dd70d
NeedsCompilation: no
Title: LoomExperiment container
Description: The LoomExperiment package provide a means to easily
convert the Bioconductor "Experiment" classes to loom files and
vice versa.
biocViews: ImmunoOncology, DataRepresentation, DataImport,
Infrastructure, SingleCell
Author: Martin Morgan, Daniel Van Twisk
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/LoomExperiment
git_branch: devel
git_last_commit: 0f3982f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/LoomExperiment_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/LoomExperiment_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/LoomExperiment_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/LoomExperiment_1.25.0.tgz
vignettes: vignettes/LoomExperiment/inst/doc/LoomExperiment.html
vignetteTitles: An introduction to the LoomExperiment class
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/LoomExperiment/inst/doc/LoomExperiment.R
dependsOnMe: OSCA.intro
suggestsMe: adverSCarial, hca
dependencyCount: 51
Package: LPE
Version: 1.81.0
Depends: R (>= 2.10)
Imports: stats
License: LGPL
MD5sum: 5b7b5b701904d47f44302eef1b30a295
NeedsCompilation: no
Title: Methods for analyzing microarray data using Local Pooled Error
(LPE) method
Description: This LPE library is used to do significance analysis of
microarray data with small number of replicates. It uses
resampling based FDR adjustment, and gives less conservative
results than traditional 'BH' or 'BY' procedures. Data accepted
is raw data in txt format from MAS4, MAS5 or dChip. Data can
also be supplied after normalization. LPE library is primarily
used for analyzing data between two conditions. To use it for
paired data, see LPEP library. For using LPE in multiple
conditions, use HEM library.
biocViews: Microarray, DifferentialExpression
Author: Nitin Jain <emailnitinjain@gmail.com>, Michael O'Connell
<michaelo@warath.com>, Jae K. Lee <jaeklee@virginia.edu>.
Includes R source code contributed by HyungJun Cho
<hcho@virginia.edu>
Maintainer: Nitin Jain <emailnitinjain@gmail.com>
URL: http://www.r-project.org,
http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/,
http://sourceforge.net/projects/r-lpe/
git_url: https://git.bioconductor.org/packages/LPE
git_branch: devel
git_last_commit: 8bda3fa
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/LPE_1.81.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/LPE_1.81.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/LPE_1.81.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/LPE_1.81.0.tgz
vignettes: vignettes/LPE/inst/doc/LPE.pdf
vignetteTitles: LPE test for microarray data with small number of
replicates
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/LPE/inst/doc/LPE.R
dependsOnMe: PLPE
suggestsMe: ABarray
dependencyCount: 1
Package: lpNet
Version: 2.39.0
Depends: lpSolve, KEGGgraph
License: Artistic License 2.0
MD5sum: f9819edb87c43ea5d390033b60269c6b
NeedsCompilation: no
Title: Linear Programming Model for Network Inference
Description: lpNet aims at infering biological networks, in particular
signaling and gene networks. For that it takes perturbation
data, either steady-state or time-series, as input and
generates an LP model which allows the inference of signaling
networks. For parameter identification either leave-one-out
cross-validation or stratified n-fold cross-validation can be
used.
biocViews: NetworkInference
Author: Bettina Knapp, Marta R. A. Matos, Johanna Mazur, Lars Kaderali
Maintainer: Lars Kaderali <lars.kaderali@uni-greifswald.de>
git_url: https://git.bioconductor.org/packages/lpNet
git_branch: devel
git_last_commit: b90bc0c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/lpNet_2.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/lpNet_2.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/lpNet_2.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/lpNet_2.39.0.tgz
vignettes: vignettes/lpNet/inst/doc/vignette_lpNet.pdf
vignetteTitles: lpNet,, network inference with a linear optimization
program.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/lpNet/inst/doc/vignette_lpNet.R
dependencyCount: 16
Package: lpsymphony
Version: 1.35.0
Depends: R (>= 3.0.0)
Suggests: BiocStyle, knitr, testthat
Enhances: slam
License: EPL
MD5sum: e893d4b6265274a8dad484928a11dcfc
NeedsCompilation: yes
Title: Symphony integer linear programming solver in R
Description: This package was derived from Rsymphony_0.1-17 from CRAN.
These packages provide an R interface to SYMPHONY, an
open-source linear programming solver written in C++. The main
difference between this package and Rsymphony is that it
includes the solver source code (SYMPHONY version 5.6), while
Rsymphony expects to find header and library files on the
users' system. Thus the intention of lpsymphony is to provide
an easy to install interface to SYMPHONY. For Windows,
precompiled DLLs are included in this package.
biocViews: Infrastructure, ThirdPartyClient
Author: Vladislav Kim [aut, cre], Ted Ralphs [ctb], Menal Guzelsoy
[ctb], Ashutosh Mahajan [ctb], Reinhard Harter [ctb], Kurt
Hornik [ctb], Cyrille Szymanski [ctb], Stefan Theussl [ctb],
Mike Smith [ctb] (ORCID:
<https://orcid.org/0000-0002-7800-3848>)
Maintainer: Vladislav Kim <Vladislav.Kim@embl.de>
URL: http://R-Forge.R-project.org/projects/rsymphony,
https://projects.coin-or.org/SYMPHONY,
http://www.coin-or.org/download/source/SYMPHONY/
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/Huber-group-EMBL/lpsymphony/issues
git_url: https://git.bioconductor.org/packages/lpsymphony
git_branch: devel
git_last_commit: 464901c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/lpsymphony_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/lpsymphony_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/lpsymphony_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/lpsymphony_1.35.0.tgz
vignettes: vignettes/lpsymphony/inst/doc/lpsymphony.pdf
vignetteTitles: Introduction to lpsymphony
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/lpsymphony/inst/doc/lpsymphony.R
importsMe: IHW
suggestsMe: oppr, prioritizr
dependencyCount: 0
Package: LRBaseDbi
Version: 2.17.0
Depends: R (>= 3.5.0)
Imports: methods, stats, utils, AnnotationDbi, RSQLite, DBI, Biobase
Suggests: testthat, BiocStyle, AnnotationHub
License: Artistic-2.0
MD5sum: 63da5e711d8adbd89aaa7e98ebeac09c
NeedsCompilation: no
Title: DBI to construct LRBase-related package
Description: Interface to construct LRBase package (LRBase.XXX.eg.db).
biocViews: Infrastructure
Author: Koki Tsuyuzaki
Maintainer: Koki Tsuyuzaki <k.t.the-answer@hotmail.co.jp>
VignetteBuilder: utils
git_url: https://git.bioconductor.org/packages/LRBaseDbi
git_branch: devel
git_last_commit: caa85c6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/LRBaseDbi_2.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/LRBaseDbi_2.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/LRBaseDbi_2.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/LRBaseDbi_2.17.0.tgz
vignettes: vignettes/LRBaseDbi/inst/doc/LRBaseDbi.pdf
vignetteTitles: LRBaseDbi
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/LRBaseDbi/inst/doc/LRBaseDbi.R
suggestsMe: scTensor
dependencyCount: 45
Package: LRcell
Version: 1.15.0
Depends: R (>= 4.1), ExperimentHub, AnnotationHub
Imports: BiocParallel, dplyr, ggplot2, ggrepel, magrittr, stats, utils
Suggests: LRcellTypeMarkers, BiocStyle, knitr, rmarkdown, roxygen2,
testthat
License: MIT + file LICENSE
MD5sum: ce4351b489a5c27c0995f3f38eae0afa
NeedsCompilation: no
Title: Differential cell type change analysis using Logistic/linear
Regression
Description: The goal of LRcell is to identify specific sub-cell types
that drives the changes observed in a bulk RNA-seq differential
gene expression experiment. To achieve this, LRcell utilizes
sets of cell marker genes acquired from single-cell
RNA-sequencing (scRNA-seq) as indicators for various cell types
in the tissue of interest. Next, for each cell type, using its
marker genes as indicators, we apply Logistic Regression on the
complete set of genes with differential expression p-values to
calculate a cell-type significance p-value. Finally, these
p-values are compared to predict which one(s) are likely to be
responsible for the differential gene expression pattern
observed in the bulk RNA-seq experiments. LRcell is inspired by
the LRpath[@sartor2009lrpath] algorithm developed by Sartor et
al., originally designed for pathway/gene set enrichment
analysis. LRcell contains three major components: LRcell
analysis, plot generation and marker gene selection. All
modules in this package are written in R. This package also
provides marker genes in the Prefrontal Cortex (pFC) human
brain region, human PBMC and nine mouse brain regions (Frontal
Cortex, Cerebellum, Globus Pallidus, Hippocampus,
Entopeduncular, Posterior Cortex, Striatum, Substantia Nigra
and Thalamus).
biocViews: SingleCell, GeneSetEnrichment, Sequencing, Regression,
GeneExpression, DifferentialExpression
Author: Wenjing Ma [cre, aut] (ORCID:
<https://orcid.org/0000-0001-8757-651X>)
Maintainer: Wenjing Ma <wenjing.ma@emory.edu>
VignetteBuilder: knitr
BugReports: https://github.com/marvinquiet/LRcell/issues
git_url: https://git.bioconductor.org/packages/LRcell
git_branch: devel
git_last_commit: 0eb2d9f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/LRcell_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/LRcell_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/LRcell_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/LRcell_1.15.0.tgz
vignettes: vignettes/LRcell/inst/doc/LRcell-vignette.html
vignetteTitles: LRcell Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/LRcell/inst/doc/LRcell-vignette.R
suggestsMe: LRcellTypeMarkers
dependencyCount: 94
Package: lumi
Version: 2.59.0
Depends: R (>= 2.10), Biobase (>= 2.5.5)
Imports: affy (>= 1.23.4), methylumi (>= 2.3.2), GenomicFeatures,
GenomicRanges, annotate, lattice, mgcv (>= 1.4-0), nleqslv,
KernSmooth, preprocessCore, RSQLite, DBI, AnnotationDbi, MASS,
graphics, stats, stats4, methods
Suggests: beadarray, limma, vsn, lumiBarnes, lumiHumanAll.db,
lumiHumanIDMapping, genefilter, RColorBrewer
License: LGPL (>= 2)
MD5sum: 28e1ca205848f036da048212f25e96e4
NeedsCompilation: no
Title: BeadArray Specific Methods for Illumina Methylation and
Expression Microarrays
Description: The lumi package provides an integrated solution for the
Illumina microarray data analysis. It includes functions of
Illumina BeadStudio (GenomeStudio) data input, quality control,
BeadArray-specific variance stabilization, normalization and
gene annotation at the probe level. It also includes the
functions of processing Illumina methylation microarrays,
especially Illumina Infinium methylation microarrays.
biocViews: Microarray, OneChannel, Preprocessing, DNAMethylation,
QualityControl, TwoChannel
Author: Pan Du, Richard Bourgon, Gang Feng, Simon Lin
Maintainer: Lei Huang <lhuang1998@gmail.com>
git_url: https://git.bioconductor.org/packages/lumi
git_branch: devel
git_last_commit: 130abf1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/lumi_2.59.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/lumi_2.59.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/lumi_2.59.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/lumi_2.59.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: iCheck, wateRmelon, lumiHumanIDMapping,
lumiMouseIDMapping, lumiRatIDMapping, ffpeExampleData,
lumiBarnes, MAQCsubset, mvoutData
importsMe: arrayMvout, ffpe, MineICA
suggestsMe: beadarray, blima, Harman, methylumi, tigre, maGUI
dependencyCount: 165
Package: lute
Version: 1.3.0
Depends: R (>= 4.3.0), stats, methods, utils, SummarizedExperiment,
SingleCellExperiment, BiocGenerics
Imports: S4Vectors, Biobase, scran, dplyr, ggplot2
Suggests: nnls, knitr, testthat, rmarkdown, BiocStyle, GenomicRanges,
limma, ExperimentHub, AnnotationHub, DelayedMatrixStats,
BisqueRNA, DelayedArray
License: Artistic-2.0
Archs: x64
MD5sum: b8687466aff61cc3bbb5ba2857027de6
NeedsCompilation: no
Title: Framework for cell size scale factor normalized bulk
transcriptomics deconvolution experiments
Description: Provides a framework for adjustment on cell type size when
performing bulk transcripomics deconvolution. The main
framework function provides a means of reference normalization
using cell size scale factors. It allows for marker selection
and deconvolution using non-negative least squares (NNLS) by
default. The framework is extensible for other marker selection
and deconvolution algorithms, and users may reuse the generics,
methods, and classes for these when developing new algorithms.
biocViews: RNASeq, Sequencing, SingleCell, Coverage, Transcriptomics,
Normalization
Author: Sean K Maden [cre, aut] (ORCID:
<https://orcid.org/0000-0002-2212-4894>), Stephanie Hicks [aut]
(ORCID: <https://orcid.org/0000-0002-7858-0231>)
Maintainer: Sean K Maden <maden.sean@gmail.com>
URL: https://github.com/metamaden/lute
VignetteBuilder: knitr
BugReports: https://github.com/metamaden/lute/issues
git_url: https://git.bioconductor.org/packages/lute
git_branch: devel
git_last_commit: 449b0aa
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/lute_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/lute_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/lute_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/lute_1.3.0.tgz
vignettes: vignettes/lute/inst/doc/lute_algorithm_classes.html,
vignettes/lute/inst/doc/lute_pseudobulk_example.html,
vignettes/lute/inst/doc/lute_users_guide.html
vignetteTitles: lute algorithm classes, Pseudobulk cell size rescaling
example, The lute user's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/lute/inst/doc/lute_algorithm_classes.R,
vignettes/lute/inst/doc/lute_pseudobulk_example.R,
vignettes/lute/inst/doc/lute_users_guide.R
dependencyCount: 95
Package: LymphoSeq
Version: 1.35.0
Depends: R (>= 3.3), LymphoSeqDB
Imports: data.table, plyr, dplyr, reshape, VennDiagram, ggplot2, ineq,
RColorBrewer, circlize, grid, utils, stats, ggtree, msa,
Biostrings, phangorn, stringdist, UpSetR
Suggests: knitr, pheatmap, wordcloud, rmarkdown
License: Artistic-2.0
MD5sum: 6588aae39db741b0243ef7851eaf756f
NeedsCompilation: no
Title: Analyze high-throughput sequencing of T and B cell receptors
Description: This R package analyzes high-throughput sequencing of T
and B cell receptor complementarity determining region 3 (CDR3)
sequences generated by Adaptive Biotechnologies' ImmunoSEQ
assay. Its input comes from tab-separated value (.tsv) files
exported from the ImmunoSEQ analyzer.
biocViews: Software, Technology, Sequencing, TargetedResequencing,
Alignment, MultipleSequenceAlignment
Author: David Coffey <dcoffey@fredhutch.org>
Maintainer: David Coffey <dcoffey@fredhutch.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/LymphoSeq
git_branch: devel
git_last_commit: 1b80972
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/LymphoSeq_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/LymphoSeq_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/LymphoSeq_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/LymphoSeq_1.35.0.tgz
vignettes: vignettes/LymphoSeq/inst/doc/LymphoSeq.html
vignetteTitles: Analysis of high-throughput sequencing of T and B cell
receptors with LymphoSeq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/LymphoSeq/inst/doc/LymphoSeq.R
dependencyCount: 97
Package: M3C
Version: 1.29.0
Depends: R (>= 3.5.0)
Imports: ggplot2, Matrix, doSNOW, cluster, parallel, foreach,
doParallel, matrixcalc, Rtsne, corpcor, umap
Suggests: knitr, rmarkdown
License: AGPL-3
MD5sum: e29d84f459a19b7d3660b91a851de577
NeedsCompilation: no
Title: Monte Carlo Reference-based Consensus Clustering
Description: M3C is a consensus clustering algorithm that uses a Monte
Carlo simulation to eliminate overestimation of K and can
reject the null hypothesis K=1.
biocViews: Clustering, GeneExpression, Transcription, RNASeq,
Sequencing, ImmunoOncology
Author: Christopher John, David Watson
Maintainer: Christopher John <chris.r.john86@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/M3C
git_branch: devel
git_last_commit: cbbb878
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/M3C_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/M3C_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/M3C_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/M3C_1.29.0.tgz
vignettes: vignettes/M3C/inst/doc/M3Cvignette.pdf
vignetteTitles: M3C
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/M3C/inst/doc/M3Cvignette.R
importsMe: lilikoi
suggestsMe: parameters
dependencyCount: 60
Package: M3Drop
Version: 1.33.0
Depends: R (>= 3.4), numDeriv
Imports: RColorBrewer, gplots, bbmle, statmod, grDevices, graphics,
stats, matrixStats, Matrix, irlba, reldist, Hmisc, methods,
scater
Suggests: ROCR, knitr, M3DExampleData, SingleCellExperiment, Seurat,
Biobase
License: GPL (>=2)
MD5sum: cc80f385313821a8002a83a847d7350d
NeedsCompilation: no
Title: Michaelis-Menten Modelling of Dropouts in single-cell RNASeq
Description: This package fits a model to the pattern of dropouts in
single-cell RNASeq data. This model is used as a null to
identify significantly variable (i.e. differentially expressed)
genes for use in downstream analysis, such as clustering cells.
Also includes an method for calculating exact Pearson residuals
in UMI-tagged data using a library-size aware negative binomial
model.
biocViews: RNASeq, Sequencing, Transcriptomics, GeneExpression,
Software, DifferentialExpression, DimensionReduction,
FeatureExtraction
Author: Tallulah Andrews <tallulandrews@gmail.com>
Maintainer: Tallulah Andrews <tallulandrews@gmail.com>
URL: https://github.com/tallulandrews/M3Drop
VignetteBuilder: knitr
BugReports: https://github.com/tallulandrews/M3Drop/issues
git_url: https://git.bioconductor.org/packages/M3Drop
git_branch: devel
git_last_commit: a036ab7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/M3Drop_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/M3Drop_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/M3Drop_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/M3Drop_1.33.0.tgz
vignettes: vignettes/M3Drop/inst/doc/M3Drop_Vignette.pdf
vignetteTitles: Introduction to M3Drop
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/M3Drop/inst/doc/M3Drop_Vignette.R
importsMe: scMerge
dependencyCount: 167
Package: m6Aboost
Version: 1.13.0
Depends: S4Vectors, adabag, GenomicRanges, R (>= 4.1)
Imports: dplyr, rtracklayer, BSgenome, Biostrings, utils, methods,
IRanges, ExperimentHub
Suggests: knitr, rmarkdown, bookdown, testthat, BiocStyle,
BSgenome.Mmusculus.UCSC.mm10
License: Artistic-2.0
MD5sum: fae8b724965d4ea206ac7ef7e375415e
NeedsCompilation: no
Title: m6Aboost
Description: This package can help user to run the m6Aboost model on
their own miCLIP2 data. The package includes functions to
assign the read counts and get the features to run the m6Aboost
model. The miCLIP2 data should be stored in a GRanges object.
More details can be found in the vignette.
biocViews: Sequencing, Epigenetics, Genetics, ExperimentHubSoftware
Author: You Zhou [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1755-9073>), Kathi Zarnack [aut]
(ORCID: <https://orcid.org/0000-0003-3527-3378>)
Maintainer: You Zhou <youzhoulearning@gmail.com>
URL: https://github.com/ZarnackGroup/m6Aboost
VignetteBuilder: knitr
BugReports: https://github.com/ZarnackGroup/m6Aboost/issues
git_url: https://git.bioconductor.org/packages/m6Aboost
git_branch: devel
git_last_commit: 123d4ec
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/m6Aboost_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/m6Aboost_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/m6Aboost_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/m6Aboost_1.13.0.tgz
vignettes: vignettes/m6Aboost/inst/doc/m6AboosVignettes.html
vignetteTitles: m6Aboost Vignettes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/m6Aboost/inst/doc/m6AboosVignettes.R
dependencyCount: 170
Package: Maaslin2
Version: 1.21.0
Depends: R (>= 3.6)
Imports: robustbase, biglm, pcaPP, edgeR, metagenomeSeq, pbapply, car,
dplyr, vegan, chemometrics, ggplot2, pheatmap, logging,
data.table, lmerTest, hash, optparse, grDevices, stats, utils,
glmmTMB, MASS, cplm, pscl, lme4, tibble
Suggests: knitr, testthat (>= 2.1.0), rmarkdown, markdown
License: MIT + file LICENSE
MD5sum: 58399be64acc8898df5c1ece40b6f857
NeedsCompilation: no
Title: "Multivariable Association Discovery in Population-scale
Meta-omics Studies"
Description: MaAsLin2 is comprehensive R package for efficiently
determining multivariable association between clinical metadata
and microbial meta'omic features. MaAsLin2 relies on general
linear models to accommodate most modern epidemiological study
designs, including cross-sectional and longitudinal, and offers
a variety of data exploration, normalization, and
transformation methods. MaAsLin2 is the next generation of
MaAsLin.
biocViews: Metagenomics, Software, Microbiome, Normalization
Author: Himel Mallick [aut], Ali Rahnavard [aut], Lauren McIver [aut,
cre]
Maintainer: Lauren McIver <lauren.j.mciver@gmail.com>
URL: http://huttenhower.sph.harvard.edu/maaslin2
VignetteBuilder: knitr
BugReports: https://github.com/biobakery/maaslin2/issues
git_url: https://git.bioconductor.org/packages/Maaslin2
git_branch: devel
git_last_commit: 3dacd30
git_last_commit_date: 2024-10-29
Date/Publication: 2024-12-18
source.ver: src/contrib/Maaslin2_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Maaslin2_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Maaslin2_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Maaslin2_1.21.0.tgz
vignettes: vignettes/Maaslin2/inst/doc/maaslin2.html
vignetteTitles: Maaslin2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Maaslin2/inst/doc/maaslin2.R
importsMe: benchdamic, Macarron, MMUPHin
suggestsMe: dar
dependencyCount: 127
Package: maaslin3
Version: 0.99.16
Depends: R (>= 4.4)
Imports: dplyr, plyr, pbapply, lmerTest, parallel, lme4, optparse,
logging, multcomp, ggplot2, RColorBrewer, patchwork, scales,
rlang, tibble, ggnewscale, survival, methods, BiocGenerics,
SummarizedExperiment, TreeSummarizedExperiment
Suggests: knitr, testthat (>= 2.1.0), rmarkdown, markdown, kableExtra
License: MIT + file LICENSE
MD5sum: 2ad43325a5a6ddc72ab919acfe2a3470
NeedsCompilation: no
Title: "Refining and extending generalized multivariate linear models
for meta-omic association discovery"
Description: MaAsLin 3 refines and extends generalized multivariate
linear models for meta-omicron association discovery. It finds
abundance and prevalence associations between microbiome
meta-omics features and complex metadata in population-scale
epidemiological studies. The software includes multiple
analysis methods (including support for multiple covariates,
repeated measures, and ordered predictors), filtering,
normalization, and transform options to customize analysis for
your specific study.
biocViews: Metagenomics, Software, Microbiome, Normalization,
MultipleComparison
Author: William Nickols [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8214-9746>), Jacob Nearing [aut]
Maintainer: William Nickols <willnickols@g.harvard.edu>
URL: http://huttenhower.sph.harvard.edu/maaslin3
VignetteBuilder: knitr
BugReports: https://github.com/biobakery/maaslin3/issues
git_url: https://git.bioconductor.org/packages/maaslin3
git_branch: devel
git_last_commit: b0643b2
git_last_commit_date: 2025-03-25
Date/Publication: 2025-03-26
source.ver: src/contrib/maaslin3_0.99.16.tar.gz
win.binary.ver: bin/windows/contrib/4.5/maaslin3_0.99.16.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/maaslin3_0.99.16.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/maaslin3_0.99.16.tgz
vignettes: vignettes/maaslin3/inst/doc/maaslin3_manual.html,
vignettes/maaslin3/inst/doc/maaslin3_tutorial.html
vignetteTitles: Manual, Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/maaslin3/inst/doc/maaslin3_manual.R,
vignettes/maaslin3/inst/doc/maaslin3_tutorial.R
dependencyCount: 112
Package: Macarron
Version: 1.11.0
Depends: R (>= 4.2.0), SummarizedExperiment
Imports: BiocParallel, DelayedArray, WGCNA, ff, data.table,
dynamicTreeCut, Maaslin2, plyr, stats, psych, xml2, httr,
RJSONIO, logging, methods, utils
Suggests: knitr, BiocStyle, optparse, testthat (>= 2.1.0), rmarkdown,
markdown
License: MIT + file LICENSE
MD5sum: 3bc52e549adcf74a774deb03ff89c7a8
NeedsCompilation: no
Title: Prioritization of potentially bioactive metabolic features from
epidemiological and environmental metabolomics datasets
Description: Macarron is a workflow for the prioritization of
potentially bioactive metabolites from metabolomics
experiments. Prioritization integrates strengths of evidences
of bioactivity such as covariation with a known metabolite,
abundance relative to a known metabolite and association with
an environmental or phenotypic indicator of bioactivity.
Broadly, the workflow consists of stratified clustering of
metabolic spectral features which co-vary in abundance in a
condition, transfer of functional annotations, estimation of
relative abundance and differential abundance analysis to
identify associations between features and phenotype/condition.
biocViews: Sequencing, Metabolomics, Coverage, FunctionalPrediction,
Clustering
Author: Amrisha Bhosle [aut], Ludwig Geistlinger [aut], Sagun Maharjan
[aut, cre]
Maintainer: Sagun Maharjan <sagunmaharjann@gmail.com>
URL: http://huttenhower.sph.harvard.edu/macarron
VignetteBuilder: knitr
BugReports:
https://forum.biobakery.org/c/microbial-community-profiling/macarron
git_url: https://git.bioconductor.org/packages/Macarron
git_branch: devel
git_last_commit: 60ff933
git_last_commit_date: 2024-10-29
Date/Publication: 2024-12-19
source.ver: src/contrib/Macarron_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Macarron_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Macarron_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Macarron_1.11.0.tgz
vignettes: vignettes/Macarron/inst/doc/Macarron.html
vignetteTitles: Macarron
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Macarron/inst/doc/Macarron.R
dependencyCount: 204
Package: maCorrPlot
Version: 1.77.0
Depends: lattice
Imports: graphics, grDevices, lattice, stats
License: GPL (>= 2)
MD5sum: f0bbcb146935882624964755d530fb4a
NeedsCompilation: no
Title: Visualize artificial correlation in microarray data
Description: Graphically displays correlation in microarray data that
is due to insufficient normalization
biocViews: Microarray, Preprocessing, Visualization
Author: Alexander Ploner <Alexander.Ploner@ki.se>
Maintainer: Alexander Ploner <Alexander.Ploner@ki.se>
URL:
http://www.pubmedcentral.gov/articlerender.fcgi?tool=pubmed&pubmedid=15799785
git_url: https://git.bioconductor.org/packages/maCorrPlot
git_branch: devel
git_last_commit: 25a3a06
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/maCorrPlot_1.77.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/maCorrPlot_1.77.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/maCorrPlot_1.77.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/maCorrPlot_1.77.0.tgz
vignettes: vignettes/maCorrPlot/inst/doc/maCorrPlot.pdf
vignetteTitles: maCorrPlot Introduction
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/maCorrPlot/inst/doc/maCorrPlot.R
dependencyCount: 6
Package: MACSQuantifyR
Version: 1.21.0
Imports: readxl, graphics, tools, utils, grDevices, ggplot2, ggrepel,
methods, stats, latticeExtra, lattice, rmarkdown, png, grid,
gridExtra, prettydoc, rvest, xml2
Suggests: knitr, testthat, R.utils, spelling
License: Artistic-2.0
MD5sum: 9ef89ae19d9e9ecd54e2fc6bd6fd3133
NeedsCompilation: no
Title: Fast treatment of MACSQuantify FACS data
Description: Automatically process the metadata of MACSQuantify FACS
sorter. It runs multiple modules: i) imports of raw file and
graphical selection of duplicates in well plate, ii) computes
statistics on data and iii) can compute combination index.
biocViews: DataImport, Preprocessing, Normalization, FlowCytometry,
DataRepresentation, GUI
Author: Raphaël Bonnet [aut, cre], Marielle Nebout [dtc],Giulia
Biondani [dtc], Jean-François Peyron[aut,ths], Inserm [fnd]
Maintainer: Raphaël Bonnet <raphael.bonnet@univ-cotedazur.fr>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MACSQuantifyR
git_branch: devel
git_last_commit: 9364d97
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MACSQuantifyR_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MACSQuantifyR_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MACSQuantifyR_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MACSQuantifyR_1.21.0.tgz
vignettes: vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_combo.html,
vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_pipeline.html,
vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR.html
vignetteTitles: MACSQuantifyR_step_by_step_analysis,
MACSQuantifyR_simple_pipeline, MACSQuantifyR_quick_introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_combo.R,
vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_pipeline.R,
vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR.R
dependencyCount: 85
Package: MACSr
Version: 1.15.0
Depends: R (>= 4.1.0)
Imports: utils, reticulate, S4Vectors, methods, basilisk,
ExperimentHub, AnnotationHub
Suggests: testthat, knitr, rmarkdown, BiocStyle, MACSdata
License: BSD_3_clause + file LICENSE
MD5sum: 2a76a759c9641a3fc76c0f9e2bdc9cae
NeedsCompilation: no
Title: MACS: Model-based Analysis for ChIP-Seq
Description: The Model-based Analysis of ChIP-Seq (MACS) is a widely
used toolkit for identifying transcript factor binding sites.
This package is an R wrapper of the lastest MACS3.
biocViews: Software, ChIPSeq, ATACSeq, ImmunoOncology
Author: Philippa Doherty [aut], Qiang Hu [aut, cre]
Maintainer: Qiang Hu <Qiang.Hu@roswellpark.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MACSr
git_branch: devel
git_last_commit: 260457d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MACSr_1.15.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MACSr_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MACSr_1.15.0.tgz
vignettes: vignettes/MACSr/inst/doc/MACSr.html
vignetteTitles: MACSr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MACSr/inst/doc/MACSr.R
dependencyCount: 78
Package: made4
Version: 1.81.0
Depends: RColorBrewer,gplots,scatterplot3d, Biobase,
SummarizedExperiment
Imports: ade4
Suggests: affy, BiocStyle, knitr, rmarkdown
License: Artistic-2.0
MD5sum: 299454c9160173d1875c7957cdc1c520
NeedsCompilation: no
Title: Multivariate analysis of microarray data using ADE4
Description: Multivariate data analysis and graphical display of
microarray data. Functions include for supervised dimension
reduction (between group analysis) and joint dimension
reduction of 2 datasets (coinertia analysis). It contains
functions that require R package ade4.
biocViews: Clustering, Classification, DimensionReduction,
PrincipalComponent,Transcriptomics, MultipleComparison,
GeneExpression, Sequencing, Microarray
Author: Aedin Culhane
Maintainer: Aedin Culhane <Aedin@jimmy.harvard.edu>
URL: http://www.hsph.harvard.edu/aedin-culhane/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/made4
git_branch: devel
git_last_commit: 654689e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/made4_1.81.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/made4_1.81.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/made4_1.81.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/made4_1.81.0.tgz
vignettes: vignettes/made4/inst/doc/introduction.html
vignetteTitles: Authoring R Markdown vignettes
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/made4/inst/doc/introduction.R
importsMe: omicade4
dependencyCount: 49
Package: MADSEQ
Version: 1.33.0
Depends: R (>= 3.5.0), rjags (>= 4.6)
Imports: VGAM, coda, BSgenome, BSgenome.Hsapiens.UCSC.hg19, S4Vectors,
methods, preprocessCore, GenomicAlignments, Rsamtools,
Biostrings, GenomicRanges, IRanges, VariantAnnotation,
SummarizedExperiment, GenomeInfoDb, rtracklayer, graphics,
stats, grDevices, utils, zlibbioc, vcfR
Suggests: knitr
License: GPL(>=2)
MD5sum: 1bea13413919a5ca6773026477217be2
NeedsCompilation: no
Title: Mosaic Aneuploidy Detection and Quantification using Massive
Parallel Sequencing Data
Description: The MADSEQ package provides a group of hierarchical
Bayeisan models for the detection of mosaic aneuploidy, the
inference of the type of aneuploidy and also for the
quantification of the fraction of aneuploid cells in the
sample.
biocViews: GenomicVariation, SomaticMutation, VariantDetection,
Bayesian, CopyNumberVariation, Sequencing, Coverage
Author: Yu Kong, Adam Auton, John Murray Greally
Maintainer: Yu Kong <yu.kong@phd.einstein.yu.edu>
URL: https://github.com/ykong2/MADSEQ
VignetteBuilder: knitr
BugReports: https://github.com/ykong2/MADSEQ/issues
git_url: https://git.bioconductor.org/packages/MADSEQ
git_branch: devel
git_last_commit: 4a2b4e3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MADSEQ_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MADSEQ_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MADSEQ_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MADSEQ_1.33.0.tgz
vignettes: vignettes/MADSEQ/inst/doc/MADSEQ-vignette.html
vignetteTitles: R Package MADSEQ
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MADSEQ/inst/doc/MADSEQ-vignette.R
dependencyCount: 109
Package: maftools
Version: 2.23.0
Depends: R (>= 3.3)
Imports: data.table, grDevices, methods, RColorBrewer, Rhtslib,
survival, DNAcopy, pheatmap
LinkingTo: Rhtslib, zlibbioc
Suggests: berryFunctions, Biostrings, BSgenome,
BSgenome.Hsapiens.UCSC.hg19, GenomicRanges, IRanges, knitr,
mclust, MultiAssayExperiment, NMF, R.utils, RaggedExperiment,
rmarkdown, S4Vectors, curl
License: MIT + file LICENSE
MD5sum: 18a09feeacc5f123dd0bfadf466fbd29
NeedsCompilation: yes
Title: Summarize, Analyze and Visualize MAF Files
Description: Analyze and visualize Mutation Annotation Format (MAF)
files from large scale sequencing studies. This package
provides various functions to perform most commonly used
analyses in cancer genomics and to create feature rich
customizable visualzations with minimal effort.
biocViews: DataRepresentation, DNASeq, Visualization, DriverMutation,
VariantAnnotation, FeatureExtraction, Classification,
SomaticMutation, Sequencing, FunctionalGenomics, Survival
Author: Anand Mayakonda [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1162-687X>)
Maintainer: Anand Mayakonda <anand_mt@hotmail.com>
URL: https://github.com/PoisonAlien/maftools
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/PoisonAlien/maftools/issues
git_url: https://git.bioconductor.org/packages/maftools
git_branch: devel
git_last_commit: 689045c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/maftools_2.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/maftools_2.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/maftools_2.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/maftools_2.23.0.tgz
vignettes: vignettes/maftools/inst/doc/cancer_hotspots.html,
vignettes/maftools/inst/doc/cnv_analysis.html,
vignettes/maftools/inst/doc/maftools.html,
vignettes/maftools/inst/doc/oncoplots.html
vignetteTitles: 03: Cancer report, 04: Copy number analysis, 01:
Summarize,, Analyze,, and Visualize MAF Files, 02: Customizing
oncoplots
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/maftools/inst/doc/cancer_hotspots.R,
vignettes/maftools/inst/doc/cnv_analysis.R,
vignettes/maftools/inst/doc/maftools.R,
vignettes/maftools/inst/doc/oncoplots.R
dependsOnMe: GNOSIS
importsMe: CaMutQC, CIMICE, katdetectr, musicatk, aplotExtra,
pathwayTMB, PMAPscore, ProgModule, Rediscover, sigminer, SMDIC,
ssMutPA
suggestsMe: GenomicDataCommons, MultiAssayExperiment, survtype,
TCGAbiolinks, oncoPredict
dependencyCount: 29
Package: MAGAR
Version: 1.15.1
Depends: R (>= 4.1), HDF5Array, RnBeads, snpStats, crlmm
Imports: doParallel, igraph, bigstatsr, rjson, plyr, data.table,
UpSetR, reshape2, jsonlite, methods, ff, argparse, impute,
RnBeads.hg19, RnBeads.hg38, utils, stats
Suggests: gridExtra, VennDiagram, qqman, LOLA, RUnit, rmutil,
rmarkdown, JASPAR2018, TFBSTools, seqLogo, knitr, devtools,
BiocGenerics, BiocManager
License: GPL-3
MD5sum: 0b432e4b4c4696aab3b071981144ecef
NeedsCompilation: no
Title: MAGAR: R-package to compute methylation Quantitative Trait Loci
(methQTL) from DNA methylation and genotyping data
Description: "Methylation-Aware Genotype Association in R" (MAGAR)
computes methQTL from DNA methylation and genotyping data from
matched samples. MAGAR uses a linear modeling stragety to call
CpGs/SNPs that are methQTLs. MAGAR accounts for the local
correlation structure of CpGs.
biocViews: Regression, Epigenetics, DNAMethylation, SNP,
GeneticVariability, MethylationArray, Microarray, CpGIsland,
MethylSeq, Sequencing, mRNAMicroarray, Preprocessing,
CopyNumberVariation, TwoChannel, ImmunoOncology,
DifferentialMethylation, BatchEffect, QualityControl,
DataImport, Network, Clustering, GraphAndNetwork
Author: Michael Scherer [cre, aut] (ORCID:
<https://orcid.org/0000-0001-7990-6179>)
Maintainer: Michael Scherer <michael.scherer@dkfz.de>
URL: https://github.com/MPIIComputationalEpigenetics/MAGAR
VignetteBuilder: knitr
BugReports:
https://github.com/MPIIComputationalEpigenetics/MAGAR/issues
git_url: https://git.bioconductor.org/packages/MAGAR
git_branch: devel
git_last_commit: 42a555a
git_last_commit_date: 2024-11-13
Date/Publication: 2024-12-04
source.ver: src/contrib/MAGAR_1.15.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MAGAR_1.15.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MAGAR_1.15.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MAGAR_1.15.1.tgz
vignettes: vignettes/MAGAR/inst/doc/MAGAR.html
vignetteTitles: MAGAR: Methylation-Aware Genotype Association in R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MAGAR/inst/doc/MAGAR.R
dependencyCount: 201
Package: magpie
Version: 1.7.0
Depends: R (>= 4.3.0)
Imports: utils, rtracklayer, Matrix, matrixStats, stats, S4Vectors,
methods, graphics, GenomicRanges, GenomicFeatures, IRanges,
Rsamtools, AnnotationDbi, aod, BiocParallel, DESeq2, openxlsx,
RColorBrewer, reshape2, TRESS
Suggests: knitr, rmarkdown, kableExtra, RUnit, TBX20BamSubset,
BiocGenerics, BiocStyle
License: MIT + file LICENSE
MD5sum: ddbf635aebbf9eb2adc5ca4d196282a7
NeedsCompilation: no
Title: MeRIP-Seq data Analysis for Genomic Power Investigation and
Evaluation
Description: This package aims to perform power analysis for the
MeRIP-seq study. It calculates FDR, FDC, power, and precision
under various study design parameters, including but not
limited to sample size, sequencing depth, and testing method.
It can also output results into .xlsx files or produce
corresponding figures of choice.
biocViews: Epitranscriptomics, DifferentialMethylation, Sequencing,
RNASeq, Software
Author: Daoyu Duan [aut, cre], Zhenxing Guo [aut]
Maintainer: Daoyu Duan <dxd429@case.edu>
URL: https://github.com/dxd429/magpie
VignetteBuilder: knitr
BugReports: https://github.com/dxd429/magpie/issues
git_url: https://git.bioconductor.org/packages/magpie
git_branch: devel
git_last_commit: c0a41dc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/magpie_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/magpie_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/magpie_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/magpie_1.7.0.tgz
vignettes: vignettes/magpie/inst/doc/magpie.html
vignetteTitles: magpie Package User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/magpie/inst/doc/magpie.R
dependencyCount: 109
Package: magrene
Version: 1.9.0
Depends: R (>= 4.2.0)
Imports: utils, stats, BiocParallel
Suggests: BiocStyle, covr, knitr, rmarkdown, ggplot2, sessioninfo,
testthat (>= 3.0.0)
License: GPL-3
MD5sum: f2d799330e076b034554601ca78a7e16
NeedsCompilation: no
Title: Motif Analysis In Gene Regulatory Networks
Description: magrene allows the identification and analysis of graph
motifs in (duplicated) gene regulatory networks (GRNs),
including lambda, V, PPI V, delta, and bifan motifs. GRNs can
be tested for motif enrichment by comparing motif frequencies
to a null distribution generated from degree-preserving
simulated GRNs. Motif frequencies can be analyzed in the
context of gene duplications to explore the impact of
small-scale and whole-genome duplications on gene regulatory
networks. Finally, users can calculate interaction similarity
for gene pairs based on the Sorensen-Dice similarity index.
biocViews: Software, MotifDiscovery, NetworkEnrichment, SystemsBiology,
GraphAndNetwork
Author: FabrÃcio Almeida-Silva [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5314-2964>), Yves Van de Peer
[aut] (ORCID: <https://orcid.org/0000-0003-4327-3730>)
Maintainer: FabrÃcio Almeida-Silva <fabricio_almeidasilva@hotmail.com>
URL: https://github.com/almeidasilvaf/magrene
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/magrene
git_url: https://git.bioconductor.org/packages/magrene
git_branch: devel
git_last_commit: 99513a8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/magrene_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/magrene_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/magrene_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/magrene_1.9.0.tgz
vignettes: vignettes/magrene/inst/doc/magrene.html
vignetteTitles: Introduction to magrene
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/magrene/inst/doc/magrene.R
dependencyCount: 13
Package: MAI
Version: 1.13.0
Depends: R (>= 3.5.0)
Imports: caret, parallel, doParallel, foreach, e1071, future.apply,
future, missForest, pcaMethods, tidyverse, stats, utils,
methods, SummarizedExperiment, S4Vectors
Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0)
License: GPL-3
Archs: x64
MD5sum: d6c551de5de0d3ef93bdbb99bbe46aae
NeedsCompilation: no
Title: Mechanism-Aware Imputation
Description: A two-step approach to imputing missing data in
metabolomics. Step 1 uses a random forest classifier to
classify missing values as either Missing Completely at
Random/Missing At Random (MCAR/MAR) or Missing Not At Random
(MNAR). MCAR/MAR are combined because it is often difficult to
distinguish these two missing types in metabolomics data. Step
2 imputes the missing values based on the classified missing
mechanisms, using the appropriate imputation algorithms.
Imputation algorithms tested and available for MCAR/MAR include
Bayesian Principal Component Analysis (BPCA), Multiple
Imputation No-Skip K-Nearest Neighbors (Multi_nsKNN), and
Random Forest. Imputation algorithms tested and available for
MNAR include nsKNN and a single imputation approach for
imputation of metabolites where left-censoring is present.
biocViews: Software, Metabolomics, StatisticalMethod, Classification
Author: Jonathan Dekermanjian [aut, cre], Elin Shaddox [aut], Debmalya
Nandy [aut], Debashis Ghosh [aut], Katerina Kechris [aut]
Maintainer: Jonathan Dekermanjian
<Jonathan.Dekermanjian@CUAnschutz.edu>
URL: https://github.com/KechrisLab/MAI
VignetteBuilder: knitr
BugReports: https://github.com/KechrisLab/MAI/issues
git_url: https://git.bioconductor.org/packages/MAI
git_branch: devel
git_last_commit: 300bbe0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MAI_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MAI_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MAI_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MAI_1.13.0.tgz
vignettes: vignettes/MAI/inst/doc/UsingMAI.html
vignetteTitles: Utilizing Mechanism-Aware Imputation (MAI)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MAI/inst/doc/UsingMAI.R
dependencyCount: 174
Package: MAIT
Version: 1.41.0
Depends: R (>= 2.10), CAMERA, Rcpp, pls
Imports:
gplots,e1071,class,MASS,plsgenomics,agricolae,xcms,methods,caret
Suggests: faahKO
Enhances: rgl
License: GPL-2
MD5sum: cf000a22ce3df1611fd5baa5df66347d
NeedsCompilation: no
Title: Statistical Analysis of Metabolomic Data
Description: The MAIT package contains functions to perform end-to-end
statistical analysis of LC/MS Metabolomic Data. Special
emphasis is put on peak annotation and in modular function
design of the functions.
biocViews: ImmunoOncology, MassSpectrometry, Metabolomics, Software
Author: Francesc Fernandez-Albert, Rafael Llorach, Cristina
Andres-LaCueva, Alexandre Perera
Maintainer: Pol Sola-Santos <pol.soladelossantos@gmail.com>
git_url: https://git.bioconductor.org/packages/MAIT
git_branch: devel
git_last_commit: 2f8e62b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MAIT_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MAIT_1.41.0.zip
vignettes: vignettes/MAIT/inst/doc/MAIT_Vignette.pdf
vignetteTitles: \maketitleMAIT Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MAIT/inst/doc/MAIT_Vignette.R
dependencyCount: 204
Package: makecdfenv
Version: 1.83.1
Depends: R (>= 2.6.0), affyio
Imports: Biobase, affy, methods, stats, utils
License: GPL (>= 2)
MD5sum: 2f75b0a268f2571af182a376eaf4bb7e
NeedsCompilation: yes
Title: CDF Environment Maker
Description: This package has two functions. One reads a Affymetrix
chip description file (CDF) and creates a hash table
environment containing the location/probe set membership
mapping. The other creates a package that automatically loads
that environment.
biocViews: OneChannel, DataImport, Preprocessing
Author: Rafael A. Irizarry <rafa@jhu.edu>, Laurent Gautier
<laurent@cbs.dtu.dk>, Wolfgang Huber
<w.huber@dkfz-heidelberg.de>, Ben Bolstad <bmb@bmbolstad.com>
Maintainer: James W. MacDonald <jmacdon@u.washington.edu>
git_url: https://git.bioconductor.org/packages/makecdfenv
git_branch: devel
git_last_commit: df48c18
git_last_commit_date: 2025-01-10
Date/Publication: 2025-01-10
source.ver: src/contrib/makecdfenv_1.83.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/makecdfenv_1.83.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/makecdfenv_1.83.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/makecdfenv_1.83.1.tgz
vignettes: vignettes/makecdfenv/inst/doc/makecdfenv.pdf
vignetteTitles: makecdfenv primer
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/makecdfenv/inst/doc/makecdfenv.R
dependsOnMe: altcdfenvs
dependencyCount: 12
Package: MANOR
Version: 1.79.2
Depends: R (>= 2.10)
Imports: GLAD, graphics, grDevices, stats, utils
Suggests: knitr, rmarkdown, bookdown
License: GPL-2
MD5sum: 5939e7df08c466cafbb9e8c02c0e4e3c
NeedsCompilation: yes
Title: CGH Micro-Array NORmalization
Description: Importation, normalization, visualization, and quality
control functions to correct identified sources of variability
in array-CGH experiments.
biocViews: Microarray, TwoChannel, DataImport, QualityControl,
Preprocessing, CopyNumberVariation, Normalization
Author: Pierre Neuvial <pierre.neuvial@math.cnrs.fr>, Philippe Hupé
<philippe.hupe@curie.fr>
Maintainer: Pierre Neuvial <pierre.neuvial@math.cnrs.fr>
URL: http://bioinfo.curie.fr/projects/manor/index.html
VignetteBuilder: knitr
BugReports: https://github.com/pneuvial/MANOR/issues
git_url: https://git.bioconductor.org/packages/MANOR
git_branch: devel
git_last_commit: 8aeff0e
git_last_commit_date: 2025-03-12
Date/Publication: 2025-03-13
source.ver: src/contrib/MANOR_1.79.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MANOR_1.79.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MANOR_1.79.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MANOR_1.79.2.tgz
vignettes: vignettes/MANOR/inst/doc/MANOR.html
vignetteTitles: Overview of the MANOR package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MANOR/inst/doc/MANOR.R
dependencyCount: 9
Package: MantelCorr
Version: 1.77.0
Depends: R (>= 2.10)
Imports: stats
License: GPL (>= 2)
MD5sum: 1340722a0b75db9b6e0e7cc933b5a886
NeedsCompilation: no
Title: Compute Mantel Cluster Correlations
Description: Computes Mantel cluster correlations from a (p x n)
numeric data matrix (e.g. microarray gene-expression data).
biocViews: Clustering
Author: Brian Steinmeyer and William Shannon
Maintainer: Brian Steinmeyer <steinmeb@ilya.wustl.edu>
git_url: https://git.bioconductor.org/packages/MantelCorr
git_branch: devel
git_last_commit: e26a745
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MantelCorr_1.77.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MantelCorr_1.77.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MantelCorr_1.77.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MantelCorr_1.77.0.tgz
vignettes: vignettes/MantelCorr/inst/doc/MantelCorrVignette.pdf
vignetteTitles: MantelCorrVignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MantelCorr/inst/doc/MantelCorrVignette.R
dependencyCount: 1
Package: MAPFX
Version: 1.3.0
Depends: R (>= 4.4.0)
Imports: flowCore, Biobase, stringr, uwot, iCellR, igraph, ggplot2,
RColorBrewer, Rfast, ComplexHeatmap, circlize, glmnetUtils,
e1071, xgboost, parallel, pbapply, reshape2, gtools, utils,
stats, cowplot, methods, grDevices, graphics
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: GPL-2
MD5sum: 3de62c929db78d0e5eee7a6f9987ff80
NeedsCompilation: no
Title: MAssively Parallel Flow cytometry Xplorer (MAPFX): A Toolbox for
Analysing Data from the Massively-Parallel Cytometry
Experiments
Description: MAPFX is an end-to-end toolbox that pre-processes the raw
data from MPC experiments (e.g., BioLegend's LEGENDScreen and
BD Lyoplates assays), and further imputes the ‘missing’
infinity markers in the wells without those measurements. The
pipeline starts by performing background correction on raw
intensities to remove the noise from electronic baseline
restoration and fluorescence compensation by adapting a
normal-exponential convolution model. Unwanted technical
variation, from sources such as well effects, is then removed
using a log-normal model with plate, column, and row factors,
after which infinity markers are imputed using the informative
backbone markers as predictors. The completed dataset can then
be used for clustering and other statistical analyses.
Additionally, MAPFX can be used to normalise data from FFC
assays as well.
biocViews: Software, FlowCytometry, CellBasedAssays, SingleCell,
Proteomics, Clustering
Author: Hsiao-Chi Liao [aut, cre] (ORCID:
<https://orcid.org/0000-0002-9586-1246>), Agus Salim [ctb],
infinityFlow [ctb]
Maintainer: Hsiao-Chi Liao <chelsea.acad@gmail.com>
URL: https://github.com/HsiaoChiLiao/MAPFX
VignetteBuilder: knitr
BugReports: https://github.com/HsiaoChiLiao/MAPFX/issues
git_url: https://git.bioconductor.org/packages/MAPFX
git_branch: devel
git_last_commit: 0dd36ba
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MAPFX_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MAPFX_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MAPFX_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MAPFX_1.3.0.tgz
vignettes: vignettes/MAPFX/inst/doc/MAPFX_Vignette.html
vignetteTitles: MAPFX: MAssively Parallel Flow cytometry Xplorer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MAPFX/inst/doc/MAPFX_Vignette.R
dependencyCount: 188
Package: maPredictDSC
Version: 1.45.0
Depends: R (>= 2.15.0),
MASS,affy,limma,gcrma,ROC,class,e1071,caret,hgu133plus2.db,ROCR,AnnotationDbi,LungCancerACvsSCCGEO
Suggests: parallel
License: GPL-2
Archs: x64
MD5sum: 23436536a1c669e8bae065e6d8a14c0e
NeedsCompilation: no
Title: Phenotype prediction using microarray data: approach of the best
overall team in the IMPROVER Diagnostic Signature Challenge
Description: This package implements the classification pipeline of the
best overall team (Team221) in the IMPROVER Diagnostic
Signature Challenge. Additional functionality is added to
compare 27 combinations of data preprocessing, feature
selection and classifier types.
biocViews: Microarray, Classification
Author: Adi Laurentiu Tarca <atarca@med.wayne.edu>
Maintainer: Adi Laurentiu Tarca <atarca@med.wayne.edu>
URL: http://bioinformaticsprb.med.wayne.edu/maPredictDSC
git_url: https://git.bioconductor.org/packages/maPredictDSC
git_branch: devel
git_last_commit: 0d81f55
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/maPredictDSC_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/maPredictDSC_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/maPredictDSC_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/maPredictDSC_1.45.0.tgz
vignettes: vignettes/maPredictDSC/inst/doc/maPredictDSC.pdf
vignetteTitles: maPredictDSC
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/maPredictDSC/inst/doc/maPredictDSC.R
dependencyCount: 136
Package: mapscape
Version: 1.31.0
Depends: R (>= 3.3)
Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), base64enc (>=
0.1-3), stringr (>= 1.0.0)
Suggests: knitr, rmarkdown
License: GPL-3
Archs: x64
MD5sum: 7d22d916214ef55aea2d28e48805b932
NeedsCompilation: no
Title: mapscape
Description: MapScape integrates clonal prevalence, clonal hierarchy,
anatomic and mutational information to provide interactive
visualization of spatial clonal evolution. There are four
inputs to MapScape: (i) the clonal phylogeny, (ii) clonal
prevalences, (iii) an image reference, which may be a medical
image or drawing and (iv) pixel locations for each sample on
the referenced image. Optionally, MapScape can accept a data
table of mutations for each clone and their variant allele
frequencies in each sample. The output of MapScape consists of
a cropped anatomical image surrounded by two representations of
each tumour sample. The first, a cellular aggregate, visually
displays the prevalence of each clone. The second shows a
skeleton of the clonal phylogeny while highlighting only those
clones present in the sample. Together, these representations
enable the analyst to visualize the distribution of clones
throughout anatomic space.
biocViews: Visualization
Author: Maia Smith [aut, cre]
Maintainer: Maia Smith <maiaannesmith@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/mapscape
git_branch: devel
git_last_commit: 49e78f8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mapscape_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mapscape_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mapscape_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mapscape_1.31.0.tgz
vignettes: vignettes/mapscape/inst/doc/mapscape_vignette.html
vignetteTitles: MapScape vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mapscape/inst/doc/mapscape_vignette.R
dependencyCount: 36
Package: mariner
Version: 1.7.0
Depends: R (>= 4.2.0)
Imports: methods, S4Vectors, BiocGenerics, BiocManager, GenomicRanges,
InteractionSet, data.table, stats, rlang, glue, assertthat,
plyranges, magrittr, dbscan, purrr, progress, GenomeInfoDb,
strawr (>= 0.0.91), DelayedArray, HDF5Array, abind,
BiocParallel, IRanges, SummarizedExperiment, rhdf5,
plotgardener, RColorBrewer, colourvalues, utils, grDevices,
graphics, grid
Suggests: knitr, testthat (>= 3.0.0), dplyr, rmarkdown, ExperimentHub,
marinerData
License: GPL-3
MD5sum: cf9eb7c62347bee10b72f2c94b965573
NeedsCompilation: no
Title: Mariner: Explore the Hi-Cs
Description: Tools for manipulating paired ranges and working with Hi-C
data in R. Functionality includes manipulating/merging paired
regions, generating paired ranges, extracting/aggregating
interactions from `.hic` files, and visualizing the results.
Designed for compatibility with plotgardener for visualization.
biocViews: FunctionalGenomics, Visualization, HiC
Author: Eric Davis [aut, cre] (ORCID:
<https://orcid.org/0000-0003-4051-3217>)
Maintainer: Eric Davis <ericscottdavis@outlook.com>
URL: http://ericscottdavis.com/mariner/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/mariner
git_branch: devel
git_last_commit: 58efcf6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mariner_1.7.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mariner_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mariner_1.7.0.tgz
vignettes: vignettes/mariner/inst/doc/introduction_to_mariner.html
vignetteTitles: Introduction to mariner
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mariner/inst/doc/introduction_to_mariner.R
dependencyCount: 110
Package: marr
Version: 1.17.0
Depends: R (>= 4.0)
Imports: Rcpp, SummarizedExperiment, utils, methods, ggplot2, dplyr,
magrittr, rlang, S4Vectors
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, BiocStyle, testthat, covr
License: GPL (>= 3)
MD5sum: 4a75724c83dd168bddd9d8e5992cd476
NeedsCompilation: yes
Title: Maximum rank reproducibility
Description: marr (Maximum Rank Reproducibility) is a nonparametric
approach that detects reproducible signals using a maximal rank
statistic for high-dimensional biological data. In this R
package, we implement functions that measures the
reproducibility of features per sample pair and sample pairs
per feature in high-dimensional biological replicate
experiments. The user-friendly plot functions in this package
also plot histograms of the reproducibility of features per
sample pair and sample pairs per feature. Furthermore, our
approach also allows the users to select optimal filtering
threshold values for the identification of reproducible
features and sample pairs based on output visualization checks
(histograms). This package also provides the subset of data
filtered by reproducible features and/or sample pairs.
biocViews: QualityControl, Metabolomics, MassSpectrometry, RNASeq,
ChIPSeq
Author: Tusharkanti Ghosh [aut, cre], Max McGrath [aut], Daisy Philtron
[aut], Katerina Kechris [aut], Debashis Ghosh [aut, cph]
Maintainer: Tusharkanti Ghosh <tusharkantighosh30@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/Ghoshlab/marr/issues
git_url: https://git.bioconductor.org/packages/marr
git_branch: devel
git_last_commit: 88e44ee
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/marr_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/marr_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/marr_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/marr_1.17.0.tgz
vignettes: vignettes/marr/inst/doc/MarrVignette.html
vignetteTitles: The marr user's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/marr/inst/doc/MarrVignette.R
dependencyCount: 65
Package: marray
Version: 1.85.0
Depends: R (>= 2.10.0), limma, methods
Suggests: tkWidgets
License: LGPL
MD5sum: e49a983ac059de43679075ec7db44546
NeedsCompilation: no
Title: Exploratory analysis for two-color spotted microarray data
Description: Class definitions for two-color spotted microarray data.
Fuctions for data input, diagnostic plots, normalization and
quality checking.
biocViews: Microarray, TwoChannel, Preprocessing
Author: Yee Hwa (Jean) Yang <jeany@maths.usyd.edu.au> with
contributions from Agnes Paquet and Sandrine Dudoit.
Maintainer: Yee Hwa (Jean) Yang <jean@biostat.ucsf.edu>
URL: http://www.maths.usyd.edu.au/u/jeany/
git_url: https://git.bioconductor.org/packages/marray
git_branch: devel
git_last_commit: a8c55f6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/marray_1.85.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/marray_1.85.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/marray_1.85.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/marray_1.85.0.tgz
vignettes: vignettes/marray/inst/doc/marray.pdf,
vignettes/marray/inst/doc/marrayClasses.pdf,
vignettes/marray/inst/doc/marrayClassesShort.pdf,
vignettes/marray/inst/doc/marrayInput.pdf,
vignettes/marray/inst/doc/marrayNorm.pdf,
vignettes/marray/inst/doc/marrayPlots.pdf
vignetteTitles: marray Overview, marrayClasses Overview, marrayClasses
Tutorial (short), marrayInput Introduction, marray
Normalization, marrayPlots Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/marray/inst/doc/marray.R,
vignettes/marray/inst/doc/marrayClasses.R,
vignettes/marray/inst/doc/marrayClassesShort.R,
vignettes/marray/inst/doc/marrayInput.R,
vignettes/marray/inst/doc/marrayNorm.R,
vignettes/marray/inst/doc/marrayPlots.R
dependsOnMe: CGHbase, convert, dyebias, MineICA, nnNorm, OLIN, RBM,
stepNorm, TurboNorm, beta7, dyebiasexamples
importsMe: arrayQuality, ChAMP, methylPipe, MSstats, MSstatsShiny,
nnNorm, OLIN, OLINgui, piano, stepNorm, timecourse
suggestsMe: DEGraph, Mfuzz, hexbin
dependencyCount: 7
Package: martini
Version: 1.27.0
Depends: R (>= 4.0)
Imports: igraph (>= 1.0.1), Matrix, memoise (>= 2.0.0), methods (>=
3.3.2), Rcpp (>= 0.12.8), snpStats (>= 1.20.0), stats, utils,
LinkingTo: Rcpp, RcppEigen (>= 0.3.3.5.0)
Suggests: biomaRt (>= 2.34.1), circlize (>= 0.4.11), STRINGdb (>=
2.2.0), httr (>= 1.2.1), IRanges (>= 2.8.2), S4Vectors (>=
0.12.2), knitr, testthat, readr, rmarkdown
License: GPL-3
MD5sum: 842f347bba4d11e375539b5c8ffe5212
NeedsCompilation: yes
Title: GWAS Incorporating Networks
Description: martini deals with the low power inherent to GWAS studies
by using prior knowledge represented as a network. SNPs are the
vertices of the network, and the edges represent biological
relationships between them (genomic adjacency, belonging to the
same gene, physical interaction between protein products). The
network is scanned using SConES, which looks for groups of SNPs
maximally associated with the phenotype, that form a close
subnetwork.
biocViews: Software, GenomeWideAssociation, SNP, GeneticVariability,
Genetics, FeatureExtraction, GraphAndNetwork, Network
Author: Hector Climente-Gonzalez [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3030-7471>), Chloe-Agathe Azencott
[aut] (ORCID: <https://orcid.org/0000-0003-1003-301X>)
Maintainer: Hector Climente-Gonzalez <hector.climente@a.riken.jp>
URL: https://github.com/hclimente/martini
VignetteBuilder: knitr
BugReports: https://github.com/hclimente/martini/issues
git_url: https://git.bioconductor.org/packages/martini
git_branch: devel
git_last_commit: 13d35e1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/martini_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/martini_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/martini_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/martini_1.27.0.tgz
vignettes: vignettes/martini/inst/doc/scones_usage.html,
vignettes/martini/inst/doc/simulate_phenotype.html
vignetteTitles: Running SConES, Simulating SConES-based phenotypes
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/martini/inst/doc/scones_usage.R,
vignettes/martini/inst/doc/simulate_phenotype.R
dependencyCount: 28
Package: maser
Version: 1.25.0
Depends: R (>= 3.5.0), ggplot2, GenomicRanges
Imports: dplyr, rtracklayer, reshape2, Gviz, DT, GenomeInfoDb, stats,
utils, IRanges, methods, BiocGenerics, parallel, data.table
Suggests: testthat, knitr, rmarkdown, BiocStyle, AnnotationHub
License: MIT + file LICENSE
MD5sum: fbf8d77080dbbcb35e1f93eb9a14e7c8
NeedsCompilation: no
Title: Mapping Alternative Splicing Events to pRoteins
Description: This package provides functionalities for downstream
analysis, annotation and visualizaton of alternative splicing
events generated by rMATS.
biocViews: AlternativeSplicing, Transcriptomics, Visualization
Author: Diogo F.T. Veiga [aut, cre]
Maintainer: Diogo F.T. Veiga <diogof.veiga@gmail.com>
URL: https://github.com/DiogoVeiga/maser
VignetteBuilder: knitr
BugReports: https://github.com/DiogoVeiga/maser/issues
git_url: https://git.bioconductor.org/packages/maser
git_branch: devel
git_last_commit: 390af67
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/maser_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/maser_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/maser_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/maser_1.25.0.tgz
vignettes: vignettes/maser/inst/doc/Introduction.html,
vignettes/maser/inst/doc/Protein_mapping.html
vignetteTitles: Introduction, Mapping protein features to splicing
events
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/maser/inst/doc/Introduction.R,
vignettes/maser/inst/doc/Protein_mapping.R
dependencyCount: 163
Package: maSigPro
Version: 1.79.0
Depends: R (>= 2.3.1)
Imports: Biobase, graphics, grDevices, venn, mclust, stats, MASS
License: GPL (>= 2)
Archs: x64
MD5sum: c46be9077376258985c443dc727794ac
NeedsCompilation: no
Title: Significant Gene Expression Profile Differences in Time Course
Gene Expression Data
Description: maSigPro is a regression based approach to find genes for
which there are significant gene expression profile differences
between experimental groups in time course microarray and
RNA-Seq experiments.
biocViews: Microarray, RNA-Seq, Differential Expression, TimeCourse
Author: Ana Conesa and Maria Jose Nueda
Maintainer: Maria Jose Nueda <mj.nueda@ua.es>
git_url: https://git.bioconductor.org/packages/maSigPro
git_branch: devel
git_last_commit: 4df8268
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/maSigPro_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/maSigPro_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/maSigPro_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/maSigPro_1.79.0.tgz
vignettes: vignettes/maSigPro/inst/doc/maSigPro.pdf,
vignettes/maSigPro/inst/doc/maSigProUsersGuide.pdf
vignetteTitles: maSigPro Vignette, maSigProUsersGuide.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 12
Package: maskBAD
Version: 1.51.0
Depends: R (>= 2.10), gcrma (>= 2.27.1), affy
Suggests: hgu95av2probe, hgu95av2cdf
License: GPL (>= 2)
MD5sum: b95b5b7b5925e303cef710b5d2b9444b
NeedsCompilation: no
Title: Masking probes with binding affinity differences
Description: Package includes functions to analyze and mask microarray
expression data.
biocViews: Microarray
Author: Michael Dannemann <michael_dannemann@eva.mpg.de>
Maintainer: Michael Dannemann <michael_dannemann@eva.mpg.de>
git_url: https://git.bioconductor.org/packages/maskBAD
git_branch: devel
git_last_commit: a9dbb0e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/maskBAD_1.51.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/maskBAD_1.51.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/maskBAD_1.51.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/maskBAD_1.51.0.tgz
vignettes: vignettes/maskBAD/inst/doc/maskBAD.pdf
vignetteTitles: Package maskBAD
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/maskBAD/inst/doc/maskBAD.R
dependencyCount: 32
Package: MassArray
Version: 1.59.0
Depends: R (>= 2.10.0), methods
Imports: graphics, grDevices, stats, utils
License: GPL (>=2)
Archs: x64
MD5sum: b98aead862bab7acec1cff74f78a9710
NeedsCompilation: no
Title: Analytical Tools for MassArray Data
Description: This package is designed for the import, quality control,
analysis, and visualization of methylation data generated using
Sequenom's MassArray platform. The tools herein contain a
highly detailed amplicon prediction for optimal assay design.
Also included are quality control measures of data, such as
primer dimer and bisulfite conversion efficiency estimation.
Methylation data are calculated using the same algorithms
contained in the EpiTyper software package. Additionally,
automatic SNP-detection can be used to flag potentially
confounded data from specific CG sites. Visualization includes
barplots of methylation data as well as UCSC Genome
Browser-compatible BED tracks. Multiple assays can be
positionally combined for integrated analysis.
biocViews: ImmunoOncology, DNAMethylation, SNP, MassSpectrometry,
Genetics, DataImport, Visualization
Author: Reid F. Thompson <reid.thompson@gmail.com>, John M. Greally
<john.greally@einstein.yu.edu>
Maintainer: Reid F. Thompson <reid.thompson@gmail.com>
git_url: https://git.bioconductor.org/packages/MassArray
git_branch: devel
git_last_commit: 53960bc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MassArray_1.59.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MassArray_1.59.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MassArray_1.59.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MassArray_1.59.0.tgz
vignettes: vignettes/MassArray/inst/doc/MassArray.pdf
vignetteTitles: 1. Primer
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MassArray/inst/doc/MassArray.R
dependencyCount: 5
Package: massiR
Version: 1.43.0
Depends: cluster, gplots, diptest, Biobase, R (>= 3.0.2)
Suggests: biomaRt, RUnit, BiocGenerics
License: GPL-3
MD5sum: ddba7ec47ba72d95cb843fd873946f33
NeedsCompilation: no
Title: massiR: MicroArray Sample Sex Identifier
Description: Predicts the sex of samples in gene expression microarray
datasets
biocViews: Software, Microarray, GeneExpression, Clustering,
Classification, QualityControl
Author: Sam Buckberry
Maintainer: Sam Buckberry <sam.buckberry@adelaide.edu.au>
git_url: https://git.bioconductor.org/packages/massiR
git_branch: devel
git_last_commit: faf164e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/massiR_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/massiR_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/massiR_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/massiR_1.43.0.tgz
vignettes: vignettes/massiR/inst/doc/massiR_Vignette.pdf
vignetteTitles: massiR_Example
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/massiR/inst/doc/massiR_Vignette.R
dependencyCount: 15
Package: MassSpecWavelet
Version: 1.73.1
Suggests: signal, waveslim, BiocStyle, knitr, rmarkdown, RUnit, bench
License: LGPL (>= 2)
MD5sum: 47619cc9cbfaa9dd542f3bfc6a300088
NeedsCompilation: yes
Title: Peak Detection for Mass Spectrometry data using wavelet-based
algorithms
Description: Peak Detection in Mass Spectrometry data is one of the
important preprocessing steps. The performance of peak
detection affects subsequent processes, including protein
identification, profile alignment and biomarker identification.
Using Continuous Wavelet Transform (CWT), this package provides
a reliable algorithm for peak detection that does not require
any type of smoothing or previous baseline correction method,
providing more consistent results for different spectra. See
<doi:10.1093/bioinformatics/btl355} for further details.
biocViews: ImmunoOncology, MassSpectrometry, Proteomics, PeakDetection
Author: Pan Du [aut], Warren Kibbe [aut], Simon Lin [aut], Sergio Oller
Moreno [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8994-1549>)
Maintainer: Sergio Oller Moreno <sergioller@gmail.com>
URL: https://github.com/zeehio/MassSpecWavelet
VignetteBuilder: knitr
BugReports: http://github.com/zeehio/MassSpecWavelet/issues
git_url: https://git.bioconductor.org/packages/MassSpecWavelet
git_branch: devel
git_last_commit: 5443820
git_last_commit_date: 2024-12-18
Date/Publication: 2024-12-18
source.ver: src/contrib/MassSpecWavelet_1.73.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MassSpecWavelet_1.73.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MassSpecWavelet_1.73.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MassSpecWavelet_1.73.1.tgz
vignettes: vignettes/MassSpecWavelet/inst/doc/FindingLocalMaxima.html,
vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.html
vignetteTitles: Finding local maxima, Using the MassSpecWavelet package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MassSpecWavelet/inst/doc/FindingLocalMaxima.R,
vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.R
importsMe: cosmiq, xcms, NMRphasing, Rnmr1D, speaq
suggestsMe: downlit
dependencyCount: 0
Package: MAST
Version: 1.33.0
Depends: SingleCellExperiment (>= 1.2.0), R(>= 3.5)
Imports: Biobase, BiocGenerics, S4Vectors, data.table, ggplot2, plyr,
stringr, abind, methods, parallel, reshape2, stats, stats4,
graphics, utils, SummarizedExperiment(>= 1.5.3), progress,
Matrix
Suggests: knitr, rmarkdown, testthat, lme4(>= 1.0), blme, roxygen2(>
6.0.0), numDeriv, car, gdata, lattice, GGally, GSEABase, NMF,
TxDb.Hsapiens.UCSC.hg19.knownGene, rsvd, limma, RColorBrewer,
BiocStyle, scater, DelayedArray, HDF5Array, zinbwave, dplyr
License: GPL(>= 2)
MD5sum: 32f6eba6564fd970099df0d9e2beaa44
NeedsCompilation: no
Title: Model-based Analysis of Single Cell Transcriptomics
Description: Methods and models for handling zero-inflated single cell
assay data.
biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment,
RNASeq, Transcriptomics, SingleCell
Author: Andrew McDavid [aut, cre], Greg Finak [aut], Masanao Yajima
[aut]
Maintainer: Andrew McDavid <Andrew_McDavid@urmc.rochester.edu>
URL: https://github.com/RGLab/MAST/
VignetteBuilder: knitr
BugReports: https://github.com/RGLab/MAST/issues
git_url: https://git.bioconductor.org/packages/MAST
git_branch: devel
git_last_commit: 35654e5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MAST_1.33.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MAST_1.33.0.tgz
vignettes: vignettes/MAST/inst/doc/MAITAnalysis.html,
vignettes/MAST/inst/doc/MAST-interoperability.html,
vignettes/MAST/inst/doc/MAST-Intro.html
vignetteTitles: Using MAST for filtering,, differential expression and
gene set enrichment in MAIT cells, Interoptability between MAST
and SingleCellExperiment-derived packages, An Introduction to
MAST
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MAST/inst/doc/MAITAnalysis.R,
vignettes/MAST/inst/doc/MAST-interoperability.R,
vignettes/MAST/inst/doc/MAST-Intro.R
dependsOnMe: POWSC
importsMe: benchdamic, celaref, singleCellTK, DWLS
suggestsMe: clusterExperiment, EWCE, MARVEL, Seurat, SeuratExplorer
dependencyCount: 73
Package: mastR
Version: 1.7.0
Depends: R (>= 4.3.0)
Imports: AnnotationDbi, Biobase, dplyr, edgeR, ggplot2, ggpubr,
graphics, grDevices, GSEABase, limma, Matrix, methods, msigdb,
org.Hs.eg.db, patchwork, SeuratObject, SingleCellExperiment,
stats, SummarizedExperiment, tidyr, utils
Suggests: BiocManager, BiocStyle, BisqueRNA, clusterProfiler,
ComplexHeatmap, depmap, enrichplot, ggrepel, ggvenn, gridExtra,
jsonlite, knitr, rmarkdown, RobustRankAggreg, rvest, scuttle,
singscore, splatter, testthat (>= 3.0.0), UpSetR
License: MIT + file LICENSE
MD5sum: 252eee1ddc99ee4911e84c7a0135e774
NeedsCompilation: no
Title: Markers Automated Screening Tool in R
Description: mastR is an R package designed for automated screening of
signatures of interest for specific research questions. The
package is developed for generating refined lists of signature
genes from multiple group comparisons based on the results from
edgeR and limma differential expression (DE) analysis workflow.
It also takes into account the background noise of
tissue-specificity, which is often ignored by other marker
generation tools. This package is particularly useful for the
identification of group markers in various biological and
medical applications, including cancer research and
developmental biology.
biocViews: Software, GeneExpression, Transcriptomics,
DifferentialExpression, Visualization
Author: Jinjin Chen [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7923-5723>), Ahmed Mohamed [aut,
ctb] (ORCID: <https://orcid.org/0000-0001-6507-5300>), Chin Wee
Tan [ctb] (ORCID: <https://orcid.org/0000-0001-9695-7218>)
Maintainer: Jinjin Chen <chen.j@wehi.edu.au>
URL: https://davislaboratory.github.io/mastR
VignetteBuilder: knitr
BugReports: https://github.com/DavisLaboratory/mastR/issues
git_url: https://git.bioconductor.org/packages/mastR
git_branch: devel
git_last_commit: 492c795
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mastR_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mastR_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mastR_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mastR_1.7.0.tgz
vignettes: vignettes/mastR/inst/doc/mastR_Demo.html
vignetteTitles: mastR_Demo
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/mastR/inst/doc/mastR_Demo.R
dependencyCount: 152
Package: matchBox
Version: 1.49.0
Depends: R (>= 2.8.0)
License: Artistic-2.0
MD5sum: 7504469c7453fac03c630e09963ca421
NeedsCompilation: no
Title: Utilities to compute, compare, and plot the agreement between
ordered vectors of features (ie. distinct genomic experiments).
The package includes Correspondence-At-the-TOP (CAT) analysis.
Description: The matchBox package enables comparing ranked vectors of
features, merging multiple datasets, removing redundant
features, using CAT-plots and Venn diagrams, and computing
statistical significance.
biocViews: Software, Annotation, Microarray, MultipleComparison,
Visualization
Author: Luigi Marchionni <marchion@jhu.edu>, Anuj Gupta
<agupta52@jhu.edu>
Maintainer: Luigi Marchionni <marchion@jhu.edu>, Anuj Gupta
<agupta52@jhu.edu>
git_url: https://git.bioconductor.org/packages/matchBox
git_branch: devel
git_last_commit: 61179a3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/matchBox_1.49.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/matchBox_1.49.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/matchBox_1.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/matchBox_1.49.0.tgz
vignettes: vignettes/matchBox/inst/doc/matchBox.pdf
vignetteTitles: Working with the matchBox package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/matchBox/inst/doc/matchBox.R
dependencyCount: 0
Package: MatrixGenerics
Version: 1.19.1
Depends: matrixStats (>= 1.4.1)
Imports: methods
Suggests: Matrix, sparseMatrixStats, SparseArray, DelayedArray,
DelayedMatrixStats, SummarizedExperiment, testthat (>= 2.1.0)
License: Artistic-2.0
MD5sum: eeb11e937f5d7b6a6a923dfae3f2a248
NeedsCompilation: no
Title: S4 Generic Summary Statistic Functions that Operate on
Matrix-Like Objects
Description: S4 generic functions modeled after the 'matrixStats' API
for alternative matrix implementations. Packages with
alternative matrix implementation can depend on this package
and implement the generic functions that are defined here for a
useful set of row and column summary statistics. Other package
developers can import this package and handle a different
matrix implementations without worrying about
incompatibilities.
biocViews: Infrastructure, Software
Author: Constantin Ahlmann-Eltze [aut] (ORCID:
<https://orcid.org/0000-0002-3762-068X>), Peter Hickey [aut,
cre] (ORCID: <https://orcid.org/0000-0002-8153-6258>), Hervé
Pagès [aut]
Maintainer: Peter Hickey <peter.hickey@gmail.com>
URL: https://bioconductor.org/packages/MatrixGenerics
BugReports: https://github.com/Bioconductor/MatrixGenerics/issues
git_url: https://git.bioconductor.org/packages/MatrixGenerics
git_branch: devel
git_last_commit: 1fc0bb2
git_last_commit_date: 2025-01-08
Date/Publication: 2025-01-09
source.ver: src/contrib/MatrixGenerics_1.19.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MatrixGenerics_1.19.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MatrixGenerics_1.19.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MatrixGenerics_1.19.1.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: DelayedArray, DelayedMatrixStats, GenomicFiles,
SparseArray, sparseMatrixStats, SummarizedExperiment,
VariantAnnotation
importsMe: atena, CoreGx, crisprDesign, CTexploreR, demuxSNP, DESeq2,
dreamlet, escape, FLAMES, genefilter, glmGamPoi, imcRtools,
lemur, lineagespot, methodical, mia, miaSim, miloR,
MinimumDistance, PDATK, RaggedExperiment, RAIDS, RCSL, saseR,
scater, scFeatures, scone, scPCA, scran, scuttle, scviR,
shinyMethyl, spatzie, StabMap, tadar, TENxIO, tLOH, tpSVG,
transformGamPoi, universalmotif, VanillaICE, Voyager, zitools,
homosapienDEE2CellScore, spatialLIBD, SCIntRuler
suggestsMe: bnem, cypress, MungeSumstats, scrapper
dependencyCount: 2
Package: MatrixQCvis
Version: 1.15.0
Depends: DT (>= 0.33), SummarizedExperiment (>= 1.20.0), plotly (>=
4.9.3), shiny (>= 1.6.0)
Imports: ComplexHeatmap (>= 2.7.9), dplyr (>= 1.0.5), ExperimentHub (>=
2.6.0), ggplot2 (>= 3.3.3), grDevices (>= 4.1.0), Hmisc (>=
4.5-0), htmlwidgets (>= 1.5.3), impute (>= 1.65.0), imputeLCMD
(>= 2.0), limma (>= 3.47.12), MASS (>= 7.3-58.1), methods (>=
4.1.0), pcaMethods (>= 1.83.0), proDA (>= 1.5.0), rlang (>=
0.4.10), rmarkdown (>= 2.7), Rtsne (>= 0.15), shinydashboard
(>= 0.7.1), shinyhelper (>= 0.3.2), shinyjs (>= 2.0.0), stats
(>= 4.1.0), sva (>= 3.52.0), tibble (>= 3.1.1), tidyr (>=
1.1.3), umap (>= 0.2.7.0), UpSetR (>= 1.4.0), vsn (>= 3.59.1)
Suggests: BiocGenerics (>= 0.37.4), BiocStyle (>= 2.19.2), hexbin (>=
1.28.2), httr (>= 1.4.7), jpeg (>= 0.1-10), knitr (>= 1.33),
statmod (>= 1.5.0), testthat (>= 3.0.2)
License: GPL-3
Archs: x64
MD5sum: 7258db01c4ff90f9f63112c3dab430ef
NeedsCompilation: no
Title: Shiny-based interactive data-quality exploration for omics data
Description: Data quality assessment is an integral part of preparatory
data analysis to ensure sound biological information retrieval.
We present here the MatrixQCvis package, which provides
shiny-based interactive visualization of data quality metrics
at the per-sample and per-feature level. It is broadly
applicable to quantitative omics data types that come in
matrix-like format (features x samples). It enables the
detection of low-quality samples, drifts, outliers and batch
effects in data sets. Visualizations include amongst others
bar- and violin plots of the (count/intensity) values, mean vs
standard deviation plots, MA plots, empirical cumulative
distribution function (ECDF) plots, visualizations of the
distances between samples, and multiple types of dimension
reduction plots. Furthermore, MatrixQCvis allows for
differential expression analysis based on the limma (moderated
t-tests) and proDA (Wald tests) packages. MatrixQCvis builds
upon the popular Bioconductor SummarizedExperiment S4 class and
enables thus the facile integration into existing workflows.
The package is especially tailored towards metabolomics and
proteomics mass spectrometry data, but also allows to assess
the data quality of other data types that can be represented in
a SummarizedExperiment object.
biocViews: Visualization, ShinyApps, GUI, QualityControl,
DimensionReduction, Metabolomics, Proteomics, Transcriptomics
Author: Thomas Naake [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7917-5580>), Wolfgang Huber [aut]
(ORCID: <https://orcid.org/0000-0002-0474-2218>)
Maintainer: Thomas Naake <thomasnaake@googlemail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MatrixQCvis
git_branch: devel
git_last_commit: ae918bf
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MatrixQCvis_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MatrixQCvis_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MatrixQCvis_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MatrixQCvis_1.15.0.tgz
vignettes: vignettes/MatrixQCvis/inst/doc/MatrixQCvis.html
vignetteTitles: Shiny-based interactive data quality exploration of
omics data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MatrixQCvis/inst/doc/MatrixQCvis.R
dependencyCount: 189
Package: MatrixRider
Version: 1.39.0
Depends: R (>= 3.1.2)
Imports: methods, TFBSTools, IRanges, XVector, Biostrings
LinkingTo: IRanges, XVector, Biostrings, S4Vectors
Suggests: RUnit, BiocGenerics, BiocStyle, JASPAR2014
License: GPL-3
MD5sum: c4847c22fad2b69f68b7d3fc3bb15b2c
NeedsCompilation: yes
Title: Obtain total affinity and occupancies for binding site matrices
on a given sequence
Description: Calculates a single number for a whole sequence that
reflects the propensity of a DNA binding protein to interact
with it. The DNA binding protein has to be described with a PFM
matrix, for example gotten from Jaspar.
biocViews: GeneRegulation, Genetics, MotifAnnotation
Author: Elena Grassi
Maintainer: Elena Grassi <grassi.e@gmail.com>
git_url: https://git.bioconductor.org/packages/MatrixRider
git_branch: devel
git_last_commit: bbdf4b1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MatrixRider_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MatrixRider_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MatrixRider_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MatrixRider_1.39.0.tgz
vignettes: vignettes/MatrixRider/inst/doc/MatrixRider.pdf
vignetteTitles: Total affinity and occupancies
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MatrixRider/inst/doc/MatrixRider.R
dependencyCount: 82
Package: matter
Version: 2.9.1
Depends: R (>= 4.4), BiocParallel, Matrix, methods
Imports: BiocGenerics, ProtGenerics, digest, irlba, stats, stats4,
graphics, grDevices, parallel, utils
LinkingTo: BH
Suggests: BiocStyle, knitr, testthat, plotly
License: Artistic-2.0 | file LICENSE
MD5sum: b12c0c4344cba943826e65cf10bff3bf
NeedsCompilation: yes
Title: Out-of-core statistical computing and signal processing
Description: Toolbox for larger-than-memory scientific computing and
visualization, providing efficient out-of-core data structures
using files or shared memory, for dense and sparse vectors,
matrices, and arrays, with applications to nonuniformly sampled
signals and images.
biocViews: Infrastructure, DataRepresentation, DataImport,
DimensionReduction, Preprocessing
Author: Kylie A. Bemis <k.bemis@northeastern.edu>
Maintainer: Kylie A. Bemis <k.bemis@northeastern.edu>
URL: https://github.com/kuwisdelu/matter
VignetteBuilder: knitr
BugReports: https://github.com/kuwisdelu/matter/issues
git_url: https://git.bioconductor.org/packages/matter
git_branch: devel
git_last_commit: bcfe9fb
git_last_commit_date: 2024-12-04
Date/Publication: 2024-12-05
source.ver: src/contrib/matter_2.9.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/matter_2.9.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/matter_2.9.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/matter_2.9.1.tgz
vignettes: vignettes/matter/inst/doc/matter2-guide.html,
vignettes/matter/inst/doc/matter2-signal.html
vignetteTitles: 1. Matter 2: User guide for flexible out-of-memory data
structures, 2. Matter 2: Signal and image processing
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/matter/inst/doc/matter2-guide.R,
vignettes/matter/inst/doc/matter2-signal.R
dependsOnMe: CardinalIO
importsMe: Cardinal
dependencyCount: 24
Package: MBAmethyl
Version: 1.41.0
Depends: R (>= 2.15)
License: Artistic-2.0
MD5sum: ff2716b3d2938644363eefcf8deeb5e0
NeedsCompilation: no
Title: Model-based analysis of DNA methylation data
Description: This package provides a function for reconstructing DNA
methylation values from raw measurements. It iteratively
implements the group fused lars to smooth related-by-location
methylation values and the constrained least squares to remove
probe affinity effect across multiple sequences.
biocViews: DNAMethylation, MethylationArray
Author: Tao Wang, Mengjie Chen
Maintainer: Tao Wang <tao.wang.tw376@yale.edu>
git_url: https://git.bioconductor.org/packages/MBAmethyl
git_branch: devel
git_last_commit: 6cbb183
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MBAmethyl_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MBAmethyl_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MBAmethyl_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MBAmethyl_1.41.0.tgz
vignettes: vignettes/MBAmethyl/inst/doc/MBAmethyl.pdf
vignetteTitles: MBAmethyl Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MBAmethyl/inst/doc/MBAmethyl.R
dependencyCount: 0
Package: MBASED
Version: 1.41.0
Depends: RUnit, BiocGenerics, BiocParallel, GenomicRanges,
SummarizedExperiment
Suggests: BiocStyle
License: Artistic-2.0
MD5sum: 697dcb91cdc8971e69c0a5486536c7c4
NeedsCompilation: no
Title: Package containing functions for ASE analysis using
Meta-analysis Based Allele-Specific Expression Detection
Description: The package implements MBASED algorithm for detecting
allele-specific gene expression from RNA count data, where
allele counts at individual loci (SNVs) are integrated into a
gene-specific measure of ASE, and utilizes simulations to
appropriately assess the statistical significance of observed
ASE.
biocViews: Sequencing, GeneExpression, Transcription
Author: Oleg Mayba, Houston Gilbert
Maintainer: Oleg Mayba <mayba.oleg@gene.com>
git_url: https://git.bioconductor.org/packages/MBASED
git_branch: devel
git_last_commit: 8637000
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MBASED_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MBASED_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MBASED_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MBASED_1.41.0.tgz
vignettes: vignettes/MBASED/inst/doc/MBASED.pdf
vignetteTitles: MBASED
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MBASED/inst/doc/MBASED.R
dependencyCount: 47
Package: MBCB
Version: 1.61.0
Depends: R (>= 2.9.0), tcltk, tcltk2
Imports: preprocessCore, stats, utils
License: GPL (>=2)
Archs: x64
MD5sum: c700a207dac0eba122cf49509ed28184
NeedsCompilation: no
Title: MBCB (Model-based Background Correction for Beadarray)
Description: This package provides a model-based background correction
method, which incorporates the negative control beads to
pre-process Illumina BeadArray data.
biocViews: Microarray, Preprocessing
Author: Yang Xie <Yang.Xie@UTSouthwestern.edu>
Maintainer: Bo Yao <Bo.Yao@UTSouthwestern.edu>
URL: https://qbrc.swmed.edu/
git_url: https://git.bioconductor.org/packages/MBCB
git_branch: devel
git_last_commit: 6753198
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MBCB_1.61.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MBCB_1.61.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MBCB_1.61.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MBCB_1.61.0.tgz
vignettes: vignettes/MBCB/inst/doc/MBCB.pdf
vignetteTitles: MBCB
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MBCB/inst/doc/MBCB.R
dependencyCount: 5
Package: MBECS
Version: 1.11.0
Depends: R (>= 4.1)
Imports: methods, magrittr, phyloseq, limma, lme4, lmerTest, pheatmap,
rmarkdown, cluster, dplyr, ggplot2, gridExtra, ruv, sva,
tibble, tidyr, vegan, stats, utils, Matrix
Suggests: knitr, markdown, BiocStyle, testthat (>= 3.0.0)
License: Artistic-2.0
MD5sum: 9d05e7fcc75362b714c2635b1a7ff3db
NeedsCompilation: no
Title: Evaluation and correction of batch effects in microbiome
data-sets
Description: The Microbiome Batch Effect Correction Suite (MBECS)
provides a set of functions to evaluate and mitigate unwated
noise due to processing in batches. To that end it incorporates
a host of batch correcting algorithms (BECA) from various
packages. In addition it offers a correction and reporting
pipeline that provides a preliminary look at the
characteristics of a data-set before and after correcting for
batch effects.
biocViews: BatchEffect, Microbiome, ReportWriting, Visualization,
Normalization, QualityControl
Author: Michael Olbrich [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2789-3382>)
Maintainer: Michael Olbrich <M.Olbrich@protonmail.com>
URL: https://github.com/rmolbrich/MBECS
VignetteBuilder: knitr
BugReports: https://github.com/rmolbrich/MBECS/issues/new
git_url: https://git.bioconductor.org/packages/MBECS
git_branch: devel
git_last_commit: f568758
git_last_commit_date: 2024-10-29
Date/Publication: 2025-01-23
source.ver: src/contrib/MBECS_1.11.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MBECS_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MBECS_1.11.0.tgz
vignettes: vignettes/MBECS/inst/doc/mbecs_vignette.html
vignetteTitles: MBECS introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MBECS/inst/doc/mbecs_vignette.R
dependencyCount: 145
Package: mbkmeans
Version: 1.23.0
Depends: R (>= 3.6)
Imports: methods, DelayedArray, Rcpp, S4Vectors, SingleCellExperiment,
SummarizedExperiment, ClusterR, benchmarkme, Matrix,
BiocParallel
LinkingTo: Rcpp, RcppArmadillo (>= 0.7.2), Rhdf5lib, beachmat, ClusterR
Suggests: beachmat, HDF5Array, Rhdf5lib, BiocStyle, TENxPBMCData,
scater, DelayedMatrixStats, bluster, knitr, testthat, rmarkdown
License: MIT + file LICENSE
MD5sum: 9e40b317b70dfc708c339aba2d0b4b5a
NeedsCompilation: yes
Title: Mini-batch K-means Clustering for Single-Cell RNA-seq
Description: Implements the mini-batch k-means algorithm for large
datasets, including support for on-disk data representation.
biocViews: Clustering, GeneExpression, RNASeq, Software,
Transcriptomics, Sequencing, SingleCell
Author: Yuwei Ni [aut, cph], Davide Risso [aut, cre, cph], Stephanie
Hicks [aut, cph], Elizabeth Purdom [aut, cph]
Maintainer: Davide Risso <risso.davide@gmail.com>
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/drisso/mbkmeans/issues
git_url: https://git.bioconductor.org/packages/mbkmeans
git_branch: devel
git_last_commit: 446bb72
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-05
source.ver: src/contrib/mbkmeans_1.23.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mbkmeans_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mbkmeans_1.23.0.tgz
vignettes: vignettes/mbkmeans/inst/doc/Vignette.html
vignetteTitles: mbkmeans vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/mbkmeans/inst/doc/Vignette.R
dependsOnMe: OSCA.basic
importsMe: clusterExperiment
suggestsMe: bluster, concordexR, scDblFinder
dependencyCount: 90
Package: mBPCR
Version: 1.61.0
Depends: oligoClasses, GWASTools
Imports: Biobase, graphics, methods, utils, grDevices
Suggests: xtable
License: GPL (>= 2)
Archs: x64
MD5sum: d823908377a8c5be1a9b2c25a36707b7
NeedsCompilation: no
Title: Bayesian Piecewise Constant Regression for DNA copy number
estimation
Description: It contains functions for estimating the DNA copy number
profile using mBPCR with the aim of detecting regions with copy
number changes.
biocViews: aCGH, SNP, Microarray, CopyNumberVariation
Author: P.M.V. Rancoita <rancoita.paola@gmail.com>, with contributions
from M. Hutter <marcus.hutter@anu.edu.au>
Maintainer: P.M.V. Rancoita <rancoita.paola@gmail.com>
URL: http://www.idsia.ch/~paola/mBPCR
git_url: https://git.bioconductor.org/packages/mBPCR
git_branch: devel
git_last_commit: 4db7f5f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mBPCR_1.61.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mBPCR_1.61.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mBPCR_1.61.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mBPCR_1.61.0.tgz
vignettes: vignettes/mBPCR/inst/doc/mBPCR.pdf
vignetteTitles: mBPCR
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mBPCR/inst/doc/mBPCR.R
dependencyCount: 124
Package: MBQN
Version: 2.19.0
Depends: R (>= 3.6)
Imports: stats, graphics, utils, limma (>= 3.30.13),
SummarizedExperiment (>= 1.10.0), preprocessCore (>= 1.36.0),
BiocFileCache, rappdirs, xml2, RCurl, ggplot2, PairedData,
rmarkdown
Suggests: knitr
License: GPL-3 + file LICENSE
MD5sum: cc3d7aaf1b2951dc9ce4a89b3f257db4
NeedsCompilation: no
Title: Mean/Median-balanced quantile normalization
Description: Modified quantile normalization for omics or other
matrix-like data distorted in location and scale.
biocViews: Normalization, Preprocessing, Proteomics, Software
Author: Eva Brombacher [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5488-0985>), Clemens Kreutz [aut,
ctb] (ORCID: <https://orcid.org/0000-0002-8796-5766>), Ariane
Schad [aut, ctb] (ORCID:
<https://orcid.org/0000-0002-1921-8902>)
Maintainer: Eva Brombacher <brombach@imbi.uni-freiburg.de>
URL: https://github.com/arianeschad/mbqn
VignetteBuilder: knitr
BugReports: https://github.com/arianeschad/MBQN/issues
git_url: https://git.bioconductor.org/packages/MBQN
git_branch: devel
git_last_commit: a95ce36
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MBQN_2.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MBQN_2.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MBQN_2.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MBQN_2.19.0.tgz
vignettes: vignettes/MBQN/inst/doc/MBQNpackage.html
vignetteTitles: MBQN Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MBQN/inst/doc/MBQNpackage.R
dependencyCount: 110
Package: mbQTL
Version: 1.7.0
Depends: R (>= 4.3.0)
Imports: MatrixEQTL, dplyr, ggplot2, readxl, stringr, tidyr,
metagenomeSeq, pheatmap, broom, graphics, stats, methods
Suggests: knitr, rmarkdown, BiocStyle
License: MIT + file LICENSE
MD5sum: 85f92ce7f6aab04b800b8f2dad2976e7
NeedsCompilation: no
Title: mbQTL: A package for SNP-Taxa mGWAS analysis
Description: mbQTL is a statistical R package for simultaneous
16srRNA,16srDNA (microbial) and variant, SNP, SNV (host)
relationship, correlation, regression studies. We apply linear,
logistic and correlation based statistics to identify the
relationships of taxa, genus, species and variant, SNP, SNV in
the infected host. We produce various statistical significance
measures such as P values, FDR, BC and probability estimation
to show significance of these relationships. Further we provide
various visualization function for ease and clarification of
the results of these analysis. The package is compatible with
dataframe, MRexperiment and text formats.
biocViews: SNP, Microbiome, WholeGenome, Metagenomics,
StatisticalMethod, Regression
Author: Mercedeh Movassagh [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7690-0230>), Steven Schiff [aut],
Joseph N Paulson [aut]
Maintainer: Mercedeh Movassagh <mercedeh.movassagh@yale.edu>
URL: "https://github.com/Mercedeh66/mbQTL"
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/mbQTL
git_branch: devel
git_last_commit: 4621e93
git_last_commit_date: 2024-10-29
Date/Publication: 2024-12-18
source.ver: src/contrib/mbQTL_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mbQTL_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mbQTL_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mbQTL_1.7.0.tgz
vignettes: vignettes/mbQTL/inst/doc/mbQTL_Vignette.html
vignetteTitles: MbQTL_Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/mbQTL/inst/doc/mbQTL_Vignette.R
dependencyCount: 77
Package: MBttest
Version: 1.35.0
Depends: R (>= 3.3.0), stats, gplots, gtools,graphics,base,
utils,grDevices
Suggests: BiocStyle, BiocGenerics
License: GPL-3
MD5sum: f2d4086110f55e17d7345e8cb59d2017
NeedsCompilation: no
Title: Multiple Beta t-Tests
Description: MBttest method was developed from beta t-test method of
Baggerly et al(2003). Compared to baySeq (Hard castle and Kelly
2010), DESeq (Anders and Huber 2010) and exact test (Robinson
and Smyth 2007, 2008) and the GLM of McCarthy et al(2012),
MBttest is of high work efficiency,that is, it has high power,
high conservativeness of FDR estimation and high stability.
MBttest is suit- able to transcriptomic data, tag data, SAGE
data (count data) from small samples or a few replicate
libraries. It can be used to identify genes, mRNA isoforms or
tags differentially expressed between two conditions.
biocViews: Sequencing, DifferentialExpression, MultipleComparison,
SAGE, GeneExpression, Transcription,
AlternativeSplicing,Coverage, DifferentialSplicing
Author: Yuan-De Tan
Maintainer: Yuan-De Tan <tanyuande@gmail.com>
git_url: https://git.bioconductor.org/packages/MBttest
git_branch: devel
git_last_commit: 63b7016
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MBttest_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MBttest_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MBttest_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MBttest_1.35.0.tgz
vignettes: vignettes/MBttest/inst/doc/MBttest-manual.pdf,
vignettes/MBttest/inst/doc/MBttest.pdf
vignetteTitles: MBttest-manual.pdf, Analysing RNA-Seq count data with
the "MBttest" package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MBttest/inst/doc/MBttest.R
dependencyCount: 11
Package: MCbiclust
Version: 1.31.0
Depends: R (>= 3.4)
Imports: BiocParallel, graphics, utils, stats, AnnotationDbi, GO.db,
org.Hs.eg.db, GGally, ggplot2, scales, cluster, WGCNA
Suggests: gplots, knitr, rmarkdown, BiocStyle, gProfileR, MASS, dplyr,
pander, devtools, testthat, GSVA
License: GPL-2
MD5sum: 4659db6642e5713b4d7bf8a7e9f3ac48
NeedsCompilation: no
Title: Massive correlating biclusters for gene expression data and
associated methods
Description: Custom made algorithm and associated methods for finding,
visualising and analysing biclusters in large gene expression
data sets. Algorithm is based on with a supplied gene set of
size n, finding the maximum strength correlation matrix
containing m samples from the data set.
biocViews: ImmunoOncology, Clustering, Microarray, StatisticalMethod,
Software, RNASeq, GeneExpression
Author: Robert Bentham
Maintainer: Robert Bentham <robert.bentham.11@ucl.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MCbiclust
git_branch: devel
git_last_commit: 990c62b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MCbiclust_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MCbiclust_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MCbiclust_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MCbiclust_1.31.0.tgz
vignettes: vignettes/MCbiclust/inst/doc/MCbiclust_vignette.html
vignetteTitles: Introduction to MCbiclust
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MCbiclust/inst/doc/MCbiclust_vignette.R
dependencyCount: 134
Package: mCSEA
Version: 1.27.2
Depends: R (>= 3.5), mCSEAdata, Homo.sapiens
Imports: biomaRt, fgsea, GenomicFeatures, GenomicRanges, ggplot2,
graphics, grDevices, Gviz, IRanges, limma, methods, parallel,
S4Vectors, stats, SummarizedExperiment, utils
Suggests: Biobase, BiocGenerics, BiocStyle, FlowSorted.Blood.450k,
knitr, leukemiasEset, minfi, minfiData, rmarkdown, RUnit
License: GPL-2
MD5sum: 3d0465dc8bfd663a894bf4f52e21753e
NeedsCompilation: no
Title: Methylated CpGs Set Enrichment Analysis
Description: Identification of diferentially methylated regions (DMRs)
in predefined regions (promoters, CpG islands...) from the
human genome using Illumina's 450K or EPIC microarray data.
Provides methods to rank CpG probes based on linear models and
includes plotting functions.
biocViews: ImmunoOncology, DifferentialMethylation, DNAMethylation,
Epigenetics, Genetics, GenomeAnnotation, MethylationArray,
Microarray, MultipleComparison, TwoChannel
Author: Jordi Martorell-Marugán and Pedro Carmona-Sáez
Maintainer: Jordi Martorell-Marugán <jmartorellm@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/mCSEA
git_branch: devel
git_last_commit: 641202d
git_last_commit_date: 2024-12-03
Date/Publication: 2024-12-03
source.ver: src/contrib/mCSEA_1.27.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mCSEA_1.27.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mCSEA_1.27.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mCSEA_1.27.2.tgz
vignettes: vignettes/mCSEA/inst/doc/mCSEA.pdf
vignetteTitles: Predefined DMRs identification with mCSEA package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mCSEA/inst/doc/mCSEA.R
suggestsMe: shinyepico
dependencyCount: 171
Package: mdp
Version: 1.27.0
Depends: R (>= 4.0)
Imports: ggplot2, gridExtra, grid, stats, utils
Suggests: testthat, knitr, rmarkdown, fgsea, BiocManager
License: GPL-3
MD5sum: 588ab4d7442accae88bd5e9d980ec566
NeedsCompilation: no
Title: Molecular Degree of Perturbation calculates scores for
transcriptome data samples based on their perturbation from
controls
Description: The Molecular Degree of Perturbation webtool quantifies
the heterogeneity of samples. It takes a data.frame of omic
data that contains at least two classes (control and test) and
assigns a score to all samples based on how perturbed they are
compared to the controls. It is based on the Molecular Distance
to Health (Pankla et al. 2009), and expands on this algorithm
by adding the options to calculate the z-score using the
modified z-score (using median absolute deviation), change the
z-score zeroing threshold, and look at genes that are most
perturbed in the test versus control classes.
biocViews: BiomedicalInformatics, QualityControl, Transcriptomics,
SystemsBiology, Microarray, QualityControl
Author: Melissa Lever [aut], Pedro Russo [aut], Helder Nakaya [aut,
cre]
Maintainer: Helder Nakaya <hnakaya@usp.br>
URL: https://mdp.sysbio.tools/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/mdp
git_branch: devel
git_last_commit: ddf480d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mdp_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mdp_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mdp_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mdp_1.27.0.tgz
vignettes: vignettes/mdp/inst/doc/my-vignette.html
vignetteTitles: Running the mdp package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mdp/inst/doc/my-vignette.R
dependencyCount: 36
Package: mdqc
Version: 1.69.0
Depends: R (>= 2.2.1), cluster, MASS
License: LGPL (>= 2)
MD5sum: fc049aaeeda5d92ceaf9fce4d7bfef1e
NeedsCompilation: no
Title: Mahalanobis Distance Quality Control for microarrays
Description: MDQC is a multivariate quality assessment method for
microarrays based on quality control (QC) reports. The
Mahalanobis distance of an array's quality attributes is used
to measure the similarity of the quality of that array against
the quality of the other arrays. Then, arrays with unusually
high distances can be flagged as potentially low-quality.
biocViews: Microarray, QualityControl
Author: Justin Harrington
Maintainer: Gabriela Cohen-Freue <gcohen@mrl.ubc.ca>
git_url: https://git.bioconductor.org/packages/mdqc
git_branch: devel
git_last_commit: cdd5233
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mdqc_1.69.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mdqc_1.69.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mdqc_1.69.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mdqc_1.69.0.tgz
vignettes: vignettes/mdqc/inst/doc/mdqcvignette.pdf
vignetteTitles: Introduction to MDQC
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mdqc/inst/doc/mdqcvignette.R
importsMe: arrayMvout
dependencyCount: 7
Package: MDTS
Version: 1.27.0
Depends: R (>= 3.5.0)
Imports: GenomicAlignments, GenomicRanges, IRanges, Biostrings,
DNAcopy, Rsamtools, parallel, stringr
Suggests: testthat, knitr
License: Artistic-2.0
MD5sum: 28425c9dd25093d6107d0cc9a782e8bd
NeedsCompilation: no
Title: Detection of de novo deletion in targeted sequencing trios
Description: A package for the detection of de novo copy number
deletions in targeted sequencing of trios with high sensitivity
and positive predictive value.
biocViews: StatisticalMethod, Technology, Sequencing,
TargetedResequencing, Coverage, DataImport
Author: Jack M.. Fu [aut, cre]
Maintainer: Jack M.. Fu <jmfu@jhsph.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MDTS
git_branch: devel
git_last_commit: fe18875
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MDTS_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MDTS_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MDTS_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MDTS_1.27.0.tgz
vignettes: vignettes/MDTS/inst/doc/mdts.html
vignetteTitles: Title of your vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MDTS/inst/doc/mdts.R
dependencyCount: 60
Package: MEAL
Version: 1.37.0
Depends: R (>= 3.6.0), Biobase, MultiDataSet
Imports: GenomicRanges, limma, vegan, BiocGenerics, minfi, IRanges,
S4Vectors, methods, parallel, ggplot2 (>= 2.0.0), permute,
Gviz, missMethyl, isva, SummarizedExperiment, SmartSVA,
graphics, stats, utils, matrixStats
Suggests: testthat, IlluminaHumanMethylationEPICanno.ilm10b2.hg19,
IlluminaHumanMethylation450kanno.ilmn12.hg19, knitr, minfiData,
BiocStyle, rmarkdown, brgedata
License: Artistic-2.0
MD5sum: dedc3428aacb39a205b0526c49b4d42d
NeedsCompilation: no
Title: Perform methylation analysis
Description: Package to integrate methylation and expression data. It
can also perform methylation or expression analysis alone.
Several plotting functionalities are included as well as a new
region analysis based on redundancy analysis. Effect of SNPs on
a region can also be estimated.
biocViews: DNAMethylation, Microarray, Software, WholeGenome
Author: Carlos Ruiz-Arenas [aut, cre], Juan R. Gonzalez [aut]
Maintainer: Xavier Escribà Montagut <xavier.escriba@isglobal.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MEAL
git_branch: devel
git_last_commit: e76b5d7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MEAL_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MEAL_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MEAL_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MEAL_1.37.0.tgz
vignettes: vignettes/MEAL/inst/doc/caseExample.html,
vignettes/MEAL/inst/doc/MEAL.html
vignetteTitles: Expression and Methylation Analysis with MEAL,
Methylation Analysis with MEAL
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MEAL/inst/doc/caseExample.R,
vignettes/MEAL/inst/doc/MEAL.R
dependencyCount: 229
Package: MeasurementError.cor
Version: 1.79.0
License: LGPL
MD5sum: dcaa2d67fc9df1c6bf5e718ab80e69aa
NeedsCompilation: no
Title: Measurement Error model estimate for correlation coefficient
Description: Two-stage measurement error model for correlation
estimation with smaller bias than the usual sample correlation
biocViews: StatisticalMethod
Author: Beiying Ding
Maintainer: Beiying Ding <bding@amgen.com>
git_url: https://git.bioconductor.org/packages/MeasurementError.cor
git_branch: devel
git_last_commit: c1d9386
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MeasurementError.cor_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MeasurementError.cor_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MeasurementError.cor_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MeasurementError.cor_1.79.0.tgz
vignettes:
vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.pdf
vignetteTitles: MeasurementError.cor Tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.R
dependencyCount: 0
Package: MEAT
Version: 1.19.0
Depends: R (>= 4.0)
Imports: impute (>= 1.58), dynamicTreeCut (>= 1.63), glmnet (>= 2.0),
grDevices, graphics, stats, utils, stringr, tibble, RPMM (>=
1.25), minfi (>= 1.30), dplyr, SummarizedExperiment, wateRmelon
Suggests: knitr, markdown, rmarkdown, BiocStyle, testthat (>= 2.1.0)
License: MIT + file LICENSE
MD5sum: 8bb2b1e7c10068df11a3eb7f301580b8
NeedsCompilation: no
Title: Muscle Epigenetic Age Test
Description: This package estimates epigenetic age in skeletal muscle,
using DNA methylation data generated with the Illumina Infinium
technology (HM27, HM450 and HMEPIC).
biocViews: Epigenetics, DNAMethylation, Microarray, Normalization,
BiomedicalInformatics, MethylationArray, Preprocessing
Author: Sarah Voisin [aut, cre]
(<https://orcid.org/0000-0002-4074-7083>), Steve Horvath [ctb]
(<https://orcid.org/0000-0002-4110-3589>)
Maintainer: Sarah Voisin <sarah.voisin.aeris@gmail.com>
URL: https://github.com/sarah-voisin/MEAT
VignetteBuilder: knitr
BugReports: https://github.com/sarah-voisin/MEAT/issues
git_url: https://git.bioconductor.org/packages/MEAT
git_branch: devel
git_last_commit: 035a45b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MEAT_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MEAT_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MEAT_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MEAT_1.19.0.tgz
vignettes: vignettes/MEAT/inst/doc/MEAT.html
vignetteTitles: MEAT
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MEAT/inst/doc/MEAT.R
dependencyCount: 180
Package: MEB
Version: 1.21.0
Depends: R (>= 3.6.0)
Imports: e1071, edgeR, scater, stats, wrswoR, SummarizedExperiment,
SingleCellExperiment
Suggests: knitr,rmarkdown,BiocStyle
License: GPL-2
MD5sum: f9dca4ee9030303b13f6c2d9b4408cba
NeedsCompilation: no
Title: A normalization-invariant minimum enclosing ball method to
detect differentially expressed genes for RNA-seq and scRNA-seq
data
Description: This package provides a method to identify differential
expression genes in the same or different species. Given that
non-DE genes have some similarities in features, a scaling-free
minimum enclosing ball (SFMEB) model is built to cover those
non-DE genes in feature space, then those DE genes, which are
enormously different from non-DE genes, being regarded as
outliers and rejected outside the ball. The method on this
package is described in the article 'A minimum enclosing ball
method to detect differential expression genes for RNA-seq
data'. The SFMEB method is extended to the scMEB method that
considering two or more potential types of cells or unknown
labels scRNA-seq dataset DEGs identification.
biocViews: DifferentialExpression, GeneExpression, Normalization,
Classification, Sequencing
Author: Yan Zhou, Jiadi Zhu
Maintainer: Jiadi Zhu <2160090406@email.szu.edu.cn>, Yan Zhou
<zhouy1016@szu.edu.cn>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MEB
git_branch: devel
git_last_commit: a09a3ea
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MEB_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MEB_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MEB_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MEB_1.21.0.tgz
vignettes: vignettes/MEB/inst/doc/NIMEB.html
vignetteTitles: MEB Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MEB/inst/doc/NIMEB.R
dependencyCount: 116
Package: MEDIPS
Version: 1.59.0
Depends: R (>= 3.0), BSgenome, Rsamtools
Imports: GenomicRanges, Biostrings, graphics, gtools, IRanges, methods,
stats, utils, edgeR, DNAcopy, biomaRt, rtracklayer,
preprocessCore
Suggests: BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, BiocStyle
License: GPL (>=2)
MD5sum: 6e2e0bf791cb1f5b8f86c554685c8eb1
NeedsCompilation: no
Title: DNA IP-seq data analysis
Description: MEDIPS was developed for analyzing data derived from
methylated DNA immunoprecipitation (MeDIP) experiments followed
by sequencing (MeDIP-seq). However, MEDIPS provides
functionalities for the analysis of any kind of quantitative
sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others)
including calculation of differential coverage between groups
of samples and saturation and correlation analysis.
biocViews: DNAMethylation, CpGIsland, DifferentialExpression,
Sequencing, ChIPSeq, Preprocessing, QualityControl,
Visualization, Microarray, Genetics, Coverage,
GenomeAnnotation, CopyNumberVariation, SequenceMatching
Author: Lukas Chavez, Matthias Lienhard, Joern Dietrich, Isaac Lopez
Moyado
Maintainer: Lukas Chavez <lukaschavez@ucsd.edu>
git_url: https://git.bioconductor.org/packages/MEDIPS
git_branch: devel
git_last_commit: 34d1345
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MEDIPS_1.59.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MEDIPS_1.59.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MEDIPS_1.59.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MEDIPS_1.59.0.tgz
vignettes: vignettes/MEDIPS/inst/doc/MEDIPS.pdf
vignetteTitles: MEDIPS
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MEDIPS/inst/doc/MEDIPS.R
dependencyCount: 107
Package: MEDME
Version: 1.67.0
Depends: R (>= 2.15), grDevices, graphics, methods, stats, utils
Imports: Biostrings, MASS, drc
Suggests: BSgenome.Hsapiens.UCSC.hg18, BSgenome.Mmusculus.UCSC.mm9
License: GPL (>= 2)
MD5sum: cbda960041707868b250c4ce738b3fd0
NeedsCompilation: yes
Title: Modelling Experimental Data from MeDIP Enrichment
Description: MEDME allows the prediction of absolute and relative
methylation levels based on measures obtained by
MeDIP-microarray experiments
biocViews: Microarray, CpGIsland, DNAMethylation
Author: Mattia Pelizzola and Annette Molinaro
Maintainer: Mattia Pelizzola <mattia.pelizzola@gmail.com>
git_url: https://git.bioconductor.org/packages/MEDME
git_branch: devel
git_last_commit: 0fb1ead
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MEDME_1.67.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MEDME_1.67.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MEDME_1.67.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MEDME_1.67.0.tgz
vignettes: vignettes/MEDME/inst/doc/MEDME.pdf
vignetteTitles: MEDME.pdf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MEDME/inst/doc/MEDME.R
dependencyCount: 98
Package: megadepth
Version: 1.17.0
Imports: xfun, utils, fs, GenomicRanges, readr, cmdfun, dplyr, magrittr
Suggests: covr, knitr, BiocStyle, sessioninfo, rmarkdown, rtracklayer,
derfinder, GenomeInfoDb, tools, RefManageR, testthat
License: Artistic-2.0
MD5sum: aad19f0a9047a62d2421b6d9f6af1124
NeedsCompilation: no
Title: megadepth: BigWig and BAM related utilities
Description: This package provides an R interface to Megadepth by
Christopher Wilks available at
https://github.com/ChristopherWilks/megadepth. It is
particularly useful for computing the coverage of a set of
genomic regions across bigWig or BAM files. With this package,
you can build base-pair coverage matrices for regions or
annotations of your choice from BigWig files. Megadepth was
used to create the raw files provided by
https://bioconductor.org/packages/recount3.
biocViews: Software, Coverage, DataImport, Transcriptomics, RNASeq,
Preprocessing
Author: Leonardo Collado-Torres [aut] (ORCID:
<https://orcid.org/0000-0003-2140-308X>), David Zhang [aut,
cre] (ORCID: <https://orcid.org/0000-0003-2382-8460>)
Maintainer: David Zhang <david.zhang.12@ucl.ac.uk>
URL: https://github.com/LieberInstitute/megadepth
SystemRequirements: megadepth
(<https://github.com/ChristopherWilks/megadepth>)
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/megadepth
git_url: https://git.bioconductor.org/packages/megadepth
git_branch: devel
git_last_commit: 019b9f0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/megadepth_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/megadepth_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/megadepth_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/megadepth_1.17.0.tgz
vignettes: vignettes/megadepth/inst/doc/megadepth.html
vignetteTitles: megadepth quick start guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: FALSE
Rfiles: vignettes/megadepth/inst/doc/megadepth.R
importsMe: chevreulProcess
dependencyCount: 82
Package: MEIGOR
Version: 1.41.0
Depends: R (>= 4.0), Rsolnp, snowfall, deSolve, CNORode
Suggests: CellNOptR, knitr, BiocStyle
License: GPL-3
MD5sum: 7815f22ea399df020271024135d76611
NeedsCompilation: no
Title: MEIGOR - MEtaheuristics for bIoinformatics Global Optimization
Description: MEIGOR provides a comprehensive environment for performing
global optimization tasks in bioinformatics and systems
biology. It leverages advanced metaheuristic algorithms to
efficiently search the solution space and is specifically
tailored to handle the complexity and high-dimensionality of
biological datasets. This package supports various optimization
routines and is integrated with Bioconductor's infrastructure
for a seamless analysis workflow.
biocViews: SystemsBiology, Optimization, Software
Author: Jose A. Egea [aut, cre], David Henriques [aut], Alexandre Fdez.
Villaverde [aut], Thomas Cokelaer [aut]
Maintainer: Jose A. Egea <josea.egea@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MEIGOR
git_branch: devel
git_last_commit: 16a8117
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MEIGOR_1.41.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MEIGOR_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MEIGOR_1.41.0.tgz
vignettes: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.html
vignetteTitles: MEIGOR: a software suite based on metaheuristics for
global optimization in systems biology and bioinformatics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.R
dependencyCount: 80
Package: Melissa
Version: 1.23.0
Depends: R (>= 3.5.0), BPRMeth, GenomicRanges
Imports: data.table, parallel, ROCR, matrixcalc, mclust, ggplot2,
doParallel, foreach, MCMCpack, cowplot, magrittr, mvtnorm,
truncnorm, assertthat, BiocStyle, stats, utils
Suggests: testthat, knitr, rmarkdown
License: GPL-3 | file LICENSE
MD5sum: 87343b3d5cbab1919694bd3d7770bc40
NeedsCompilation: no
Title: Bayesian clustering and imputationa of single cell methylomes
Description: Melissa is a Baysian probabilistic model for jointly
clustering and imputing single cell methylomes. This is done by
taking into account local correlations via a Generalised Linear
Model approach and global similarities using a mixture
modelling approach.
biocViews: ImmunoOncology, DNAMethylation, GeneExpression,
GeneRegulation, Epigenetics, Genetics, Clustering,
FeatureExtraction, Regression, RNASeq, Bayesian, KEGG,
Sequencing, Coverage, SingleCell
Author: C. A. Kapourani [aut, cre]
Maintainer: C. A. Kapourani <kapouranis.andreas@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Melissa
git_branch: devel
git_last_commit: cffba8a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Melissa_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Melissa_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Melissa_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Melissa_1.23.0.tgz
vignettes: vignettes/Melissa/inst/doc/process_files.html,
vignettes/Melissa/inst/doc/run_melissa.html
vignetteTitles: 1: Process and filter scBS-seq data, 2: Cluster and
impute scBS-seq data using Melissa
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Melissa/inst/doc/process_files.R,
vignettes/Melissa/inst/doc/run_melissa.R
dependencyCount: 112
Package: memes
Version: 1.15.0
Depends: R (>= 4.1)
Imports: Biostrings, dplyr, cmdfun (>= 1.0.2), GenomicRanges, ggplot2,
ggseqlogo, magrittr, matrixStats, methods, patchwork, processx,
purrr, rlang, readr, stats, tools, tibble, tidyr, utils,
usethis, universalmotif (>= 1.9.3), xml2
Suggests: cowplot, BSgenome.Dmelanogaster.UCSC.dm3,
BSgenome.Dmelanogaster.UCSC.dm6, forcats, testthat (>= 2.1.0),
knitr, MotifDb, pheatmap, PMCMRplus, plyranges (>= 1.9.1),
rmarkdown, covr
License: MIT + file LICENSE
MD5sum: 234b6f00b317a9645289b0a2d7e98930
NeedsCompilation: no
Title: motif matching, comparison, and de novo discovery using the MEME
Suite
Description: A seamless interface to the MEME Suite family of tools for
motif analysis. 'memes' provides data aware utilities for using
GRanges objects as entrypoints to motif analysis, data
structures for examining & editing motif lists, and novel data
visualizations. 'memes' functions and data structures are
amenable to both base R and tidyverse workflows.
biocViews: DataImport, FunctionalGenomics, GeneRegulation,
MotifAnnotation, MotifDiscovery, SequenceMatching, Software
Author: Spencer Nystrom [aut, cre, cph] (ORCID:
<https://orcid.org/0000-0003-1000-1579>)
Maintainer: Spencer Nystrom <nystromdev@gmail.com>
URL: https://snystrom.github.io/memes/,
https://github.com/snystrom/memes
SystemRequirements: Meme Suite (v5.3.3 or above)
<http://meme-suite.org/doc/download.html>
VignetteBuilder: knitr
BugReports: https://github.com/snystrom/memes/issues
git_url: https://git.bioconductor.org/packages/memes
git_branch: devel
git_last_commit: ba252fc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/memes_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/memes_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/memes_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/memes_1.15.0.tgz
vignettes: vignettes/memes/inst/doc/core_ame.html,
vignettes/memes/inst/doc/core_dreme.html,
vignettes/memes/inst/doc/core_fimo.html,
vignettes/memes/inst/doc/core_tomtom.html,
vignettes/memes/inst/doc/install_guide.html,
vignettes/memes/inst/doc/integrative_analysis.html,
vignettes/memes/inst/doc/tidy_motifs.html
vignetteTitles: Motif Enrichment Testing using AME, Denovo Motif
Discovery Using DREME, Motif Scanning using FIMO, Motif
Comparison using TomTom, Install MEME, ChIP-seq Analysis,
Tidying Motif Metadata
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/memes/inst/doc/core_ame.R,
vignettes/memes/inst/doc/core_dreme.R,
vignettes/memes/inst/doc/core_fimo.R,
vignettes/memes/inst/doc/core_tomtom.R,
vignettes/memes/inst/doc/install_guide.R,
vignettes/memes/inst/doc/integrative_analysis.R,
vignettes/memes/inst/doc/tidy_motifs.R
importsMe: MotifPeeker
dependencyCount: 110
Package: Mergeomics
Version: 1.35.0
Depends: R (>= 3.0.1)
Suggests: RUnit, BiocGenerics
License: GPL (>= 2)
Archs: x64
MD5sum: 8f674d70061e68b3b5b0ca8b790fbc37
NeedsCompilation: no
Title: Integrative network analysis of omics data
Description: The Mergeomics pipeline serves as a flexible framework for
integrating multidimensional omics-disease associations,
functional genomics, canonical pathways and gene-gene
interaction networks to generate mechanistic hypotheses. It
includes two main parts, 1) Marker set enrichment analysis
(MSEA); 2) Weighted Key Driver Analysis (wKDA).
biocViews: Software
Author: Ville-Petteri Makinen, Le Shu, Yuqi Zhao, Zeyneb Kurt, Bin
Zhang, Xia Yang
Maintainer: Zeyneb Kurt <zeynebkurt@gmail.com>
git_url: https://git.bioconductor.org/packages/Mergeomics
git_branch: devel
git_last_commit: 2b53e79
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Mergeomics_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Mergeomics_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Mergeomics_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Mergeomics_1.35.0.tgz
vignettes: vignettes/Mergeomics/inst/doc/Mergeomics.pdf
vignetteTitles: Mergeomics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Mergeomics/inst/doc/Mergeomics.R
dependencyCount: 0
Package: MeSHDbi
Version: 1.43.0
Depends: R (>= 3.0.1)
Imports: methods, AnnotationDbi (>= 1.31.19), RSQLite, Biobase
Suggests: testthat
License: Artistic-2.0
MD5sum: e5ed009cacf9955b5b767ea0ed0e79cf
NeedsCompilation: no
Title: DBI to construct MeSH-related package from sqlite file
Description: The package is unified implementation of MeSH.db,
MeSH.AOR.db, and MeSH.PCR.db and also is interface to construct
Gene-MeSH package (MeSH.XXX.eg.db). loadMeSHDbiPkg import
sqlite file and generate MeSH.XXX.eg.db.
biocViews: Annotation, AnnotationData, Infrastructure
Author: Koki Tsuyuzaki
Maintainer: Koki Tsuyuzaki <k.t.the-answer@hotmail.co.jp>
git_url: https://git.bioconductor.org/packages/MeSHDbi
git_branch: devel
git_last_commit: cae5246
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MeSHDbi_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MeSHDbi_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MeSHDbi_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MeSHDbi_1.43.0.tgz
vignettes: vignettes/MeSHDbi/inst/doc/MeSHDbi.pdf
vignetteTitles: MeSH.db
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
importsMe: meshes, meshr, scTensor
dependencyCount: 45
Package: meshes
Version: 1.33.0
Depends: R (>= 4.1.0)
Imports: AnnotationDbi, DOSE, enrichplot, GOSemSim (>= 2.31.2),
methods, utils, AnnotationHub, MeSHDbi, yulab.utils (>= 0.1.5)
Suggests: knitr, rmarkdown, prettydoc
License: Artistic-2.0
MD5sum: fd832c17ee3212d61c969dfd173b078a
NeedsCompilation: no
Title: MeSH Enrichment and Semantic analyses
Description: MeSH (Medical Subject Headings) is the NLM controlled
vocabulary used to manually index articles for MEDLINE/PubMed.
MeSH terms were associated by Entrez Gene ID by three methods,
gendoo, gene2pubmed and RBBH. This association is fundamental
for enrichment and semantic analyses. meshes supports
enrichment analysis (over-representation and gene set
enrichment analysis) of gene list or whole expression profile.
The semantic comparisons of MeSH terms provide quantitative
ways to compute similarities between genes and gene groups.
meshes implemented five methods proposed by Resnik, Schlicker,
Jiang, Lin and Wang respectively and supports more than 70
species.
biocViews: Annotation, Clustering, MultipleComparison, Software
Author: Guangchuang Yu [aut, cre]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
URL: https://yulab-smu.top/biomedical-knowledge-mining-book/
VignetteBuilder: knitr
BugReports: https://github.com/GuangchuangYu/meshes/issues
git_url: https://git.bioconductor.org/packages/meshes
git_branch: devel
git_last_commit: 708aa6d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/meshes_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/meshes_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/meshes_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/meshes_1.33.0.tgz
vignettes: vignettes/meshes/inst/doc/meshes.html
vignetteTitles: meshes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/meshes/inst/doc/meshes.R
dependencyCount: 130
Package: meshr
Version: 2.13.0
Depends: R (>= 4.1.0)
Imports: markdown, rmarkdown, BiocStyle, knitr, methods, stats, utils,
fdrtool, MeSHDbi, Category, S4Vectors, BiocGenerics, RSQLite
License: Artistic-2.0
MD5sum: 9ff5c4327428edbd764ceb42460fb78d
NeedsCompilation: no
Title: Tools for conducting enrichment analysis of MeSH
Description: A set of annotation maps describing the entire MeSH
assembled using data from MeSH.
biocViews: AnnotationData, FunctionalAnnotation, Bioinformatics,
Statistics, Annotation, MultipleComparisons, MeSHDb
Author: Koki Tsuyuzaki, Itoshi Nikaido, Gota Morota
Maintainer: Koki Tsuyuzaki <k.t.the-answer@hotmail.co.jp>
VignetteBuilder: knitr
BugReports: https://github.com/rikenbit/meshr/issues
git_url: https://git.bioconductor.org/packages/meshr
git_branch: devel
git_last_commit: 5a2870d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/meshr_2.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/meshr_2.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/meshr_2.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/meshr_2.13.0.tgz
vignettes: vignettes/meshr/inst/doc/MeSH.html
vignetteTitles: AnnotationHub-style MeSH ORA Framework from BioC 3.14
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/meshr/inst/doc/MeSH.R
importsMe: scTensor
dependencyCount: 85
Package: MesKit
Version: 1.17.0
Depends: R (>= 4.0.0)
Imports: methods, data.table, Biostrings, dplyr, tidyr (>= 1.0.0), ape
(>= 5.4.1), ggrepel, pracma, ggridges, AnnotationDbi, IRanges,
circlize, cowplot, mclust, phangorn, ComplexHeatmap (>= 1.9.3),
ggplot2, RColorBrewer, grDevices, stats, utils, S4Vectors
Suggests: shiny, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 (>=
1.4.0), org.Hs.eg.db, clusterProfiler,
TxDb.Hsapiens.UCSC.hg19.knownGene
License: GPL-3
MD5sum: 075ff91e7f217840b51e32fde48aef05
NeedsCompilation: no
Title: A tool kit for dissecting cancer evolution from multi-region
derived tumor biopsies via somatic alterations
Description: MesKit provides commonly used analysis and visualization
modules based on mutational data generated by multi-region
sequencing (MRS). This package allows to depict mutational
profiles, measure heterogeneity within or between tumors from
the same patient, track evolutionary dynamics, as well as
characterize mutational patterns on different levels. Shiny
application was also developed for a need of GUI-based
analysis. As a handy tool, MesKit can facilitate the
interpretation of tumor heterogeneity and the understanding of
evolutionary relationship between regions in MRS study.
Author: Mengni Liu [aut, cre] (ORCID:
<https://orcid.org/0000-0001-9938-9973>), Jianyu Chen [aut,
ctb] (ORCID: <https://orcid.org/0000-0003-4491-9265>), Xin Wang
[aut, ctb] (ORCID: <https://orcid.org/0000-0002-6072-599X>)
Maintainer: Mengni Liu <niinleslie@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MesKit
git_branch: devel
git_last_commit: 471c27c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MesKit_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MesKit_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MesKit_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MesKit_1.17.0.tgz
vignettes: vignettes/MesKit/inst/doc/MesKit.html
vignetteTitles: Analyze and Visualize Multi-region Whole-exome
Sequencing Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MesKit/inst/doc/MesKit.R
importsMe: CaMutQC
dependencyCount: 101
Package: messina
Version: 1.43.0
Depends: R (>= 3.1.0), survival (>= 2.37-4), methods
Imports: Rcpp (>= 0.11.1), plyr (>= 1.8), ggplot2 (>= 0.9.3.1), grid
(>= 3.1.0), foreach (>= 1.4.1), graphics
LinkingTo: Rcpp
Suggests: knitr (>= 1.5), antiProfilesData (>= 0.99.2), Biobase (>=
2.22.0), BiocStyle
Enhances: doMC (>= 1.3.3)
License: EPL (>= 1.0)
MD5sum: b65784727fd19bb657c1b19caa1b17ac
NeedsCompilation: yes
Title: Single-gene classifiers and outlier-resistant detection of
differential expression for two-group and survival problems
Description: Messina is a collection of algorithms for constructing
optimally robust single-gene classifiers, and for identifying
differential expression in the presence of outliers or unknown
sample subgroups. The methods have application in identifying
lead features to develop into clinical tests (both diagnostic
and prognostic), and in identifying differential expression
when a fraction of samples show unusual patterns of expression.
biocViews: GeneExpression, DifferentialExpression,
BiomedicalInformatics, Classification, Survival
Author: Mark Pinese [aut], Mark Pinese [cre], Mark Pinese [cph]
Maintainer: Mark Pinese <mpinese@ccia.org.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/messina
git_branch: devel
git_last_commit: c06bc28
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/messina_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/messina_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/messina_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/messina_1.43.0.tgz
vignettes: vignettes/messina/inst/doc/messina.pdf
vignetteTitles: Using Messina
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/messina/inst/doc/messina.R
dependencyCount: 41
Package: metabCombiner
Version: 1.17.0
Depends: R (>= 4.0)
Imports: dplyr (>= 1.0), methods, mgcv, caret, S4Vectors, stats, utils,
rlang, graphics, matrixStats, tidyr
Suggests: knitr, rmarkdown, testthat, BiocStyle
License: GPL-3
MD5sum: 45cb07d515c3f0429c1f56bb845bb569
NeedsCompilation: yes
Title: Method for Combining LC-MS Metabolomics Feature Measurements
Description: This package aligns LC-HRMS metabolomics datasets acquired
from biologically similar specimens analyzed under similar, but
not necessarily identical, conditions. Peak-picked and simply
aligned metabolomics feature tables (consisting of m/z, rt, and
per-sample abundance measurements, plus optional identifiers &
adduct annotations) are accepted as input. The package outputs
a combined table of feature pair alignments, organized into
groups of similar m/z, and ranked by a similarity score. Input
tables are assumed to be acquired using similar (but not
necessarily identical) analytical methods.
biocViews: Software, MassSpectrometry, Metabolomics
Author: Hani Habra [aut, cre], Alla Karnovsky [ths]
Maintainer: Hani Habra <hhabra1@gmail.com>
VignetteBuilder: knitr
BugReports: https://www.github.com/hhabra/metabCombiner/issues
git_url: https://git.bioconductor.org/packages/metabCombiner
git_branch: devel
git_last_commit: e8a8053
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/metabCombiner_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/metabCombiner_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/metabCombiner_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/metabCombiner_1.17.0.tgz
vignettes: vignettes/metabCombiner/inst/doc/metabCombiner_vignette.html
vignetteTitles: Combine LC-MS Metabolomics Datasets with metabCombiner
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/metabCombiner/inst/doc/metabCombiner_vignette.R
dependencyCount: 89
Package: metabinR
Version: 1.9.0
Depends: R (>= 4.3)
Imports: methods, rJava
Suggests: BiocStyle, cvms, data.table, dplyr, ggplot2, gridExtra,
knitr, R.utils, rmarkdown, sabre, spelling, testthat (>= 3.0.0)
License: GPL-3
MD5sum: 6c7384560261ef376c9202d803b1cf51
NeedsCompilation: no
Title: Abundance and Compositional Based Binning of Metagenomes
Description: Provide functions for performing abundance and
compositional based binning on metagenomic samples, directly
from FASTA or FASTQ files. Functions are implemented in Java
and called via rJava. Parallel implementation that operates
directly on input FASTA/FASTQ files for fast execution.
biocViews: Classification, Clustering, Microbiome, Sequencing, Software
Author: Anestis Gkanogiannis [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6441-0688>)
Maintainer: Anestis Gkanogiannis <anestis@gkanogiannis.com>
URL: https://github.com/gkanogiannis/metabinR
SystemRequirements: Java (>= 8)
VignetteBuilder: knitr
BugReports: https://github.com/gkanogiannis/metabinR/issues
git_url: https://git.bioconductor.org/packages/metabinR
git_branch: devel
git_last_commit: e7264a2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/metabinR_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/metabinR_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/metabinR_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/metabinR_1.9.0.tgz
vignettes: vignettes/metabinR/inst/doc/metabinR_vignette.html
vignetteTitles: metabinR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/metabinR/inst/doc/metabinR_vignette.R
dependencyCount: 2
Package: MetaboAnnotation
Version: 1.11.1
Depends: R (>= 4.0.0)
Imports: BiocGenerics, MsCoreUtils, MetaboCoreUtils, ProtGenerics,
methods, S4Vectors, Spectra (>= 1.13.2), BiocParallel,
SummarizedExperiment, QFeatures, AnnotationHub, graphics,
CompoundDb
Suggests: testthat, knitr, msdata, BiocStyle, rmarkdown, plotly, shiny,
shinyjs, DT, microbenchmark, mzR
Enhances: RMariaDB, RSQLite
License: Artistic-2.0
MD5sum: 282801ac454fe53196e1de2ac2ff7e41
NeedsCompilation: no
Title: Utilities for Annotation of Metabolomics Data
Description: High level functions to assist in annotation of
(metabolomics) data sets. These include functions to perform
simple tentative annotations based on mass matching but also
functions to consider m/z and retention times for annotation of
LC-MS features given that respective reference values are
available. In addition, the function provides high-level
functions to simplify matching of LC-MS/MS spectra against
spectral libraries and objects and functionality to represent
and manage such matched data.
biocViews: Infrastructure, Metabolomics, MassSpectrometry
Author: Michael Witting [aut] (ORCID:
<https://orcid.org/0000-0002-1462-4426>), Johannes Rainer [aut,
cre] (ORCID: <https://orcid.org/0000-0002-6977-7147>), Andrea
Vicini [aut] (ORCID: <https://orcid.org/0000-0001-9438-6909>),
Carolin Huber [aut] (ORCID:
<https://orcid.org/0000-0002-9355-8948>), Philippine Louail
[aut] (ORCID: <https://orcid.org/0009-0007-5429-6846>), Nir
Shachaf [ctb]
Maintainer: Johannes Rainer <Johannes.Rainer@eurac.edu>
URL: https://github.com/RforMassSpectrometry/MetaboAnnotation
VignetteBuilder: knitr
BugReports:
https://github.com/RforMassSpectrometry/MetaboAnnotation/issues
git_url: https://git.bioconductor.org/packages/MetaboAnnotation
git_branch: devel
git_last_commit: 05c3086
git_last_commit_date: 2024-11-21
Date/Publication: 2024-11-21
source.ver: src/contrib/MetaboAnnotation_1.11.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MetaboAnnotation_1.11.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MetaboAnnotation_1.11.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MetaboAnnotation_1.11.1.tgz
vignettes: vignettes/MetaboAnnotation/inst/doc/MetaboAnnotation.html
vignetteTitles: Annotation of MS-based Metabolomics Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MetaboAnnotation/inst/doc/MetaboAnnotation.R
dependencyCount: 144
Package: MetaboCoreUtils
Version: 1.15.0
Depends: R (>= 4.0)
Imports: utils, MsCoreUtils, BiocParallel, methods, stats
Suggests: BiocStyle, testthat, knitr, rmarkdown, robustbase
License: Artistic-2.0
Archs: x64
MD5sum: a2426892090d2a6dbe93d0a4460e8dea
NeedsCompilation: no
Title: Core Utils for Metabolomics Data
Description: MetaboCoreUtils defines metabolomics-related core
functionality provided as low-level functions to allow a data
structure-independent usage across various R packages. This
includes functions to calculate between ion (adduct) and
compound mass-to-charge ratios and masses or functions to work
with chemical formulas. The package provides also a set of
adduct definitions and information on some commercially
available internal standard mixes commonly used in MS
experiments.
biocViews: Infrastructure, Metabolomics, MassSpectrometry
Author: Johannes Rainer [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6977-7147>), Michael Witting [aut]
(ORCID: <https://orcid.org/0000-0002-1462-4426>), Andrea Vicini
[aut], Liesa Salzer [ctb] (ORCID:
<https://orcid.org/0000-0003-0761-0656>), Sebastian Gibb [aut]
(ORCID: <https://orcid.org/0000-0001-7406-4443>), Michael
Stravs [ctb] (ORCID: <https://orcid.org/0000-0002-1426-8572>),
Roger Gine [aut] (ORCID:
<https://orcid.org/0000-0003-0288-9619>), Philippine Louail
[aut] (ORCID: <https://orcid.org/0009-0007-5429-6846>)
Maintainer: Johannes Rainer <Johannes.Rainer@eurac.edu>
URL: https://github.com/RforMassSpectrometry/MetaboCoreUtils
VignetteBuilder: knitr
BugReports:
https://github.com/RforMassSpectrometry/MetaboCoreUtils/issues
git_url: https://git.bioconductor.org/packages/MetaboCoreUtils
git_branch: devel
git_last_commit: 8267815
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MetaboCoreUtils_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MetaboCoreUtils_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MetaboCoreUtils_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MetaboCoreUtils_1.15.0.tgz
vignettes: vignettes/MetaboCoreUtils/inst/doc/MetaboCoreUtils.html
vignetteTitles: Core Utils for Metabolomics Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MetaboCoreUtils/inst/doc/MetaboCoreUtils.R
importsMe: CompoundDb, MetaboAnnotation, Spectra, xcms
dependencyCount: 24
Package: MetaboDynamics
Version: 0.99.23
Depends: R (>= 4.4.0)
Imports: dplyr, ggplot2, KEGGREST, methods, Rcpp (>= 0.12.0),
RcppParallel (>= 5.0.1), rstan (>= 2.18.1), rstantools (>=
2.4.0), S4Vectors, stringr, SummarizedExperiment, tidyr
LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0),
RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>=
2.18.0)
Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0)
License: GPL (>= 3)
Archs: x64
MD5sum: 906374e037c5e76693aa21020c7805f1
NeedsCompilation: yes
Title: Bayesian analysis of longitudinal metabolomics data
Description: MetaboDynamics is an R-package that provides a framework
of probabilistic models to analyze longitudinal metabolomics
data. It enables robust estimation of mean concentrations
despite varying spread between timepoints and reports
differences between timepoints as well as metabolite specific
dynamics profiles that can be used for identifying "dynamics
clusters" of metabolites of similar dynamics. Provides
probabilistic over-representation analysis of KEGG functional
modules and pathways as well as comparison between clusters of
different experimental conditions.
biocViews:
Software,Metabolomics,Bayesian,FunctionalPrediction,MultipleComparison,KEGG,Pathways
Author: Katja Danielzik [aut, cre] (ORCID:
<https://orcid.org/0009-0007-5021-6212>), Simo Kitanovski [ctb]
(ORCID: <https://orcid.org/0000-0003-2909-5376>), Johann
Matschke [ctb] (ORCID:
<https://orcid.org/0000-0003-4878-8741>), Daniel Hoffmann [ctb]
(ORCID: <https://orcid.org/0000-0003-2973-7869>)
Maintainer: Katja Danielzik <katja.danielzik@uni-due.de>
URL: https://github.com/KatjaDanielzik/MetaboDynamics
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/KatjaDanielzik/MetaboDynamics/issues
git_url: https://git.bioconductor.org/packages/MetaboDynamics
git_branch: devel
git_last_commit: 680a446
git_last_commit_date: 2025-03-26
Date/Publication: 2025-03-27
source.ver: src/contrib/MetaboDynamics_0.99.23.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MetaboDynamics_0.99.23.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MetaboDynamics_0.99.23.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MetaboDynamics_0.99.23.tgz
vignettes: vignettes/MetaboDynamics/inst/doc/MetaboDynamics.html
vignetteTitles: 1. MetaboDynamics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MetaboDynamics/inst/doc/MetaboDynamics.R
dependencyCount: 95
Package: metabolomicsWorkbenchR
Version: 1.17.0
Depends: R (>= 4.0)
Imports: data.table, httr, jsonlite, methods, MultiAssayExperiment,
struct, SummarizedExperiment, utils
Suggests: BiocStyle, covr, knitr, HDF5Array, httptest, rmarkdown,
structToolbox, testthat, pmp, grid, png
License: GPL-3
MD5sum: 246e73924cf2c8511a44523b28721ec0
NeedsCompilation: no
Title: Metabolomics Workbench in R
Description: This package provides functions for interfacing with the
Metabolomics Workbench RESTful API. Study, compound, protein
and gene information can be searched for using the API. Methods
to obtain study data in common Bioconductor formats such as
SummarizedExperiment and MultiAssayExperiment are also
included.
biocViews: Software, Metabolomics
Author: Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut]
Maintainer: Gavin Rhys Lloyd <g.r.lloyd@bham.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/metabolomicsWorkbenchR
git_branch: devel
git_last_commit: 3a7c458
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/metabolomicsWorkbenchR_1.17.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.5/metabolomicsWorkbenchR_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/metabolomicsWorkbenchR_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/metabolomicsWorkbenchR_1.17.0.tgz
vignettes:
vignettes/metabolomicsWorkbenchR/inst/doc/example_using_structToolbox.html,
vignettes/metabolomicsWorkbenchR/inst/doc/Introduction_to_metabolomicsWorkbenchR.html
vignetteTitles: Example using structToolbox,
Introduction_to_metabolomicsWorkbenchR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/metabolomicsWorkbenchR/inst/doc/example_using_structToolbox.R,
vignettes/metabolomicsWorkbenchR/inst/doc/Introduction_to_metabolomicsWorkbenchR.R
suggestsMe: fobitools, MetMashR
dependencyCount: 69
Package: metabomxtr
Version: 1.41.0
Depends: methods,Biobase
Imports: optimx, Formula, plyr, multtest, BiocParallel, ggplot2
Suggests: xtable, reshape2
License: GPL-2
MD5sum: 28e7955f78ad73da48c11e90b94c36ad
NeedsCompilation: no
Title: A package to run mixture models for truncated metabolomics data
with normal or lognormal distributions
Description: The functions in this package return optimized parameter
estimates and log likelihoods for mixture models of truncated
data with normal or lognormal distributions.
biocViews: ImmunoOncology, Metabolomics, MassSpectrometry
Author: Michael Nodzenski, Anna Reisetter, Denise Scholtens
Maintainer: Michael Nodzenski <michael.nodzenski@gmail.com>
git_url: https://git.bioconductor.org/packages/metabomxtr
git_branch: devel
git_last_commit: 26b046c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/metabomxtr_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/metabomxtr_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/metabomxtr_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/metabomxtr_1.41.0.tgz
vignettes: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.pdf,
vignettes/metabomxtr/inst/doc/mixnorm_Vignette.pdf
vignetteTitles: metabomxtr, mixnorm
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.R,
vignettes/metabomxtr/inst/doc/mixnorm_Vignette.R
dependencyCount: 58
Package: MetaboSignal
Version: 1.37.0
Depends: R(>= 3.3)
Imports: KEGGgraph, hpar, igraph, RCurl, KEGGREST, EnsDb.Hsapiens.v75,
stats, graphics, utils, org.Hs.eg.db, biomaRt, AnnotationDbi,
MWASTools, mygene
Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown
License: GPL-3
MD5sum: 6a7bba34e719c3c949dc28f4f2f8f886
NeedsCompilation: no
Title: MetaboSignal: a network-based approach to overlay and explore
metabolic and signaling KEGG pathways
Description: MetaboSignal is an R package that allows merging,
analyzing and customizing metabolic and signaling KEGG
pathways. It is a network-based approach designed to explore
the topological relationship between genes (signaling- or
enzymatic-genes) and metabolites, representing a powerful tool
to investigate the genetic landscape and regulatory networks of
metabolic phenotypes.
biocViews: GraphAndNetwork, GeneSignaling, GeneTarget, Network,
Pathways, KEGG, Reactome, Software
Author: Andrea Rodriguez-Martinez, Rafael Ayala, Joram M. Posma, Ana L.
Neves, Maryam Anwar, Jeremy K. Nicholson, Marc-Emmanuel Dumas
Maintainer: Andrea Rodriguez-Martinez
<andrea.rodriguez-martinez13@imperial.ac.uk>, Rafael Ayala
<rafaelayalahernandez@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MetaboSignal
git_branch: devel
git_last_commit: 3fa4cf5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MetaboSignal_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MetaboSignal_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MetaboSignal_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MetaboSignal_1.37.0.tgz
vignettes: vignettes/MetaboSignal/inst/doc/MetaboSignal.html,
vignettes/MetaboSignal/inst/doc/MetaboSignal2.html
vignetteTitles: MetaboSignal, MetaboSignal 2: merging KEGG with
additional interaction resources
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MetaboSignal/inst/doc/MetaboSignal.R,
vignettes/MetaboSignal/inst/doc/MetaboSignal2.R
dependencyCount: 200
Package: metaCCA
Version: 1.35.0
Suggests: knitr
License: MIT + file LICENSE
Archs: x64
MD5sum: 88769fb8eca66677a89e1ad14cd6c966
NeedsCompilation: no
Title: Summary Statistics-Based Multivariate Meta-Analysis of
Genome-Wide Association Studies Using Canonical Correlation
Analysis
Description: metaCCA performs multivariate analysis of a single or
multiple GWAS based on univariate regression coefficients. It
allows multivariate representation of both phenotype and
genotype. metaCCA extends the statistical technique of
canonical correlation analysis to the setting where original
individual-level records are not available, and employs a
covariance shrinkage algorithm to achieve robustness.
biocViews: GenomeWideAssociation, SNP, Genetics, Regression,
StatisticalMethod, Software
Author: Anna Cichonska <anna.cichonska@gmail.com>
Maintainer: Anna Cichonska <anna.cichonska@gmail.com>
URL: https://doi.org/10.1093/bioinformatics/btw052
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/metaCCA
git_branch: devel
git_last_commit: e5665b0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/metaCCA_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/metaCCA_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/metaCCA_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/metaCCA_1.35.0.tgz
vignettes: vignettes/metaCCA/inst/doc/metaCCA.pdf
vignetteTitles: metaCCA
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/metaCCA/inst/doc/metaCCA.R
dependencyCount: 0
Package: MetaCyto
Version: 1.29.0
Depends: R (>= 3.4)
Imports: flowCore (>= 1.4),tidyr (>=
0.7),fastcluster,ggplot2,metafor,cluster,FlowSOM, grDevices,
graphics, stats, utils
Suggests: knitr, dplyr, rmarkdown
License: GPL (>= 2)
MD5sum: e647a7d4b668f6bec0f8e3b3180cb833
NeedsCompilation: no
Title: MetaCyto: A package for meta-analysis of cytometry data
Description: This package provides functions for preprocessing,
automated gating and meta-analysis of cytometry data. It also
provides functions that facilitate the collection of cytometry
data from the ImmPort database.
biocViews: ImmunoOncology, CellBiology, FlowCytometry, Clustering,
StatisticalMethod, Software, CellBasedAssays, Preprocessing
Author: Zicheng Hu, Chethan Jujjavarapu, Sanchita Bhattacharya, Atul J.
Butte
Maintainer: Zicheng Hu <zicheng.hu@ucsf.edu>
VignetteBuilder: knitr, rmarkdown
git_url: https://git.bioconductor.org/packages/MetaCyto
git_branch: devel
git_last_commit: 19f7705
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MetaCyto_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MetaCyto_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MetaCyto_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MetaCyto_1.29.0.tgz
vignettes: vignettes/MetaCyto/inst/doc/MetaCyto_Vignette.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MetaCyto/inst/doc/MetaCyto_Vignette.R
dependencyCount: 110
Package: metagene2
Version: 1.23.0
Depends: R (>= 4.0), R6 (>= 2.0), GenomicRanges, BiocParallel
Imports: rtracklayer, tools, GenomicAlignments, GenomeInfoDb, IRanges,
ggplot2, Rsamtools, purrr, data.table, methods, dplyr,
magrittr, reshape2
Suggests: BiocGenerics, RUnit, knitr, BiocStyle, rmarkdown
License: Artistic-2.0
MD5sum: 368fe76b0052ba6800b2c546db3234e4
NeedsCompilation: no
Title: A package to produce metagene plots
Description: This package produces metagene plots to compare coverages
of sequencing experiments at selected groups of genomic
regions. It can be used for such analyses as assessing the
binding of DNA-interacting proteins at promoter regions or
surveying antisense transcription over the length of a gene.
The metagene2 package can manage all aspects of the analysis,
from normalization of coverages to plot facetting according to
experimental metadata. Bootstraping analysis is used to provide
confidence intervals of per-sample mean coverages.
biocViews: ChIPSeq, Genetics, MultipleComparison, Coverage, Alignment,
Sequencing
Author: Eric Fournier [cre, aut], Charles Joly Beauparlant [aut],
Cedric Lippens [aut], Arnaud Droit [aut]
Maintainer: Eric Fournier <ericfournier2@yahoo.ca>
URL: https://github.com/ArnaudDroitLab/metagene2
VignetteBuilder: knitr
BugReports: https://github.com/ArnaudDroitLab/metagene2/issues
git_url: https://git.bioconductor.org/packages/metagene2
git_branch: devel
git_last_commit: 2d45109
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/metagene2_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/metagene2_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/metagene2_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/metagene2_1.23.0.tgz
vignettes: vignettes/metagene2/inst/doc/metagene2.html
vignetteTitles: Introduction to metagene2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/metagene2/inst/doc/metagene2.R
dependencyCount: 93
Package: metagenomeSeq
Version: 1.49.1
Depends: R(>= 3.0), Biobase, limma, glmnet, methods, RColorBrewer
Imports: parallel, matrixStats, foreach, Matrix, gplots, graphics,
grDevices, stats, utils, Wrench
Suggests: annotate, BiocGenerics, biomformat, knitr, gss, testthat (>=
0.8), vegan, interactiveDisplay, IHW
License: Artistic-2.0
MD5sum: 45962347184bd5bf032f51f1b76d1661
NeedsCompilation: no
Title: Statistical analysis for sparse high-throughput sequencing
Description: metagenomeSeq is designed to determine features (be it
Operational Taxanomic Unit (OTU), species, etc.) that are
differentially abundant between two or more groups of multiple
samples. metagenomeSeq is designed to address the effects of
both normalization and under-sampling of microbial communities
on disease association detection and the testing of feature
correlations.
biocViews: ImmunoOncology, Classification, Clustering,
GeneticVariability, DifferentialExpression, Microbiome,
Metagenomics, Normalization, Visualization, MultipleComparison,
Sequencing, Software
Author: Joseph Nathaniel Paulson, Nathan D. Olson, Domenick J. Braccia,
Justin Wagner, Hisham Talukder, Mihai Pop, Hector Corrada Bravo
Maintainer: Joseph N. Paulson <josephpaulson@gmail.com>
URL: https://github.com/nosson/metagenomeSeq/
VignetteBuilder: knitr
BugReports: https://github.com/nosson/metagenomeSeq/issues
git_url: https://git.bioconductor.org/packages/metagenomeSeq
git_branch: devel
git_last_commit: 96adc06
git_last_commit_date: 2024-12-18
Date/Publication: 2024-12-18
source.ver: src/contrib/metagenomeSeq_1.49.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/metagenomeSeq_1.49.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/metagenomeSeq_1.49.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/metagenomeSeq_1.49.1.tgz
vignettes: vignettes/metagenomeSeq/inst/doc/fitTimeSeries.pdf,
vignettes/metagenomeSeq/inst/doc/metagenomeSeq.pdf
vignetteTitles: fitTimeSeries: differential abundance analysis through
time or location, metagenomeSeq: statistical analysis for
sparse high-throughput sequencing
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/metagenomeSeq/inst/doc/fitTimeSeries.R,
vignettes/metagenomeSeq/inst/doc/metagenomeSeq.R
dependsOnMe: microbiomeExplorer, etec16s
importsMe: benchdamic, Maaslin2, mbQTL, microbiomeDASim
suggestsMe: dar, interactiveDisplay, phyloseq, scTreeViz, Wrench
dependencyCount: 32
Package: metahdep
Version: 1.65.0
Depends: R (>= 2.10), methods
Suggests: affyPLM
License: GPL-3
MD5sum: e77a3bf50c2297d6876c9f58552166d9
NeedsCompilation: yes
Title: Hierarchical Dependence in Meta-Analysis
Description: Tools for meta-analysis in the presence of hierarchical
(and/or sampling) dependence, including with gene expression
studies
biocViews: Microarray, DifferentialExpression
Author: John R. Stevens, Gabriel Nicholas
Maintainer: John R. Stevens <john.r.stevens@usu.edu>
git_url: https://git.bioconductor.org/packages/metahdep
git_branch: devel
git_last_commit: c7cca38
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/metahdep_1.65.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/metahdep_1.65.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/metahdep_1.65.0.tgz
vignettes: vignettes/metahdep/inst/doc/metahdep.pdf
vignetteTitles: metahdep Primer
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/metahdep/inst/doc/metahdep.R
dependencyCount: 1
Package: metaMS
Version: 1.43.0
Depends: R (>= 4.0), methods, CAMERA, xcms (>= 1.35)
Imports: Matrix, tools, robustbase, BiocGenerics, graphics, stats,
utils
Suggests: metaMSdata, RUnit
License: GPL (>= 2)
MD5sum: 0502c32326efc36cd9dbbfcff2291688
NeedsCompilation: no
Title: MS-based metabolomics annotation pipeline
Description: MS-based metabolomics data processing and compound
annotation pipeline.
biocViews: ImmunoOncology, MassSpectrometry, Metabolomics
Author: Ron Wehrens [aut] (author of GC-MS part, Initial Maintainer),
Pietro Franceschi [aut] (author of LC-MS part), Nir Shahaf
[ctb], Matthias Scholz [ctb], Georg Weingart [ctb] (development
of GC-MS approach), Elisabete Carvalho [ctb] (testing and
feedback of GC-MS pipeline), Yann Guitton [ctb, cre] (ORCID:
<https://orcid.org/0000-0002-4479-0636>), Julien Saint-Vanne
[ctb]
Maintainer: Yann Guitton <yann.guitton@gmail.com>
URL: https://github.com/yguitton/metaMS
git_url: https://git.bioconductor.org/packages/metaMS
git_branch: devel
git_last_commit: 5510242
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/metaMS_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/metaMS_1.43.0.zip
vignettes: vignettes/metaMS/inst/doc/runGC.pdf,
vignettes/metaMS/inst/doc/runLC.pdf
vignetteTitles: runGC, runLC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/metaMS/inst/doc/runGC.R,
vignettes/metaMS/inst/doc/runLC.R
suggestsMe: CluMSID
dependencyCount: 162
Package: MetaNeighbor
Version: 1.27.0
Depends: R(>= 3.5)
Imports: grDevices, graphics, methods, stats (>= 3.4), utils (>= 3.4),
Matrix (>= 1.2), matrixStats (>= 0.54), beanplot (>= 1.2),
gplots (>= 3.0.1), RColorBrewer (>= 1.1.2),
SummarizedExperiment (>= 1.12), SingleCellExperiment, igraph,
dplyr, tidyr, tibble, ggplot2
Suggests: knitr (>= 1.17), rmarkdown (>= 1.6), testthat (>= 1.0.2),
UpSetR
License: MIT + file LICENSE
MD5sum: 22151c9d5519939d9718fc25825d775d
NeedsCompilation: no
Title: Single cell replicability analysis
Description: MetaNeighbor allows users to quantify cell type
replicability across datasets using neighbor voting.
biocViews: ImmunoOncology, GeneExpression, GO, MultipleComparison,
SingleCell, Transcriptomics
Author: Megan Crow [aut, cre], Sara Ballouz [ctb], Manthan Shah [ctb],
Stephan Fischer [ctb], Jesse Gillis [aut]
Maintainer: Stephan Fischer <fischer@cshl.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MetaNeighbor
git_branch: devel
git_last_commit: 7dda94c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MetaNeighbor_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MetaNeighbor_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MetaNeighbor_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MetaNeighbor_1.27.0.tgz
vignettes: vignettes/MetaNeighbor/inst/doc/MetaNeighbor.pdf
vignetteTitles: MetaNeighbor user guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MetaNeighbor/inst/doc/MetaNeighbor.R
dependencyCount: 77
Package: MetaPhOR
Version: 1.9.0
Depends: R (>= 4.2.0)
Imports: utils, ggplot2, ggrepel, stringr, pheatmap, grDevices, stats,
clusterProfiler, RecordLinkage, RCy3
Suggests: BiocStyle, knitr, rmarkdown, kableExtra
License: Artistic-2.0
MD5sum: d4d489f98830118b0968c75f6ccea5f3
NeedsCompilation: no
Title: Metabolic Pathway Analysis of RNA
Description: MetaPhOR was developed to enable users to assess metabolic
dysregulation using transcriptomic-level data (RNA-sequencing
and Microarray data) and produce publication-quality figures. A
list of differentially expressed genes (DEGs), which includes
fold change and p value, from DESeq2 or limma, can be used as
input, with sample size for MetaPhOR, and will produce a data
frame of scores for each KEGG pathway. These scores represent
the magnitude and direction of transcriptional change within
the pathway, along with estimated p-values.MetaPhOR then uses
these scores to visualize metabolic profiles within and between
samples through a variety of mechanisms, including: bubble
plots, heatmaps, and pathway models.
biocViews: Metabolomics, RNASeq, Pathways, GeneExpression,
DifferentialExpression, KEGG, Sequencing, Microarray
Author: Emily Isenhart [aut, cre], Spencer Rosario [aut]
Maintainer: Emily Isenhart <emily.isenhart@roswellpark.org>
SystemRequirements: Cytoscape (>= 3.9.0) for the cytoPath() examples
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MetaPhOR
git_branch: devel
git_last_commit: ffbf2ed
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MetaPhOR_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MetaPhOR_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MetaPhOR_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MetaPhOR_1.9.0.tgz
vignettes: vignettes/MetaPhOR/inst/doc/MetaPhOR-vignette.html
vignetteTitles: MetaPhOR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MetaPhOR/inst/doc/MetaPhOR-vignette.R
dependencyCount: 168
Package: metapod
Version: 1.15.0
Imports: Rcpp
LinkingTo: Rcpp
Suggests: testthat, knitr, BiocStyle, rmarkdown
License: GPL-3
MD5sum: 83055b599c8739cef63a9715b7024ec0
NeedsCompilation: yes
Title: Meta-Analyses on P-Values of Differential Analyses
Description: Implements a variety of methods for combining p-values in
differential analyses of genome-scale datasets. Functions can
combine p-values across different tests in the same analysis
(e.g., genomic windows in ChIP-seq, exons in RNA-seq) or for
corresponding tests across separate analyses (e.g., replicated
comparisons, effect of different treatment conditions). Support
is provided for handling log-transformed input p-values,
missing values and weighting where appropriate.
biocViews: MultipleComparison, DifferentialPeakCalling
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/metapod
git_branch: devel
git_last_commit: 25083c8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/metapod_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/metapod_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/metapod_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/metapod_1.15.0.tgz
vignettes: vignettes/metapod/inst/doc/overview.html
vignetteTitles: Meta-analysis strategies
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/metapod/inst/doc/overview.R
importsMe: csaw, mumosa, scp, scran
suggestsMe: TSCAN
dependencyCount: 3
Package: metapone
Version: 1.13.0
Depends: R (>= 4.1.0), BiocParallel, fields, markdown, fdrtool, fgsea,
ggplot2, ggrepel
Imports: methods
Suggests: rmarkdown, knitr
License: Artistic-2.0
MD5sum: c6d646b0b9e814967c1a78eee4da9cd7
NeedsCompilation: no
Title: Conducts pathway test of metabolomics data using a weighted
permutation test
Description: The package conducts pathway testing from untargetted
metabolomics data. It requires the user to supply feature-level
test results, from case-control testing, regression, or other
suitable feature-level tests for the study design. Weights are
given to metabolic features based on how many metabolites they
could potentially match to. The package can combine positive
and negative mode results in pathway tests.
biocViews: Technology, MassSpectrometry, Metabolomics, Pathways
Author: Leqi Tian [aut], Tianwei Yu [aut], Tianwei Yu [cre]
Maintainer: Tianwei Yu <yutianwei@cuhk.edu.cn>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/metapone
git_branch: devel
git_last_commit: 9fe5c68
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/metapone_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/metapone_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/metapone_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/metapone_1.13.0.tgz
vignettes: vignettes/metapone/inst/doc/metapone.html
vignetteTitles: metapone
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/metapone/inst/doc/metapone.R
dependencyCount: 61
Package: metaSeq
Version: 1.47.0
Depends: R (>= 2.13.0), NOISeq, snow, Rcpp
License: Artistic-2.0
MD5sum: 8d614846aea4a4df54b6abc1ea578648
NeedsCompilation: no
Title: Meta-analysis of RNA-Seq count data in multiple studies
Description: The probabilities by one-sided NOISeq are combined by
Fisher's method or Stouffer's method
biocViews: RNASeq, DifferentialExpression, Sequencing, ImmunoOncology
Author: Koki Tsuyuzaki, Itoshi Nikaido
Maintainer: Koki Tsuyuzaki <k.t.the-answer@hotmail.co.jp>
git_url: https://git.bioconductor.org/packages/metaSeq
git_branch: devel
git_last_commit: dbdffb0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/metaSeq_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/metaSeq_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/metaSeq_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/metaSeq_1.47.0.tgz
vignettes: vignettes/metaSeq/inst/doc/metaSeq.pdf
vignetteTitles: metaSeq
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/metaSeq/inst/doc/metaSeq.R
dependencyCount: 15
Package: metaseqR2
Version: 1.19.1
Depends: R (>= 4.0.0), DESeq2, limma, locfit, splines
Imports: ABSSeq, Biobase, BiocGenerics, BiocParallel, biomaRt,
Biostrings, corrplot, DSS, DT, EDASeq, edgeR, genefilter,
GenomeInfoDb, GenomicAlignments, GenomicFeatures,
GenomicRanges, gplots, graphics, grDevices, harmonicmeanp,
heatmaply, htmltools, httr, IRanges, jsonlite, lattice, log4r,
magrittr, MASS, Matrix, methods, NBPSeq, pander, parallel,
qvalue, rmarkdown, rmdformats, Rsamtools, RSQLite, rtracklayer,
S4Vectors, stats, stringr, SummarizedExperiment, survcomp,
txdbmaker, utils, VennDiagram, vsn, yaml, zoo
Suggests: BiocStyle, BiocManager, BSgenome, knitr, RMySQL, RUnit
Enhances: TCC
License: GPL (>= 3)
MD5sum: 02eb0e6e4dda6853e4c154ad0e58f8fc
NeedsCompilation: yes
Title: An R package for the analysis and result reporting of RNA-Seq
data by combining multiple statistical algorithms
Description: Provides an interface to several normalization and
statistical testing packages for RNA-Seq gene expression data.
Additionally, it creates several diagnostic plots, performs
meta-analysis by combinining the results of several statistical
tests and reports the results in an interactive way.
biocViews: Software, GeneExpression, DifferentialExpression,
WorkflowStep, Preprocessing, QualityControl, Normalization,
ReportWriting, RNASeq, Transcription, Sequencing,
Transcriptomics, Bayesian, Clustering, CellBiology,
BiomedicalInformatics, FunctionalGenomics, SystemsBiology,
ImmunoOncology, AlternativeSplicing, DifferentialSplicing,
MultipleComparison, TimeCourse, DataImport, ATACSeq,
Epigenetics, Regression, ProprietaryPlatforms,
GeneSetEnrichment, BatchEffect, ChIPSeq
Author: Panagiotis Moulos [aut, cre]
Maintainer: Panagiotis Moulos <moulos@fleming.gr>
URL: http://www.fleming.gr
VignetteBuilder: knitr
BugReports: https://github.com/pmoulos/metaseqR2/issues
git_url: https://git.bioconductor.org/packages/metaseqR2
git_branch: devel
git_last_commit: 3767ef3
git_last_commit_date: 2025-03-12
Date/Publication: 2025-03-12
source.ver: src/contrib/metaseqR2_1.19.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/metaseqR2_1.19.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/metaseqR2_1.19.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/metaseqR2_1.19.1.tgz
vignettes: vignettes/metaseqR2/inst/doc/metaseqr2-annotation.html,
vignettes/metaseqR2/inst/doc/metaseqr2-statistics.html
vignetteTitles: Building an annotation database for metaseqR2, RNA-Seq
data analysis with metaseqR2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/metaseqR2/inst/doc/metaseqr2-annotation.R,
vignettes/metaseqR2/inst/doc/metaseqr2-statistics.R
dependencyCount: 239
Package: MetCirc
Version: 1.37.0
Depends: R (>= 4.4), amap (>= 0.8), circlize (>= 0.4.16), scales (>=
1.3.0), shiny (>= 1.8.1.1), Spectra (>= 1.15.3)
Imports: ggplot2 (>= 3.5.1), MsCoreUtils (>= 1.17.0), S4Vectors (>=
0.43.1)
Suggests: BiocGenerics, graphics (>= 4.4), grDevices (>= 4.4), knitr
(>= 1.48), testthat (>= 3.2.1.1)
License: GPL (>= 3)
Archs: x64
MD5sum: 1fddca9edfc5ebb0a015e28297366668
NeedsCompilation: no
Title: Navigating mass spectral similarity in high-resolution MS/MS
metabolomics data metabolomics data
Description: MetCirc comprises a workflow to interactively explore
high-resolution MS/MS metabolomics data. MetCirc uses the
Spectra object infrastructure defined in the package Spectra
that stores MS/MS spectra. MetCirc offers functionality to
calculate similarity between precursors based on the normalised
dot product, neutral losses or user-defined functions and
visualise similarities in a circular layout. Within the
interactive framework the user can annotate MS/MS features
based on their similarity to (known) related MS/MS features.
biocViews: ShinyApps, Metabolomics, MassSpectrometry, Visualization
Author: Thomas Naake <thomasnaake@googlemail.com>, Johannes Rainer
<johannes.rainer@eurac.edu> and Emmanuel Gaquerel
<emmanuel.gaquerel@ibmp-cnrs.unistra.fr>
Maintainer: Thomas Naake <thomasnaake@googlemail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MetCirc
git_branch: devel
git_last_commit: 6a5a2cb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MetCirc_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MetCirc_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MetCirc_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MetCirc_1.37.0.tgz
vignettes: vignettes/MetCirc/inst/doc/MetCirc.html
vignetteTitles: Workflow for Metabolomics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MetCirc/inst/doc/MetCirc.R
dependencyCount: 84
Package: methimpute
Version: 1.29.0
Depends: R (>= 3.5.0), GenomicRanges, ggplot2
Imports: Rcpp (>= 0.12.4.5), methods, utils, grDevices, stats,
GenomeInfoDb, IRanges, Biostrings, reshape2, minpack.lm,
data.table
LinkingTo: Rcpp
Suggests: knitr, BiocStyle
License: Artistic-2.0
MD5sum: f46dc5293a86c0863ac128d35e338c4e
NeedsCompilation: yes
Title: Imputation-guided re-construction of complete methylomes from
WGBS data
Description: This package implements functions for calling methylation
for all cytosines in the genome.
biocViews: ImmunoOncology, Software, DNAMethylation, Epigenetics,
HiddenMarkovModel, Sequencing, Coverage
Author: Aaron Taudt
Maintainer: Aaron Taudt <aaron.taudt@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/methimpute
git_branch: devel
git_last_commit: 4287169
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/methimpute_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/methimpute_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/methimpute_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/methimpute_1.29.0.tgz
vignettes: vignettes/methimpute/inst/doc/methimpute.pdf
vignetteTitles: Methylation status calling with METHimpute
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methimpute/inst/doc/methimpute.R
dependencyCount: 62
Package: methInheritSim
Version: 1.29.0
Depends: R (>= 3.5.0)
Imports: methylKit, GenomicRanges, GenomeInfoDb, parallel,
BiocGenerics, S4Vectors, methods, stats, IRanges, msm
Suggests: BiocStyle, knitr, rmarkdown, RUnit, methylInheritance
License: Artistic-2.0
MD5sum: 626ae577ea8cf5d83fdd2db864ea53c5
NeedsCompilation: no
Title: Simulating Whole-Genome Inherited Bisulphite Sequencing Data
Description: Simulate a multigeneration methylation case versus control
experiment with inheritance relation using a real control
dataset.
biocViews: BiologicalQuestion, Epigenetics, DNAMethylation,
DifferentialMethylation, MethylSeq, Software, ImmunoOncology,
StatisticalMethod, WholeGenome, Sequencing
Author: Pascal Belleau, Astrid Deschênes and Arnaud Droit
Maintainer: Pascal Belleau <pascal_belleau@hotmail.com>
URL: https://github.com/belleau/methInheritSim
VignetteBuilder: knitr
BugReports: https://github.com/belleau/methInheritSim/issues
git_url: https://git.bioconductor.org/packages/methInheritSim
git_branch: devel
git_last_commit: 7089998
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-27
source.ver: src/contrib/methInheritSim_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/methInheritSim_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/methInheritSim_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/methInheritSim_1.29.0.tgz
vignettes: vignettes/methInheritSim/inst/doc/methInheritSim.html
vignetteTitles: Simulating Whole-Genome Inherited Bisulphite Sequencing
Data
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methInheritSim/inst/doc/methInheritSim.R
suggestsMe: methylInheritance
dependencyCount: 110
Package: methodical
Version: 1.3.0
Depends: GenomicRanges, ggplot2, R (>= 4.0), SummarizedExperiment
Imports: AnnotationHub, annotatr, BiocCheck, BiocManager, BiocParallel,
BiocStyle, Biostrings, BSgenome, BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Hsapiens.UCSC.hg38, cowplot, data.table, DelayedArray,
devtools, dplyr, ExperimentHub, foreach, GenomeInfoDb,
HDF5Array, IRanges, knitr, MatrixGenerics, R.utils, rcmdcheck,
RcppRoll, remotes, rhdf5, rtracklayer, S4Vectors, scales,
tibble, tidyr, tools, TumourMethData, usethis
Suggests: BSgenome.Athaliana.TAIR.TAIR9, DESeq2, methrix, rmarkdown
License: GPL (>= 3)
MD5sum: 9ba90d3e5d864d91c16c604c4a9cc50c
NeedsCompilation: no
Title: Discovering genomic regions where methylation is strongly
associated with transcriptional activity
Description: DNA methylation is generally considered to be associated
with transcriptional silencing. However, comprehensive,
genome-wide investigation of this relationship requires the
evaluation of potentially millions of correlation values
between the methylation of individual genomic loci and
expression of associated transcripts in a relatively large
numbers of samples. Methodical makes this process quick and
easy while keeping a low memory footprint. It also provides a
novel method for identifying regions where a number of
methylation sites are consistently strongly associated with
transcriptional expression. In addition, Methodical enables
housing DNA methylation data from diverse sources (e.g. WGBS,
RRBS and methylation arrays) with a common framework, lifting
over DNA methylation data between different genome builds and
creating base-resolution plots of the association between DNA
methylation and transcriptional activity at transcriptional
start sites.
biocViews: DNAMethylation, MethylationArray, Transcription,
GenomeWideAssociation, Software
Author: Richard Heery [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8067-3114>)
Maintainer: Richard Heery <richardheery@gmail.com>
URL: https://github.com/richardheery/methodical
SystemRequirements: kallisto
VignetteBuilder: knitr
BugReports: https://github.com/richardheery/methodical/issues
git_url: https://git.bioconductor.org/packages/methodical
git_branch: devel
git_last_commit: e681cbe
git_last_commit_date: 2024-12-26
Date/Publication: 2024-12-26
source.ver: src/contrib/methodical_1.3.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/methodical_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/methodical_1.3.0.tgz
vignettes:
vignettes/methodical/inst/doc/calculating_methylation_transcription_correlations.html,
vignettes/methodical/inst/doc/working_with_meth_rses.html
vignetteTitles: calculating_methylation_transcription_correlations,
working_with_meth_rses
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: TRUE
Rfiles:
vignettes/methodical/inst/doc/calculating_methylation_transcription_correlations.R,
vignettes/methodical/inst/doc/working_with_meth_rses.R
dependencyCount: 213
Package: MethPed
Version: 1.35.0
Depends: R (>= 3.0.0), Biobase
Imports: randomForest, grDevices, graphics, stats
Suggests: BiocStyle, knitr, markdown, impute
License: GPL-2
Archs: x64
MD5sum: ede50947817c844604e578df05c1340b
NeedsCompilation: no
Title: A DNA methylation classifier tool for the identification of
pediatric brain tumor subtypes
Description: Classification of pediatric tumors into biologically
defined subtypes is challenging and multifaceted approaches are
needed. For this aim, we developed a diagnostic classifier
based on DNA methylation profiles. We offer MethPed as an
easy-to-use toolbox that allows researchers and clinical
diagnosticians to test single samples as well as large cohorts
for subclass prediction of pediatric brain tumors. The current
version of MethPed can classify the following tumor
diagnoses/subgroups: Diffuse Intrinsic Pontine Glioma (DIPG),
Ependymoma, Embryonal tumors with multilayered rosettes (ETMR),
Glioblastoma (GBM), Medulloblastoma (MB) - Group 3 (MB_Gr3),
Group 4 (MB_Gr3), Group WNT (MB_WNT), Group SHH (MB_SHH) and
Pilocytic Astrocytoma (PiloAstro).
biocViews: ImmunoOncology, DNAMethylation, Classification, Epigenetics
Author: Mohammad Tanvir Ahamed [aut, trl], Anna Danielsson [aut],
Szilárd Nemes [aut, trl], Helena Carén [aut, cre, cph]
Maintainer: Helena Carén <helenacarenlab@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MethPed
git_branch: devel
git_last_commit: 0b7abdc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MethPed_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MethPed_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MethPed_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MethPed_1.35.0.tgz
vignettes: vignettes/MethPed/inst/doc/MethPed-vignette.html
vignetteTitles: MethPed User Guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MethPed/inst/doc/MethPed-vignette.R
dependencyCount: 9
Package: MethReg
Version: 1.17.0
Depends: R (>= 4.0)
Imports: dplyr, plyr, GenomicRanges, SummarizedExperiment,
DelayedArray, ggplot2, ggpubr, tibble, tidyr, S4Vectors,
sesameData, sesame, AnnotationHub, ExperimentHub, stringr,
readr, methods, stats, Matrix, MASS, rlang, pscl, IRanges,
sfsmisc, progress, utils, openxlsx, JASPAR2024, RSQLite,
TFBSTools
Suggests: rmarkdown, BiocStyle, testthat (>= 2.1.0), parallel, R.utils,
doParallel, reshape2, motifmatchr, matrixStats, biomaRt,
dorothea, viper, stageR, BiocFileCache, png, htmltools, knitr,
jpeg, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg19,
data.table, downloader
License: GPL-3
MD5sum: 1778e4faf112a29a20b0735a383248ab
NeedsCompilation: no
Title: Assessing the regulatory potential of DNA methylation regions or
sites on gene transcription
Description: Epigenome-wide association studies (EWAS) detects a large
number of DNA methylation differences, often hundreds of
differentially methylated regions and thousands of CpGs, that
are significantly associated with a disease, many are located
in non-coding regions. Therefore, there is a critical need to
better understand the functional impact of these CpG
methylations and to further prioritize the significant changes.
MethReg is an R package for integrative modeling of DNA
methylation, target gene expression and transcription factor
binding sites data, to systematically identify and rank
functional CpG methylations. MethReg evaluates, prioritizes and
annotates CpG sites with high regulatory potential using
matched methylation and gene expression data, along with
external TF-target interaction databases based on manually
curation, ChIP-seq experiments or gene regulatory network
analysis.
biocViews: MethylationArray, Regression, GeneExpression, Epigenetics,
GeneTarget, Transcription
Author: Tiago Silva [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1343-6850>), Lily Wang [aut]
Maintainer: Tiago Silva <tiagochst@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/TransBioInfoLab/MethReg/issues/
git_url: https://git.bioconductor.org/packages/MethReg
git_branch: devel
git_last_commit: ba3e766
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-13
source.ver: src/contrib/MethReg_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MethReg_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MethReg_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MethReg_1.17.0.tgz
vignettes: vignettes/MethReg/inst/doc/MethReg.html
vignetteTitles: MethReg: estimating regulatory potential of DNA
methylation in gene transcription
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MethReg/inst/doc/MethReg.R
dependencyCount: 171
Package: methrix
Version: 1.21.0
Depends: R (>= 3.6), data.table (>= 1.12.4), SummarizedExperiment
Imports: rtracklayer, DelayedArray, HDF5Array, BSgenome,
DelayedMatrixStats, parallel, methods, ggplot2, S4Vectors,
matrixStats, graphics, stats, utils, GenomicRanges, IRanges
Suggests: knitr, rmarkdown, DSS, bsseq, plotly,
BSgenome.Mmusculus.UCSC.mm9, MafDb.1Kgenomes.phase3.GRCh38,
MafDb.1Kgenomes.phase3.hs37d5, BSgenome.Hsapiens.UCSC.hg19,
GenomicScores, Biostrings, RColorBrewer, GenomeInfoDb, testthat
(>= 2.1.0)
License: MIT + file LICENSE
Archs: x64
MD5sum: 951d21b904aafafc8e8d367f5d3b0e42
NeedsCompilation: no
Title: Fast and efficient summarization of generic bedGraph files from
Bisufite sequencing
Description: Bedgraph files generated by Bisulfite pipelines often come
in various flavors. Critical downstream step requires
summarization of these files into methylation/coverage
matrices. This step of data aggregation is done by Methrix,
including many other useful downstream functions.
biocViews: DNAMethylation, Sequencing, Coverage
Author: Anand Mayakonda [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1162-687X>), Reka Toth [aut]
(ORCID: <https://orcid.org/0000-0002-6096-1052>), Rajbir Batra
[ctb], Clarissa Feuerstein-Akgöz [ctb], Joschka Hey [ctb],
Maximilian Schönung [ctb], Pavlo Lutsik [ctb]
Maintainer: Anand Mayakonda <anand_mt@hotmail.com>
URL: https://github.com/CompEpigen/methrix
VignetteBuilder: knitr
BugReports: https://github.com/CompEpigen/methrix/issues
git_url: https://git.bioconductor.org/packages/methrix
git_branch: devel
git_last_commit: 7d44855
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/methrix_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/methrix_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/methrix_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/methrix_1.21.0.tgz
vignettes: vignettes/methrix/inst/doc/methrix.html
vignetteTitles: Methrix tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/methrix/inst/doc/methrix.R
suggestsMe: methodical
dependencyCount: 94
Package: MethTargetedNGS
Version: 1.39.0
Depends: R (>= 3.1.2), stringr, seqinr, gplots, Biostrings, pwalign
Imports: utils, graphics, stats
License: Artistic-2.0
MD5sum: 5afeaea1d57c8a230f658e49b5a3b97b
NeedsCompilation: no
Title: Perform Methylation Analysis on Next Generation Sequencing Data
Description: Perform step by step methylation analysis of Next
Generation Sequencing data.
biocViews: ResearchField, Genetics, Sequencing, Alignment,
SequenceMatching, DataImport
Author: Muhammad Ahmer Jamil with Contribution of Prof. Holger Frohlich
and Priv.-Doz. Dr. Osman El-Maarri
Maintainer: Muhammad Ahmer Jamil <engr.ahmerjamil@gmail.com>
SystemRequirements: HMMER3
git_url: https://git.bioconductor.org/packages/MethTargetedNGS
git_branch: devel
git_last_commit: 70f3c0d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MethTargetedNGS_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MethTargetedNGS_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MethTargetedNGS_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MethTargetedNGS_1.39.0.tgz
vignettes: vignettes/MethTargetedNGS/inst/doc/MethTargetedNGS.pdf
vignetteTitles: Introduction to MethTargetedNGS
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MethTargetedNGS/inst/doc/MethTargetedNGS.R
dependencyCount: 50
Package: MethylAid
Version: 1.41.0
Depends: R (>= 3.4)
Imports: Biobase, BiocParallel, BiocGenerics, ggplot2, grid, gridBase,
grDevices, graphics, hexbin, matrixStats, minfi (>= 1.22.0),
methods, RColorBrewer, shiny, stats, SummarizedExperiment,
utils
Suggests: BiocStyle, knitr, MethylAidData, minfiData, minfiDataEPIC,
RUnit
License: GPL (>= 2)
MD5sum: 89d2ad8277efe9fcbffe1f74a8a6e7c1
NeedsCompilation: no
Title: Visual and interactive quality control of large Illumina DNA
Methylation array data sets
Description: A visual and interactive web application using RStudio's
shiny package. Bad quality samples are detected using
sample-dependent and sample-independent controls present on the
array and user adjustable thresholds. In depth exploration of
bad quality samples can be performed using several interactive
diagnostic plots of the quality control probes present on the
array. Furthermore, the impact of any batch effect provided by
the user can be explored.
biocViews: DNAMethylation, MethylationArray, Microarray, TwoChannel,
QualityControl, BatchEffect, Visualization, GUI
Author: Maarten van Iterson [aut, cre], Elmar Tobi[ctb], Roderick
Slieker[ctb], Wouter den Hollander[ctb], Rene Luijk[ctb] and
Bas Heijmans[ctb]
Maintainer: L.J.Sinke <L.J.Sinke@lumc.nl>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MethylAid
git_branch: devel
git_last_commit: 1f7b25a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MethylAid_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MethylAid_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MethylAid_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MethylAid_1.41.0.tgz
vignettes: vignettes/MethylAid/inst/doc/MethylAid.pdf
vignetteTitles: MethylAid: Visual and Interactive quality control of
Illumina Human DNA Methylation array data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MethylAid/inst/doc/MethylAid.R
dependsOnMe: MethylAidData
dependencyCount: 170
Package: methylCC
Version: 1.21.0
Depends: R (>= 3.6), FlowSorted.Blood.450k
Imports: Biobase, GenomicRanges, IRanges, S4Vectors, dplyr, magrittr,
minfi, bsseq, quadprog, plyranges, stats, utils, bumphunter,
genefilter, methods, IlluminaHumanMethylation450kmanifest,
IlluminaHumanMethylation450kanno.ilmn12.hg19
Suggests: rmarkdown, knitr, testthat (>= 2.1.0), BiocGenerics,
BiocStyle, tidyr, ggplot2
License: GPL-3
MD5sum: 0f251a910b62982276cf18939c553e25
NeedsCompilation: no
Title: Estimate the cell composition of whole blood in DNA methylation
samples
Description: A tool to estimate the cell composition of DNA methylation
whole blood sample measured on any platform technology
(microarray and sequencing).
biocViews: Microarray, Sequencing, DNAMethylation, MethylationArray,
MethylSeq, WholeGenome
Author: Stephanie C. Hicks [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7858-0231>), Rafael Irizarry [aut]
(ORCID: <https://orcid.org/0000-0002-3944-4309>)
Maintainer: Stephanie C. Hicks <shicks19@jhu.edu>
URL: https://github.com/stephaniehicks/methylCC/
VignetteBuilder: knitr
BugReports: https://github.com/stephaniehicks/methylCC/
git_url: https://git.bioconductor.org/packages/methylCC
git_branch: devel
git_last_commit: 109b2f6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-05
source.ver: src/contrib/methylCC_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/methylCC_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/methylCC_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/methylCC_1.21.0.tgz
vignettes: vignettes/methylCC/inst/doc/methylCC.html
vignetteTitles: The methylCC user's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methylCC/inst/doc/methylCC.R
dependencyCount: 161
Package: methylclock
Version: 1.13.0
Depends: R (>= 4.1.0), methylclockData, devtools, quadprog
Imports: Rcpp (>= 1.0.6), ExperimentHub, dplyr, impute,
PerformanceAnalytics, Biobase, ggpmisc, tidyverse, ggplot2,
ggpubr, minfi, tibble, RPMM, stats, graphics, tidyr, gridExtra,
preprocessCore, dynamicTreeCut, planet
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, GEOquery, rmarkdown
License: MIT + file LICENSE
MD5sum: d1bfc0736c547fc4faf5fe26d5fb224d
NeedsCompilation: yes
Title: Methylclock - DNA methylation-based clocks
Description: This package allows to estimate chronological and
gestational DNA methylation (DNAm) age as well as biological
age using different methylation clocks. Chronological DNAm age
(in years) : Horvath's clock, Hannum's clock, BNN, Horvath's
skin+blood clock, PedBE clock and Wu's clock. Gestational DNAm
age : Knight's clock, Bohlin's clock, Mayne's clock and Lee's
clocks. Biological DNAm clocks : Levine's clock and Telomere
Length's clock.
biocViews: DNAMethylation, BiologicalQuestion, Preprocessing,
StatisticalMethod, Normalization
Author: Dolors Pelegri-Siso [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5993-3003>), Juan R. Gonzalez
[aut] (ORCID: <https://orcid.org/0000-0003-3267-2146>)
Maintainer: Dolors Pelegri-Siso <dolors.pelegri@isglobal.org>
URL: https://github.com/isglobal-brge/methylclock
VignetteBuilder: knitr
BugReports: https://github.com/isglobal-brge/methylclock/issues
git_url: https://git.bioconductor.org/packages/methylclock
git_branch: devel
git_last_commit: 5639edc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-05
source.ver: src/contrib/methylclock_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/methylclock_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/methylclock_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/methylclock_1.13.0.tgz
vignettes: vignettes/methylclock/inst/doc/methylclock.html
vignetteTitles: DNAm age using diffrent methylation clocks
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/methylclock/inst/doc/methylclock.R
dependencyCount: 301
Package: methylGSA
Version: 1.25.0
Depends: R (>= 3.5)
Imports: RobustRankAggreg, ggplot2, stringr, stats, clusterProfiler,
missMethyl, org.Hs.eg.db, reactome.db, BiocParallel, GO.db,
AnnotationDbi, shiny,
IlluminaHumanMethylation450kanno.ilmn12.hg19,
IlluminaHumanMethylationEPICanno.ilm10b4.hg19
Suggests: knitr, rmarkdown, testthat, enrichplot
License: GPL-2
MD5sum: ee554c9854b75d29eb846989d4ecf7ae
NeedsCompilation: no
Title: Gene Set Analysis Using the Outcome of Differential Methylation
Description: The main functions for methylGSA are methylglm and
methylRRA. methylGSA implements logistic regression adjusting
number of probes as a covariate. methylRRA adjusts multiple
p-values of each gene by Robust Rank Aggregation. For more
detailed help information, please see the vignette.
biocViews:
DNAMethylation,DifferentialMethylation,GeneSetEnrichment,Regression,
GeneRegulation,Pathways
Author: Xu Ren [aut, cre], Pei Fen Kuan [aut]
Maintainer: Xu Ren <xuren2120@gmail.com>
URL: https://github.com/reese3928/methylGSA
VignetteBuilder: knitr
BugReports: https://github.com/reese3928/methylGSA/issues
git_url: https://git.bioconductor.org/packages/methylGSA
git_branch: devel
git_last_commit: fb33fc3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/methylGSA_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/methylGSA_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/methylGSA_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/methylGSA_1.25.0.tgz
vignettes: vignettes/methylGSA/inst/doc/methylGSA-vignette.html
vignetteTitles: methylGSA: Gene Set Analysis for DNA Methylation
Datasets
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methylGSA/inst/doc/methylGSA-vignette.R
dependencyCount: 216
Package: methyLImp2
Version: 1.3.1
Depends: R (>= 4.3.0), ChAMPdata
Imports: BiocParallel, parallel, stats, methods, corpcor,
SummarizedExperiment, utils
Suggests: BiocStyle, knitr, rmarkdown, spelling, testthat (>= 3.0.0)
License: GPL-3
MD5sum: 515f8fe5fdab0e10da21bc9a59885d6e
NeedsCompilation: no
Title: Missing value estimation of DNA methylation data
Description: This package allows to estimate missing values in DNA
methylation data. methyLImp method is based on linear
regression since methylation levels show a high degree of
inter-sample correlation. Implementation is parallelised over
chromosomes since probes on different chromosomes are usually
independent. Mini-batch approach to reduce the runtime in case
of large number of samples is available.
biocViews: DNAMethylation, Microarray, Software, MethylationArray,
Regression
Author: Pietro Di Lena [aut] (ORCID:
<https://orcid.org/0000-0002-1838-8918>), Anna Plaksienko [aut,
cre] (ORCID: <https://orcid.org/0000-0001-9607-057X>), Claudia
Angelini [aut] (ORCID:
<https://orcid.org/0000-0001-8350-8464>), Christine Nardini
[aut] (ORCID: <https://orcid.org/0000-0001-7601-321X>)
Maintainer: Anna Plaksienko <anna@plaxienko.com>
URL: https://github.com/annaplaksienko/methyLImp2
VignetteBuilder: knitr
BugReports: https://github.com/annaplaksienko/methyLImp2/issues
git_url: https://git.bioconductor.org/packages/methyLImp2
git_branch: devel
git_last_commit: 38134b5
git_last_commit_date: 2025-02-04
Date/Publication: 2025-02-04
source.ver: src/contrib/methyLImp2_1.3.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/methyLImp2_1.3.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/methyLImp2_1.3.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/methyLImp2_1.3.1.tgz
vignettes: vignettes/methyLImp2/inst/doc/methyLImp2_vignette.html
vignetteTitles: methyLImp2 vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methyLImp2/inst/doc/methyLImp2_vignette.R
dependencyCount: 48
Package: methylInheritance
Version: 1.31.0
Depends: R (>= 3.5)
Imports: methylKit, BiocParallel, GenomicRanges, IRanges, S4Vectors,
methods, parallel, ggplot2, gridExtra, rebus
Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, RUnit,
methInheritSim
License: Artistic-2.0
MD5sum: 539d83c776186fa5499766439f911ca0
NeedsCompilation: no
Title: Permutation-Based Analysis associating Conserved Differentially
Methylated Elements Across Multiple Generations to a Treatment
Effect
Description: Permutation analysis, based on Monte Carlo sampling, for
testing the hypothesis that the number of conserved
differentially methylated elements, between several
generations, is associated to an effect inherited from a
treatment and that stochastic effect can be dismissed.
biocViews: BiologicalQuestion, Epigenetics, DNAMethylation,
DifferentialMethylation, MethylSeq, Software, ImmunoOncology,
StatisticalMethod, WholeGenome, Sequencing
Author: Astrid Deschênes [cre, aut] (ORCID:
<https://orcid.org/0000-0001-7846-6749>), Pascal Belleau [aut]
(ORCID: <https://orcid.org/0000-0002-0802-1071>), Arnaud Droit
[aut]
Maintainer: Astrid Deschênes <adeschen@hotmail.com>
URL: https://github.com/adeschen/methylInheritance
VignetteBuilder: knitr
BugReports: https://github.com/adeschen/methylInheritance/issues
git_url: https://git.bioconductor.org/packages/methylInheritance
git_branch: devel
git_last_commit: 9150e3e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-27
source.ver: src/contrib/methylInheritance_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/methylInheritance_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/methylInheritance_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/methylInheritance_1.31.0.tgz
vignettes: vignettes/methylInheritance/inst/doc/methylInheritance.html
vignetteTitles: Permutation-Based Analysis associating Conserved
Differentially Methylated Elements Across Multiple Generations
to a Treatment Effect
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methylInheritance/inst/doc/methylInheritance.R
suggestsMe: methInheritSim
dependencyCount: 113
Package: methylKit
Version: 1.33.3
Depends: R (>= 3.5.0), GenomicRanges (>= 1.18.1), methods
Imports: IRanges, data.table (>= 1.9.6), parallel, S4Vectors (>=
0.13.13), GenomeInfoDb, KernSmooth, qvalue, emdbook, Rsamtools,
gtools, fastseg, rtracklayer, mclust, mgcv, Rcpp, R.utils,
limma, grDevices, graphics, stats, utils
LinkingTo: Rcpp, Rhtslib (>= 1.13.1)
Suggests: testthat (>= 2.1.0), knitr, rmarkdown, genomation,
BiocManager
License: Artistic-2.0
Archs: x64
MD5sum: 5d5ed12bad8d2aab8ba9a232cb6f2d83
NeedsCompilation: yes
Title: DNA methylation analysis from high-throughput bisulfite
sequencing results
Description: methylKit is an R package for DNA methylation analysis and
annotation from high-throughput bisulfite sequencing. The
package is designed to deal with sequencing data from RRBS and
its variants, but also target-capture methods and whole genome
bisulfite sequencing. It also has functions to analyze
base-pair resolution 5hmC data from experimental protocols such
as oxBS-Seq and TAB-Seq. Methylation calling can be performed
directly from Bismark aligned BAM files.
biocViews: DNAMethylation, Sequencing, MethylSeq
Author: Altuna Akalin [aut, cre], Matthias Kormaksson [aut], Sheng Li
[aut], Arsene Wabo [ctb], Adrian Bierling [aut], Alexander
Blume [aut], Katarzyna Wreczycka [ctb]
Maintainer: Altuna Akalin <aakalin@gmail.com>, Alexander Blume
<alex.gos90@gmail.com>
URL: https://github.com/al2na/methylKit
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/al2na/methylKit/issues
git_url: https://git.bioconductor.org/packages/methylKit
git_branch: devel
git_last_commit: ecf8584
git_last_commit_date: 2025-02-28
Date/Publication: 2025-02-28
source.ver: src/contrib/methylKit_1.33.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/methylKit_1.33.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/methylKit_1.33.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/methylKit_1.33.3.tgz
vignettes: vignettes/methylKit/inst/doc/methylKit.html
vignetteTitles: methylKit: User Guide v`r packageVersion('methylKit')`
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methylKit/inst/doc/methylKit.R
importsMe: deconvR, methInheritSim, methylInheritance
dependencyCount: 106
Package: MethylMix
Version: 2.37.0
Depends: R (>= 3.2.0)
Imports: foreach, RPMM, RColorBrewer, ggplot2, RCurl, impute,
data.table, limma, R.matlab, digest
Suggests: BiocStyle, doParallel, testthat, knitr, rmarkdown
License: GPL-2
MD5sum: 79cb2a389812aa8a9504b8f336fadcd5
NeedsCompilation: no
Title: MethylMix: Identifying methylation driven cancer genes
Description: MethylMix is an algorithm implemented to identify hyper
and hypomethylated genes for a disease. MethylMix is based on a
beta mixture model to identify methylation states and compares
them with the normal DNA methylation state. MethylMix uses a
novel statistic, the Differential Methylation value or DM-value
defined as the difference of a methylation state with the
normal methylation state. Finally, matched gene expression data
is used to identify, besides differential, functional
methylation states by focusing on methylation changes that
effect gene expression. References: Gevaert 0. MethylMix: an R
package for identifying DNA methylation-driven genes.
Bioinformatics (Oxford, England). 2015;31(11):1839-41.
doi:10.1093/bioinformatics/btv020. Gevaert O, Tibshirani R,
Plevritis SK. Pancancer analysis of DNA methylation-driven
genes using MethylMix. Genome Biology. 2015;16(1):17.
doi:10.1186/s13059-014-0579-8.
biocViews:
DNAMethylation,StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray,DifferentialExpression,Pathways,Network
Author: Olivier Gevaert
Maintainer: Olivier Gevaert <olivier.gevaert@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MethylMix
git_branch: devel
git_last_commit: 6007130
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MethylMix_2.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MethylMix_2.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MethylMix_2.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MethylMix_2.37.0.tgz
vignettes: vignettes/MethylMix/inst/doc/vignettes.html
vignetteTitles: MethylMix
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MethylMix/inst/doc/vignettes.R
dependencyCount: 52
Package: methylMnM
Version: 1.45.0
Depends: R (>= 2.12.1), edgeR, statmod
License: GPL-3
MD5sum: 146c0c3f7a0fde28cf65238fb4176c24
NeedsCompilation: yes
Title: detect different methylation level (DMR)
Description: To give the exactly p-value and q-value of MeDIP-seq and
MRE-seq data for different samples comparation.
biocViews: Software, DNAMethylation, Sequencing
Author: Yan Zhou, Bo Zhang, Nan Lin, BaoXue Zhang and Ting Wang
Maintainer: Yan Zhou<zhouy1016@163.com>
git_url: https://git.bioconductor.org/packages/methylMnM
git_branch: devel
git_last_commit: ed89f51
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/methylMnM_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/methylMnM_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/methylMnM_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/methylMnM_1.45.0.tgz
vignettes: vignettes/methylMnM/inst/doc/methylMnM.pdf
vignetteTitles: methylMnM
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methylMnM/inst/doc/methylMnM.R
importsMe: SIMD
dependencyCount: 11
Package: methylPipe
Version: 1.41.0
Depends: R (>= 3.5.0), methods, grDevices, graphics, stats, utils,
GenomicRanges, SummarizedExperiment (>= 0.2.0), Rsamtools
Imports: marray, gplots, IRanges, BiocGenerics, Gviz,
GenomicAlignments, Biostrings, parallel, data.table,
GenomeInfoDb, S4Vectors
Suggests: BSgenome.Hsapiens.UCSC.hg18,
TxDb.Hsapiens.UCSC.hg18.knownGene, knitr, MethylSeekR
License: GPL(>=2)
MD5sum: 9e13412107e56ef86841dd664400369e
NeedsCompilation: yes
Title: Base resolution DNA methylation data analysis
Description: Memory efficient analysis of base resolution DNA
methylation data in both the CpG and non-CpG sequence context.
Integration of DNA methylation data derived from any
methodology providing base- or low-resolution data.
biocViews: MethylSeq, DNAMethylation, Coverage, Sequencing
Author: Mattia Pelizzola [aut], Kamal Kishore [aut], Mattia Furlan
[ctb, cre]
Maintainer: Mattia Furlan <mattia.furlan@iit.it>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/methylPipe
git_branch: devel
git_last_commit: 9ff081a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/methylPipe_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/methylPipe_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/methylPipe_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/methylPipe_1.41.0.tgz
vignettes: vignettes/methylPipe/inst/doc/methylPipe.pdf
vignetteTitles: methylPipe.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methylPipe/inst/doc/methylPipe.R
dependsOnMe: ListerEtAlBSseq
importsMe: compEpiTools
dependencyCount: 163
Package: methylscaper
Version: 1.15.0
Depends: R (>= 4.4.0)
Imports: shiny, shinyjs, seriation, BiocParallel, seqinr, Biostrings,
pwalign, Rfast, grDevices, graphics, stats, utils, tools,
methods, shinyFiles, data.table, SummarizedExperiment
Suggests: BiocStyle, knitr, rmarkdown, devtools, R.utils
License: GPL-2
Archs: x64
MD5sum: 0ef9c5099e9e130e40d4e4a2bb480fc1
NeedsCompilation: no
Title: Visualization of Methylation Data
Description: methylscaper is an R package for processing and
visualizing data jointly profiling methylation and chromatin
accessibility (MAPit, NOMe-seq, scNMT-seq, nanoNOMe, etc.). The
package supports both single-cell and single-molecule data, and
a common interface for jointly visualizing both data types
through the generation of ordered representational
methylation-state matrices. The Shiny app allows for an
interactive seriation process of refinement and re-weighting
that optimally orders the cells or DNA molecules to discover
methylation patterns and nucleosome positioning.
biocViews: DNAMethylation, Epigenetics, Sequencing, Visualization,
SingleCell, NucleosomePositioning
Author: Bacher Rhonda [aut, cre], Parker Knight [aut]
Maintainer: Bacher Rhonda <rbacher@ufl.edu>
URL: https://github.com/rhondabacher/methylscaper/
VignetteBuilder: knitr
BugReports: https://github.com/rhondabacher/methylscaper/issues
git_url: https://git.bioconductor.org/packages/methylscaper
git_branch: devel
git_last_commit: 271ec91
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/methylscaper_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/methylscaper_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/methylscaper_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/methylscaper_1.15.0.tgz
vignettes: vignettes/methylscaper/inst/doc/methylScaper.html
vignetteTitles: Using methylscaper to visualize joint methylation and
nucleosome occupancy data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methylscaper/inst/doc/methylScaper.R
dependencyCount: 108
Package: MethylSeekR
Version: 1.47.0
Depends: rtracklayer (>= 1.16.3), parallel (>= 2.15.1), mhsmm (>=
0.4.4)
Imports: IRanges (>= 1.16.3), BSgenome (>= 1.26.1), GenomicRanges (>=
1.10.5), geneplotter (>= 1.34.0), graphics (>= 2.15.2),
grDevices (>= 2.15.2), parallel (>= 2.15.2), stats (>= 2.15.2),
utils (>= 2.15.2)
Suggests: BSgenome.Hsapiens.UCSC.hg18
License: GPL (>=2)
MD5sum: 84f4645d7ced612df68dab046006111f
NeedsCompilation: no
Title: Segmentation of Bis-seq data
Description: This is a package for the discovery of regulatory regions
from Bis-seq data
biocViews: Sequencing, MethylSeq, DNAMethylation
Author: Lukas Burger, Dimos Gaidatzis, Dirk Schubeler and Michael
Stadler
Maintainer: Lukas Burger <Lukas.Burger@fmi.ch>
git_url: https://git.bioconductor.org/packages/MethylSeekR
git_branch: devel
git_last_commit: bd272df
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MethylSeekR_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MethylSeekR_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MethylSeekR_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MethylSeekR_1.47.0.tgz
vignettes: vignettes/MethylSeekR/inst/doc/MethylSeekR.pdf
vignetteTitles: MethylSeekR
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MethylSeekR/inst/doc/MethylSeekR.R
suggestsMe: methylPipe, RnBeads
dependencyCount: 83
Package: methylSig
Version: 1.19.0
Depends: R (>= 3.6)
Imports: bsseq, DelayedArray, DelayedMatrixStats, DSS, IRanges,
GenomeInfoDb, GenomicRanges, methods, parallel, stats,
S4Vectors
Suggests: BiocStyle, bsseqData, knitr, rmarkdown, testthat (>= 2.1.0),
covr
License: GPL-3
Archs: x64
MD5sum: c1800d6484851cf0105f11f7a20e675d
NeedsCompilation: no
Title: MethylSig: Differential Methylation Testing for WGBS and RRBS
Data
Description: MethylSig is a package for testing for differentially
methylated cytosines (DMCs) or regions (DMRs) in whole-genome
bisulfite sequencing (WGBS) or reduced representation bisulfite
sequencing (RRBS) experiments. MethylSig uses a beta binomial
model to test for significant differences between groups of
samples. Several options exist for either site-specific or
sliding window tests, and variance estimation.
biocViews: DNAMethylation, DifferentialMethylation, Epigenetics,
Regression, MethylSeq
Author: Yongseok Park [aut], Raymond G. Cavalcante [aut, cre]
Maintainer: Raymond G. Cavalcante <rcavalca@umich.edu>
VignetteBuilder: knitr
BugReports: https://github.com/sartorlab/methylSig/issues
git_url: https://git.bioconductor.org/packages/methylSig
git_branch: devel
git_last_commit: 3088278
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/methylSig_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/methylSig_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/methylSig_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/methylSig_1.19.0.tgz
vignettes: vignettes/methylSig/inst/doc/updating-methylSig-code.html,
vignettes/methylSig/inst/doc/using-methylSig.html
vignetteTitles: Updating methylSig code, Using methylSig
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methylSig/inst/doc/updating-methylSig-code.R,
vignettes/methylSig/inst/doc/using-methylSig.R
dependencyCount: 92
Package: methylumi
Version: 2.53.0
Depends: Biobase, methods, R (>= 2.13), scales, reshape2, ggplot2,
matrixStats, FDb.InfiniumMethylation.hg19 (>= 2.2.0), minfi
Imports: BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges,
SummarizedExperiment, Biobase, graphics, lattice, annotate,
genefilter, AnnotationDbi, minfi, stats4, illuminaio,
GenomicFeatures
Suggests: lumi, lattice, limma, xtable, SQN, MASS, matrixStats,
parallel, rtracklayer, Biostrings, TCGAMethylation450k,
IlluminaHumanMethylation450kanno.ilmn12.hg19,
FDb.InfiniumMethylation.hg18 (>= 2.2.0), Homo.sapiens, knitr
License: GPL-2
Archs: x64
MD5sum: 31cc8c7298e5e9841808cf20d5baa8d2
NeedsCompilation: no
Title: Handle Illumina methylation data
Description: This package provides classes for holding and manipulating
Illumina methylation data. Based on eSet, it can contain MIAME
information, sample information, feature information, and
multiple matrices of data. An "intelligent" import function,
methylumiR can read the Illumina text files and create a
MethyLumiSet. methylumIDAT can directly read raw IDAT files
from HumanMethylation27 and HumanMethylation450 microarrays.
Normalization, background correction, and quality control
features for GoldenGate, Infinium, and Infinium HD arrays are
also included.
biocViews: DNAMethylation, TwoChannel, Preprocessing, QualityControl,
CpGIsland
Author: Sean Davis, Pan Du, Sven Bilke, Tim Triche, Jr., Moiz Bootwalla
Maintainer: Sean Davis <seandavi@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/seandavi/methylumi/issues/new
git_url: https://git.bioconductor.org/packages/methylumi
git_branch: devel
git_last_commit: 491e60e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/methylumi_2.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/methylumi_2.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/methylumi_2.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/methylumi_2.53.0.tgz
vignettes: vignettes/methylumi/inst/doc/methylumi.pdf,
vignettes/methylumi/inst/doc/methylumi450k.pdf
vignetteTitles: An Introduction to the methylumi package, Working with
Illumina 450k Arrays using methylumi
hasREADME: TRUE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/methylumi/inst/doc/methylumi.R,
vignettes/methylumi/inst/doc/methylumi450k.R
dependsOnMe: bigmelon, RnBeads, skewr, wateRmelon
importsMe: ffpe, lumi, missMethyl
dependencyCount: 159
Package: MetID
Version: 1.25.0
Depends: R (>= 3.5)
Imports: utils (>= 3.3.1), stats (>= 3.4.2), devtools (>= 1.13.0),
stringr (>= 1.3.0), Matrix (>= 1.2-12), igraph (>= 1.2.1),
ChemmineR (>= 2.30.2)
Suggests: knitr (>= 1.19), rmarkdown (>= 1.8)
License: Artistic-2.0
MD5sum: ea17309fd7fc3ebe061d85dcec0c97bf
NeedsCompilation: no
Title: Network-based prioritization of putative metabolite IDs
Description: This package uses an innovative network-based approach
that will enhance our ability to determine the identities of
significant ions detected by LC-MS.
biocViews: AssayDomain, BiologicalQuestion, Infrastructure,
ResearchField, StatisticalMethod, Technology, WorkflowStep,
Network, KEGG
Author: Zhenzhi Li <zzrickli@gmail.com>
Maintainer: Zhenzhi Li <zzrickli@gmail.com>
URL: https://github.com/ressomlab/MetID
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MetID
git_branch: devel
git_last_commit: 0715d00
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MetID_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MetID_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MetID_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MetID_1.25.0.tgz
vignettes: vignettes/MetID/inst/doc/Introduction_to_MetID.html
vignetteTitles: Introduction to MetID
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MetID/inst/doc/Introduction_to_MetID.R
dependencyCount: 132
Package: MetMashR
Version: 1.1.0
Depends: R (>= 4.3.0), struct
Imports: dplyr, methods, httr, scales, ggthemes, ggplot2, utils, rlang,
cowplot, stats
Suggests: covr, httptest, knitr, rmarkdown, testthat (>= 3.0.0),
rgoslin, DT, RSQLite, CompoundDb, BiocStyle, BiocFileCache,
msPurity, ChemmineOB, rsvg, metabolomicsWorkbenchR, KEGGREST,
plyr, magick, structToolbox, RVenn, ggVennDiagram, patchwork,
XML, GO.db, tidytext, tidyr, tidyselect, ComplexUpset,
jsonlite, openxlsx
License: GPL-3
MD5sum: 2542c9686fad9562d67df4419c4679a1
NeedsCompilation: no
Title: Metabolite Mashing with R
Description: A package to merge, filter sort, organise and otherwise
mash together metabolite annotation tables. Metabolite
annotations can be imported from multiple sources (software)
and combined using workflow steps based on S4 class templates
derived from the `struct` package. Other modular workflow steps
such as filtering, merging, splitting, normalisation and
rest-api queries are included.
biocViews: WorkflowStep, Metabolomics, KEGG
Author: Gavin Rhys Lloyd [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7989-6695>), Ralf Johannes Maria
Weber [aut]
Maintainer: Gavin Rhys Lloyd <g.r.lloyd@bham.ac.uk>
URL: https://computational-metabolomics.github.io/MetMashR/
VignetteBuilder: knitr
BugReports:
https://github.com/computational-metabolomics/MetMashR/issues
git_url: https://git.bioconductor.org/packages/MetMashR
git_branch: devel
git_last_commit: e797206
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MetMashR_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MetMashR_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MetMashR_1.1.0.tgz
vignettes: vignettes/MetMashR/inst/doc/annotate_mixtures.html,
vignettes/MetMashR/inst/doc/exploring_mtox.html,
vignettes/MetMashR/inst/doc/Extending_MetMashR.html,
vignettes/MetMashR/inst/doc/using_MetMashR.html
vignetteTitles: Annotation of mixtures of standards, Exploring the
MTox700+ library, Extending MetMashR, Using MetMashR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MetMashR/inst/doc/annotate_mixtures.R,
vignettes/MetMashR/inst/doc/exploring_mtox.R,
vignettes/MetMashR/inst/doc/Extending_MetMashR.R,
vignettes/MetMashR/inst/doc/using_MetMashR.R
dependencyCount: 80
Package: MetNet
Version: 1.25.0
Depends: R (>= 4.0), S4Vectors (>= 0.28.1), SummarizedExperiment (>=
1.20.0)
Imports: bnlearn (>= 4.3), BiocParallel (>= 1.12.0), corpcor (>=
1.6.10), dplyr (>= 1.0.3), ggplot2 (>= 3.3.3), GeneNet (>=
1.2.15), GENIE3 (>= 1.7.0), methods (>= 3.5), parmigene (>=
1.0.2), psych (>= 2.1.6), rlang (>= 0.4.10), stabs (>= 0.6),
stats (>= 3.6), tibble (>= 3.0.5), tidyr (>= 1.1.2)
Suggests: BiocGenerics (>= 0.24.0), BiocStyle (>= 2.6.1), glmnet (>=
4.1-1), igraph (>= 1.1.2), knitr (>= 1.11), rmarkdown (>=
1.15), testthat (>= 2.2.1), Spectra (>= 1.4.1), MsCoreUtils (>=
1.6.0)
License: GPL (>= 3)
MD5sum: 8621732d1186aacdb2650d01ec8263fa
NeedsCompilation: no
Title: Inferring metabolic networks from untargeted high-resolution
mass spectrometry data
Description: MetNet contains functionality to infer metabolic network
topologies from quantitative data and high-resolution
mass/charge information. Using statistical models (including
correlation, mutual information, regression and Bayes
statistics) and quantitative data (intensity values of
features) adjacency matrices are inferred that can be combined
to a consensus matrix. Mass differences calculated between
mass/charge values of features will be matched against a data
frame of supplied mass/charge differences referring to
transformations of enzymatic activities. In a third step, the
two levels of information are combined to form a adjacency
matrix inferred from both quantitative and structure
information.
biocViews: ImmunoOncology, Metabolomics, MassSpectrometry, Network,
Regression
Author: Thomas Naake [aut, cre], Liesa Salzer [ctb]
Maintainer: Thomas Naake <thomasnaake@googlemail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MetNet
git_branch: devel
git_last_commit: 3d26654
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MetNet_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MetNet_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MetNet_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MetNet_1.25.0.tgz
vignettes: vignettes/MetNet/inst/doc/MetNet.html
vignetteTitles: Workflow for high-resolution metabolomics data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MetNet/inst/doc/MetNet.R
dependencyCount: 92
Package: mfa
Version: 1.29.0
Depends: R (>= 3.4.0)
Imports: methods, stats, ggplot2, Rcpp, dplyr, ggmcmc, MCMCpack,
MCMCglmm, coda, magrittr, tibble, Biobase
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, BiocStyle, testthat
License: GPL (>= 2)
MD5sum: a67a938b85c271eee088bd744cde3b2e
NeedsCompilation: yes
Title: Bayesian hierarchical mixture of factor analyzers for modelling
genomic bifurcations
Description: MFA models genomic bifurcations using a Bayesian
hierarchical mixture of factor analysers.
biocViews: ImmunoOncology, RNASeq, GeneExpression, Bayesian, SingleCell
Author: Kieran Campbell [aut, cre]
Maintainer: Kieran Campbell <kieranrcampbell@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/mfa
git_branch: devel
git_last_commit: cd71406
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mfa_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mfa_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mfa_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mfa_1.29.0.tgz
vignettes: vignettes/mfa/inst/doc/introduction_to_mfa.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mfa/inst/doc/introduction_to_mfa.R
suggestsMe: splatter
dependencyCount: 71
Package: Mfuzz
Version: 2.67.0
Depends: R (>= 2.5.0), Biobase (>= 2.5.5), e1071
Imports: tcltk, tkWidgets
Suggests: marray
License: GPL-2
MD5sum: 0b43e9ae45320117984fa5c57bcfeb9b
NeedsCompilation: no
Title: Soft clustering of omics time series data
Description: The Mfuzz package implements noise-robust soft clustering
of omics time-series data, including transcriptomic, proteomic
or metabolomic data. It is based on the use of c-means
clustering. For convenience, it includes a graphical user
interface.
biocViews: Microarray, Clustering, TimeCourse, Preprocessing,
Visualization
Author: Matthias Futschik <matthias.futschik@sysbiolab.eu>
Maintainer: Matthias Futschik <matthias.futschik@sysbiolab.eu>
URL: http://mfuzz.sysbiolab.eu/
git_url: https://git.bioconductor.org/packages/Mfuzz
git_branch: devel
git_last_commit: c882261
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Mfuzz_2.67.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Mfuzz_2.67.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Mfuzz_2.67.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Mfuzz_2.67.0.tgz
vignettes: vignettes/Mfuzz/inst/doc/Mfuzz.pdf
vignetteTitles: Introduction to Mfuzz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Mfuzz/inst/doc/Mfuzz.R
dependsOnMe: cycle, MultiRNAflow
importsMe: Patterns
suggestsMe: DAPAR, pctax
dependencyCount: 17
Package: MGFM
Version: 1.41.0
Depends: AnnotationDbi,annotate
Suggests: hgu133a.db
License: GPL-3
MD5sum: f97c6d5399eec487078e8de969698eae
NeedsCompilation: no
Title: Marker Gene Finder in Microarray gene expression data
Description: The package is designed to detect marker genes from
Microarray gene expression data sets
biocViews: Genetics, GeneExpression, Microarray
Author: Khadija El Amrani
Maintainer: Khadija El Amrani <khadija.el-amrani@charite.de>
git_url: https://git.bioconductor.org/packages/MGFM
git_branch: devel
git_last_commit: b6b997c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MGFM_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MGFM_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MGFM_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MGFM_1.41.0.tgz
vignettes: vignettes/MGFM/inst/doc/MGFM.pdf
vignetteTitles: Using MGFM
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MGFM/inst/doc/MGFM.R
dependsOnMe: sampleClassifier
dependencyCount: 48
Package: MGFR
Version: 1.33.0
Depends: R (>= 3.5)
Imports: biomaRt, annotate
License: GPL-3
MD5sum: 856241c3a0436d2983ce4a6f70287cfa
NeedsCompilation: no
Title: Marker Gene Finder in RNA-seq data
Description: The package is designed to detect marker genes from
RNA-seq data.
biocViews: ImmunoOncology, Genetics, GeneExpression, RNASeq
Author: Khadija El Amrani
Maintainer: Khadija El Amrani <a.khadija@gmx.de>
git_url: https://git.bioconductor.org/packages/MGFR
git_branch: devel
git_last_commit: 3887a1b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MGFR_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MGFR_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MGFR_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MGFR_1.33.0.tgz
vignettes: vignettes/MGFR/inst/doc/MGFR.pdf
vignetteTitles: Using MGFR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MGFR/inst/doc/MGFR.R
dependsOnMe: sampleClassifier
dependencyCount: 71
Package: MGnifyR
Version: 1.3.0
Depends: R(>= 4.4.0), MultiAssayExperiment, TreeSummarizedExperiment,
SummarizedExperiment, BiocGenerics
Imports: mia, ape, dplyr, httr, methods, plyr, reshape2, S4Vectors,
urltools, utils, tidyjson
Suggests: biomformat, broom, ggplot2, knitr, rmarkdown, testthat, xml2,
BiocStyle, miaViz, vegan, scater, phyloseq, magick
License: Artistic-2.0 | file LICENSE
Archs: x64
MD5sum: 0babca2499c753a778cd83618a0533ac
NeedsCompilation: no
Title: R interface to EBI MGnify metagenomics resource
Description: Utility package to facilitate integration and analysis of
EBI MGnify data in R. The package can be used to import
microbial data for instance into TreeSummarizedExperiment
(TreeSE). In TreeSE format, the data is directly compatible
with miaverse framework.
biocViews: Infrastructure, DataImport, Metagenomics
Author: Tuomas Borman [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8563-8884>), Ben Allen [aut], Leo
Lahti [aut] (ORCID: <https://orcid.org/0000-0001-5537-637X>)
Maintainer: Tuomas Borman <tuomas.v.borman@utu.fi>
URL: https://github.com/EBI-Metagenomics/MGnifyR
VignetteBuilder: knitr
BugReports: https://github.com/EBI-Metagenomics/MGnifyR/issues
git_url: https://git.bioconductor.org/packages/MGnifyR
git_branch: devel
git_last_commit: da1771c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MGnifyR_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MGnifyR_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MGnifyR_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MGnifyR_1.3.0.tgz
vignettes: vignettes/MGnifyR/inst/doc/MGnifyR_long.html,
vignettes/MGnifyR/inst/doc/MGnifyR.html
vignetteTitles: MGnifyR,, extended vignette, MGnifyR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MGnifyR/inst/doc/MGnifyR_long.R,
vignettes/MGnifyR/inst/doc/MGnifyR.R
suggestsMe: HoloFoodR
dependencyCount: 171
Package: mgsa
Version: 1.55.0
Depends: R (>= 2.14.0), methods, gplots
Imports: graphics, stats, utils
Suggests: DBI, RSQLite, GO.db, testthat
License: Artistic-2.0
MD5sum: 5fc28fbf350fc811940cde8bb5c85bde
NeedsCompilation: yes
Title: Model-based gene set analysis
Description: Model-based Gene Set Analysis (MGSA) is a Bayesian
modeling approach for gene set enrichment. The package mgsa
implements MGSA and tools to use MGSA together with the Gene
Ontology.
biocViews: Pathways, GO, GeneSetEnrichment
Author: Sebastian Bauer <mail@sebastianbauer.info>, Julien Gagneur
<gagneur@genzentrum.lmu.de>
Maintainer: Sebastian Bauer <mail@sebastianbauer.info>
URL: https://github.com/sba1/mgsa-bioc
git_url: https://git.bioconductor.org/packages/mgsa
git_branch: devel
git_last_commit: d873604
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mgsa_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mgsa_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mgsa_1.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mgsa_1.55.0.tgz
vignettes: vignettes/mgsa/inst/doc/mgsa.pdf
vignetteTitles: Overview of the mgsa package.
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mgsa/inst/doc/mgsa.R
dependencyCount: 9
Package: mia
Version: 1.15.35
Depends: R (>= 4.1.0), MultiAssayExperiment, SingleCellExperiment,
SummarizedExperiment, TreeSummarizedExperiment (>= 1.99.3)
Imports: ape, BiocGenerics, BiocParallel, Biostrings, bluster,
DECIPHER, decontam, DelayedArray, DelayedMatrixStats,
DirichletMultinomial, dplyr, IRanges, MASS, MatrixGenerics,
methods, rbiom, rlang, S4Vectors, scater, scuttle, stats,
stringr, tibble, tidyr, utils, vegan, Rcpp
LinkingTo: Rcpp
Suggests: ade4, BiocStyle, biomformat, dada2, knitr, mediation, miaViz,
microbiomeDataSets, NMF, patchwork, philr, phyloseq, reldist,
rhdf5, rmarkdown, testthat, topicdoc, topicmodels, yaml
License: Artistic-2.0 | file LICENSE
MD5sum: c33f71e9f5335c67454ea8567f994977
NeedsCompilation: yes
Title: Microbiome analysis
Description: mia implements tools for microbiome analysis based on the
SummarizedExperiment, SingleCellExperiment and
TreeSummarizedExperiment infrastructure. Data wrangling and
analysis in the context of taxonomic data is the main scope.
Additional functions for common task are implemented such as
community indices calculation and summarization.
biocViews: Microbiome, Software, DataImport
Author: Tuomas Borman [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8563-8884>), Felix G.M. Ernst
[aut] (ORCID: <https://orcid.org/0000-0001-5064-0928>),
Sudarshan A. Shetty [aut] (ORCID:
<https://orcid.org/0000-0001-7280-9915>), Leo Lahti [aut]
(ORCID: <https://orcid.org/0000-0001-5537-637X>), Yang Cao
[ctb], Nathan D. Olson [ctb], Levi Waldron [ctb], Marcel Ramos
[ctb], Héctor Corrada Bravo [ctb], Jayaram Kancherla [ctb],
Domenick Braccia [ctb], Basil Courbayre [ctb], Muluh Muluh
[ctb], Giulio Benedetti [ctb], Moritz Emanuel Beber [ctb]
(ORCID: <https://orcid.org/0000-0003-2406-1978>), Nitesh Turaga
[ctb], Chouaib Benchraka [ctb], Akewak Jeba [ctb], Himmi
Lindgren [ctb], Noah De Gunst [ctb], Théotime Pralas [ctb],
Shadman Ishraq [ctb], Eineje Ameh [ctb], Artur Sannikov [ctb],
Hervé Pagès [ctb], Rajesh Shigdel [ctb], Katariina Pärnänen
[ctb], Pande Erawijantari [ctb], Danielle Callan [ctb], Jesse
Pasanen [ctb]
Maintainer: Tuomas Borman <tuomas.v.borman@utu.fi>
URL: https://github.com/microbiome/mia
VignetteBuilder: knitr
BugReports: https://github.com/microbiome/mia/issues
git_url: https://git.bioconductor.org/packages/mia
git_branch: devel
git_last_commit: 6c9ceb2
git_last_commit_date: 2025-03-27
Date/Publication: 2025-03-27
source.ver: src/contrib/mia_1.15.35.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mia_1.15.6.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mia_1.15.35.tgz
vignettes: vignettes/mia/inst/doc/mia.html
vignetteTitles: mia
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/mia/inst/doc/mia.R
dependsOnMe: miaViz
importsMe: dar, iSEEtree, lefser, MGnifyR, curatedMetagenomicData
suggestsMe: ANCOMBC, HoloFoodR, miaSim, philr, bugphyzz,
MicrobiomeBenchmarkData, MiscMetabar
dependencyCount: 166
Package: miaSim
Version: 1.13.0
Depends: TreeSummarizedExperiment
Imports: SummarizedExperiment, deSolve, stats, poweRlaw,
MatrixGenerics, S4Vectors
Suggests: ape, cluster, foreach, doParallel, dplyr, GGally, ggplot2,
igraph, network, reshape2, sna, vegan, rmarkdown, knitr,
BiocStyle, testthat, mia, miaViz, colourvalues, philentropy
License: Artistic-2.0 | file LICENSE
MD5sum: 0c7431bc8644aef121f5aaf96542d4dd
NeedsCompilation: no
Title: Microbiome Data Simulation
Description: Microbiome time series simulation with generalized
Lotka-Volterra model, Self-Organized Instability (SOI), and
other models. Hubbell's Neutral model is used to determine the
abundance matrix. The resulting abundance matrix is applied to
(Tree)SummarizedExperiment objects.
biocViews: Microbiome, Software, Sequencing, DNASeq, ATACSeq, Coverage,
Network
Author: Yagmur Simsek [cre, aut], Karoline Faust [aut], Yu Gao [aut],
Emma Gheysen [aut], Daniel Rios Garza [aut], Tuomas Borman
[aut] (ORCID: <https://orcid.org/0000-0002-8563-8884>), Leo
Lahti [aut] (ORCID: <https://orcid.org/0000-0001-5537-637X>)
Maintainer: Yagmur Simsek <yagmur.simsek.98@gmail.com>
URL: https://github.com/microbiome/miaSim
VignetteBuilder: knitr
BugReports: https://github.com/microbiome/miaSim/issues
git_url: https://git.bioconductor.org/packages/miaSim
git_branch: devel
git_last_commit: 667f35c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/miaSim_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/miaSim_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/miaSim_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/miaSim_1.13.0.tgz
vignettes: vignettes/miaSim/inst/doc/miaSim.html
vignetteTitles: miaSim
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/miaSim/inst/doc/miaSim.R
dependencyCount: 79
Package: miaViz
Version: 1.15.12
Depends: R (>= 4.1.0), ggplot2, ggraph (>= 2.0), mia (>= 1.13.0),
SummarizedExperiment, TreeSummarizedExperiment
Imports: ape, BiocGenerics, BiocParallel, DelayedArray,
DirichletMultinomial, dplyr, ggnewscale, ggrepel, ggtree,
methods, rlang, S4Vectors, scales, scater,
SingleCellExperiment, stats, tibble, tidygraph, tidyr,
tidytext, tidytree, viridis
Suggests: BiocStyle, bluster, circlize, ComplexHeatmap, ggh4x, knitr,
patchwork, rmarkdown, shadowtext, testthat, vegan
License: Artistic-2.0 | file LICENSE
MD5sum: 988094e745c67fb5e2d7be7123966502
NeedsCompilation: no
Title: Microbiome Analysis Plotting and Visualization
Description: The miaViz package implements functions to visualize
TreeSummarizedExperiment objects especially in the context of
microbiome analysis. Part of the mia family of R/Bioconductor
packages.
biocViews: Microbiome, Software, Visualization
Author: Tuomas Borman [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8563-8884>), Felix G.M. Ernst
[aut] (ORCID: <https://orcid.org/0000-0001-5064-0928>), Leo
Lahti [aut] (ORCID: <https://orcid.org/0000-0001-5537-637X>),
Basil Courbayre [ctb], Giulio Benedetti [ctb] (ORCID:
<https://orcid.org/0000-0002-8732-7692>), Théotime Pralas
[ctb], Nitesh Turaga [ctb], Chouaib Benchraka [ctb], Sam
Hillman [ctb], Muluh Muluh [ctb], Noah De Gunst [ctb], Ely
Seraidarian [ctb], Himmi Lindgren [ctb], Vivian Ikeh [ctb]
Maintainer: Tuomas Borman <tuomas.v.borman@utu.fi>
URL: https://github.com/microbiome/miaViz
VignetteBuilder: knitr
BugReports: https://github.com/microbiome/miaViz/issues
git_url: https://git.bioconductor.org/packages/miaViz
git_branch: devel
git_last_commit: fdb4ded
git_last_commit_date: 2025-03-19
Date/Publication: 2025-03-19
source.ver: src/contrib/miaViz_1.15.12.tar.gz
win.binary.ver: bin/windows/contrib/4.5/miaViz_1.15.12.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/miaViz_1.15.12.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/miaViz_1.15.12.tgz
vignettes: vignettes/miaViz/inst/doc/miaViz.html
vignetteTitles: miaViz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/miaViz/inst/doc/miaViz.R
importsMe: iSEEtree
suggestsMe: HoloFoodR, MGnifyR, mia, miaSim
dependencyCount: 186
Package: MiChip
Version: 1.61.0
Depends: R (>= 2.3.0), Biobase
Imports: Biobase
License: GPL (>= 2)
MD5sum: 4cbbe78f97364667ee4dc2ebb4bc549b
NeedsCompilation: no
Title: MiChip Parsing and Summarizing Functions
Description: This package takes the MiChip miRNA microarray .grp
scanner output files and parses these out, providing summary
and plotting functions to analyse MiChip hybridizations. A set
of hybridizations is packaged into an ExpressionSet allowing it
to be used by other BioConductor packages.
biocViews: Microarray, Preprocessing
Author: Jonathon Blake <blake@embl.de>
Maintainer: Jonathon Blake <blake@embl.de>
git_url: https://git.bioconductor.org/packages/MiChip
git_branch: devel
git_last_commit: 8c80304
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MiChip_1.61.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MiChip_1.61.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MiChip_1.61.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MiChip_1.61.0.tgz
vignettes: vignettes/MiChip/inst/doc/MiChip.pdf
vignetteTitles: MiChip miRNA Microarray Processing
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MiChip/inst/doc/MiChip.R
dependencyCount: 7
Package: microbiome
Version: 1.29.0
Depends: R (>= 3.6.0), phyloseq, ggplot2
Imports: Biostrings, compositions, dplyr, reshape2, Rtsne, scales,
stats, tibble, tidyr, utils, vegan
Suggests: BiocGenerics, BiocStyle, Cairo, knitr, rmarkdown, testthat
License: BSD_2_clause + file LICENSE
Archs: x64
MD5sum: 18b69e36495ede8da15f89c55841286f
NeedsCompilation: no
Title: Microbiome Analytics
Description: Utilities for microbiome analysis.
biocViews: Metagenomics,Microbiome,Sequencing,SystemsBiology
Author: Leo Lahti [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5537-637X>), Sudarshan Shetty
[aut] (ORCID: <https://orcid.org/0000-0001-7280-9915>)
Maintainer: Leo Lahti <leo.lahti@iki.fi>
URL: http://microbiome.github.io/microbiome
VignetteBuilder: knitr
BugReports: https://github.com/microbiome/microbiome/issues
git_url: https://git.bioconductor.org/packages/microbiome
git_branch: devel
git_last_commit: be82090
git_last_commit_date: 2024-10-29
Date/Publication: 2025-01-23
source.ver: src/contrib/microbiome_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/microbiome_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/microbiome_1.29.0.tgz
vignettes: vignettes/microbiome/inst/doc/vignette.html
vignetteTitles: microbiome R package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/microbiome/inst/doc/vignette.R
importsMe: benchdamic, MicrobiomeSurv
suggestsMe: ANCOMBC, dar, zitools
dependencyCount: 93
Package: microbiomeDASim
Version: 1.21.0
Depends: R (>= 3.6.0)
Imports: graphics, ggplot2, MASS, tmvtnorm, Matrix, mvtnorm, pbapply,
stats, phyloseq, metagenomeSeq, Biobase
Suggests: testthat (>= 2.1.0), knitr, devtools
License: MIT + file LICENSE
MD5sum: 444c21dea269a307b202d25edda162c9
NeedsCompilation: no
Title: Microbiome Differential Abundance Simulation
Description: A toolkit for simulating differential microbiome data
designed for longitudinal analyses. Several functional forms
may be specified for the mean trend. Observations are drawn
from a multivariate normal model. The objective of this package
is to be able to simulate data in order to accurately compare
different longitudinal methods for differential abundance.
biocViews: Microbiome, Visualization, Software
Author: Justin Williams, Hector Corrada Bravo, Jennifer Tom, Joseph
Nathaniel Paulson
Maintainer: Justin Williams <williazo@ucla.edu>
URL: https://github.com/williazo/microbiomeDASim
VignetteBuilder: knitr
BugReports: https://github.com/williazo/microbiomeDASim/issues
git_url: https://git.bioconductor.org/packages/microbiomeDASim
git_branch: devel
git_last_commit: a5f0a43
git_last_commit_date: 2024-10-29
Date/Publication: 2025-01-23
source.ver: src/contrib/microbiomeDASim_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/microbiomeDASim_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/microbiomeDASim_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/microbiomeDASim_1.21.0.tgz
vignettes: vignettes/microbiomeDASim/inst/doc/microbiomeDASim.pdf
vignetteTitles: microbiomeDASim
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/microbiomeDASim/inst/doc/microbiomeDASim.R
dependencyCount: 103
Package: microbiomeExplorer
Version: 1.17.0
Depends: shiny, magrittr, metagenomeSeq, Biobase
Imports: shinyjs (>= 2.0.0), shinydashboard, shinycssloaders,
shinyWidgets, rmarkdown (>= 1.9.0), DESeq2, RColorBrewer,
dplyr, tidyr, purrr, rlang, knitr, readr, DT (>= 0.12.0),
biomformat, tools, stringr, vegan, matrixStats, heatmaply, car,
broom, limma, reshape2, tibble, forcats, lubridate, methods,
plotly (>= 4.9.1)
Suggests: V8, testthat (>= 2.1.0)
License: MIT + file LICENSE
MD5sum: 106a659d2b3affb302d44b3417b9bb02
NeedsCompilation: no
Title: Microbiome Exploration App
Description: The MicrobiomeExplorer R package is designed to facilitate
the analysis and visualization of marker-gene survey feature
data. It allows a user to perform and visualize typical
microbiome analytical workflows either through the command line
or an interactive Shiny application included with the package.
In addition to applying common analytical workflows the
application enables automated analysis report generation.
biocViews: Classification, Clustering, GeneticVariability,
DifferentialExpression, Microbiome, Metagenomics,
Normalization, Visualization, MultipleComparison, Sequencing,
Software, ImmunoOncology
Author: Joseph Paulson [aut], Janina Reeder [aut, cre], Mo Huang [aut],
Genentech [cph, fnd]
Maintainer: Janina Reeder <reederj1@gene.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/microbiomeExplorer
git_branch: devel
git_last_commit: f28fc46
git_last_commit_date: 2024-10-29
Date/Publication: 2024-12-18
source.ver: src/contrib/microbiomeExplorer_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/microbiomeExplorer_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/microbiomeExplorer_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/microbiomeExplorer_1.17.0.tgz
vignettes: vignettes/microbiomeExplorer/inst/doc/exploreMouseData.html
vignetteTitles: microbiomeExplorer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/microbiomeExplorer/inst/doc/exploreMouseData.R
dependencyCount: 194
Package: MicrobiomeProfiler
Version: 1.13.0
Depends: R (>= 4.2.0)
Imports: clusterProfiler (>= 4.5.2), config, DT, enrichplot, golem,
gson, methods, magrittr, shiny (>= 1.6.0), shinyWidgets,
shinycustomloader, htmltools, ggplot2, graphics, stats, utils,
yulab.utils
Suggests: rmarkdown, knitr, testthat (>= 3.0.0), prettydoc
License: GPL-2
MD5sum: 3ff817046593001a77935a80cca99b6d
NeedsCompilation: no
Title: An R/shiny package for microbiome functional enrichment analysis
Description: This is an R/shiny package to perform functional
enrichment analysis for microbiome data. This package was based
on clusterProfiler. Moreover, MicrobiomeProfiler support KEGG
enrichment analysis, COG enrichment analysis, Microbe-Disease
association enrichment analysis, Metabo-Pathway analysis.
biocViews: Microbiome, Software, Visualization,KEGG
Author: Guangchuang Yu [cre, aut] (ORCID:
<https://orcid.org/0000-0002-6485-8781>), Meijun Chen [aut]
(ORCID: <https://orcid.org/0000-0003-2486-8106>)
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
URL: https://github.com/YuLab-SMU/MicrobiomeProfiler/,
https://yulab-smu.top/contribution-knowledge-mining/
VignetteBuilder: knitr
BugReports: https://github.com/YuLab-SMU/MicrobiomeProfiler/issues
git_url: https://git.bioconductor.org/packages/MicrobiomeProfiler
git_branch: devel
git_last_commit: 2233f8a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MicrobiomeProfiler_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MicrobiomeProfiler_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MicrobiomeProfiler_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MicrobiomeProfiler_1.13.0.tgz
vignettes:
vignettes/MicrobiomeProfiler/inst/doc/MicrobiomeProfiler.html
vignetteTitles: Introduction to MicrobiotaProcess
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MicrobiomeProfiler/inst/doc/MicrobiomeProfiler.R
dependencyCount: 154
Package: MicrobiotaProcess
Version: 1.19.0
Depends: R (>= 4.0.0)
Imports: ape, tidyr, ggplot2, magrittr, dplyr, Biostrings, ggrepel,
vegan, zoo, ggtree, tidytree (>= 0.4.2), MASS, methods, rlang,
tibble, grDevices, stats, utils, coin, ggsignif, patchwork,
ggstar, tidyselect, SummarizedExperiment, foreach, treeio (>=
1.17.2), pillar, cli, plyr, dtplyr, ggtreeExtra, data.table,
ggfun (>= 0.1.1)
Suggests: rmarkdown, prettydoc, testthat, knitr, nlme, phangorn,
DECIPHER, randomForest, jsonlite, biomformat, scales, yaml,
withr, S4Vectors, purrr, seqmagick, glue, ggupset,
ggVennDiagram, gghalves, ggalluvial (>= 0.11.1), forcats,
phyloseq, aplot, ggnewscale, ggside, ggh4x, hopach, parallel,
shadowtext, DirichletMultinomial, ggpp, BiocManager
License: GPL (>= 3.0)
MD5sum: 8825c1a383ddd335a51218b5b16eeee2
NeedsCompilation: no
Title: A comprehensive R package for managing and analyzing microbiome
and other ecological data within the tidy framework
Description: MicrobiotaProcess is an R package for analysis,
visualization and biomarker discovery of microbial datasets. It
introduces MPSE class, this make it more interoperable with the
existing computing ecosystem. Moreover, it introduces a tidy
microbiome data structure paradigm and analysis grammar. It
provides a wide variety of microbiome data analysis procedures
under the unified and common framework (tidy-like framework).
biocViews: Visualization, Microbiome, Software, MultipleComparison,
FeatureExtraction
Author: Shuangbin Xu [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3513-5362>), Guangchuang Yu [aut,
ctb] (ORCID: <https://orcid.org/0000-0002-6485-8781>)
Maintainer: Shuangbin Xu <xshuangbin@163.com>
URL: https://github.com/YuLab-SMU/MicrobiotaProcess/
VignetteBuilder: knitr
BugReports: https://github.com/YuLab-SMU/MicrobiotaProcess/issues
git_url: https://git.bioconductor.org/packages/MicrobiotaProcess
git_branch: devel
git_last_commit: b30acb4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MicrobiotaProcess_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MicrobiotaProcess_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MicrobiotaProcess_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MicrobiotaProcess_1.19.0.tgz
vignettes: vignettes/MicrobiotaProcess/inst/doc/MicrobiotaProcess.html
vignetteTitles: Introduction to MicrobiotaProcess
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MicrobiotaProcess/inst/doc/MicrobiotaProcess.R
suggestsMe: parafac4microbiome
dependencyCount: 109
Package: microRNA
Version: 1.65.1
Depends: R (>= 2.10)
Imports: Biostrings (>= 2.11.32)
License: Artistic-2.0
MD5sum: 2aee572aebf26f15ef63bbf609464f05
NeedsCompilation: yes
Title: Data and functions for dealing with microRNAs
Description: Different data resources for microRNAs and some functions
for manipulating them.
biocViews: Infrastructure, GenomeAnnotation, SequenceMatching
Author: R. Gentleman, S. Falcon
Maintainer: "Michael Lawrence" <michafla@gene.com>
git_url: https://git.bioconductor.org/packages/microRNA
git_branch: devel
git_last_commit: a40bf50
git_last_commit_date: 2025-02-04
Date/Publication: 2025-02-05
source.ver: src/contrib/microRNA_1.65.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/microRNA_1.65.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/microRNA_1.65.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/microRNA_1.65.1.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
suggestsMe: rtracklayer
dependencyCount: 25
Package: microSTASIS
Version: 1.7.0
Depends: R (>= 4.2.0)
Imports: BiocParallel, ggplot2, ggside, grid, rlang, stats, stringr,
TreeSummarizedExperiment
Suggests: BiocStyle, gghighlight, knitr, rmarkdown, methods,
RefManageR, sessioninfo, SingleCellExperiment,
SummarizedExperiment, testthat (>= 3.0.0)
License: GPL-3
MD5sum: 799daca72ae582ee03265a331de93605
NeedsCompilation: no
Title: Microbiota STability ASsessment via Iterative cluStering
Description: The toolkit 'µSTASIS', or microSTASIS, has been developed
for the stability analysis of microbiota in a temporal
framework by leveraging on iterative clustering. Concretely,
the core function uses Hartigan-Wong k-means algorithm as many
times as possible for stressing out paired samples from the
same individuals to test if they remain together for multiple
numbers of clusters over a whole data set of individuals.
Moreover, the package includes multiple functions to subset
samples from paired times, validate the results or visualize
the output.
biocViews: GeneticVariability, BiomedicalInformatics, Clustering,
MultipleComparison, Microbiome
Author: Pedro Sánchez-Sánchez [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4846-1813>), Alfonso BenÃtez-Páez
[aut] (ORCID: <https://orcid.org/0000-0001-5707-4340>)
Maintainer: Pedro Sánchez-Sánchez <bio.pedro.technology@gmail.com>
URL: https://doi.org/10.1093/bib/bbac055
VignetteBuilder: knitr
BugReports: https://github.com/BiotechPedro/microSTASIS
git_url: https://git.bioconductor.org/packages/microSTASIS
git_branch: devel
git_last_commit: cc85111
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/microSTASIS_1.7.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/microSTASIS_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/microSTASIS_1.7.0.tgz
vignettes: vignettes/microSTASIS/inst/doc/microSTASIS.html
vignetteTitles: Introduction to microSTASIS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/microSTASIS/inst/doc/microSTASIS.R
dependencyCount: 90
Package: MICSQTL
Version: 1.5.0
Depends: R (>= 4.3.0), SummarizedExperiment, stats
Imports: TCA, nnls, purrr, TOAST, magrittr, BiocParallel, ggplot2,
ggpubr, ggridges, glue, S4Vectors, dirmult
Suggests: testthat (>= 3.0.0), rmarkdown, knitr, BiocStyle
License: GPL-3
MD5sum: d55d30ed4d380e2402b880c71b358e77
NeedsCompilation: no
Title: MICSQTL (Multi-omic deconvolution, Integration and
Cell-type-specific Quantitative Trait Loci)
Description: Our pipeline, MICSQTL, utilizes scRNA-seq reference and
bulk transcriptomes to estimate cellular composition in the
matched bulk proteomes. The expression of genes and proteins at
either bulk level or cell type level can be integrated by
Angle-based Joint and Individual Variation Explained (AJIVE)
framework. Meanwhile, MICSQTL can perform cell-type-specic
quantitative trait loci (QTL) mapping to proteins or
transcripts based on the input of bulk expression data and the
estimated cellular composition per molecule type, without the
need for single cell sequencing. We use matched
transcriptome-proteome from human brain frontal cortex tissue
samples to demonstrate the input and output of our tool.
biocViews: GeneExpression, Genetics, Proteomics, RNASeq, Sequencing,
SingleCell, Software, Visualization, CellBasedAssays, Coverage
Author: Yue Pan [aut] (ORCID: <https://orcid.org/0000-0003-1958-2744>),
Qian Li [aut, cre] (ORCID:
<https://orcid.org/0000-0001-9874-3555>), Iain Carmichael [ctb]
Maintainer: Qian Li <qian.li@stjude.org>
URL: https://bioconductor.org/packages/MICSQTL,
https://github.com/YuePan027/MICSQTL
VignetteBuilder: knitr
BugReports: https://github.com/YuePan027/MICSQTL/issues
git_url: https://git.bioconductor.org/packages/MICSQTL
git_branch: devel
git_last_commit: 3dd556e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MICSQTL_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MICSQTL_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MICSQTL_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MICSQTL_1.5.0.tgz
vignettes: vignettes/MICSQTL/inst/doc/MICSQTL.html
vignetteTitles: MICSQTL: Multi-omic deconvolution,, Integration and
Cell-type-specific Quantitative Trait Loci
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MICSQTL/inst/doc/MICSQTL.R
dependencyCount: 147
Package: midasHLA
Version: 1.15.0
Depends: R (>= 4.1), MultiAssayExperiment (>= 1.8.3)
Imports: assertthat (>= 0.2.0), broom (>= 0.5.1), dplyr (>= 0.8.0.1),
formattable (>= 0.2.0.1), HardyWeinberg (>= 1.6.3), kableExtra
(>= 1.1.0), knitr (>= 1.21), magrittr (>= 1.5), methods,
stringi (>= 1.2.4), rlang (>= 0.3.1), S4Vectors (>= 0.20.1),
stats, SummarizedExperiment (>= 1.12.0), tibble (>= 2.0.1),
utils, qdapTools (>= 1.3.3)
Suggests: broom.mixed (>= 0.2.4), cowplot (>= 1.0.0), devtools (>=
2.0.1), ggplot2 (>= 3.1.0), ggpubr (>= 0.2.5), rmarkdown,
seqinr (>= 3.4-5), survival (>= 2.43-3), testthat (>= 2.0.1),
tidyr (>= 1.1.2)
License: MIT + file LICENCE
MD5sum: 61ec868e8abeb24fb6c006ac8f4f12be
NeedsCompilation: no
Title: R package for immunogenomics data handling and association
analysis
Description: MiDAS is a R package for immunogenetics data
transformation and statistical analysis. MiDAS accepts input
data in the form of HLA alleles and KIR types, and can
transform it into biologically meaningful variables, enabling
HLA amino acid fine mapping, analyses of HLA evolutionary
divergence, KIR gene presence, as well as validated HLA-KIR
interactions. Further, it allows comprehensive statistical
association analysis workflows with phenotypes of diverse
measurement scales. MiDAS closes a gap between the inference of
immunogenetic variation and its efficient utilization to make
relevant discoveries related to T cell, Natural Killer cell,
and disease biology.
biocViews: CellBiology, Genetics, StatisticalMethod
Author: Christian Hammer [aut], Maciej Migdał [aut, cre]
Maintainer: Maciej Migdał <mcjmigdal@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/midasHLA
git_branch: devel
git_last_commit: b204d6f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/midasHLA_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/midasHLA_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/midasHLA_1.15.0.tgz
vignettes: vignettes/midasHLA/inst/doc/MiDAS_tutorial.html,
vignettes/midasHLA/inst/doc/MiDAS_vignette.html
vignetteTitles: MiDAS tutorial, MiDAS quick start
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/midasHLA/inst/doc/MiDAS_tutorial.R,
vignettes/midasHLA/inst/doc/MiDAS_vignette.R
dependencyCount: 141
Package: miloR
Version: 2.3.0
Depends: R (>= 4.0.0), edgeR
Imports: BiocNeighbors, BiocGenerics, SingleCellExperiment, Matrix (>=
1.3-0), MatrixGenerics, S4Vectors, stats, stringr, methods,
igraph, irlba, utils, cowplot, BiocParallel, BiocSingular,
limma, ggplot2, tibble, matrixStats, ggraph, gtools,
SummarizedExperiment, patchwork, tidyr, dplyr, ggrepel,
ggbeeswarm, RColorBrewer, grDevices, Rcpp, pracma, numDeriv
LinkingTo: Rcpp, RcppArmadillo, RcppEigen, RcppML
Suggests: testthat, mvtnorm, scater, scran, covr, knitr, rmarkdown,
uwot, scuttle, BiocStyle, MouseGastrulationData,
MouseThymusAgeing, magick, RCurl, MASS, curl, scRNAseq,
graphics, sparseMatrixStats
License: GPL-3 + file LICENSE
MD5sum: c790f0f45dc67696dbabf619b2205035
NeedsCompilation: yes
Title: Differential neighbourhood abundance testing on a graph
Description: Milo performs single-cell differential abundance testing.
Cell states are modelled as representative neighbourhoods on a
nearest neighbour graph. Hypothesis testing is performed using
either a negative bionomial generalized linear model or
negative binomial generalized linear mixed model.
biocViews: SingleCell, MultipleComparison, FunctionalGenomics, Software
Author: Mike Morgan [aut, cre] (ORCID:
<https://orcid.org/0000-0003-0757-0711>), Emma Dann [aut, ctb]
Maintainer: Mike Morgan <michael.morgan@abdn.ac.uk>
URL: https://marionilab.github.io/miloR
VignetteBuilder: knitr
BugReports: https://github.com/MarioniLab/miloR/issues
git_url: https://git.bioconductor.org/packages/miloR
git_branch: devel
git_last_commit: 959804d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/miloR_2.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/miloR_2.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/miloR_2.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/miloR_2.3.0.tgz
vignettes: vignettes/miloR/inst/doc/milo_contrasts.html,
vignettes/miloR/inst/doc/milo_demo.html,
vignettes/miloR/inst/doc/milo_gastrulation.html,
vignettes/miloR/inst/doc/milo_glmm.html
vignetteTitles: Using contrasts for differential abundance testing,
Differential abundance testing with Milo, Differential
abundance testing with Milo - Mouse gastrulation example, Mixed
effect models for Milo DA testing
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/miloR/inst/doc/milo_contrasts.R,
vignettes/miloR/inst/doc/milo_demo.R,
vignettes/miloR/inst/doc/milo_gastrulation.R,
vignettes/miloR/inst/doc/milo_glmm.R
importsMe: dandelionR
dependencyCount: 116
Package: mimager
Version: 1.31.0
Depends: Biobase
Imports: BiocGenerics, S4Vectors, preprocessCore, grDevices, methods,
grid, gtable, scales, DBI, affy, affyPLM, oligo, oligoClasses
Suggests: knitr, rmarkdown, BiocStyle, testthat, lintr, Matrix, abind,
affydata, hgu95av2cdf, oligoData, pd.hugene.1.0.st.v1
License: MIT + file LICENSE
Archs: x64
MD5sum: 7e367bff081b072678f5a2b536d73c2c
NeedsCompilation: no
Title: mimager: The Microarray Imager
Description: Easily visualize and inspect microarrays for spatial
artifacts.
biocViews: Infrastructure, Visualization, Microarray
Author: Aaron Wolen [aut, cre, cph]
Maintainer: Aaron Wolen <aaron@wolen.com>
URL: https://github.com/aaronwolen/mimager
VignetteBuilder: knitr
BugReports: https://github.com/aaronwolen/mimager/issues
git_url: https://git.bioconductor.org/packages/mimager
git_branch: devel
git_last_commit: b2c3bfc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mimager_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mimager_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mimager_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mimager_1.31.0.tgz
vignettes: vignettes/mimager/inst/doc/introduction.html
vignetteTitles: mimager overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/mimager/inst/doc/introduction.R
dependencyCount: 75
Package: mina
Version: 1.15.0
Depends: R (>= 4.0.0)
Imports: methods, stats, Rcpp, MCL, RSpectra, apcluster, bigmemory,
foreach, ggplot2, parallel, parallelDist, reshape2, plyr,
biganalytics, stringr, Hmisc, utils
LinkingTo: Rcpp, RcppParallel, RcppArmadillo
Suggests: knitr, rmarkdown
Enhances: doMC
License: GPL
MD5sum: 8f10396e8d39186158bb3c75315c649d
NeedsCompilation: yes
Title: Microbial community dIversity and Network Analysis
Description: An increasing number of microbiome datasets have been
generated and analyzed with the help of rapidly developing
sequencing technologies. At present, analysis of taxonomic
profiling data is mainly conducted using composition-based
methods, which ignores interactions between community members.
Besides this, a lack of efficient ways to compare microbial
interaction networks limited the study of community dynamics.
To better understand how community diversity is affected by
complex interactions between its members, we developed a
framework (Microbial community dIversity and Network Analysis,
mina), a comprehensive framework for microbial community
diversity analysis and network comparison. By defining and
integrating network-derived community features, we greatly
reduce noise-to-signal ratio for diversity analyses. A
bootstrap and permutation-based method was implemented to
assess community network dissimilarities and extract
discriminative features in a statistically principled way.
biocViews: Software, WorkflowStep
Author: Rui Guan [aut, cre], Ruben Garrido-Oter [ctb]
Maintainer: Rui Guan <guan@mpipz.mpg.de>
VignetteBuilder: knitr
BugReports: https://github.com/Guan06/mina
git_url: https://git.bioconductor.org/packages/mina
git_branch: devel
git_last_commit: e5829ce
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mina_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mina_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mina_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mina_1.15.0.tgz
vignettes: vignettes/mina/inst/doc/mina.html
vignetteTitles: Microbial dIversity and Network Analysis with MINA
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mina/inst/doc/mina.R
dependencyCount: 95
Package: MineICA
Version: 1.47.0
Depends: R (>= 2.10), methods, BiocGenerics (>= 0.13.8), Biobase, plyr,
ggplot2, scales, foreach, xtable, biomaRt, gtools, GOstats,
cluster, marray, mclust, RColorBrewer, colorspace, igraph,
Rgraphviz, graph, annotate, Hmisc, fastICA, JADE
Imports: AnnotationDbi, lumi, fpc, lumiHumanAll.db
Suggests: biomaRt, GOstats, cluster, hgu133a.db, mclust, igraph,
breastCancerMAINZ, breastCancerTRANSBIG, breastCancerUPP,
breastCancerVDX, future, future.apply
Enhances: doMC
License: GPL-2
MD5sum: bf39ae61deb73fffdda3a3a8b7a335ea
NeedsCompilation: no
Title: Analysis of an ICA decomposition obtained on genomics data
Description: The goal of MineICA is to perform Independent Component
Analysis (ICA) on multiple transcriptome datasets, integrating
additional data (e.g molecular, clinical and pathological).
This Integrative ICA helps the biological interpretation of the
components by studying their association with variables (e.g
sample annotations) and gene sets, and enables the comparison
of components from different datasets using correlation-based
graph.
biocViews: Visualization, MultipleComparison
Author: Anne Biton
Maintainer: Anne Biton <anne.biton@gmail.com>
git_url: https://git.bioconductor.org/packages/MineICA
git_branch: devel
git_last_commit: 0cd440c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MineICA_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MineICA_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MineICA_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MineICA_1.47.0.tgz
vignettes: vignettes/MineICA/inst/doc/MineICA.pdf
vignetteTitles: MineICA: Independent component analysis of genomic data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MineICA/inst/doc/MineICA.R
dependencyCount: 220
Package: minet
Version: 3.65.0
Imports: infotheo
License: Artistic-2.0
Archs: x64
MD5sum: 99e8824bddd66161401595f5b9e1fdb4
NeedsCompilation: yes
Title: Mutual Information NETworks
Description: This package implements various algorithms for inferring
mutual information networks from data.
biocViews: Microarray, GraphAndNetwork, Network, NetworkInference
Author: Patrick E. Meyer, Frederic Lafitte, Gianluca Bontempi
Maintainer: Patrick E. Meyer <software@meyerp.com>
URL: http://minet.meyerp.com
git_url: https://git.bioconductor.org/packages/minet
git_branch: devel
git_last_commit: 7fc4954
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/minet_3.65.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/minet_3.65.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/minet_3.65.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/minet_3.65.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: BUS, geNetClassifier, netresponse
importsMe: BioNERO, epiNEM, RTN, PRANA, TGS
suggestsMe: CNORfeeder, TCGAbiolinks, dnapath, WGCNA
dependencyCount: 1
Package: minfi
Version: 1.53.1
Depends: methods, BiocGenerics (>= 0.15.3), GenomicRanges,
SummarizedExperiment (>= 1.1.6), Biostrings, bumphunter (>=
1.1.9)
Imports: S4Vectors, GenomeInfoDb, Biobase (>= 2.33.2), IRanges,
beanplot, RColorBrewer, lattice, nor1mix, siggenes, limma,
preprocessCore, illuminaio (>= 0.23.2), DelayedMatrixStats (>=
1.3.4), mclust, genefilter, nlme, reshape, MASS, quadprog,
data.table, GEOquery, stats, grDevices, graphics, utils,
DelayedArray (>= 0.15.16), HDF5Array, BiocParallel
Suggests: IlluminaHumanMethylation450kmanifest (>= 0.2.0),
IlluminaHumanMethylation450kanno.ilmn12.hg19 (>= 0.2.1),
minfiData (>= 0.18.0), minfiDataEPIC, FlowSorted.Blood.450k (>=
1.0.1), RUnit, digest, BiocStyle, knitr, rmarkdown, tools
License: Artistic-2.0
MD5sum: c22693e1aff1841fdbcffaa6282166d8
NeedsCompilation: no
Title: Analyze Illumina Infinium DNA methylation arrays
Description: Tools to analyze & visualize Illumina Infinium methylation
arrays.
biocViews: ImmunoOncology, DNAMethylation, DifferentialMethylation,
Epigenetics, Microarray, MethylationArray, MultiChannel,
TwoChannel, DataImport, Normalization, Preprocessing,
QualityControl
Author: Kasper Daniel Hansen [cre, aut], Martin Aryee [aut], Rafael A.
Irizarry [aut], Andrew E. Jaffe [ctb], Jovana Maksimovic [ctb],
E. Andres Houseman [ctb], Jean-Philippe Fortin [ctb], Tim
Triche [ctb], Shan V. Andrews [ctb], Peter F. Hickey [ctb]
Maintainer: Kasper Daniel Hansen <kasperdanielhansen@gmail.com>
URL: https://github.com/hansenlab/minfi
VignetteBuilder: knitr
BugReports: https://github.com/hansenlab/minfi/issues
git_url: https://git.bioconductor.org/packages/minfi
git_branch: devel
git_last_commit: 3852366
git_last_commit_date: 2024-11-19
Date/Publication: 2024-11-19
source.ver: src/contrib/minfi_1.53.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/minfi_1.53.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/minfi_1.53.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/minfi_1.53.1.tgz
vignettes: vignettes/minfi/inst/doc/minfi.html
vignetteTitles: minfi User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/minfi/inst/doc/minfi.R
dependsOnMe: bigmelon, ChAMP, conumee, methylumi, REMP,
IlluminaHumanMethylation27kanno.ilmn12.hg19,
IlluminaHumanMethylation27kmanifest,
IlluminaHumanMethylation450kanno.ilmn12.hg19,
IlluminaHumanMethylation450kmanifest,
IlluminaHumanMethylationEPICanno.ilm10b2.hg19,
IlluminaHumanMethylationEPICanno.ilm10b3.hg19,
IlluminaHumanMethylationEPICanno.ilm10b4.hg19,
IlluminaHumanMethylationEPICmanifest,
IlluminaHumanMethylationEPICv2anno.20a1.hg38,
IlluminaHumanMethylationEPICv2manifest,
IlluminaHumanMethylationMSAanno.ilm10a1.hg38,
IlluminaHumanMethylationMSAmanifest, BeadSorted.Saliva.EPIC,
FlowSorted.Blood.450k, FlowSorted.Blood.EPIC,
FlowSorted.CordBlood.450k, FlowSorted.CordBloodCombined.450k,
FlowSorted.CordBloodNorway.450k, FlowSorted.DLPFC.450k,
minfiData, minfiDataEPIC
importsMe: deconvR, DMRcate, epimutacions, funtooNorm, iNETgrate, MEAL,
MEAT, MethylAid, methylCC, methylclock, methylumi, missMethyl,
quantro, recountmethylation, shinyepico, shinyMethyl, skewr,
HiBED, EMAS
suggestsMe: epivizr, epivizrChart, GeoTcgaData, Harman, mCSEA,
MultiDataSet, planet, RnBeads, brgedata, epimutacionsData,
GSE159526, MLML2R
dependencyCount: 144
Package: MinimumDistance
Version: 1.51.0
Depends: R (>= 3.5.0), VanillaICE (>= 1.47.1)
Imports: methods, BiocGenerics, MatrixGenerics, Biobase, S4Vectors (>=
0.23.18), IRanges, GenomeInfoDb, GenomicRanges (>= 1.17.16),
SummarizedExperiment (>= 1.15.4), oligoClasses, DNAcopy, ff,
foreach, matrixStats, lattice, data.table, grid, stats, utils
Suggests: human610quadv1bCrlmm (>= 1.0.3), BSgenome.Hsapiens.UCSC.hg18,
BSgenome.Hsapiens.UCSC.hg19, RUnit
Enhances: snow, doSNOW
License: Artistic-2.0
Archs: x64
MD5sum: 6c446d351a854c25de7b9cd7848bf916
NeedsCompilation: no
Title: A Package for De Novo CNV Detection in Case-Parent Trios
Description: Analysis of de novo copy number variants in trios from
high-dimensional genotyping platforms.
biocViews: Microarray, SNP, CopyNumberVariation
Author: Robert B Scharpf and Ingo Ruczinski
Maintainer: Robert Scharpf <rscharpf@jhu.edu>
git_url: https://git.bioconductor.org/packages/MinimumDistance
git_branch: devel
git_last_commit: 4c9f42d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MinimumDistance_1.51.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MinimumDistance_1.51.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MinimumDistance_1.51.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MinimumDistance_1.51.0.tgz
vignettes: vignettes/MinimumDistance/inst/doc/MinimumDistance.pdf
vignetteTitles: Detection of de novo copy number alterations in
case-parent trios
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MinimumDistance/inst/doc/MinimumDistance.R
dependencyCount: 97
Package: MiPP
Version: 1.79.0
Depends: R (>= 2.4)
Imports: Biobase, e1071, MASS, stats
License: GPL (>= 2)
MD5sum: 00a2e2ed6c1148ed40aa7ad33ce803f2
NeedsCompilation: no
Title: Misclassification Penalized Posterior Classification
Description: This package finds optimal sets of genes that seperate
samples into two or more classes.
biocViews: Microarray, Classification
Author: HyungJun Cho <hj4cho@korea.ac.kr>, Sukwoo Kim
<s4kim@korea.ac.kr>, Mat Soukup <soukup@fda.gov>, and Jae K.
Lee <jaeklee@virginia.edu>
Maintainer: Sukwoo Kim <s4kim@korea.ac.kr>
URL:
http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/
git_url: https://git.bioconductor.org/packages/MiPP
git_branch: devel
git_last_commit: 6b61592
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MiPP_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MiPP_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MiPP_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MiPP_1.79.0.tgz
vignettes: vignettes/MiPP/inst/doc/MiPP.pdf
vignetteTitles: MiPP Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 12
Package: miQC
Version: 1.15.0
Depends: R (>= 3.5.0)
Imports: SingleCellExperiment, flexmix, ggplot2, splines
Suggests: scRNAseq, scater, BiocStyle, knitr, rmarkdown
License: BSD_3_clause + file LICENSE
Archs: x64
MD5sum: ca67888bba35670b75acf030ff0af54f
NeedsCompilation: no
Title: Flexible, probabilistic metrics for quality control of scRNA-seq
data
Description: Single-cell RNA-sequencing (scRNA-seq) has made it
possible to profile gene expression in tissues at high
resolution. An important preprocessing step prior to
performing downstream analyses is to identify and remove cells
with poor or degraded sample quality using quality control (QC)
metrics. Two widely used QC metrics to identify a
‘low-quality’ cell are (i) if the cell includes a high
proportion of reads that map to mitochondrial DNA encoded genes
(mtDNA) and (ii) if a small number of genes are detected. miQC
is data-driven QC metric that jointly models both the
proportion of reads mapping to mtDNA and the number of detected
genes with mixture models in a probabilistic framework to
predict the low-quality cells in a given dataset.
biocViews: SingleCell, QualityControl, GeneExpression, Preprocessing,
Sequencing
Author: Ariel Hippen [aut, cre], Stephanie Hicks [aut]
Maintainer: Ariel Hippen <ariel.hippen@gmail.com>
URL: https://github.com/greenelab/miQC
VignetteBuilder: knitr
BugReports: https://github.com/greenelab/miQC/issues
git_url: https://git.bioconductor.org/packages/miQC
git_branch: devel
git_last_commit: c01c77f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/miQC_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/miQC_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/miQC_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/miQC_1.15.0.tgz
vignettes: vignettes/miQC/inst/doc/miQC.html
vignetteTitles: miQC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/miQC/inst/doc/miQC.R
dependencyCount: 66
Package: MIRA
Version: 1.29.0
Depends: R (>= 3.5)
Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, data.table,
ggplot2, Biobase, stats, bsseq, methods
Suggests: knitr, parallel, testthat, BiocStyle, rmarkdown,
AnnotationHub, LOLA
License: GPL-3
MD5sum: 0538d6088b26105cb62f537d3a409580
NeedsCompilation: no
Title: Methylation-Based Inference of Regulatory Activity
Description: DNA methylation contains information about the regulatory
state of the cell. MIRA aggregates genome-scale DNA methylation
data into a DNA methylation profile for a given region set with
shared biological annotation. Using this profile, MIRA infers
and scores the collective regulatory activity for the region
set. MIRA facilitates regulatory analysis in situations where
classical regulatory assays would be difficult and allows
public sources of region sets to be leveraged for novel insight
into the regulatory state of DNA methylation datasets.
biocViews: ImmunoOncology, DNAMethylation, GeneRegulation,
GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq,
MethylSeq, Sequencing, Epigenetics, Coverage
Author: Nathan Sheffield <http://www.databio.org> [aut], Christoph Bock
[ctb], John Lawson [aut, cre]
Maintainer: John Lawson <jtl2hk@virginia.edu>
URL: http://databio.org/mira
VignetteBuilder: knitr
BugReports: https://github.com/databio/MIRA
git_url: https://git.bioconductor.org/packages/MIRA
git_branch: devel
git_last_commit: 699390a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MIRA_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MIRA_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MIRA_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MIRA_1.29.0.tgz
vignettes: vignettes/MIRA/inst/doc/BiologicalApplication.html,
vignettes/MIRA/inst/doc/GettingStarted.html
vignetteTitles: Applying MIRA to a Biological Question, Getting Started
with Methylation-based Inference of Regulatory Activity
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MIRA/inst/doc/BiologicalApplication.R,
vignettes/MIRA/inst/doc/GettingStarted.R
importsMe: COCOA
dependencyCount: 105
Package: MiRaGE
Version: 1.49.0
Depends: R (>= 3.1.0), Biobase(>= 2.23.3)
Imports: BiocGenerics, S4Vectors, AnnotationDbi, BiocManager
Suggests: seqinr (>= 3.0.7), biomaRt (>= 2.19.1), GenomicFeatures (>=
1.15.4), Biostrings (>= 2.31.3), BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Mmusculus.UCSC.mm10, miRNATarget, humanStemCell,
IRanges, GenomicRanges (>= 1.8.3), BSgenome,
beadarrayExampleData
License: GPL
MD5sum: 34d1d98a87d6700f85bd7d68e29f8667
NeedsCompilation: no
Title: MiRNA Ranking by Gene Expression
Description: The package contains functions for inferece of target gene
regulation by miRNA, based on only target gene expression
profile.
biocViews: ImmunoOncology, Microarray, GeneExpression, RNASeq,
Sequencing, SAGE
Author: Y-h. Taguchi <tag@granular.com>
Maintainer: Y-h. Taguchi <tag@granular.com>
git_url: https://git.bioconductor.org/packages/MiRaGE
git_branch: devel
git_last_commit: 99be42d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MiRaGE_1.49.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MiRaGE_1.49.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MiRaGE_1.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MiRaGE_1.49.0.tgz
vignettes: vignettes/MiRaGE/inst/doc/MiRaGE.pdf
vignetteTitles: How to use MiRaGE Package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MiRaGE/inst/doc/MiRaGE.R
dependencyCount: 46
Package: miRBaseConverter
Version: 1.31.0
Depends: R (>= 3.4)
Imports: stats
Suggests: BiocGenerics, RUnit, knitr, rtracklayer, utils, rmarkdown
License: GPL (>= 2)
MD5sum: 8af75bd48820d767827c48df9c187f62
NeedsCompilation: no
Title: A comprehensive and high-efficiency tool for converting and
retrieving the information of miRNAs in different miRBase
versions
Description: A comprehensive tool for converting and retrieving the
miRNA Name, Accession, Sequence, Version, History and Family
information in different miRBase versions. It can process a
huge number of miRNAs in a short time without other depends.
biocViews: Software, miRNA
Author: Taosheng Xu Taosheng Xu [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8283-7274>)
Maintainer: Taosheng Xu Taosheng Xu <taosheng.x@gmail.com>
URL: https://github.com/taoshengxu/miRBaseConverter
VignetteBuilder: knitr
BugReports: https://github.com/taoshengxu/miRBaseConverter/issues
git_url: https://git.bioconductor.org/packages/miRBaseConverter
git_branch: devel
git_last_commit: 163132a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/miRBaseConverter_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/miRBaseConverter_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/miRBaseConverter_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/miRBaseConverter_1.31.0.tgz
vignettes:
vignettes/miRBaseConverter/inst/doc/miRBaseConverter-vignette.html
vignetteTitles: "miRBaseConverter: A comprehensive and high-efficiency
tool for converting and retrieving the information of miRNAs in
different miRBase versions"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/miRBaseConverter/inst/doc/miRBaseConverter-vignette.R
suggestsMe: EpiMix
dependencyCount: 1
Package: miRcomp
Version: 1.37.0
Depends: R (>= 3.2), Biobase (>= 2.22.0), miRcompData
Imports: utils, methods, graphics, KernSmooth, stats
Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics, shiny
License: GPL-3 | file LICENSE
MD5sum: fc5d44e1b95b91ffcdf4aec16bc7f2e3
NeedsCompilation: no
Title: Tools to assess and compare miRNA expression estimatation
methods
Description: Based on a large miRNA dilution study, this package
provides tools to read in the raw amplification data and use
these data to assess the performance of methods that estimate
expression from the amplification curves.
biocViews: Software, qPCR, Preprocessing, QualityControl
Author: Matthew N. McCall <mccallm@gmail.com>, Lauren Kemperman
<lkemperm@u.rochester.edu>
Maintainer: Matthew N. McCall <mccallm@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/miRcomp
git_branch: devel
git_last_commit: f81710e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/miRcomp_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/miRcomp_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/miRcomp_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/miRcomp_1.37.0.tgz
vignettes: vignettes/miRcomp/inst/doc/miRcomp.html
vignetteTitles: Assessment and comparison of miRNA expression
estimation methods (miRcomp)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/miRcomp/inst/doc/miRcomp.R
dependencyCount: 9
Package: mirIntegrator
Version: 1.37.0
Depends: R (>= 3.3)
Imports: graph,ROntoTools, ggplot2, org.Hs.eg.db, AnnotationDbi,
Rgraphviz
Suggests: RUnit, BiocGenerics
License: GPL (>=3)
MD5sum: 743b6444050b42ee24034c9c9549767f
NeedsCompilation: no
Title: Integrating microRNA expression into signaling pathways for
pathway analysis
Description: Tools for augmenting signaling pathways to perform pathway
analysis of microRNA and mRNA expression levels.
biocViews: Network, Microarray, GraphAndNetwork, Pathways, KEGG
Author: Diana Diaz <dmd at wayne dot edu>
Maintainer: Diana Diaz <dmd@wayne.edu>
URL: http://datad.github.io/mirIntegrator/
git_url: https://git.bioconductor.org/packages/mirIntegrator
git_branch: devel
git_last_commit: f0be694
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mirIntegrator_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mirIntegrator_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mirIntegrator_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mirIntegrator_1.37.0.tgz
vignettes: vignettes/mirIntegrator/inst/doc/mirIntegrator.pdf
vignetteTitles: mirIntegrator Overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mirIntegrator/inst/doc/mirIntegrator.R
dependencyCount: 77
Package: MIRit
Version: 1.3.4
Depends: MultiAssayExperiment, R (>= 4.4.0)
Imports: AnnotationDbi, BiocFileCache, BiocParallel, DESeq2, edgeR,
fgsea, genekitr, geneset, ggplot2, ggpubr, graph, graphics,
graphite, grDevices, httr, limma, methods, Rcpp, Rgraphviz (>=
2.44.0), rlang, stats, utils
LinkingTo: Rcpp
Suggests: BiocStyle, biomaRt, BSgenome.Hsapiens.UCSC.hg38,
GenomicRanges, ggrepel, ggridges, Gviz, gwasrapidd, knitr,
MonoPoly, org.Hs.eg.db, rmarkdown, testthat (>= 3.0.0)
License: GPL (>= 3)
MD5sum: 16c61ecded1a1518db38e8727456c492
NeedsCompilation: yes
Title: Integrate microRNA and gene expression to decipher pathway
complexity
Description: MIRit is an R package that provides several methods for
investigating the relationships between miRNAs and genes in
different biological conditions. In particular, MIRit allows to
explore the functions of dysregulated miRNAs, and makes it
possible to identify miRNA-gene regulatory axes that control
biological pathways, thus enabling the users to unveil the
complexity of miRNA biology. MIRit is an all-in-one framework
that aims to help researchers in all the central aspects of an
integrative miRNA-mRNA analyses, from differential expression
analysis to network characterization.
biocViews: Software, GeneRegulation, NetworkEnrichment,
NetworkInference, Epigenetics, FunctionalGenomics,
SystemsBiology, Network, Pathways, GeneExpression,
DifferentialExpression
Author: Jacopo Ronchi [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5520-4631>), Maria Foti [fnd]
(ORCID: <https://orcid.org/0000-0002-4481-1900>)
Maintainer: Jacopo Ronchi <jacopo.ronchi@unimib.it>
URL: https://github.com/jacopo-ronchi/MIRit
VignetteBuilder: knitr
BugReports: https://github.com/jacopo-ronchi/MIRit/issues
git_url: https://git.bioconductor.org/packages/MIRit
git_branch: devel
git_last_commit: 65dbe7b
git_last_commit_date: 2025-03-19
Date/Publication: 2025-03-19
source.ver: src/contrib/MIRit_1.3.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MIRit_1.3.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MIRit_1.3.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MIRit_1.3.4.tgz
vignettes: vignettes/MIRit/inst/doc/MIRit.html
vignetteTitles: Integrate miRNA and gene expression data with MIRit
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MIRit/inst/doc/MIRit.R
dependencyCount: 196
Package: miRLAB
Version: 1.37.0
Imports: methods, stats, utils, RCurl, httr, stringr, Hmisc, energy,
entropy, gplots, glmnet, impute, limma,
pcalg,TCGAbiolinks,dplyr,SummarizedExperiment, ctc,
InvariantCausalPrediction, Category, GOstats, org.Hs.eg.db
Suggests: knitr,BiocGenerics, AnnotationDbi,RUnit,rmarkdown
License: GPL (>=2)
MD5sum: 480edb6ab8ee3290d8b96efb20418223
NeedsCompilation: no
Title: Dry lab for exploring miRNA-mRNA relationships
Description: Provide tools exploring miRNA-mRNA relationships,
including popular miRNA target prediction methods, ensemble
methods that integrate individual methods, functions to get
data from online resources, functions to validate the results,
and functions to conduct enrichment analyses.
biocViews: miRNA, GeneExpression, NetworkInference, Network
Author: Thuc Duy Le, Junpeng Zhang, Mo Chen, Vu Viet Hoang Pham
Maintainer: Thuc Duy Le <Thuc.Le@unisa.edu.au>
URL: https://github.com/pvvhoang/miRLAB
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/miRLAB
git_branch: devel
git_last_commit: 8fe9ef1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/miRLAB_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/miRLAB_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/miRLAB_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/miRLAB_1.37.0.tgz
vignettes: vignettes/miRLAB/inst/doc/miRLAB-vignette.html
vignetteTitles: miRLAB
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/miRLAB/inst/doc/miRLAB-vignette.R
dependencyCount: 196
Package: miRNAmeConverter
Version: 1.35.0
Depends: miRBaseVersions.db
Imports: DBI, AnnotationDbi, reshape2
Suggests: methods, testthat, knitr, rmarkdown
License: Artistic-2.0
MD5sum: fdc01469baf2925d054a4d02ecad0b93
NeedsCompilation: no
Title: Convert miRNA Names to Different miRBase Versions
Description: Translating mature miRNA names to different miRBase
versions, sequence retrieval, checking names for validity and
detecting miRBase version of a given set of names (data from
http://www.mirbase.org/).
biocViews: Preprocessing, miRNA
Author: Stefan Haunsberger [aut, cre]
Maintainer: Stefan J. Haunsberger <stefan.haunsberger@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/miRNAmeConverter
git_branch: devel
git_last_commit: 9ede4bb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/miRNAmeConverter_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/miRNAmeConverter_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/miRNAmeConverter_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/miRNAmeConverter_1.35.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 53
Package: miRNApath
Version: 1.67.0
Depends: methods, R(>= 2.7.0)
License: LGPL-2.1
MD5sum: 763f8cf122ffc9d261bc827e60f77d34
NeedsCompilation: no
Title: miRNApath: Pathway Enrichment for miRNA Expression Data
Description: This package provides pathway enrichment techniques for
miRNA expression data. Specifically, the set of methods handles
the many-to-many relationship between miRNAs and the multiple
genes they are predicted to target (and thus affect.) It also
handles the gene-to-pathway relationships separately. Both
steps are designed to preserve the additive effects of miRNAs
on genes, many miRNAs affecting one gene, one miRNA affecting
multiple genes, or many miRNAs affecting many genes.
biocViews: Annotation, Pathways, DifferentialExpression,
NetworkEnrichment, miRNA
Author: James M. Ward <jmw86069@gmail.com> with contributions from
Yunling Shi, Cindy Richards, John P. Cogswell
Maintainer: James M. Ward <jmw86069@gmail.com>
git_url: https://git.bioconductor.org/packages/miRNApath
git_branch: devel
git_last_commit: 510d73a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/miRNApath_1.67.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/miRNApath_1.67.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/miRNApath_1.67.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/miRNApath_1.67.0.tgz
vignettes: vignettes/miRNApath/inst/doc/miRNApath.pdf
vignetteTitles: miRNApath: Pathway Enrichment for miRNA Expression Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/miRNApath/inst/doc/miRNApath.R
dependencyCount: 1
Package: miRNAtap
Version: 1.41.0
Depends: R (>= 3.3.0), AnnotationDbi
Imports: DBI, RSQLite, stringr, sqldf, plyr, methods
Suggests: topGO, org.Hs.eg.db, miRNAtap.db, testthat
License: GPL-2
Archs: x64
MD5sum: 63262de4887759a5cae1ffbf02f2c939
NeedsCompilation: no
Title: miRNAtap: microRNA Targets - Aggregated Predictions
Description: The package facilitates implementation of workflows
requiring miRNA predictions, it allows to integrate ranked
miRNA target predictions from multiple sources available online
and aggregate them with various methods which improves quality
of predictions above any of the single sources. Currently
predictions are available for Homo sapiens, Mus musculus and
Rattus norvegicus (the last one through homology translation).
biocViews: Software, Classification, Microarray, Sequencing, miRNA
Author: Maciej Pajak, T. Ian Simpson
Maintainer: T. Ian Simpson <ian.simpson@ed.ac.uk>
git_url: https://git.bioconductor.org/packages/miRNAtap
git_branch: devel
git_last_commit: 18c2dc7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/miRNAtap_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/miRNAtap_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/miRNAtap_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/miRNAtap_1.41.0.tgz
vignettes: vignettes/miRNAtap/inst/doc/miRNAtap.pdf
vignetteTitles: miRNAtap
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/miRNAtap/inst/doc/miRNAtap.R
dependsOnMe: miRNAtap.db
importsMe: miRNAtap.db
dependencyCount: 54
Package: miRSM
Version: 2.3.0
Depends: R (>= 4.4.0)
Imports: WGCNA, flashClust, dynamicTreeCut, GFA, igraph, linkcomm, MCL,
fabia, NMF, biclust, iBBiG, BicARE, isa2, s4vd, BiBitR, rqubic,
Biobase, PMA, stats, dbscan, subspace, mclust, SOMbrero,
ppclust, Rcpp, utils, SummarizedExperiment, GSEABase,
org.Hs.eg.db, clusterProfiler, ReactomePA, DOSE,
MatrixCorrelation, energy
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: GPL-3
MD5sum: cd76f3ef01f5d2a392b8b476f68e648f
NeedsCompilation: yes
Title: Inferring miRNA sponge modules in heterogeneous data
Description: The package aims to identify miRNA sponge or ceRNA modules
in heterogeneous data. It provides several functions to study
miRNA sponge modules at single-sample and multi-sample levels,
including popular methods for inferring gene modules (candidate
miRNA sponge or ceRNA modules), and two functions to identify
miRNA sponge modules at single-sample and multi-sample levels,
as well as several functions to conduct modular analysis of
miRNA sponge modules.
biocViews: GeneExpression, BiomedicalInformatics, Clustering,
GeneSetEnrichment, Microarray, Software, GeneRegulation,
GeneTarget
Author: Junpeng Zhang [aut, cre]
Maintainer: Junpeng Zhang <zjp@dali.edu.cn>
URL: https://github.com/zhangjunpeng411/miRSM
VignetteBuilder: knitr
BugReports: https://github.com/zhangjunpeng411/miRSM/issues
git_url: https://git.bioconductor.org/packages/miRSM
git_branch: devel
git_last_commit: b0bce7f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/miRSM_2.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/miRSM_2.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/miRSM_2.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/miRSM_2.3.0.tgz
vignettes: vignettes/miRSM/inst/doc/miRSM.html
vignetteTitles: miRSM: inferring miRNA sponge modules in heterogeneous
data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/miRSM/inst/doc/miRSM.R
dependencyCount: 262
Package: miRspongeR
Version: 2.11.0
Depends: R (>= 4.4.0)
Imports: corpcor, SPONGE, parallel, igraph, MCL, clusterProfiler,
ReactomePA, DOSE, survival, grDevices, graphics, stats,
linkcomm, utils, Rcpp, org.Hs.eg.db, foreach, doParallel
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: GPL-3
MD5sum: 0f1b678ed813c4c15ef45e35c02c79e4
NeedsCompilation: yes
Title: Identification and analysis of miRNA sponge regulation
Description: This package provides several functions to explore miRNA
sponge (also called ceRNA or miRNA decoy) regulation from
putative miRNA-target interactions or/and transcriptomics data
(including bulk, single-cell and spatial gene expression data).
It provides eight popular methods for identifying miRNA sponge
interactions, and an integrative method to integrate miRNA
sponge interactions from different methods, as well as the
functions to validate miRNA sponge interactions, and infer
miRNA sponge modules, conduct enrichment analysis of miRNA
sponge modules, and conduct survival analysis of miRNA sponge
modules. By using a sample control variable strategy, it
provides a function to infer sample-specific miRNA sponge
interactions. In terms of sample-specific miRNA sponge
interactions, it implements three similarity methods to
construct sample-sample correlation network.
biocViews: GeneExpression, BiomedicalInformatics, NetworkEnrichment,
Survival, Microarray, Software, SingleCell, Spatial, RNASeq
Author: Junpeng Zhang [aut, cre]
Maintainer: Junpeng Zhang <zjp@dali.edu.cn>
URL: <https://github.com/zhangjunpeng411/miRspongeR>
VignetteBuilder: knitr
BugReports: https://github.com/zhangjunpeng411/miRspongeR/issues
git_url: https://git.bioconductor.org/packages/miRspongeR
git_branch: devel
git_last_commit: c3dd126
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/miRspongeR_2.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/miRspongeR_2.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/miRspongeR_2.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/miRspongeR_2.11.0.tgz
vignettes: vignettes/miRspongeR/inst/doc/miRspongeR.html
vignetteTitles: Identification and analysis of miRNA sponge regulation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/miRspongeR/inst/doc/miRspongeR.R
dependencyCount: 279
Package: mirTarRnaSeq
Version: 1.15.0
Depends: R (>= 4.1.0), ggplot2
Imports: purrr, MASS, pscl, assertthat, caTools, dplyr, pheatmap,
reshape2, corrplot, grDevices, graphics, stats, utils,
data.table, R.utils, viridis
Suggests: BiocStyle, knitr, rmarkdown, R.cache, SPONGE
License: MIT + file LICENSE
MD5sum: 971c94fc259fce478aec52cbb7b75bde
NeedsCompilation: no
Title: mirTarRnaSeq
Description: mirTarRnaSeq R package can be used for interactive mRNA
miRNA sequencing statistical analysis. This package utilizes
expression or differential expression mRNA and miRNA sequencing
results and performs interactive correlation and various GLMs
(Regular GLM, Multivariate GLM, and Interaction GLMs ) analysis
between mRNA and miRNA expriments. These experiments can be
time point experiments, and or condition expriments.
biocViews: miRNA, Regression, Software, Sequencing, SmallRNA,
TimeCourse, DifferentialExpression
Author: Mercedeh Movassagh [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7690-0230>), Sarah Morton [aut],
Rafael Irizarry [aut], Jeffrey Bailey [aut], Joseph N Paulson
[aut]
Maintainer: Mercedeh Movassagh <mercedeh@ds.dfci.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/mirTarRnaSeq
git_branch: devel
git_last_commit: 60572a9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mirTarRnaSeq_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mirTarRnaSeq_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mirTarRnaSeq_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mirTarRnaSeq_1.15.0.tgz
vignettes: vignettes/mirTarRnaSeq/inst/doc/mirTarRnaSeq.pdf
vignetteTitles: mirTarRnaSeq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/mirTarRnaSeq/inst/doc/mirTarRnaSeq.R
dependencyCount: 58
Package: missMethyl
Version: 1.41.3
Depends: R (>= 3.6.0), IlluminaHumanMethylation450kanno.ilmn12.hg19,
IlluminaHumanMethylationEPICanno.ilm10b4.hg19,
IlluminaHumanMethylationEPICv2anno.20a1.hg38
Imports: AnnotationDbi, BiasedUrn, Biobase, BiocGenerics,
GenomicRanges, GO.db, IlluminaHumanMethylation450kmanifest,
IlluminaHumanMethylationEPICmanifest,
IlluminaHumanMethylationEPICv2manifest, IRanges, limma,
methods, methylumi, minfi, org.Hs.eg.db, ruv, S4Vectors,
statmod, stringr, SummarizedExperiment
Suggests: BiocStyle, edgeR, knitr, minfiData, rmarkdown,
tweeDEseqCountData, DMRcate, ExperimentHub
License: GPL-2
MD5sum: e481ab7ec89c96bb48e42a1cf0db0ceb
NeedsCompilation: no
Title: Analysing Illumina HumanMethylation BeadChip Data
Description: Normalisation, testing for differential variability and
differential methylation and gene set testing for data from
Illumina's Infinium HumanMethylation arrays. The normalisation
procedure is subset-quantile within-array normalisation (SWAN),
which allows Infinium I and II type probes on a single array to
be normalised together. The test for differential variability
is based on an empirical Bayes version of Levene's test.
Differential methylation testing is performed using RUV, which
can adjust for systematic errors of unknown origin in
high-dimensional data by using negative control probes. Gene
ontology analysis is performed by taking into account the
number of probes per gene on the array, as well as taking into
account multi-gene associated probes.
biocViews: Normalization, DNAMethylation, MethylationArray,
GenomicVariation, GeneticVariability, DifferentialMethylation,
GeneSetEnrichment
Author: Belinda Phipson and Jovana Maksimovic
Maintainer: Belinda Phipson <phipson.b@wehi.edu.au>, Jovana Maksimovic
<jovana.maksimovic@petermac.org>, Andrew Lonsdale
<andrew.lonsdale@petermac.org>, Calandra Grima
<calandra.grima@petermac.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/missMethyl
git_branch: devel
git_last_commit: 010948c
git_last_commit_date: 2025-03-02
Date/Publication: 2025-03-04
source.ver: src/contrib/missMethyl_1.41.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/missMethyl_1.41.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/missMethyl_1.41.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/missMethyl_1.41.3.tgz
vignettes: vignettes/missMethyl/inst/doc/missMethyl.html
vignetteTitles: missMethyl: Analysing Illumina HumanMethylation
BeadChip Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/missMethyl/inst/doc/missMethyl.R
importsMe: DMRcate, MEAL, methylGSA
suggestsMe: RnBeads
dependencyCount: 170
Package: missRows
Version: 1.27.0
Depends: R (>= 3.5), methods, ggplot2, grDevices, MultiAssayExperiment
Imports: plyr, stats, gtools, S4Vectors
Suggests: BiocStyle, knitr, testthat
License: Artistic-2.0
Archs: x64
MD5sum: 25d78c7e290b7a15bfe385d05d1ce732
NeedsCompilation: no
Title: Handling Missing Individuals in Multi-Omics Data Integration
Description: The missRows package implements the MI-MFA method to deal
with missing individuals ('biological units') in multi-omics
data integration. The MI-MFA method generates multiple imputed
datasets from a Multiple Factor Analysis model, then the yield
results are combined in a single consensus solution. The
package provides functions for estimating coordinates of
individuals and variables, imputing missing individuals, and
various diagnostic plots to inspect the pattern of missingness
and visualize the uncertainty due to missing values.
biocViews: Software, StatisticalMethod, DimensionReduction,
PrincipalComponent, MathematicalBiology, Visualization
Author: Ignacio Gonzalez and Valentin Voillet
Maintainer: Gonzalez Ignacio <ignacio.gonzalez@bbox.fr>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/missRows
git_branch: devel
git_last_commit: 560ef6b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/missRows_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/missRows_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/missRows_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/missRows_1.27.0.tgz
vignettes: vignettes/missRows/inst/doc/missRows.pdf
vignetteTitles: missRows
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/missRows/inst/doc/missRows.R
dependencyCount: 74
Package: mist
Version: 0.99.18
Depends: R (>= 4.5.0)
Imports: BiocParallel, MCMCpack, Matrix, S4Vectors, methods,
rtracklayer, car, mvtnorm, SummarizedExperiment,
SingleCellExperiment, BiocGenerics, stats, rlang
Suggests: knitr, rmarkdown, RUnit, ggplot2, BiocStyle
License: MIT + file LICENSE
Archs: x64
MD5sum: 7ba7aa86a511d358856f8e3e49c6a16a
NeedsCompilation: no
Title: Differential Methylation Analysis for scDNAm Data
Description: mist (Methylation Inference for Single-cell along
Trajectory) is a hierarchical Bayesian framework for modeling
DNA methylation trajectories and performing differential
methylation (DM) analysis in single-cell DNA methylation
(scDNAm) data. It estimates developmental-stage-specific
variations, identifies genomic features with drastic changes
along pseudotime, and, for two phenotypic groups, detects
features with distinct temporal methylation patterns. mist uses
Gibbs sampling to estimate parameters for temporal changes and
stage-specific variations.
biocViews: Epigenetics, DifferentialMethylation, DNAMethylation,
SingleCell, Software
Author: Daoyu Duan [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3147-2006>)
Maintainer: Daoyu Duan <dxd429@case.edu>
URL: https://https://github.com/dxd429/mist
VignetteBuilder: knitr
BugReports: https://https://github.com/dxd429/mist/issues
git_url: https://git.bioconductor.org/packages/mist
git_branch: devel
git_last_commit: 85e7fb6
git_last_commit_date: 2025-01-21
Date/Publication: 2025-01-22
source.ver: src/contrib/mist_0.99.18.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mist_0.99.18.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mist_0.99.18.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mist_0.99.18.tgz
vignettes: vignettes/mist/inst/doc/mist_vignette.html
vignetteTitles: mist_vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/mist/inst/doc/mist_vignette.R
dependencyCount: 121
Package: mistyR
Version: 1.15.0
Depends: R (>= 4.0)
Imports: assertthat, caret, deldir, digest, distances, dplyr (>=
1.1.0), filelock, furrr (>= 0.2.0), ggplot2, methods, purrr,
ranger, readr (>= 2.0.0), ridge, rlang, rlist, R.utils, stats,
stringr, tibble, tidyr, tidyselect (>= 1.2.0), utils, withr
Suggests: BiocStyle, covr, earth, future, igraph (>= 1.2.7), iml,
kernlab, knitr, MASS, rmarkdown, RSNNS, testthat (>= 3.0.0),
xgboost
License: GPL-3
MD5sum: b05d468c6b542805b9a2f5ad6f453bc9
NeedsCompilation: no
Title: Multiview Intercellular SpaTial modeling framework
Description: mistyR is an implementation of the Multiview Intercellular
SpaTialmodeling framework (MISTy). MISTy is an explainable
machine learning framework for knowledge extraction and
analysis of single-cell, highly multiplexed, spatially resolved
data. MISTy facilitates an in-depth understanding of marker
interactions by profiling the intra- and intercellular
relationships. MISTy is a flexible framework able to process a
custom number of views. Each of these views can describe a
different spatial context, i.e., define a relationship among
the observed expressions of the markers, such as intracellular
regulation or paracrine regulation, but also, the views can
also capture cell-type specific relationships, capture
relations between functional footprints or focus on relations
between different anatomical regions. Each MISTy view is
considered as a potential source of variability in the measured
marker expressions. Each MISTy view is then analyzed for its
contribution to the total expression of each marker and is
explained in terms of the interactions with other measurements
that led to the observed contribution.
biocViews: Software, BiomedicalInformatics, CellBiology,
SystemsBiology, Regression, DecisionTree, SingleCell, Spatial
Author: Jovan Tanevski [cre, aut] (ORCID:
<https://orcid.org/0000-0001-7177-1003>), Ricardo Omar Ramirez
Flores [ctb] (ORCID: <https://orcid.org/0000-0003-0087-371X>),
Philipp Schäfer [ctb]
Maintainer: Jovan Tanevski <jovan.tanevski@uni-heidelberg.de>
URL: https://saezlab.github.io/mistyR/
VignetteBuilder: knitr
BugReports: https://github.com/saezlab/mistyR/issues
git_url: https://git.bioconductor.org/packages/mistyR
git_branch: devel
git_last_commit: 89f8c5d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mistyR_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mistyR_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mistyR_1.15.0.tgz
vignettes: vignettes/mistyR/inst/doc/mistyR.html
vignetteTitles: Getting started
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mistyR/inst/doc/mistyR.R
dependencyCount: 110
Package: mitch
Version: 1.19.3
Depends: R (>= 4.4)
Imports: stats, grDevices, graphics, utils, MASS, plyr, reshape2,
parallel, GGally, grid, gridExtra, knitr, rmarkdown, ggplot2,
gplots, beeswarm, echarts4r, kableExtra
Suggests: stringi, testthat (>= 2.1.0), HGNChelper,
IlluminaHumanMethylation450kanno.ilmn12.hg19,
IlluminaHumanMethylationEPICanno.ilm10b4.hg19
License: CC BY-SA 4.0 + file LICENSE
MD5sum: c245734bcbd86f4d596354292a4adca9
NeedsCompilation: no
Title: Multi-Contrast Gene Set Enrichment Analysis
Description: mitch is an R package for multi-contrast enrichment
analysis. At it’s heart, it uses a rank-MANOVA based
statistical approach to detect sets of genes that exhibit
enrichment in the multidimensional space as compared to the
background. The rank-MANOVA concept dates to work by Cox and
Mann (https://doi.org/10.1186/1471-2105-13-S16-S12). mitch is
useful for pathway analysis of profiling studies with one, two
or more contrasts, or in studies with multiple omics profiling,
for example proteomic, transcriptomic, epigenomic analysis of
the same samples. mitch is perfectly suited for pathway level
differential analysis of scRNA-seq data. We have an established
routine for pathway enrichment of Infinium Methylation Array
data (see vignette). The main strengths of mitch are that it
can import datasets easily from many upstream tools and has
advanced plotting features to visualise these enrichments.
biocViews: GeneExpression, GeneSetEnrichment, SingleCell,
Transcriptomics, Epigenetics, Proteomics,
DifferentialExpression, Reactome, DNAMethylation,
MethylationArray
Author: Mark Ziemann [aut, cre, cph] (ORCID:
<https://orcid.org/0000-0002-7688-6974>), Antony Kaspi [aut,
cph]
Maintainer: Mark Ziemann <mark.ziemann@gmail.com>
URL: https://github.com/markziemann/mitch
VignetteBuilder: knitr
BugReports: https://github.com/markziemann/mitch
git_url: https://git.bioconductor.org/packages/mitch
git_branch: devel
git_last_commit: 6550cbb
git_last_commit_date: 2024-11-26
Date/Publication: 2024-11-26
source.ver: src/contrib/mitch_1.19.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mitch_1.19.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mitch_1.19.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mitch_1.19.3.tgz
vignettes: vignettes/mitch/inst/doc/infiniumMethArrayWorkflow.html,
vignettes/mitch/inst/doc/mitchWorkflow.html
vignetteTitles: Applying mitch to pathway analysis of Infinium
Methylation array data, mitch Workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/mitch/inst/doc/infiniumMethArrayWorkflow.R,
vignettes/mitch/inst/doc/mitchWorkflow.R
dependencyCount: 102
Package: mitoClone2
Version: 1.13.0
Depends: R (>= 4.1.0)
Imports: reshape2, GenomicRanges, pheatmap, deepSNV, grDevices,
graphics, stats, utils, S4Vectors, Rhtslib, parallel, methods,
ggplot2
LinkingTo: Rhtslib (>= 1.13.1)
Suggests: knitr, rmarkdown, Biostrings, testthat
License: GPL-3
MD5sum: 2cb47a06670bc0200fd9dda643c53b3e
NeedsCompilation: yes
Title: Clonal Population Identification in Single-Cell RNA-Seq Data
using Mitochondrial and Somatic Mutations
Description: This package primarily identifies variants in
mitochondrial genomes from BAM alignment files. It filters
these variants to remove RNA editing events then estimates
their evolutionary relationship (i.e. their phylogenetic tree)
and groups single cells into clones. It also visualizes the
mutations and providing additional genomic context.
biocViews: Annotation, DataImport, Genetics, SNP, Software, SingleCell,
Alignment
Author: Benjamin Story [aut, cre], Lars Velten [aut], Gregor Mönke
[aut]
Maintainer: Benjamin Story <story.benjamin@gmail.com>
URL: https://github.com/benstory/mitoClone2
SystemRequirements: GNU make, PhISCS (optional)
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/mitoClone2
git_branch: devel
git_last_commit: 020fb39
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mitoClone2_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mitoClone2_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mitoClone2_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mitoClone2_1.13.0.tgz
vignettes: vignettes/mitoClone2/inst/doc/clustering.html,
vignettes/mitoClone2/inst/doc/overview.html
vignetteTitles: Computation of phylogenetic trees and clustering of
mutations, Variant Calling
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mitoClone2/inst/doc/clustering.R,
vignettes/mitoClone2/inst/doc/overview.R
dependencyCount: 107
Package: mixOmics
Version: 6.31.4
Depends: R (>= 4.4.0), MASS, lattice, ggplot2
Imports: igraph, ellipse, corpcor, RColorBrewer, parallel, dplyr,
tidyr, reshape2, methods, matrixStats, rARPACK, gridExtra,
grDevices, graphics, stats, ggrepel, BiocParallel, utils,
gsignal, rgl
Suggests: BiocStyle, knitr, rmarkdown, testthat, microbenchmark, magick
License: GPL (>= 2)
MD5sum: e49d7ee0b2c3d682afbab6187eab1523
NeedsCompilation: no
Title: Omics Data Integration Project
Description: Multivariate methods are well suited to large omics data
sets where the number of variables (e.g. genes, proteins,
metabolites) is much larger than the number of samples
(patients, cells, mice). They have the appealing properties of
reducing the dimension of the data by using instrumental
variables (components), which are defined as combinations of
all variables. Those components are then used to produce useful
graphical outputs that enable better understanding of the
relationships and correlation structures between the different
data sets that are integrated. mixOmics offers a wide range of
multivariate methods for the exploration and integration of
biological datasets with a particular focus on variable
selection. The package proposes several sparse multivariate
models we have developed to identify the key variables that are
highly correlated, and/or explain the biological outcome of
interest. The data that can be analysed with mixOmics may come
from high throughput sequencing technologies, such as omics
data (transcriptomics, metabolomics, proteomics, metagenomics
etc) but also beyond the realm of omics (e.g. spectral
imaging). The methods implemented in mixOmics can also handle
missing values without having to delete entire rows with
missing data. A non exhaustive list of methods include variants
of generalised Canonical Correlation Analysis, sparse Partial
Least Squares and sparse Discriminant Analysis. Recently we
implemented integrative methods to combine multiple data sets:
N-integration with variants of Generalised Canonical
Correlation Analysis and P-integration with variants of
multi-group Partial Least Squares.
biocViews: ImmunoOncology, Microarray, Sequencing, Metabolomics,
Metagenomics, Proteomics, GenePrediction, MultipleComparison,
Classification, Regression
Author: Kim-Anh Le Cao [aut], Florian Rohart [aut], Ignacio Gonzalez
[aut], Sebastien Dejean [aut], Al J Abadi [ctb], Max Bladen
[ctb], Benoit Gautier [ctb], Francois Bartolo [ctb], Pierre
Monget [ctb], Jeff Coquery [ctb], FangZou Yao [ctb], Benoit
Liquet [ctb], Eva Hamrud [ctb, cre]
Maintainer: Eva Hamrud <mixomicsdeveloper@gmail.com>
URL: http://www.mixOmics.org
VignetteBuilder: knitr
BugReports: https://github.com/mixOmicsTeam/mixOmics/issues/
git_url: https://git.bioconductor.org/packages/mixOmics
git_branch: devel
git_last_commit: 2dc5da1
git_last_commit_date: 2025-01-12
Date/Publication: 2025-01-13
source.ver: src/contrib/mixOmics_6.31.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mixOmics_6.31.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mixOmics_6.31.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mixOmics_6.31.4.tgz
vignettes: vignettes/mixOmics/inst/doc/vignette.html
vignetteTitles: mixOmics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mixOmics/inst/doc/vignette.R
dependsOnMe: timeOmics, mixKernel, sgPLS
importsMe: AlpsNMR, benchdamic, DepecheR, PLSDAbatch, POMA, Coxmos,
Holomics, iTensor, MSclassifR, plsmod, plsRcox, SISIR
suggestsMe: autonomics, planet, eoPredData, MetabolomicsBasics, pctax,
RVAideMemoire, SelectBoost, sharp
dependencyCount: 90
Package: MLInterfaces
Version: 1.87.0
Depends: R (>= 3.5), Rcpp, methods, BiocGenerics (>= 0.13.11), Biobase,
annotate, cluster
Imports: gdata, pls, sfsmisc, MASS, rpart, genefilter, fpc, ggvis,
shiny, gbm, RColorBrewer, hwriter, threejs (>= 0.2.2), mlbench,
stats4, tools, grDevices, graphics, stats, magrittr,
SummarizedExperiment
Suggests: class, e1071, ipred, randomForest, gpls, pamr, nnet, ALL,
hgu95av2.db, som, hu6800.db, lattice, caret (>= 5.07),
golubEsets, ada, keggorthology, kernlab, mboost, party, klaR,
BiocStyle, knitr, testthat
Enhances: parallel
License: LGPL
MD5sum: 0ff110bf271f614007d2da209f554f57
NeedsCompilation: no
Title: Uniform interfaces to R machine learning procedures for data in
Bioconductor containers
Description: This package provides uniform interfaces to machine
learning code for data in R and Bioconductor containers.
biocViews: Classification, Clustering
Author: Vincent Carey [cre, aut] (ORCID:
<https://orcid.org/0000-0003-4046-0063>), Jess Mar [aut], Jason
Vertrees [ctb], Laurent Gatto [ctb], Phylis Atieno [ctb]
(Translated vignettes from Sweave to Rmarkdown / HTML.)
Maintainer: Vincent Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MLInterfaces
git_branch: devel
git_last_commit: 7982d3a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MLInterfaces_1.87.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MLInterfaces_1.87.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MLInterfaces_1.87.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MLInterfaces_1.87.0.tgz
vignettes: vignettes/MLInterfaces/inst/doc/xvalComputerClusters.pdf,
vignettes/MLInterfaces/inst/doc/MLint_devel.html,
vignettes/MLInterfaces/inst/doc/MLprac2_2.html
vignetteTitles: MLInterfaces Computer Cluster, MLInterfaces 2.0 -- a
new design, A machine learning tutorial tutorial: applications
of the Bioconductor MLInterfaces package to gene expression
data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MLInterfaces/inst/doc/MLint_devel.R,
vignettes/MLInterfaces/inst/doc/MLprac2_2.R,
vignettes/MLInterfaces/inst/doc/xvalComputerClusters.R
dependsOnMe: pRoloc, SigCheck, dGAselID, nlcv
dependencyCount: 123
Package: MLP
Version: 1.55.0
Imports: AnnotationDbi, gplots, graphics, stats, utils
Suggests: GO.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db,
org.Cf.eg.db, org.Mmu.eg.db, KEGGREST, annotate, Rgraphviz,
GOstats, graph, limma, mouse4302.db, reactome.db
License: GPL-3
MD5sum: a47af6ec8389d393f63eb638b6f065ae
NeedsCompilation: no
Title: Mean Log P Analysis
Description: Pathway analysis based on p-values associated to genes
from a genes expression analysis of interest. Utility functions
enable to extract pathways from the Gene Ontology Biological
Process (GOBP), Molecular Function (GOMF) and Cellular
Component (GOCC), Kyoto Encyclopedia of Genes of Genomes (KEGG)
and Reactome databases. Methodology, and helper functions to
display the results as a table, barplot of pathway
significance, Gene Ontology graph and pathway significance are
available.
biocViews: Genetics, GeneExpression, Pathways, Reactome, KEGG, GO
Author: Nandini Raghavan [aut], Tobias Verbeke [aut], An De Bondt
[aut], Javier Cabrera [ctb], Dhammika Amaratunga [ctb], Tine
Casneuf [ctb], Willem Ligtenberg [ctb], Laure Cougnaud [cre],
Katarzyna Gorczak [ctb]
Maintainer: Tobias Verbeke <tobias.verbeke@openanalytics.eu>
git_url: https://git.bioconductor.org/packages/MLP
git_branch: devel
git_last_commit: 679711e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MLP_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MLP_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MLP_1.55.0.tgz
vignettes: vignettes/MLP/inst/doc/UsingMLP.pdf
vignetteTitles: UsingMLP
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MLP/inst/doc/UsingMLP.R
importsMe: esetVis
suggestsMe: a4
dependencyCount: 50
Package: MLSeq
Version: 2.25.0
Depends: caret, ggplot2
Imports: testthat, VennDiagram, pamr, methods, DESeq2, edgeR, limma,
Biobase, SummarizedExperiment, plyr, foreach, utils, sSeq,
xtable
Suggests: knitr, e1071, kernlab
License: GPL(>=2)
Archs: x64
MD5sum: 0b555115dc4fccffda24aeab8e231544
NeedsCompilation: no
Title: Machine Learning Interface for RNA-Seq Data
Description: This package applies several machine learning methods,
including SVM, bagSVM, Random Forest and CART to RNA-Seq data.
biocViews: ImmunoOncology, Sequencing, RNASeq, Classification,
Clustering
Author: Gokmen Zararsiz [aut, cre], Dincer Goksuluk [aut], Selcuk
Korkmaz [aut], Vahap Eldem [aut], Izzet Parug Duru [ctb], Ahmet
Ozturk [aut], Ahmet Ergun Karaagaoglu [aut, ths]
Maintainer: Gokmen Zararsiz <gokmenzararsiz@hotmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MLSeq
git_branch: devel
git_last_commit: 97e5509
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MLSeq_2.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MLSeq_2.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MLSeq_2.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MLSeq_2.25.0.tgz
vignettes: vignettes/MLSeq/inst/doc/MLSeq.pdf
vignetteTitles: Beginner's guide to the "MLSeq" package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MLSeq/inst/doc/MLSeq.R
importsMe: GARS
dependencyCount: 144
Package: MMDiff2
Version: 1.35.0
Depends: R (>= 3.5.0), Rsamtools, Biobase
Imports: GenomicRanges, locfit, BSgenome, Biostrings, shiny, ggplot2,
RColorBrewer, graphics, grDevices, parallel, S4Vectors, methods
Suggests: MMDiffBamSubset, MotifDb, knitr, BiocStyle,
BSgenome.Mmusculus.UCSC.mm9
License: Artistic-2.0
MD5sum: 95b1132e7c1a771c13ac15a2e6bc1c79
NeedsCompilation: no
Title: Statistical Testing for ChIP-Seq data sets
Description: This package detects statistically significant differences
between read enrichment profiles in different ChIP-Seq samples.
To take advantage of shape differences it uses Kernel methods
(Maximum Mean Discrepancy, MMD).
biocViews: ChIPSeq, DifferentialPeakCalling, Sequencing, Software
Author: Gabriele Schweikert [cre, aut], David Kuo [aut]
Maintainer: Gabriele Schweikert <gschweik@staffmail.ed.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MMDiff2
git_branch: devel
git_last_commit: d7fad9d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MMDiff2_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MMDiff2_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MMDiff2_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MMDiff2_1.35.0.tgz
vignettes: vignettes/MMDiff2/inst/doc/MMDiff2.pdf
vignetteTitles: An Introduction to the MMDiff2 method
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MMDiff2/inst/doc/MMDiff2.R
suggestsMe: MMDiffBamSubset
dependencyCount: 106
Package: MMUPHin
Version: 1.21.0
Depends: R (>= 3.6)
Imports: Maaslin2, metafor, fpc, igraph, ggplot2, dplyr, tidyr,
stringr, cowplot, utils, stats, grDevices
Suggests: testthat, BiocStyle, knitr, rmarkdown, magrittr, vegan,
phyloseq, curatedMetagenomicData, genefilter
License: MIT + file LICENSE
MD5sum: d8a1366dbace6878af8c8aef4dd61cac
NeedsCompilation: no
Title: Meta-analysis Methods with Uniform Pipeline for Heterogeneity in
Microbiome Studies
Description: MMUPHin is an R package for meta-analysis tasks of
microbiome cohorts. It has function interfaces for: a)
covariate-controlled batch- and cohort effect adjustment, b)
meta-analysis differential abundance testing, c) meta-analysis
unsupervised discrete structure (clustering) discovery, and d)
meta-analysis unsupervised continuous structure discovery.
biocViews: Metagenomics, Microbiome, BatchEffect
Author: Siyuan Ma
Maintainer: Siyuan MA <syma.research@gmail.com>
SystemRequirements: glpk (>= 4.57)
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MMUPHin
git_branch: devel
git_last_commit: 6b438ce
git_last_commit_date: 2024-10-29
Date/Publication: 2024-12-18
source.ver: src/contrib/MMUPHin_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MMUPHin_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MMUPHin_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MMUPHin_1.21.0.tgz
vignettes: vignettes/MMUPHin/inst/doc/MMUPHin.html
vignetteTitles: MMUPHin
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MMUPHin/inst/doc/MMUPHin.R
dependencyCount: 139
Package: mnem
Version: 1.23.1
Depends: R (>= 4.1)
Imports: cluster, graph, Rgraphviz, flexclust, lattice, naturalsort,
snowfall, stats4, tsne, methods, graphics, stats, utils,
Linnorm, data.table, Rcpp, RcppEigen, matrixStats, grDevices,
e1071, ggplot2, wesanderson
LinkingTo: Rcpp, RcppEigen
Suggests: knitr, devtools, rmarkdown, BiocGenerics, RUnit, epiNEM,
BiocStyle
License: GPL-3
MD5sum: 4a5d41e7ee27fbb92b0ccf7c643b70a1
NeedsCompilation: yes
Title: Mixture Nested Effects Models
Description: Mixture Nested Effects Models (mnem) is an extension of
Nested Effects Models and allows for the analysis of single
cell perturbation data provided by methods like Perturb-Seq
(Dixit et al., 2016) or Crop-Seq (Datlinger et al., 2017). In
those experiments each of many cells is perturbed by a
knock-down of a specific gene, i.e. several cells are perturbed
by a knock-down of gene A, several by a knock-down of gene B,
... and so forth. The observed read-out has to be multi-trait
and in the case of the Perturb-/Crop-Seq gene are expression
profiles for each cell. mnem uses a mixture model to
simultaneously cluster the cell population into k clusters and
and infer k networks causally linking the perturbed genes for
each cluster. The mixture components are inferred via an
expectation maximization algorithm.
biocViews: Pathways, SystemsBiology, NetworkInference, Network, RNASeq,
PooledScreens, SingleCell, CRISPR, ATACSeq, DNASeq,
GeneExpression
Author: Martin Pirkl [aut, cre]
Maintainer: Martin Pirkl <martinpirkl@yahoo.de>
URL: https://github.com/cbg-ethz/mnem/
VignetteBuilder: knitr
BugReports: https://github.com/cbg-ethz/mnem/issues
git_url: https://git.bioconductor.org/packages/mnem
git_branch: devel
git_last_commit: e2453b1
git_last_commit_date: 2025-03-26
Date/Publication: 2025-03-26
source.ver: src/contrib/mnem_1.23.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mnem_1.23.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mnem_1.23.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mnem_1.23.1.tgz
vignettes: vignettes/mnem/inst/doc/mnem.html
vignetteTitles: mnem
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mnem/inst/doc/mnem.R
dependsOnMe: nempi
importsMe: bnem, dce, epiNEM
dependencyCount: 83
Package: moanin
Version: 1.15.0
Depends: R (>= 4.0), SummarizedExperiment, topGO, stats
Imports: S4Vectors, MASS (>= 1.0.0), limma, viridis, edgeR, graphics,
methods, grDevices, reshape2, NMI, zoo, ClusterR, splines,
matrixStats
Suggests: testthat (>= 1.0.0), timecoursedata, knitr, rmarkdown,
markdown, covr, BiocStyle
License: BSD 3-clause License + file LICENSE
MD5sum: 9dd4b481d1521d2db2c558b427bf9a31
NeedsCompilation: no
Title: An R Package for Time Course RNASeq Data Analysis
Description: Simple and efficient workflow for time-course gene
expression data, built on publictly available open-source
projects hosted on CRAN and bioconductor. moanin provides
helper functions for all the steps required for analysing
time-course data using functional data analysis: (1) functional
modeling of the timecourse data; (2) differential expression
analysis; (3) clustering; (4) downstream analysis.
biocViews: TimeCourse, GeneExpression, RNASeq, Microarray,
DifferentialExpression, Clustering
Author: Elizabeth Purdom [aut] (ORCID:
<https://orcid.org/0000-0001-9455-7990>), Nelle Varoquaux [aut,
cre] (ORCID: <https://orcid.org/0000-0002-8748-6546>)
Maintainer: Nelle Varoquaux <nelle.varoquaux@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/moanin
git_branch: devel
git_last_commit: 24c12c7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/moanin_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/moanin_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/moanin_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/moanin_1.15.0.tgz
vignettes: vignettes/moanin/inst/doc/documentation.html
vignetteTitles: The Moanin Package
hasREADME: TRUE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/moanin/inst/doc/documentation.R
dependencyCount: 96
Package: mobileRNA
Version: 1.3.0
Depends: R (>= 4.3.0)
Imports: dplyr, tidyr, ggplot2, BiocGenerics, DESeq2, edgeR, ggrepel,
grDevices, pheatmap, utils, tidyselect, progress, RColorBrewer,
GenomicRanges, rtracklayer, data.table, SimDesign, scales,
IRanges, stats, methods, Biostrings, reticulate, S4Vectors,
GenomeInfoDb, SummarizedExperiment, rlang, bioseq, grid
Suggests: knitr, rmarkdown, BiocStyle
License: MIT + file LICENSE
MD5sum: 907d073bcfa05a20e658fbc20b5c5124
NeedsCompilation: no
Title: mobileRNA: Investigate the RNA mobilome & population-scale
changes
Description: Genomic analysis can be utilised to identify differences
between RNA populations in two conditions, both in production
and abundance. This includes the identification of RNAs
produced by multiple genomes within a biological system. For
example, RNA produced by pathogens within a host or mobile RNAs
in plant graft systems. The mobileRNA package provides methods
to pre-process, analyse and visualise the sRNA and mRNA
populations based on the premise of mapping reads to all
genotypes at the same time.
biocViews: Visualization, RNASeq, Sequencing, SmallRNA, GenomeAssembly,
Clustering, ExperimentalDesign, QualityControl, WorkflowStep,
Alignment, Preprocessing
Author: Katie Jeynes-Cupper [aut, cre] (ORCID:
<https://orcid.org/0009-0000-1350-1371>), Marco Catoni [aut]
(ORCID: <https://orcid.org/0000-0002-3258-2522>)
Maintainer: Katie Jeynes-Cupper <kej031@student.bham.ac.uk>
SystemRequirements: GNU make, ShortStack (>= 4.0), HTSeq, HISAT2,
SAMtools, Conda
VignetteBuilder: knitr
BugReports: https://github.com/KJeynesCupper/mobileRNA/issues
git_url: https://git.bioconductor.org/packages/mobileRNA
git_branch: devel
git_last_commit: d0f0d6c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mobileRNA_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mobileRNA_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mobileRNA_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mobileRNA_1.3.0.tgz
vignettes: vignettes/mobileRNA/inst/doc/mobileRNA.html
vignetteTitles: mobileRNA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/mobileRNA/inst/doc/mobileRNA.R
dependencyCount: 146
Package: MODA
Version: 1.33.0
Depends: R (>= 3.3)
Imports: grDevices, graphics, stats, utils, WGCNA, dynamicTreeCut,
igraph, cluster, AMOUNTAIN, RColorBrewer
Suggests: BiocStyle, knitr, rmarkdown
License: GPL (>= 2)
MD5sum: 0dd0d8530b1692218fd70975f3c0f459
NeedsCompilation: no
Title: MODA: MOdule Differential Analysis for weighted gene
co-expression network
Description: MODA can be used to estimate and construct
condition-specific gene co-expression networks, and identify
differentially expressed subnetworks as conserved or condition
specific modules which are potentially associated with relevant
biological processes.
biocViews: GeneExpression, Microarray, DifferentialExpression, Network
Author: Dong Li, James B. Brown, Luisa Orsini, Zhisong Pan, Guyu Hu and
Shan He
Maintainer: Dong Li <dxl466@cs.bham.ac.uk>
git_url: https://git.bioconductor.org/packages/MODA
git_branch: devel
git_last_commit: 1417781
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MODA_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MODA_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MODA_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MODA_1.33.0.tgz
vignettes: vignettes/MODA/inst/doc/MODA.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 116
Package: ModCon
Version: 1.15.0
Depends: data.table, parallel, utils, stats, R (>= 4.1)
Suggests: testthat, knitr, rmarkdown, dplyr, shinycssloaders, shiny,
shinyFiles, shinydashboard, shinyjs
License: GPL-3 + file LICENSE
Archs: x64
MD5sum: efddabe88c0a481318b2e408fb9111d9
NeedsCompilation: no
Title: Modifying splice site usage by changing the mRNP code, while
maintaining the genetic code
Description: Collection of functions to calculate a nucleotide sequence
surrounding for splice donors sites to either activate or
repress donor usage. The proposed alternative nucleotide
sequence encodes the same amino acid and could be applied e.g.
in reporter systems to silence or activate cryptic splice donor
sites.
biocViews: FunctionalGenomics, AlternativeSplicing
Author: Johannes Ptok [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0322-5649>)
Maintainer: Johannes Ptok <Johannes.Ptok@posteo.de>
URL: https://github.com/caggtaagtat/ModCon
SystemRequirements: Perl
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ModCon
git_branch: devel
git_last_commit: b573fd3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ModCon_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ModCon_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ModCon_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ModCon_1.15.0.tgz
vignettes: vignettes/ModCon/inst/doc/ModCon.html
vignetteTitles: Designing SD context with ModCon
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ModCon/inst/doc/ModCon.R
dependencyCount: 5
Package: Modstrings
Version: 1.23.0
Depends: R (>= 3.6), Biostrings (>= 2.51.5)
Imports: methods, BiocGenerics, GenomicRanges, S4Vectors, IRanges,
XVector, stringi, stringr, crayon, grDevices
Suggests: BiocStyle, knitr, rmarkdown, testthat, usethis
License: Artistic-2.0
MD5sum: 3d33a97a2cffb1100e508c979acca450
NeedsCompilation: no
Title: Working with modified nucleotide sequences
Description: Representing nucleotide modifications in a nucleotide
sequence is usually done via special characters from a number
of sources. This represents a challenge to work with in R and
the Biostrings package. The Modstrings package implements this
functionallity for RNA and DNA sequences containing modified
nucleotides by translating the character internally in order to
work with the infrastructure of the Biostrings package. For
this the ModRNAString and ModDNAString classes and derivates
and functions to construct and modify these objects despite the
encoding issues are implemenented. In addition the conversion
from sequences to list like location information (and the
reverse operation) is implemented as well.
biocViews: DataImport, DataRepresentation, Infrastructure, Sequencing,
Software
Author: Felix G.M. Ernst [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5064-0928>), Denis L.J. Lafontaine
[ctb, fnd]
Maintainer: Felix G.M. Ernst <felix.gm.ernst@outlook.com>
VignetteBuilder: knitr
BugReports: https://github.com/FelixErnst/Modstrings/issues
git_url: https://git.bioconductor.org/packages/Modstrings
git_branch: devel
git_last_commit: 7af36f0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Modstrings_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Modstrings_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Modstrings_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Modstrings_1.23.0.tgz
vignettes: vignettes/Modstrings/inst/doc/ModDNAString-alphabet.html,
vignettes/Modstrings/inst/doc/ModRNAString-alphabet.html,
vignettes/Modstrings/inst/doc/Modstrings.html
vignetteTitles: Modstrings-DNA-alphabet, Modstrings-RNA-alphabet,
Modstrings
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Modstrings/inst/doc/ModDNAString-alphabet.R,
vignettes/Modstrings/inst/doc/ModRNAString-alphabet.R,
vignettes/Modstrings/inst/doc/Modstrings.R
dependsOnMe: EpiTxDb, RNAmodR, tRNAdbImport
importsMe: tRNA
suggestsMe: EpiTxDb.Hs.hg38, EpiTxDb.Sc.sacCer3
dependencyCount: 34
Package: MOFA2
Version: 1.17.0
Depends: R (>= 4.0)
Imports: rhdf5, dplyr, tidyr, reshape2, pheatmap, ggplot2, methods,
RColorBrewer, cowplot, ggrepel, reticulate, HDF5Array,
grDevices, stats, magrittr, forcats, utils, corrplot,
DelayedArray, Rtsne, uwot, basilisk, stringi
Suggests: knitr, testthat, Seurat, SeuratObject, ggpubr, foreach,
psych, MultiAssayExperiment, SummarizedExperiment,
SingleCellExperiment, ggrastr, mvtnorm, GGally, rmarkdown,
data.table, tidyverse, BiocStyle, Matrix, markdown
License: file LICENSE
MD5sum: 1e79cc54a97aaa0f33e40ee21a84802b
NeedsCompilation: yes
Title: Multi-Omics Factor Analysis v2
Description: The MOFA2 package contains a collection of tools for
training and analysing multi-omic factor analysis (MOFA). MOFA
is a probabilistic factor model that aims to identify principal
axes of variation from data sets that can comprise multiple
omic layers and/or groups of samples. Additional time or space
information on the samples can be incorporated using the
MEFISTO framework, which is part of MOFA2. Downstream analysis
functions to inspect molecular features underlying each factor,
vizualisation, imputation etc are available.
biocViews: DimensionReduction, Bayesian, Visualization
Author: Ricard Argelaguet [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3199-3722>), Damien Arnol [aut]
(ORCID: <https://orcid.org/0000-0003-2462-534X>), Danila
Bredikhin [aut] (ORCID:
<https://orcid.org/0000-0001-8089-6983>), Britta Velten [aut]
(ORCID: <https://orcid.org/0000-0002-8397-3515>)
Maintainer: Ricard Argelaguet <ricard.argelaguet@gmail.com>
URL: https://biofam.github.io/MOFA2/index.html
SystemRequirements: Python (>=3), numpy, pandas, h5py, scipy, argparse,
sklearn, mofapy2
VignetteBuilder: knitr
BugReports: https://github.com/bioFAM/MOFA2
git_url: https://git.bioconductor.org/packages/MOFA2
git_branch: devel
git_last_commit: 12bde39
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MOFA2_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MOFA2_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MOFA2_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MOFA2_1.17.0.tgz
vignettes: vignettes/MOFA2/inst/doc/downstream_analysis.html,
vignettes/MOFA2/inst/doc/getting_started_R.html,
vignettes/MOFA2/inst/doc/MEFISTO_temporal.html
vignetteTitles: Downstream analysis: Overview, MOFA2: How to train a
model in R, MEFISTO on simulated data (temporal)
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MOFA2/inst/doc/downstream_analysis.R,
vignettes/MOFA2/inst/doc/getting_started_R.R,
vignettes/MOFA2/inst/doc/MEFISTO_temporal.R
suggestsMe: HoloFoodR
dependencyCount: 92
Package: MOGAMUN
Version: 1.17.0
Imports: stats, utils, RCy3, stringr, graphics, grDevices, RUnit,
BiocParallel, igraph
Suggests: knitr, markdown
License: GPL-3 + file LICENSE
MD5sum: 5aef2862885955f563882c8b85c4578b
NeedsCompilation: no
Title: MOGAMUN: A Multi-Objective Genetic Algorithm to Find Active
Modules in Multiplex Biological Networks
Description: MOGAMUN is a multi-objective genetic algorithm that
identifies active modules in a multiplex biological network.
This allows analyzing different biological networks at the same
time. MOGAMUN is based on NSGA-II (Non-Dominated Sorting
Genetic Algorithm, version II), which we adapted to work on
networks.
biocViews: SystemsBiology, GraphAndNetwork, DifferentialExpression,
BiomedicalInformatics, Transcriptomics, Clustering, Network
Author: Elva-MarÃa Novoa-del-Toro [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6135-5839>)
Maintainer: Elva-MarÃa Novoa-del-Toro <elvanov@hotmail.com>
URL: https://github.com/elvanov/MOGAMUN
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MOGAMUN
git_branch: devel
git_last_commit: cb76f52
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MOGAMUN_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MOGAMUN_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MOGAMUN_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MOGAMUN_1.17.0.tgz
vignettes: vignettes/MOGAMUN/inst/doc/MOGAMUN_Vignette.html
vignetteTitles: Finding active modules with MOGAMUN
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MOGAMUN/inst/doc/MOGAMUN_Vignette.R
dependencyCount: 68
Package: mogsa
Version: 1.41.0
Depends: R (>= 3.4.0)
Imports: methods, graphite, genefilter, BiocGenerics, gplots, GSEABase,
Biobase, parallel, corpcor, svd, cluster, grDevices, graphics,
stats, utils
Suggests: BiocStyle, knitr, org.Hs.eg.db
License: GPL-2
MD5sum: 5511debeb0e35d73f3eb9b0aba832576
NeedsCompilation: no
Title: Multiple omics data integrative clustering and gene set analysis
Description: This package provide a method for doing gene set analysis
based on multiple omics data.
biocViews: GeneExpression, PrincipalComponent, StatisticalMethod,
Clustering, Software
Author: Chen Meng
Maintainer: Chen Meng <mengchen18@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/mogsa
git_branch: devel
git_last_commit: b1cb18d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mogsa_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mogsa_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mogsa_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mogsa_1.41.0.tgz
vignettes: vignettes/mogsa/inst/doc/moCluster-knitr.pdf,
vignettes/mogsa/inst/doc/mogsa-knitr.pdf
vignetteTitles: moCluster: Integrative clustering using multiple omics
data, mogsa: gene set analysis on multiple omics data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mogsa/inst/doc/moCluster-knitr.R,
vignettes/mogsa/inst/doc/mogsa-knitr.R
dependencyCount: 71
Package: MoleculeExperiment
Version: 1.7.0
Depends: R (>= 2.10)
Imports: SpatialExperiment, Matrix, purrr, data.table, dplyr (>=
1.1.1), magrittr, rjson, utils, methods, terra, ggplot2, rlang,
cli, EBImage, rhdf5, BiocParallel, S4Vectors, stats
Suggests: knitr, BiocStyle, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: 7172a79d20705d6a44b8a527b7f48b4c
NeedsCompilation: no
Title: Prioritising a molecule-level storage of Spatial Transcriptomics
Data
Description: MoleculeExperiment contains functions to create and work
with objects from the new MoleculeExperiment class. We
introduce this class for analysing molecule-based spatial
transcriptomics data (e.g., Xenium by 10X, Cosmx SMI by
Nanostring, and Merscope by Vizgen). This allows researchers to
analyse spatial transcriptomics data at the molecule level, and
to have standardised data formats accross vendors.
biocViews: DataImport, DataRepresentation, Infrastructure, Software,
Spatial, Transcriptomics
Author: Bárbara Zita Peters Couto [aut], Nicholas Robertson [aut],
Ellis Patrick [aut], Shila Ghazanfar [aut, cre]
Maintainer: Shila Ghazanfar <shazanfar@gmail.com>
URL: https://github.com/SydneyBioX/MoleculeExperiment
VignetteBuilder: knitr
BugReports: https://github.com/SydneyBioX/MoleculeExperiment/issues
git_url: https://git.bioconductor.org/packages/MoleculeExperiment
git_branch: devel
git_last_commit: d50404d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MoleculeExperiment_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MoleculeExperiment_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MoleculeExperiment_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MoleculeExperiment_1.7.0.tgz
vignettes:
vignettes/MoleculeExperiment/inst/doc/MoleculeExperiment.html
vignetteTitles: "Introduction to MoleculeExperiment"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MoleculeExperiment/inst/doc/MoleculeExperiment.R
dependencyCount: 125
Package: MOMA
Version: 1.19.0
Depends: R (>= 4.0)
Imports: circlize, cluster, ComplexHeatmap, dplyr, ggplot2, graphics,
grid, grDevices, magrittr, methods, MKmisc,
MultiAssayExperiment, parallel, qvalue, RColorBrewer, readr,
reshape2, rlang, stats, stringr, tibble, tidyr, utils
Suggests: BiocStyle, knitr, rmarkdown, testthat, viper
License: GPL-3
MD5sum: 6ecda9e59b439a8d406630c820c53ef8
NeedsCompilation: no
Title: Multi Omic Master Regulator Analysis
Description: This package implements the inference of candidate master
regulator proteins from multi-omics' data (MOMA) algorithm, as
well as ancillary analysis and visualization functions.
biocViews: Software, NetworkEnrichment, NetworkInference, Network,
FeatureExtraction, Clustering, FunctionalGenomics,
Transcriptomics, SystemsBiology
Author: Evan Paull [aut], Sunny Jones [aut, cre], Mariano Alvarez [aut]
Maintainer: Sunny Jones <sunnyjjones@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/califano-lab/MOMA/issues
git_url: https://git.bioconductor.org/packages/MOMA
git_branch: devel
git_last_commit: cd609f2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MOMA_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MOMA_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MOMA_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MOMA_1.19.0.tgz
vignettes: vignettes/MOMA/inst/doc/moma.html
vignetteTitles: MOMA - Multi Omic Master Regulator Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MOMA/inst/doc/moma.R
dependencyCount: 104
Package: monaLisa
Version: 1.13.3
Depends: R (>= 4.1)
Imports: BiocGenerics, BiocParallel, Biostrings, BSgenome, circlize,
ComplexHeatmap (>= 2.11.1), GenomeInfoDb, GenomicRanges, cli,
ggplot2, glmnet, grDevices, grid, IRanges, methods, rlang,
RSQLite, stabs, stats, SummarizedExperiment, S4Vectors,
TFBSTools, tidyr, tools, utils, XVector
Suggests: BiocManager, BiocStyle, BSgenome.Mmusculus.UCSC.mm10,
ggrepel, gridExtra, JASPAR2020, JASPAR2024, knitr, rmarkdown,
testthat, TxDb.Mmusculus.UCSC.mm10.knownGene
License: GPL (>= 3)
MD5sum: 75916615beb21b3603d7ebe6022ca271
NeedsCompilation: no
Title: Binned Motif Enrichment Analysis and Visualization
Description: Useful functions to work with sequence motifs in the
analysis of genomics data. These include methods to annotate
genomic regions or sequences with predicted motif hits and to
identify motifs that drive observed changes in accessibility or
expression. Functions to produce informative visualizations of
the obtained results are also provided.
biocViews: MotifAnnotation, Visualization, FeatureExtraction,
Epigenetics
Author: Dania Machlab [aut] (ORCID:
<https://orcid.org/0000-0002-2578-6930>), Lukas Burger [aut]
(ORCID: <https://orcid.org/0000-0001-5596-7567>), Charlotte
Soneson [aut] (ORCID: <https://orcid.org/0000-0003-3833-2169>),
Dany Mukesha [ctb] (ORCID:
<https://orcid.org/0009-0001-9514-751X>), Michael Stadler [aut,
cre] (ORCID: <https://orcid.org/0000-0002-2269-4934>)
Maintainer: Michael Stadler <michael.stadler@fmi.ch>
URL: https://github.com/fmicompbio/monaLisa,
https://bioconductor.org/packages/monaLisa/,
https://fmicompbio.github.io/monaLisa/
VignetteBuilder: knitr
BugReports: https://github.com/fmicompbio/monaLisa/issues
git_url: https://git.bioconductor.org/packages/monaLisa
git_branch: devel
git_last_commit: f95fd41
git_last_commit_date: 2025-03-24
Date/Publication: 2025-03-24
source.ver: src/contrib/monaLisa_1.13.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/monaLisa_1.13.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/monaLisa_1.13.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/monaLisa_1.13.3.tgz
vignettes: vignettes/monaLisa/inst/doc/monaLisa.html,
vignettes/monaLisa/inst/doc/selecting_motifs_with_randLassoStabSel.html
vignetteTitles: monaLisa - MOtif aNAlysis with Lisa,
selecting_motifs_with_randLassoStabSel
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/monaLisa/inst/doc/monaLisa.R,
vignettes/monaLisa/inst/doc/selecting_motifs_with_randLassoStabSel.R
dependencyCount: 124
Package: monocle
Version: 2.35.0
Depends: R (>= 2.10.0), methods, Matrix (>= 1.2-6), Biobase, ggplot2
(>= 1.0.0), VGAM (>= 1.0-6), DDRTree (>= 0.1.4),
Imports: parallel, igraph (>= 1.0.1), BiocGenerics, HSMMSingleCell (>=
0.101.5), plyr, cluster, combinat, fastICA, grid, irlba (>=
2.0.0), matrixStats, Rtsne, MASS, reshape2, leidenbase (>=
0.1.9), limma, tibble, dplyr, pheatmap, stringr, proxy, slam,
viridis, stats, biocViews, RANN(>= 2.5), Rcpp (>= 0.12.0)
LinkingTo: Rcpp
Suggests: destiny, Hmisc, knitr, Seurat, scater, testthat
License: Artistic-2.0
MD5sum: c086fffdc67fac83696c419f2c65b470
NeedsCompilation: yes
Title: Clustering, differential expression, and trajectory analysis for
single- cell RNA-Seq
Description: Monocle performs differential expression and time-series
analysis for single-cell expression experiments. It orders
individual cells according to progress through a biological
process, without knowing ahead of time which genes define
progress through that process. Monocle also performs
differential expression analysis, clustering, visualization,
and other useful tasks on single cell expression data. It is
designed to work with RNA-Seq and qPCR data, but could be used
with other types as well.
biocViews: ImmunoOncology, Sequencing, RNASeq, GeneExpression,
DifferentialExpression, Infrastructure, DataImport,
DataRepresentation, Visualization, Clustering,
MultipleComparison, QualityControl
Author: Cole Trapnell
Maintainer: Cole Trapnell <coletrap@uw.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/monocle
git_branch: devel
git_last_commit: f50c2db
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/monocle_2.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/monocle_2.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/monocle_2.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/monocle_2.35.0.tgz
vignettes: vignettes/monocle/inst/doc/monocle-vignette.pdf
vignetteTitles: Monocle: Cell counting,, differential expression,, and
trajectory analysis for single-cell RNA-Seq experiments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/monocle/inst/doc/monocle-vignette.R
dependsOnMe: cicero
importsMe: uSORT
suggestsMe: sincell, ClusterGVis, grandR, Seurat
dependencyCount: 78
Package: Moonlight2R
Version: 1.5.4
Depends: R (>= 4.4), doParallel, foreach
Imports: parmigene, randomForest, gplots, circlize, RColorBrewer,
HiveR, clusterProfiler, DOSE, Biobase, grDevices, graphics,
GEOquery, stats, purrr, RISmed, grid, utils, ComplexHeatmap,
GenomicRanges, dplyr, fuzzyjoin, rtracklayer, magrittr, qpdf,
readr, seqminer, stringr, tibble, tidyHeatmap, tidyr,
AnnotationHub, easyPubMed, org.Hs.eg.db, EpiMix, BiocGenerics,
ggplot2, ExperimentHub, rlang, withr, data.table
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), devtools,
roxygen2, png
License: GPL-3
MD5sum: f1bced56a3ea6a4ab14097033d2deca4
NeedsCompilation: no
Title: Identify oncogenes and tumor suppressor genes from omics data
Description: The understanding of cancer mechanism requires the
identification of genes playing a role in the development of
the pathology and the characterization of their role (notably
oncogenes and tumor suppressors). We present an updated version
of the R/bioconductor package called MoonlightR, namely
Moonlight2R, which returns a list of candidate driver genes for
specific cancer types on the basis of omics data integration.
The Moonlight framework contains a primary layer where gene
expression data and information about biological processes are
integrated to predict genes called oncogenic mediators, divided
into putative tumor suppressors and putative oncogenes. This is
done through functional enrichment analyses, gene regulatory
networks and upstream regulator analyses to score the
importance of well-known biological processes with respect to
the studied cancer type. By evaluating the effect of the
oncogenic mediators on biological processes or through random
forests, the primary layer predicts two putative roles for the
oncogenic mediators: i) tumor suppressor genes (TSGs) and ii)
oncogenes (OCGs). As gene expression data alone is not enough
to explain the deregulation of the genes, a second layer of
evidence is needed. We have automated the integration of a
secondary mutational layer through new functionalities in
Moonlight2R. These functionalities analyze mutations in the
cancer cohort and classifies these into driver and passenger
mutations using the driver mutation prediction tool,
CScape-somatic. Those oncogenic mediators with at least one
driver mutation are retained as the driver genes. As a
consequence, this methodology does not only identify genes
playing a dual role (e.g. TSG in one cancer type and OCG in
another) but also helps in elucidating the biological processes
underlying their specific roles. In particular, Moonlight2R can
be used to discover OCGs and TSGs in the same cancer type. This
may for instance help in answering the question whether some
genes change role between early stages (I, II) and late stages
(III, IV). In the future, this analysis could be useful to
determine the causes of different resistances to
chemotherapeutic treatments. An additional mechanistic layer
evaluates if there are mutations affecting the protein
stability of the transcription factors (TFs) of the TSGs and
OCGs, as that may have an effect on the expression of the
genes.
biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation,
GeneExpression, MethylationArray, DifferentialExpression,
Pathways, Network, Survival, GeneSetEnrichment,
NetworkEnrichment
Author: Mona Nourbakhsh [aut], Astrid Saksager [aut], Nikola Tom [aut],
Katrine Meldgård [aut], Anna Melidi [aut], Xi Steven Chen
[aut], Antonio Colaprico [aut], Catharina Olsen [aut], Matteo
Tiberti [cre, aut], Elena Papaleo [aut]
Maintainer: Matteo Tiberti <tiberti@cancer.dk>
URL: https://github.com/ELELAB/Moonlight2R
SystemRequirements: CScapeSomatic
VignetteBuilder: knitr
BugReports: https://github.com/ELELAB/Moonlight2R/issues
git_url: https://git.bioconductor.org/packages/Moonlight2R
git_branch: devel
git_last_commit: 552fc09
git_last_commit_date: 2025-01-24
Date/Publication: 2025-01-24
source.ver: src/contrib/Moonlight2R_1.5.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Moonlight2R_1.5.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Moonlight2R_1.5.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Moonlight2R_1.5.4.tgz
vignettes: vignettes/Moonlight2R/inst/doc/Moonlight2R.html
vignetteTitles: A workflow to study mechanistic indicators for driver
gene prediction with Moonlight
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: FALSE
Rfiles: vignettes/Moonlight2R/inst/doc/Moonlight2R.R
dependencyCount: 223
Package: MoonlightR
Version: 1.33.0
Depends: R (>= 3.5), doParallel, foreach
Imports: parmigene, randomForest, SummarizedExperiment, gplots,
circlize, RColorBrewer, HiveR, clusterProfiler, DOSE, Biobase,
limma, grDevices, graphics, TCGAbiolinks, GEOquery, stats,
RISmed, grid, utils
Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2,
png, edgeR
License: GPL (>= 3)
MD5sum: f241d49777f3c3056102beff18f3edc7
NeedsCompilation: no
Title: Identify oncogenes and tumor suppressor genes from omics data
Description: Motivation: The understanding of cancer mechanism requires
the identification of genes playing a role in the development
of the pathology and the characterization of their role
(notably oncogenes and tumor suppressors). Results: We present
an R/bioconductor package called MoonlightR which returns a
list of candidate driver genes for specific cancer types on the
basis of TCGA expression data. The method first infers gene
regulatory networks and then carries out a functional
enrichment analysis (FEA) (implementing an upstream regulator
analysis, URA) to score the importance of well-known biological
processes with respect to the studied cancer type. Eventually,
by means of random forests, MoonlightR predicts two specific
roles for the candidate driver genes: i) tumor suppressor genes
(TSGs) and ii) oncogenes (OCGs). As a consequence, this
methodology does not only identify genes playing a dual role
(e.g. TSG in one cancer type and OCG in another) but also helps
in elucidating the biological processes underlying their
specific roles. In particular, MoonlightR can be used to
discover OCGs and TSGs in the same cancer type. This may help
in answering the question whether some genes change role
between early stages (I, II) and late stages (III, IV) in
breast cancer. In the future, this analysis could be useful to
determine the causes of different resistances to
chemotherapeutic treatments.
biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation,
GeneExpression, MethylationArray, DifferentialExpression,
Pathways, Network, Survival, GeneSetEnrichment,
NetworkEnrichment
Author: Antonio Colaprico [aut], Catharina Olsen [aut], Matthew H.
Bailey [aut], Gabriel J. Odom [aut], Thilde Terkelsen [aut],
Mona Nourbakhsh [aut], Astrid Saksager [aut], Tiago C. Silva
[aut], André V. Olsen [aut], Laura Cantini [aut], Andrei
Zinovyev [aut], Emmanuel Barillot [aut], Houtan Noushmehr
[aut], Gloria Bertoli [aut], Isabella Castiglioni [aut],
Claudia Cava [aut], Gianluca Bontempi [aut], Xi Steven Chen
[aut], Elena Papaleo [aut], Matteo Tiberti [cre, aut]
Maintainer: Matteo Tiberti <tiberti@cancer.dk>
URL: https://github.com/ELELAB/MoonlightR
VignetteBuilder: knitr
BugReports: https://github.com/ELELAB/MoonlightR/issues
git_url: https://git.bioconductor.org/packages/MoonlightR
git_branch: devel
git_last_commit: e51fead
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MoonlightR_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MoonlightR_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MoonlightR_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MoonlightR_1.33.0.tgz
vignettes: vignettes/MoonlightR/inst/doc/Moonlight.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MoonlightR/inst/doc/Moonlight.R
dependencyCount: 188
Package: mosaics
Version: 2.45.0
Depends: R (>= 3.0.0), methods, graphics, Rcpp
Imports: MASS, splines, lattice, IRanges, GenomicRanges,
GenomicAlignments, Rsamtools, GenomeInfoDb, S4Vectors
LinkingTo: Rcpp
Suggests: mosaicsExample
Enhances: parallel
License: GPL (>= 2)
MD5sum: dd506943324f7d75456f812653505e15
NeedsCompilation: yes
Title: MOSAiCS (MOdel-based one and two Sample Analysis and Inference
for ChIP-Seq)
Description: This package provides functions for fitting MOSAiCS and
MOSAiCS-HMM, a statistical framework to analyze one-sample or
two-sample ChIP-seq data of transcription factor binding and
histone modification.
biocViews: ChIPseq, Sequencing, Transcription, Genetics, Bioinformatics
Author: Dongjun Chung, Pei Fen Kuan, Rene Welch, Sunduz Keles
Maintainer: Dongjun Chung <dongjun.chung@gmail.com>
URL: http://groups.google.com/group/mosaics_user_group
SystemRequirements: Perl
git_url: https://git.bioconductor.org/packages/mosaics
git_branch: devel
git_last_commit: 43771c2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mosaics_2.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mosaics_2.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mosaics_2.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mosaics_2.45.0.tgz
vignettes: vignettes/mosaics/inst/doc/mosaics-example.pdf
vignetteTitles: MOSAiCS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mosaics/inst/doc/mosaics-example.R
dependencyCount: 54
Package: mosbi
Version: 1.13.0
Depends: R (>= 4.1)
Imports: Rcpp, BH, xml2, methods, igraph, fabia, RcppParallel, biclust,
isa2, QUBIC, akmbiclust, RColorBrewer
LinkingTo: Rcpp, BH, RcppParallel
Suggests: knitr, rmarkdown, BiocGenerics, runibic, BiocStyle, testthat
(>= 3.0.0)
License: AGPL-3 + file LICENSE
Archs: x64
MD5sum: 58b3ebff2c6f295d02bc4345a37c893a
NeedsCompilation: yes
Title: Molecular Signature identification using Biclustering
Description: This package is a implementation of biclustering ensemble
method MoSBi (Molecular signature Identification from
Biclustering). MoSBi provides standardized interfaces for
biclustering results and can combine their results with a
multi-algorithm ensemble approach to compute robust ensemble
biclusters on molecular omics data. This is done by computing
similarity networks of biclusters and filtering for overlaps
using a custom error model. After that, the louvain modularity
it used to extract bicluster communities from the similarity
network, which can then be converted to ensemble biclusters.
Additionally, MoSBi includes several network visualization
methods to give an intuitive and scalable overview of the
results. MoSBi comes with several biclustering algorithms, but
can be easily extended to new biclustering algorithms.
biocViews: Software, StatisticalMethod, Clustering, Network
Author: Tim Daniel Rose [cre, aut], Josch Konstantin Pauling [aut],
Nikolai Koehler [aut]
Maintainer: Tim Daniel Rose <tim.rose@wzw.tum.de>
SystemRequirements: C++17, GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/mosbi
git_branch: devel
git_last_commit: ba952ac
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mosbi_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mosbi_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mosbi_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mosbi_1.13.0.tgz
vignettes: vignettes/mosbi/inst/doc/example-workflow.html,
vignettes/mosbi/inst/doc/similarity-metrics-evaluation.html
vignetteTitles: example-workflow, similarity-metrics-evaluation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/mosbi/inst/doc/example-workflow.R,
vignettes/mosbi/inst/doc/similarity-metrics-evaluation.R
dependencyCount: 63
Package: MOSClip
Version: 1.1.0
Depends: R (>= 4.4.0)
Imports: MultiAssayExperiment, methods, survminer, graph, graphite,
AnnotationDbi, checkmate, ggplot2, gridExtra, igraph, pheatmap,
survival, RColorBrewer, SuperExactTest, reshape, NbClust,
S4Vectors, grDevices, graphics, stats, utils, ComplexHeatmap,
FactoMineR, circlize, corpcor, coxrobust, elasticnet, gRbase,
ggplotify, qpgraph, org.Hs.eg.db, Matrix
Suggests: RUnit, BiocGenerics, MASS, BiocStyle, knitr, EDASeq,
rmarkdown, kableExtra, testthat (>= 3.0.0)
License: AGPL-3
MD5sum: 1fbf147a8d3216b22125600ec23fe926
NeedsCompilation: no
Title: Multi Omics Survival Clip
Description: Topological pathway analysis tool able to integrate
multi-omics data. It finds survival-associated modules or
significant modules for two-class analysis. This tool have two
main methods: pathway tests and module tests. The latter method
allows the user to dig inside the pathways itself.
biocViews: Software, StatisticalMethod, GraphAndNetwork, Survival,
Regression, DimensionReduction, Pathways, Reactome
Author: Paolo Martini [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0146-1031>), Anna Bortolato [aut]
(ORCID: <https://orcid.org/0009-0009-9327-6084>), Anna Tanada
[aut] (ORCID: <https://orcid.org/0000-0003-3224-0538>), Enrica
Calura [aut] (ORCID: <https://orcid.org/0000-0001-8463-2432>),
Stefania Pirrotta [aut] (ORCID:
<https://orcid.org/0009-0004-0030-217X>), Federico Agostinis
[aut]
Maintainer: Paolo Martini <paolo.martini@unibs.it>
URL: https://github.com/CaluraLab/MOSClip/
VignetteBuilder: knitr
BugReports: https://github.com/CaluraLab/MOSClip/issues
git_url: https://git.bioconductor.org/packages/MOSClip
git_branch: devel
git_last_commit: 412bda8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MOSClip_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MOSClip_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MOSClip_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MOSClip_1.1.0.tgz
vignettes: vignettes/MOSClip/inst/doc/mosclip_vignette.html
vignetteTitles: MOSClip
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MOSClip/inst/doc/mosclip_vignette.R
dependencyCount: 219
Package: mosdef
Version: 1.3.1
Depends: R (>= 4.4.0)
Imports: DT, ggplot2, ggforce, ggrepel, graphics, grDevices, htmltools,
methods, AnnotationDbi, topGO, GO.db, clusterProfiler, goseq,
utils, RColorBrewer, rlang, DESeq2, scales,
SummarizedExperiment, S4Vectors, stats
Suggests: knitr, rmarkdown, macrophage, org.Hs.eg.db, GeneTonic,
testthat (>= 3.0.0), TxDb.Hsapiens.UCSC.hg38.knownGene,
BiocStyle
License: MIT + file LICENSE
MD5sum: 9598b0e72678a992178c8fa58e91c3a4
NeedsCompilation: no
Title: MOSt frequently used and useful Differential Expression
Functions
Description: This package provides functionality to run a number of
tasks in the differential expression analysis workflow. This
encompasses the most widely used steps, from running various
enrichment analysis tools with a unified interface to creating
plots and beautifying table components linking to external
websites and databases. This streamlines the generation of
comprehensive analysis reports.
biocViews: GeneExpression, Software, Transcription, Transcriptomics,
DifferentialExpression, Visualization, ReportWriting,
GeneSetEnrichment, GO
Author: Leon Dammer [aut] (ORCID:
<https://orcid.org/0009-0008-4132-7639>), Federico Marini [aut,
cre] (ORCID: <https://orcid.org/0000-0003-3252-7758>)
Maintainer: Federico Marini <marinif@uni-mainz.de>
URL: https://github.com/imbeimainz/mosdef
VignetteBuilder: knitr
BugReports: https://github.com/imbeimainz/mosdef/issues
git_url: https://git.bioconductor.org/packages/mosdef
git_branch: devel
git_last_commit: de8bb7a
git_last_commit_date: 2024-12-19
Date/Publication: 2024-12-20
source.ver: src/contrib/mosdef_1.3.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mosdef_1.3.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mosdef_1.3.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mosdef_1.3.1.tgz
vignettes: vignettes/mosdef/inst/doc/mosdef_userguide.html
vignetteTitles: The mosdef User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/mosdef/inst/doc/mosdef_userguide.R
importsMe: GeneTonic, ideal, pcaExplorer
dependencyCount: 181
Package: MOSim
Version: 2.3.0
Depends: R (>= 4.2.0)
Imports: HiddenMarkov, zoo, IRanges, S4Vectors, dplyr, ggplot2,
lazyeval, matrixStats, methods, rlang, stringi, stringr, scran,
Seurat, Signac, edgeR, Rcpp
LinkingTo: cpp11, Rcpp
Suggests: testthat, knitr, rmarkdown, codetools, BiocStyle, stats,
utils, purrr, scales, tibble, tidyr, Biobase, scater,
SingleCellExperiment, decor, markdown, Rsamtools, igraph,
leiden, bluster
License: GPL-3
Archs: x64
MD5sum: 2f58b2bf23b7a3bfe11716c6b3944ad6
NeedsCompilation: yes
Title: Multi-Omics Simulation (MOSim)
Description: MOSim package simulates multi-omic experiments that mimic
regulatory mechanisms within the cell, allowing flexible
experimental design including time course and multiple groups.
biocViews: Software, TimeCourse, ExperimentalDesign, RNASeq
Author: Carolina Monzó [aut], Carlos MartÃnez [aut], Sonia Tarazona
[cre, aut]
Maintainer: Sonia Tarazona <sotacam@gmail.com>
URL: https://github.com/ConesaLab/MOSim
VignetteBuilder: knitr
BugReports: https://github.com/ConesaLab/MOSim/issues
git_url: https://git.bioconductor.org/packages/MOSim
git_branch: devel
git_last_commit: 8e78c32
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MOSim_2.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MOSim_2.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MOSim_2.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MOSim_2.3.0.tgz
vignettes: vignettes/MOSim/inst/doc/MOSim.html,
vignettes/MOSim/inst/doc/scMOSim.html
vignetteTitles: %\VignetteEngine{knitr::knitr}Wiki of how to use mosim,
Wiki of how to use sc_mosim
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MOSim/inst/doc/MOSim.R,
vignettes/MOSim/inst/doc/scMOSim.R
dependencyCount: 198
Package: Motif2Site
Version: 1.11.0
Depends: R (>= 4.1)
Imports: S4Vectors, stats, utils, methods, grDevices, graphics,
BiocGenerics, BSgenome, GenomeInfoDb, MASS, IRanges,
GenomicRanges, Biostrings, GenomicAlignments, edgeR, mixtools
Suggests: BiocStyle, rmarkdown, knitr, BSgenome.Hsapiens.UCSC.hg38,
BSgenome.Mmusculus.UCSC.mm10,
BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Ecoli.NCBI.20080805
License: GPL-2
MD5sum: 2b9d46dad244176b365c4d8d79f31594
NeedsCompilation: no
Title: Detect binding sites from motifs and ChIP-seq experiments, and
compare binding sites across conditions
Description: Detect binding sites using motifs IUPAC sequence or bed
coordinates and ChIP-seq experiments in bed or bam format.
Combine/compare binding sites across experiments, tissues, or
conditions. All normalization and differential steps are done
using TMM-GLM method. Signal decomposition is done by setting
motifs as the centers of the mixture of normal distribution
curves.
biocViews: Software, Sequencing, ChIPSeq, DifferentialPeakCalling,
Epigenetics, SequenceMatching
Author: Peyman Zarrineh [cre, aut] (ORCID:
<https://orcid.org/0000-0003-4820-4101>)
Maintainer: Peyman Zarrineh <peyman.zarrineh@manchester.ac.uk>
VignetteBuilder: knitr
BugReports: https://github.com/ManchesterBioinference/Motif2Site/issues
git_url: https://git.bioconductor.org/packages/Motif2Site
git_branch: devel
git_last_commit: 9d1afd0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Motif2Site_1.11.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Motif2Site_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Motif2Site_1.11.0.tgz
vignettes: vignettes/Motif2Site/inst/doc/Motif2Site.html
vignetteTitles: Motif2Site
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Motif2Site/inst/doc/Motif2Site.R
dependencyCount: 125
Package: motifbreakR
Version: 2.21.0
Depends: R (>= 4.4.0), grid, MotifDb
Imports: methods, grDevices, stringr, parallel, BiocGenerics, S4Vectors
(>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges, Biostrings,
BSgenome, rtracklayer, VariantAnnotation, BiocParallel,
motifStack, Gviz, matrixStats, TFMPvalue, SummarizedExperiment,
pwalign, DT, bsicons, BiocFileCache, biomaRt, bslib, shiny,
vroom
Suggests: BSgenome.Hsapiens.UCSC.hg19,
SNPlocs.Hsapiens.dbSNP155.GRCh37, knitr, rmarkdown,
BSgenome.Drerio.UCSC.danRer7, BiocStyle,
BSgenome.Hsapiens.1000genomes.hs37d5,
BSgenome.Hsapiens.UCSC.hg19.masked,
BSgenome.Hsapiens.NCBI.GRCh38,
BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Hsapiens.UCSC.hg38
License: GPL-2
Archs: x64
MD5sum: 9fdf33eaa1dd098155fa573a39abcc82
NeedsCompilation: no
Title: A Package For Predicting The Disruptiveness Of Single Nucleotide
Polymorphisms On Transcription Factor Binding Sites
Description: We introduce motifbreakR, which allows the biologist to
judge in the first place whether the sequence surrounding the
polymorphism is a good match, and in the second place how much
information is gained or lost in one allele of the polymorphism
relative to another. MotifbreakR is both flexible and
extensible over previous offerings; giving a choice of
algorithms for interrogation of genomes with motifs from public
sources that users can choose from; these are 1) a weighted-sum
probability matrix, 2) log-probabilities, and 3) weighted by
relative entropy. MotifbreakR can predict effects for novel or
previously described variants in public databases, making it
suitable for tasks beyond the scope of its original design.
Lastly, it can be used to interrogate any genome curated within
Bioconductor (currently there are 32 species, a total of 109
versions).
biocViews: ChIPSeq, Visualization, MotifAnnotation, Transcription
Author: Simon Gert Coetzee [aut, cre] (ORCID:
<https://orcid.org/0000-0003-4267-5930>), Dennis J. Hazelett
[aut]
Maintainer: Simon Gert Coetzee <coetzee@uthscsa.edu>
VignetteBuilder: knitr
BugReports: https://github.com/Simon-Coetzee/motifbreakR/issues
git_url: https://git.bioconductor.org/packages/motifbreakR
git_branch: devel
git_last_commit: 81a16d2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/motifbreakR_2.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/motifbreakR_2.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/motifbreakR_2.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/motifbreakR_2.21.0.tgz
vignettes: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.html
vignetteTitles: motifbreakR: an Introduction
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.R
dependencyCount: 182
Package: motifcounter
Version: 1.31.0
Depends: R(>= 3.0)
Imports: Biostrings, methods
Suggests: knitr, rmarkdown, testthat, MotifDb, seqLogo, prettydoc
License: GPL-2
MD5sum: 0d381bd7135b436646920100113f46f0
NeedsCompilation: yes
Title: R package for analysing TFBSs in DNA sequences
Description: 'motifcounter' provides motif matching, motif counting and
motif enrichment functionality based on position frequency
matrices. The main features of the packages include the
utilization of higher-order background models and accounting
for self-overlapping motif matches when determining motif
enrichment. The background model allows to capture dinucleotide
(or higher-order nucleotide) composition adequately which may
reduced model biases and misleading results compared to using
simple GC background models. When conducting a motif enrichment
analysis based on the motif match count, the package relies on
a compound Poisson distribution or alternatively a
combinatorial model. These distribution account for
self-overlapping motif structures as exemplified by repeat-like
or palindromic motifs, and allow to determine the p-value and
fold-enrichment for a set of observed motif matches.
biocViews: Transcription,MotifAnnotation,SequenceMatching,Software
Author: Wolfgang Kopp [aut, cre]
Maintainer: Wolfgang Kopp <wolfgang.kopp@mdc-berlin.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/motifcounter
git_branch: devel
git_last_commit: 08f5123
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/motifcounter_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/motifcounter_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/motifcounter_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/motifcounter_1.31.0.tgz
vignettes: vignettes/motifcounter/inst/doc/motifcounter.html
vignetteTitles: Introduction to the `motifcounter` package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/motifcounter/inst/doc/motifcounter.R
dependencyCount: 25
Package: MotifDb
Version: 1.49.3
Depends: R (>= 3.5.0), methods, BiocGenerics, S4Vectors, IRanges,
GenomicRanges, Biostrings
Imports: rtracklayer, splitstackshape
Suggests: RUnit, seqLogo, BiocStyle, knitr, rmarkdown, formatR,
markdown
License: Artistic-2.0 | file LICENSE
License_is_FOSS: no
License_restricts_use: yes
MD5sum: e570ed0fe0ff1761b7b19334fa7134b6
NeedsCompilation: no
Title: An Annotated Collection of Protein-DNA Binding Sequence Motifs
Description: More than 9900 annotated position frequency matrices from
14 public sources, for multiple organisms.
biocViews: MotifAnnotation
Author: Paul Shannon, Matt Richards
Maintainer: Paul Shannon <pshannon@systemsbiology.org>
VignetteBuilder: knitr, rmarkdown, formatR, markdown
git_url: https://git.bioconductor.org/packages/MotifDb
git_branch: devel
git_last_commit: 625db80
git_last_commit_date: 2025-03-12
Date/Publication: 2025-03-13
source.ver: src/contrib/MotifDb_1.49.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MotifDb_1.49.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MotifDb_1.49.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MotifDb_1.49.3.tgz
vignettes: vignettes/MotifDb/inst/doc/MotifDb.html
vignetteTitles: "A collection of PWMs"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MotifDb/inst/doc/MotifDb.R
dependsOnMe: motifbreakR, generegulation
importsMe: rTRMui
suggestsMe: ATACseqQC, DiffLogo, enhancerHomologSearch, igvR, memes,
MMDiff2, motifcounter, motifStack, motifTestR,
profileScoreDist, PWMEnrich, rTRM, TENET, TFutils,
universalmotif, vtpnet
dependencyCount: 60
Package: motifmatchr
Version: 1.29.0
Depends: R (>= 3.3)
Imports: Matrix, Rcpp, methods, TFBSTools, Biostrings, BSgenome,
S4Vectors, SummarizedExperiment, GenomicRanges, IRanges,
Rsamtools, GenomeInfoDb
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19
License: GPL-3 + file LICENSE
Archs: x64
MD5sum: df20eee2c1360463a4fa803ba4a16a69
NeedsCompilation: yes
Title: Fast Motif Matching in R
Description: Quickly find motif matches for many motifs and many
sequences. Wraps C++ code from the MOODS motif calling library,
which was developed by Pasi Rastas, Janne Korhonen, and Petri
Martinmäki.
biocViews: MotifAnnotation
Author: Alicia Schep [aut, cre], Stanford University [cph]
Maintainer: Alicia Schep <aschep@gmail.com>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/motifmatchr
git_branch: devel
git_last_commit: 0c15309
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/motifmatchr_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/motifmatchr_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/motifmatchr_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/motifmatchr_1.29.0.tgz
vignettes: vignettes/motifmatchr/inst/doc/motifmatchr.html
vignetteTitles: motifmatchr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/motifmatchr/inst/doc/motifmatchr.R
importsMe: ATACseqTFEA, enhancerHomologSearch, epiregulon, esATAC,
pageRank, spatzie
suggestsMe: chromVAR, GRaNIE, MethReg, CAGEWorkflow, Signac
dependencyCount: 83
Package: MotifPeeker
Version: 0.99.13
Depends: R (>= 4.4.0)
Imports: BiocFileCache, BiocParallel, DT, ggplot2, plotly,
universalmotif, GenomicRanges, IRanges, rtracklayer, tools,
htmltools, rmarkdown, viridis, SummarizedExperiment,
htmlwidgets, Rsamtools, GenomicAlignments, GenomeInfoDb,
Biostrings, BSgenome, memes, S4Vectors, dplyr, purrr, tidyr,
heatmaply, stats, utils
Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38,
downloadthis, knitr, markdown, methods, remotes, rworkflows,
testthat (>= 3.0.0), withr, emoji, curl, jsonlite
License: GPL (>= 3)
MD5sum: b9d03f93a4050c340a95b945c0628ce2
NeedsCompilation: no
Title: Benchmarking Epigenomic Profiling Methods Using Motif Enrichment
Description: MotifPeeker is used to compare and analyse datasets from
epigenomic profiling methods with motif enrichment as the key
benchmark. The package outputs an HTML report consisting of
three sections: (1. General Metrics) Overview of peaks-related
general metrics for the datasets (FRiP scores, peak widths and
motif-summit distances). (2. Known Motif Enrichment Analysis)
Statistics for the frequency of user-provided motifs enriched
in the datasets. (3. De-Novo Motif Enrichment Analysis)
Statistics for the frequency of de-novo discovered motifs
enriched in the datasets and compared with known motifs.
biocViews: Epigenetics, Genetics, QualityControl, ChIPSeq,
MultipleComparison, FunctionalGenomics, MotifDiscovery,
SequenceMatching, Software, Alignment
Author: Hiranyamaya Dash [cre, aut] (ORCID:
<https://orcid.org/0009-0005-5514-505X>), Thomas Roberts [aut]
(ORCID: <https://orcid.org/0009-0006-6244-8670>), Maria Weinert
[aut] (ORCID: <https://orcid.org/0000-0001-6187-1000>), Nathan
Skene [aut] (ORCID: <https://orcid.org/0000-0002-6807-3180>)
Maintainer: Hiranyamaya Dash <hdash.work@gmail.com>
URL: https://github.com/neurogenomics/MotifPeeker
SystemRequirements: MEME Suite (v5.3.3 or above)
<http://meme-suite.org/doc/download.html>
VignetteBuilder: knitr
BugReports: https://github.com/neurogenomics/MotifPeeker/issues
git_url: https://git.bioconductor.org/packages/MotifPeeker
git_branch: devel
git_last_commit: 45eb2ac
git_last_commit_date: 2024-12-19
Date/Publication: 2024-12-19
source.ver: src/contrib/MotifPeeker_0.99.13.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MotifPeeker_0.99.13.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MotifPeeker_0.99.13.tgz
vignettes: vignettes/MotifPeeker/inst/doc/MotifPeeker.html,
vignettes/MotifPeeker/inst/doc/troubleshooting.html
vignetteTitles: MotifPeeker, troubleshooting
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: FALSE
Rfiles: vignettes/MotifPeeker/inst/doc/MotifPeeker.R,
vignettes/MotifPeeker/inst/doc/troubleshooting.R
dependencyCount: 188
Package: motifStack
Version: 1.51.1
Depends: R (>= 2.15.1), methods, grid
Imports: ade4, Biostrings, ggplot2, grDevices, graphics, htmlwidgets,
stats, stats4, utils, XML, TFBSTools
Suggests: Cairo, grImport, grImport2, BiocGenerics, MotifDb,
RColorBrewer, BiocStyle, knitr, RUnit, rmarkdown, JASPAR2020
License: GPL (>= 2)
MD5sum: fd9b03e033a72da2a718ff17b51d8aef
NeedsCompilation: no
Title: Plot stacked logos for single or multiple DNA, RNA and amino
acid sequence
Description: The motifStack package is designed for graphic
representation of multiple motifs with different similarity
scores. It works with both DNA/RNA sequence motif and amino
acid sequence motif. In addition, it provides the flexibility
for users to customize the graphic parameters such as the font
type and symbol colors.
biocViews: SequenceMatching, Visualization, Sequencing, Microarray,
Alignment, ChIPchip, ChIPSeq, MotifAnnotation, DataImport
Author: Jianhong Ou, Michael Brodsky, Scot Wolfe and Lihua Julie Zhu
Maintainer: Jianhong Ou <jou@morgridge.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/motifStack
git_branch: devel
git_last_commit: a56a420
git_last_commit_date: 2025-01-03
Date/Publication: 2025-01-03
source.ver: src/contrib/motifStack_1.51.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/motifStack_1.51.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/motifStack_1.51.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/motifStack_1.51.1.tgz
vignettes: vignettes/motifStack/inst/doc/motifStack_HTML.html
vignetteTitles: motifStack Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/motifStack/inst/doc/motifStack_HTML.R
dependsOnMe: generegulation
importsMe: ATACseqQC, atSNP, dagLogo, motifbreakR, ribosomeProfilingQC
suggestsMe: ChIPpeakAnno, TFutils, trackViewer, tripr, universalmotif
dependencyCount: 122
Package: motifTestR
Version: 1.3.4
Depends: Biostrings, GenomicRanges, ggplot2 (>= 3.5.0), R (>= 4.3.0),
Imports: GenomeInfoDb, graphics, harmonicmeanp, IRanges, matrixStats,
methods, parallel, patchwork, rlang, S4Vectors, stats,
universalmotif,
Suggests: AnnotationHub, BiocStyle, BSgenome.Hsapiens.UCSC.hg19,
ComplexUpset, extraChIPs, ggdendro, knitr, MotifDb, rmarkdown,
rtracklayer, testthat (>= 3.0.0), VGAM
License: GPL-3
Archs: x64
MD5sum: b44382d55f591185a4c256c0f52b009d
NeedsCompilation: no
Title: Perform key tests for binding motifs in sequence data
Description: Taking a set of sequence motifs as PWMs, test a set of
sequences for over-representation of these motifs, as well as
any positional features within the set of motifs. Enrichment
analysis can be undertaken using multiple statistical
approaches. The package also contains core functions to prepare
data for analysis, and to visualise results.
biocViews: MotifAnnotation, ChIPSeq, ChipOnChip, SequenceMatching,
Software
Author: Stevie Pederson [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8197-3303>)
Maintainer: Stevie Pederson <stephen.pederson.au@gmail.com>
URL: https://github.com/smped/motifTestR
VignetteBuilder: knitr
BugReports: https://github.com/smped/motifTestR/issues
git_url: https://git.bioconductor.org/packages/motifTestR
git_branch: devel
git_last_commit: d2b25a7
git_last_commit_date: 2025-03-07
Date/Publication: 2025-03-09
source.ver: src/contrib/motifTestR_1.3.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/motifTestR_1.3.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/motifTestR_1.3.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/motifTestR_1.3.4.tgz
vignettes: vignettes/motifTestR/inst/doc/motifAnalysis.html
vignetteTitles: Motif Analysis Using motifTestR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/motifTestR/inst/doc/motifAnalysis.R
dependencyCount: 65
Package: MouseFM
Version: 1.17.0
Depends: R (>= 4.0.0)
Imports: httr, curl, GenomicRanges, dplyr, ggplot2, reshape2, scales,
gtools, tidyr, data.table, jsonlite, rlist, GenomeInfoDb,
methods, biomaRt, stats, IRanges
Suggests: BiocStyle, testthat, knitr, rmarkdown
License: GPL-3
MD5sum: 6837b92bc761521efaa75c2d2a75372f
NeedsCompilation: no
Title: In-silico methods for genetic finemapping in inbred mice
Description: This package provides methods for genetic finemapping in
inbred mice by taking advantage of their very high homozygosity
rate (>95%).
biocViews: Genetics, SNP, GeneTarget, VariantAnnotation,
GenomicVariation, MultipleComparison, SystemsBiology,
MathematicalBiology, PatternLogic, GenePrediction,
BiomedicalInformatics, FunctionalGenomics
Author: Matthias Munz [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4728-3357>), Inken Wohlers [aut]
(ORCID: <https://orcid.org/0000-0003-4004-0464>), Hauke Busch
[aut] (ORCID: <https://orcid.org/0000-0003-4763-4521>)
Maintainer: Matthias Munz <matthias.munz@gmx.de>
VignetteBuilder: knitr
BugReports: https://github.com/matmu/MouseFM/issues
git_url: https://git.bioconductor.org/packages/MouseFM
git_branch: devel
git_last_commit: 16b409c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/MouseFM_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MouseFM_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MouseFM_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MouseFM_1.17.0.tgz
vignettes: vignettes/MouseFM/inst/doc/fetch.html,
vignettes/MouseFM/inst/doc/finemap.html,
vignettes/MouseFM/inst/doc/prio.html
vignetteTitles: Fetch, Finemapping, Prioritization
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MouseFM/inst/doc/fetch.R,
vignettes/MouseFM/inst/doc/finemap.R,
vignettes/MouseFM/inst/doc/prio.R
dependencyCount: 94
Package: MPAC
Version: 1.1.6
Depends: R (>= 4.4.0)
Imports: data.table (>= 1.14.2), SummarizedExperiment (>= 1.30.2),
BiocParallel (>= 1.28.3), fitdistrplus (>= 1.1), igraph (>=
1.4.3), BiocSingular (>= 1.10.0), S4Vectors (>= 0.32.3),
SingleCellExperiment (>= 1.16.0), bluster (>= 1.4.0), fgsea (>=
1.20.0), scran (>= 1.22.1), ComplexHeatmap (>= 2.16.0),
circlize (>= 0.4.16), scales (>= 1.3.0), stringr (>= 1.5.1),
viridis (>= 0.6.5), ggplot2 (>= 3.5.1), ggraph (>= 2.2.1),
survival (>= 3.7), survminer (>= 0.4.9), grid, stats
Suggests: rmarkdown, knitr, svglite, bookdown(>= 0.34), testthat (>=
3.0.0)
License: GPL-3
Archs: x64
MD5sum: f9e17bc13a151db01a1f8f1a98895ae9
NeedsCompilation: no
Title: Multi-omic Pathway Analysis of Cells
Description: Multi-omic Pathway Analysis of Cells (MPAC), integrates
multi-omic data for understanding cellular mechanisms. It
predicts novel patient groups with distinct pathway profiles as
well as identifying key pathway proteins with potential
clinical associations. From CNA and RNA-seq data, it determines
genes’ DNA and RNA states (i.e., repressed, normal, or
activated), which serve as the input for PARADIGM to calculate
Inferred Pathway Levels (IPLs). It also permutes DNA and RNA
states to create a background distribution to filter IPLs as a
way to remove events observed by chance. It provides multiple
methods for downstream analysis and visualization.
biocViews: Software, Technology, Sequencing, RNASeq, Survival,
Clustering, ImmunoOncology
Author: Peng Liu [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5655-2259>), Paul Ahlquist [aut],
Irene Ong [aut], Anthony Gitter [aut]
Maintainer: Peng Liu <pliu55.wisc+bioconductor@gmail.com>
URL: https://github.com/pliu55/MPAC
VignetteBuilder: knitr
BugReports: https://github.com/pliu55/MPAC/issues
git_url: https://git.bioconductor.org/packages/MPAC
git_branch: devel
git_last_commit: 31140d5
git_last_commit_date: 2025-03-14
Date/Publication: 2025-03-17
source.ver: src/contrib/MPAC_1.1.6.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MPAC_1.1.6.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MPAC_1.1.6.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MPAC_1.1.6.tgz
vignettes: vignettes/MPAC/inst/doc/MPAC.html
vignetteTitles: MPAC: Multi-omic Pathway Analysis of Cells
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MPAC/inst/doc/MPAC.R
dependencyCount: 181
Package: MPFE
Version: 1.43.0
License: GPL (>= 3)
MD5sum: fa116110e11e331c446f82b8aba12381
NeedsCompilation: no
Title: Estimation of the amplicon methylation pattern distribution from
bisulphite sequencing data
Description: Estimate distribution of methylation patterns from a table
of counts from a bisulphite sequencing experiment given a
non-conversion rate and read error rate.
biocViews: HighThroughputSequencingData, DNAMethylation, MethylSeq
Author: Peijie Lin, Sylvain Foret, Conrad Burden
Maintainer: Conrad Burden <conrad.burden@anu.edu.au>
git_url: https://git.bioconductor.org/packages/MPFE
git_branch: devel
git_last_commit: 939ad78
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MPFE_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MPFE_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MPFE_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MPFE_1.43.0.tgz
vignettes: vignettes/MPFE/inst/doc/MPFE.pdf
vignetteTitles: MPFE
hasREADME: TRUE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MPFE/inst/doc/MPFE.R
dependencyCount: 0
Package: mpra
Version: 1.29.0
Depends: R (>= 3.5.0), methods, BiocGenerics, SummarizedExperiment,
limma
Imports: S4Vectors, scales, stats, graphics, statmod
Suggests: BiocStyle, knitr, rmarkdown, RUnit
License: Artistic-2.0
MD5sum: 51cfa1dd391f8882b8f32fdd0b60b054
NeedsCompilation: no
Title: Analyze massively parallel reporter assays
Description: Tools for data management, count preprocessing, and
differential analysis in massively parallel report assays
(MPRA).
biocViews: Software, GeneRegulation, Sequencing, FunctionalGenomics
Author: Leslie Myint [cre, aut], Kasper D. Hansen [aut]
Maintainer: Leslie Myint <leslie.myint@gmail.com>
URL: https://github.com/hansenlab/mpra
VignetteBuilder: knitr
BugReports: https://github.com/hansenlab/mpra/issues
git_url: https://git.bioconductor.org/packages/mpra
git_branch: devel
git_last_commit: 033bb6a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mpra_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mpra_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mpra_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mpra_1.29.0.tgz
vignettes: vignettes/mpra/inst/doc/mpra.html
vignetteTitles: mpra User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mpra/inst/doc/mpra.R
dependencyCount: 49
Package: MPRAnalyze
Version: 1.25.0
Imports: BiocParallel, methods, progress, stats, SummarizedExperiment
Suggests: knitr
License: GPL-3
MD5sum: 8b4a43db43ae08b83a24c4c6c460701f
NeedsCompilation: no
Title: Statistical Analysis of MPRA data
Description: MPRAnalyze provides statistical framework for the analysis
of data generated by Massively Parallel Reporter Assays
(MPRAs), used to directly measure enhancer activity. MPRAnalyze
can be used for quantification of enhancer activity,
classification of active enhancers and comparative analyses of
enhancer activity between conditions. MPRAnalyze construct a
nested pair of generalized linear models (GLMs) to relate the
DNA and RNA observations, easily adjustable to various
experimental designs and conditions, and provides a set of
rigorous statistical testig schemes.
biocViews: ImmunoOncology, Software, StatisticalMethod, Sequencing,
GeneExpression, CellBiology, CellBasedAssays,
DifferentialExpression, ExperimentalDesign, Classification
Author: Tal Ashuach [aut, cre], David S Fischer [aut], Anat Kriemer
[ctb], Fabian J Theis [ctb], Nir Yosef [ctb],
Maintainer: Tal Ashuach <tal_ashuach@berkeley.edu>
URL: https://github.com/YosefLab/MPRAnalyze
VignetteBuilder: knitr
BugReports: https://github.com/YosefLab/MPRAnalyze
git_url: https://git.bioconductor.org/packages/MPRAnalyze
git_branch: devel
git_last_commit: a9ca154
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MPRAnalyze_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MPRAnalyze_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MPRAnalyze_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MPRAnalyze_1.25.0.tgz
vignettes: vignettes/MPRAnalyze/inst/doc/vignette.html
vignetteTitles: Analyzing MPRA data with MPRAnalyze
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MPRAnalyze/inst/doc/vignette.R
dependencyCount: 55
Package: msa
Version: 1.39.2
Depends: R (>= 3.3.0), methods, Biostrings (>= 2.40.0)
Imports: Rcpp (>= 0.11.1), BiocGenerics, IRanges (>= 1.20.0),
S4Vectors, tools
LinkingTo: Rcpp
Suggests: Biobase, knitr, seqinr, ape (>= 5.1), phangorn, pwalign
License: GPL (>= 2)
MD5sum: 721b2a9e5c4da9d389fb292143f9bd94
NeedsCompilation: yes
Title: Multiple Sequence Alignment
Description: The 'msa' package provides a unified R/Bioconductor
interface to the multiple sequence alignment algorithms
ClustalW, ClustalOmega, and Muscle. All three algorithms are
integrated in the package, therefore, they do not depend on any
external software tools and are available for all major
platforms. The multiple sequence alignment algorithms are
complemented by a function for pretty-printing multiple
sequence alignments using the LaTeX package TeXshade.
biocViews: MultipleSequenceAlignment, Alignment, MultipleComparison,
Sequencing
Author: Enrico Bonatesta [aut], Christoph Kainrath [aut], Ulrich
Bodenhofer [aut,cre]
Maintainer: Ulrich Bodenhofer <ulrich@bodenhofer.com>
URL: https://github.com/UBod/msa
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/msa
git_branch: devel
git_last_commit: 20dabf2
git_last_commit_date: 2025-02-14
Date/Publication: 2025-02-14
source.ver: src/contrib/msa_1.39.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/msa_1.39.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/msa_1.39.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/msa_1.39.2.tgz
vignettes: vignettes/msa/inst/doc/msa.pdf
vignetteTitles: msa - An R Package for Multiple Sequence Alignment
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/msa/inst/doc/msa.R
importsMe: LymphoSeq, odseq, surfaltr, SeedMatchR
suggestsMe: idpr, AntibodyForests, bio3d
dependencyCount: 26
Package: MSA2dist
Version: 1.11.2
Depends: R (>= 4.4.0)
Imports: Rcpp, Biostrings, GenomicRanges, IRanges, ape, doParallel,
dplyr, foreach, methods, parallel, pwalign, rlang, seqinr,
stats, stringi, stringr, tibble, tidyr, utils
LinkingTo: Rcpp, RcppThread
Suggests: rmarkdown, knitr, devtools, testthat, ggplot2, BiocStyle
License: GPL-3 + file LICENSE
MD5sum: 3c49b415e3c8a7f5244715ce2b4cb4f7
NeedsCompilation: yes
Title: MSA2dist calculates pairwise distances between all sequences of
a DNAStringSet or a AAStringSet using a custom score matrix and
conducts codon based analysis
Description: MSA2dist calculates pairwise distances between all
sequences of a DNAStringSet or a AAStringSet using a custom
score matrix and conducts codon based analysis. It uses scoring
matrices to be used in these pairwise distance calculations
which can be adapted to any scoring for DNA or AA characters.
E.g. by using literal distances MSA2dist calculates pairwise
IUPAC distances. DNAStringSet alignments can be analysed as
codon alignments to look for synonymous and nonsynonymous
substitutions (dN/dS) in a parallelised fashion using a variety
of substitution models. Non-aligned coding sequences can be
directly used to construct pairwise codon alignments
(global/local) and calculate dN/dS without any external
dependencies.
biocViews: Alignment, Sequencing, Genetics, GO
Author: Kristian K Ullrich [aut, cre] (ORCID:
<https://orcid.org/0000-0003-4308-9626>)
Maintainer: Kristian K Ullrich <ullrich@evolbio.mpg.de>
URL: https://gitlab.gwdg.de/mpievolbio-it/MSA2dist,
https://mpievolbio-it.pages.gwdg.de/MSA2dist/
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://gitlab.gwdg.de/mpievolbio-it/MSA2dist/issues
git_url: https://git.bioconductor.org/packages/MSA2dist
git_branch: devel
git_last_commit: dcadc7a
git_last_commit_date: 2025-03-28
Date/Publication: 2025-03-28
source.ver: src/contrib/MSA2dist_1.11.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MSA2dist_1.11.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MSA2dist_1.11.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MSA2dist_1.11.2.tgz
vignettes: vignettes/MSA2dist/inst/doc/MSA2dist.html
vignetteTitles: MSA2dist Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MSA2dist/inst/doc/MSA2dist.R
importsMe: doubletrouble
dependencyCount: 65
Package: MsBackendMassbank
Version: 1.15.3
Depends: R (>= 4.0), Spectra (>= 1.15.10)
Imports: BiocParallel, S4Vectors, IRanges, methods, ProtGenerics (>=
1.35.3), MsCoreUtils, DBI, utils
Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19),
RSQLite, rmarkdown
License: Artistic-2.0
MD5sum: 9e40f739f092e3161a8397250305c4a8
NeedsCompilation: no
Title: Mass Spectrometry Data Backend for MassBank record Files
Description: Mass spectrometry (MS) data backend supporting import and
export of MS/MS library spectra from MassBank record files.
Different backends are available that allow handling of data in
plain MassBank text file format or allow also to interact
directly with MassBank SQL databases. Objects from this package
are supposed to be used with the Spectra Bioconductor package.
This package thus adds MassBank support to the Spectra package.
biocViews: Infrastructure, MassSpectrometry, Metabolomics, DataImport
Author: RforMassSpectrometry Package Maintainer [cre], Michael Witting
[aut] (ORCID: <https://orcid.org/0000-0002-1462-4426>),
Johannes Rainer [aut] (ORCID:
<https://orcid.org/0000-0002-6977-7147>), Michael Stravs [ctb]
Maintainer: RforMassSpectrometry Package Maintainer
<maintainer@rformassspectrometry.org>
URL: https://github.com/RforMassSpectrometry/MsBackendMassbank
VignetteBuilder: knitr
BugReports:
https://github.com/RforMassSpectrometry/MsBackendMassbank/issues
git_url: https://git.bioconductor.org/packages/MsBackendMassbank
git_branch: devel
git_last_commit: 0349704
git_last_commit_date: 2025-02-26
Date/Publication: 2025-02-26
source.ver: src/contrib/MsBackendMassbank_1.15.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MsBackendMassbank_1.15.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MsBackendMassbank_1.15.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MsBackendMassbank_1.15.3.tgz
vignettes: vignettes/MsBackendMassbank/inst/doc/MsBackendMassbank.html
vignetteTitles: Description and usage of MsBackendMassbank
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MsBackendMassbank/inst/doc/MsBackendMassbank.R
dependencyCount: 31
Package: MsBackendMetaboLights
Version: 1.1.4
Depends: R (>= 4.2.0), Spectra (>= 1.15.12)
Imports: curl, ProtGenerics, BiocFileCache, S4Vectors, methods,
progress
Suggests: testthat, rmarkdown, mzR, knitr, BiocStyle
License: Artistic-2.0
MD5sum: 9049d60980b4e0b7072202be6df6f66b
NeedsCompilation: no
Title: Retrieve Mass Spectrometry Data from MetaboLights
Description: MetaboLights is one of the main public repositories for
storage of metabolomics experiments, which includes analysis
results as well as raw data. The MsBackendMetaboLights package
provides functionality to retrieve and represent mass
spectrometry (MS) data from MetaboLights. Data files are
downloaded and cached locally avoiding repetitive downloads. MS
data from metabolomics experiments can thus be directly and
seamlessly integrated into R-based analysis workflows with the
Spectra and MsBackendMetaboLights package.
biocViews: Infrastructure, MassSpectrometry, Metabolomics, DataImport,
Proteomics
Author: Johannes Rainer [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6977-7147>), Philippine Louail
[aut] (ORCID: <https://orcid.org/0009-0007-5429-6846>)
Maintainer: Johannes Rainer <Johannes.Rainer@eurac.edu>
URL: https://github.com/RforMassSpectrometry/MsBackendMetaboLights
VignetteBuilder: knitr
BugReports:
https://github.com/RforMassSpectrometry/MsBackendMetaboLights/issues
git_url: https://git.bioconductor.org/packages/MsBackendMetaboLights
git_branch: devel
git_last_commit: 4566726
git_last_commit_date: 2025-03-26
Date/Publication: 2025-03-26
source.ver: src/contrib/MsBackendMetaboLights_1.1.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MsBackendMetaboLights_1.1.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MsBackendMetaboLights_1.1.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MsBackendMetaboLights_1.1.4.tgz
vignettes:
vignettes/MsBackendMetaboLights/inst/doc/MsBackendMetaboLights.html
vignetteTitles: Retrieve and Use Mass Spectrometry Data from
MetaboLights
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/MsBackendMetaboLights/inst/doc/MsBackendMetaboLights.R
dependencyCount: 72
Package: MsBackendMgf
Version: 1.15.2
Depends: R (>= 4.0), Spectra (>= 1.5.14)
Imports: ProtGenerics (>= 1.35.3), BiocParallel, S4Vectors, IRanges,
MsCoreUtils, methods, stats
Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19),
rmarkdown
License: Artistic-2.0
Archs: x64
MD5sum: 48a3999380ba772a97b38c31160aea5b
NeedsCompilation: no
Title: Mass Spectrometry Data Backend for Mascot Generic Format (mgf)
Files
Description: Mass spectrometry (MS) data backend supporting import and
export of MS/MS spectra data from Mascot Generic Format (mgf)
files. Objects defined in this package are supposed to be used
with the Spectra Bioconductor package. This package thus adds
mgf file support to the Spectra package.
biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics,
DataImport
Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto
[aut] (ORCID: <https://orcid.org/0000-0002-1520-2268>),
Johannes Rainer [aut] (ORCID:
<https://orcid.org/0000-0002-6977-7147>), Sebastian Gibb [aut]
(ORCID: <https://orcid.org/0000-0001-7406-4443>), Michael
Witting [ctb] (ORCID: <https://orcid.org/0000-0002-1462-4426>),
Adriano Rutz [ctb] (ORCID:
<https://orcid.org/0000-0003-0443-9902>)
Maintainer: RforMassSpectrometry Package Maintainer
<maintainer@rformassspectrometry.org>
URL: https://github.com/RforMassSpectrometry/MsBackendMgf
VignetteBuilder: knitr
BugReports: https://github.com/RforMassSpectrometry/MsBackendMgf/issues
git_url: https://git.bioconductor.org/packages/MsBackendMgf
git_branch: devel
git_last_commit: 35c6333
git_last_commit_date: 2025-01-17
Date/Publication: 2025-01-17
source.ver: src/contrib/MsBackendMgf_1.15.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MsBackendMgf_1.15.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MsBackendMgf_1.15.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MsBackendMgf_1.15.2.tgz
vignettes: vignettes/MsBackendMgf/inst/doc/MsBackendMgf.html
vignetteTitles: Description and usage of MsBackendMgf
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MsBackendMgf/inst/doc/MsBackendMgf.R
suggestsMe: CompoundDb, MsBackendRawFileReader, xcms
dependencyCount: 30
Package: MsBackendMsp
Version: 1.11.1
Depends: R (>= 4.1.0), Spectra (>= 1.5.14)
Imports: ProtGenerics (>= 1.35.3), BiocParallel, S4Vectors, IRanges,
MsCoreUtils, methods, stats
Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19),
rmarkdown
License: Artistic-2.0
Archs: x64
MD5sum: 9deb3a47cac4eae6743d91bf7048454b
NeedsCompilation: no
Title: Mass Spectrometry Data Backend for NIST msp Files
Description: Mass spectrometry (MS) data backend supporting import and
handling of MS/MS spectra from NIST MSP Format (msp) files.
Import of data from files with different MSP *flavours* is
supported. Objects from this package add support for MSP files
to Bioconductor's Spectra package. This package is thus not
supposed to be used without the Spectra package that provides a
complete infrastructure for MS data handling.
biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics,
DataImport
Author: Neumann Steffen [aut] (ORCID:
<https://orcid.org/0000-0002-7899-7192>), Johannes Rainer [aut,
cre] (ORCID: <https://orcid.org/0000-0002-6977-7147>), Michael
Witting [ctb] (ORCID: <https://orcid.org/0000-0002-1462-4426>)
Maintainer: Johannes Rainer <Johannes.Rainer@eurac.edu>
URL: https://github.com/RforMassSpectrometry/MsBackendMsp
VignetteBuilder: knitr
BugReports: https://github.com/RforMassSpectrometry/MsBackendMsp/issues
git_url: https://git.bioconductor.org/packages/MsBackendMsp
git_branch: devel
git_last_commit: 75db366
git_last_commit_date: 2025-01-17
Date/Publication: 2025-01-17
source.ver: src/contrib/MsBackendMsp_1.11.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MsBackendMsp_1.11.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MsBackendMsp_1.11.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MsBackendMsp_1.11.1.tgz
vignettes: vignettes/MsBackendMsp/inst/doc/MsBackendMsp.html
vignetteTitles: MsBackendMsp
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MsBackendMsp/inst/doc/MsBackendMsp.R
dependencyCount: 30
Package: MsBackendRawFileReader
Version: 1.13.1
Depends: R (>= 4.1), methods, Spectra (>= 1.15.10)
Imports: ProtGenerics (>= 1.35.3), MsCoreUtils, S4Vectors, IRanges,
rawrr (>= 1.13.1), utils, BiocParallel
Suggests: BiocStyle (>= 2.5), ExperimentHub, MsBackendMgf, knitr,
lattice, mzR, protViz (>= 0.7), rmarkdown, tartare (>= 1.5),
testthat
License: GPL-3
MD5sum: 1b5c402dbf65a6265481b2a05757092b
NeedsCompilation: yes
Title: Mass Spectrometry Backend for Reading Thermo Fisher Scientific
raw Files
Description: implements a MsBackend for the Spectra package using
Thermo Fisher Scientific's NewRawFileReader .Net libraries. The
package is generalizing the functionality introduced by the
rawrr package Methods defined in this package are supposed to
extend the Spectra Bioconductor package.
biocViews: MassSpectrometry, Proteomics, Metabolomics
Author: Christian Panse [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1975-3064>), Tobias Kockmann [aut]
(ORCID: <https://orcid.org/0000-0002-1847-885X>), Roger Gine
Bertomeu [ctb] (ORCID: <https://orcid.org/0000-0003-0288-9619>)
Maintainer: Christian Panse <cp@fgcz.ethz.ch>
URL: https://github.com/fgcz/MsBackendRawFileReader
SystemRequirements: mono-runtime 4.x or higher (including System.Data
library) on Linux/macOS, .Net Framework (>= 4.5.1) on Microsoft
Windows.
VignetteBuilder: knitr
BugReports: https://github.com/fgcz/MsBackendRawFileReader/issues
git_url: https://git.bioconductor.org/packages/MsBackendRawFileReader
git_branch: devel
git_last_commit: d2c3762
git_last_commit_date: 2024-11-07
Date/Publication: 2024-11-07
source.ver: src/contrib/MsBackendRawFileReader_1.13.1.tar.gz
win.binary.ver:
bin/windows/contrib/4.5/MsBackendRawFileReader_1.13.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MsBackendRawFileReader_1.13.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MsBackendRawFileReader_1.13.1.tgz
vignettes:
vignettes/MsBackendRawFileReader/inst/doc/MsBackendRawFileReader.html
vignetteTitles: On Using and Extending the `MsBackendRawFileReader`
Backend.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: FALSE
Rfiles:
vignettes/MsBackendRawFileReader/inst/doc/MsBackendRawFileReader.R
dependencyCount: 31
Package: MsBackendSql
Version: 1.7.4
Depends: R (>= 4.2.0), Spectra (>= 1.17.9)
Imports: BiocParallel, S4Vectors, methods, ProtGenerics (>= 1.35.3),
DBI, MsCoreUtils, IRanges, data.table, progress, stringi,
fastmatch, BiocGenerics
Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19),
RSQLite, msdata, rmarkdown, microbenchmark, mzR
License: Artistic-2.0
MD5sum: dba8c6293022c117db387ee11e3f2d33
NeedsCompilation: no
Title: SQL-based Mass Spectrometry Data Backend
Description: SQL-based mass spectrometry (MS) data backend supporting
also storange and handling of very large data sets. Objects
from this package are supposed to be used with the Spectra
Bioconductor package. Through the MsBackendSql with its minimal
memory footprint, this package thus provides an alternative MS
data representation for very large or remote MS data sets.
biocViews: Infrastructure, MassSpectrometry, Metabolomics, DataImport,
Proteomics
Author: Johannes Rainer [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6977-7147>), Chong Tang [ctb],
Laurent Gatto [ctb] (ORCID:
<https://orcid.org/0000-0002-1520-2268>)
Maintainer: Johannes Rainer <Johannes.Rainer@eurac.edu>
URL: https://github.com/RforMassSpectrometry/MsBackendSql
VignetteBuilder: knitr
BugReports: https://github.com/RforMassSpectrometry/MsBackendSql/issues
git_url: https://git.bioconductor.org/packages/MsBackendSql
git_branch: devel
git_last_commit: a5f890a
git_last_commit_date: 2025-03-17
Date/Publication: 2025-03-17
source.ver: src/contrib/MsBackendSql_1.7.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MsBackendSql_1.7.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MsBackendSql_1.7.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MsBackendSql_1.7.4.tgz
vignettes: vignettes/MsBackendSql/inst/doc/MsBackendSql.html
vignetteTitles: Storing Mass Spectrometry Data in SQL Databases
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MsBackendSql/inst/doc/MsBackendSql.R
suggestsMe: MsExperiment
dependencyCount: 45
Package: MsCoreUtils
Version: 1.19.2
Depends: R (>= 3.6.0)
Imports: methods, S4Vectors, MASS, stats, clue
LinkingTo: Rcpp
Suggests: testthat, knitr, BiocStyle, rmarkdown, roxygen2, imputeLCMD,
impute, norm, pcaMethods, vsn, Matrix, preprocessCore,
missForest
Enhances: HDF5Array
License: Artistic-2.0
MD5sum: f3775387bc1a133684c35ba0c3b2e317
NeedsCompilation: yes
Title: Core Utils for Mass Spectrometry Data
Description: MsCoreUtils defines low-level functions for mass
spectrometry data and is independent of any high-level data
structures. These functions include mass spectra processing
functions (noise estimation, smoothing, binning, baseline
estimation), quantitative aggregation functions (median polish,
robust summarisation, ...), missing data imputation, data
normalisation (quantiles, vsn, ...), misc helper functions,
that are used across high-level data structure within the R for
Mass Spectrometry packages.
biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics
Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto
[aut] (ORCID: <https://orcid.org/0000-0002-1520-2268>),
Johannes Rainer [aut] (ORCID:
<https://orcid.org/0000-0002-6977-7147>), Sebastian Gibb [aut]
(ORCID: <https://orcid.org/0000-0001-7406-4443>), Philippine
Louail [aut] (ORCID: <https://orcid.org/0009-0007-5429-6846>),
Adriaan Sticker [ctb], Sigurdur Smarason [ctb], Thomas Naake
[ctb], Josep Maria Badia Aparicio [ctb] (ORCID:
<https://orcid.org/0000-0002-5704-1124>), Michael Witting [ctb]
(ORCID: <https://orcid.org/0000-0002-1462-4426>), Samuel
Wieczorek [ctb], Roger Gine Bertomeu [ctb] (ORCID:
<https://orcid.org/0000-0003-0288-9619>), Mar Garcia-Aloy [ctb]
(ORCID: <https://orcid.org/0000-0002-1330-6610>)
Maintainer: RforMassSpectrometry Package Maintainer
<maintainer@rformassspectrometry.org>
URL: https://github.com/RforMassSpectrometry/MsCoreUtils
VignetteBuilder: knitr
BugReports: https://github.com/RforMassSpectrometry/MsCoreUtils/issues
git_url: https://git.bioconductor.org/packages/MsCoreUtils
git_branch: devel
git_last_commit: e34fb5c
git_last_commit_date: 2025-03-21
Date/Publication: 2025-03-21
source.ver: src/contrib/MsCoreUtils_1.19.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MsCoreUtils_1.19.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MsCoreUtils_1.19.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MsCoreUtils_1.19.2.tgz
vignettes: vignettes/MsCoreUtils/inst/doc/MsCoreUtils.html
vignetteTitles: Core Utils for Mass Spectrometry Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MsCoreUtils/inst/doc/MsCoreUtils.R
importsMe: CompoundDb, hdxmsqc, MetaboAnnotation, MetaboCoreUtils,
MetCirc, MsBackendMassbank, MsBackendMgf, MsBackendMsp,
MsBackendRawFileReader, MsBackendSql, MsFeatures, MSnbase,
PSMatch, QFeatures, qmtools, scp, Spectra, SpectraQL, xcms
suggestsMe: MetNet, msqrob2
dependencyCount: 13
Package: MsDataHub
Version: 1.7.2
Imports: ExperimentHub, utils
Suggests: ExperimentHubData, DT, BiocStyle, knitr, rmarkdown, testthat
(>= 3.0.0), Spectra, mzR, PSMatch, QFeatures (>= 1.13.3)
License: Artistic-2.0
MD5sum: c892aeaadb9bbdd51d51fe61be4a4487
NeedsCompilation: no
Title: Mass Spectrometry Data on ExperimentHub
Description: The MsDataHub package uses the ExperimentHub
infrastructure to distribute raw mass spectrometry data files,
peptide spectrum matches or quantitative data from proteomics
and metabolomics experiments.
biocViews: ExperimentHubSoftware, MassSpectrometry, Proteomics,
Metabolomics
Author: Laurent Gatto [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1520-2268>), Kristina Gomoryova
[ctb] (ORCID: <https://orcid.org/0000-0003-4407-3917>),
Johannes Rainer [aut] (ORCID:
<https://orcid.org/0000-0002-6977-7147>)
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
URL: https://rformassspectrometry.github.io/MsDataHub
VignetteBuilder: knitr
BugReports: https://github.com/RforMassSpectrometry/MsDataHub/issues
git_url: https://git.bioconductor.org/packages/MsDataHub
git_branch: devel
git_last_commit: b459a13
git_last_commit_date: 2025-02-21
Date/Publication: 2025-02-21
source.ver: src/contrib/MsDataHub_1.7.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MsDataHub_1.7.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MsDataHub_1.7.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MsDataHub_1.7.2.tgz
vignettes: vignettes/MsDataHub/inst/doc/MsDataHub.html
vignetteTitles: Mass Spectrometry Data on ExperimentHub
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MsDataHub/inst/doc/MsDataHub.R
suggestsMe: msqrob2, QFeatures, scp
dependencyCount: 66
Package: MsExperiment
Version: 1.9.1
Depends: R (>= 4.2), ProtGenerics (>= 1.35.2),
Imports: methods, S4Vectors, IRanges, Spectra, SummarizedExperiment,
QFeatures, DBI, BiocGenerics
Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19),
rmarkdown, rpx, mzR, msdata, MsBackendSql (>= 1.3.2), RSQLite
License: Artistic-2.0
MD5sum: 88af7d5d0def67bd4e0cbc22efb629d0
NeedsCompilation: no
Title: Infrastructure for Mass Spectrometry Experiments
Description: Infrastructure to store and manage all aspects related to
a complete proteomics or metabolomics mass spectrometry (MS)
experiment. The MsExperiment package provides light-weight and
flexible containers for MS experiments building on the new MS
infrastructure provided by the Spectra, QFeatures and related
packages. Along with raw data representations, links to
original data files and sample annotations, additional metadata
or annotations can also be stored within the MsExperiment
container. To guarantee maximum flexibility only minimal
constraints are put on the type and content of the data within
the containers.
biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics,
ExperimentalDesign, DataImport
Author: Laurent Gatto [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1520-2268>), Johannes Rainer [aut]
(ORCID: <https://orcid.org/0000-0002-6977-7147>), Sebastian
Gibb [aut] (ORCID: <https://orcid.org/0000-0001-7406-4443>)
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
URL: https://github.com/RforMassSpectrometry/MsExperiment
VignetteBuilder: knitr
BugReports: https://github.com/RforMassSpectrometry/MsExperiment/issues
git_url: https://git.bioconductor.org/packages/MsExperiment
git_branch: devel
git_last_commit: bc06ba2
git_last_commit_date: 2025-02-11
Date/Publication: 2025-02-11
source.ver: src/contrib/MsExperiment_1.9.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MsExperiment_1.9.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MsExperiment_1.9.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MsExperiment_1.9.1.tgz
vignettes: vignettes/MsExperiment/inst/doc/MsExperiment.html
vignetteTitles: Managing Mass Spectrometry Experiments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MsExperiment/inst/doc/MsExperiment.R
importsMe: MsQuality, squallms, xcms
dependencyCount: 119
Package: MsFeatures
Version: 1.15.0
Depends: R (>= 4.1)
Imports: methods, ProtGenerics (>= 1.23.5), MsCoreUtils,
SummarizedExperiment, stats
Suggests: testthat, roxygen2, BiocStyle, pheatmap, knitr, rmarkdown
License: Artistic-2.0
MD5sum: 3607e6d9b7f1694f876e59f09fafa109
NeedsCompilation: no
Title: Functionality for Mass Spectrometry Features
Description: The MsFeature package defines functionality for Mass
Spectrometry features. This includes functions to group (LC-MS)
features based on some of their properties, such as retention
time (coeluting features), or correlation of signals across
samples. This packge hence allows to group features, and its
results can be used as an input for the `QFeatures` package
which allows to aggregate abundance levels of features within
each group. This package defines concepts and functions for
base and common data types, implementations for more specific
data types are expected to be implemented in the respective
packages (such as e.g. `xcms`). All functionality of this
package is implemented in a modular way which allows
combination of different grouping approaches and enables its
re-use in other R packages.
biocViews: Infrastructure, MassSpectrometry, Metabolomics
Author: Johannes Rainer [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6977-7147>)
Maintainer: Johannes Rainer <Johannes.Rainer@eurac.edu>
URL: https://github.com/RforMassSpectrometry/MsFeatures
VignetteBuilder: knitr
BugReports: https://github.com/RforMassSpectrometry/MsFeatures/issues
git_url: https://git.bioconductor.org/packages/MsFeatures
git_branch: devel
git_last_commit: 2fb27a6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MsFeatures_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MsFeatures_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MsFeatures_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MsFeatures_1.15.0.tgz
vignettes: vignettes/MsFeatures/inst/doc/MsFeatures.html
vignetteTitles: Grouping Mass Spectrometry Features
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MsFeatures/inst/doc/MsFeatures.R
importsMe: xcms
suggestsMe: qmtools
dependencyCount: 42
Package: msgbsR
Version: 1.31.0
Depends: R (>= 3.5.0), GenomicRanges, methods
Imports: BSgenome, easyRNASeq, edgeR, GenomicAlignments,
GenomicFeatures, GenomeInfoDb, ggbio, ggplot2, IRanges,
parallel, plyr, Rsamtools, R.utils, stats,
SummarizedExperiment, S4Vectors, utils
Suggests: roxygen2, BSgenome.Rnorvegicus.UCSC.rn6
License: GPL-2
Archs: x64
MD5sum: a7967b5a29d89101b3721b884007a2bf
NeedsCompilation: no
Title: msgbsR: methylation sensitive genotyping by sequencing (MS-GBS)
R functions
Description: Pipeline for the anaysis of a MS-GBS experiment.
biocViews: ImmunoOncology, DifferentialMethylation, DataImport,
Epigenetics, MethylSeq
Author: Benjamin Mayne
Maintainer: Benjamin Mayne <benjamin.mayne@adelaide.edu.au>
git_url: https://git.bioconductor.org/packages/msgbsR
git_branch: devel
git_last_commit: c7b70de
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/msgbsR_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/msgbsR_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/msgbsR_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/msgbsR_1.31.0.tgz
vignettes: vignettes/msgbsR/inst/doc/msgbsR_Vignette.pdf
vignetteTitles: msgbsR_Example
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/msgbsR/inst/doc/msgbsR_Vignette.R
dependencyCount: 181
Package: msImpute
Version: 1.17.0
Depends: R (>= 3.5.0)
Imports: softImpute, methods, stats, graphics, pdist, reticulate,
scran, data.table, FNN, matrixStats, limma, mvtnorm, tidyr,
dplyr
Suggests: BiocStyle, knitr, rmarkdown, ComplexHeatmap, imputeLCMD
License: GPL (>=2)
MD5sum: b4763d49a01f3f0bc622212b8497ca70
NeedsCompilation: no
Title: Imputation of label-free mass spectrometry peptides
Description: MsImpute is a package for imputation of peptide intensity
in proteomics experiments. It additionally contains tools for
MAR/MNAR diagnosis and assessment of distortions to the
probability distribution of the data post imputation. The
missing values are imputed by low-rank approximation of the
underlying data matrix if they are MAR (method = "v2"), by
Barycenter approach if missingness is MNAR ("v2-mnar"), or by
Peptide Identity Propagation (PIP).
biocViews: MassSpectrometry, Proteomics, Software
Author: Soroor Hediyeh-zadeh [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7513-6779>)
Maintainer: Soroor Hediyeh-zadeh <hediyehzadeh.s@wehi.edu.au>
SystemRequirements: python
VignetteBuilder: knitr
BugReports: https://github.com/DavisLaboratory/msImpute/issues
git_url: https://git.bioconductor.org/packages/msImpute
git_branch: devel
git_last_commit: 1dd79da
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/msImpute_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/msImpute_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/msImpute_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/msImpute_1.17.0.tgz
vignettes: vignettes/msImpute/inst/doc/msImpute-vignette.html
vignetteTitles: msImpute: proteomics missing values imputation and
diagnosis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/msImpute/inst/doc/msImpute-vignette.R
dependencyCount: 96
Package: mslp
Version: 1.9.0
Depends: R (>= 4.2.0)
Imports: data.table (>= 1.13.0), doRNG, fmsb, foreach, magrittr,
org.Hs.eg.db, pROC, randomForest, RankProd, stats, utils
Suggests: BiocStyle, doFuture, future, knitr, rmarkdown, roxygen2,
tinytest
License: GPL-3
MD5sum: 9a8e2d83d966760d465e1fb900f68b2f
NeedsCompilation: no
Title: Predict synthetic lethal partners of tumour mutations
Description: An integrated pipeline to predict the potential synthetic
lethality partners (SLPs) of tumour mutations, based on gene
expression, mutation profiling and cell line genetic screens
data. It has builtd-in support for data from cBioPortal. The
primary SLPs correlating with muations in WT and compensating
for the loss of function of mutations are predicted by random
forest based methods (GENIE3) and Rank Products, respectively.
Genetic screens are employed to identfy consensus SLPs leads to
reduced cell viability when perturbed.
biocViews: Pharmacogenetics, Pharmacogenomics
Author: Chunxuan Shao [aut, cre]
Maintainer: Chunxuan Shao <chunxuan@outlook.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/mslp
git_branch: devel
git_last_commit: ee73d2b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mslp_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mslp_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mslp_1.9.0.tgz
vignettes: vignettes/mslp/inst/doc/mslp.html
vignetteTitles: mslp
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mslp/inst/doc/mslp.R
dependencyCount: 63
Package: msmsEDA
Version: 1.45.0
Depends: R (>= 3.0.1), MSnbase
Imports: MASS, gplots, RColorBrewer
License: GPL-2
MD5sum: 669f1d42b72544607956faf5d98266c7
NeedsCompilation: no
Title: Exploratory Data Analysis of LC-MS/MS data by spectral counts
Description: Exploratory data analysis to assess the quality of a set
of LC-MS/MS experiments, and visualize de influence of the
involved factors.
biocViews: ImmunoOncology, Software, MassSpectrometry, Proteomics
Author: Josep Gregori, Alex Sanchez, and Josep Villanueva
Maintainer: Josep Gregori <josep.gregori@gmail.com>
git_url: https://git.bioconductor.org/packages/msmsEDA
git_branch: devel
git_last_commit: 42fe18d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/msmsEDA_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/msmsEDA_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/msmsEDA_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/msmsEDA_1.45.0.tgz
vignettes: vignettes/msmsEDA/inst/doc/msmsData-Vignette.pdf
vignetteTitles: msmsEDA: Batch effects detection in LC-MSMS experiments
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/msmsEDA/inst/doc/msmsData-Vignette.R
dependsOnMe: msmsTests
suggestsMe: Harman, RforProteomics
dependencyCount: 141
Package: msmsTests
Version: 1.45.0
Depends: R (>= 3.0.1), MSnbase, msmsEDA
Imports: edgeR, qvalue
Suggests: xtable
License: GPL-2
MD5sum: 4d417dc06f3f9985677f8843f5d5cd5a
NeedsCompilation: no
Title: LC-MS/MS Differential Expression Tests
Description: Statistical tests for label-free LC-MS/MS data by spectral
counts, to discover differentially expressed proteins between
two biological conditions. Three tests are available: Poisson
GLM regression, quasi-likelihood GLM regression, and the
negative binomial of the edgeR package.The three models admit
blocking factors to control for nuissance variables.To assure a
good level of reproducibility a post-test filter is available,
where we may set the minimum effect size considered biologicaly
relevant, and the minimum expression of the most abundant
condition.
biocViews: ImmunoOncology, Software, MassSpectrometry, Proteomics
Author: Josep Gregori, Alex Sanchez, and Josep Villanueva
Maintainer: Josep Gregori i Font <josep.gregori@gmail.com>
git_url: https://git.bioconductor.org/packages/msmsTests
git_branch: devel
git_last_commit: 0615a12
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/msmsTests_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/msmsTests_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/msmsTests_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/msmsTests_1.45.0.tgz
vignettes: vignettes/msmsTests/inst/doc/msmsTests-Vignette.pdf,
vignettes/msmsTests/inst/doc/msmsTests-Vignette2.pdf
vignetteTitles: msmsTests: post test filters to improve
reproducibility, msmsTests: controlling batch effects by
blocking
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/msmsTests/inst/doc/msmsTests-Vignette.R,
vignettes/msmsTests/inst/doc/msmsTests-Vignette2.R
importsMe: MSnID
suggestsMe: RforProteomics
dependencyCount: 145
Package: MSnbase
Version: 2.33.4
Depends: R (>= 3.5), methods, BiocGenerics (>= 0.7.1), Biobase (>=
2.15.2), mzR (>= 2.29.3), S4Vectors, ProtGenerics (>= 1.29.1)
Imports: MsCoreUtils, PSMatch, BiocParallel, IRanges (>= 2.13.28),
plyr, vsn, grid, stats4, affy, impute, pcaMethods, MALDIquant
(>= 1.16), mzID (>= 1.5.2), digest, lattice, ggplot2, scales,
MASS, Rcpp
LinkingTo: Rcpp
Suggests: testthat, pryr, gridExtra, microbenchmark, zoo, knitr (>=
1.1.0), rols, Rdisop, pRoloc, pRolocdata (>= 1.43.3), magick,
msdata (>= 0.19.3), roxygen2, rgl, rpx, AnnotationHub,
BiocStyle (>= 2.5.19), rmarkdown, imputeLCMD, norm, gplots,
XML, shiny, magrittr, SummarizedExperiment, Spectra
License: Artistic-2.0
MD5sum: 168fed4d0483a7dffa67d5926862c4d9
NeedsCompilation: yes
Title: Base Functions and Classes for Mass Spectrometry and Proteomics
Description: MSnbase provides infrastructure for manipulation,
processing and visualisation of mass spectrometry and
proteomics data, ranging from raw to quantitative and annotated
data.
biocViews: ImmunoOncology, Infrastructure, Proteomics,
MassSpectrometry, QualityControl, DataImport
Author: Laurent Gatto, Johannes Rainer and Sebastian Gibb with
contributions from Guangchuang Yu, Samuel Wieczorek,
Vasile-Cosmin Lazar, Vladislav Petyuk, Thomas Naake, Richie
Cotton, Arne Smits, Martina Fisher, Ludger Goeminne, Adriaan
Sticker, Lieven Clement and Pascal Maas.
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
URL: https://lgatto.github.io/MSnbase
VignetteBuilder: knitr
BugReports: https://github.com/lgatto/MSnbase/issues
git_url: https://git.bioconductor.org/packages/MSnbase
git_branch: devel
git_last_commit: 65bd7651
git_last_commit_date: 2025-03-14
Date/Publication: 2025-03-17
source.ver: src/contrib/MSnbase_2.33.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MSnbase_2.33.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MSnbase_2.33.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MSnbase_2.33.4.tgz
vignettes: vignettes/MSnbase/inst/doc/v01-MSnbase-demo.html,
vignettes/MSnbase/inst/doc/v02-MSnbase-io.html,
vignettes/MSnbase/inst/doc/v03-MSnbase-centroiding.html,
vignettes/MSnbase/inst/doc/v04-benchmarking.html,
vignettes/MSnbase/inst/doc/v05-MSnbase-development.html
vignetteTitles: Base Functions and Classes for MS-based Proteomics,
MSnbase IO capabilities, MSnbase: centroiding of profile-mode
MS data, MSnbase benchmarking, A short introduction to
`MSnbase` development
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSnbase/inst/doc/v01-MSnbase-demo.R,
vignettes/MSnbase/inst/doc/v02-MSnbase-io.R,
vignettes/MSnbase/inst/doc/v03-MSnbase-centroiding.R,
vignettes/MSnbase/inst/doc/v04-benchmarking.R,
vignettes/MSnbase/inst/doc/v05-MSnbase-development.R
dependsOnMe: bandle, msmsEDA, msmsTests, pRoloc, pRolocGUI,
qPLEXanalyzer, synapter, DAPARdata, pRolocdata, RforProteomics
importsMe: cliqueMS, CluMSID, DAPAR, DEP, MSnID, MSstatsQC, omXplore,
peakPantheR, PrInCE, PRONE, ptairMS, RMassBank, squallms,
topdownr, xcms, qPLEXdata
suggestsMe: AnnotationHub, biobroom, BiocGenerics, isobar, msPurity,
msqrob2, proDA, qcmetrics, wpm, msdata, LCMSQA, RAMClustR
dependencyCount: 135
Package: MSnID
Version: 1.41.0
Depends: R (>= 2.10), Rcpp
Imports: MSnbase (>= 1.12.1), mzID (>= 1.3.5), R.cache, foreach,
doParallel, parallel, methods, iterators, data.table, Biobase,
ProtGenerics, reshape2, dplyr, mzR, BiocStyle, msmsTests,
ggplot2, RUnit, BiocGenerics, Biostrings, purrr, rlang,
stringr, tibble, AnnotationHub, AnnotationDbi, xtable
License: Artistic-2.0
MD5sum: ce3e90b4b981d7e679ae742a19e6de81
NeedsCompilation: no
Title: Utilities for Exploration and Assessment of Confidence of LC-MSn
Proteomics Identifications
Description: Extracts MS/MS ID data from mzIdentML (leveraging mzID
package) or text files. After collating the search results from
multiple datasets it assesses their identification quality and
optimize filtering criteria to achieve the maximum number of
identifications while not exceeding a specified false discovery
rate. Also contains a number of utilities to explore the MS/MS
results and assess missed and irregular enzymatic cleavages,
mass measurement accuracy, etc.
biocViews: Proteomics, MassSpectrometry, ImmunoOncology
Author: Vlad Petyuk with contributions from Laurent Gatto
Maintainer: Vlad Petyuk <petyuk@gmail.com>
git_url: https://git.bioconductor.org/packages/MSnID
git_branch: devel
git_last_commit: dc343c5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MSnID_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MSnID_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MSnID_1.41.0.tgz
vignettes: vignettes/MSnID/inst/doc/handling_mods.pdf,
vignettes/MSnID/inst/doc/msnid_vignette.pdf
vignetteTitles: Handling Modifications with MSnID, MSnID Package for
Handling MS/MS Identifications
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSnID/inst/doc/handling_mods.R,
vignettes/MSnID/inst/doc/msnid_vignette.R
suggestsMe: RforProteomics
dependencyCount: 169
Package: mspms
Version: 0.99.7
Depends: R (>= 4.4.0)
Imports: QFeatures, SummarizedExperiment, magrittr, rlang, dplyr,
purrr, stats, tidyr, stringr, ggplot2, ggseqlogo, heatmaply,
readr, rstatix, tibble, ggpubr
Suggests: knitr, testthat (>= 3.0.0), downloadthis, DT, rmarkdown,
BiocStyle, imputeLCMD
License: MIT + file LICENSE
MD5sum: 7746d22ee2df667053eac599cd567a7f
NeedsCompilation: no
Title: Tools for the analysis of MSP-MS data
Description: This package provides functions for the analysis of data
generated by the multiplex substrate profiling by mass
spectrometry for proteases (MSP-MS) method. Data exported from
upstream proteomics software is accepted as input and
subsequently processed for analysis. Tools for statistical
analysis, visualization, and interpretation of the data are
provided.
biocViews: Proteomics, MassSpectrometry, Preprocessing
Author: Charlie Bayne [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1870-5298>)
Maintainer: Charlie Bayne <baynec2@gmail.com>
URL: https://github.com/baynec2/mspms
VignetteBuilder: knitr
BugReports: https://github.com/baynec2/mspms/issues
git_url: https://git.bioconductor.org/packages/mspms
git_branch: devel
git_last_commit: 9653326
git_last_commit_date: 2024-12-02
Date/Publication: 2024-12-03
source.ver: src/contrib/mspms_0.99.7.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mspms_0.99.7.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mspms_0.99.7.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mspms_0.99.7.tgz
vignettes: vignettes/mspms/inst/doc/mspms_vignette.html
vignetteTitles: mspms_vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/mspms/inst/doc/mspms_vignette.R
dependencyCount: 172
Package: MSPrep
Version: 1.17.0
Depends: R (>= 4.1.0)
Imports: SummarizedExperiment, S4Vectors, pcaMethods (>= 1.24.0), crmn,
preprocessCore, dplyr (>= 0.7), tidyr, tibble (>= 1.2),
magrittr, rlang, stats, stringr, methods, missForest, sva, VIM,
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 1.0.2)
License: GPL-3
MD5sum: aee1b9f3b7822c6a1815a634342214eb
NeedsCompilation: no
Title: Package for Summarizing, Filtering, Imputing, and Normalizing
Metabolomics Data
Description: Package performs summarization of replicates, filtering by
frequency, several different options for imputing missing data,
and a variety of options for transforming, batch correcting,
and normalizing data.
biocViews: Metabolomics, MassSpectrometry, Preprocessing
Author: Max McGrath [aut, cre], Matt Mulvahill [aut], Grant Hughes
[aut], Sean Jacobson [aut], Harrison Pielke-lombardo [aut],
Katerina Kechris [aut, cph, ths]
Maintainer: Max McGrath <max.mcgrath@ucdenver.edu>
URL: https://github.com/KechrisLab/MSPrep
VignetteBuilder: knitr
BugReports: https://github.com/KechrisLab/MSPrep/issues
git_url: https://git.bioconductor.org/packages/MSPrep
git_branch: devel
git_last_commit: 5202f3a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MSPrep_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MSPrep_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MSPrep_1.17.0.tgz
vignettes: vignettes/MSPrep/inst/doc/using_MSPrep.html
vignetteTitles: Using MSPrep
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSPrep/inst/doc/using_MSPrep.R
dependencyCount: 150
Package: msPurity
Version: 1.33.0
Depends: Rcpp
Imports: plyr, dplyr, dbplyr, magrittr, foreach, parallel, doSNOW,
stringr, mzR, reshape2, fastcluster, ggplot2, DBI, RSQLite
Suggests: MSnbase, testthat, xcms, BiocStyle, knitr, rmarkdown,
msPurityData, CAMERA, RPostgres, RMySQL
License: GPL-3 + file LICENSE
Archs: x64
MD5sum: b8cb1ac24bcd199e0a5c453a6bc71c02
NeedsCompilation: no
Title: Automated Evaluation of Precursor Ion Purity for Mass
Spectrometry Based Fragmentation in Metabolomics
Description: msPurity R package was developed to: 1) Assess the
spectral quality of fragmentation spectra by evaluating the
"precursor ion purity". 2) Process fragmentation spectra. 3)
Perform spectral matching. What is precursor ion purity? -What
we call "Precursor ion purity" is a measure of the contribution
of a selected precursor peak in an isolation window used for
fragmentation. The simple calculation involves dividing the
intensity of the selected precursor peak by the total intensity
of the isolation window. When assessing MS/MS spectra this
calculation is done before and after the MS/MS scan of interest
and the purity is interpolated at the recorded time of the
MS/MS acquisition. Additionally, isotopic peaks can be removed,
low abundance peaks are removed that are thought to have
limited contribution to the resulting MS/MS spectra and the
isolation efficiency of the mass spectrometer can be used to
normalise the intensities used for the calculation.
biocViews: MassSpectrometry, Metabolomics, Software
Author: Thomas N. Lawson [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5915-7980>), Ralf Weber [ctb],
Martin Jones [ctb], Julien Saint-Vanne [ctb], Andris Jankevics
[ctb], Mark Viant [ths], Warwick Dunn [ths]
Maintainer: Thomas N. Lawson <thomas.nigel.lawson@gmail.com>
URL: https://github.com/computational-metabolomics/msPurity/
VignetteBuilder: knitr
BugReports:
https://github.com/computational-metabolomics/msPurity/issues/new
git_url: https://git.bioconductor.org/packages/msPurity
git_branch: devel
git_last_commit: 6c0644f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/msPurity_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/msPurity_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/msPurity_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/msPurity_1.33.0.tgz
vignettes:
vignettes/msPurity/inst/doc/msPurity-lcmsms-data-processing-and-spectral-matching-vignette.html,
vignettes/msPurity/inst/doc/msPurity-spectral-database-vignette.html,
vignettes/msPurity/inst/doc/msPurity-vignette.html
vignetteTitles: msPurity spectral matching, msPurity spectral database
schema, msPurity
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles:
vignettes/msPurity/inst/doc/msPurity-lcmsms-data-processing-and-spectral-matching-vignette.R,
vignettes/msPurity/inst/doc/msPurity-spectral-database-vignette.R,
vignettes/msPurity/inst/doc/msPurity-vignette.R
suggestsMe: MetMashR
dependencyCount: 70
Package: msqrob2
Version: 1.15.1
Depends: R (>= 4.1), QFeatures (>= 1.1.2)
Imports: stats, methods, lme4, purrr, BiocParallel, Matrix, MASS,
limma, SummarizedExperiment, MultiAssayExperiment, codetools
Suggests: multcomp, gridExtra, knitr, BiocStyle, RefManageR,
sessioninfo, rmarkdown, testthat, tidyverse, plotly, MsDataHub,
MSnbase, matrixStats, MsCoreUtils, covr
License: Artistic-2.0
MD5sum: bd3f2d1f3bd6f92a662368a78a08db0b
NeedsCompilation: no
Title: Robust statistical inference for quantitative LC-MS proteomics
Description: msqrob2 provides a robust linear mixed model framework for
assessing differential abundance in MS-based Quantitative
proteomics experiments. Our workflows can start from raw
peptide intensities or summarised protein expression values.
The model parameter estimates can be stabilized by ridge
regression, empirical Bayes variance estimation and robust
M-estimation. msqrob2's hurde workflow can handle missing data
without having to rely on hard-to-verify imputation
assumptions, and, outcompetes state-of-the-art methods with and
without imputation for both high and low missingness. It builds
on QFeature infrastructure for quantitative mass spectrometry
data to store the model results together with the raw data and
preprocessed data.
biocViews: Proteomics, MassSpectrometry, DifferentialExpression,
MultipleComparison, Regression, ExperimentalDesign, Software,
ImmunoOncology, Normalization, TimeCourse, Preprocessing
Author: Lieven Clement [aut, cre] (ORCID:
<https://orcid.org/0000-0002-9050-4370>), Laurent Gatto [aut]
(ORCID: <https://orcid.org/0000-0002-1520-2268>), Oliver M.
Crook [aut] (ORCID: <https://orcid.org/0000-0001-5669-8506>),
Adriaan Sticker [ctb], Ludger Goeminne [ctb], Milan Malfait
[ctb] (ORCID: <https://orcid.org/0000-0001-9144-3701>), Stijn
Vandenbulcke [aut]
Maintainer: Lieven Clement <lieven.clement@ugent.be>
URL: https://github.com/statOmics/msqrob2
VignetteBuilder: knitr
BugReports: https://github.com/statOmics/msqrob2/issues
git_url: https://git.bioconductor.org/packages/msqrob2
git_branch: devel
git_last_commit: 3d53253
git_last_commit_date: 2025-02-26
Date/Publication: 2025-02-28
source.ver: src/contrib/msqrob2_1.15.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/msqrob2_1.15.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/msqrob2_1.15.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/msqrob2_1.15.1.tgz
vignettes: vignettes/msqrob2/inst/doc/cptac.html
vignetteTitles: A. label-free workflow with two group design
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/msqrob2/inst/doc/cptac.R
dependencyCount: 126
Package: MsQuality
Version: 1.7.1
Depends: R (>= 4.2.0)
Imports: BiocParallel (>= 1.32.0), ggplot2 (>= 3.3.5), htmlwidgets (>=
1.5.3), methods (>= 4.2.0), msdata (>= 0.32.0), MsExperiment
(>= 0.99.0), plotly (>= 4.9.4.1), ProtGenerics (>= 1.24.0),
rlang (>= 1.1.1), rmzqc (>= 0.5.0), shiny (>= 1.6.0),
shinydashboard (>= 0.7.1), Spectra (>= 1.13.2), stats (>=
4.2.0), stringr (>= 1.4.0), tibble (>= 3.1.4), tidyr (>=
1.1.3), utils (>= 4.2.0)
Suggests: BiocGenerics (>= 0.24.0), BiocStyle (>= 2.6.1), dplyr (>=
1.0.5), knitr (>= 1.11), mzR (>= 2.32.0), rmarkdown (>= 2.7),
S4Vectors (>= 0.29.17), testthat (>= 2.2.1)
License: GPL-3
MD5sum: 280a458c0bc0107c3681f227e7618286
NeedsCompilation: no
Title: MsQuality - Quality metric calculation from Spectra and
MsExperiment objects
Description: The MsQuality provides functionality to calculate quality
metrics for mass spectrometry-derived, spectral data at the
per-sample level. MsQuality relies on the mzQC framework of
quality metrics defined by the Human Proteom
Organization-Proteomics Standards Initiative (HUPO-PSI). These
metrics quantify the quality of spectral raw files using a
controlled vocabulary. The package is especially addressed
towards users that acquire mass spectrometry data on a large
scale (e.g. data sets from clinical settings consisting of
several thousands of samples). The MsQuality package allows to
calculate low-level quality metrics that require minimum
information on mass spectrometry data: retention time, m/z
values, and associated intensities. MsQuality relies on the
Spectra package, or alternatively the MsExperiment package, and
its infrastructure to store spectral data.
biocViews: Metabolomics, Proteomics, MassSpectrometry, QualityControl
Author: Thomas Naake [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7917-5580>), Johannes Rainer [aut]
(ORCID: <https://orcid.org/0000-0002-6977-7147>)
Maintainer: Thomas Naake <thomasnaake@googlemail.com>
URL: https://www.github.com/tnaake/MsQuality/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MsQuality
git_branch: devel
git_last_commit: 8207c19
git_last_commit_date: 2025-01-22
Date/Publication: 2025-01-22
source.ver: src/contrib/MsQuality_1.7.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MsQuality_1.7.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MsQuality_1.7.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MsQuality_1.7.1.tgz
vignettes: vignettes/MsQuality/inst/doc/MsQuality.html
vignetteTitles: QC for metabolomics and proteomics data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MsQuality/inst/doc/MsQuality.R
dependencyCount: 145
Package: MSstats
Version: 4.15.2
Depends: R (>= 4.0)
Imports: MSstatsConvert, data.table, checkmate, MASS, htmltools, limma,
lme4, preprocessCore, survival, utils, Rcpp, ggplot2, ggrepel,
gplots, plotly, marray, stats, grDevices, graphics, methods,
statmod, parallel
LinkingTo: Rcpp, RcppArmadillo
Suggests: BiocStyle, knitr, rmarkdown, tinytest, covr, markdown,
mockery, kableExtra
License: Artistic-2.0
MD5sum: 41a5bc72149877a16e637a2022aedad5
NeedsCompilation: yes
Title: Protein Significance Analysis in DDA, SRM and DIA for Label-free
or Label-based Proteomics Experiments
Description: A set of tools for statistical relative protein
significance analysis in DDA, SRM and DIA experiments.
biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software,
Normalization, QualityControl, TimeCourse
Author: Meena Choi [aut, cre], Mateusz Staniak [aut], Devon Kohler
[aut], Tony Wu [aut], Deril Raju [aut], Tsung-Heng Tsai [aut],
Ting Huang [aut], Olga Vitek [aut]
Maintainer: Meena Choi <mnchoi67@gmail.com>
URL: http://msstats.org
VignetteBuilder: knitr
BugReports: https://groups.google.com/forum/#!forum/msstats
git_url: https://git.bioconductor.org/packages/MSstats
git_branch: devel
git_last_commit: 9014bcf
git_last_commit_date: 2025-03-05
Date/Publication: 2025-03-05
source.ver: src/contrib/MSstats_4.15.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MSstats_4.15.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MSstats_4.15.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MSstats_4.15.2.tgz
vignettes: vignettes/MSstats/inst/doc/MSstats.html,
vignettes/MSstats/inst/doc/MSstatsWorkflow.html
vignetteTitles: MSstats: Protein/Peptide significance analysis,
MSstats: End to End Workflow
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSstats/inst/doc/MSstats.R,
vignettes/MSstats/inst/doc/MSstatsWorkflow.R
dependsOnMe: MSstatsBioNet
importsMe: artMS, MSstatsBig, MSstatsLiP, MSstatsPTM, MSstatsShiny,
MSstatsTMT
dependencyCount: 103
Package: MSstatsBig
Version: 1.5.0
Imports: arrow, DBI, dplyr, MSstats, MSstatsConvert, readr, sparklyr,
utils
Suggests: knitr, rmarkdown
License: Artistic-2.0
MD5sum: c3002316043715283b27f4a4771a9965
NeedsCompilation: no
Title: MSstats Preprocessing for Larger than Memory Data
Description: MSstats package provide tools for preprocessing,
summarization and differential analysis of mass spectrometry
(MS) proteomics data. Recently, some MS protocols enable
acquisition of data sets that result in larger than memory
quantitative data. MSstats functions are not able to process
such data. MSstatsBig package provides additional converter
functions that enable processing larger than memory data sets.
biocViews: MassSpectrometry, Proteomics, Software
Author: Mateusz Staniak [aut, cre], Devon Kohler [aut]
Maintainer: Mateusz Staniak <mtst@mstaniak.pl>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MSstatsBig
git_branch: devel
git_last_commit: 0338d8f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MSstatsBig_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MSstatsBig_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsBig_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MSstatsBig_1.5.0.tgz
vignettes: vignettes/MSstatsBig/inst/doc/MSstatsBig_Workflow.html
vignetteTitles: MSstatsBig Workflow
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSstatsBig/inst/doc/MSstatsBig_Workflow.R
dependencyCount: 126
Package: MSstatsBioNet
Version: 0.99.9
Depends: R (>= 4.4.0), MSstats
Imports: RCy3, httr, jsonlite, r2r, tidyr
Suggests: data.table, BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0),
mockery, MSstatsConvert
License: file LICENSE
MD5sum: 901997364f00369f30e22784ea539765
NeedsCompilation: no
Title: Network Analysis for MS-based Proteomics Experiments
Description: A set of tools for network analysis using mass
spectrometry-based proteomics data and network databases. The
package takes as input the output of MSstats differential
abundance analysis and provides functions to perform enrichment
analysis and visualization in the context of prior knowledge
from past literature. Notably, this package integrates with
INDRA, which is a database of biological networks extracted
from the literature using text mining techniques.
biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software,
QualityControl, NetworkEnrichment, Network
Author: Anthony Wu [aut, cre], Olga Vitek [aut] (ORCID:
<https://orcid.org/0000-0003-1728-1104>)
Maintainer: Anthony Wu <wu.anthon@northeastern.edu>
URL: http://msstats.org, https://vitek-lab.github.io/MSstatsBioNet/
VignetteBuilder: knitr
BugReports: https://groups.google.com/forum/#!forum/msstats
git_url: https://git.bioconductor.org/packages/MSstatsBioNet
git_branch: devel
git_last_commit: 3dbd252
git_last_commit_date: 2025-02-06
Date/Publication: 2025-02-19
source.ver: src/contrib/MSstatsBioNet_0.99.9.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MSstatsBioNet_0.99.9.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsBioNet_0.99.9.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MSstatsBioNet_0.99.9.tgz
vignettes: vignettes/MSstatsBioNet/inst/doc/MSstatsBioNet.html
vignetteTitles: MSstatsBioNet: Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MSstatsBioNet/inst/doc/MSstatsBioNet.R
dependencyCount: 119
Package: MSstatsConvert
Version: 1.17.1
Depends: R (>= 4.0)
Imports: data.table, log4r, methods, checkmate, utils, stringi
Suggests: tinytest, covr, knitr, rmarkdown
License: Artistic-2.0
MD5sum: 641aca8297322470b87122ade727c0fb
NeedsCompilation: no
Title: Import Data from Various Mass Spectrometry Signal Processing
Tools to MSstats Format
Description: MSstatsConvert provides tools for importing reports of
Mass Spectrometry data processing tools into R format suitable
for statistical analysis using the MSstats and MSstatsTMT
packages.
biocViews: MassSpectrometry, Proteomics, Software, DataImport,
QualityControl
Author: Mateusz Staniak [aut, cre], Devon Kohler [aut], Anthony Wu
[aut], Meena Choi [aut], Ting Huang [aut], Olga Vitek [aut]
Maintainer: Mateusz Staniak <mtst@mstaniak.pl>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MSstatsConvert
git_branch: devel
git_last_commit: d3a2847
git_last_commit_date: 2024-12-12
Date/Publication: 2024-12-12
source.ver: src/contrib/MSstatsConvert_1.17.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MSstatsConvert_1.17.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsConvert_1.17.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MSstatsConvert_1.17.1.tgz
vignettes: vignettes/MSstatsConvert/inst/doc/msstats_data_format.html
vignetteTitles: Working with MSstatsConvert
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSstatsConvert/inst/doc/msstats_data_format.R
importsMe: MSstats, MSstatsBig, MSstatsLiP, MSstatsPTM, MSstatsShiny,
MSstatsTMT
suggestsMe: MSstatsBioNet
dependencyCount: 9
Package: MSstatsLiP
Version: 1.13.0
Depends: R (>= 4.1)
Imports: dplyr, gridExtra, stringr, ggplot2, grDevices, MSstats,
MSstatsConvert, data.table, Biostrings, MSstatsPTM, Rcpp,
checkmate, factoextra, ggpubr, purrr, tibble, tidyr, tidyverse,
scales, stats
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, rmarkdown, covr, tinytest, gghighlight
License: Artistic-2.0
MD5sum: e5539d5b80514cb200595b9a3b4dca7c
NeedsCompilation: yes
Title: LiP Significance Analysis in shotgun mass spectrometry-based
proteomic experiments
Description: Tools for LiP peptide and protein significance analysis.
Provides functions for summarization, estimation of LiP peptide
abundance, and detection of changes across conditions. Utilizes
functionality across the MSstats family of packages.
biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software,
DifferentialExpression, OneChannel, TwoChannel, Normalization,
QualityControl
Author: Devon Kohler [aut, cre], Tsung-Heng Tsai [aut], Ting Huang
[aut], Mateusz Staniak [aut], Meena Choi [aut], Valentina
Cappelletti [aut], Liliana Malinovska [aut], Olga Vitek [aut]
Maintainer: Devon Kohler <kohler.d@northeastern.edu>
VignetteBuilder: knitr
BugReports: https://github.com/Vitek-Lab/MSstatsLiP/issues
git_url: https://git.bioconductor.org/packages/MSstatsLiP
git_branch: devel
git_last_commit: 256206e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-26
source.ver: src/contrib/MSstatsLiP_1.13.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsLiP_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MSstatsLiP_1.13.0.tgz
vignettes: vignettes/MSstatsLiP/inst/doc/MSstatsLiP_Workflow.html,
vignettes/MSstatsLiP/inst/doc/Proteolytic_resistance_notebook.html
vignetteTitles: MSstatsLiP Workflow: An example workflow and analysis
of the MSstatsLiP package, MSstatsLiP Proteolytic Workflow
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSstatsLiP/inst/doc/MSstatsLiP_Workflow.R,
vignettes/MSstatsLiP/inst/doc/Proteolytic_resistance_notebook.R
dependencyCount: 197
Package: MSstatsLOBD
Version: 1.15.0
Depends: R (>= 4.0)
Imports: minpack.lm, ggplot2, utils, stats, grDevices
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, rmarkdown, covr, tinytest, dplyr
License: Artistic-2.0
Archs: x64
MD5sum: 823aa6c839ecfe76c20b60573dcb2445
NeedsCompilation: no
Title: Assay characterization: estimation of limit of blanc(LoB) and
limit of detection(LOD)
Description: The MSstatsLOBD package allows calculation and
visualization of limit of blac (LOB) and limit of detection
(LOD). We define the LOB as the highest apparent concentration
of a peptide expected when replicates of a blank sample
containing no peptides are measured. The LOD is defined as the
measured concentration value for which the probability of
falsely claiming the absence of a peptide in the sample is
0.05, given a probability 0.05 of falsely claiming its
presence. These functionalities were previously a part of the
MSstats package. The methodology is described in Galitzine
(2018) <doi:10.1074/mcp.RA117.000322>.
biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software,
DifferentialExpression, OneChannel, TwoChannel, Normalization,
QualityControl
Author: Devon Kohler [aut, cre], Mateusz Staniak [aut], Cyril Galitzine
[aut], Meena Choi [aut], Olga Vitek [aut]
Maintainer: Devon Kohler <kohler.d@northeastern.edu>
VignetteBuilder: knitr
BugReports: https://github.com/Vitek-Lab/MSstatsLODQ/issues
git_url: https://git.bioconductor.org/packages/MSstatsLOBD
git_branch: devel
git_last_commit: 3e3285d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MSstatsLOBD_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MSstatsLOBD_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsLOBD_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MSstatsLOBD_1.15.0.tgz
vignettes: vignettes/MSstatsLOBD/inst/doc/MSstatsLOBD_workflow.html
vignetteTitles: LOB/LOD Estimation Workflow
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSstatsLOBD/inst/doc/MSstatsLOBD_workflow.R
dependencyCount: 37
Package: MSstatsPTM
Version: 2.9.1
Depends: R (>= 4.3)
Imports: dplyr, gridExtra, stringr, stats, ggplot2, stringi, grDevices,
MSstatsTMT, MSstatsConvert, MSstats, data.table, Rcpp,
Biostrings, checkmate, ggrepel
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, tinytest, covr, mockery, testthat (>=
3.0.0)
License: Artistic-2.0
MD5sum: 735c3f9a531c1c16f62030b67d737ab2
NeedsCompilation: yes
Title: Statistical Characterization of Post-translational Modifications
Description: MSstatsPTM provides general statistical methods for
quantitative characterization of post-translational
modifications (PTMs). Supports DDA, DIA, SRM, and tandem mass
tag (TMT) labeling. Typically, the analysis involves the
quantification of PTM sites (i.e., modified residues) and their
corresponding proteins, as well as the integration of the
quantification results. MSstatsPTM provides functions for
summarization, estimation of PTM site abundance, and detection
of changes in PTMs across experimental conditions.
biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software,
DifferentialExpression, OneChannel, TwoChannel, Normalization,
QualityControl
Author: Devon Kohler [aut, cre], Tsung-Heng Tsai [aut], Ting Huang
[aut], Mateusz Staniak [aut], Meena Choi [aut], Olga Vitek
[aut]
Maintainer: Devon Kohler <kohler.d@northeastern.edu>
VignetteBuilder: knitr
BugReports: https://github.com/Vitek-Lab/MSstatsPTM/issues
git_url: https://git.bioconductor.org/packages/MSstatsPTM
git_branch: devel
git_last_commit: f1d70f4
git_last_commit_date: 2024-11-25
Date/Publication: 2024-11-26
source.ver: src/contrib/MSstatsPTM_2.9.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MSstatsPTM_2.9.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsPTM_2.9.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MSstatsPTM_2.9.1.tgz
vignettes:
vignettes/MSstatsPTM/inst/doc/MSstatsPTM_LabelFree_Workflow.html,
vignettes/MSstatsPTM/inst/doc/MSstatsPTM_TMT_Workflow.html
vignetteTitles: MSstatsPTM LabelFree Workflow, MSstatsPTM TMT Workflow
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSstatsPTM/inst/doc/MSstatsPTM_LabelFree_Workflow.R,
vignettes/MSstatsPTM/inst/doc/MSstatsPTM_TMT_Workflow.R
importsMe: MSstatsLiP, MSstatsShiny
dependencyCount: 118
Package: MSstatsQC
Version: 2.25.0
Depends: R (>= 3.5.0)
Imports: dplyr,plotly,ggplot2,ggExtra, stats,grid, MSnbase, qcmetrics
Suggests: knitr,rmarkdown, testthat, RforProteomics
License: Artistic License 2.0
MD5sum: 370bcc5f149d778873363af83bf0a59d
NeedsCompilation: no
Title: Longitudinal system suitability monitoring and quality control
for proteomic experiments
Description: MSstatsQC is an R package which provides longitudinal
system suitability monitoring and quality control tools for
proteomic experiments.
biocViews: Software, QualityControl, Proteomics, MassSpectrometry
Author: Eralp Dogu [aut, cre], Sara Taheri [aut], Olga Vitek [aut]
Maintainer: Eralp Dogu <eralp.dogu@gmail.com>
URL: http://msstats.org/msstatsqc
VignetteBuilder: knitr
BugReports: https://groups.google.com/forum/#!forum/msstatsqc
git_url: https://git.bioconductor.org/packages/MSstatsQC
git_branch: devel
git_last_commit: 0ea25be
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MSstatsQC_2.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MSstatsQC_2.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsQC_2.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MSstatsQC_2.25.0.tgz
vignettes: vignettes/MSstatsQC/inst/doc/MSstatsQC.html
vignetteTitles: MSstatsQC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSstatsQC/inst/doc/MSstatsQC.R
importsMe: MSstatsQCgui
dependencyCount: 147
Package: MSstatsQCgui
Version: 1.27.0
Imports: shiny, MSstatsQC, ggExtra, gridExtra, plotly, dplyr, grid
Suggests: knitr
License: Artistic License 2.0
MD5sum: 4c0fa732e4260fd8af17101766107483
NeedsCompilation: no
Title: A graphical user interface for MSstatsQC package
Description: MSstatsQCgui is a Shiny app which provides longitudinal
system suitability monitoring and quality control tools for
proteomic experiments.
biocViews: Software, QualityControl, Proteomics, MassSpectrometry, GUI
Author: Eralp Dogu [aut, cre], Sara Taheri [aut], Olga Vitek [aut]
Maintainer: Eralp Dogu <eralp.dogu@gmail.com>
URL: http://msstats.org/msstatsqc
VignetteBuilder: knitr
BugReports: https://groups.google.com/forum/#!forum/msstatsqc
git_url: https://git.bioconductor.org/packages/MSstatsQCgui
git_branch: devel
git_last_commit: 7c1f776
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MSstatsQCgui_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MSstatsQCgui_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsQCgui_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MSstatsQCgui_1.27.0.tgz
vignettes: vignettes/MSstatsQCgui/inst/doc/MSstatsQCgui.html
vignetteTitles: MSstatsQCgui
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSstatsQCgui/inst/doc/MSstatsQCgui.R
dependencyCount: 149
Package: MSstatsShiny
Version: 1.9.0
Depends: R (>= 4.2)
Imports: shiny, shinyBS, shinyjs, shinybusy, dplyr, ggplot2,plotly,
data.table, Hmisc, MSstats, MSstatsTMT, MSstatsPTM,
MSstatsConvert, gplots, marray, DT, readxl, ggrepel, uuid,
utils, stats, htmltools, methods, tidyr, grDevices,
graphics,mockery
Suggests: rmarkdown, tinytest, sessioninfo, knitr, testthat (>= 3.0.0),
shinytest2,
License: Artistic-2.0
MD5sum: 53f7ec33dbc56b0874dd79d6616e5676
NeedsCompilation: no
Title: MSstats GUI for Statistical Anaylsis of Proteomics Experiments
Description: MSstatsShiny is an R-Shiny graphical user interface (GUI)
integrated with the R packages MSstats, MSstatsTMT, and
MSstatsPTM. It provides a point and click end-to-end analysis
pipeline applicable to a wide variety of experimental designs.
These include data-dependedent acquisitions (DDA) which are
label-free or tandem mass tag (TMT)-based, as well as DIA, SRM,
and PRM acquisitions and those targeting post-translational
modifications (PTMs). The application automatically saves users
selections and builds an R script that recreates their
analysis, supporting reproducible data analysis.
biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software,
ShinyApps, DifferentialExpression, OneChannel, TwoChannel,
Normalization, QualityControl, GUI
Author: Devon Kohler [aut, cre], Deril Raju [aut], Maanasa Kaza [aut],
Cristina Pasi [aut], Ting Huang [aut], Mateusz Staniak [aut],
Dhaval Mohandas [aut], Eduard Sabido [aut], Meena Choi [aut],
Olga Vitek [aut]
Maintainer: Devon Kohler <kohler.d@northeastern.edu>
VignetteBuilder: knitr
BugReports: https://github.com/Vitek-Lab/MSstatsShiny/issues
git_url: https://git.bioconductor.org/packages/MSstatsShiny
git_branch: devel
git_last_commit: 41427c3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-26
source.ver: src/contrib/MSstatsShiny_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MSstatsShiny_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsShiny_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MSstatsShiny_1.9.0.tgz
vignettes:
vignettes/MSstatsShiny/inst/doc/MSstatsShiny_Launch_Instructions.html
vignetteTitles: MSstatsPTM LabelFree Workflow
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/MSstatsShiny/inst/doc/MSstatsShiny_Launch_Instructions.R
dependencyCount: 157
Package: MSstatsTMT
Version: 2.15.2
Depends: R (>= 4.2)
Imports: limma, lme4, lmerTest, methods, data.table, stats, utils,
ggplot2, grDevices, graphics, MSstats, MSstatsConvert,
checkmate, plotly, htmltools
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: Artistic-2.0
Archs: x64
MD5sum: 12474f18dbbfd84c41280c70010655d3
NeedsCompilation: no
Title: Protein Significance Analysis in shotgun mass spectrometry-based
proteomic experiments with tandem mass tag (TMT) labeling
Description: The package provides statistical tools for detecting
differentially abundant proteins in shotgun mass
spectrometry-based proteomic experiments with tandem mass tag
(TMT) labeling. It provides multiple functionalities, including
aata visualization, protein quantification and normalization,
and statistical modeling and inference. Furthermore, it is
inter-operable with other data processing tools, such as
Proteome Discoverer, MaxQuant, OpenMS and SpectroMine.
biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software
Author: Devon Kohler [aut, cre], Ting Huang [aut], Meena Choi [aut],
Mateusz Staniak [aut], Tony Wu [aut], Deril Raju [aut], Sicheng
Hao [aut], Olga Vitek [aut]
Maintainer: Devon Kohler <kohler.d@northeastern.edu>
URL: http://msstats.org/msstatstmt/
VignetteBuilder: knitr
BugReports: https://groups.google.com/forum/#!forum/msstats
git_url: https://git.bioconductor.org/packages/MSstatsTMT
git_branch: devel
git_last_commit: 1eed116
git_last_commit_date: 2025-03-05
Date/Publication: 2025-03-05
source.ver: src/contrib/MSstatsTMT_2.15.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MSstatsTMT_2.15.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MSstatsTMT_2.15.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MSstatsTMT_2.15.2.tgz
vignettes: vignettes/MSstatsTMT/inst/doc/MSstatsTMT.html
vignetteTitles: MSstatsTMT User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MSstatsTMT/inst/doc/MSstatsTMT.R
importsMe: MSstatsPTM, MSstatsShiny
dependencyCount: 106
Package: MuData
Version: 1.11.1
Depends: Matrix, S4Vectors, rhdf5 (>= 2.45)
Imports: methods, stats, MultiAssayExperiment, SingleCellExperiment,
SummarizedExperiment, DelayedArray, S4Vectors
Suggests: HDF5Array, rmarkdown, knitr, fs, testthat, BiocStyle, covr,
SingleCellMultiModal, CiteFuse, scater
License: GPL-3
MD5sum: 525fb1e9c60bf8eb8e481128e163917e
NeedsCompilation: no
Title: Serialization for MultiAssayExperiment Objects
Description: Save MultiAssayExperiments to h5mu files supported by muon
and mudata. Muon is a Python framework for multimodal omics
data analysis. It uses an HDF5-based format for data storage.
biocViews: DataImport
Author: Danila Bredikhin [aut] (ORCID:
<https://orcid.org/0000-0001-8089-6983>), Ilia Kats [aut, cre]
(ORCID: <https://orcid.org/0000-0001-5220-5671>)
Maintainer: Ilia Kats <i.kats@dkfz-heidelberg.de>
URL: https://github.com/ilia-kats/MuData
VignetteBuilder: knitr
BugReports: https://github.com/ilia-kats/MuData/issues
git_url: https://git.bioconductor.org/packages/MuData
git_branch: devel
git_last_commit: aa2290d
git_last_commit_date: 2025-02-24
Date/Publication: 2025-02-24
source.ver: src/contrib/MuData_1.11.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MuData_1.11.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MuData_1.11.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MuData_1.11.1.tgz
vignettes: vignettes/MuData/inst/doc/Blood-CITE-seq.html,
vignettes/MuData/inst/doc/Cord-Blood-CITE-seq.html,
vignettes/MuData/inst/doc/Getting-Started.html
vignetteTitles: Blood CITE-seq with MuData, Cord Blood CITE-seq with
MuData, Getting started with MuDataMae
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MuData/inst/doc/Blood-CITE-seq.R,
vignettes/MuData/inst/doc/Cord-Blood-CITE-seq.R,
vignettes/MuData/inst/doc/Getting-Started.R
dependencyCount: 61
Package: Mulcom
Version: 1.57.0
Depends: R (>= 2.10), Biobase
Imports: graphics, grDevices, stats, methods, fields
License: GPL-2
MD5sum: b7e70e7d6a6df45746ee345d52fa8068
NeedsCompilation: yes
Title: Calculates Mulcom test
Description: Identification of differentially expressed genes and false
discovery rate (FDR) calculation by Multiple Comparison test.
biocViews: StatisticalMethod, MultipleComparison, Microarray,
DifferentialExpression, GeneExpression
Author: Claudio Isella
Maintainer: Claudio Isella <claudio.isella@ircc.it>
git_url: https://git.bioconductor.org/packages/Mulcom
git_branch: devel
git_last_commit: 9ce9679
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Mulcom_1.57.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Mulcom_1.57.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Mulcom_1.57.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Mulcom_1.57.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 15
Package: MultiAssayExperiment
Version: 1.33.9
Depends: SummarizedExperiment (>= 1.3.81), R (>= 3.5.0)
Imports: Biobase, BiocBaseUtils, BiocGenerics (>= 0.53.4),
DelayedArray, GenomicRanges, IRanges, methods, S4Vectors,
tidyr, utils
Suggests: BiocStyle, HDF5Array, h5mread, knitr, maftools, R.rsp,
RaggedExperiment, reshape2, rmarkdown, survival, survminer,
testthat, UpSetR
License: Artistic-2.0
MD5sum: 09ea6cbe4bf73eecee37371142f67ea4
NeedsCompilation: no
Title: Software for the integration of multi-omics experiments in
Bioconductor
Description: Harmonize data management of multiple experimental assays
performed on an overlapping set of specimens. It provides a
familiar Bioconductor user experience by extending concepts
from SummarizedExperiment, supporting an open-ended mix of
standard data classes for individual assays, and allowing
subsetting by genomic ranges or rownames. Facilities are
provided for reshaping data into wide and long formats for
adaptability to graphing and downstream analysis.
biocViews: Infrastructure, DataRepresentation
Author: Marcel Ramos [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3242-0582>), Martin Morgan [aut,
ctb], Lori Shepherd [ctb], Hervé Pagès [ctb], Vincent J Carey
[aut, ctb], Levi Waldron [aut], MultiAssay SIG [ctb]
Maintainer: Marcel Ramos <marcel.ramos@sph.cuny.edu>
URL: http://waldronlab.io/MultiAssayExperiment/
VignetteBuilder: knitr, R.rsp
Video: https://youtu.be/w6HWAHaDpyk, https://youtu.be/Vh0hVVUKKFM
BugReports: https://github.com/waldronlab/MultiAssayExperiment/issues
git_url: https://git.bioconductor.org/packages/MultiAssayExperiment
git_branch: devel
git_last_commit: 3128ca1
git_last_commit_date: 2025-01-28
Date/Publication: 2025-01-29
source.ver: src/contrib/MultiAssayExperiment_1.33.9.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MultiAssayExperiment_1.33.9.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MultiAssayExperiment_1.33.9.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MultiAssayExperiment_1.33.9.tgz
vignettes:
vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment_cheatsheet.pdf,
vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.html,
vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html,
vignettes/MultiAssayExperiment/inst/doc/UsingHDF5Array.html
vignetteTitles: MultiAssayExperiment_cheatsheet.pdf, Coordinating
Analysis of Multi-Assay Experiments, Quick-start Guide,
HDF5Array and MultiAssayExperiment
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.R,
vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.R,
vignettes/MultiAssayExperiment/inst/doc/UsingHDF5Array.R
dependsOnMe: alabaster.mae, CAGEr, cBioPortalData, ClassifyR,
evaluomeR, hipathia, HoloFoodR, InTAD, MGnifyR, mia, midasHLA,
MIRit, missRows, QFeatures, terraTCGAdata, curatedPCaData,
curatedTCGAData, microbiomeDataSets, OMICsPCAdata, scMultiome,
SingleCellMultiModal
importsMe: AffiXcan, AMARETTO, animalcules, autonomics, biosigner,
CoreGx, corral, ELMER, FindIT2, gDRcore, gDRimport, gDRutils,
gINTomics, glmSparseNet, GOpro, hermes, LinkHD,
metabolomicsWorkbenchR, MOMA, MOSClip, msqrob2, MuData,
MultiBaC, MultimodalExperiment, nipalsMCIA, OMICsPCA,
omicsPrint, omXplore, padma, PDATK, PharmacoGx, phenomis,
ropls, scp, scPipe, survClust, TCGAutils, TENET, vsclust,
xcore, curatedTBData, HMP2Data, LegATo, MetaScope,
TENET.ExperimentHub
suggestsMe: BiocGenerics, CNVRanger, funOmics, maftools, MOFA2,
MultiDataSet, RaggedExperiment, updateObject, brgedata,
MOFAdata, teal, teal.slice
dependencyCount: 56
Package: MultiBaC
Version: 1.17.0
Imports: Matrix, ggplot2, MultiAssayExperiment, ropls, graphics,
methods, plotrix, grDevices, pcaMethods
Suggests: knitr, rmarkdown, BiocStyle, devtools
License: GPL-3
MD5sum: fb8ba80f2156a4c502c9cb17ee10a2fb
NeedsCompilation: no
Title: Multiomic Batch effect Correction
Description: MultiBaC is a strategy to correct batch effects from
multiomic datasets distributed across different labs or data
acquisition events. MultiBaC is the first Batch effect
correction algorithm that dealing with batch effect correction
in multiomics datasets. MultiBaC is able to remove batch
effects across different omics generated within separate
batches provided that at least one common omic data type is
included in all the batches considered.
biocViews: Software, StatisticalMethod, PrincipalComponent,
DataRepresentation, GeneExpression, Transcription, BatchEffect
Author: person("Manuel", "Ugidos", email = "manuelugidos@gmail.com"),
person("Sonia", "Tarazona", email = "sotacam@gmail.com"),
person("MarÃa José", "Nueda", email = "mjnueda@ua.es")
Maintainer: The package maintainer <manuelugidos@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MultiBaC
git_branch: devel
git_last_commit: 032c958
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MultiBaC_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MultiBaC_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MultiBaC_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MultiBaC_1.17.0.tgz
vignettes: vignettes/MultiBaC/inst/doc/MultiBaC.html
vignetteTitles: MultiBaC
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MultiBaC/inst/doc/MultiBaC.R
dependencyCount: 107
Package: multiClust
Version: 1.37.0
Imports: mclust, ctc, survival, cluster, dendextend, amap, graphics,
grDevices
Suggests: knitr, rmarkdown, gplots, RUnit, BiocGenerics,
preprocessCore, Biobase, GEOquery
License: GPL (>= 2)
MD5sum: 868b295c1be5befb4057896ec60c0237
NeedsCompilation: no
Title: multiClust: An R-package for Identifying Biologically Relevant
Clusters in Cancer Transcriptome Profiles
Description: Clustering is carried out to identify patterns in
transcriptomics profiles to determine clinically relevant
subgroups of patients. Feature (gene) selection is a critical
and an integral part of the process. Currently, there are many
feature selection and clustering methods to identify the
relevant genes and perform clustering of samples. However,
choosing an appropriate methodology is difficult. In addition,
extensive feature selection methods have not been supported by
the available packages. Hence, we developed an integrative
R-package called multiClust that allows researchers to
experiment with the choice of combination of methods for gene
selection and clustering with ease. Using multiClust, we
identified the best performing clustering methodology in the
context of clinical outcome. Our observations demonstrate that
simple methods such as variance-based ranking perform well on
the majority of data sets, provided that the appropriate number
of genes is selected. However, different gene ranking and
selection methods remain relevant as no methodology works for
all studies.
biocViews: FeatureExtraction, Clustering, GeneExpression, Survival
Author: Nathan Lawlor [aut, cre], Peiyong Guan [aut], Alec Fabbri
[aut], Krish Karuturi [aut], Joshy George [aut]
Maintainer: Nathan Lawlor <nathan.lawlor03@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/multiClust
git_branch: devel
git_last_commit: 9909f55
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/multiClust_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/multiClust_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/multiClust_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/multiClust_1.37.0.tgz
vignettes: vignettes/multiClust/inst/doc/multiClust.html
vignetteTitles: "A Guide to multiClust"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/multiClust/inst/doc/multiClust.R
dependencyCount: 44
Package: multicrispr
Version: 1.17.1
Depends: R (>= 4.0)
Imports: BiocGenerics, Biostrings, BSgenome, CRISPRseek, data.table,
GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, grid,
karyoploteR, magrittr, methods, parallel, plyranges, Rbowtie,
reticulate, rtracklayer, stats, stringi, tidyr, tidyselect,
utils
Suggests: AnnotationHub, BiocStyle, BSgenome.Hsapiens.UCSC.hg38,
BSgenome.Mmusculus.UCSC.mm10,
BSgenome.Scerevisiae.UCSC.sacCer1, ensembldb, IRanges, knitr,
magick, rmarkdown, testthat, TxDb.Mmusculus.UCSC.mm10.knownGene
License: GPL-2
Archs: x64
MD5sum: 1ffbfa8515bfa736b90eb7ac5a59ddfd
NeedsCompilation: no
Title: Multi-locus multi-purpose Crispr/Cas design
Description: This package is for designing Crispr/Cas9 and Prime
Editing experiments. It contains functions to (1) define and
transform genomic targets, (2) find spacers (4) count offtarget
(mis)matches, and (5) compute Doench2016/2014 targeting
efficiency. Care has been taken for multicrispr to scale well
towards large target sets, enabling the design of large
Crispr/Cas9 libraries.
biocViews: CRISPR, Software
Author: Aditya Bhagwat [aut, cre], Richie ´Cotton [aut], Rene Wiegandt
[ctb], Mette Bentsen [ctb], Jens Preussner [ctb], Michael
Lawrence [ctb], Hervé Pagès [ctb], Johannes Graumann [sad],
Mario Looso [sad, rth]
Maintainer: Aditya Bhagwat <aditya.bhagwat@uni-marburg.de>
URL: https://github.com/bhagwataditya/multicrispr
VignetteBuilder: knitr
BugReports: https://github.com/bhagwataditya/multicrispr/issues
git_url: https://git.bioconductor.org/packages/multicrispr
git_branch: devel
git_last_commit: 2519b98
git_last_commit_date: 2024-11-27
Date/Publication: 2024-11-27
source.ver: src/contrib/multicrispr_1.17.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/multicrispr_1.17.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/multicrispr_1.17.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/multicrispr_1.17.1.tgz
vignettes: vignettes/multicrispr/inst/doc/crispr_grna_design.html,
vignettes/multicrispr/inst/doc/genome_arithmetics.html,
vignettes/multicrispr/inst/doc/prime_editing.html
vignetteTitles: grna_design, genome_arithmetics, prime_editing
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/multicrispr/inst/doc/crispr_grna_design.R,
vignettes/multicrispr/inst/doc/genome_arithmetics.R,
vignettes/multicrispr/inst/doc/prime_editing.R
dependencyCount: 193
Package: MultiDataSet
Version: 1.35.0
Depends: R (>= 4.1), Biobase
Imports: BiocGenerics, GenomicRanges, IRanges, S4Vectors,
SummarizedExperiment, methods, utils, ggplot2, ggrepel, qqman,
limma
Suggests: brgedata, minfi, minfiData, knitr, rmarkdown, testthat,
omicade4, iClusterPlus, GEOquery, MultiAssayExperiment,
BiocStyle, RaggedExperiment
License: file LICENSE
MD5sum: d2a318cce05873cad8a7a9be91c6ed25
NeedsCompilation: no
Title: Implementation of MultiDataSet and ResultSet
Description: Implementation of the BRGE's (Bioinformatic Research Group
in Epidemiology from Center for Research in Environmental
Epidemiology) MultiDataSet and ResultSet. MultiDataSet is
designed for integrating multi omics data sets and ResultSet is
a container for omics results. This package contains base
classes for MEAL and rexposome packages.
biocViews: Software, DataRepresentation
Author: Carlos Ruiz-Arenas [aut, cre], Carles Hernandez-Ferrer [aut],
Juan R. Gonzalez [aut]
Maintainer: Xavier Escrib<c3><a0> Montagut
<xavier.escriba@isglobal.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MultiDataSet
git_branch: devel
git_last_commit: f5c8571
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MultiDataSet_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MultiDataSet_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MultiDataSet_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MultiDataSet_1.35.0.tgz
vignettes:
vignettes/MultiDataSet/inst/doc/MultiDataSet_Extending_Proteome.html,
vignettes/MultiDataSet/inst/doc/MultiDataSet.html
vignetteTitles: Adding a new type of data to MultiDataSet objects,
Introduction to MultiDataSet
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles:
vignettes/MultiDataSet/inst/doc/MultiDataSet_Extending_Proteome.R,
vignettes/MultiDataSet/inst/doc/MultiDataSet.R
dependsOnMe: MEAL
importsMe: biosigner, omicRexposome, phenomis, ropls
dependencyCount: 68
Package: multiGSEA
Version: 1.17.3
Depends: R (>= 4.0.0)
Imports: magrittr, graphite, AnnotationDbi, metaboliteIDmapping, dplyr,
fgsea, metap, rappdirs, rlang, methods
Suggests: org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Ss.eg.db,
org.Bt.eg.db, org.Ce.eg.db, org.Dm.eg.db, org.Dr.eg.db,
org.Gg.eg.db, org.Xl.eg.db, org.Cf.eg.db, knitr, rmarkdown,
BiocStyle, testthat (>= 2.1.0)
License: GPL-3
MD5sum: d552ff0345533b905526603d72f74f78
NeedsCompilation: no
Title: Combining GSEA-based pathway enrichment with multi omics data
integration
Description: Extracted features from pathways derived from 8 different
databases (KEGG, Reactome, Biocarta, etc.) can be used on
transcriptomic, proteomic, and/or metabolomic level to
calculate a combined GSEA-based enrichment score.
biocViews: GeneSetEnrichment, Pathways, Reactome, BioCarta
Author: Sebastian Canzler [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7935-9582>), Jörg Hackermüller
[aut] (ORCID: <https://orcid.org/0000-0003-4920-7072>)
Maintainer: Sebastian Canzler <sebastian.canzler@ufz.de>
URL: https://github.com/yigbt/multiGSEA
VignetteBuilder: knitr
BugReports: https://github.com/yigbt/multiGSEA/issues
git_url: https://git.bioconductor.org/packages/multiGSEA
git_branch: devel
git_last_commit: 6b0e4cd
git_last_commit_date: 2025-01-06
Date/Publication: 2025-01-08
source.ver: src/contrib/multiGSEA_1.17.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/multiGSEA_1.17.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/multiGSEA_1.17.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/multiGSEA_1.17.3.tgz
vignettes: vignettes/multiGSEA/inst/doc/multiGSEA.html
vignetteTitles: multiGSEA.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/multiGSEA/inst/doc/multiGSEA.R
dependencyCount: 122
Package: multiHiCcompare
Version: 1.25.0
Depends: R (>= 4.0.0)
Imports: data.table, dplyr, HiCcompare, edgeR, BiocParallel, qqman,
pheatmap, methods, GenomicRanges, graphics, stats, utils,
pbapply, GenomeInfoDbData, GenomeInfoDb, aggregation
Suggests: knitr, rmarkdown, testthat, BiocStyle
License: MIT + file LICENSE
MD5sum: 1fe82ca51339595182302435441c1ee0
NeedsCompilation: no
Title: Normalize and detect differences between Hi-C datasets when
replicates of each experimental condition are available
Description: multiHiCcompare provides functions for joint normalization
and difference detection in multiple Hi-C datasets. This
extension of the original HiCcompare package now allows for
Hi-C experiments with more than 2 groups and multiple samples
per group. multiHiCcompare operates on processed Hi-C data in
the form of sparse upper triangular matrices. It accepts four
column (chromosome, region1, region2, IF) tab-separated text
files storing chromatin interaction matrices. multiHiCcompare
provides cyclic loess and fast loess (fastlo) methods adapted
to jointly normalizing Hi-C data. Additionally, it provides a
general linear model (GLM) framework adapting the edgeR package
to detect differences in Hi-C data in a distance dependent
manner.
biocViews: Software, HiC, Sequencing, Normalization
Author: Mikhail Dozmorov [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0086-8358>), John Stansfield [aut]
Maintainer: Mikhail Dozmorov <mikhail.dozmorov@gmail.com>
URL: https://github.com/dozmorovlab/multiHiCcompare
VignetteBuilder: knitr
BugReports: https://github.com/dozmorovlab/multiHiCcompare/issues
git_url: https://git.bioconductor.org/packages/multiHiCcompare
git_branch: devel
git_last_commit: 62d7e43
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/multiHiCcompare_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/multiHiCcompare_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/multiHiCcompare_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/multiHiCcompare_1.25.0.tgz
vignettes:
vignettes/multiHiCcompare/inst/doc/juiceboxVisualization.html,
vignettes/multiHiCcompare/inst/doc/multiHiCcompare.html
vignetteTitles: juiceboxVisualization, multiHiCcompare
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/multiHiCcompare/inst/doc/juiceboxVisualization.R,
vignettes/multiHiCcompare/inst/doc/multiHiCcompare.R
importsMe: HiCDOC, OHCA
suggestsMe: HiCcompare
dependencyCount: 93
Package: MultiMed
Version: 2.29.0
Depends: R (>= 3.1.0)
Suggests: RUnit, BiocGenerics
License: GPL (>= 2) + file LICENSE
MD5sum: 48a0a7fcb67017cf1144eb755ad4170b
NeedsCompilation: no
Title: Testing multiple biological mediators simultaneously
Description: Implements methods for testing multiple mediators
biocViews: MultipleComparison, StatisticalMethod, Software
Author: Simina M. Boca, Ruth Heller, Joshua N. Sampson
Maintainer: Simina M. Boca <smb310@georgetown.edu>
git_url: https://git.bioconductor.org/packages/MultiMed
git_branch: devel
git_last_commit: e0a8cee
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MultiMed_2.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MultiMed_2.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MultiMed_2.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MultiMed_2.29.0.tgz
vignettes: vignettes/MultiMed/inst/doc/MultiMed.pdf
vignetteTitles: MultiMedTutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MultiMed/inst/doc/MultiMed.R
dependencyCount: 0
Package: multiMiR
Version: 1.29.0
Depends: R (>= 3.4)
Imports: stats, XML, RCurl, purrr (>= 0.2.2), tibble (>= 2.0), methods,
BiocGenerics, AnnotationDbi, dplyr,
Suggests: BiocStyle, edgeR, knitr, rmarkdown, testthat (>= 1.0.2)
License: MIT + file LICENSE
MD5sum: 2d3d9b1ee6c26900075aef22a6f00903
NeedsCompilation: no
Title: Integration of multiple microRNA-target databases with their
disease and drug associations
Description: A collection of microRNAs/targets from external resources,
including validated microRNA-target databases (miRecords,
miRTarBase and TarBase), predicted microRNA-target databases
(DIANA-microT, ElMMo, MicroCosm, miRanda, miRDB, PicTar, PITA
and TargetScan) and microRNA-disease/drug databases
(miR2Disease, Pharmaco-miR VerSe and PhenomiR).
biocViews: miRNAData, Homo_sapiens_Data, Mus_musculus_Data,
Rattus_norvegicus_Data, OrganismData
Author: Yuanbin Ru [aut], Matt Mulvahill [aut], Spencer Mahaffey [cre,
aut], Katerina Kechris [aut, cph, ths]
Maintainer: Spencer Mahaffey <Spencer.Mahaffey@cuanschutz.edu>
URL: https://github.com/KechrisLab/multiMiR
VignetteBuilder: knitr
BugReports: https://github.com/KechrisLab/multiMiR/issues
git_url: https://git.bioconductor.org/packages/multiMiR
git_branch: devel
git_last_commit: 2e6eb45
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/multiMiR_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/multiMiR_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/multiMiR_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/multiMiR_1.29.0.tgz
vignettes: vignettes/multiMiR/inst/doc/multiMiR.html
vignetteTitles: The multiMiR user's guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/multiMiR/inst/doc/multiMiR.R
suggestsMe: EpiMix
dependencyCount: 57
Package: MultimodalExperiment
Version: 1.7.0
Depends: R (>= 4.3.0), IRanges, S4Vectors
Imports: BiocGenerics, MultiAssayExperiment, methods, utils
Suggests: BiocStyle, knitr, rmarkdown
License: Artistic-2.0
MD5sum: d09ed84d87473852430d6bb8cabf009c
NeedsCompilation: no
Title: Integrative Bulk and Single-Cell Experiment Container
Description: MultimodalExperiment is an S4 class that integrates bulk
and single-cell experiment data; it is optimally
storage-efficient, and its methods are exceptionally fast. It
effortlessly represents multimodal data of any nature and
features normalized experiment, subject, sample, and cell
annotations, which are related to underlying biological
experiments through maps. Its coordination methods are opt-in
and employ database-like join operations internally to deliver
fast and flexible management of multimodal data.
biocViews: DataRepresentation, Infrastructure, SingleCell
Author: Lucas Schiffer [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3628-0326>)
Maintainer: Lucas Schiffer <schiffer.lucas@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MultimodalExperiment
git_branch: devel
git_last_commit: d92c351
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MultimodalExperiment_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MultimodalExperiment_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MultimodalExperiment_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MultimodalExperiment_1.7.0.tgz
vignettes:
vignettes/MultimodalExperiment/inst/doc/MultimodalExperiment.html
vignetteTitles: MultimodalExperiment
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MultimodalExperiment/inst/doc/MultimodalExperiment.R
dependencyCount: 57
Package: MultiRNAflow
Version: 1.5.0
Depends: Mfuzz (>= 2.58.0), R (>= 4.3)
Imports: Biobase (>= 2.54.0), ComplexHeatmap (>= 2.14.0), DESeq2 (>=
1.38.1), factoextra (>= 1.0.7), FactoMineR (>= 2.6), ggalluvial
(>= 0.12.3), ggplot2 (>= 3.4.0), ggplotify (>= 0.1.2), ggrepel
(>= 0.9.2), gprofiler2 (>= 0.2.1), graphics (>= 4.2.2),
grDevices (>= 4.2.2), grid (>= 4.2.2), plot3D (>= 1.4),
plot3Drgl (>= 1.0.3), reshape2 (>= 1.4.4), S4Vectors (>=
0.36.2), stats (>= 4.2.2), SummarizedExperiment (>= 1.28.0),
UpSetR (>= 1.4.0), utils (>= 4.2.2)
Suggests: BiocGenerics (>= 0.40.0), BiocStyle, e1071 (>= 1.7.12),
knitr, rmarkdown, testthat (>= 3.0.0)
License: GPL-3 | file LICENSE
MD5sum: 458417663840e6c3de32e10cfc393072
NeedsCompilation: no
Title: An R package for integrated analysis of temporal RNA-seq data
with multiple biological conditions
Description: Our R package MultiRNAflow provides an easy to use unified
framework allowing to automatically make both unsupervised and
supervised (DE) analysis for datasets with an arbitrary number
of biological conditions and time points. In particular, our
code makes a deep downstream analysis of DE information, e.g.
identifying temporal patterns across biological conditions and
DE genes which are specific to a biological condition for each
time.
biocViews: Sequencing, RNASeq, GeneExpression, Transcription,
TimeCourse, Preprocessing, Visualization, Normalization,
PrincipalComponent, Clustering, DifferentialExpression,
GeneSetEnrichment, Pathways
Author: Rodolphe Loubaton [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1442-7270>), Nicolas Champagnat
[aut, ths] (ORCID: <https://orcid.org/0000-0002-5128-2357>),
Laurent Vallat [aut, ths] (ORCID:
<https://orcid.org/0000-0002-5226-7706>), Pierre Vallois [aut]
(ORCID: <https://orcid.org/0000-0002-2123-0142>), Région Grand
Est [fnd], Cancéropôle Est [fnd]
Maintainer: Rodolphe Loubaton <loubaton.rodolphe@gmail.com>
URL: https://github.com/loubator/MultiRNAflow
VignetteBuilder: knitr
BugReports: https://github.com/loubator/MultiRNAflow/issues
git_url: https://git.bioconductor.org/packages/MultiRNAflow
git_branch: devel
git_last_commit: 63c2461
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MultiRNAflow_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MultiRNAflow_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MultiRNAflow_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MultiRNAflow_1.5.0.tgz
vignettes:
vignettes/MultiRNAflow/inst/doc/MultiRNAflow_vignette-knitr.pdf,
vignettes/MultiRNAflow/inst/doc/Running_analysis_with_MultiRNAflow.html
vignetteTitles: MultiRNAflow: A R package for analysing RNA-seq raw
counts with different time points and several biological
conditions., Running_analysis_with_MultiRNAflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/MultiRNAflow/inst/doc/MultiRNAflow_vignette-knitr.R,
vignettes/MultiRNAflow/inst/doc/Running_analysis_with_MultiRNAflow.R
dependencyCount: 190
Package: multiscan
Version: 1.67.0
Depends: R (>= 2.3.0)
Imports: Biobase, utils
License: GPL (>= 2)
MD5sum: 28304602c7eb1a50f32aff98300e9eb6
NeedsCompilation: yes
Title: R package for combining multiple scans
Description: Estimates gene expressions from several laser scans of the
same microarray
biocViews: Microarray, Preprocessing
Author: Mizanur Khondoker <mizanur.khondoker@ed.ac.uk>, Chris Glasbey,
Bruce Worton.
Maintainer: Mizanur Khondoker <mizanur.khondoker@ed.ac.uk>
git_url: https://git.bioconductor.org/packages/multiscan
git_branch: devel
git_last_commit: b974c24
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/multiscan_1.67.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/multiscan_1.67.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/multiscan_1.67.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/multiscan_1.67.0.tgz
vignettes: vignettes/multiscan/inst/doc/multiscan.pdf
vignetteTitles: An R Package for Estimating Gene Expressions using
Multiple Scans
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/multiscan/inst/doc/multiscan.R
dependencyCount: 7
Package: multistateQTL
Version: 1.99.5
Depends: QTLExperiment, SummarizedExperiment, ComplexHeatmap,
data.table, collapse
Imports: methods, S4Vectors, grid, dplyr, tidyr, matrixStats, stats,
fitdistrplus, viridis, ggplot2, circlize, mashr, grDevices
Suggests: testthat, BiocStyle, knitr, covr, rmarkdown
License: GPL-3
MD5sum: d8a60c2d5af53b1d1b76de4870eff6e2
NeedsCompilation: no
Title: Toolkit for the analysis of multi-state QTL data
Description: A collection of tools for doing various analyses of
multi-state QTL data, with a focus on visualization and
interpretation. The package 'multistateQTL' contains functions
which can remove or impute missing data, identify significant
associations, as well as categorise features into global,
multi-state or unique. The analysis results are stored in a
'QTLExperiment' object, which is based on the
'SummarisedExperiment' framework.
biocViews: FunctionalGenomics, GeneExpression, Sequencing,
Visualization, SNP, Software
Author: Christina Del Azodi [aut], Davis McCarthy [ctb], Amelia
Dunstone [cre, ctb] (ORCID:
<https://orcid.org/0009-0009-6426-1529>)
Maintainer: Amelia Dunstone <amelia.dunstone@svi.edu.au>
URL: https://github.com/dunstone-a/multistateQTL
VignetteBuilder: knitr
BugReports: https://github.com/dunstone-a/multistateQTL/issues
git_url: https://git.bioconductor.org/packages/multistateQTL
git_branch: devel
git_last_commit: 20d79c1
git_last_commit_date: 2025-03-17
Date/Publication: 2025-03-17
source.ver: src/contrib/multistateQTL_1.99.5.tar.gz
win.binary.ver: bin/windows/contrib/4.5/multistateQTL_1.99.5.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/multistateQTL_1.99.5.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/multistateQTL_1.99.5.tgz
vignettes: vignettes/multistateQTL/inst/doc/multistateQTL.html
vignetteTitles: multistateQTL: Orchestrating multi-state QTL analysis
in R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/multistateQTL/inst/doc/multistateQTL.R
dependencyCount: 116
Package: multiWGCNA
Version: 1.5.0
Depends: R (>= 4.3.0), ggalluvial
Imports: stringr, readr, WGCNA, dplyr, reshape2, data.table, patchwork,
scales, igraph, flashClust, ggplot2, dcanr, cowplot, ggrepel,
methods, SummarizedExperiment
Suggests: BiocStyle, doParallel, ExperimentHub, knitr, markdown,
rmarkdown, testthat (>= 3.0.0), vegan
License: GPL-3
MD5sum: 69e3eb1596d6f70b6c2861302a9f994c
NeedsCompilation: no
Title: multiWGCNA
Description: An R package for deeping mining gene co-expression
networks in multi-trait expression data. Provides functions for
analyzing, comparing, and visualizing WGCNA networks across
conditions. multiWGCNA was designed to handle the common case
where there are multiple biologically meaningful sample traits,
such as disease vs wildtype across development or anatomical
region.
biocViews: Sequencing, RNASeq, GeneExpression, DifferentialExpression,
Regression, Clustering
Author: Dario Tommasini [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1214-6547>), Brent Fogel [aut,
ctb]
Maintainer: Dario Tommasini <dtommasini0@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/multiWGCNA
git_branch: devel
git_last_commit: b2881a4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/multiWGCNA_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/multiWGCNA_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/multiWGCNA_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/multiWGCNA_1.5.0.tgz
vignettes: vignettes/multiWGCNA/inst/doc/astrocyte_map_v2.html,
vignettes/multiWGCNA/inst/doc/autism_full_workflow.html
vignetteTitles: Astrocyte multiWGCNA network, General Workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/multiWGCNA/inst/doc/astrocyte_map_v2.R,
vignettes/multiWGCNA/inst/doc/autism_full_workflow.R
suggestsMe: multiWGCNAdata
dependencyCount: 147
Package: multtest
Version: 2.63.0
Depends: R (>= 2.10), methods, BiocGenerics, Biobase
Imports: survival, MASS, stats4
Suggests: snow
License: LGPL
MD5sum: be7aca1943b19bca8473ab3a9ae64e32
NeedsCompilation: yes
Title: Resampling-based multiple hypothesis testing
Description: Non-parametric bootstrap and permutation resampling-based
multiple testing procedures (including empirical Bayes methods)
for controlling the family-wise error rate (FWER), generalized
family-wise error rate (gFWER), tail probability of the
proportion of false positives (TPPFP), and false discovery rate
(FDR). Several choices of bootstrap-based null distribution
are implemented (centered, centered and scaled,
quantile-transformed). Single-step and step-wise methods are
available. Tests based on a variety of t- and F-statistics
(including t-statistics based on regression parameters from
linear and survival models as well as those based on
correlation parameters) are included. When probing hypotheses
with t-statistics, users may also select a potentially faster
null distribution which is multivariate normal with mean zero
and variance covariance matrix derived from the vector
influence function. Results are reported in terms of adjusted
p-values, confidence regions and test statistic cutoffs. The
procedures are directly applicable to identifying
differentially expressed genes in DNA microarray experiments.
biocViews: Microarray, DifferentialExpression, MultipleComparison
Author: Katherine S. Pollard, Houston N. Gilbert, Yongchao Ge, Sandra
Taylor, Sandrine Dudoit
Maintainer: Katherine S. Pollard <katherine.pollard@gladstone.ucsf.edu>
git_url: https://git.bioconductor.org/packages/multtest
git_branch: devel
git_last_commit: baeb862
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/multtest_2.63.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/multtest_2.63.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/multtest_2.63.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/multtest_2.63.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: aCGH, BicARE, KCsmart, PREDA, rain, REDseq, siggenes,
webbioc, cp4p, DiffCorr, PCS
importsMe: a4Base, ABarray, adSplit, ALDEx2, anota, BulkSignalR,
ChIPpeakAnno, GUIDEseq, metabomxtr, nethet, OCplus, phyloseq,
RTopper, SARC, SingleCellSignalR, singleCellTK, synapter,
webbioc, mutoss, nlcv, pRF, TcGSA
suggestsMe: annaffy, CAMERA, ecolitk, factDesign, GOstats, GSEAlm,
ropls, topGO, xcms, cherry, POSTm
dependencyCount: 15
Package: mumosa
Version: 1.15.0
Depends: SingleCellExperiment
Imports: stats, utils, methods, igraph, Matrix, BiocGenerics,
BiocParallel, IRanges, S4Vectors, DelayedArray,
DelayedMatrixStats, SummarizedExperiment, BiocNeighbors,
BiocSingular, ScaledMatrix, beachmat, scuttle, metapod, scran,
batchelor, uwot
Suggests: testthat, knitr, BiocStyle, rmarkdown, scater, bluster,
DropletUtils, scRNAseq
License: GPL-3
Archs: x64
MD5sum: 6f503d8cd183b1dabe19e1daa63dd0d7
NeedsCompilation: no
Title: Multi-Modal Single-Cell Analysis Methods
Description: Assorted utilities for multi-modal analyses of single-cell
datasets. Includes functions to combine multiple modalities for
downstream analysis, perform MNN-based batch correction across
multiple modalities, and to compute correlations between assay
values for different modalities.
biocViews: ImmunoOncology, SingleCell, RNASeq
Author: Aaron Lun [aut, cre]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: http://bioconductor.org/packages/mumosa
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/
git_url: https://git.bioconductor.org/packages/mumosa
git_branch: devel
git_last_commit: df075a8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mumosa_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mumosa_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mumosa_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mumosa_1.15.0.tgz
vignettes: vignettes/mumosa/inst/doc/overview.html
vignetteTitles: Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mumosa/inst/doc/overview.R
dependencyCount: 84
Package: MungeSumstats
Version: 1.15.12
Depends: R(>= 4.1)
Imports: data.table, utils, R.utils, dplyr, stats, GenomicRanges,
GenomeInfoDb, IRanges, ieugwasr(>= 1.0.1), BSgenome,
Biostrings, stringr, VariantAnnotation, methods, parallel,
rtracklayer(>= 1.59.1), RCurl
Suggests: SNPlocs.Hsapiens.dbSNP144.GRCh37,
SNPlocs.Hsapiens.dbSNP144.GRCh38,
SNPlocs.Hsapiens.dbSNP155.GRCh37,
SNPlocs.Hsapiens.dbSNP155.GRCh38,
BSgenome.Hsapiens.1000genomes.hs37d5,
BSgenome.Hsapiens.NCBI.GRCh38, BiocGenerics, S4Vectors,
rmarkdown, markdown, knitr, testthat (>= 3.0.0), UpSetR,
BiocStyle, covr, Rsamtools, MatrixGenerics, badger,
BiocParallel, GenomicFiles
License: Artistic-2.0
MD5sum: 31d2b1990a2198e894bca269ac90be6d
NeedsCompilation: no
Title: Standardise summary statistics from GWAS
Description: The *MungeSumstats* package is designed to facilitate the
standardisation of GWAS summary statistics. It reformats
inputted summary statisitics to include SNP, CHR, BP and can
look up these values if any are missing. It also pefrorms
dozens of QC and filtering steps to ensure high data quality
and minimise inter-study differences.
biocViews: SNP, WholeGenome, Genetics, ComparativeGenomics,
GenomeWideAssociation, GenomicVariation, Preprocessing
Author: Alan Murphy [aut, cre] (ORCID:
<https://orcid.org/0000-0002-2487-8753>), Brian Schilder [aut,
ctb] (ORCID: <https://orcid.org/0000-0001-5949-2191>), Nathan
Skene [aut] (ORCID: <https://orcid.org/0000-0002-6807-3180>)
Maintainer: Alan Murphy <alanmurph94@hotmail.com>
URL: https://github.com/neurogenomics/MungeSumstats,
https://al-murphy.github.io/MungeSumstats/
VignetteBuilder: knitr
BugReports: https://github.com/neurogenomics/MungeSumstats/issues
git_url: https://git.bioconductor.org/packages/MungeSumstats
git_branch: devel
git_last_commit: 07eee45
git_last_commit_date: 2024-12-18
Date/Publication: 2024-12-18
source.ver: src/contrib/MungeSumstats_1.15.12.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MungeSumstats_1.15.12.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MungeSumstats_1.15.12.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MungeSumstats_1.15.12.tgz
vignettes: vignettes/MungeSumstats/inst/doc/docker.html,
vignettes/MungeSumstats/inst/doc/MungeSumstats.html,
vignettes/MungeSumstats/inst/doc/OpenGWAS.html
vignetteTitles: docker, MungeSumstats, OpenGWAS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MungeSumstats/inst/doc/docker.R,
vignettes/MungeSumstats/inst/doc/MungeSumstats.R,
vignettes/MungeSumstats/inst/doc/OpenGWAS.R
dependencyCount: 94
Package: muscat
Version: 1.21.0
Depends: R (>= 4.4)
Imports: BiocParallel, blme, ComplexHeatmap, data.table, DESeq2, dplyr,
edgeR, ggplot2, glmmTMB, grDevices, grid, limma, lmerTest,
lme4, Matrix, matrixStats, methods, progress, purrr, rlang,
S4Vectors, scales, scater, scuttle, sctransform, stats,
SingleCellExperiment, SummarizedExperiment, variancePartition,
viridis
Suggests: BiocStyle, countsimQC, ExperimentHub, iCOBRA, knitr,
patchwork, phylogram, RColorBrewer, reshape2, rmarkdown,
statmod, stageR, testthat, UpSetR
License: GPL-3
MD5sum: b12eed2af5c7172844ea91e2b61b9d61
NeedsCompilation: no
Title: Multi-sample multi-group scRNA-seq data analysis tools
Description: `muscat` provides various methods and visualization tools
for DS analysis in multi-sample, multi-group,
multi-(cell-)subpopulation scRNA-seq data, including cell-level
mixed models and methods based on aggregated “pseudobulk†data,
as well as a flexible simulation platform that mimics both
single and multi-sample scRNA-seq data.
biocViews: ImmunoOncology, DifferentialExpression, Sequencing,
SingleCell, Software, StatisticalMethod, Visualization
Author: Helena L. Crowell [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4801-1767>), Pierre-Luc Germain
[aut], Charlotte Soneson [aut], Anthony Sonrel [aut], Jeroen
Gilis [aut], Davide Risso [aut], Lieven Clement [aut], Mark D.
Robinson [aut, fnd]
Maintainer: Helena L. Crowell <helena@crowell.eu>
URL: https://github.com/HelenaLC/muscat
VignetteBuilder: knitr
BugReports: https://github.com/HelenaLC/muscat/issues
git_url: https://git.bioconductor.org/packages/muscat
git_branch: devel
git_last_commit: 0bf2615
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-13
source.ver: src/contrib/muscat_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/muscat_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/muscat_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/muscat_1.21.0.tgz
vignettes: vignettes/muscat/inst/doc/analysis.html,
vignettes/muscat/inst/doc/detection.html,
vignettes/muscat/inst/doc/simulation.html
vignetteTitles: "1. DS analysis", "3. Differential detection", "2. Data
simulation"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/muscat/inst/doc/analysis.R,
vignettes/muscat/inst/doc/detection.R,
vignettes/muscat/inst/doc/simulation.R
importsMe: DESpace
suggestsMe: dreamlet, muscData
dependencyCount: 177
Package: muscle
Version: 3.49.0
Depends: Biostrings
License: Unlimited
Archs: x64
MD5sum: 144d601eea9a36461e1c4c31db78a254
NeedsCompilation: yes
Title: Multiple Sequence Alignment with MUSCLE
Description: MUSCLE performs multiple sequence alignments of nucleotide
or amino acid sequences.
biocViews: MultipleSequenceAlignment, Alignment, Sequencing, Genetics,
SequenceMatching, DataImport
Author: Algorithm by Robert C. Edgar. R port by Alex T. Kalinka.
Maintainer: Alex T. Kalinka <alex.t.kalinka@gmail.com>
URL: http://www.drive5.com/muscle/
git_url: https://git.bioconductor.org/packages/muscle
git_branch: devel
git_last_commit: 25320f4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/muscle_3.49.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/muscle_3.49.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/muscle_3.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/muscle_3.49.0.tgz
vignettes: vignettes/muscle/inst/doc/muscle-vignette.pdf
vignetteTitles: A guide to using muscle
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/muscle/inst/doc/muscle-vignette.R
suggestsMe: seqmagick
dependencyCount: 25
Package: musicatk
Version: 2.1.0
Depends: R (>= 4.4.0), NMF
Imports: SummarizedExperiment, VariantAnnotation, Biostrings, base,
methods, magrittr, tibble, tidyr, gtools, gridExtra,
MCMCprecision, MASS, matrixTests, data.table, dplyr, rlang,
BSgenome, GenomeInfoDb, GenomicFeatures, GenomicRanges,
IRanges, S4Vectors, uwot, ggplot2, stringr,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm9,
BSgenome.Mmusculus.UCSC.mm10, decompTumor2Sig, topicmodels,
ggrepel, plotly, utils, factoextra, cluster, ComplexHeatmap,
philentropy, maftools, shiny, stringi, tidyverse, ggpubr,
Matrix (>= 1.6.1), scales, conclust
Suggests: TCGAbiolinks, shinyBS, shinyalert, shinybusy, shinydashboard,
shinyjs, shinyjqui, sortable, testthat, BiocStyle, knitr,
rmarkdown, survival, XVector, qpdf, covr, shinyWidgets,
cowplot, withr
License: LGPL-3
MD5sum: 9ddb9df34fa7487261a5925cca11b3ac
NeedsCompilation: no
Title: Mutational Signature Comprehensive Analysis Toolkit
Description: Mutational signatures are carcinogenic exposures or
aberrant cellular processes that can cause alterations to the
genome. We created musicatk (MUtational SIgnature Comprehensive
Analysis ToolKit) to address shortcomings in versatility and
ease of use in other pre-existing computational tools. Although
many different types of mutational data have been generated,
current software packages do not have a flexible framework to
allow users to mix and match different types of mutations in
the mutational signature inference process. Musicatk enables
users to count and combine multiple mutation types, including
SBS, DBS, and indels. Musicatk calculates replication strand,
transcription strand and combinations of these features along
with discovery from unique and proprietary genomic feature
associated with any mutation type. Musicatk also implements
several methods for discovery of new signatures as well as
methods to infer exposure given an existing set of signatures.
Musicatk provides functions for visualization and downstream
exploratory analysis including the ability to compare
signatures between cohorts and find matching signatures in
COSMIC V2 or COSMIC V3.
biocViews: Software, BiologicalQuestion, SomaticMutation,
VariantAnnotation
Author: Aaron Chevalier [aut] (ORCHID: 0000-0002-3968-9250), Natasha
Gurevich [aut] (ORCHID: 0000-0002-0747-6840), Tao Guo [aut]
(ORCHID: 0009-0005-8960-9203), Joshua D. Campbell [aut, cre]
(ORCID: <https://orcid.org/0000-0003-0780-8662>)
Maintainer: Joshua D. Campbell <camp@bu.edu>
VignetteBuilder: knitr
BugReports: https://github.com/campbio/musicatk/issues
git_url: https://git.bioconductor.org/packages/musicatk
git_branch: devel
git_last_commit: 45539aa
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/musicatk_2.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/musicatk_2.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/musicatk_2.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/musicatk_2.1.0.tgz
vignettes: vignettes/musicatk/inst/doc/musicatk.html
vignetteTitles: Mutational Signature Comprehensive Analysis Toolkit
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/musicatk/inst/doc/musicatk.R
dependencyCount: 268
Package: MutationalPatterns
Version: 3.17.0
Depends: R (>= 4.2.0), GenomicRanges (>= 1.24.0), NMF (>= 0.20.6)
Imports: stats, S4Vectors, BiocGenerics (>= 0.18.0), BSgenome (>=
1.40.0), VariantAnnotation (>= 1.18.1), dplyr (>= 0.8.3),
tibble(>= 2.1.3), purrr (>= 0.3.2), tidyr (>= 1.0.0), stringr
(>= 1.4.0), magrittr (>= 1.5), ggplot2 (>= 2.1.0), pracma (>=
1.8.8), IRanges (>= 2.6.0), GenomeInfoDb (>= 1.12.0),
Biostrings (>= 2.40.0), ggdendro (>= 0.1-20), cowplot (>=
0.9.2), ggalluvial (>= 0.12.2), RColorBrewer, methods
Suggests: BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.0), BiocStyle (>= 2.0.3),
TxDb.Hsapiens.UCSC.hg19.knownGene (>= 3.2.2), biomaRt (>=
2.28.0), gridExtra (>= 2.2.1), rtracklayer (>= 1.32.2), ccfindR
(>= 1.6.0), GenomicFeatures, AnnotationDbi, testthat, knitr,
rmarkdown
License: MIT + file LICENSE
MD5sum: cb6e81e6ada005066c267820a8a284aa
NeedsCompilation: no
Title: Comprehensive genome-wide analysis of mutational processes
Description: Mutational processes leave characteristic footprints in
genomic DNA. This package provides a comprehensive set of
flexible functions that allows researchers to easily evaluate
and visualize a multitude of mutational patterns in base
substitution catalogues of e.g. healthy samples, tumour
samples, or DNA-repair deficient cells. The package covers a
wide range of patterns including: mutational signatures,
transcriptional and replicative strand bias, lesion
segregation, genomic distribution and association with genomic
features, which are collectively meaningful for studying the
activity of mutational processes. The package works with single
nucleotide variants (SNVs), insertions and deletions (Indels),
double base substitutions (DBSs) and larger multi base
substitutions (MBSs). The package provides functionalities for
both extracting mutational signatures de novo and determining
the contribution of previously identified mutational signatures
on a single sample level. MutationalPatterns integrates with
common R genomic analysis workflows and allows easy association
with (publicly available) annotation data.
biocViews: Genetics, SomaticMutation
Author: Freek Manders [aut] (ORCID:
<https://orcid.org/0000-0001-6197-347X>), Francis Blokzijl
[aut] (ORCID: <https://orcid.org/0000-0002-8084-8444>), Roel
Janssen [aut] (ORCID: <https://orcid.org/0000-0003-4324-5350>),
Jurrian de Kanter [ctb] (ORCID:
<https://orcid.org/0000-0001-5665-3711>), Rurika Oka [ctb]
(ORCID: <https://orcid.org/0000-0003-4107-7250>), Mark van
Roosmalen [cre], Ruben van Boxtel [aut, cph] (ORCID:
<https://orcid.org/0000-0003-1285-2836>), Edwin Cuppen [aut]
(ORCID: <https://orcid.org/0000-0002-0400-9542>)
Maintainer: Mark van Roosmalen
<vanBoxtelBioinformatics@prinsesmaximacentrum.nl>
URL: https://doi.org/doi:10.1186/s12864-022-08357-3
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MutationalPatterns
git_branch: devel
git_last_commit: 1bdb319
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/MutationalPatterns_3.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MutationalPatterns_3.17.0.zip
vignettes:
vignettes/MutationalPatterns/inst/doc/Introduction_to_MutationalPatterns.html
vignetteTitles: Introduction to MutationalPatterns
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles:
vignettes/MutationalPatterns/inst/doc/Introduction_to_MutationalPatterns.R
importsMe: RESOLVE
suggestsMe: SUITOR
dependencyCount: 123
Package: MVCClass
Version: 1.81.0
Depends: R (>= 2.1.0), methods
License: LGPL
MD5sum: 21893935ad32913fe46c1fb2ad4164e3
NeedsCompilation: no
Title: Model-View-Controller (MVC) Classes
Description: Creates classes used in model-view-controller (MVC) design
biocViews: Visualization, Infrastructure, GraphAndNetwork
Author: Elizabeth Whalen
Maintainer: Elizabeth Whalen <ewhalen@hsph.harvard.edu>
git_url: https://git.bioconductor.org/packages/MVCClass
git_branch: devel
git_last_commit: 72d5a1c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MVCClass_1.81.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MVCClass_1.81.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MVCClass_1.81.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MVCClass_1.81.0.tgz
vignettes: vignettes/MVCClass/inst/doc/MVCClass.pdf
vignetteTitles: MVCClass
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: BioMVCClass
dependencyCount: 1
Package: MWASTools
Version: 1.31.0
Depends: R (>= 3.5.0)
Imports: glm2, ppcor, qvalue, car, boot, grid, ggplot2, gridExtra,
igraph, SummarizedExperiment, KEGGgraph, RCurl, KEGGREST,
ComplexHeatmap, stats, utils
Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown
License: CC BY-NC-ND 4.0
Archs: x64
MD5sum: 5ed75f82cb770ab70c0c620c4b30033f
NeedsCompilation: no
Title: MWASTools: an integrated pipeline to perform metabolome-wide
association studies
Description: MWASTools provides a complete pipeline to perform
metabolome-wide association studies. Key functionalities of the
package include: quality control analysis of metabonomic data;
MWAS using different association models (partial correlations;
generalized linear models); model validation using
non-parametric bootstrapping; visualization of MWAS results;
NMR metabolite identification using STOCSY; and biological
interpretation of MWAS results.
biocViews: Metabolomics, Lipidomics, Cheminformatics, SystemsBiology,
QualityControl
Author: Andrea Rodriguez-Martinez, Joram M. Posma, Rafael Ayala, Ana L.
Neves, Maryam Anwar, Jeremy K. Nicholson, Marc-Emmanuel Dumas
Maintainer: Andrea Rodriguez-Martinez
<andrea.rodriguez-martinez13@imperial.ac.uk>, Rafael Ayala
<rafael.ayala@oist.jp>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/MWASTools
git_branch: devel
git_last_commit: f47f1f8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/MWASTools_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/MWASTools_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/MWASTools_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/MWASTools_1.31.0.tgz
vignettes: vignettes/MWASTools/inst/doc/MWASTools.html
vignetteTitles: MWASTools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/MWASTools/inst/doc/MWASTools.R
importsMe: MetaboSignal
dependencyCount: 125
Package: mygene
Version: 1.43.0
Depends: R (>= 3.2.1), GenomicFeatures, txdbmaker
Imports: methods, utils, stats, httr (>= 0.3), jsonlite (>= 0.9.7),
Hmisc, sqldf, plyr, S4Vectors
Suggests: BiocStyle
License: Artistic-2.0
MD5sum: 6949c47dba9ad742603226325aa74f70
NeedsCompilation: no
Title: Access MyGene.Info_ services
Description: MyGene.Info_ provides simple-to-use REST web services to
query/retrieve gene annotation data. It's designed with
simplicity and performance emphasized. *mygene*, is an
easy-to-use R wrapper to access MyGene.Info_ services.
biocViews: Annotation
Author: Adam Mark, Ryan Thompson, Cyrus Afrasiabi, Chunlei Wu
Maintainer: Adam Mark, Cyrus Afrasiabi, Chunlei Wu <cwu@scripps.edu>
git_url: https://git.bioconductor.org/packages/mygene
git_branch: devel
git_last_commit: 66cce1e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mygene_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mygene_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mygene_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mygene_1.43.0.tgz
vignettes: vignettes/mygene/inst/doc/mygene.pdf
vignetteTitles: Using mygene.R
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mygene/inst/doc/mygene.R
importsMe: MetaboSignal
suggestsMe: CRISPRball
dependencyCount: 148
Package: myvariant
Version: 1.37.0
Depends: R (>= 3.2.1), VariantAnnotation
Imports: httr, jsonlite, S4Vectors, Hmisc, plyr, magrittr, GenomeInfoDb
Suggests: BiocStyle
License: Artistic-2.0
MD5sum: 87210f6ba43340b0f6946a05edba0514
NeedsCompilation: no
Title: Accesses MyVariant.info variant query and annotation services
Description: MyVariant.info is a comprehensive aggregation of variant
annotation resources. myvariant is a wrapper for querying
MyVariant.info services
biocViews: VariantAnnotation, Annotation, GenomicVariation
Author: Adam Mark
Maintainer: Adam Mark, Chunlei Wu <cwu@scripps.edu>
git_url: https://git.bioconductor.org/packages/myvariant
git_branch: devel
git_last_commit: ef53b5c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/myvariant_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/myvariant_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/myvariant_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/myvariant_1.37.0.tgz
vignettes: vignettes/myvariant/inst/doc/myvariant.pdf
vignetteTitles: Using MyVariant.R
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/myvariant/inst/doc/myvariant.R
dependencyCount: 132
Package: mzID
Version: 1.45.0
Depends: methods
Imports: XML, plyr, parallel, doParallel, foreach, iterators,
ProtGenerics
Suggests: knitr, testthat
License: GPL (>= 2)
MD5sum: 4fed8b4cc7058bb0fd5a0863c1b28ecb
NeedsCompilation: no
Title: An mzIdentML parser for R
Description: A parser for mzIdentML files implemented using the XML
package. The parser tries to be general and able to handle all
types of mzIdentML files with the drawback of having less
'pretty' output than a vendor specific parser. Please contact
the maintainer with any problems and supply an mzIdentML file
so the problems can be fixed quickly.
biocViews: ImmunoOncology, DataImport, MassSpectrometry, Proteomics
Author: Laurent Gatto [ctb, cre] (ORCID:
<https://orcid.org/0000-0002-1520-2268>), Thomas Pedersen [aut]
(ORCID: <https://orcid.org/0000-0002-6977-7147>), Vladislav
Petyuk [ctb]
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/mzID
git_branch: devel
git_last_commit: 3c854c5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/mzID_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mzID_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mzID_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mzID_1.45.0.tgz
vignettes: vignettes/mzID/inst/doc/HOWTO_mzID.pdf
vignetteTitles: Using mzID
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mzID/inst/doc/HOWTO_mzID.R
importsMe: MSnbase, MSnID, TargetDecoy
suggestsMe: mzR, PSMatch, RforProteomics
dependencyCount: 11
Package: mzR
Version: 2.41.4
Depends: R (>= 4.0.0), Rcpp (>= 0.10.1), methods, utils
Imports: Biobase, BiocGenerics (>= 0.13.6), ProtGenerics (>= 1.17.3),
ncdf4
LinkingTo: Rcpp, Rhdf5lib (>= 1.1.4)
Suggests: msdata (>= 0.15.1), RUnit, mzID, BiocStyle (>= 2.5.19),
knitr, XML, rmarkdown
License: Artistic-2.0
MD5sum: 76fd2162b473b3223333ce385bcf8312
NeedsCompilation: yes
Title: parser for netCDF, mzXML and mzML and mzIdentML files (mass
spectrometry data)
Description: mzR provides a unified API to the common file formats and
parsers available for mass spectrometry data. It comes with a
subset of the proteowizard library for mzXML, mzML and
mzIdentML. The netCDF reading code has previously been used in
XCMS.
biocViews: ImmunoOncology, Infrastructure, DataImport, Proteomics,
Metabolomics, MassSpectrometry
Author: Bernd Fischer, Steffen Neumann, Laurent Gatto, Qiang Kou,
Johannes Rainer
Maintainer: Steffen Neumann <sneumann@ipb-halle.de>
URL: https://github.com/sneumann/mzR/
SystemRequirements: C++11, GNU make
VignetteBuilder: knitr
BugReports: https://github.com/sneumann/mzR/issues/
git_url: https://git.bioconductor.org/packages/mzR
git_branch: devel
git_last_commit: f9ec8cf
git_last_commit_date: 2025-02-11
Date/Publication: 2025-02-12
source.ver: src/contrib/mzR_2.41.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/mzR_2.41.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/mzR_2.41.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/mzR_2.41.4.tgz
vignettes: vignettes/mzR/inst/doc/mzR.html
vignetteTitles: Accessin raw mass spectrometry and identification data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/mzR/inst/doc/mzR.R
dependsOnMe: MSnbase
importsMe: adductomicsR, CluMSID, MSnID, msPurity, peakPantheR,
RMassBank, SIMAT, TargetDecoy, topdownr, xcms, yamss
suggestsMe: AnnotationHub, koinar, MetaboAnnotation,
MsBackendMetaboLights, MsBackendRawFileReader, MsBackendSql,
MsDataHub, MsExperiment, MsQuality, PSMatch, qcmetrics,
Spectra, SpectraQL, msdata, RforProteomics, chromConverter,
erah
dependencyCount: 11
Package: NADfinder
Version: 1.31.1
Depends: R (>= 3.5.0), BiocGenerics, IRanges, GenomicRanges, S4Vectors,
SummarizedExperiment
Imports: graphics, methods, baseline, signal, GenomicAlignments,
GenomeInfoDb, rtracklayer, limma, trackViewer, stats, utils,
Rsamtools, metap, EmpiricalBrownsMethod,ATACseqQC, corrplot,
csaw
Suggests: RUnit, BiocStyle, knitr, BSgenome.Mmusculus.UCSC.mm10,
testthat, BiocManager, rmarkdown
License: GPL (>= 2)
MD5sum: 0a60cac96586799945a2674ffbb8c489
NeedsCompilation: no
Title: Call wide peaks for sequencing data
Description: Nucleolus is an important structure inside the nucleus in
eukaryotic cells. It is the site for transcribing rDNA into
rRNA and for assembling ribosomes, aka ribosome biogenesis. In
addition, nucleoli are dynamic hubs through which numerous
proteins shuttle and contact specific non-rDNA genomic loci.
Deep sequencing analyses of DNA associated with isolated
nucleoli (NAD- seq) have shown that specific loci, termed
nucleolus- associated domains (NADs) form frequent three-
dimensional associations with nucleoli. NAD-seq has been used
to study the biological functions of NAD and the dynamics of
NAD distribution during embryonic stem cell (ESC)
differentiation. Here, we developed a Bioconductor package
NADfinder for bioinformatic analysis of the NAD-seq data,
including baseline correction, smoothing, normalization, peak
calling, and annotation.
biocViews: Sequencing, DNASeq, GeneRegulation, PeakDetection
Author: Jianhong Ou, Haibo Liu, Jun Yu, Hervé Pagès, Paul Kaufman,
Lihua Julie Zhu
Maintainer: Jianhong Ou <jou@morgridge.org>, Lihua Julie Zhu
<julie.zhu@umassmed.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/NADfinder
git_branch: devel
git_last_commit: f62bc38
git_last_commit_date: 2025-01-03
Date/Publication: 2025-01-03
source.ver: src/contrib/NADfinder_1.31.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/NADfinder_1.31.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/NADfinder_1.31.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/NADfinder_1.31.1.tgz
vignettes: vignettes/NADfinder/inst/doc/NADfinder.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NADfinder/inst/doc/NADfinder.R
dependencyCount: 230
Package: NanoMethViz
Version: 3.3.4
Depends: R (>= 4.0.0), methods, ggplot2 (>= 3.4.0)
Imports: cpp11 (>= 0.2.5), readr, cli, S4Vectors, SummarizedExperiment,
BiocSingular, bsseq, forcats, assertthat, AnnotationDbi, Rcpp,
dplyr, dbscan, e1071, fs, GenomicRanges, Biostrings, ggrastr,
glue, graphics, IRanges, limma (>= 3.44.0), patchwork, purrr,
rlang, R.utils, Rsamtools, scales (>= 1.2.0), stats, stringr,
tibble, tidyr, utils, withr
LinkingTo: Rcpp
Suggests: BiocStyle, DSS, Mus.musculus (>= 1.3.1), Homo.sapiens (>=
1.3.1), org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Hsapiens.UCSC.hg38.knownGene, org.Mm.eg.db,
TxDb.Mmusculus.UCSC.mm10.knownGene,
TxDb.Mmusculus.UCSC.mm39.refGene, knitr, rmarkdown,
rtracklayer, testthat (>= 3.0.0), covr
License: Apache License (>= 2.0)
MD5sum: 74d5c94b1827a671a2413c20813a7f24
NeedsCompilation: yes
Title: Visualise methylation data from Oxford Nanopore sequencing
Description: NanoMethViz is a toolkit for visualising methylation data
from Oxford Nanopore sequencing. It can be used to explore
methylation patterns from reads derived from Oxford Nanopore
direct DNA sequencing with methylation called by callers
including nanopolish, f5c and megalodon. The plots in this
package allow the visualisation of methylation profiles
aggregated over experimental groups and across classes of
genomic features.
biocViews: Software, LongRead, Visualization, DifferentialMethylation,
DNAMethylation, Epigenetics, DataImport
Author: Shian Su [cre, aut]
Maintainer: Shian Su <su.s@wehi.edu.au>
URL: https://github.com/shians/NanoMethViz,
https://shians.github.io/NanoMethViz/
SystemRequirements: C++20
VignetteBuilder: knitr
BugReports: https://github.com/Shians/NanoMethViz/issues
git_url: https://git.bioconductor.org/packages/NanoMethViz
git_branch: devel
git_last_commit: a484423
git_last_commit_date: 2025-03-09
Date/Publication: 2025-03-10
source.ver: src/contrib/NanoMethViz_3.3.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/NanoMethViz_3.3.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/NanoMethViz_3.3.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/NanoMethViz_3.3.4.tgz
vignettes: vignettes/NanoMethViz/inst/doc/UsersGuide.html
vignetteTitles: User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NanoMethViz/inst/doc/UsersGuide.R
dependencyCount: 150
Package: NanoStringDiff
Version: 1.37.0
Depends: Biobase
Imports: matrixStats, methods, Rcpp
LinkingTo: Rcpp
Suggests: testthat, BiocStyle
License: GPL
MD5sum: fd9c7ddb654eab15b7b39e125d3c5edd
NeedsCompilation: yes
Title: Differential Expression Analysis of NanoString nCounter Data
Description: This Package utilizes a generalized linear model(GLM) of
the negative binomial family to characterize count data and
allows for multi-factor design. NanoStrongDiff incorporate size
factors, calculated from positive controls and housekeeping
controls, and background level, obtained from negative
controls, in the model framework so that all the normalization
information provided by NanoString nCounter Analyzer is fully
utilized.
biocViews: DifferentialExpression, Normalization
Author: hong wang <hong.wang@uky.edu>, tingting zhai
<tingting.zhai@uky.edu>, chi wang <chi.wang@uky.edu>
Maintainer: tingting zhai <tingting.zhai@uky.edu>,hong wang
<hong.wang@uky.edu>
git_url: https://git.bioconductor.org/packages/NanoStringDiff
git_branch: devel
git_last_commit: ceaa04c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/NanoStringDiff_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/NanoStringDiff_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/NanoStringDiff_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/NanoStringDiff_1.37.0.tgz
vignettes: vignettes/NanoStringDiff/inst/doc/NanoStringDiff.pdf
vignetteTitles: NanoStringDiff Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NanoStringDiff/inst/doc/NanoStringDiff.R
suggestsMe: NanoTube
dependencyCount: 9
Package: NanoStringNCTools
Version: 1.15.0
Depends: R (>= 3.6), Biobase, S4Vectors, ggplot2
Imports: BiocGenerics, Biostrings, ggbeeswarm, ggiraph, ggthemes,
grDevices, IRanges, methods, pheatmap, RColorBrewer, stats,
utils
Suggests: biovizBase, ggbio, RUnit, rmarkdown, knitr, qpdf
License: MIT
Archs: x64
MD5sum: 83604c71b14e0e5766bad817719a44a1
NeedsCompilation: no
Title: NanoString nCounter Tools
Description: Tools for NanoString Technologies nCounter Technology.
Provides support for reading RCC files into an ExpressionSet
derived object. Also includes methods for QC and
normalizaztion of NanoString data.
biocViews: GeneExpression, Transcription, CellBasedAssays, DataImport,
Transcriptomics, Proteomics, mRNAMicroarray,
ProprietaryPlatforms, RNASeq
Author: Patrick Aboyoun [aut], Nicole Ortogero [aut], Maddy Griswold
[cre], Zhi Yang [ctb]
Maintainer: Maddy Griswold <mgriswold@nanostring.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/NanoStringNCTools
git_branch: devel
git_last_commit: daac9af
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/NanoStringNCTools_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/NanoStringNCTools_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/NanoStringNCTools_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/NanoStringNCTools_1.15.0.tgz
vignettes: vignettes/NanoStringNCTools/inst/doc/Introduction.html
vignetteTitles: Introduction to the NanoStringRCCSet Class
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/NanoStringNCTools/inst/doc/Introduction.R
dependsOnMe: GeomxTools, GeoMxWorkflows
importsMe: GeoDiff
dependencyCount: 88
Package: NanoTube
Version: 1.13.1
Depends: R (>= 4.1), Biobase, ggplot2, limma
Imports: fgsea, methods, reshape, stats, utils
Suggests: grid, kableExtra, knitr, NanoStringDiff, pheatmap, plotly,
rlang, rmarkdown, ruv, RUVSeq, shiny, testthat, xlsx
License: GPL-3 + file LICENSE
MD5sum: 33a32a53d5334bc9af5dcb236b9f342c
NeedsCompilation: no
Title: An Easy Pipeline for NanoString nCounter Data Analysis
Description: NanoTube includes functions for the processing, quality
control, analysis, and visualization of NanoString nCounter
data. Analysis functions include differential analysis and gene
set analysis methods, as well as postprocessing steps to help
understand the results. Additional functions are included to
enable interoperability with other Bioconductor NanoString data
analysis packages.
biocViews: Software, GeneExpression, DifferentialExpression,
QualityControl
Author: Caleb Class [cre, aut] (ORCID:
<https://orcid.org/0000-0003-3130-3613>), Caiden Lukan [ctb]
Maintainer: Caleb Class <cclass@butler.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/NanoTube
git_branch: devel
git_last_commit: 86c2245
git_last_commit_date: 2025-01-24
Date/Publication: 2025-01-26
source.ver: src/contrib/NanoTube_1.13.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/NanoTube_1.13.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/NanoTube_1.13.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/NanoTube_1.13.1.tgz
vignettes: vignettes/NanoTube/inst/doc/NanoTube.html
vignetteTitles: NanoTube Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/NanoTube/inst/doc/NanoTube.R
dependencyCount: 57
Package: NBAMSeq
Version: 1.23.0
Depends: R (>= 3.6), SummarizedExperiment, S4Vectors
Imports: DESeq2, mgcv(>= 1.8-24), BiocParallel, genefilter, methods,
stats,
Suggests: knitr, rmarkdown, testthat, ggplot2
License: GPL-2
Archs: x64
MD5sum: abab2b087a76451bb6e6571ed2bc0dcc
NeedsCompilation: no
Title: Negative Binomial Additive Model for RNA-Seq Data
Description: High-throughput sequencing experiments followed by
differential expression analysis is a widely used approach to
detect genomic biomarkers. A fundamental step in differential
expression analysis is to model the association between gene
counts and covariates of interest. NBAMSeq a flexible
statistical model based on the generalized additive model and
allows for information sharing across genes in variance
estimation.
biocViews: RNASeq, DifferentialExpression, GeneExpression, Sequencing,
Coverage
Author: Xu Ren [aut, cre], Pei Fen Kuan [aut]
Maintainer: Xu Ren <xuren2120@gmail.com>
URL: https://github.com/reese3928/NBAMSeq
VignetteBuilder: knitr
BugReports: https://github.com/reese3928/NBAMSeq/issues
git_url: https://git.bioconductor.org/packages/NBAMSeq
git_branch: devel
git_last_commit: fbb8e45
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/NBAMSeq_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/NBAMSeq_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/NBAMSeq_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/NBAMSeq_1.23.0.tgz
vignettes: vignettes/NBAMSeq/inst/doc/NBAMSeq-vignette.html
vignetteTitles: Negative Binomial Additive Model for RNA-Seq Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NBAMSeq/inst/doc/NBAMSeq-vignette.R
dependencyCount: 94
Package: ncdfFlow
Version: 2.53.1
Depends: R (>= 2.14.0), flowCore(>= 1.51.7), methods, BH
Imports: Biobase,BiocGenerics,flowCore
LinkingTo: cpp11,BH, Rhdf5lib
Suggests: testthat,parallel,flowStats,knitr
License: AGPL-3.0-only
Archs: x64
MD5sum: cf5a0caa1b805671b8b66d5bfc42acca
NeedsCompilation: yes
Title: ncdfFlow: A package that provides HDF5 based storage for flow
cytometry data.
Description: Provides HDF5 storage based methods and functions for
manipulation of flow cytometry data.
biocViews: ImmunoOncology, FlowCytometry
Author: Mike Jiang,Greg Finak,N. Gopalakrishnan
Maintainer: Mike Jiang <mike@ozette.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ncdfFlow
git_branch: devel
git_last_commit: 5a88de0
git_last_commit_date: 2025-01-13
Date/Publication: 2025-01-14
source.ver: src/contrib/ncdfFlow_2.53.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ncdfFlow_2.53.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ncdfFlow_2.53.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ncdfFlow_2.53.1.tgz
vignettes: vignettes/ncdfFlow/inst/doc/ncdfFlow.pdf
vignetteTitles: Basic Functions for Flow Cytometry Data
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ncdfFlow/inst/doc/ncdfFlow.R
dependsOnMe: ggcyto
importsMe: flowStats, flowWorkspace, openCyto
suggestsMe: COMPASS, cydar
dependencyCount: 18
Package: ncGTW
Version: 1.21.0
Depends: methods, BiocParallel, xcms
Imports: Rcpp, grDevices, graphics, stats
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, testthat, rmarkdown
License: GPL-2
MD5sum: ab868ba3f17505a26b0b5116f87cc27d
NeedsCompilation: yes
Title: Alignment of LC-MS Profiles by Neighbor-wise Compound-specific
Graphical Time Warping with Misalignment Detection
Description: The purpose of ncGTW is to help XCMS for LC-MS data
alignment. Currently, ncGTW can detect the misaligned feature
groups by XCMS, and the user can choose to realign these
feature groups by ncGTW or not.
biocViews: Software, MassSpectrometry, Metabolomics, Alignment
Author: Chiung-Ting Wu <ctwu@vt.edu>
Maintainer: Chiung-Ting Wu <ctwu@vt.edu>
VignetteBuilder: knitr
BugReports: https://github.com/ChiungTingWu/ncGTW/issues
git_url: https://git.bioconductor.org/packages/ncGTW
git_branch: devel
git_last_commit: 5de9a8f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ncGTW_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ncGTW_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ncGTW_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ncGTW_1.21.0.tgz
vignettes: vignettes/ncGTW/inst/doc/ncGTW.html
vignetteTitles: ncGTW User Manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ncGTW/inst/doc/ncGTW.R
dependencyCount: 146
Package: NCIgraph
Version: 1.55.0
Depends: R (>= 4.0.0)
Imports: graph, KEGGgraph, methods, RBGL, RCy3, R.oo
Suggests: Rgraphviz
Enhances: DEGraph
License: GPL-3
MD5sum: 6b0e8d949277d08403424779584cc26e
NeedsCompilation: no
Title: Pathways from the NCI Pathways Database
Description: Provides various methods to load the pathways from the NCI
Pathways Database in R graph objects and to re-format them.
biocViews: Pathways, GraphAndNetwork
Author: Laurent Jacob
Maintainer: Laurent Jacob <laurent.jacob@gmail.com>
git_url: https://git.bioconductor.org/packages/NCIgraph
git_branch: devel
git_last_commit: bbee7a4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/NCIgraph_1.55.0.tar.gz
vignettes: vignettes/NCIgraph/inst/doc/NCIgraph.pdf
vignetteTitles: NCIgraph: networks from the NCI pathway integrated
database as graphNEL objects.
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NCIgraph/inst/doc/NCIgraph.R
importsMe: DEGraph
suggestsMe: DEGraph
dependencyCount: 57
Package: ncRNAtools
Version: 1.17.0
Imports: httr, xml2, utils, methods, grDevices, ggplot2, IRanges,
GenomicRanges, S4Vectors
Suggests: knitr, BiocStyle, rmarkdown, RUnit, BiocGenerics
License: GPL-3
MD5sum: 5faa7ae333b4fcb0f345bd8243b29433
NeedsCompilation: no
Title: An R toolkit for non-coding RNA
Description: ncRNAtools provides a set of basic tools for handling and
analyzing non-coding RNAs. These include tools to access the
RNAcentral database and to predict and visualize the secondary
structure of non-coding RNAs. The package also provides tools
to read, write and interconvert the file formats most commonly
used for representing such secondary structures.
biocViews: FunctionalGenomics, DataImport, ThirdPartyClient,
Visualization, StructuralPrediction
Author: Lara Selles Vidal [cre, aut] (ORCID:
<https://orcid.org/0000-0003-2537-6824>), Rafael Ayala [aut]
(ORCID: <https://orcid.org/0000-0002-9332-4623>), Guy-Bart Stan
[aut] (ORCID: <https://orcid.org/0000-0002-5560-902X>), Rodrigo
Ledesma-Amaro [aut] (ORCID:
<https://orcid.org/0000-0003-2631-5898>)
Maintainer: Lara Selles Vidal <lara.selles@oist.jp>
VignetteBuilder: knitr
BugReports: https://github.com/LaraSellesVidal/ncRNAtools/issues
git_url: https://git.bioconductor.org/packages/ncRNAtools
git_branch: devel
git_last_commit: d839ffb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ncRNAtools_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ncRNAtools_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ncRNAtools_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ncRNAtools_1.17.0.tgz
vignettes: vignettes/ncRNAtools/inst/doc/ncRNAtools.html
vignetteTitles: rfaRm
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ncRNAtools/inst/doc/ncRNAtools.R
dependencyCount: 54
Package: ndexr
Version: 1.29.0
Depends: RCX
Imports: httr, jsonlite, plyr, tidyr
Suggests: BiocStyle, testthat, knitr, rmarkdown
License: BSD_3_clause + file LICENSE
MD5sum: 87271ed2fb58adcf2a91fa67b0cdc54a
NeedsCompilation: no
Title: NDEx R client library
Description: This package offers an interface to NDEx servers, e.g. the
public server at http://ndexbio.org/. It can retrieve and save
networks via the API. Networks are offered as RCX object and as
igraph representation.
biocViews: Pathways, DataImport, Network
Author: Florian Auer [cre, aut] (ORCID:
<https://orcid.org/0000-0002-5320-8900>), Frank Kramer [ctb],
Alex Ishkin [ctb], Dexter Pratt [ctb]
Maintainer: Florian Auer <florian.auer@informatik.uni-augsburg.de>
URL: https://github.com/frankkramer-lab/ndexr
VignetteBuilder: knitr
BugReports: https://github.com/frankkramer-lab/ndexr/issues
git_url: https://git.bioconductor.org/packages/ndexr
git_branch: devel
git_last_commit: b913e3a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ndexr_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ndexr_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ndexr_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ndexr_1.29.0.tgz
vignettes: vignettes/ndexr/inst/doc/ndexr-vignette.html
vignetteTitles: NDExR Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ndexr/inst/doc/ndexr-vignette.R
dependencyCount: 41
Package: nearBynding
Version: 1.17.0
Depends: R (>= 4.0)
Imports: R.utils, matrixStats, plyranges, transport, Rsamtools,
S4Vectors, grDevices, graphics, rtracklayer, dplyr,
GenomeInfoDb, methods, GenomicRanges, utils, stats, magrittr,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Hsapiens.UCSC.hg38.knownGene, ggplot2, gplots,
BiocGenerics, rlang
Suggests: knitr, rmarkdown
License: Artistic-2.0
Archs: x64
MD5sum: 8196a3fee43b7189be10dc2571f6437f
NeedsCompilation: no
Title: Discern RNA structure proximal to protein binding
Description: Provides a pipeline to discern RNA structure at and
proximal to the site of protein binding within regions of the
transcriptome defined by the user. CLIP protein-binding data
can be input as either aligned BAM or peak-called bedGraph
files. RNA structure can either be predicted internally from
sequence or users have the option to input their own RNA
structure data. RNA structure binding profiles can be visually
and quantitatively compared across multiple formats.
biocViews: Visualization, MotifDiscovery, DataRepresentation,
StructuralPrediction, Clustering, MultipleComparison
Author: Veronica Busa [cre]
Maintainer: Veronica Busa <vbusa1@jhmi.edu>
SystemRequirements: bedtools (>= 2.28.0), Stereogene (>= v2.22), CapR
(>= 1.1.1)
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/nearBynding
git_branch: devel
git_last_commit: 287e346
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/nearBynding_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/nearBynding_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/nearBynding_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/nearBynding_1.17.0.tgz
vignettes: vignettes/nearBynding/inst/doc/nearBynding.pdf
vignetteTitles: nearBynding Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/nearBynding/inst/doc/nearBynding.R
dependencyCount: 113
Package: Nebulosa
Version: 1.17.0
Depends: R (>= 4.0), ggplot2, patchwork
Imports: SingleCellExperiment, SummarizedExperiment, SeuratObject, ks,
Matrix, stats, methods, ggrastr
Suggests: testthat, BiocStyle, knitr, rmarkdown, covr, scater, scran,
DropletUtils, igraph, BiocFileCache, Seurat
License: GPL-3
Archs: x64
MD5sum: d4aa735b6712adec7c40f4b11bee5344
NeedsCompilation: no
Title: Single-Cell Data Visualisation Using Kernel Gene-Weighted
Density Estimation
Description: This package provides a enhanced visualization of
single-cell data based on gene-weighted density estimation.
Nebulosa recovers the signal from dropped-out features and
allows the inspection of the joint expression from multiple
features (e.g. genes). Seurat and SingleCellExperiment objects
can be used within Nebulosa.
biocViews: Software, GeneExpression, SingleCell, Visualization,
DimensionReduction
Author: Jose Alquicira-Hernandez [aut, cre] (ORCID:
<https://orcid.org/0000-0002-9049-7780>)
Maintainer: Jose Alquicira-Hernandez <alquicirajose@gmail.com>
URL: https://github.com/powellgenomicslab/Nebulosa
VignetteBuilder: knitr
BugReports: https://github.com/powellgenomicslab/Nebulosa/issues
git_url: https://git.bioconductor.org/packages/Nebulosa
git_branch: devel
git_last_commit: 29d6923
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Nebulosa_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Nebulosa_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Nebulosa_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Nebulosa_1.17.0.tgz
vignettes: vignettes/Nebulosa/inst/doc/introduction.html,
vignettes/Nebulosa/inst/doc/nebulosa_seurat.html
vignetteTitles: Visualization of gene expression with Nebulosa,
Visualization of gene expression with Nebulosa (in Seurat)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Nebulosa/inst/doc/introduction.R,
vignettes/Nebulosa/inst/doc/nebulosa_seurat.R
suggestsMe: scCustomize, SCpubr
dependencyCount: 98
Package: nempi
Version: 1.15.0
Depends: R (>= 4.1), mnem
Imports: e1071, nnet, randomForest, naturalsort, graphics, stats,
utils, matrixStats, epiNEM
Suggests: knitr, BiocGenerics, rmarkdown, RUnit, BiocStyle
License: GPL-3
MD5sum: 3a9a4a76d1c66dbeb6318fb976bf4e6a
NeedsCompilation: no
Title: Inferring unobserved perturbations from gene expression data
Description: Takes as input an incomplete perturbation profile and
differential gene expression in log odds and infers unobserved
perturbations and augments observed ones. The inference is done
by iteratively inferring a network from the perturbations and
inferring perturbations from the network. The network inference
is done by Nested Effects Models.
biocViews: Software, GeneExpression, DifferentialExpression,
DifferentialMethylation, GeneSignaling, Pathways, Network,
Classification, NeuralNetwork, NetworkInference, ATACSeq,
DNASeq, RNASeq, PooledScreens, CRISPR, SingleCell,
SystemsBiology
Author: Martin Pirkl [aut, cre]
Maintainer: Martin Pirkl <martinpirkl@yahoo.de>
URL: https://github.com/cbg-ethz/nempi/
VignetteBuilder: knitr
BugReports: https://github.com/cbg-ethz/nempi/issues
git_url: https://git.bioconductor.org/packages/nempi
git_branch: devel
git_last_commit: f9f289f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/nempi_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/nempi_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/nempi_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/nempi_1.15.0.tgz
vignettes: vignettes/nempi/inst/doc/nempi.html
vignetteTitles: nempi
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/nempi/inst/doc/nempi.R
dependencyCount: 115
Package: NetActivity
Version: 1.9.0
Depends: R (>= 4.1.0)
Imports: airway, DelayedArray, DelayedMatrixStats, DESeq2, methods,
methods, NetActivityData, SummarizedExperiment, utils
Suggests: AnnotationDbi, BiocStyle, Fletcher2013a, knitr, org.Hs.eg.db,
rmarkdown, testthat (>= 3.0.0), tidyverse
License: MIT + file LICENSE
MD5sum: 54ea588982b8187960f92539230b9213
NeedsCompilation: no
Title: Compute gene set scores from a deep learning framework
Description: #' NetActivity enables to compute gene set scores from
previously trained sparsely-connected autoencoders. The package
contains a function to prepare the data
(`prepareSummarizedExperiment`) and a function to compute the
gene set scores (`computeGeneSetScores`). The package
`NetActivityData` contains different pre-trained models to be
directly applied to the data. Alternatively, the users might
use the package to compute gene set scores using custom models.
biocViews: RNASeq, Microarray, Transcription, FunctionalGenomics, GO,
GeneExpression, Pathways, Software
Author: Carlos Ruiz-Arenas [aut, cre]
Maintainer: Carlos Ruiz-Arenas <carlos.ruiza@upf.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/NetActivity
git_branch: devel
git_last_commit: 8c47a8c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/NetActivity_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/NetActivity_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/NetActivity_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/NetActivity_1.9.0.tgz
vignettes: vignettes/NetActivity/inst/doc/NetActivity.html
vignetteTitles: "Gene set scores computation with NetActivity"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/NetActivity/inst/doc/NetActivity.R
dependencyCount: 80
Package: netboost
Version: 2.15.0
Depends: R (>= 4.0.0)
Imports: Rcpp, RcppParallel, parallel, grDevices, graphics, stats,
utils, dynamicTreeCut, WGCNA, impute, colorspace, methods,
BiocStyle, R.utils
LinkingTo: Rcpp, RcppParallel
Suggests: knitr, rmarkdown
License: GPL-3
OS_type: unix
MD5sum: 1e40f74b6ce418c58afe29fc3aefd3c4
NeedsCompilation: yes
Title: Network Analysis Supported by Boosting
Description: Boosting supported network analysis for high-dimensional
omics applications. This package comes bundled with the
MC-UPGMA clustering package by Yaniv Loewenstein.
biocViews: Software, StatisticalMethod, GraphAndNetwork, Network,
Clustering, DimensionReduction, BiomedicalInformatics,
Epigenetics, Metabolomics, Transcriptomics
Author: Pascal Schlosser [aut, cre], Jochen Knaus [aut, ctb], Yaniv
Loewenstein [aut]
Maintainer: Pascal Schlosser <pascal.schlosser@uniklinik-freiburg.de>
URL: https://bioconductor.org/packages/release/bioc/html/netboost.html
SystemRequirements: GNU make, Bash, Perl, Gzip
VignetteBuilder: knitr
BugReports: pascal.schlosser@uniklinik-freiburg.de
git_url: https://git.bioconductor.org/packages/netboost
git_branch: devel
git_last_commit: 5fe0a60
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/netboost_2.15.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/netboost_2.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/netboost_2.15.0.tgz
vignettes: vignettes/netboost/inst/doc/netboost.html
vignetteTitles: The Netboost users guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/netboost/inst/doc/netboost.R
dependencyCount: 121
Package: nethet
Version: 1.39.0
Imports: glasso, mvtnorm, GeneNet, huge, CompQuadForm, ggm, mclust,
parallel, GSA, limma, multtest, ICSNP, glmnet, network,
ggplot2, grDevices, graphics, stats, utils
Suggests: knitr, xtable, BiocStyle, testthat
License: GPL-2
MD5sum: c63e0e882caea11af11d8eebba01e156
NeedsCompilation: yes
Title: A bioconductor package for high-dimensional exploration of
biological network heterogeneity
Description: Package nethet is an implementation of statistical solid
methodology enabling the analysis of network heterogeneity from
high-dimensional data. It combines several implementations of
recent statistical innovations useful for estimation and
comparison of networks in a heterogeneous, high-dimensional
setting. In particular, we provide code for formal two-sample
testing in Gaussian graphical models (differential network and
GGM-GSA; Stadler and Mukherjee, 2013, 2014) and make a novel
network-based clustering algorithm available (mixed graphical
lasso, Stadler and Mukherjee, 2013).
biocViews: Clustering, GraphAndNetwork
Author: Nicolas Staedler, Frank Dondelinger
Maintainer: Nicolas Staedler <staedler.n@gmail.com>, Frank Dondelinger
<fdondelinger.work@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/nethet
git_branch: devel
git_last_commit: 585f30a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/nethet_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/nethet_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/nethet_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/nethet_1.39.0.tgz
vignettes: vignettes/nethet/inst/doc/nethet.pdf
vignetteTitles: nethet
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/nethet/inst/doc/nethet.R
dependencyCount: 78
Package: NetPathMiner
Version: 1.43.1
Depends: R (>= 3.0.2), igraph (>= 1.0)
Suggests: rBiopaxParser (>= 2.1), RCurl, graph, knitr, rmarkdown,
BiocStyle
License: GPL (>= 2)
MD5sum: 9370b227d7831c87ed204069854dead0
NeedsCompilation: yes
Title: NetPathMiner for Biological Network Construction, Path Mining
and Visualization
Description: NetPathMiner is a general framework for network path
mining using genome-scale networks. It constructs networks from
KGML, SBML and BioPAX files, providing three network
representations, metabolic, reaction and gene representations.
NetPathMiner finds active paths and applies machine learning
methods to summarize found paths for easy interpretation. It
also provides static and interactive visualizations of networks
and paths to aid manual investigation.
biocViews: GraphAndNetwork, Pathways, Network, Clustering,
Classification
Author: Ahmed Mohamed [aut, cre] (ORCID:
<https://orcid.org/0000-0001-6507-5300>), Tim Hancock [aut],
Tim Hancock [aut]
Maintainer: Ahmed Mohamed <mohamed@kuicr.kyoto-u.ac.jp>
URL: https://github.com/ahmohamed/NetPathMiner
SystemRequirements: libxml2, libSBML (>= 5.5)
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/NetPathMiner
git_branch: devel
git_last_commit: b338534
git_last_commit_date: 2024-11-27
Date/Publication: 2024-11-27
source.ver: src/contrib/NetPathMiner_1.43.1.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/NetPathMiner_1.43.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/NetPathMiner_1.43.1.tgz
vignettes: vignettes/NetPathMiner/inst/doc/NPMVignette.html
vignetteTitles: NetPathMiner Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NetPathMiner/inst/doc/NPMVignette.R
dependencyCount: 17
Package: netprioR
Version: 1.33.0
Depends: methods, graphics, R(>= 3.3)
Imports: stats, Matrix, dplyr, doParallel, foreach, parallel,
sparseMVN, ggplot2, gridExtra, pROC
Suggests: knitr, BiocStyle, pander
License: GPL-3
MD5sum: 7bd5c4c40d2144d6f3fcc8d2b707529f
NeedsCompilation: no
Title: A model for network-based prioritisation of genes
Description: A model for semi-supervised prioritisation of genes
integrating network data, phenotypes and additional prior
knowledge about TP and TN gene labels from the literature or
experts.
biocViews: ImmunoOncology, CellBasedAssays, Preprocessing, Network
Author: Fabian Schmich
Maintainer: Fabian Schmich <fabian.schmich@bsse.ethz.ch>
URL: http://bioconductor.org/packages/netprioR
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/netprioR
git_branch: devel
git_last_commit: 9afdf83
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/netprioR_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/netprioR_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/netprioR_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/netprioR_1.33.0.tgz
vignettes: vignettes/netprioR/inst/doc/netprioR.html
vignetteTitles: netprioR Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/netprioR/inst/doc/netprioR.R
dependencyCount: 48
Package: netresponse
Version: 1.67.0
Depends: R (>= 2.15.1), BiocStyle, Rgraphviz, rmarkdown, methods,
minet, mclust, reshape2
Imports: ggplot2, graph, igraph, parallel, plyr, qvalue, RColorBrewer
Suggests: knitr
License: GPL (>=2)
MD5sum: 558241aae920cf98f6d80e09b6363628
NeedsCompilation: yes
Title: Functional Network Analysis
Description: Algorithms for functional network analysis. Includes an
implementation of a variational Dirichlet process Gaussian
mixture model for nonparametric mixture modeling.
biocViews: CellBiology, Clustering, GeneExpression, Genetics, Network,
GraphAndNetwork, DifferentialExpression, Microarray,
NetworkInference, Transcription
Author: Leo Lahti, Olli-Pekka Huovilainen, Antonio Gusmao and Juuso
Parkkinen
Maintainer: Leo Lahti <leo.lahti@iki.fi>
URL: https://github.com/antagomir/netresponse
VignetteBuilder: knitr
BugReports: https://github.com/antagomir/netresponse/issues
git_url: https://git.bioconductor.org/packages/netresponse
git_branch: devel
git_last_commit: 71a1a40
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/netresponse_1.67.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/netresponse_1.67.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/netresponse_1.67.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/netresponse_1.67.0.tgz
vignettes: vignettes/netresponse/inst/doc/NetResponse.html
vignetteTitles: microbiome R package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/netresponse/inst/doc/NetResponse.R
dependencyCount: 77
Package: NetSAM
Version: 1.47.0
Depends: R (>= 3.0.0), seriation (>= 1.0-6), igraph (>= 2.0.0), tools
(>= 3.0.0), WGCNA (>= 1.34.0), biomaRt (>= 2.18.0)
Imports: methods, AnnotationDbi (>= 1.28.0), doParallel (>= 1.0.10),
foreach (>= 1.4.0), survival (>= 2.37-7), GO.db (>= 2.10.0),
R2HTML (>= 2.2.0), DBI (>= 0.5-1)
Suggests: RUnit, BiocGenerics, org.Sc.sgd.db, org.Hs.eg.db,
org.Mm.eg.db, org.Rn.eg.db, org.Dr.eg.db, org.Ce.eg.db,
org.Cf.eg.db, org.Dm.eg.db, org.At.tair.db, rmarkdown, knitr,
markdown
License: LGPL
MD5sum: 3bb23fdb9594b014d78082ff32f3efc0
NeedsCompilation: no
Title: Network Seriation And Modularization
Description: The NetSAM (Network Seriation and Modularization) package
takes an edge-list representation of a weighted or unweighted
network as an input, performs network seriation and
modularization analysis, and generates as files that can be
used as an input for the one-dimensional network visualization
tool NetGestalt (http://www.netgestalt.org) or other network
analysis. The NetSAM package can also generate correlation
network (e.g. co-expression network) based on the input matrix
data, perform seriation and modularization analysis for the
correlation network and calculate the associations between the
sample features and modules or identify the associated GO terms
for the modules.
Author: Jing Wang <jing.wang@bcm.edu>
Maintainer: Zhiao Shi <zhiao.shi@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/NetSAM
git_branch: devel
git_last_commit: c829ddb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/NetSAM_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/NetSAM_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/NetSAM_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/NetSAM_1.47.0.tgz
vignettes: vignettes/NetSAM/inst/doc/NetSAM.pdf
vignetteTitles: NetSAM User Guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NetSAM/inst/doc/NetSAM.R
dependencyCount: 137
Package: netSmooth
Version: 1.27.0
Depends: R (>= 3.5), scater (>= 1.15.11), clusterExperiment (>= 2.1.6)
Imports: entropy, SummarizedExperiment, SingleCellExperiment, Matrix,
cluster, data.table, stats, methods, DelayedArray, HDF5Array
(>= 1.15.13)
Suggests: knitr, testthat, Rtsne, biomaRt, igraph, STRINGdb, NMI,
pheatmap, ggplot2, BiocStyle, rmarkdown, BiocParallel, uwot
License: GPL-3
MD5sum: 194984c4e926d64b87e37446df6b9299
NeedsCompilation: no
Title: Network smoothing for scRNAseq
Description: netSmooth is an R package for network smoothing of single
cell RNA sequencing data. Using bio networks such as
protein-protein interactions as priors for gene co-expression,
netsmooth improves cell type identification from noisy, sparse
scRNAseq data.
biocViews: Network, GraphAndNetwork, SingleCell, RNASeq,
GeneExpression, Sequencing, Transcriptomics, Normalization,
Preprocessing, Clustering, DimensionReduction
Author: Jonathan Ronen [aut, cre], Altuna Akalin [aut]
Maintainer: Jonathan Ronen <yablee@gmail.com>
URL: https://github.com/BIMSBbioinfo/netSmooth
VignetteBuilder: knitr
BugReports: https://github.com/BIMSBbioinfo/netSmooth/issues
git_url: https://git.bioconductor.org/packages/netSmooth
git_branch: devel
git_last_commit: 4d3aa76
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/netSmooth_1.27.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/netSmooth_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/netSmooth_1.27.0.tgz
vignettes: vignettes/netSmooth/inst/doc/buildingPPIsFromStringDB.html,
vignettes/netSmooth/inst/doc/netSmoothIntro.html
vignetteTitles: Generation of PPI graph, netSmooth example
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/netSmooth/inst/doc/buildingPPIsFromStringDB.R,
vignettes/netSmooth/inst/doc/netSmoothIntro.R
dependencyCount: 177
Package: NewWave
Version: 1.17.0
Depends: R (>= 4.0), SummarizedExperiment
Imports: methods, SingleCellExperiment, parallel, irlba, Matrix,
DelayedArray, BiocSingular, SharedObject, stats
Suggests: testthat, rmarkdown, splatter, mclust, Rtsne, ggplot2, Rcpp,
BiocStyle, knitr
License: GPL-3
Archs: x64
MD5sum: d622f6a38e2c1a03c3aff17f6fd0556d
NeedsCompilation: no
Title: Negative binomial model for scRNA-seq
Description: A model designed for dimensionality reduction and batch
effect removal for scRNA-seq data. It is designed to be
massively parallelizable using shared objects that prevent
memory duplication, and it can be used with different
mini-batch approaches in order to reduce time consumption. It
assumes a negative binomial distribution for the data with a
dispersion parameter that can be both commonwise across gene
both genewise.
biocViews: Software, GeneExpression, Transcriptomics, SingleCell,
BatchEffect, Sequencing, Coverage, Regression
Author: Federico Agostinis [aut, cre], Chiara Romualdi [aut], Gabriele
Sales [aut], Davide Risso [aut]
Maintainer: Federico Agostinis <federico.agostinis@outlook.com>
VignetteBuilder: knitr
BugReports: https://github.com/fedeago/NewWave/issues
git_url: https://git.bioconductor.org/packages/NewWave
git_branch: devel
git_last_commit: ef0cec5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/NewWave_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/NewWave_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/NewWave_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/NewWave_1.17.0.tgz
vignettes: vignettes/NewWave/inst/doc/vignette.html
vignetteTitles: vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NewWave/inst/doc/vignette.R
dependencyCount: 55
Package: ngsReports
Version: 2.9.0
Depends: R (>= 4.2.0), BiocGenerics, ggplot2 (>= 3.5.0), patchwork (>=
1.1.1), tibble (>= 1.3.1)
Imports: Biostrings, checkmate, dplyr (>= 1.1.0), forcats, ggdendro,
grDevices (>= 3.6.0), grid, jsonlite, lifecycle, lubridate,
methods, plotly (>= 4.9.4), reshape2, rlang, rmarkdown, scales,
stats, stringr, tidyr, tidyselect (>= 0.2.3), utils, zoo
Suggests: BiocStyle, Cairo, DT, knitr, pander, readr, testthat,
truncnorm
License: LGPL-3
MD5sum: 55b5f3b0553180ef1f656f6e201115c4
NeedsCompilation: no
Title: Load FastqQC reports and other NGS related files
Description: This package provides methods and object classes for
parsing FastQC reports and output summaries from other NGS
tools into R. As well as parsing files, multiple plotting
methods have been implemented for visualising the parsed data.
Plots can be generated as static ggplot objects or interactive
plotly objects.
biocViews: QualityControl, ReportWriting
Author: Stevie Pederson [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8197-3303>), Christopher Ward
[aut], Thu-Hien To [aut]
Maintainer: Stevie Pederson <stephen.pederson.au@gmail.com>
URL: https://github.com/smped/ngsReports
VignetteBuilder: knitr
BugReports: https://github.com/smped/ngsReports/issues
git_url: https://git.bioconductor.org/packages/ngsReports
git_branch: devel
git_last_commit: eb02955
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ngsReports_2.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ngsReports_2.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ngsReports_2.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ngsReports_2.9.0.tgz
vignettes: vignettes/ngsReports/inst/doc/ngsReportsIntroduction.html
vignetteTitles: An Introduction To ngsReports
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ngsReports/inst/doc/ngsReportsIntroduction.R
dependencyCount: 98
Package: nipalsMCIA
Version: 1.5.4
Depends: R (>= 4.3.0)
Imports: ComplexHeatmap, dplyr, fgsea, ggplot2 (>= 3.0.0), graphics,
grid, methods, MultiAssayExperiment, SummarizedExperiment,
pracma, rlang, RSpectra, scales, stats
Suggests: BiocFileCache, BiocStyle, circlize, ggpubr, KernSmooth,
knitr, piggyback, reshape2, rmarkdown, rpart, Seurat (>=
4.0.0), spatstat.explore, stringr, survival, tidyverse,
testthat (>= 3.0.0)
License: GPL-3
Archs: x64
MD5sum: 3fa4f3335869ddd303f5277dd7e32b95
NeedsCompilation: no
Title: Multiple Co-Inertia Analysis via the NIPALS Method
Description: Computes Multiple Co-Inertia Analysis (MCIA), a
dimensionality reduction (jDR) algorithm, for a multi-block
dataset using a modification to the Nonlinear Iterative Partial
Least Squares method (NIPALS) proposed in (Hanafi et. al,
2010). Allows multiple options for row- and table-level
preprocessing, and speeds up computation of variance explained.
Vignettes detail application to bulk- and single cell-
multi-omics studies.
biocViews: Software, Clustering, Classification, MultipleComparison,
Normalization, Preprocessing, SingleCell
Author: Maximilian Mattessich [cre] (ORCID:
<https://orcid.org/0000-0002-1233-1240>), Joaquin Reyna [aut]
(ORCID: <https://orcid.org/0000-0002-8468-2840>), Edel Aron
[aut] (ORCID: <https://orcid.org/0000-0002-8683-4772>), Ferhat
Ay [aut] (ORCID: <https://orcid.org/0000-0002-0708-6914>),
Steven Kleinstein [aut] (ORCID:
<https://orcid.org/0000-0003-4957-1544>), Anna Konstorum [aut]
(ORCID: <https://orcid.org/0000-0003-4024-2058>)
Maintainer: Maximilian Mattessich
<maximilian.mattessich@northwestern.edu>
URL: https://github.com/Muunraker/nipalsMCIA
VignetteBuilder: knitr
BugReports: https://github.com/Muunraker/nipalsMCIA/issues
git_url: https://git.bioconductor.org/packages/nipalsMCIA
git_branch: devel
git_last_commit: 73689dc
git_last_commit_date: 2025-02-17
Date/Publication: 2025-02-18
source.ver: src/contrib/nipalsMCIA_1.5.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/nipalsMCIA_1.5.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/nipalsMCIA_1.5.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/nipalsMCIA_1.5.4.tgz
vignettes:
vignettes/nipalsMCIA/inst/doc/Analysis-of-MCIA-Decomposition.html,
vignettes/nipalsMCIA/inst/doc/Predicting-New-Scores.html,
vignettes/nipalsMCIA/inst/doc/Single-Cell-Analysis.html
vignetteTitles: Analysis of MCIA Decomposition, Predicting New MCIA
scores, Single Cell Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/nipalsMCIA/inst/doc/Analysis-of-MCIA-Decomposition.R,
vignettes/nipalsMCIA/inst/doc/Predicting-New-Scores.R,
vignettes/nipalsMCIA/inst/doc/Single-Cell-Analysis.R
dependencyCount: 101
Package: nnNorm
Version: 2.71.0
Depends: R(>= 2.2.0), marray
Imports: graphics, grDevices, marray, methods, nnet, stats
License: LGPL
MD5sum: 4c1fd87352174e61b49cc88ed980223b
NeedsCompilation: no
Title: Spatial and intensity based normalization of cDNA microarray
data based on robust neural nets
Description: This package allows to detect and correct for spatial and
intensity biases with two-channel microarray data. The
normalization method implemented in this package is based on
robust neural networks fitting.
biocViews: Microarray, TwoChannel, Preprocessing
Author: Adi Laurentiu Tarca <atarca@med.wayne.edu>
Maintainer: Adi Laurentiu Tarca <atarca@med.wayne.edu>
URL: http://bioinformaticsprb.med.wayne.edu/tarca/
git_url: https://git.bioconductor.org/packages/nnNorm
git_branch: devel
git_last_commit: 18cab7b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/nnNorm_2.71.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/nnNorm_2.71.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/nnNorm_2.71.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/nnNorm_2.71.0.tgz
vignettes: vignettes/nnNorm/inst/doc/nnNorm.pdf
vignetteTitles: nnNorm Tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/nnNorm/inst/doc/nnNorm.R
dependencyCount: 9
Package: nnSVG
Version: 1.11.4
Depends: R (>= 4.2)
Imports: SpatialExperiment, SingleCellExperiment, SummarizedExperiment,
BRISC, BiocParallel, Matrix, matrixStats, stats, methods
Suggests: BiocStyle, knitr, rmarkdown, STexampleData,
WeberDivechaLCdata, scran, ggplot2, testthat
License: MIT + file LICENSE
MD5sum: 2409591d8cdef8c193fbec01a5d9fbb0
NeedsCompilation: no
Title: Scalable identification of spatially variable genes in
spatially-resolved transcriptomics data
Description: Method for scalable identification of spatially variable
genes (SVGs) in spatially-resolved transcriptomics data. The
method is based on nearest-neighbor Gaussian processes and uses
the BRISC algorithm for model fitting and parameter estimation.
Allows identification and ranking of SVGs with flexible length
scales across a tissue slide or within spatial domains defined
by covariates. Scales linearly with the number of spatial
locations and can be applied to datasets containing thousands
or more spatial locations.
biocViews: Spatial, SingleCell, Transcriptomics, GeneExpression,
Preprocessing
Author: Lukas M. Weber [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3282-1730>), Stephanie C. Hicks
[aut] (ORCID: <https://orcid.org/0000-0002-7858-0231>)
Maintainer: Lukas M. Weber <lmweb012@gmail.com>
URL: https://github.com/lmweber/nnSVG
VignetteBuilder: knitr
BugReports: https://github.com/lmweber/nnSVG/issues
git_url: https://git.bioconductor.org/packages/nnSVG
git_branch: devel
git_last_commit: 4e6427e
git_last_commit_date: 2025-02-25
Date/Publication: 2025-02-26
source.ver: src/contrib/nnSVG_1.11.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/nnSVG_1.11.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/nnSVG_1.11.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/nnSVG_1.11.4.tgz
vignettes: vignettes/nnSVG/inst/doc/nnSVG.html
vignetteTitles: nnSVG Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/nnSVG/inst/doc/nnSVG.R
importsMe: spoon
suggestsMe: tpSVG
dependencyCount: 86
Package: NOISeq
Version: 2.51.0
Depends: R (>= 2.13.0), methods, Biobase (>= 2.13.11), splines (>=
3.0.1), Matrix (>= 1.2)
License: Artistic-2.0
MD5sum: 4b1fbace1be3e90f1f2893dc73a1bf6c
NeedsCompilation: no
Title: Exploratory analysis and differential expression for RNA-seq
data
Description: Analysis of RNA-seq expression data or other similar kind
of data. Exploratory plots to evualuate saturation, count
distribution, expression per chromosome, type of detected
features, features length, etc. Differential expression between
two experimental conditions with no parametric assumptions.
biocViews: ImmunoOncology, RNASeq, DifferentialExpression,
Visualization, Sequencing
Author: Sonia Tarazona, Pedro Furio-Tari, Maria Jose Nueda, Alberto
Ferrer and Ana Conesa
Maintainer: Sonia Tarazona <sotacam@eio.upv.es>
git_url: https://git.bioconductor.org/packages/NOISeq
git_branch: devel
git_last_commit: bf39f9c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/NOISeq_2.51.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/NOISeq_2.51.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/NOISeq_2.51.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/NOISeq_2.51.0.tgz
vignettes: vignettes/NOISeq/inst/doc/NOISeq.pdf,
vignettes/NOISeq/inst/doc/QCreport.pdf
vignetteTitles: NOISeq User's Guide, QCreport.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NOISeq/inst/doc/NOISeq.R
dependsOnMe: metaSeq
importsMe: benchdamic, broadSeq, CNVPanelizer, ExpHunterSuite
suggestsMe: compcodeR, GeoTcgaData
dependencyCount: 12
Package: NoRCE
Version: 1.19.0
Depends: R (>= 4.2.0)
Imports:
KEGGREST,png,dplyr,graphics,RSQLite,DBI,tidyr,grDevices,stringr,GenomeInfoDb,
S4Vectors,SummarizedExperiment,reactome.db,rWikiPathways,RCurl,
dbplyr,utils,ggplot2,igraph,stats,reshape2,readr,
GO.db,zlibbioc,
biomaRt,rtracklayer,IRanges,GenomicRanges,GenomicFeatures,AnnotationDbi
Suggests: knitr,
TxDb.Hsapiens.UCSC.hg38.knownGene,TxDb.Drerio.UCSC.danRer10.refGene,
TxDb.Mmusculus.UCSC.mm10.knownGene,TxDb.Dmelanogaster.UCSC.dm6.ensGene,
testthat,TxDb.Celegans.UCSC.ce11.refGene,rmarkdown,
TxDb.Rnorvegicus.UCSC.rn6.refGene,TxDb.Hsapiens.UCSC.hg19.knownGene,
org.Mm.eg.db,
org.Rn.eg.db,org.Hs.eg.db,org.Dr.eg.db,BiocGenerics,
org.Sc.sgd.db, org.Ce.eg.db,org.Dm.eg.db, methods,markdown
License: MIT + file LICENSE
MD5sum: b2066a1609888adcb5bd47e6f7240b2a
NeedsCompilation: no
Title: NoRCE: Noncoding RNA Sets Cis Annotation and Enrichment
Description: While some non-coding RNAs (ncRNAs) are assigned critical
regulatory roles, most remain functionally uncharacterized.
This presents a challenge whenever an interesting set of ncRNAs
needs to be analyzed in a functional context. Transcripts
located close-by on the genome are often regulated together.
This genomic proximity on the sequence can hint to a functional
association. We present a tool, NoRCE, that performs cis
enrichment analysis for a given set of ncRNAs. Enrichment is
carried out using the functional annotations of the coding
genes located proximal to the input ncRNAs. Other biologically
relevant information such as topologically associating domain
(TAD) boundaries, co-expression patterns, and miRNA target
prediction information can be incorporated to conduct a richer
enrichment analysis. To this end, NoRCE includes several
relevant datasets as part of its data repository, including
cell-line specific TAD boundaries, functional gene sets, and
expression data for coding & ncRNAs specific to cancer.
Additionally, the users can utilize custom data files in their
investigation. Enrichment results can be retrieved in a tabular
format or visualized in several different ways. NoRCE is
currently available for the following species: human, mouse,
rat, zebrafish, fruit fly, worm, and yeast.
biocViews: BiologicalQuestion, DifferentialExpression,
GenomeAnnotation, GeneSetEnrichment, GeneTarget,
GenomeAssembly, GO
Author: Gulden Olgun [aut, cre]
Maintainer: Gulden Olgun <gulden@cs.bilkent.edu.tr>
VignetteBuilder: knitr
BugReports: https://github.com/guldenolgun/NoRCE/issues
git_url: https://git.bioconductor.org/packages/NoRCE
git_branch: devel
git_last_commit: 18045b1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/NoRCE_1.19.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/NoRCE_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/NoRCE_1.19.0.tgz
vignettes: vignettes/NoRCE/inst/doc/NoRCE.html
vignetteTitles: Noncoding RNA Set Cis Annotation and Enrichment
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/NoRCE/inst/doc/NoRCE.R
dependencyCount: 129
Package: normalize450K
Version: 1.35.0
Depends: R (>= 3.3), Biobase, illuminaio, quadprog
Imports: utils
License: BSD_2_clause + file LICENSE
MD5sum: 78484d03102ea8c39d04117448bd2b97
NeedsCompilation: no
Title: Preprocessing of Illumina Infinium 450K data
Description: Precise measurements are important for epigenome-wide
studies investigating DNA methylation in whole blood samples,
where effect sizes are expected to be small in magnitude. The
450K platform is often affected by batch effects and proper
preprocessing is recommended. This package provides functions
to read and normalize 450K '.idat' files. The normalization
corrects for dye bias and biases related to signal intensity
and methylation of probes using local regression. No adjustment
for probe type bias is performed to avoid the trade-off of
precision for accuracy of beta-values.
biocViews: Normalization, DNAMethylation, Microarray, TwoChannel,
Preprocessing, MethylationArray
Author: Jonathan Alexander Heiss
Maintainer: Jonathan Alexander Heiss <jonathan.heiss@posteo.de>
git_url: https://git.bioconductor.org/packages/normalize450K
git_branch: devel
git_last_commit: 089eb29
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/normalize450K_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/normalize450K_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/normalize450K_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/normalize450K_1.35.0.tgz
vignettes: vignettes/normalize450K/inst/doc/read_and_normalize450K.pdf
vignetteTitles: Normalization of 450K data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/normalize450K/inst/doc/read_and_normalize450K.R
dependencyCount: 13
Package: NormalyzerDE
Version: 1.25.0
Depends: R (>= 4.1.0)
Imports: vsn, preprocessCore, limma, MASS, ape, car, ggplot2, methods,
utils, stats, SummarizedExperiment, matrixStats, ggforce
Suggests: knitr, testthat, rmarkdown, roxygen2, hexbin, BiocStyle
License: Artistic-2.0
MD5sum: 98e96afa5fc4b9ea485284bcdc2f5aed
NeedsCompilation: no
Title: Evaluation of normalization methods and calculation of
differential expression analysis statistics
Description: NormalyzerDE provides screening of normalization methods
for LC-MS based expression data. It calculates a range of
normalized matrices using both existing approaches and a novel
time-segmented approach, calculates performance measures and
generates an evaluation report. Furthermore, it provides an
easy utility for Limma- or ANOVA- based differential expression
analysis.
biocViews: Normalization, MultipleComparison, Visualization, Bayesian,
Proteomics, Metabolomics, DifferentialExpression
Author: Jakob Willforss
Maintainer: Jakob Willforss <jakob.willforss@hotmail.com>
URL: https://github.com/ComputationalProteomics/NormalyzerDE
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/NormalyzerDE
git_branch: devel
git_last_commit: cfe738a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/NormalyzerDE_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/NormalyzerDE_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/NormalyzerDE_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/NormalyzerDE_1.25.0.tgz
vignettes: vignettes/NormalyzerDE/inst/doc/vignette.html
vignetteTitles: Differential expression and countering technical biases
using NormalyzerDE
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NormalyzerDE/inst/doc/vignette.R
importsMe: PRONE
dependencyCount: 109
Package: NormqPCR
Version: 1.53.0
Depends: R(>= 2.14.0), stats, RColorBrewer, Biobase, methods, ReadqPCR,
qpcR
License: LGPL-3
MD5sum: 0432eadf6037ee41bc21dfebfaaf7d60
NeedsCompilation: no
Title: Functions for normalisation of RT-qPCR data
Description: Functions for the selection of optimal reference genes and
the normalisation of real-time quantitative PCR data.
biocViews: MicrotitrePlateAssay, GeneExpression, qPCR
Author: Matthias Kohl, James Perkins, Nor Izayu Abdul Rahman
Maintainer: James Perkins <jimrperkins@gmail.com>
URL: www.bioconductor.org/packages/release/bioc/html/NormqPCR.html
git_url: https://git.bioconductor.org/packages/NormqPCR
git_branch: devel
git_last_commit: 291a5b8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/NormqPCR_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/NormqPCR_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/NormqPCR_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/NormqPCR_1.53.0.tgz
vignettes: vignettes/NormqPCR/inst/doc/NormqPCR.pdf
vignetteTitles: NormqPCR: Functions for normalisation of RT-qPCR data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NormqPCR/inst/doc/NormqPCR.R
dependencyCount: 48
Package: normr
Version: 1.33.0
Depends: R (>= 3.3.0)
Imports: methods, stats, utils, grDevices, parallel, GenomeInfoDb,
GenomicRanges, IRanges, Rcpp (>= 0.11), qvalue (>= 2.2),
bamsignals (>= 1.4), rtracklayer (>= 1.32)
LinkingTo: Rcpp
Suggests: BiocStyle, testthat (>= 1.0), knitr, rmarkdown
Enhances: BiocParallel
License: GPL-2
MD5sum: 393c7ceb3efa5ec4f9e7e0fca3546aea
NeedsCompilation: yes
Title: Normalization and difference calling in ChIP-seq data
Description: Robust normalization and difference calling procedures for
ChIP-seq and alike data. Read counts are modeled jointly as a
binomial mixture model with a user-specified number of
components. A fitted background estimate accounts for the
effect of enrichment in certain regions and, therefore,
represents an appropriate null hypothesis. This robust
background is used to identify significantly enriched or
depleted regions.
biocViews: Bayesian, DifferentialPeakCalling, Classification,
DataImport, ChIPSeq, RIPSeq, FunctionalGenomics, Genetics,
MultipleComparison, Normalization, PeakDetection,
Preprocessing, Alignment
Author: Johannes Helmuth [aut, cre], Ho-Ryun Chung [aut]
Maintainer: Johannes Helmuth <johannes.helmuth@laborberlin.com>
URL: https://github.com/your-highness/normR
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/your-highness/normR/issues
git_url: https://git.bioconductor.org/packages/normr
git_branch: devel
git_last_commit: 828cac9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/normr_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/normr_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/normr_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/normr_1.33.0.tgz
vignettes: vignettes/normr/inst/doc/normr.html
vignetteTitles: Introduction to the normR package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/normr/inst/doc/normr.R
dependencyCount: 92
Package: NPARC
Version: 1.19.0
Depends: R (>= 4.0.0)
Imports: dplyr, tidyr, BiocParallel, broom, MASS, rlang, magrittr,
stats, methods
Suggests: testthat, devtools, knitr, rprojroot, rmarkdown, ggplot2,
BiocStyle
License: GPL-3
MD5sum: 13cf50fe79a1a2db0b4b40e37f9ab2c7
NeedsCompilation: no
Title: Non-parametric analysis of response curves for thermal proteome
profiling experiments
Description: Perform non-parametric analysis of response curves as
described by Childs, Bach, Franken et al. (2019):
Non-parametric analysis of thermal proteome profiles reveals
novel drug-binding proteins.
biocViews: Software, Proteomics
Author: Dorothee Childs, Nils Kurzawa
Maintainer: Nils Kurzawa <nilskurzawa@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/NPARC
git_branch: devel
git_last_commit: 6cd65a1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/NPARC_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/NPARC_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/NPARC_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/NPARC_1.19.0.tgz
vignettes: vignettes/NPARC/inst/doc/NPARC.html
vignetteTitles: Analysing thermal proteome profiling data with the
NPARC package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NPARC/inst/doc/NPARC.R
dependencyCount: 39
Package: npGSEA
Version: 1.43.0
Depends: GSEABase (>= 1.24.0)
Imports: Biobase, methods, BiocGenerics, graphics, stats
Suggests: ALL, genefilter, limma, hgu95av2.db, ReportingTools,
BiocStyle
License: Artistic-2.0
MD5sum: fc99ca3500439255b5e38f2e20b3ab47
NeedsCompilation: no
Title: Permutation approximation methods for gene set enrichment
analysis (non-permutation GSEA)
Description: Current gene set enrichment methods rely upon permutations
for inference. These approaches are computationally expensive
and have minimum achievable p-values based on the number of
permutations, not on the actual observed statistics. We have
derived three parametric approximations to the permutation
distributions of two gene set enrichment test statistics. We
are able to reduce the computational burden and granularity
issues of permutation testing with our method, which is
implemented in this package. npGSEA calculates gene set
enrichment statistics and p-values without the computational
cost of permutations. It is applicable in settings where one
or many gene sets are of interest. There are also built-in
plotting functions to help users visualize results.
biocViews: GeneSetEnrichment, Microarray, StatisticalMethod, Pathways
Author: Jessica Larson and Art Owen
Maintainer: Jessica Larson <larson.jess@gmail.com>
git_url: https://git.bioconductor.org/packages/npGSEA
git_branch: devel
git_last_commit: 0cb512d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/npGSEA_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/npGSEA_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/npGSEA_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/npGSEA_1.43.0.tgz
vignettes: vignettes/npGSEA/inst/doc/npGSEA.pdf
vignetteTitles: Running gene set enrichment analysis with the "npGSEA"
package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/npGSEA/inst/doc/npGSEA.R
dependencyCount: 50
Package: NTW
Version: 1.57.0
Depends: R (>= 2.3.0)
Imports: mvtnorm, stats, utils
License: GPL-2
MD5sum: 7738655c0d448bc2a87e4a1fb50e4dbf
NeedsCompilation: no
Title: Predict gene network using an Ordinary Differential Equation
(ODE) based method
Description: This package predicts the gene-gene interaction network
and identifies the direct transcriptional targets of the
perturbation using an ODE (Ordinary Differential Equation)
based method.
biocViews: Preprocessing
Author: Wei Xiao, Yin Jin, Darong Lai, Xinyi Yang, Yuanhua Liu,
Christine Nardini
Maintainer: Yuanhua Liu <liuyuanhua@picb.ac.cn>
git_url: https://git.bioconductor.org/packages/NTW
git_branch: devel
git_last_commit: 1d0ba97
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/NTW_1.57.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/NTW_1.57.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/NTW_1.57.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/NTW_1.57.0.tgz
vignettes: vignettes/NTW/inst/doc/NTW.pdf
vignetteTitles: NTW vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NTW/inst/doc/NTW.R
dependencyCount: 3
Package: nucleoSim
Version: 1.35.0
Imports: stats, IRanges, S4Vectors, graphics, methods
Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, testthat
License: Artistic-2.0
Archs: x64
MD5sum: 1e78d58be37b69699a2144aa06c1c63e
NeedsCompilation: no
Title: Generate synthetic nucleosome maps
Description: This package can generate a synthetic map with reads
covering the nucleosome regions as well as a synthetic map with
forward and reverse reads emulating next-generation sequencing.
The synthetic hybridization data of “Tiling Arrays†can also be
generated. The user has choice between three different
distributions for the read positioning: Normal, Student and
Uniform. In addition, a visualization tool is provided to
explore the synthetic nucleosome maps.
biocViews: Genetics, Sequencing, Software, StatisticalMethod, Alignment
Author: Rawane Samb [aut], Astrid Deschênes [cre, aut] (ORCID:
<https://orcid.org/0000-0001-7846-6749>), Pascal Belleau [aut]
(ORCID: <https://orcid.org/0000-0002-0802-1071>), Arnaud Droit
[aut]
Maintainer: Astrid Deschênes <adeschen@hotmail.com>
URL: https://github.com/arnauddroitlab/nucleoSim
VignetteBuilder: knitr
BugReports: https://github.com/arnauddroitlab/nucleoSim/issues
git_url: https://git.bioconductor.org/packages/nucleoSim
git_branch: devel
git_last_commit: 334622c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/nucleoSim_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/nucleoSim_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/nucleoSim_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/nucleoSim_1.35.0.tgz
vignettes: vignettes/nucleoSim/inst/doc/nucleoSim.html
vignetteTitles: Generate synthetic nucleosome maps
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/nucleoSim/inst/doc/nucleoSim.R
suggestsMe: RJMCMCNucleosomes
dependencyCount: 9
Package: nucleR
Version: 2.39.0
Depends: R (>= 3.5.0), methods
Imports: Biobase, BiocGenerics, Biostrings, GenomeInfoDb,
GenomicRanges, IRanges, Rsamtools, S4Vectors, ShortRead, dplyr,
ggplot2, magrittr, parallel, stats, utils, grDevices
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: LGPL (>= 3)
MD5sum: bc7c033a79a42ac8e58571d47700e343
NeedsCompilation: no
Title: Nucleosome positioning package for R
Description: Nucleosome positioning for Tiling Arrays and NGS
experiments.
biocViews: NucleosomePositioning, Coverage, ChIPSeq, Microarray,
Sequencing, Genetics, QualityControl, DataImport
Author: Oscar Flores, Ricard Illa
Maintainer: Alba Sala <alba.sala@irbbarcelona.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/nucleR
git_branch: devel
git_last_commit: 242e3bb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/nucleR_2.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/nucleR_2.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/nucleR_2.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/nucleR_2.39.0.tgz
vignettes: vignettes/nucleR/inst/doc/nucleR.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/nucleR/inst/doc/nucleR.R
dependencyCount: 89
Package: nuCpos
Version: 1.25.0
Depends: R (>= 4.2.0)
Imports: graphics, methods
Suggests: NuPoP, Biostrings, testthat
License: GPL-2
MD5sum: 6d0e72d7066c2d6c91d88e7874b55efe
NeedsCompilation: yes
Title: An R package for prediction of nucleosome positions
Description: nuCpos, a derivative of NuPoP, is an R package for
prediction of nucleosome positions. nuCpos calculates local and
whole nucleosomal histone binding affinity (HBA) scores for a
given 147-bp sequence. Note: This package was designed to
demonstrate the use of chemical maps in prediction. As the
parental package NuPoP now provides chemical-map-based
prediction, the function for dHMM-based prediction was removed
from this package. nuCpos continues to provide functions for
HBA calculation.
biocViews: Genetics, Epigenetics, NucleosomePositioning
Author: Hiroaki Kato, Takeshi Urano
Maintainer: Hiroaki Kato <hkato@med.shimane-u.ac.jp>
git_url: https://git.bioconductor.org/packages/nuCpos
git_branch: devel
git_last_commit: 8f49c5e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/nuCpos_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/nuCpos_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/nuCpos_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/nuCpos_1.25.0.tgz
vignettes: vignettes/nuCpos/inst/doc/nuCpos-intro.pdf
vignetteTitles: An R package for prediction of nucleosome positioning
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/nuCpos/inst/doc/nuCpos-intro.R
dependencyCount: 2
Package: nullranges
Version: 1.13.0
Imports: stats, IRanges, GenomicRanges, GenomeInfoDb, methods, rlang,
S4Vectors, scales, InteractionSet, ggplot2, grDevices,
plyranges, data.table, progress, ggridges
Suggests: testthat, knitr, rmarkdown, ks, DNAcopy, RcppHMM,
AnnotationHub, ExperimentHub, nullrangesData, excluderanges,
ensembldb, EnsDb.Hsapiens.v86, BSgenome.Hsapiens.UCSC.hg38,
patchwork, plotgardener, dplyr, purrr, magrittr, tidyr, cobalt,
DiagrammeR, tidySummarizedExperiment
License: GPL-3
MD5sum: 5980e2646decf5d6c0bdd149e810420c
NeedsCompilation: no
Title: Generation of null ranges via bootstrapping or covariate
matching
Description: Modular package for generation of sets of ranges
representing the null hypothesis. These can take the form of
bootstrap samples of ranges (using the block bootstrap
framework of Bickel et al 2010), or sets of control ranges that
are matched across one or more covariates. nullranges is
designed to be inter-operable with other packages for analysis
of genomic overlap enrichment, including the plyranges
Bioconductor package.
biocViews: Visualization, GeneSetEnrichment, FunctionalGenomics,
Epigenetics, GeneRegulation, GeneTarget, GenomeAnnotation,
Annotation, GenomeWideAssociation, HistoneModification,
ChIPSeq, ATACSeq, DNaseSeq, RNASeq, HiddenMarkovModel
Author: Michael Love [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8401-0545>), Wancen Mu [aut]
(ORCID: <https://orcid.org/0000-0002-5061-7581>), Eric Davis
[aut] (ORCID: <https://orcid.org/0000-0003-4051-3217>), Douglas
Phanstiel [aut] (ORCID:
<https://orcid.org/0000-0003-2123-0051>), Stuart Lee [aut]
(ORCID: <https://orcid.org/0000-0003-1179-8436>), Mikhail
Dozmorov [ctb], Tim Triche [ctb], CZI [fnd]
Maintainer: Michael Love <michaelisaiahlove@gmail.com>
URL: https://nullranges.github.io/nullranges,
https://github.com/nullranges/nullranges
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/tag/nullranges/
git_url: https://git.bioconductor.org/packages/nullranges
git_branch: devel
git_last_commit: 8b24586
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/nullranges_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/nullranges_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/nullranges_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/nullranges_1.13.0.tgz
vignettes: vignettes/nullranges/inst/doc/bootRanges.html,
vignettes/nullranges/inst/doc/matching_ginteractions.html,
vignettes/nullranges/inst/doc/matching_granges.html,
vignettes/nullranges/inst/doc/matching_pool_set.html,
vignettes/nullranges/inst/doc/matchRanges.html,
vignettes/nullranges/inst/doc/nullranges.html
vignetteTitles: 1. Introduction to bootRanges, 4. Matching case study
II: CTCF orientation, 3. Matching case study I: CTCF occupancy,
5. Creating a pool set for matchRanges, 2. Introduction to
matchRanges, 0. Introduction to nullranges
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/nullranges/inst/doc/bootRanges.R,
vignettes/nullranges/inst/doc/matching_ginteractions.R,
vignettes/nullranges/inst/doc/matching_granges.R,
vignettes/nullranges/inst/doc/matching_pool_set.R,
vignettes/nullranges/inst/doc/matchRanges.R,
vignettes/nullranges/inst/doc/nullranges.R
importsMe: tidyomics
dependencyCount: 94
Package: NuPoP
Version: 2.15.0
Depends: R (>= 4.0)
Imports: graphics, utils
Suggests: knitr, rmarkdown
License: GPL-2
MD5sum: 7f5f067ac4d6c00f11b2268b57b3b9ed
NeedsCompilation: yes
Title: An R package for nucleosome positioning prediction
Description: NuPoP is an R package for Nucleosome Positioning
Prediction.This package is built upon a duration hidden Markov
model proposed in Xi et al, 2010; Wang et al, 2008. The core of
the package was written in Fotran. In addition to the R
package, a stand-alone Fortran software tool is also available
at https://github.com/jipingw. The Fortran codes have complete
functonality as the R package. Note: NuPoP has two separate
functions for prediction of nucleosome positioning, one for
MNase-map trained models and the other for chemical map-trained
models. The latter was implemented for four species including
yeast, S.pombe, mouse and human, trained based on our recent
publications. We noticed there is another package nuCpos by
another group for prediction of nucleosome positioning trained
with chemicals. A report to compare recent versions of NuPoP
with nuCpos can be found at
https://github.com/jiping/NuPoP_doc. Some more information can
be found and will be posted at
https://github.com/jipingw/NuPoP.
biocViews: Genetics,Visualization,Classification,NucleosomePositioning,
HiddenMarkovModel
Author: Ji-Ping Wang <jzwang@northwestern.edu>; Liqun Xi
<liqunxi02@gmail.com>; Oscar Zarate <zarate@u.northwestern.edu>
Maintainer: Ji-Ping Wang<jzwang@northwestern.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/NuPoP
git_branch: devel
git_last_commit: 6983e4d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/NuPoP_2.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/NuPoP_2.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/NuPoP_2.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/NuPoP_2.15.0.tgz
vignettes: vignettes/NuPoP/inst/doc/NuPoP.html
vignetteTitles: NuPoP
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/NuPoP/inst/doc/NuPoP.R
suggestsMe: nuCpos
dependencyCount: 2
Package: occugene
Version: 1.67.0
Depends: R (>= 2.0.0)
License: GPL (>= 2)
MD5sum: 6eebfe10442f3c757a3ebb0dac4874fd
NeedsCompilation: no
Title: Functions for Multinomial Occupancy Distribution
Description: Statistical tools for building random mutagenesis
libraries for prokaryotes. The package has functions for
handling the occupancy distribution for a multinomial and for
estimating the number of essential genes in random transposon
mutagenesis libraries.
biocViews: Annotation, Pathways
Author: Oliver Will <oliverrreader@gmail.com>
Maintainer: Oliver Will <oliverrreader@gmail.com>
git_url: https://git.bioconductor.org/packages/occugene
git_branch: devel
git_last_commit: c94c038
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/occugene_1.67.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/occugene_1.67.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/occugene_1.67.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/occugene_1.67.0.tgz
vignettes: vignettes/occugene/inst/doc/occugene.pdf
vignetteTitles: occugene
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/occugene/inst/doc/occugene.R
dependencyCount: 0
Package: OCplus
Version: 1.81.0
Depends: R (>= 2.1.0)
Imports: multtest (>= 1.7.3), graphics, grDevices, stats, interp
License: LGPL
MD5sum: 23dee7a7069666cd5179cbd06af28655
NeedsCompilation: no
Title: Operating characteristics plus sample size and local fdr for
microarray experiments
Description: This package allows to characterize the operating
characteristics of a microarray experiment, i.e. the trade-off
between false discovery rate and the power to detect truly
regulated genes. The package includes tools both for planned
experiments (for sample size assessment) and for already
collected data (identification of differentially expressed
genes).
biocViews: Microarray, DifferentialExpression, MultipleComparison
Author: Yudi Pawitan <Yudi.Pawitan@ki.se> and Alexander Ploner
<Alexander.Ploner@ki.se>
Maintainer: Alexander Ploner <Alexander.Ploner@ki.se>
git_url: https://git.bioconductor.org/packages/OCplus
git_branch: devel
git_last_commit: 530dc28
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/OCplus_1.81.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/OCplus_1.81.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/OCplus_1.81.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/OCplus_1.81.0.tgz
vignettes: vignettes/OCplus/inst/doc/OCplus.pdf
vignetteTitles: OCplus Introduction
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OCplus/inst/doc/OCplus.R
dependencyCount: 20
Package: octad
Version: 1.9.0
Depends: R (>= 4.2.0), magrittr, dplyr, ggplot2, edgeR, RUVSeq, DESeq2,
limma, rhdf5, foreach, Rfast, octad.db, stats, httr, qpdf,
ExperimentHub, AnnotationHub, Biobase, S4Vectors
Imports: EDASeq, GSVA, data.table, htmlwidgets, plotly, reshape2,
grDevices, utils
Suggests: knitr, rmarkdown
License: Artistic-2.0
MD5sum: ef7599273b99997b87f05720915b9b4b
NeedsCompilation: no
Title: Open Cancer TherApeutic Discovery (OCTAD)
Description: OCTAD provides a platform for virtually screening
compounds targeting precise cancer patient groups. The
essential idea is to identify drugs that reverse the gene
expression signature of disease by tamping down over-expressed
genes and stimulating weakly expressed ones. The package offers
deep-learning based reference tissue selection, disease gene
expression signature creation, pathway enrichment analysis,
drug reversal potency scoring, cancer cell line selection, drug
enrichment analysis and in silico hit validation. It currently
covers ~20,000 patient tissue samples covering 50 cancer types,
and expression profiles for ~12,000 distinct compounds.
biocViews: Classification, GeneExpression, Pharmacogenetics,
Pharmacogenomics, Software, GeneSetEnrichment
Author: E. Chekalin [aut, cre], S. Paithankar [aut], B. Zeng [aut], B.
Glicksberg [ctb], P. Newbury [ctb], J. Xing [ctb], K. Liu
[ctb], A. Wen [ctb], D. Joseph [ctb], B. Chen [aut]
Maintainer: E. Chekalin <eygen.chekalin@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/octad
git_branch: devel
git_last_commit: 33eb152
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/octad_1.9.0.tar.gz
vignettes: vignettes/octad/inst/doc/octad.html
vignetteTitles: octad
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/octad/inst/doc/octad.R
dependencyCount: 189
Package: odseq
Version: 1.35.0
Depends: R (>= 3.2.3)
Imports: msa (>= 1.2.1), kebabs (>= 1.4.1), mclust (>= 5.1)
Suggests: knitr(>= 1.11)
License: MIT + file LICENSE
MD5sum: 5b6cb03994667432b1986fd1df905296
NeedsCompilation: no
Title: Outlier detection in multiple sequence alignments
Description: Performs outlier detection of sequences in a multiple
sequence alignment using bootstrap of predefined distance
metrics. Outlier sequences can make downstream analyses
unreliable or make the alignments less accurate while they are
being constructed. This package implements the OD-seq algorithm
proposed by Jehl et al (doi 10.1186/s12859-015-0702-1) for
aligned sequences and a variant using string kernels for
unaligned sequences.
biocViews: Alignment, MultipleSequenceAlignment
Author: José Jiménez
Maintainer: José Jiménez <jose@jimenezluna.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/odseq
git_branch: devel
git_last_commit: c7de581
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/odseq_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/odseq_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/odseq_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/odseq_1.35.0.tgz
vignettes: vignettes/odseq/inst/doc/vignette.pdf
vignetteTitles: A quick tutorial to outlier detection in MSAs
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/odseq/inst/doc/vignette.R
dependencyCount: 39
Package: OGRE
Version: 1.11.0
Depends: R (>= 4.2.0), S4Vectors
Imports: GenomicRanges, methods, data.table, assertthat, ggplot2, Gviz,
IRanges, AnnotationHub, grDevices, stats, GenomeInfoDb, shiny,
shinyFiles, DT, rtracklayer, shinydashboard, shinyBS,tidyr
Suggests: testthat (>= 3.0.0), knitr (>= 1.36), rmarkdown (>= 2.11)
License: Artistic-2.0
Archs: x64
MD5sum: 3b3ecdf353e81735378038f40666bce6
NeedsCompilation: no
Title: Calculate, visualize and analyse overlap between genomic regions
Description: OGRE calculates overlap between user defined genomic
region datasets. Any regions can be supplied i.e. genes, SNPs,
or reads from sequencing experiments. Key numbers help analyse
the extend of overlaps which can also be visualized at a
genomic level.
biocViews: Software, WorkflowStep, BiologicalQuestion, Annotation,
Metagenomics, Visualization, Sequencing
Author: Sven Berres [aut, cre], Jörg Gromoll [ctb], Marius Wöste [ctb],
Sarah Sandmann [ctb], Sandra Laurentino [ctb]
Maintainer: Sven Berres <svenbioinf@gmail.com>
URL: https://github.com/svenbioinf/OGRE/
VignetteBuilder: knitr
BugReports: https://github.com/svenbioinf/OGRE/issues
git_url: https://git.bioconductor.org/packages/OGRE
git_branch: devel
git_last_commit: 817241b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/OGRE_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/OGRE_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/OGRE_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/OGRE_1.11.0.tgz
vignettes: vignettes/OGRE/inst/doc/OGRE.html
vignetteTitles: OGRE
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OGRE/inst/doc/OGRE.R
dependencyCount: 172
Package: oligo
Version: 1.71.7
Depends: R (>= 3.2.0), BiocGenerics (>= 0.13.11), oligoClasses (>=
1.29.6), Biobase (>= 2.27.3), Biostrings (>= 2.35.12)
Imports: affyio (>= 1.35.0), affxparser (>= 1.39.4), DBI (>= 0.3.1),
ff, graphics, methods, preprocessCore (>= 1.29.0), RSQLite (>=
1.0.0), splines, stats, stats4, utils, bit
LinkingTo: preprocessCore
Suggests: BSgenome.Hsapiens.UCSC.hg18, hapmap100kxba, pd.hg.u95av2,
pd.mapping50k.xba240, pd.huex.1.0.st.v2, pd.hg18.60mer.expr,
pd.hugene.1.0.st.v1, maqcExpression4plex, genefilter, limma,
RColorBrewer, oligoData, BiocStyle, knitr, RUnit, biomaRt,
AnnotationDbi, ACME, RCurl
Enhances: doMC, doMPI
License: LGPL (>= 2)
MD5sum: 7e97bd8075dd6dad6c267245790298d2
NeedsCompilation: yes
Title: Preprocessing tools for oligonucleotide arrays
Description: A package to analyze oligonucleotide arrays
(expression/SNP/tiling/exon) at probe-level. It currently
supports Affymetrix (CEL files) and NimbleGen arrays (XYS
files).
biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, SNP,
DifferentialExpression, ExonArray, GeneExpression, DataImport
Author: Benilton Carvalho and Rafael Irizarry
Maintainer: Benilton Carvalho <benilton@unicamp.br>
URL: https://github.com/benilton/oligo
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/oligo
git_branch: devel
git_last_commit: e073acb
git_last_commit_date: 2025-03-08
Date/Publication: 2025-03-09
source.ver: src/contrib/oligo_1.71.7.tar.gz
win.binary.ver: bin/windows/contrib/4.5/oligo_1.71.7.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/oligo_1.71.7.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/oligo_1.71.7.tgz
vignettes: vignettes/oligo/inst/doc/oug.pdf
vignetteTitles: oligo User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: ITALICS, pdInfoBuilder, puma, SCAN.UPC, oligoData,
pd.081229.hg18.promoter.medip.hx1,
pd.2006.07.18.hg18.refseq.promoter,
pd.2006.07.18.mm8.refseq.promoter,
pd.2006.10.31.rn34.refseq.promoter, pd.ag, pd.aragene.1.0.st,
pd.aragene.1.1.st, pd.ath1.121501, pd.barley1,
pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine, pd.bsubtilis,
pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine, pd.canine.2,
pd.celegans, pd.charm.hg18.example, pd.chicken,
pd.chigene.1.0.st, pd.chigene.1.1.st, pd.chogene.2.0.st,
pd.chogene.2.1.st, pd.citrus, pd.clariom.d.human,
pd.clariom.s.human, pd.clariom.s.human.ht, pd.clariom.s.mouse,
pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht,
pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st,
pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array,
pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1,
pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2,
pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st,
pd.equgene.1.1.st, pd.feinberg.hg18.me.hx1,
pd.feinberg.mm8.me.hx1, pd.felgene.1.0.st, pd.felgene.1.1.st,
pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5,
pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st,
pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a,
pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219,
pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d,
pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm,
pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800,
pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1,
pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize,
pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240,
pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st,
pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a,
pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c,
pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0,
pd.mirna.3.1, pd.mirna.4.0, pd.moe430a, pd.moe430b,
pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1,
pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2,
pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb,
pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st,
pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles,
pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st,
pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b,
pd.raex.1.0.st.v1, pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1,
pd.ragene.2.0.st, pd.ragene.2.1.st, pd.rat230.2,
pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b,
pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus,
pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34,
pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus,
pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st,
pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera,
pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis,
pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st,
pd.zebrafish, pd.atdschip.tiling, pumadata, maEndToEnd
importsMe: ArrayExpress, cn.farms, frma, ITALICS, mimager
suggestsMe: frmaTools, hapmap100khind, hapmap100kxba, hapmap500knsp,
hapmap500ksty, hapmapsnp5, hapmapsnp6, maqcExpression4plex,
aroma.affymetrix, maGUI, RCPA
dependencyCount: 63
Package: oligoClasses
Version: 1.69.0
Depends: R (>= 2.14)
Imports: BiocGenerics (>= 0.27.1), Biobase (>= 2.17.8), methods,
graphics, IRanges (>= 2.5.17), GenomicRanges (>= 1.23.7),
SummarizedExperiment, Biostrings (>= 2.23.6), affyio (>=
1.23.2), foreach, BiocManager, utils, S4Vectors (>= 0.9.25),
RSQLite, DBI, ff
Suggests: hapmapsnp5, hapmapsnp6, pd.genomewidesnp.6,
pd.genomewidesnp.5, pd.mapping50k.hind240,
pd.mapping50k.xba240, pd.mapping250k.sty, pd.mapping250k.nsp,
genomewidesnp6Crlmm (>= 1.0.7), genomewidesnp5Crlmm (>= 1.0.6),
RUnit, human370v1cCrlmm, VanillaICE, crlmm
Enhances: doMC, doMPI, doSNOW, doParallel, doRedis
License: GPL (>= 2)
MD5sum: 73401f7f1c51d7554ee9a98bbce1ee71
NeedsCompilation: no
Title: Classes for high-throughput arrays supported by oligo and crlmm
Description: This package contains class definitions, validity checks,
and initialization methods for classes used by the oligo and
crlmm packages.
biocViews: Infrastructure
Author: Benilton Carvalho and Robert Scharpf
Maintainer: Benilton Carvalho <beniltoncarvalho@gmail.com> and Robert
Scharpf <rscharpf@jhsph.edu>
git_url: https://git.bioconductor.org/packages/oligoClasses
git_branch: devel
git_last_commit: 37e0a0e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/oligoClasses_1.69.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/oligoClasses_1.69.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/oligoClasses_1.69.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/oligoClasses_1.69.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: cn.farms, crlmm, mBPCR, oligo, puma,
pd.081229.hg18.promoter.medip.hx1,
pd.2006.07.18.hg18.refseq.promoter,
pd.2006.07.18.mm8.refseq.promoter,
pd.2006.10.31.rn34.refseq.promoter, pd.ag, pd.aragene.1.0.st,
pd.aragene.1.1.st, pd.ath1.121501, pd.barley1,
pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine, pd.bsubtilis,
pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine, pd.canine.2,
pd.celegans, pd.charm.hg18.example, pd.chicken,
pd.chigene.1.0.st, pd.chigene.1.1.st, pd.chogene.2.0.st,
pd.chogene.2.1.st, pd.citrus, pd.clariom.d.human,
pd.clariom.s.human, pd.clariom.s.human.ht, pd.clariom.s.mouse,
pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht,
pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st,
pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array,
pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1,
pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2,
pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st,
pd.equgene.1.1.st, pd.feinberg.hg18.me.hx1,
pd.feinberg.mm8.me.hx1, pd.felgene.1.0.st, pd.felgene.1.1.st,
pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5,
pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st,
pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a,
pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219,
pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d,
pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm,
pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800,
pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1,
pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize,
pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240,
pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st,
pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a,
pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c,
pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0,
pd.mirna.3.1, pd.mirna.4.0, pd.moe430a, pd.moe430b,
pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1,
pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2,
pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb,
pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st,
pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles,
pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st,
pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b,
pd.raex.1.0.st.v1, pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1,
pd.ragene.2.0.st, pd.ragene.2.1.st, pd.rat230.2,
pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b,
pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus,
pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34,
pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus,
pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st,
pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera,
pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis,
pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st,
pd.zebrafish, pd.atdschip.tiling, maEndToEnd
importsMe: affycoretools, frma, ITALICS, mimager, MinimumDistance,
pdInfoBuilder, puma, VanillaICE
suggestsMe: hapmapsnp6, aroma.affymetrix, scrime
dependencyCount: 59
Package: OLIN
Version: 1.85.0
Depends: R (>= 2.10), methods, locfit, marray
Imports: graphics, grDevices, limma, marray, methods, stats
Suggests: convert
License: GPL-2
MD5sum: ddfcf6f50ba1cd6309668ae7279f83d4
NeedsCompilation: no
Title: Optimized local intensity-dependent normalisation of two-color
microarrays
Description: Functions for normalisation of two-color microarrays by
optimised local regression and for detection of artefacts in
microarray data
biocViews: Microarray, TwoChannel, QualityControl, Preprocessing,
Visualization
Author: Matthias Futschik <mfutschik@ualg.pt>
Maintainer: Matthias Futschik <mfutschik@ualg.pt>
URL: http://olin.sysbiolab.eu
git_url: https://git.bioconductor.org/packages/OLIN
git_branch: devel
git_last_commit: f6d5168
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/OLIN_1.85.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/OLIN_1.85.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/OLIN_1.85.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/OLIN_1.85.0.tgz
vignettes: vignettes/OLIN/inst/doc/OLIN.pdf
vignetteTitles: Introduction to OLIN
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OLIN/inst/doc/OLIN.R
dependsOnMe: OLINgui
importsMe: OLINgui
dependencyCount: 11
Package: OLINgui
Version: 1.81.0
Depends: R (>= 2.0.0), OLIN (>= 1.4.0)
Imports: graphics, marray, OLIN, tcltk, tkWidgets, widgetTools
License: GPL-2
MD5sum: 46217904e7b2610c3c5d0432573f3f71
NeedsCompilation: no
Title: Graphical user interface for OLIN
Description: Graphical user interface for the OLIN package
biocViews: Microarray, TwoChannel, QualityControl, Preprocessing,
Visualization
Author: Matthias Futschik <mfutschik@ualg.pt>
Maintainer: Matthias Futschik <mfutschik@ualg.pt>
URL: http://olin.sysbiolab.eu
git_url: https://git.bioconductor.org/packages/OLINgui
git_branch: devel
git_last_commit: 49b185c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/OLINgui_1.81.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/OLINgui_1.81.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/OLINgui_1.81.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/OLINgui_1.81.0.tgz
vignettes: vignettes/OLINgui/inst/doc/OLINgui.pdf
vignetteTitles: Introduction to OLINgui
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OLINgui/inst/doc/OLINgui.R
dependencyCount: 17
Package: omada
Version: 1.9.0
Depends: pdfCluster (>= 1.0-3), kernlab (>= 0.9-29), R (>= 4.2), fpc
(>= 2.2-9), Rcpp (>= 1.0.7), diceR (>= 0.6.0), ggplot2 (>=
3.3.5), reshape (>= 0.8.8), genieclust (>= 1.1.3), clValid (>=
0.7), glmnet (>= 4.1.3), dplyr(>= 1.0.7), stats (>= 4.1.2),
clValid(>= 0.7)
Suggests: rmarkdown, knitr, testthat
License: GPL-3
Archs: x64
MD5sum: d15e85b820a271eeff7a9a584a9ca784
NeedsCompilation: no
Title: Machine learning tools for automated transcriptome clustering
analysis
Description: Symptomatic heterogeneity in complex diseases reveals
differences in molecular states that need to be investigated.
However, selecting the numerous parameters of an exploratory
clustering analysis in RNA profiling studies requires deep
understanding of machine learning and extensive computational
experimentation. Tools that assist with such decisions without
prior field knowledge are nonexistent and further gene
association analyses need to be performed independently. We
have developed a suite of tools to automate these processes and
make robust unsupervised clustering of transcriptomic data more
accessible through automated machine learning based functions.
The efficiency of each tool was tested with four datasets
characterised by different expression signal strengths. Our
toolkit’s decisions reflected the real number of stable
partitions in datasets where the subgroups are discernible.
Even in datasets with less clear biological distinctions,
stable subgroups with different expression profiles and
clinical associations were found.
biocViews: Software, Clustering, RNASeq, GeneExpression
Author: Sokratis Kariotis [aut, cre] (ORCID:
<https://orcid.org/0000-0001-9993-6017>)
Maintainer: Sokratis Kariotis <sokratiskariotis@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/omada
git_branch: devel
git_last_commit: 5caa0b1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/omada_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/omada_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/omada_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/omada_1.9.0.tgz
vignettes: vignettes/omada/inst/doc/omada-vignette.html
vignetteTitles: my-vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/omada/inst/doc/omada-vignette.R
dependencyCount: 140
Package: OmaDB
Version: 2.23.0
Depends: R (>= 3.5), httr (>= 1.2.1), plyr(>= 1.8.4)
Imports: utils, ape, Biostrings, GenomicRanges, IRanges, methods,
topGO, jsonlite
Suggests: knitr, rmarkdown, testthat
License: GPL-3
MD5sum: f6e1f4ca353ab611a8b41583836a37b4
NeedsCompilation: no
Title: R wrapper for the OMA REST API
Description: A package for the orthology prediction data download from
OMA database.
biocViews: Software, ComparativeGenomics, FunctionalGenomics, Genetics,
Annotation, GO, FunctionalPrediction
Author: Klara Kaleb
Maintainer: Klara Kaleb <klara.kaleb18@ic.ac.uk>, Adrian Altenhoff
<adrian.altenhoff@inf.ethz.ch>
URL: https://github.com/DessimozLab/OmaDB
VignetteBuilder: knitr
BugReports: https://github.com/DessimozLab/OmaDB/issues
git_url: https://git.bioconductor.org/packages/OmaDB
git_branch: devel
git_last_commit: c8dc611
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/OmaDB_2.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/OmaDB_2.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/OmaDB_2.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/OmaDB_2.23.0.tgz
vignettes: vignettes/OmaDB/inst/doc/exploring_hogs.html,
vignettes/OmaDB/inst/doc/OmaDB.html,
vignettes/OmaDB/inst/doc/sequence_mapping.html,
vignettes/OmaDB/inst/doc/tree_visualisation.html
vignetteTitles: Exploring Hierarchical orthologous groups with OmaDB,
Get started with OmaDB, Sequence Mapping with OmaDB, Exploring
Taxonomic trees with OmaDB
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OmaDB/inst/doc/exploring_hogs.R,
vignettes/OmaDB/inst/doc/OmaDB.R,
vignettes/OmaDB/inst/doc/sequence_mapping.R,
vignettes/OmaDB/inst/doc/tree_visualisation.R
suggestsMe: orthogene, PhyloProfile
dependencyCount: 59
Package: omicade4
Version: 1.47.0
Depends: R (>= 3.0.0), ade4
Imports: made4, Biobase
Suggests: BiocStyle
License: GPL-2
MD5sum: 4d54298833335f07f5a47553bac29bb7
NeedsCompilation: no
Title: Multiple co-inertia analysis of omics datasets
Description: This package performes multiple co-inertia analysis of
omics datasets.
biocViews: Software, Clustering, Classification, MultipleComparison
Author: Chen Meng, Aedin Culhane, Amin M. Gholami.
Maintainer: Chen Meng <mengchen18@gmail.com>
git_url: https://git.bioconductor.org/packages/omicade4
git_branch: devel
git_last_commit: 8db5f0e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/omicade4_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/omicade4_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/omicade4_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/omicade4_1.47.0.tgz
vignettes: vignettes/omicade4/inst/doc/omicade4.pdf
vignetteTitles: Using omicade4
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/omicade4/inst/doc/omicade4.R
importsMe: omicRexposome
suggestsMe: biosigner, MultiDataSet, phenomis, ropls
dependencyCount: 50
Package: OmicCircos
Version: 1.45.0
Depends: R (>= 2.14.0), methods,GenomicRanges
License: GPL-2
MD5sum: d21546f0aa2e3c8f3321fbc83fcbad61
NeedsCompilation: no
Title: High-quality circular visualization of omics data
Description: OmicCircos is an R application and package for generating
high-quality circular plots for omics data.
biocViews: Visualization,Statistics,Annotation
Author: Ying Hu <yhu@mail.nih.gov> Chunhua Yan <yanch@mail.nih.gov>
Maintainer: Ying Hu <yhu@mail.nih.gov>
git_url: https://git.bioconductor.org/packages/OmicCircos
git_branch: devel
git_last_commit: 852429c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/OmicCircos_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/OmicCircos_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/OmicCircos_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/OmicCircos_1.45.0.tgz
vignettes: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.pdf
vignetteTitles: OmicCircos vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.R
dependencyCount: 23
Package: omicplotR
Version: 1.27.0
Depends: R (>= 3.6), ALDEx2 (>= 1.18.0)
Imports: compositions, DT, grDevices, knitr, jsonlite, matrixStats,
rmarkdown, shiny, stats, vegan, zCompositions
License: MIT + file LICENSE
MD5sum: 2acf081db8a439edb112a30b510060d3
NeedsCompilation: no
Title: Visual Exploration of Omic Datasets Using a Shiny App
Description: A Shiny app for visual exploration of omic datasets as
compositions, and differential abundance analysis using ALDEx2.
Useful for exploring RNA-seq, meta-RNA-seq, 16s rRNA gene
sequencing with visualizations such as principal component
analysis biplots (coloured using metadata for visualizing each
variable), dendrograms and stacked bar plots, and effect plots
(ALDEx2). Input is a table of counts and metadata file (if
metadata exists), with options to filter data by count or by
metadata to remove low counts, or to visualize select samples
according to selected metadata.
biocViews: Software, DifferentialExpression, GeneExpression, GUI,
RNASeq, DNASeq, Metagenomics, Transcriptomics, Bayesian,
Microbiome, Visualization, Sequencing, ImmunoOncology
Author: Daniel Giguere [aut, cre], Jean Macklaim [aut], Greg Gloor
[aut]
Maintainer: Daniel Giguere <dgiguer@uwo.ca>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/omicplotR
git_branch: devel
git_last_commit: 3f163b1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/omicplotR_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/omicplotR_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/omicplotR_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/omicplotR_1.27.0.tgz
vignettes: vignettes/omicplotR/inst/doc/omicplotR.html
vignetteTitles: omicplotR: A tool for visualization of omic datasets as
compositions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/omicplotR/inst/doc/omicplotR.R
dependencyCount: 114
Package: omicRexposome
Version: 1.29.0
Depends: R (>= 3.5.0), Biobase
Imports: stats, utils, grDevices, graphics, methods, rexposome, limma,
sva, ggplot2, ggrepel, PMA, omicade4, gridExtra, MultiDataSet,
SmartSVA, isva, parallel, SummarizedExperiment, stringr
Suggests: BiocStyle, knitr, rmarkdown, snpStats, brgedata
License: MIT + file LICENSE
MD5sum: a82506f29faf3c15f6561fd146e6397a
NeedsCompilation: no
Title: Exposome and omic data associatin and integration analysis
Description: omicRexposome systematizes the association evaluation
between exposures and omic data, taking advantage of
MultiDataSet for coordinated data management, rexposome for
exposome data definition and limma for association testing.
Also to perform data integration mixing exposome and omic data
using multi co-inherent analysis (omicade4) and multi-canonical
correlation analysis (PMA).
biocViews: ImmunoOncology, WorkflowStep, MultipleComparison,
Visualization, GeneExpression, DifferentialExpression,
DifferentialMethylation, GeneRegulation, Epigenetics,
Proteomics, Transcriptomics, StatisticalMethod, Regression
Author: Carles Hernandez-Ferrer [aut, cre], Juan R. González [aut]
Maintainer: Xavier Escribà Montagut <xavier.escriba@isglobal.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/omicRexposome
git_branch: devel
git_last_commit: 0f24216
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/omicRexposome_1.29.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/omicRexposome_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/omicRexposome_1.29.0.tgz
vignettes:
vignettes/omicRexposome/inst/doc/exposome_omic_integration.html
vignetteTitles: Exposome Data Integration with Omic Data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/omicRexposome/inst/doc/exposome_omic_integration.R
dependencyCount: 228
Package: OmicsMLRepoR
Version: 1.1.5
Depends: R (>= 4.4.0)
Imports: dplyr, stringr, rols, tidyr, methods, stats, tibble,
data.tree, jsonlite, plyr, BiocFileCache, readr, DiagrammeR,
rlang, lubridate
Suggests: arrow, knitr, BiocStyle, curatedMetagenomicData, testthat (>=
3.0.0), cBioPortalData
License: Artistic-2.0
MD5sum: 12efbac49668c53a40666fba8378d377
NeedsCompilation: no
Title: Search harmonized metadata created under the OmicsMLRepo project
Description: This package provides functions to browse the harmonized
metadata for large omics databases. This package also supports
data navigation if the metadata incorporates ontology.
biocViews: Software, Infrastructure, DataRepresentation
Author: Sehyun Oh [aut, cre] (ORCID:
<https://orcid.org/0000-0002-9490-3061>), Kaelyn Long [aut]
Maintainer: Sehyun Oh <shbrief@gmail.com>
URL: https://github.com/shbrief/OmicsMLRepoR
VignetteBuilder: knitr
BugReports: https://github.com/shbrief/OmicsMLRepoR/issues
git_url: https://git.bioconductor.org/packages/OmicsMLRepoR
git_branch: devel
git_last_commit: cd56020
git_last_commit_date: 2025-03-22
Date/Publication: 2025-03-23
source.ver: src/contrib/OmicsMLRepoR_1.1.5.tar.gz
win.binary.ver: bin/windows/contrib/4.5/OmicsMLRepoR_1.1.5.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/OmicsMLRepoR_1.1.5.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/OmicsMLRepoR_1.1.5.tgz
vignettes: vignettes/OmicsMLRepoR/inst/doc/Quickstart.html
vignetteTitles: Quickstart
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OmicsMLRepoR/inst/doc/Quickstart.R
dependencyCount: 94
Package: OMICsPCA
Version: 1.25.0
Depends: R (>= 3.5.0), OMICsPCAdata
Imports: HelloRanges, fpc, stats, MultiAssayExperiment, pdftools,
methods, grDevices, utils,clValid, NbClust, cowplot, rmarkdown,
kableExtra, rtracklayer, IRanges, GenomeInfoDb, reshape2,
ggplot2, factoextra, rgl, corrplot, MASS, graphics, FactoMineR,
PerformanceAnalytics, tidyr, data.table, cluster, magick
Suggests: knitr, RUnit, BiocGenerics
License: GPL-3
MD5sum: cd2fee889fd952ef082f84dda7cdb928
NeedsCompilation: no
Title: An R package for quantitative integration and analysis of
multiple omics assays from heterogeneous samples
Description: OMICsPCA is an analysis pipeline designed to integrate
multi OMICs experiments done on various subjects (e.g. Cell
lines, individuals), treatments (e.g. disease/control) or time
points and to analyse such integrated data from various various
angles and perspectives. In it's core OMICsPCA uses Principal
Component Analysis (PCA) to integrate multiomics experiments
from various sources and thus has ability to over data
insufficiency issues by using the ingegrated data as
representatives. OMICsPCA can be used in various application
including analysis of overall distribution of OMICs assays
across various samples /individuals /time points; grouping
assays by user-defined conditions; identification of source of
variation, similarity/dissimilarity between assays, variables
or individuals.
biocViews: ImmunoOncology, MultipleComparison, PrincipalComponent,
DataRepresentation, Workflow, Visualization,
DimensionReduction, Clustering, BiologicalQuestion,
EpigeneticsWorkflow, Transcription, GeneticVariability, GUI,
BiomedicalInformatics, Epigenetics, FunctionalGenomics,
SingleCell
Author: Subhadeep Das [aut, cre], Dr. Sucheta Tripathy [ctb]
Maintainer: Subhadeep Das <subhadeep1024@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/OMICsPCA
git_branch: devel
git_last_commit: 1659da8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/OMICsPCA_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/OMICsPCA_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/OMICsPCA_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/OMICsPCA_1.25.0.tgz
vignettes: vignettes/OMICsPCA/inst/doc/vignettes.html
vignetteTitles: OMICsPCA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OMICsPCA/inst/doc/vignettes.R
dependencyCount: 208
Package: omicsPrint
Version: 1.27.0
Depends: R (>= 3.5), MASS
Imports: methods, matrixStats, graphics, stats, SummarizedExperiment,
MultiAssayExperiment, RaggedExperiment
Suggests: BiocStyle, knitr, rmarkdown, testthat, GEOquery,
VariantAnnotation, Rsamtools, BiocParallel, GenomicRanges,
FDb.InfiniumMethylation.hg19, snpStats
License: GPL (>= 2)
MD5sum: df82c7bf6bcb8e9a92a503c8b96ddc99
NeedsCompilation: no
Title: Cross omic genetic fingerprinting
Description: omicsPrint provides functionality for cross omic genetic
fingerprinting, for example, to verify sample relationships
between multiple omics data types, i.e. genomic, transcriptomic
and epigenetic (DNA methylation).
biocViews: QualityControl, Genetics, Epigenetics, Transcriptomics,
DNAMethylation, Transcription, GeneticVariability,
ImmunoOncology
Author: Maarten van Iterson [aut], Davy Cats [cre]
Maintainer: Davy Cats <davycats.dc@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/omicsPrint
git_branch: devel
git_last_commit: 8c45530
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/omicsPrint_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/omicsPrint_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/omicsPrint_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/omicsPrint_1.27.0.tgz
vignettes: vignettes/omicsPrint/inst/doc/omicsPrint.html
vignetteTitles: omicsPrint: detection of data linkage errors in
multiple omics studies
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/omicsPrint/inst/doc/omicsPrint.R
dependencyCount: 59
Package: omicsViewer
Version: 1.11.1
Depends: R (>= 4.2)
Imports: survminer, survival, fastmatch, reshape2, stringr, beeswarm,
grDevices, DT, shiny, shinythemes, shinyWidgets, plotly,
networkD3, httr, matrixStats, RColorBrewer, Biobase, fgsea,
openxlsx, psych, shinybusy, ggseqlogo, htmlwidgets, graphics,
grid, stats, utils, methods, shinyjs, curl, flatxml, ggplot2,
S4Vectors, SummarizedExperiment, RSQLite, Matrix,
shinycssloaders, ROCR, drc
Suggests: BiocStyle, knitr, rmarkdown, unittest
License: GPL-2
MD5sum: bd48086908bd04f60aa02b310faf4b00
NeedsCompilation: no
Title: Interactive and explorative visualization of
SummarizedExperssionSet or ExpressionSet using omicsViewer
Description: omicsViewer visualizes ExpressionSet (or
SummarizedExperiment) in an interactive way. The omicsViewer
has a separate back- and front-end. In the back-end, users need
to prepare an ExpressionSet that contains all the necessary
information for the downstream data interpretation. Some extra
requirements on the headers of phenotype data or feature data
are imposed so that the provided information can be clearly
recognized by the front-end, at the same time, keep a minimum
modification on the existing ExpressionSet object. The pure
dependency on R/Bioconductor guarantees maximum flexibility in
the statistical analysis in the back-end. Once the
ExpressionSet is prepared, it can be visualized using the
front-end, implemented by shiny and plotly. Both features and
samples could be selected from (data) tables or graphs (scatter
plot/heatmap). Different types of analyses, such as enrichment
analysis (using Bioconductor package fgsea or fisher's exact
test) and STRING network analysis, will be performed on the fly
and the results are visualized simultaneously. When a subset of
samples and a phenotype variable is selected, a significance
test on means (t-test or ranked based test; when phenotype
variable is quantitative) or test of independence (chi-square
or fisher’s exact test; when phenotype data is categorical)
will be performed to test the association between the phenotype
of interest with the selected samples. Additionally, other
analyses can be easily added as extra shiny modules. Therefore,
omicsViewer will greatly facilitate data exploration, many
different hypotheses can be explored in a short time without
the need for knowledge of R. In addition, the resulting data
could be easily shared using a shiny server. Otherwise, a
standalone version of omicsViewer together with designated
omics data could be easily created by integrating it with
portable R, which can be shared with collaborators or submitted
as supplementary data together with a manuscript.
biocViews: Software, Visualization, GeneSetEnrichment,
DifferentialExpression, MotifDiscovery, Network,
NetworkEnrichment
Author: Chen Meng [aut, cre]
Maintainer: Chen Meng <mengchen18@gmail.com>
URL: https://github.com/mengchen18/omicsViewer
VignetteBuilder: knitr
Video:
https://www.youtube.com/watch?v=0nirB-exquY&list=PLo2m88lJf-RRoLKMY8UEGqCpraKYrX5lk
BugReports: https://github.com/mengchen18/omicsViewer
git_url: https://git.bioconductor.org/packages/omicsViewer
git_branch: devel
git_last_commit: 7951298
git_last_commit_date: 2025-01-29
Date/Publication: 2025-01-30
source.ver: src/contrib/omicsViewer_1.11.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/omicsViewer_1.11.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/omicsViewer_1.11.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/omicsViewer_1.11.1.tgz
vignettes: vignettes/omicsViewer/inst/doc/quickStart.html
vignetteTitles: quickStart.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/omicsViewer/inst/doc/quickStart.R
dependencyCount: 196
Package: Omixer
Version: 1.17.0
Depends: R (>= 4.0.0)
Imports: dplyr, ggplot2, forcats, tibble, gridExtra, magrittr, readr,
tidyselect, grid, stats, stringr
Suggests: knitr, rmarkdown, BiocStyle, magick, testthat
License: MIT + file LICENSE
MD5sum: cbc36a3ac0bee6ebcb1fc14fb995316d
NeedsCompilation: no
Title: Omixer: multivariate and reproducible sample randomization to
proactively counter batch effects in omics studies
Description: Omixer - an Bioconductor package for multivariate and
reproducible sample randomization, which ensures optimal sample
distribution across batches with well-documented methods. It
outputs lab-friendly sample layouts, reducing the risk of
sample mixups when manually pipetting randomized samples.
biocViews: DataRepresentation, ExperimentalDesign, QualityControl,
Software, Visualization
Author: Lucy Sinke [cre, aut]
Maintainer: Lucy Sinke <l.j.sinke@lumc.nl>
VignetteBuilder: knitr
BugReports: https://github.com/molepi/Omixer/issues
git_url: https://git.bioconductor.org/packages/Omixer
git_branch: devel
git_last_commit: c6bc854
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Omixer_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Omixer_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Omixer_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Omixer_1.17.0.tgz
vignettes: vignettes/Omixer/inst/doc/omixer-vignette.html
vignetteTitles: my-vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Omixer/inst/doc/omixer-vignette.R
dependencyCount: 54
Package: OmnipathR
Version: 3.15.0
Depends: R(>= 4.0)
Imports: checkmate, crayon, curl, digest, dplyr(>= 1.1.0), httr,
igraph, jsonlite, later, logger, lubridate, magrittr, progress,
purrr, rappdirs, readr(>= 2.0.0), readxl, rlang, rmarkdown,
RSQLite, R.utils, rvest, stats, stringi, stringr, tibble,
tidyr, tidyselect, tools, utils, vctrs, withr, XML, xml2, yaml,
zip
Suggests: BiocStyle, bookdown, ggplot2, ggraph, gprofiler2, knitr,
mlrMBO, parallelMap, ParamHelpers, Rgraphviz, R.matlab,
sigmajs, smoof, supraHex, testthat
License: MIT + file LICENSE
MD5sum: 4424da5802837e2b0ba6bf1d3ab7f4a6
NeedsCompilation: no
Title: OmniPath web service client and more
Description: A client for the OmniPath web service
(https://www.omnipathdb.org) and many other resources. It also
includes functions to transform and pretty print some of the
downloaded data, functions to access a number of other
resources such as BioPlex, ConsensusPathDB, EVEX, Gene
Ontology, Guide to Pharmacology (IUPHAR/BPS), Harmonizome,
HTRIdb, Human Phenotype Ontology, InWeb InBioMap, KEGG Pathway,
Pathway Commons, Ramilowski et al. 2015, RegNetwork, ReMap, TF
census, TRRUST and Vinayagam et al. 2011. Furthermore,
OmnipathR features a close integration with the NicheNet method
for ligand activity prediction from transcriptomics data, and
its R implementation `nichenetr` (available only on github).
biocViews: GraphAndNetwork, Network, Pathways, Software,
ThirdPartyClient, DataImport, DataRepresentation,
GeneSignaling, GeneRegulation, SystemsBiology, Transcriptomics,
SingleCell, Annotation, KEGG
Author: Alberto Valdeolivas [aut] (ORCID:
<https://orcid.org/0000-0001-5482-9023>), Denes Turei [cre,
aut] (ORCID: <https://orcid.org/0000-0002-7249-9379>), Attila
Gabor [aut] (ORCID: <https://orcid.org/0000-0002-0776-1182>),
Diego Mananes [aut] (ORCID:
<https://orcid.org/0000-0001-7247-6794>), Aurelien Dugourd
[aut] (ORCID: <https://orcid.org/0000-0002-0714-028X>)
Maintainer: Denes Turei <turei.denes@gmail.com>
URL: https://r.omnipathdb.org/
VignetteBuilder: knitr
BugReports: https://github.com/saezlab/OmnipathR/issues
git_url: https://git.bioconductor.org/packages/OmnipathR
git_branch: devel
git_last_commit: bc4d2dd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/OmnipathR_3.15.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/OmnipathR_3.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/OmnipathR_3.15.0.tgz
vignettes: vignettes/OmnipathR/inst/doc/bioc_workshop.html,
vignettes/OmnipathR/inst/doc/cosmos.html,
vignettes/OmnipathR/inst/doc/db_manager.html,
vignettes/OmnipathR/inst/doc/drug_targets.html,
vignettes/OmnipathR/inst/doc/extra_attrs.html,
vignettes/OmnipathR/inst/doc/nichenet.html,
vignettes/OmnipathR/inst/doc/omnipath_intro.html,
vignettes/OmnipathR/inst/doc/paths.html
vignetteTitles: OmniPath Bioconductor workshop, COSMOS PKN, Database
manager, Building networks around drug-targets using OmnipathR,
Extra attributes, Using NicheNet with OmnipathR, OmnipathR: an
R client for the OmniPath web service, Pathway construction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/OmnipathR/inst/doc/bioc_workshop.R,
vignettes/OmnipathR/inst/doc/cosmos.R,
vignettes/OmnipathR/inst/doc/db_manager.R,
vignettes/OmnipathR/inst/doc/drug_targets.R,
vignettes/OmnipathR/inst/doc/extra_attrs.R,
vignettes/OmnipathR/inst/doc/nichenet.R,
vignettes/OmnipathR/inst/doc/omnipath_intro.R,
vignettes/OmnipathR/inst/doc/paths.R
importsMe: gINTomics, wppi
suggestsMe: decoupleR, dorothea
dependencyCount: 89
Package: ompBAM
Version: 1.11.0
Imports: utils, Rcpp
Suggests: RcppProgress, knitr, rmarkdown, roxygen2, devtools, usethis,
desc, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: 08954273a47228a1a4f3b9f52faaff79
NeedsCompilation: no
Title: C++ Library for OpenMP-based multi-threaded sequential profiling
of Binary Alignment Map (BAM) files
Description: This packages provides C++ header files for developers
wishing to create R packages that processes BAM files. ompBAM
automates file access, memory management, and handling of
multiple threads 'behind the scenes', so developers can focus
on creating domain-specific functionality. The included
vignette contains detailed documentation of this API, including
quick-start instructions to create a new ompBAM-based package,
and step-by-step explanation of the functionality behind the
example packaged included within ompBAM.
biocViews: Alignment, DataImport, RNASeq, Software, Sequencing,
Transcriptomics, SingleCell
Author: Alex Chit Hei Wong [aut, cre, cph]
Maintainer: Alex Chit Hei Wong <alexchwong.github@gmail.com>
URL: https://github.com/alexchwong/ompBAM
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/
git_url: https://git.bioconductor.org/packages/ompBAM
git_branch: devel
git_last_commit: 08d7d83
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ompBAM_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ompBAM_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ompBAM_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ompBAM_1.11.0.tgz
vignettes: vignettes/ompBAM/inst/doc/ompBAM-API-Docs.html
vignetteTitles: ompBAM API Documentation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ompBAM/inst/doc/ompBAM-API-Docs.R
importsMe: SpliceWiz
linksToMe: SpliceWiz
dependencyCount: 3
Package: omXplore
Version: 1.1.2
Depends: R (>= 4.4.0), methods
Imports: DT, shiny, MSnbase, PSMatch, SummarizedExperiment,
MultiAssayExperiment, shinyBS, shinyjs, shinyjqui,
RColorBrewer, gplots, highcharter, visNetwork, tibble,
grDevices, stats, utils, htmlwidgets, vioplot, graphics,
FactoMineR, dendextend, dplyr, factoextra, tidyr, nipals
Suggests: knitr, rmarkdown, BiocStyle, testthat, Matrix, graph
License: Artistic-2.0
MD5sum: a2e2f5892bafb0a9e028465c957f32a0
NeedsCompilation: no
Title: Vizualization tools for 'omics' datasets with R
Description: This package contains a collection of functions (written
as shiny modules) for the visualisation and the statistical
analysis of omics data. These plots can be displayed
individually or embedded in a global Shiny module. Additionaly,
it is possible to integrate third party modules to the main
interface of the package omXplore.
biocViews: Software, ShinyApps, MassSpectrometry, DataRepresentation,
GUI, QualityControl
Author: Samuel Wieczorek [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5016-1203>), Thomas Burger [aut],
Enora Fremy [ctb], Cyril Ariztegui [ctb]
Maintainer: Samuel Wieczorek <samuel.wieczorek@cea.fr>
URL: https://github.com/prostarproteomics/omXplore,
https://prostarproteomics.github.io/omXplore/
VignetteBuilder: knitr
BugReports: https://github.com/prostarproteomics/omXplore/issues
git_url: https://git.bioconductor.org/packages/omXplore
git_branch: devel
git_last_commit: d15a99a
git_last_commit_date: 2025-03-04
Date/Publication: 2025-03-04
source.ver: src/contrib/omXplore_1.1.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/omXplore_1.1.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/omXplore_1.1.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/omXplore_1.1.2.tgz
vignettes: vignettes/omXplore/inst/doc/addingThirdPartyPlots.html,
vignettes/omXplore/inst/doc/omXplore.html
vignetteTitles: Adding third party plots, omXplore
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/omXplore/inst/doc/addingThirdPartyPlots.R,
vignettes/omXplore/inst/doc/omXplore.R
dependencyCount: 210
Package: oncomix
Version: 1.29.0
Depends: R (>= 3.4.0)
Imports: ggplot2, ggrepel, RColorBrewer, mclust, stats,
SummarizedExperiment
Suggests: knitr, rmarkdown, testthat, RMySQL
License: GPL-3
MD5sum: 8c70422ced1f22bf8be74b31c95377ac
NeedsCompilation: no
Title: Identifying Genes Overexpressed in Subsets of Tumors from
Tumor-Normal mRNA Expression Data
Description: This package helps identify mRNAs that are overexpressed
in subsets of tumors relative to normal tissue. Ideal inputs
would be paired tumor-normal data from the same tissue from
many patients (>15 pairs). This unsupervised approach relies on
the observation that oncogenes are characteristically
overexpressed in only a subset of tumors in the population, and
may help identify oncogene candidates purely based on
differences in mRNA expression between previously unknown
subtypes.
biocViews: GeneExpression, Sequencing
Author: Daniel Pique, John Greally, Jessica Mar
Maintainer: Daniel Pique <daniel.pique@med.einstein.yu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/oncomix
git_branch: devel
git_last_commit: 34c2f12
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/oncomix_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/oncomix_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/oncomix_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/oncomix_1.29.0.tgz
vignettes: vignettes/oncomix/inst/doc/oncomix.html
vignetteTitles: OncoMix Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/oncomix/inst/doc/oncomix.R
dependencyCount: 65
Package: oncoscanR
Version: 1.9.0
Depends: R (>= 4.2), IRanges (>= 2.30.0), GenomicRanges (>= 1.48.0),
magrittr
Imports: readr, S4Vectors, methods, utils
Suggests: testthat (>= 3.1.4), jsonlite, knitr, rmarkdown, BiocStyle
License: MIT + file LICENSE
MD5sum: 73a4e5e5706d0624c02639c4d5ac0c60
NeedsCompilation: no
Title: Secondary analyses of CNV data (HRD and more)
Description: The software uses the copy number segments from a text
file and identifies all chromosome arms that are globally
altered and computes various genome-wide scores. The following
HRD scores (characteristic of BRCA-mutated cancers) are
included: LST, HR-LOH, nLST and gLOH. the package is tailored
for the ThermoFisher Oncoscan assay analyzed with their
Chromosome Alteration Suite (ChAS) but can be adapted to any
input.
biocViews: CopyNumberVariation, Microarray, Software
Author: Yann Christinat [aut, cre], Geneva University Hospitals [aut,
cph]
Maintainer: Yann Christinat <yann.christinat@hcuge.ch>
URL: https://github.com/yannchristinat/oncoscanR
VignetteBuilder: knitr
BugReports: https://github.com/yannchristinat/oncoscanR/issues
git_url: https://git.bioconductor.org/packages/oncoscanR
git_branch: devel
git_last_commit: bef9380
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/oncoscanR_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/oncoscanR_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/oncoscanR_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/oncoscanR_1.9.0.tgz
vignettes: vignettes/oncoscanR/inst/doc/oncoscanR.html
vignetteTitles: oncoscanR vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/oncoscanR/inst/doc/oncoscanR.R
dependencyCount: 48
Package: OncoScore
Version: 1.35.1
Depends: R (>= 4.1.0),
Imports: biomaRt, grDevices, graphics, utils, methods,
Suggests: BiocGenerics, BiocStyle, knitr, testthat,
License: file LICENSE
MD5sum: 142472a8d1739175610cf62c11796c06
NeedsCompilation: no
Title: A tool to identify potentially oncogenic genes
Description: OncoScore is a tool to measure the association of genes to
cancer based on citation frequencies in biomedical literature.
The score is evaluated from PubMed literature by dynamically
updatable web queries.
biocViews: BiomedicalInformatics
Author: Luca De Sano [cre, aut] (ORCID:
<https://orcid.org/0000-0002-9618-3774>), Carlo Gambacorti
Passerini [ctb], Rocco Piazza [ctb], Daniele Ramazzotti [aut]
(ORCID: <https://orcid.org/0000-0002-6087-2666>), Roberta
Spinelli [ctb]
Maintainer: Luca De Sano <luca.desano@gmail.com>
URL: https://github.com/danro9685/OncoScore
VignetteBuilder: knitr
BugReports: https://github.com/danro9685/OncoScore
git_url: https://git.bioconductor.org/packages/OncoScore
git_branch: devel
git_last_commit: 14f40b2
git_last_commit_date: 2025-03-25
Date/Publication: 2025-03-26
source.ver: src/contrib/OncoScore_1.35.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/OncoScore_1.35.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/OncoScore_1.35.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/OncoScore_1.35.1.tgz
vignettes: vignettes/OncoScore/inst/doc/v1_introduction.html,
vignettes/OncoScore/inst/doc/v2_running_OncoScore.html
vignetteTitles: Introduction, Running OncoScore
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/OncoScore/inst/doc/v1_introduction.R,
vignettes/OncoScore/inst/doc/v2_running_OncoScore.R
dependencyCount: 68
Package: OncoSimulR
Version: 4.9.1
Depends: R (>= 3.5.0)
Imports: Rcpp (>= 0.12.4), parallel, data.table, graph, Rgraphviz,
gtools, igraph, methods, RColorBrewer, grDevices, car, dplyr,
smatr, ggplot2, ggrepel, stringr
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, Oncotree, testthat (>= 1.0.0), rmarkdown,
bookdown, pander
License: GPL (>= 3)
MD5sum: 53c30206d56ce40469e4fae865e05c0c
NeedsCompilation: yes
Title: Forward Genetic Simulation of Cancer Progression with Epistasis
Description: Functions for forward population genetic simulation in
asexual populations, with special focus on cancer progression.
Fitness can be an arbitrary function of genetic interactions
between multiple genes or modules of genes, including
epistasis, order restrictions in mutation accumulation, and
order effects. Fitness (including just birth, just death, or
both birth and death) can also be a function of the relative
and absolute frequencies of other genotypes (i.e.,
frequency-dependent fitness). Mutation rates can differ between
genes, and we can include mutator/antimutator genes (to model
mutator phenotypes). Simulating multi-species scenarios and
therapeutic interventions, including adaptive therapy, is also
possible. Simulations use continuous-time models and can
include driver and passenger genes and modules. Also included
are functions for: simulating random DAGs of the type found in
Oncogenetic Trees, Conjunctive Bayesian Networks, and other
cancer progression models; plotting and sampling from single or
multiple realizations of the simulations, including single-cell
sampling; plotting the parent-child relationships of the
clones; generating random fitness landscapes (Rough Mount Fuji,
House of Cards, additive, NK, Ising, and Eggbox models) and
plotting them.
biocViews: BiologicalQuestion, SomaticMutation
Author: Ramon Diaz-Uriarte [aut, cre], Sergio Sanchez-Carrillo [aut],
Juan Antonio Miguel Gonzalez [aut], Alberto Gonzalez Klein
[aut], Javier Mu\~noz Haro [aut], Javier Lopez Cano [aut],
Niklas Endres [ctb], Mark Taylor [ctb], Arash Partow [ctb],
Sophie Brouillet [ctb], Sebastian Matuszewski [ctb], Harry
Annoni [ctb], Luca Ferretti [ctb], Guillaume Achaz [ctb],
Tymoteusz Wolodzko [ctb], Guillermo Gorines Cordero [ctb], Ivan
Lorca Alonso [ctb], Francisco Mu\~noz Lopez [ctb], David
Roncero Moro\~no [ctb], Alvaro Quevedo [ctb], Pablo Perez
[ctb], Cristina Devesa [ctb], Alejandro Herrador [ctb], Holger
Froehlich [ctb], Florian Markowetz [ctb], Achim Tresch [ctb],
Theresa Niederberger [ctb], Christian Bender [ctb], Matthias
Maneck [ctb], Claudio Lottaz [ctb], Tim Beissbarth [ctb], Sara
Dorado Alfaro [ctb], Miguel Hernandez del Valle [ctb], Alvaro
Huertas Garcia [ctb], Diego Ma\~nanes Cayero [ctb], Alejandro
Martin Mu\~noz [ctb], Marta Couce Iglesias [ctb], Silvia Garcia
Cobos [ctb], Carlos Madariaga Aramendi [ctb], Ana Rodriguez
Ronchel [ctb], Lucia Sanchez Garcia [ctb], Yolanda Benitez
Quesada [ctb], Asier Fernandez Pato [ctb], Esperanza Lopez
Lopez [ctb], Alberto Manuel Parra Perez [ctb], Jorge Garcia
Calleja [ctb], Ana del Ramo Galian [ctb], Alejandro de los
Reyes Benitez [ctb], Guillermo Garcia Hoyos [ctb], Rosalia
Palomino Cabrera [ctb], Rafael Barrero Rodriguez [ctb], Silvia
Talavera Marcos [ctb]
Maintainer: Ramon Diaz-Uriarte <rdiaz02@gmail.com>
URL: https://github.com/rdiaz02/OncoSimul,
https://popmodels.cancercontrol.cancer.gov/gsr/packages/oncosimulr/
VignetteBuilder: knitr
BugReports: https://github.com/rdiaz02/OncoSimul/issues
git_url: https://git.bioconductor.org/packages/OncoSimulR
git_branch: devel
git_last_commit: aeb944c
git_last_commit_date: 2025-03-04
Date/Publication: 2025-03-25
source.ver: src/contrib/OncoSimulR_4.9.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/OncoSimulR_4.9.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/OncoSimulR_4.9.1.tgz
vignettes: vignettes/OncoSimulR/inst/doc/OncoSimulR.html
vignetteTitles: OncoSimulR: forward genetic simulation in asexual
populations with arbitrary epistatic interactions and a focus
on modeling tumor progression.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OncoSimulR/inst/doc/OncoSimulR.R
dependencyCount: 81
Package: onlineFDR
Version: 2.15.0
Imports: stats, Rcpp, progress
LinkingTo: Rcpp, RcppProgress
Suggests: knitr, rmarkdown, testthat, covr
License: GPL-3
MD5sum: dd4b56870118f4d8731d2efb251d6191
NeedsCompilation: yes
Title: Online error rate control
Description: This package allows users to control the false discovery
rate (FDR) or familywise error rate (FWER) for online multiple
hypothesis testing, where hypotheses arrive in a stream. In
this framework, a null hypothesis is rejected based on the
evidence against it and on the previous rejection decisions.
biocViews: MultipleComparison, Software, StatisticalMethod
Author: David S. Robertson [aut, cre], Lathan Liou [aut], Aaditya
Ramdas [aut], Adel Javanmard [ctb], Andrea Montanari [ctb],
Jinjin Tian [ctb], Tijana Zrnic [ctb], Natasha A. Karp [aut]
Maintainer: David S. Robertson <david.robertson@mrc-bsu.cam.ac.uk>
URL: https://dsrobertson.github.io/onlineFDR/index.html
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/onlineFDR
git_branch: devel
git_last_commit: 4d673fd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/onlineFDR_2.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/onlineFDR_2.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/onlineFDR_2.15.0.tgz
vignettes: vignettes/onlineFDR/inst/doc/advanced-usage.html,
vignettes/onlineFDR/inst/doc/onlineFDR.html,
vignettes/onlineFDR/inst/doc/theory.html
vignetteTitles: Advanced usage of onlineFDR, Using the onlineFDR
package, The theory behind onlineFDR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/onlineFDR/inst/doc/advanced-usage.R,
vignettes/onlineFDR/inst/doc/onlineFDR.R,
vignettes/onlineFDR/inst/doc/theory.R
dependencyCount: 17
Package: ontoProc
Version: 2.1.7
Depends: R (>= 4.1.0), ontologyIndex
Imports: Biobase, S4Vectors, methods, stats, utils, BiocFileCache,
shiny, graph, Rgraphviz, ontologyPlot, dplyr, magrittr, DT,
igraph, AnnotationHub, SummarizedExperiment, reticulate,
R.utils, httr, basilisk
Suggests: knitr, org.Hs.eg.db, org.Mm.eg.db, testthat, BiocStyle,
SingleCellExperiment, celldex, rmarkdown, AnnotationDbi, magick
License: Artistic-2.0
MD5sum: 4eab2a80407a2f4fda0623a172a5c327
NeedsCompilation: no
Title: processing of ontologies of anatomy, cell lines, and so on
Description: Support harvesting of diverse bioinformatic ontologies,
making particular use of the ontologyIndex package on CRAN. We
provide snapshots of key ontologies for terms about cells, cell
lines, chemical compounds, and anatomy, to help analyze
genome-scale experiments, particularly cell x compound screens.
Another purpose is to strengthen development of compelling use
cases for richer interfaces to emerging ontologies.
biocViews: Infrastructure, GO
Author: Vincent Carey [ctb, cre] (ORCID:
<https://orcid.org/0000-0003-4046-0063>), Sara Stankiewicz
[ctb], Victor Tarca [ctb] (ORCID:
<https://orcid.org/0009-0003-1976-0392>)
Maintainer: Vincent Carey <stvjc@channing.harvard.edu>
URL: https://github.com/vjcitn/ontoProc
VignetteBuilder: knitr
BugReports: https://github.com/vjcitn/ontoProc/issues
git_url: https://git.bioconductor.org/packages/ontoProc
git_branch: devel
git_last_commit: 091f8da
git_last_commit_date: 2025-03-13
Date/Publication: 2025-03-13
source.ver: src/contrib/ontoProc_2.1.7.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ontoProc_2.1.7.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ontoProc_2.1.7.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ontoProc_2.1.7.tgz
vignettes: vignettes/ontoProc/inst/doc/ontoProc.html,
vignettes/ontoProc/inst/doc/owlents.html
vignetteTitles: ontoProc: some ontology-oriented utilites with
single-cell focus for Bioconductor, owlents: using OWL directly
in ontoProc
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ontoProc/inst/doc/ontoProc.R,
vignettes/ontoProc/inst/doc/owlents.R
dependsOnMe: SingleRBook
importsMe: pogos, tenXplore
suggestsMe: scDiffCom
dependencyCount: 119
Package: openCyto
Version: 2.19.2
Depends: R (>= 3.5.0)
Imports: methods, Biobase, BiocGenerics, flowCore(>= 1.99.17), flowViz,
ncdfFlow(>= 2.11.34), flowWorkspace(>= 3.99.1), flowClust(>=
3.11.4), RBGL, graph, data.table, RColorBrewer, grDevices
LinkingTo: cpp11, BH(>= 1.62.0-1)
Suggests: flowWorkspaceData, knitr, rmarkdown, markdown, testthat,
utils, tools, parallel, ggcyto, CytoML, flowStats(>= 4.5.2),
MASS
License: AGPL-3.0-only
MD5sum: 30698200ee99ed9135b32c1c2afebc99
NeedsCompilation: yes
Title: Hierarchical Gating Pipeline for flow cytometry data
Description: This package is designed to facilitate the automated
gating methods in sequential way to mimic the manual gating
strategy.
biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing,
DataRepresentation
Author: Mike Jiang, John Ramey, Greg Finak, Raphael Gottardo
Maintainer: Mike Jiang <mike@ozette.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/openCyto
git_branch: devel
git_last_commit: 1968c83
git_last_commit_date: 2025-03-26
Date/Publication: 2025-03-27
source.ver: src/contrib/openCyto_2.19.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/openCyto_2.19.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/openCyto_2.19.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/openCyto_2.19.2.tgz
vignettes: vignettes/openCyto/inst/doc/HowToAutoGating.html,
vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.html,
vignettes/openCyto/inst/doc/openCytoVignette.html
vignetteTitles: How to use different auto gating functions, How to
write a csv gating template, An Introduction to the openCyto
package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/openCyto/inst/doc/HowToAutoGating.R,
vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.R,
vignettes/openCyto/inst/doc/openCytoVignette.R
importsMe: CytoML
suggestsMe: CATALYST, flowClust, flowCore, flowStats, flowTime,
flowWorkspace, ggcyto
dependencyCount: 78
Package: openPrimeR
Version: 1.29.0
Depends: R (>= 4.0.0)
Imports: Biostrings (>= 2.38.4), pwalign, XML (>= 3.98-1.4), scales (>=
0.4.0), reshape2 (>= 1.4.1), seqinr (>= 3.3-3), IRanges (>=
2.4.8), GenomicRanges (>= 1.22.4), ggplot2 (>= 2.1.0), plyr (>=
1.8.4), dplyr (>= 0.5.0), stringdist (>= 0.9.4.1), stringr (>=
1.0.0), RColorBrewer (>= 1.1-2), DECIPHER (>= 1.16.1),
lpSolveAPI (>= 5.5.2.0-17), digest (>= 0.6.9), Hmisc (>=
3.17-4), ape (>= 3.5), BiocGenerics (>= 0.16.1), S4Vectors (>=
0.8.11), foreach (>= 1.4.3), magrittr (>= 1.5), uniqtag (>=
1.0), openxlsx (>= 4.0.17), grid (>= 3.1.0), grDevices (>=
3.1.0), stats (>= 3.1.0), utils (>= 3.1.0), methods (>= 3.1.0)
Suggests: testthat (>= 1.0.2), knitr (>= 1.13), rmarkdown (>= 1.0),
devtools (>= 1.12.0), doParallel (>= 1.0.10), pander (>=
0.6.0), learnr (>= 0.9)
License: GPL-2
MD5sum: 14375d1dd4c8ddafdf2775398c0e254b
NeedsCompilation: no
Title: Multiplex PCR Primer Design and Analysis
Description: An implementation of methods for designing, evaluating,
and comparing primer sets for multiplex PCR. Primers are
designed by solving a set cover problem such that the number of
covered template sequences is maximized with the smallest
possible set of primers. To guarantee that high-quality primers
are generated, only primers fulfilling constraints on their
physicochemical properties are selected. A Shiny app providing
a user interface for the functionalities of this package is
provided by the 'openPrimeRui' package.
biocViews: Software, Technology, Coverage, MultipleComparison
Author: Matthias Döring [aut, cre], Nico Pfeifer [aut]
Maintainer: Matthias Döring <matthias-doering@gmx.de>
SystemRequirements: MAFFT (>= 7.305), OligoArrayAux (>= 3.8), ViennaRNA
(>= 2.4.1), MELTING (>= 5.1.1), Pandoc (>= 1.12.3)
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/openPrimeR
git_branch: devel
git_last_commit: 74182ac
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/openPrimeR_1.29.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/openPrimeR_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/openPrimeR_1.29.0.tgz
vignettes: vignettes/openPrimeR/inst/doc/openPrimeR_vignette.html
vignetteTitles: openPrimeR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/openPrimeR/inst/doc/openPrimeR_vignette.R
dependencyCount: 115
Package: OpenStats
Version: 1.19.1
Depends: nlme
Imports: MASS, jsonlite, Hmisc, methods, knitr, AICcmodavg, car, rlist,
summarytools, graphics, stats, utils
Suggests: rmarkdown
License: GPL (>= 2)
Archs: x64
MD5sum: be22256f8ac9a2d6559b1a3a969b4512
NeedsCompilation: no
Title: A Robust and Scalable Software Package for Reproducible Analysis
of High-Throughput genotype-phenotype association
Description: Package contains several methods for statistical analysis
of genotype to phenotype association in high-throughput
screening pipelines.
biocViews: StatisticalMethod, BatchEffect, Bayesian
Author: Hamed Haseli Mashhadi
Maintainer: Marina Kan <marinak@ebi.ac.uk>
URL: https://git.io/Jv5w0
VignetteBuilder: knitr
BugReports: https://git.io/Jv5wg
git_url: https://git.bioconductor.org/packages/OpenStats
git_branch: devel
git_last_commit: 4083881
git_last_commit_date: 2024-11-29
Date/Publication: 2024-11-29
source.ver: src/contrib/OpenStats_1.19.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/OpenStats_1.19.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/OpenStats_1.19.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/OpenStats_1.19.1.tgz
vignettes: vignettes/OpenStats/inst/doc/OpenStats.html
vignetteTitles: OpenStats
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OpenStats/inst/doc/OpenStats.R
dependencyCount: 129
Package: oposSOM
Version: 2.25.0
Depends: R (>= 4.0.0), igraph (>= 1.0.0)
Imports: fastICA, tsne, scatterplot3d, pixmap, fdrtool, ape, biomaRt,
Biobase, RcppParallel, Rcpp, methods, graph, XML, png, RCurl
LinkingTo: RcppParallel, Rcpp
License: GPL (>=2)
Archs: x64
MD5sum: 93a1d4b0056b2feb6f34a41c140e6272
NeedsCompilation: yes
Title: Comprehensive analysis of transcriptome data
Description: This package translates microarray expression data into
metadata of reduced dimension. It provides various
sample-centered and group-centered visualizations, sample
similarity analyses and functional enrichment analyses. The
underlying SOM algorithm combines feature clustering,
multidimensional scaling and dimension reduction, along with
strong visualization capabilities. It enables extraction and
description of functional expression modules inherent in the
data.
biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment,
DataRepresentation, Visualization
Author: Henry Loeffler-Wirth <wirth@izbi.uni-leipzig.de>, Hoang Thanh
Le <le@izbi.uni-leipzig.de> and Martin Kalcher
<mkalcher@porkbox.net>
Maintainer: Henry Loeffler-Wirth <wirth@izbi.uni-leipzig.de>
URL: http://som.izbi.uni-leipzig.de
git_url: https://git.bioconductor.org/packages/oposSOM
git_branch: devel
git_last_commit: a26ada9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/oposSOM_2.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/oposSOM_2.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/oposSOM_2.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/oposSOM_2.25.0.tgz
vignettes: vignettes/oposSOM/inst/doc/Vignette.pdf
vignetteTitles: The oposSOM users guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/oposSOM/inst/doc/Vignette.R
dependencyCount: 86
Package: oppar
Version: 1.35.0
Depends: R (>= 3.3)
Imports: Biobase, methods, GSEABase, GSVA
Suggests: knitr, rmarkdown, limma, org.Hs.eg.db, GO.db, snow, parallel
License: GPL-2
MD5sum: 356013bd608cd54e83058297509c6ecd
NeedsCompilation: yes
Title: Outlier profile and pathway analysis in R
Description: The R implementation of mCOPA package published by Wang et
al. (2012). Oppar provides methods for Cancer Outlier profile
Analysis. Although initially developed to detect outlier genes
in cancer studies, methods presented in oppar can be used for
outlier profile analysis in general. In addition, tools are
provided for gene set enrichment and pathway analysis.
biocViews: Pathways, GeneSetEnrichment, SystemsBiology, GeneExpression,
Software
Author: Chenwei Wang [aut], Alperen Taciroglu [aut], Stefan R Maetschke
[aut], Colleen C Nelson [aut], Mark Ragan [aut], Melissa Davis
[aut], Soroor Hediyeh zadeh [cre], Momeneh Foroutan [ctr]
Maintainer: Soroor Hediyeh zadeh <hediyehzadeh.s@wehi.edu.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/oppar
git_branch: devel
git_last_commit: f758652
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/oppar_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/oppar_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/oppar_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/oppar_1.35.0.tgz
vignettes: vignettes/oppar/inst/doc/oppar.html
vignetteTitles: OPPAR: Outlier Profile and Pathway Analysis in R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/oppar/inst/doc/oppar.R
dependencyCount: 104
Package: oppti
Version: 1.21.0
Depends: R (>= 3.5)
Imports: limma, stats, reshape, ggplot2, grDevices, RColorBrewer,
pheatmap, knitr, methods, devtools, parallelDist,
Suggests: markdown
License: MIT
MD5sum: b8803010bb70e46442aef7bfb14e3d1a
NeedsCompilation: no
Title: Outlier Protein and Phosphosite Target Identifier
Description: The aim of oppti is to analyze protein (and phosphosite)
expressions to find outlying markers for each sample in the
given cohort(s) for the discovery of personalized actionable
targets.
biocViews: Proteomics, Regression, DifferentialExpression,
BiomedicalInformatics, GeneTarget, GeneExpression, Network
Author: Abdulkadir Elmas
Maintainer: Abdulkadir Elmas <abdulkadir.elmas@mssm.edu>
URL: https://github.com/Huang-lab/oppti
VignetteBuilder: knitr
BugReports: https://github.com/Huang-lab/oppti/issues
git_url: https://git.bioconductor.org/packages/oppti
git_branch: devel
git_last_commit: 1c8e74d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/oppti_1.21.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/oppti_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/oppti_1.21.0.tgz
vignettes: vignettes/oppti/inst/doc/analysis.html
vignetteTitles: Outlier Protein and Phosphosite Target Identifier
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/oppti/inst/doc/analysis.R
dependencyCount: 125
Package: optimalFlow
Version: 1.19.0
Depends: dplyr, optimalFlowData, rlang (>= 0.4.0)
Imports: transport, parallel, Rfast, robustbase, dbscan, randomForest,
foreach, graphics, doParallel, stats, flowMeans, rgl, ellipse
Suggests: knitr, BiocStyle, rmarkdown, magick
License: Artistic-2.0
Archs: x64
MD5sum: 14e9c96630d7057a9c5586169d620834
NeedsCompilation: no
Title: optimalFlow
Description: Optimal-transport techniques applied to supervised flow
cytometry gating.
biocViews: Software, FlowCytometry, Technology
Author: Hristo Inouzhe <hristo.inouzhe@gmail.com>
Maintainer: Hristo Inouzhe <hristo.inouzhe@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/optimalFlow
git_branch: devel
git_last_commit: 3518f15
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/optimalFlow_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/optimalFlow_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/optimalFlow_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/optimalFlow_1.19.0.tgz
vignettes: vignettes/optimalFlow/inst/doc/optimalFlow_vignette.html
vignetteTitles: optimalFlow: optimal-transport approach to Flow
Cytometry analysis
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/optimalFlow/inst/doc/optimalFlow_vignette.R
dependencyCount: 96
Package: OPWeight
Version: 1.29.0
Depends: R (>= 3.4.0),
Imports: graphics, qvalue, MASS, tibble, stats,
Suggests: airway, BiocStyle, cowplot, DESeq2, devtools, ggplot2,
gridExtra, knitr, Matrix, rmarkdown, scales, testthat
License: Artistic-2.0
MD5sum: b69c46c788876529773682b2bdd88f52
NeedsCompilation: no
Title: Optimal p-value weighting with independent information
Description: This package perform weighted-pvalue based multiple
hypothesis test and provides corresponding information such as
ranking probability, weight, significant tests, etc . To
conduct this testing procedure, the testing method apply a
probabilistic relationship between the test rank and the
corresponding test effect size.
biocViews: ImmunoOncology, BiomedicalInformatics, MultipleComparison,
Regression, RNASeq, SNP
Author: Mohamad Hasan [aut, cre], Paul Schliekelman [aut]
Maintainer: Mohamad Hasan <shakilmohamad7@gmail.com>
URL: https://github.com/mshasan/OPWeight
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/OPWeight
git_branch: devel
git_last_commit: 1f5f472
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/OPWeight_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/OPWeight_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/OPWeight_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/OPWeight_1.29.0.tgz
vignettes: vignettes/OPWeight/inst/doc/OPWeight.html
vignetteTitles: "Introduction to OPWeight"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OPWeight/inst/doc/OPWeight.R
dependencyCount: 42
Package: OrderedList
Version: 1.79.0
Depends: R (>= 3.6.1), Biobase, twilight
Imports: methods
License: GPL (>= 2)
Archs: x64
MD5sum: c5b12b717a1773d869b1d4c8075cb2da
NeedsCompilation: no
Title: Similarities of Ordered Gene Lists
Description: Detection of similarities between ordered lists of genes.
Thereby, either simple lists can be compared or gene expression
data can be used to deduce the lists. Significance of
similarities is evaluated by shuffling lists or by resampling
in microarray data, respectively.
biocViews: Microarray, DifferentialExpression, MultipleComparison
Author: Xinan Yang, Stefanie Scheid, Claudio Lottaz
Maintainer: Claudio Lottaz <Claudio.Lottaz@klinik.uni-regensburg.de>
URL: http://compdiag.molgen.mpg.de/software/OrderedList.shtml
git_url: https://git.bioconductor.org/packages/OrderedList
git_branch: devel
git_last_commit: febc9a9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/OrderedList_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/OrderedList_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/OrderedList_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/OrderedList_1.79.0.tgz
vignettes: vignettes/OrderedList/inst/doc/tr_2006_01.pdf
vignetteTitles: Similarities of Ordered Gene Lists
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OrderedList/inst/doc/tr_2006_01.R
dependencyCount: 10
Package: ORFhunteR
Version: 1.15.0
Depends: Biostrings, rtracklayer, Peptides
Imports: Rcpp (>= 1.0.3), BSgenome.Hsapiens.UCSC.hg38, data.table,
stringr, randomForest, xfun, stats, utils, parallel, graphics
LinkingTo: Rcpp
Suggests: knitr, BiocStyle, rmarkdown
License: MIT License
Archs: x64
MD5sum: 8834a241709a6322d9aa5ea77b9f68d3
NeedsCompilation: yes
Title: Predict open reading frames in nucleotide sequences
Description: The ORFhunteR package is a R and C++ library for an
automatic determination and annotation of open reading frames
(ORF) in a large set of RNA molecules. It efficiently
implements the machine learning model based on vectorization of
nucleotide sequences and the random forest classification
algorithm. The ORFhunteR package consists of a set of functions
written in the R language in conjunction with C++. The
efficiency of the package was confirmed by the examples of the
analysis of RNA molecules from the NCBI RefSeq and Ensembl
databases. The package can be used in basic and applied
biomedical research related to the study of the transcriptome
of normal as well as altered (for example, cancer) human cells.
biocViews: Technology, StatisticalMethod, Sequencing, RNASeq,
Classification, FeatureExtraction
Author: Vasily V. Grinev [aut, cre] (ORCID:
<https://orcid.org/0000-0001-9981-7333>), Mikalai M. Yatskou
[aut], Victor V. Skakun [aut], Maryna Chepeleva [aut] (ORCID:
<https://orcid.org/0000-0003-3036-4916>), Petr V. Nazarov [aut]
(ORCID: <https://orcid.org/0000-0003-3443-0298>)
Maintainer: Vasily V. Grinev <grinev_vv@bsu.by>
VignetteBuilder: knitr
BugReports: https://github.com/rfctbio-bsu/ORFhunteR/issues
git_url: https://git.bioconductor.org/packages/ORFhunteR
git_branch: devel
git_last_commit: 7fcb3ee
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ORFhunteR_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ORFhunteR_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ORFhunteR_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ORFhunteR_1.15.0.tgz
vignettes: vignettes/ORFhunteR/inst/doc/ORFhunteR.html
vignetteTitles: The ORFhunteR package: User’s manual
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ORFhunteR/inst/doc/ORFhunteR.R
dependencyCount: 73
Package: ORFik
Version: 1.27.3
Depends: R (>= 4.1.0), IRanges (>= 2.17.1), GenomicRanges (>= 1.35.1),
GenomicAlignments (>= 1.19.0)
Imports: AnnotationDbi (>= 1.45.0), Biostrings (>= 2.51.1), biomaRt,
biomartr (>= 1.0.7), BiocFileCache, BiocGenerics (>= 0.29.1),
BiocParallel (>= 1.19.0), BSgenome, cowplot (>= 1.0.0),
data.table (>= 1.11.8), DESeq2 (>= 1.24.0), fst (>= 0.9.2),
GenomeInfoDb (>= 1.15.5), GenomicFeatures (>= 1.31.10), ggplot2
(>= 2.2.1), gridExtra (>= 2.3), httr (>= 1.3.0), jsonlite,
methods (>= 3.6.0), R.utils, Rcpp (>= 1.0.0), Rsamtools (>=
1.35.0), rtracklayer (>= 1.43.0), stats, SummarizedExperiment
(>= 1.14.0), S4Vectors (>= 0.21.3), tools, txdbmaker, utils,
XML, xml2 (>= 1.2.0), withr
LinkingTo: Rcpp
Suggests: testthat, rmarkdown, knitr, BiocStyle,
BSgenome.Hsapiens.UCSC.hg19
License: MIT + file LICENSE
MD5sum: a58e22af9aa88455b0fe3c8ab4de97a4
NeedsCompilation: yes
Title: Open Reading Frames in Genomics
Description: R package for analysis of transcript and translation
features through manipulation of sequence data and NGS data
like Ribo-Seq, RNA-Seq, TCP-Seq and CAGE. It is generalized in
the sense that any transcript region can be analysed, as the
name hints to it was made with investigation of ribosomal
patterns over Open Reading Frames (ORFs) as it's primary use
case. ORFik is extremely fast through use of C++, data.table
and GenomicRanges. Package allows to reassign starts of the
transcripts with the use of CAGE-Seq data, automatic shifting
of RiboSeq reads, finding of Open Reading Frames for whole
genomes and much more.
biocViews: ImmunoOncology, Software, Sequencing, RiboSeq, RNASeq,
FunctionalGenomics, Coverage, Alignment, DataImport
Author: Haakon Tjeldnes [aut, cre, dtc], Kornel Labun [aut, cph],
Michal Swirski [ctb], Katarzyna Chyzynska [ctb, dtc], Yamila
Torres Cleuren [ctb, ths], Eivind Valen [ths, fnd]
Maintainer: Haakon Tjeldnes <hauken_heyken@hotmail.com>
URL: https://github.com/Roleren/ORFik
VignetteBuilder: knitr
BugReports: https://github.com/Roleren/ORFik/issues
git_url: https://git.bioconductor.org/packages/ORFik
git_branch: devel
git_last_commit: 0a2b85a
git_last_commit_date: 2025-02-17
Date/Publication: 2025-02-17
source.ver: src/contrib/ORFik_1.27.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ORFik_1.27.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ORFik_1.27.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ORFik_1.27.3.tgz
vignettes: vignettes/ORFik/inst/doc/Annotation_Alignment.html,
vignettes/ORFik/inst/doc/Importing_Data.html,
vignettes/ORFik/inst/doc/ORFikExperiment.html,
vignettes/ORFik/inst/doc/ORFikOverview.html,
vignettes/ORFik/inst/doc/Ribo-seq_pipeline.html,
vignettes/ORFik/inst/doc/Working_with_transcripts.html
vignetteTitles: Annotation & Alignment, Importing data, Data
management, ORFik Overview, Ribo-seq pipeline, Working with
transcripts
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ORFik/inst/doc/Annotation_Alignment.R,
vignettes/ORFik/inst/doc/Importing_Data.R,
vignettes/ORFik/inst/doc/ORFikExperiment.R,
vignettes/ORFik/inst/doc/ORFikOverview.R,
vignettes/ORFik/inst/doc/Ribo-seq_pipeline.R,
vignettes/ORFik/inst/doc/Working_with_transcripts.R
dependsOnMe: RiboCrypt
importsMe: TFHAZ
dependencyCount: 138
Package: Organism.dplyr
Version: 1.35.1
Depends: R (>= 4.1.0), dplyr (>= 0.7.0), AnnotationFilter (>= 1.1.3)
Imports: RSQLite, S4Vectors, GenomeInfoDb, IRanges, GenomicRanges,
GenomicFeatures, AnnotationDbi, rlang, methods, tools, utils,
BiocFileCache, DBI, dbplyr, tibble
Suggests: org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene,
org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.ensGene, testthat,
knitr, rmarkdown, magick, BiocStyle, ggplot2
License: Artistic-2.0
MD5sum: 8aa564b768ada0fd928fa33f7b9de8a1
NeedsCompilation: no
Title: dplyr-based Access to Bioconductor Annotation Resources
Description: This package provides an alternative interface to
Bioconductor 'annotation' resources, in particular the gene
identifier mapping functionality of the 'org' packages (e.g.,
org.Hs.eg.db) and the genome coordinate functionality of the
'TxDb' packages (e.g., TxDb.Hsapiens.UCSC.hg38.knownGene).
biocViews: Annotation, Sequencing, GenomeAnnotation
Author: Martin Morgan [aut, cre], Daniel van Twisk [ctb], Yubo Cheng
[aut]
Maintainer: Martin Morgan <martin.morgan@roswellpark.org>
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/Organism.dplyr/issues
git_url: https://git.bioconductor.org/packages/Organism.dplyr
git_branch: devel
git_last_commit: 0b66960
git_last_commit_date: 2025-03-21
Date/Publication: 2025-03-23
source.ver: src/contrib/Organism.dplyr_1.35.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Organism.dplyr_1.35.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Organism.dplyr_1.35.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Organism.dplyr_1.35.1.tgz
vignettes: vignettes/Organism.dplyr/inst/doc/Organism.dplyr.html
vignetteTitles: Organism.dplyr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Organism.dplyr/inst/doc/Organism.dplyr.R
importsMe: Ularcirc
dependencyCount: 94
Package: OrganismDbi
Version: 1.49.0
Depends: R (>= 2.14.0), BiocGenerics (>= 0.15.10), AnnotationDbi (>=
1.33.15), GenomicFeatures (>= 1.39.4)
Imports: methods, utils, stats, DBI, BiocManager, Biobase, graph, RBGL,
S4Vectors, IRanges, GenomicRanges (>= 1.31.13), txdbmaker
Suggests: Homo.sapiens, Rattus.norvegicus, BSgenome.Hsapiens.UCSC.hg19,
AnnotationHub, FDb.UCSC.tRNAs, mirbase.db, rtracklayer,
biomaRt, RUnit, RMariaDB, BiocStyle, knitr
License: Artistic-2.0
MD5sum: 4aa38d81398e5a18a5ed24cf8fa2c833
NeedsCompilation: no
Title: Software to enable the smooth interfacing of different database
packages
Description: The package enables a simple unified interface to several
annotation packages each of which has its own schema by taking
advantage of the fact that each of these packages implements a
select methods.
biocViews: Annotation, Infrastructure
Author: Marc Carlson [aut], Martin Morgan [aut], Valerie Obenchain
[aut], Aliyu Atiku Mustapha [ctb] (Converted 'OrganismDbi'
vignette from Sweave to RMarkdown / HTML.), Bioconductor
Package Maintainer [cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/OrganismDbi
git_branch: devel
git_last_commit: 223f33d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/OrganismDbi_1.49.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/OrganismDbi_1.49.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/OrganismDbi_1.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/OrganismDbi_1.49.0.tgz
vignettes: vignettes/OrganismDbi/inst/doc/OrganismDbi.html
vignetteTitles: OrganismDbi: A meta framework for Annotation Packages
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OrganismDbi/inst/doc/OrganismDbi.R
dependsOnMe: Homo.sapiens, Mus.musculus, Rattus.norvegicus
importsMe: AnnotationHubData, epivizrData, ggbio, uncoverappLib
suggestsMe: ChIPpeakAnno, epivizrStandalone
dependencyCount: 104
Package: orthogene
Version: 1.13.0
Depends: R (>= 4.1)
Imports: dplyr, methods, stats, utils, Matrix, jsonlite, homologene,
gprofiler2, babelgene, data.table, parallel, ggplot2, ggpubr,
patchwork, DelayedArray, grr, repmis, ggtree, tools
Suggests: rworkflows, remotes, knitr, BiocStyle, markdown, rmarkdown,
testthat (>= 3.0.0), piggyback, magick, GenomeInfoDbData, ape,
phytools, rphylopic (>= 1.0.0), TreeTools, ggimage, OmaDB
License: GPL-3
Archs: x64
MD5sum: a71963eb973cb0358dee15502cd1be41
NeedsCompilation: no
Title: Interspecies gene mapping
Description: `orthogene` is an R package for easy mapping of
orthologous genes across hundreds of species. It pulls
up-to-date gene ortholog mappings across **700+ organisms**. It
also provides various utility functions to aggregate/expand
common objects (e.g. data.frames, gene expression matrices,
lists) using **1:1**, **many:1**, **1:many** or **many:many**
gene mappings, both within- and between-species.
biocViews: Genetics, ComparativeGenomics, Preprocessing, Phylogenetics,
Transcriptomics, GeneExpression
Author: Brian Schilder [cre] (ORCID:
<https://orcid.org/0000-0001-5949-2191>)
Maintainer: Brian Schilder <brian_schilder@alumni.brown.edu>
URL: https://github.com/neurogenomics/orthogene
VignetteBuilder: knitr
BugReports: https://github.com/neurogenomics/orthogene/issues
git_url: https://git.bioconductor.org/packages/orthogene
git_branch: devel
git_last_commit: cd1c983
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/orthogene_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/orthogene_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/orthogene_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/orthogene_1.13.0.tgz
vignettes: vignettes/orthogene/inst/doc/docker.html,
vignettes/orthogene/inst/doc/infer_species.html,
vignettes/orthogene/inst/doc/orthogene.html
vignetteTitles: docker, Infer species, orthogene
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/orthogene/inst/doc/docker.R,
vignettes/orthogene/inst/doc/infer_species.R,
vignettes/orthogene/inst/doc/orthogene.R
importsMe: BulkSignalR, EWCE
dependencyCount: 155
Package: orthos
Version: 1.5.1
Depends: R (>= 4.3), SummarizedExperiment
Imports: AnnotationHub, basilisk, BiocParallel, colorspace, cowplot,
DelayedArray, dplyr, ExperimentHub, ggplot2, ggpubr, ggrepel,
ggsci, grDevices, grid, HDF5Array, keras, methods, orthosData,
parallel, plyr, reticulate, rlang, S4Vectors, stats,
tensorflow, tidyr
Suggests: BiocManager, BiocStyle, htmltools, knitr, rmarkdown, testthat
(>= 3.0.0)
License: MIT + file LICENSE
Archs: x64
MD5sum: 04dcd3a180fad766c6edc2f2d6b161ed
NeedsCompilation: no
Title: `orthos` is an R package for variance decomposition using
conditional variational auto-encoders
Description: `orthos` decomposes RNA-seq contrasts, for example
obtained from a gene knock-out or compound treatment
experiment, into unspecific and experiment-specific components.
Original and decomposed contrasts can be efficiently queried
against a large database of contrasts (derived from ARCHS4,
https://maayanlab.cloud/archs4/) to identify similar
experiments. `orthos` furthermore provides plotting functions
to visualize the results of such a search for similar
contrasts.
biocViews: RNASeq, DifferentialExpression, GeneExpression
Author: Panagiotis Papasaikas [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1640-7636>), Charlotte Soneson
[aut] (ORCID: <https://orcid.org/0000-0003-3833-2169>), Michael
Stadler [aut] (ORCID: <https://orcid.org/0000-0002-2269-4934>),
Friedrich Miescher Institute for Biomedical Research [cph]
Maintainer: Panagiotis Papasaikas <panagiotis.papasaikas@fmi.ch>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/orthos
git_branch: devel
git_last_commit: cbc0f6e
git_last_commit_date: 2025-03-12
Date/Publication: 2025-03-12
source.ver: src/contrib/orthos_1.5.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/orthos_1.5.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/orthos_1.5.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/orthos_1.5.1.tgz
vignettes: vignettes/orthos/inst/doc/orthosIntro.html
vignetteTitles: 1. Introduction to orthos
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/orthos/inst/doc/orthosIntro.R
dependencyCount: 159
Package: OSAT
Version: 1.55.0
Depends: methods,stats
Suggests: xtable, Biobase
License: Artistic-2.0
MD5sum: 636c6226ebcfb56bd45541e7b6b6d81c
NeedsCompilation: no
Title: OSAT: Optimal Sample Assignment Tool
Description: A sizable genomics study such as microarray often involves
the use of multiple batches (groups) of experiment due to
practical complication. To minimize batch effects, a careful
experiment design should ensure the even distribution of
biological groups and confounding factors across batches. OSAT
(Optimal Sample Assignment Tool) is developed to facilitate the
allocation of collected samples to different batches. With
minimum steps, it produces setup that optimizes the even
distribution of samples in groups of biological interest into
different batches, reducing the confounding or correlation
between batches and the biological variables of interest. It
can also optimize the even distribution of confounding factors
across batches. Our tool can handle challenging instances where
incomplete and unbalanced sample collections are involved as
well as ideal balanced RCBD. OSAT provides a number of
predefined layout for some of the most commonly used genomics
platform. Related paper can be find at
http://www.biomedcentral.com/1471-2164/13/689 .
biocViews: DataRepresentation, Visualization, ExperimentalDesign,
QualityControl
Author: Li Yan
Maintainer: Li Yan <li.yan@roswellpark.org>
URL: http://www.biomedcentral.com/1471-2164/13/689
git_url: https://git.bioconductor.org/packages/OSAT
git_branch: devel
git_last_commit: 77f07d3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/OSAT_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/OSAT_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/OSAT_1.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/OSAT_1.55.0.tgz
vignettes: vignettes/OSAT/inst/doc/OSAT.pdf
vignetteTitles: An introduction to OSAT
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OSAT/inst/doc/OSAT.R
suggestsMe: designit
dependencyCount: 2
Package: Oscope
Version: 1.37.0
Depends: EBSeq, cluster, testthat, BiocParallel
Suggests: BiocStyle
License: Artistic-2.0
MD5sum: 0c7dacec4fbdf82d5870e4747f5667f0
NeedsCompilation: no
Title: Oscope - A statistical pipeline for identifying oscillatory
genes in unsynchronized single cell RNA-seq
Description: Oscope is a statistical pipeline developed to identifying
and recovering the base cycle profiles of oscillating genes in
an unsynchronized single cell RNA-seq experiment. The Oscope
pipeline includes three modules: a sine model module to search
for candidate oscillator pairs; a K-medoids clustering module
to cluster candidate oscillators into groups; and an extended
nearest insertion module to recover the base cycle order for
each oscillator group.
biocViews: ImmunoOncology, StatisticalMethod,RNASeq, Sequencing,
GeneExpression
Author: Ning Leng
Maintainer: Ning Leng <lengning1@gmail.com>
git_url: https://git.bioconductor.org/packages/Oscope
git_branch: devel
git_last_commit: 4ad579a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Oscope_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Oscope_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Oscope_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Oscope_1.37.0.tgz
vignettes: vignettes/Oscope/inst/doc/Oscope_vignette.pdf
vignetteTitles: Oscope_vigette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Oscope/inst/doc/Oscope_vignette.R
importsMe: scDDboost
dependencyCount: 53
Package: OSTA.data
Version: 0.99.2
Depends: R (>= 4.5)
Imports: osfr, utils, BiocFileCache
Suggests: BiocStyle, DropletUtils, knitr, VisiumIO, SpatialExperimentIO
License: Artistic-2.0
MD5sum: b224524919efe2848395fdfb36e5eaaf
NeedsCompilation: no
Title: OSTA book data
Description: 'OSTA.data' is a companion package for the "Orchestrating
Spatial Transcriptomics Analysis" (OSTA) with Bioconductor
online book. Throughout OSTA, we rely on a set of publicly
available datasets that cover different sequencing- and
imaging-based platforms, such as Visium, Visium HD, Xenium (10x
Genomics) and CosMx (NanoString). In addition, we rely on
scRNA-seq (Chromium) data for tasks, e.g., spot deconvolution
and label transfer (i.e., supervised clustering). These data
been deposited in an Open Storage Framework (OSF) repository,
and can be queried and downloaded using functions from the
'osfr' package. For convenience, we have implemented
'OSTA.data' to query and retrieve data from our OSF node, and
cache retrieved Zip archives using 'BiocFileCache'.
biocViews: DataImport, DataRepresentation, ExperimentHubSoftware,
Infrastructure, ImmunoOncology, GeneExpression,
Transcriptomics, SingleCell, Spatial
Author: Yixing E. Dong [aut, cre] (ORCID:
<https://orcid.org/0009-0003-5115-5686>), Helena L. Crowell
[aut] (ORCID: <https://orcid.org/0000-0002-4801-1767>), Vince
Carey [aut] (ORCID: <https://orcid.org/0000-0003-4046-0063>)
Maintainer: Yixing E. Dong <estelladong729@gmail.com>
URL: https://github.com/estellad/OSTA.data
VignetteBuilder: knitr
BugReports: https://github.com/estellad/OSTA.data
git_url: https://git.bioconductor.org/packages/OSTA.data
git_branch: devel
git_last_commit: d82e4e6
git_last_commit_date: 2025-02-25
Date/Publication: 2025-03-10
source.ver: src/contrib/OSTA.data_0.99.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/OSTA.data_0.99.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/OSTA.data_0.99.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/OSTA.data_0.99.2.tgz
vignettes: vignettes/OSTA.data/inst/doc/OSTA.data.html
vignetteTitles: OSTA.data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OSTA.data/inst/doc/OSTA.data.R
dependencyCount: 53
Package: OTUbase
Version: 1.57.0
Depends: R (>= 2.9.0), methods, S4Vectors, IRanges, ShortRead (>=
1.23.15), Biobase, vegan
Imports: Biostrings
License: Artistic-2.0
MD5sum: ba08a023ed03b520f05579ce106622c4
NeedsCompilation: no
Title: Provides structure and functions for the analysis of OTU data
Description: Provides a platform for Operational Taxonomic Unit based
analysis
biocViews: Sequencing, DataImport
Author: Daniel Beck, Matt Settles, and James A. Foster
Maintainer: Daniel Beck <danlbek@gmail.com>
git_url: https://git.bioconductor.org/packages/OTUbase
git_branch: devel
git_last_commit: 4de6774
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/OTUbase_1.57.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/OTUbase_1.57.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/OTUbase_1.57.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/OTUbase_1.57.0.tgz
vignettes: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.pdf
vignetteTitles: An introduction to OTUbase
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.R
dependencyCount: 69
Package: OUTRIDER
Version: 1.25.0
Depends: R (>= 3.6), BiocParallel, GenomicFeatures,
SummarizedExperiment, data.table, methods
Imports: BBmisc, BiocGenerics, DESeq2 (>= 1.16.1), generics,
GenomicRanges, ggplot2, ggrepel, grDevices, heatmaply,
pheatmap, graphics, IRanges, matrixStats, plotly, plyr,
pcaMethods, PRROC, RColorBrewer, reshape2, S4Vectors, scales,
splines, stats, txdbmaker, utils
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, knitr, rmarkdown, BiocStyle,
TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, RMariaDB,
AnnotationDbi, beeswarm, covr, GenomeInfoDb, ggbio, biovizBase
License: MIT + file LICENSE
MD5sum: d2bd205b414f05b962cb4e8d4e155d67
NeedsCompilation: yes
Title: OUTRIDER - OUTlier in RNA-Seq fInDER
Description: Identification of aberrant gene expression in RNA-seq
data. Read count expectations are modeled by an autoencoder to
control for confounders in the data. Given these expectations,
the RNA-seq read counts are assumed to follow a negative
binomial distribution with a gene-specific dispersion. Outliers
are then identified as read counts that significantly deviate
from this distribution. Furthermore, OUTRIDER provides useful
plotting functions to analyze and visualize the results.
biocViews: ImmunoOncology, RNASeq, Transcriptomics, Alignment,
Sequencing, GeneExpression, Genetics
Author: Felix Brechtmann [aut], Christian Mertes [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1091-205X>), Agne Matuseviciute
[aut], Michaela Fee Müller [ctb], Vicente Yepez [aut], Julien
Gagneur [aut]
Maintainer: Christian Mertes <mertes@in.tum.de>
URL: https://github.com/gagneurlab/OUTRIDER
VignetteBuilder: knitr
BugReports: https://github.com/gagneurlab/OUTRIDER/issues
git_url: https://git.bioconductor.org/packages/OUTRIDER
git_branch: devel
git_last_commit: e21b7d6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/OUTRIDER_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/OUTRIDER_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/OUTRIDER_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/OUTRIDER_1.25.0.tgz
vignettes: vignettes/OUTRIDER/inst/doc/OUTRIDER.pdf
vignetteTitles: OUTRIDER: OUTlier in RNA-seq fInDER
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/OUTRIDER/inst/doc/OUTRIDER.R
importsMe: FRASER
dependencyCount: 170
Package: OutSplice
Version: 1.7.1
Depends: R(>= 4.3)
Imports: AnnotationDbi (>= 1.60.0), GenomicRanges (>= 1.49.0),
GenomicFeatures (>= 1.50.2), IRanges (>= 2.32.0), org.Hs.eg.db
(>= 3.16.0), TxDb.Hsapiens.UCSC.hg19.knownGene (>= 3.2.2),
TxDb.Hsapiens.UCSC.hg38.knownGene (>= 3.16.0), S4Vectors (>=
0.36.0)
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: GPL-2
MD5sum: 286ed9c775a018d8030015d16b859e5a
NeedsCompilation: no
Title: Comparison of Splicing Events between Tumor and Normal Samples
Description: An easy to use tool that can compare splicing events in
tumor and normal tissue samples using either a user generated
matrix, or data from The Cancer Genome Atlas (TCGA). This
package generates a matrix of splicing outliers that are
significantly over or underexpressed in tumors samples compared
to normal denoted by chromosome location. The package also will
calculate the splicing burden in each tumor and characterize
the types of splicing events that occur.
biocViews: AlternativeSplicing, DifferentialExpression,
DifferentialSplicing, GeneExpression, RNASeq, Software,
VariantAnnotation
Author: Joseph Bendik [aut] (ORCID:
<https://orcid.org/0000-0003-0877-5639>), Sandhya Kalavacherla
[aut] (ORCID: <https://orcid.org/0000-0003-0485-9042>), Michael
Considine [aut] (ORCID:
<https://orcid.org/0000-0002-8666-4857>), Bahman Afsari [aut]
(ORCID: <https://orcid.org/0000-0001-8717-7199>), Michael F.
Ochs [aut], Joseph Califano [aut] (ORCID:
<https://orcid.org/0000-0002-4715-6761>), Daria A. Gaykalova
[aut] (ORCID: <https://orcid.org/0000-0001-5037-0147>), Elana
Fertig [aut] (ORCID: <https://orcid.org/0000-0003-3204-342X>),
Theresa Guo [cre, aut] (ORCID:
<https://orcid.org/0000-0002-1689-3275>)
Maintainer: Theresa Guo <twguo@health.ucsd.edu>
URL: https://github.com/GuoLabUCSD/OutSplice
VignetteBuilder: knitr
BugReports: https://github.com/GuoLabUCSD/OutSplice/issues
git_url: https://git.bioconductor.org/packages/OutSplice
git_branch: devel
git_last_commit: 683cfbe
git_last_commit_date: 2025-02-04
Date/Publication: 2025-02-05
source.ver: src/contrib/OutSplice_1.7.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/OutSplice_1.7.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/OutSplice_1.7.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/OutSplice_1.7.1.tgz
vignettes: vignettes/OutSplice/inst/doc/OutSplice.html
vignetteTitles: Find Splicing Outliers in Tumor Samples with OutSplice
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OutSplice/inst/doc/OutSplice.R
dependencyCount: 80
Package: OVESEG
Version: 1.23.0
Depends: R (>= 3.6)
Imports: stats, utils, methods, BiocParallel, SummarizedExperiment,
limma, fdrtool, Rcpp
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, BiocStyle, testthat, ggplot2, gridExtra,
grid, reshape2, scales
License: GPL-2
MD5sum: 915166a7f654b4fa6e079f7ad7b14bb5
NeedsCompilation: yes
Title: OVESEG-test to detect tissue/cell-specific markers
Description: An R package for multiple-group comparison to detect
tissue/cell-specific marker genes among subtypes. It provides
functions to compute OVESEG-test statistics, derive component
weights in the mixture null distribution model and estimate
p-values from weightedly aggregated permutations. Obtained
posterior probabilities of component null hypotheses can also
portrait all kinds of upregulation patterns among subtypes.
biocViews: Software, MultipleComparison, CellBiology, GeneExpression
Author: Lulu Chen <luluchen@vt.edu>
Maintainer: Lulu Chen <luluchen@vt.edu>
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/Lululuella/OVESEG
git_url: https://git.bioconductor.org/packages/OVESEG
git_branch: devel
git_last_commit: ec068d0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/OVESEG_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/OVESEG_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/OVESEG_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/OVESEG_1.23.0.tgz
vignettes: vignettes/OVESEG/inst/doc/OVESEG.html
vignetteTitles: OVESEG User Manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/OVESEG/inst/doc/OVESEG.R
dependencyCount: 50
Package: PAA
Version: 1.41.0
Depends: R (>= 3.2.0), Rcpp (>= 0.11.6)
Imports: e1071, gplots, gtools, limma, MASS, mRMRe, randomForest, ROCR,
sva
LinkingTo: Rcpp
Suggests: BiocStyle, RUnit, BiocGenerics, vsn
License: BSD_3_clause + file LICENSE
MD5sum: 6098d128913b6756d61e858d2d20929b
NeedsCompilation: yes
Title: PAA (Protein Array Analyzer)
Description: PAA imports single color (protein) microarray data that
has been saved in gpr file format - esp. ProtoArray data. After
preprocessing (background correction, batch filtering,
normalization) univariate feature preselection is performed
(e.g., using the "minimum M statistic" approach - hereinafter
referred to as "mMs"). Subsequently, a multivariate feature
selection is conducted to discover biomarker candidates.
Therefore, either a frequency-based backwards elimination
aproach or ensemble feature selection can be used. PAA provides
a complete toolbox of analysis tools including several
different plots for results examination and evaluation.
biocViews: Classification, Microarray, OneChannel, Proteomics
Author: Michael Turewicz [aut, cre], Martin Eisenacher [ctb, cre]
Maintainer: Michael Turewicz <michael.turewicz@rub.de>, Martin
Eisenacher <martin.eisenacher@rub.de>
URL: http://www.ruhr-uni-bochum.de/mpc/software/PAA/
SystemRequirements: C++ software package Random Jungle
git_url: https://git.bioconductor.org/packages/PAA
git_branch: devel
git_last_commit: e9dd570
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PAA_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PAA_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PAA_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PAA_1.41.0.tgz
vignettes: vignettes/PAA/inst/doc/PAA_1.7.1.pdf,
vignettes/PAA/inst/doc/PAA_vignette.pdf
vignetteTitles: PAA_1.7.1.pdf, PAA tutorial
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PAA/inst/doc/PAA_vignette.R
dependencyCount: 87
Package: packFinder
Version: 1.19.0
Depends: R (>= 4.1.0)
Imports: Biostrings, GenomicRanges, kmer, ape, methods, IRanges,
S4Vectors
Suggests: biomartr, knitr, rmarkdown, testthat, dendextend, biocViews,
BiocCheck, BiocStyle
License: GPL-2
Archs: x64
MD5sum: 8f06714849bb5ba3498d410c69e69051
NeedsCompilation: no
Title: de novo Annotation of Pack-TYPE Transposable Elements
Description: Algorithm and tools for in silico pack-TYPE transposon
discovery. Filters a given genome for properties unique to DNA
transposons and provides tools for the investigation of
returned matches. Sequences are input in DNAString format, and
ranges are returned as a dataframe (in the format returned by
as.dataframe(GRanges)).
biocViews: Genetics, SequenceMatching, Annotation
Author: Jack Gisby [aut, cre] (ORCID:
<https://orcid.org/0000-0003-0511-8123>), Marco Catoni [aut]
(ORCID: <https://orcid.org/0000-0002-3258-2522>)
Maintainer: Jack Gisby <jackgisby@gmail.com>
URL: https://github.com/jackgisby/packFinder
VignetteBuilder: knitr
BugReports: https://github.com/jackgisby/packFinder/issues
git_url: https://git.bioconductor.org/packages/packFinder
git_branch: devel
git_last_commit: 7ce4093
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/packFinder_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/packFinder_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/packFinder_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/packFinder_1.19.0.tgz
vignettes: vignettes/packFinder/inst/doc/packFinder.html
vignetteTitles: packFinder
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/packFinder/inst/doc/packFinder.R
dependencyCount: 35
Package: padma
Version: 1.17.0
Depends: R (>= 4.1.0), SummarizedExperiment, S4Vectors
Imports: FactoMineR, MultiAssayExperiment, methods, graphics, stats,
utils
Suggests: testthat, BiocStyle, knitr, rmarkdown, KEGGREST, missMDA,
ggplot2, ggrepel, car, cowplot, reshape2
License: GPL (>=3)
MD5sum: 6b830ddfa63666563e192c7bc2662305
NeedsCompilation: no
Title: Individualized Multi-Omic Pathway Deviation Scores Using
Multiple Factor Analysis
Description: Use multiple factor analysis to calculate individualized
pathway-centric scores of deviation with respect to the sampled
population based on multi-omic assays (e.g., RNA-seq, copy
number alterations, methylation, etc). Graphical and numerical
outputs are provided to identify highly aberrant individuals
for a particular pathway of interest, as well as the gene and
omics drivers of aberrant multi-omic profiles.
biocViews: Software, StatisticalMethod, PrincipalComponent,
GeneExpression, Pathways, RNASeq, BioCarta, MethylSeq
Author: Andrea Rau [cre, aut] (ORCID:
<https://orcid.org/0000-0001-6469-488X>), Regina Manansala
[aut], Florence Jaffrézic [ctb], Denis Laloë [aut], Paul Auer
[aut]
Maintainer: Andrea Rau <andrea.rau@inrae.fr>
URL: https://github.com/andreamrau/padma
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/padma
git_branch: devel
git_last_commit: 816cd17
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/padma_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/padma_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/padma_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/padma_1.17.0.tgz
vignettes: vignettes/padma/inst/doc/padma.html
vignetteTitles: padma package:Quick-start guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/padma/inst/doc/padma.R
dependencyCount: 135
Package: PADOG
Version: 1.49.4
Depends: R (>= 3.0.0), KEGGdzPathwaysGEO, methods,Biobase
Imports: limma, AnnotationDbi, GSA, foreach, doRNG, hgu133plus2.db,
hgu133a.db, KEGGREST, nlme
Suggests: doParallel, parallel
License: GPL (>= 2)
Archs: x64
MD5sum: 44427e9ca9f88916982f4499761db49c
NeedsCompilation: no
Title: Pathway Analysis with Down-weighting of Overlapping Genes
(PADOG)
Description: This package implements a general purpose gene set
analysis method called PADOG that downplays the importance of
genes that apear often accross the sets of genes to be
analyzed. The package provides also a benchmark for gene set
analysis methods in terms of sensitivity and ranking using 24
public datasets from KEGGdzPathwaysGEO package.
biocViews: Microarray, OneChannel, TwoChannel
Author: Adi Laurentiu Tarca <atarca@med.wayne.edu>; Zhonghui Xu
<zhonghui.xu@gmail.com>
Maintainer: Adi L. Tarca <atarca@med.wayne.edu>
git_url: https://git.bioconductor.org/packages/PADOG
git_branch: devel
git_last_commit: 46f8115
git_last_commit_date: 2025-01-17
Date/Publication: 2025-01-19
source.ver: src/contrib/PADOG_1.49.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PADOG_1.49.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PADOG_1.49.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PADOG_1.49.4.tgz
vignettes: vignettes/PADOG/inst/doc/PADOG.pdf
vignetteTitles: PADOG
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PADOG/inst/doc/PADOG.R
dependsOnMe: BLMA
importsMe: EGSEA
suggestsMe: ReporterScore
dependencyCount: 62
Package: pageRank
Version: 1.17.0
Depends: R (>= 4.0)
Imports: GenomicRanges, igraph, motifmatchr, stats, utils, grDevices,
graphics
Suggests: bcellViper, BSgenome.Hsapiens.UCSC.hg19, JASPAR2018,
TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, TFBSTools,
GenomicFeatures, annotate
License: GPL-2
Archs: x64
MD5sum: 913d55480cc276763cdf3a8e3bc2d4a4
NeedsCompilation: no
Title: Temporal and Multiplex PageRank for Gene Regulatory Network
Analysis
Description: Implemented temporal PageRank analysis as defined by
Rozenshtein and Gionis. Implemented multiplex PageRank as
defined by Halu et al. Applied temporal and multiplex PageRank
in gene regulatory network analysis.
biocViews: StatisticalMethod, GeneTarget, Network
Author: Hongxu Ding [aut, cre, ctb, cph]
Maintainer: Hongxu Ding <hd2326@columbia.edu>
URL: https://github.com/hd2326/pageRank
BugReports: https://github.com/hd2326/pageRank/issues
git_url: https://git.bioconductor.org/packages/pageRank
git_branch: devel
git_last_commit: a33df28
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/pageRank_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pageRank_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pageRank_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pageRank_1.17.0.tgz
vignettes: vignettes/pageRank/inst/doc/introduction.pdf
vignetteTitles: introduction.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pageRank/inst/doc/introduction.R
dependencyCount: 86
Package: PAIRADISE
Version: 1.23.0
Depends: R (>= 3.6), nloptr
Imports: SummarizedExperiment, S4Vectors, stats, methods, abind,
BiocParallel
Suggests: testthat, knitr, rmarkdown, BiocStyle
License: MIT + file LICENSE
MD5sum: 3ba6733408cd21c4e9093a12d9603a32
NeedsCompilation: no
Title: PAIRADISE: Paired analysis of differential isoform expression
Description: This package implements the PAIRADISE procedure for
detecting differential isoform expression between matched
replicates in paired RNA-Seq data.
biocViews: RNASeq, DifferentialExpression, AlternativeSplicing,
StatisticalMethod, ImmunoOncology
Author: Levon Demirdjian, Ying Nian Wu, Yi Xing
Maintainer: Qiang Hu <Qiang.Hu@roswellpark.org>, Levon Demirdjian
<levondem@ucla.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PAIRADISE
git_branch: devel
git_last_commit: 069378a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PAIRADISE_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PAIRADISE_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PAIRADISE_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PAIRADISE_1.23.0.tgz
vignettes: vignettes/PAIRADISE/inst/doc/pairadise.html
vignetteTitles: PAIRADISE
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PAIRADISE/inst/doc/pairadise.R
dependencyCount: 47
Package: paircompviz
Version: 1.45.0
Depends: R (>= 2.10), Rgraphviz
Imports: Rgraphviz
Suggests: multcomp, reshape, rpart, plyr, xtable
License: GPL (>=3.0)
MD5sum: 1bccb1b4b17524e686c637831a989cc9
NeedsCompilation: no
Title: Multiple comparison test visualization
Description: This package provides visualization of the results from
the multiple (i.e. pairwise) comparison tests such as
pairwise.t.test, pairwise.prop.test or pairwise.wilcox.test.
The groups being compared are visualized as nodes in Hasse
diagram. Such approach enables very clear and vivid depiction
of which group is significantly greater than which others,
especially if comparing a large number of groups.
biocViews: GraphAndNetwork
Author: Michal Burda
Maintainer: Michal Burda <michal.burda@osu.cz>
git_url: https://git.bioconductor.org/packages/paircompviz
git_branch: devel
git_last_commit: 78a6dbf
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/paircompviz_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/paircompviz_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/paircompviz_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/paircompviz_1.45.0.tgz
vignettes: vignettes/paircompviz/inst/doc/vignette.pdf
vignetteTitles: Using paircompviz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/paircompviz/inst/doc/vignette.R
dependencyCount: 11
Package: pairedGSEA
Version: 1.7.0
Depends: R (>= 4.3.0)
Imports: DESeq2, DEXSeq, limma, fgsea, sva, SummarizedExperiment,
S4Vectors, BiocParallel, ggplot2, aggregation, stats, utils,
methods
Suggests: writexl, readxl, readr, rhdf5, msigdbr, plotly, testthat (>=
3.0.0), knitr, rmarkdown, covr, BiocStyle
License: MIT + file LICENSE
MD5sum: a429ce7eec783e7c215a9f2199811fc3
NeedsCompilation: no
Title: Paired DGE and DGS analysis for gene set enrichment analysis
Description: pairedGSEA makes it simple to run a paired Differential
Gene Expression (DGE) and Differencital Gene Splicing (DGS)
analysis. The package allows you to store intermediate results
for further investiation, if desired. pairedGSEA comes with a
wrapper function for running an Over-Representation Analysis
(ORA) and functionalities for plotting the results.
biocViews: DifferentialExpression, AlternativeSplicing,
DifferentialSplicing, GeneExpression, ImmunoOncology,
GeneSetEnrichment, Pathways, RNASeq, Software, Transcription,
Author: Søren Helweg Dam [cre, aut] (ORCID:
<https://orcid.org/0000-0002-9895-0930>), Lars Rønn Olsen [aut]
(ORCID: <https://orcid.org/0000-0002-6725-7850>), Kristoffer
Vitting-Seerup [aut] (ORCID:
<https://orcid.org/0000-0002-6450-0608>)
Maintainer: Søren Helweg Dam <sohdam@dtu.dk>
URL: https://github.com/shdam/pairedGSEA
VignetteBuilder: knitr
BugReports: https://github.com/shdam/pairedGSEA/issues
git_url: https://git.bioconductor.org/packages/pairedGSEA
git_branch: devel
git_last_commit: 7731ffe
git_last_commit_date: 2025-02-12
Date/Publication: 2025-03-06
source.ver: src/contrib/pairedGSEA_1.7.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pairedGSEA_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pairedGSEA_1.7.0.tgz
vignettes: vignettes/pairedGSEA/inst/doc/User-Guide.html
vignetteTitles: User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/pairedGSEA/inst/doc/User-Guide.R
dependencyCount: 126
Package: pairkat
Version: 1.13.0
Depends: R (>= 4.1)
Imports: SummarizedExperiment, KEGGREST, igraph, data.table, methods,
stats, magrittr, CompQuadForm, tibble
Suggests: rmarkdown, knitr, BiocStyle, dplyr
License: GPL-3
MD5sum: 4325f6c96e87f495048fe6fac6007811
NeedsCompilation: no
Title: PaIRKAT
Description: PaIRKAT is model framework for assessing statistical
relationships between networks of metabolites (pathways) and an
outcome of interest (phenotype). PaIRKAT queries the KEGG
database to determine interactions between metabolites from
which network connectivity is constructed. This model framework
improves testing power on high dimensional data by including
graph topography in the kernel machine regression setting.
Studies on high dimensional data can struggle to include the
complex relationships between variables. The semi-parametric
kernel machine regression model is a powerful tool for
capturing these types of relationships. They provide a
framework for testing for relationships between outcomes of
interest and high dimensional data such as metabolomic,
genomic, or proteomic pathways. PaIRKAT uses known biological
connections between high dimensional variables by representing
them as edges of ‘graphs’ or ‘networks.’ It is common for nodes
(e.g. metabolites) to be disconnected from all others within
the graph, which leads to meaningful decreases in testing power
whether or not the graph information is included. We include a
graph regularization or ‘smoothing’ approach for managing this
issue.
biocViews: Software, Metabolomics, KEGG, Pathways, Network,
GraphAndNetwork, Regression
Author: Charlie Carpenter [aut], Cameron Severn [aut], Max McGrath
[cre, aut]
Maintainer: Max McGrath <max.mcgrath@ucdenver.edu>
VignetteBuilder: knitr
BugReports: https://github.com/Ghoshlab/pairkat/issues
git_url: https://git.bioconductor.org/packages/pairkat
git_branch: devel
git_last_commit: def3ebb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/pairkat_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pairkat_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pairkat_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pairkat_1.13.0.tgz
vignettes: vignettes/pairkat/inst/doc/using-pairkat.html
vignetteTitles: using-pairkat
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pairkat/inst/doc/using-pairkat.R
dependencyCount: 54
Package: pandaR
Version: 1.39.0
Depends: R (>= 3.0.0), methods, Biobase, BiocGenerics,
Imports: matrixStats, igraph, ggplot2, grid, reshape, plyr, RUnit,
hexbin
Suggests: knitr, rmarkdown
License: GPL-2
MD5sum: af18caf13f999ab456520a5797e71d58
NeedsCompilation: no
Title: PANDA Algorithm
Description: Runs PANDA, an algorithm for discovering novel network
structure by combining information from multiple complementary
data sources.
biocViews: StatisticalMethod, GraphAndNetwork, Microarray,
GeneRegulation, NetworkInference, GeneExpression,
Transcription, Network
Author: Dan Schlauch, Joseph N. Paulson, Albert Young, John
Quackenbush, Kimberly Glass
Maintainer: Joseph N. Paulson <paulson.joseph@gene.com>, Dan Schlauch
<dschlauch@genospace.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/pandaR
git_branch: devel
git_last_commit: 0d9c9dc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/pandaR_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pandaR_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pandaR_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pandaR_1.39.0.tgz
vignettes: vignettes/pandaR/inst/doc/pandaR.html
vignetteTitles: pandaR Package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pandaR/inst/doc/pandaR.R
dependencyCount: 46
Package: panelcn.mops
Version: 1.29.0
Depends: R (>= 3.5.0), cn.mops, methods, utils, stats, graphics
Imports: GenomicRanges, Rsamtools, IRanges, S4Vectors, GenomeInfoDb,
grDevices
Suggests: knitr, rmarkdown, RUnit, BiocGenerics
License: LGPL (>= 2.0)
MD5sum: cb2152e9229ddb655680d651ceb77fce
NeedsCompilation: no
Title: CNV detection tool for targeted NGS panel data
Description: CNV detection tool for targeted NGS panel data. Extension
of the cn.mops package.
biocViews: Sequencing, CopyNumberVariation, CellBiology,
GenomicVariation, VariantDetection, Genetics
Author: Verena Haunschmid [aut], Gundula Povysil [aut, cre]
Maintainer: Gundula Povysil <povysil@bioinf.jku.at>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/panelcn.mops
git_branch: devel
git_last_commit: d91ac16
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/panelcn.mops_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/panelcn.mops_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/panelcn.mops_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/panelcn.mops_1.29.0.tgz
vignettes: vignettes/panelcn.mops/inst/doc/panelcn.mops.pdf
vignetteTitles: panelcn.mops: Manual for the R package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/panelcn.mops/inst/doc/panelcn.mops.R
suggestsMe: CopyNumberPlots
dependencyCount: 41
Package: PanomiR
Version: 1.11.0
Depends: R (>= 4.2.0)
Imports: clusterProfiler, dplyr, forcats, GSEABase, igraph, limma,
metap, org.Hs.eg.db, parallel, preprocessCore, RColorBrewer,
rlang, tibble, withr, utils
Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 8ebf4e0fd1ee13a553461e4c9f610fe6
NeedsCompilation: no
Title: Detection of miRNAs that regulate interacting groups of pathways
Description: PanomiR is a package to detect miRNAs that target groups
of pathways from gene expression data. This package provides
functionality for generating pathway activity profiles,
determining differentially activated pathways between
user-specified conditions, determining clusters of pathways via
the PCxN package, and generating miRNAs targeting clusters of
pathways. These function can be used separately or sequentially
to analyze RNA-Seq data.
biocViews: GeneExpression, GeneSetEnrichment, GeneTarget, miRNA,
Pathways
Author: Pourya Naderi [aut, cre], Yue Yang (Alan) Teo [aut], Ilya
Sytchev [aut], Winston Hide [aut]
Maintainer: Pourya Naderi <pouryany@gmail.com>
URL: https://github.com/pouryany/PanomiR
VignetteBuilder: knitr
BugReports: https://github.com/pouryany/PanomiR/issues
git_url: https://git.bioconductor.org/packages/PanomiR
git_branch: devel
git_last_commit: 747888b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PanomiR_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PanomiR_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PanomiR_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PanomiR_1.11.0.tgz
vignettes: vignettes/PanomiR/inst/doc/PanomiR.html
vignetteTitles: PanomiR Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PanomiR/inst/doc/PanomiR.R
dependencyCount: 156
Package: panp
Version: 1.77.0
Depends: R (>= 2.10), affy (>= 1.23.4), Biobase (>= 2.5.5)
Imports: Biobase, methods, stats, utils
Suggests: gcrma
License: GPL (>= 2)
MD5sum: 49d0949c7c1ee6ddfbb2decf970d8b25
NeedsCompilation: no
Title: Presence-Absence Calls from Negative Strand Matching Probesets
Description: A function to make gene presence/absence calls based on
distance from negative strand matching probesets (NSMP) which
are derived from Affymetrix annotation. PANP is applied after
gene expression values are created, and therefore can be used
after any preprocessing method such as MAS5 or GCRMA, or
PM-only methods like RMA. NSMP sets have been established for
the HGU133A and HGU133-Plus-2.0 chipsets to date.
biocViews: Infrastructure
Author: Peter Warren
Maintainer: Peter Warren <peter.warren@verizon.net>
git_url: https://git.bioconductor.org/packages/panp
git_branch: devel
git_last_commit: 6b89b21
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/panp_1.77.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/panp_1.77.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/panp_1.77.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/panp_1.77.0.tgz
vignettes: vignettes/panp/inst/doc/panp.pdf
vignetteTitles: gene presence/absence calls
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/panp/inst/doc/panp.R
dependencyCount: 12
Package: PANR
Version: 1.53.0
Depends: R (>= 2.14), igraph
Imports: graphics, grDevices, MASS, methods, pvclust, stats, utils,
RedeR
Suggests: snow
License: Artistic-2.0
MD5sum: a8a5312ac394f0746f8ef4763b84b50e
NeedsCompilation: no
Title: Posterior association networks and functional modules inferred
from rich phenotypes of gene perturbations
Description: This package provides S4 classes and methods for inferring
functional gene networks with edges encoding posterior beliefs
of gene association types and nodes encoding perturbation
effects.
biocViews: ImmunoOncology, NetworkInference, Visualization,
GraphAndNetwork, Clustering, CellBasedAssays
Author: Xin Wang <xin_wang@hms.harvard.edu>
Maintainer: Xin Wang <xin_wang@hms.harvard.edu>
git_url: https://git.bioconductor.org/packages/PANR
git_branch: devel
git_last_commit: a07182d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PANR_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PANR_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PANR_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PANR_1.53.0.tgz
vignettes: vignettes/PANR/inst/doc/PANR-Vignette.pdf
vignetteTitles: Main vignette:Posterior association network and
enriched functional gene modules inferred from rich phenotypes
of gene perturbations
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PANR/inst/doc/PANR-Vignette.R
dependencyCount: 28
Package: parglms
Version: 1.39.0
Depends: methods
Imports: BiocGenerics, BatchJobs, foreach, doParallel
Suggests: RUnit, sandwich, MASS, knitr, GenomeInfoDb, GenomicRanges,
gwascat, BiocStyle, rmarkdown
License: Artistic-2.0
MD5sum: 84500c1c24dd38aeceb7fd51e0a7c08d
NeedsCompilation: no
Title: support for parallelized estimation of GLMs/GEEs
Description: This package provides support for parallelized estimation
of GLMs/GEEs, catering for dispersed data.
Author: VJ Carey <stvjc@channing.harvard.edu>
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/parglms
git_branch: devel
git_last_commit: 1e7c61f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/parglms_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/parglms_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/parglms_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/parglms_1.39.0.tgz
vignettes: vignettes/parglms/inst/doc/parglms.pdf
vignetteTitles: parglms: parallelized GLM
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/parglms/inst/doc/parglms.R
dependencyCount: 38
Package: parody
Version: 1.65.2002
Depends: R (>= 3.5.0), tools, utils
Suggests: knitr, BiocStyle, testthat, rmarkdown
License: Artistic-2.0
MD5sum: 10b97f20b5cc1d1489d5c372da4abdbf
NeedsCompilation: no
Title: Parametric And Resistant Outlier DYtection
Description: Provide routines for univariate and multivariate outlier
detection with a focus on parametric methods, but support for
some methods based on resistant statistics.
biocViews: MultipleComparison
Author: Vince Carey [aut, cre] (ORCID:
<https://orcid.org/0000-0003-4046-0063>)
Maintainer: Vince Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/parody
git_branch: devel
git_last_commit: ce11c41
git_last_commit_date: 2025-02-11
Date/Publication: 2025-02-11
source.ver: src/contrib/parody_1.65.2002.tar.gz
win.binary.ver: bin/windows/contrib/4.5/parody_1.65.2002.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/parody_1.65.2002.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/parody_1.65.2002.tgz
vignettes: vignettes/parody/inst/doc/parody.html
vignetteTitles: parody: parametric and resistant outlier dytection
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/parody/inst/doc/parody.R
dependsOnMe: arrayMvout
dependencyCount: 2
Package: partCNV
Version: 1.5.0
Depends: R (>= 3.5.0)
Imports: stats, data.table, depmixS4, Seurat, SingleCellExperiment,
AnnotationHub, magrittr, GenomicRanges, BiocStyle
Suggests: rmarkdown, knitr, IRanges, testthat (>= 3.0.0)
License: GPL-2
MD5sum: ebfc294b4460c1f4fe327838b919405c
NeedsCompilation: no
Title: Infer locally aneuploid cells using single cell RNA-seq data
Description: This package uses a statistical framework for rapid and
accurate detection of aneuploid cells with local copy number
deletion or amplification. Our method uses an EM algorithm with
mixtures of Poisson distributions while incorporating
cytogenetics information (e.g., regional deletion or
amplification) to guide the classification (partCNV). When
applicable, we further improve the accuracy by integrating a
Hidden Markov Model for feature selection (partCNVH).
biocViews: Software, CopyNumberVariation, HiddenMarkovModel,
SingleCell, Classification
Author: Ziyi Li [aut, cre, ctb], Ruoxing Li [ctb]
Maintainer: Ziyi Li <zli16@mdanderson.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/partCNV
git_branch: devel
git_last_commit: 91c5b22
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/partCNV_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/partCNV_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/partCNV_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/partCNV_1.5.0.tgz
vignettes: vignettes/partCNV/inst/doc/partCNV_vignette.html
vignetteTitles: partCNV_vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/partCNV/inst/doc/partCNV_vignette.R
dependencyCount: 192
Package: PAST
Version: 1.23.0
Depends: R (>= 4.0)
Imports: stats, utils, dplyr, rlang, iterators, parallel, foreach,
doParallel, qvalue, rtracklayer, ggplot2, GenomicRanges,
S4Vectors
Suggests: knitr, rmarkdown
License: GPL (>=3) + file LICENSE
MD5sum: 5f1adab9a9693847affa9c5035448570
NeedsCompilation: no
Title: Pathway Association Study Tool (PAST)
Description: PAST takes GWAS output and assigns SNPs to genes, uses
those genes to find pathways associated with the genes, and
plots pathways based on significance. Implements methods for
reading GWAS input data, finding genes associated with SNPs,
calculating enrichment score and significance of pathways, and
plotting pathways.
biocViews: Pathways, GeneSetEnrichment
Author: Thrash Adam [cre, aut], DeOrnellis Mason [aut]
Maintainer: Thrash Adam <thrash@igbb.msstate.edu>
URL: https://github.com/IGBB/past
VignetteBuilder: knitr
BugReports: https://github.com/IGBB/past/issues
git_url: https://git.bioconductor.org/packages/PAST
git_branch: devel
git_last_commit: 926ad93
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PAST_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PAST_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PAST_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PAST_1.23.0.tgz
vignettes: vignettes/PAST/inst/doc/past.html
vignetteTitles: PAST
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PAST/inst/doc/past.R
dependencyCount: 95
Package: Path2PPI
Version: 1.37.0
Depends: R (>= 3.2.1), igraph (>= 1.0.1), methods
Suggests: knitr, rmarkdown, RUnit, BiocGenerics, BiocStyle
License: GPL (>= 2)
MD5sum: 08ee33182780a4e9b2017e40a2c50c58
NeedsCompilation: no
Title: Prediction of pathway-related protein-protein interaction
networks
Description: Package to predict protein-protein interaction (PPI)
networks in target organisms for which only a view information
about PPIs is available. Path2PPI predicts PPI networks based
on sets of proteins which can belong to a certain pathway from
well-established model organisms. It helps to combine and
transfer information of a certain pathway or biological process
from several reference organisms to one target organism.
Path2PPI only depends on the sequence similarity of the
involved proteins.
biocViews: NetworkInference, SystemsBiology, Network, Proteomics,
Pathways
Author: Oliver Philipp [aut, cre], Ina Koch [ctb]
Maintainer: Oliver Philipp <contact@oliverphilipp.info>
URL: http://www.bioinformatik.uni-frankfurt.de/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Path2PPI
git_branch: devel
git_last_commit: 59d226c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Path2PPI_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Path2PPI_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Path2PPI_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Path2PPI_1.37.0.tgz
vignettes: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.html
vignetteTitles: Path2PPI - A brief tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.R
dependencyCount: 17
Package: pathifier
Version: 1.45.0
Imports: R.oo, princurve (>= 2.0.4)
License: Artistic-1.0
MD5sum: 46f4edddd7b93233e71e9af2ba12979f
NeedsCompilation: no
Title: Quantify deregulation of pathways in cancer
Description: Pathifier is an algorithm that infers pathway deregulation
scores for each tumor sample on the basis of expression data.
This score is determined, in a context-specific manner, for
every particular dataset and type of cancer that is being
investigated. The algorithm transforms gene-level information
into pathway-level information, generating a compact and
biologically relevant representation of each sample.
biocViews: Network
Author: Yotam Drier
Maintainer: Assif Yitzhaky <assif.yitzhaky@weizmann.ac.il>
git_url: https://git.bioconductor.org/packages/pathifier
git_branch: devel
git_last_commit: b298975
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/pathifier_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pathifier_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pathifier_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pathifier_1.45.0.tgz
vignettes: vignettes/pathifier/inst/doc/Overview.pdf
vignetteTitles: Quantify deregulation of pathways in cancer
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pathifier/inst/doc/Overview.R
importsMe: funOmics
dependencyCount: 9
Package: pathlinkR
Version: 1.3.7
Depends: R (>= 4.4.0)
Imports: circlize, clusterProfiler, ComplexHeatmap, dplyr, fgsea,
ggforce, ggplot2, ggpubr, ggraph, ggrepel, grid, igraph, purrr,
sigora, stringr, tibble, tidygraph, tidyr, vegan, visNetwork
Suggests: AnnotationDbi, BiocStyle, biomaRt, covr, DESeq2, jsonlite,
knitr, org.Hs.eg.db, rmarkdown, scales, testthat (>= 3.0.0),
vdiffr
License: GPL-3 + file LICENSE
MD5sum: 9d4c6de55c7f059791cfaaab87adad0e
NeedsCompilation: no
Title: Analyze and interpret RNA-Seq results
Description: pathlinkR is an R package designed to facilitate analysis
of RNA-Seq results. Specifically, our aim with pathlinkR was to
provide a number of tools which take a list of DE genes and
perform different analyses on them, aiding with the
interpretation of results. Functions are included to perform
pathway enrichment, with muliplte databases supported, and
tools for visualizing these results. Genes can also be used to
create and plot protein-protein interaction networks, all from
inside of R.
biocViews: GeneSetEnrichment, Network, Pathways, Reactome, RNASeq,
NetworkEnrichment
Author: Travis Blimkie [cre] (ORCID:
<https://orcid.org/0000-0001-8778-8627>), Andy An [aut]
Maintainer: Travis Blimkie <travis.m.blimkie@gmail.com>
URL: https://github.com/hancockinformatics/pathlinkR
VignetteBuilder: knitr
BugReports: https://github.com/hancockinformatics/pathlinkR/issues
git_url: https://git.bioconductor.org/packages/pathlinkR
git_branch: devel
git_last_commit: a80da73
git_last_commit_date: 2024-12-23
Date/Publication: 2024-12-24
source.ver: src/contrib/pathlinkR_1.3.7.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pathlinkR_1.3.7.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pathlinkR_1.3.7.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pathlinkR_1.3.7.tgz
vignettes: vignettes/pathlinkR/inst/doc/pathlinkR.html
vignetteTitles: Analyze and visualize RNA-Seq data with pathlinkR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/pathlinkR/inst/doc/pathlinkR.R
dependencyCount: 191
Package: PathNet
Version: 1.47.0
Suggests: PathNetData, RUnit, BiocGenerics
License: GPL-3
MD5sum: 3f844bd364cd35018e489dfb7ac5271f
NeedsCompilation: no
Title: An R package for pathway analysis using topological information
Description: PathNet uses topological information present in pathways
and differential expression levels of genes (obtained from
microarray experiment) to identify pathways that are 1)
significantly enriched and 2) associated with each other in the
context of differential expression. The algorithm is described
in: PathNet: A tool for pathway analysis using topological
information. Dutta B, Wallqvist A, and Reifman J. Source Code
for Biology and Medicine 2012 Sep 24;7(1):10.
biocViews: Pathways, DifferentialExpression, MultipleComparison, KEGG,
NetworkEnrichment, Network
Author: Bhaskar Dutta <bhaskar.dutta@gmail.com>, Anders Wallqvist
<awallqvist@bhsai.org>, and Jaques Reifman <jreifman@bhsai.org>
Maintainer: Ludwig Geistlinger <ludwig.geistlinger@gmail.com>
git_url: https://git.bioconductor.org/packages/PathNet
git_branch: devel
git_last_commit: 08e4aa3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PathNet_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PathNet_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PathNet_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PathNet_1.47.0.tgz
vignettes: vignettes/PathNet/inst/doc/PathNet.pdf
vignetteTitles: PathNet
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PathNet/inst/doc/PathNet.R
dependencyCount: 0
Package: PathoStat
Version: 1.33.0
Depends: R (>= 3.5)
Imports: limma, corpcor,matrixStats, reshape2, scales, ggplot2,
rentrez, DT, tidyr, plyr, dplyr, phyloseq, shiny, stats,
methods, XML, graphics, utils, BiocStyle, edgeR, DESeq2,
ComplexHeatmap, plotly, webshot, vegan, shinyjs, glmnet,
gmodels, ROCR, RColorBrewer, knitr, devtools, ape
Suggests: rmarkdown, testthat
License: GPL (>= 2)
MD5sum: f182ebd6bd142036bf6e7cb1cfa3df20
NeedsCompilation: no
Title: PathoStat Statistical Microbiome Analysis Package
Description: The purpose of this package is to perform Statistical
Microbiome Analysis on metagenomics results from sequencing
data samples. In particular, it supports analyses on the
PathoScope generated report files. PathoStat provides various
functionalities including Relative Abundance charts, Diversity
estimates and plots, tests of Differential Abundance, Time
Series visualization, and Core OTU analysis.
biocViews: Microbiome, Metagenomics, GraphAndNetwork, Microarray,
PatternLogic, PrincipalComponent, Sequencing, Software,
Visualization, RNASeq, ImmunoOncology
Author: Solaiappan Manimaran <manimaran_1975@hotmail.com>, Matthew
Bendall <bendall@gwmail.gwu.edu>, Sandro Valenzuela Diaz
<sandrolvalenzuelad@gmail.com>, Eduardo Castro
<castronallar@gmail.com>, Tyler Faits <tfaits@gmail.com>, Yue
Zhao <jasonzhao0307@gmail.com>, Anthony Nicholas Federico
<anfed@bu.edu>, W. Evan Johnson <wej@bu.edu>
Maintainer: Solaiappan Manimaran <manimaran_1975@hotmail.com>, Yue Zhao
<jasonzhao0307@gmail.com>
URL: https://github.com/mani2012/PathoStat
VignetteBuilder: knitr
BugReports: https://github.com/mani2012/PathoStat/issues
git_url: https://git.bioconductor.org/packages/PathoStat
git_branch: devel
git_last_commit: 1f65914
git_last_commit_date: 2024-10-29
Date/Publication: 2025-01-23
source.ver: src/contrib/PathoStat_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PathoStat_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PathoStat_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PathoStat_1.33.0.tgz
vignettes: vignettes/PathoStat/inst/doc/PathoStat-vignette.html
vignetteTitles: PathoStat intro
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PathoStat/inst/doc/PathoStat-vignette.R
dependencyCount: 206
Package: pathRender
Version: 1.75.0
Depends: graph, Rgraphviz, RColorBrewer, cMAP, AnnotationDbi, methods,
stats4
Suggests: ALL, hgu95av2.db
License: LGPL
MD5sum: 0cbc76fe9cdd7c646cd32db07efd931a
NeedsCompilation: no
Title: Render molecular pathways
Description: build graphs from pathway databases, render them by
Rgraphviz.
biocViews: GraphAndNetwork, Pathways, Visualization
Author: Li Long <lilong@isb-sib.ch>
Maintainer: Vince Carey <stvjc@channing.harvard.edu>
URL: http://www.bioconductor.org
git_url: https://git.bioconductor.org/packages/pathRender
git_branch: devel
git_last_commit: 3e88ace
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/pathRender_1.75.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pathRender_1.75.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pathRender_1.75.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pathRender_1.75.0.tgz
vignettes: vignettes/pathRender/inst/doc/pathRender.pdf,
vignettes/pathRender/inst/doc/plotExG.pdf
vignetteTitles: pathRender overview, pathway graphs colored by
expression map
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pathRender/inst/doc/pathRender.R,
vignettes/pathRender/inst/doc/plotExG.R
dependencyCount: 50
Package: pathview
Version: 1.47.0
Depends: R (>= 3.5.0)
Imports: KEGGgraph, XML, Rgraphviz, graph, png, AnnotationDbi,
org.Hs.eg.db, KEGGREST, methods, utils
Suggests: gage, org.Mm.eg.db, org.EcK12.eg.db, RUnit, BiocGenerics
License: GPL (>=3.0)
MD5sum: 9e8d5717bad51eff47f15437bf5f7b31
NeedsCompilation: no
Title: a tool set for pathway based data integration and visualization
Description: Pathview is a tool set for pathway based data integration
and visualization. It maps and renders a wide variety of
biological data on relevant pathway graphs. All users need is
to supply their data and specify the target pathway. Pathview
automatically downloads the pathway graph data, parses the data
file, maps user data to the pathway, and render pathway graph
with the mapped data. In addition, Pathview also seamlessly
integrates with pathway and gene set (enrichment) analysis
tools for large-scale and fully automated analysis.
biocViews: Pathways, GraphAndNetwork, Visualization, GeneSetEnrichment,
DifferentialExpression, GeneExpression, Microarray, RNASeq,
Genetics, Metabolomics, Proteomics, SystemsBiology, Sequencing
Author: Weijun Luo
Maintainer: Weijun Luo <luo_weijun@yahoo.com>
URL: https://github.com/datapplab/pathview, https://pathview.uncc.edu/
git_url: https://git.bioconductor.org/packages/pathview
git_branch: devel
git_last_commit: 87ffb7b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/pathview_1.47.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pathview_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pathview_1.47.0.tgz
vignettes: vignettes/pathview/inst/doc/pathview.pdf
vignetteTitles: Pathview: pathway based data integration and
visualization
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pathview/inst/doc/pathview.R
dependsOnMe: EGSEA, SBGNview
importsMe: debrowser, EnrichmentBrowser, GDCRNATools, lilikoi, SQMtools
suggestsMe: gage, TCGAbiolinks, gageData, CAGEWorkflow, ReporterScore
dependencyCount: 53
Package: pathwayPCA
Version: 1.23.0
Depends: R (>= 3.1)
Imports: lars, methods, parallel, stats, survival, utils
Suggests: airway, circlize, grDevices, knitr, RCurl, reshape2,
rmarkdown, SummarizedExperiment, survminer, testthat, tidyverse
License: GPL-3
MD5sum: c9b8548305539cf09cfec2bea8f5ad22
NeedsCompilation: no
Title: Integrative Pathway Analysis with Modern PCA Methodology and
Gene Selection
Description: pathwayPCA is an integrative analysis tool that implements
the principal component analysis (PCA) based pathway analysis
approaches described in Chen et al. (2008), Chen et al. (2010),
and Chen (2011). pathwayPCA allows users to: (1) Test pathway
association with binary, continuous, or survival phenotypes.
(2) Extract relevant genes in the pathways using the SuperPCA
and AES-PCA approaches. (3) Compute principal components (PCs)
based on the selected genes. These estimated latent variables
represent pathway activities for individual subjects, which can
then be used to perform integrative pathway analysis, such as
multi-omics analysis. (4) Extract relevant genes that drive
pathway significance as well as data corresponding to these
relevant genes for additional in-depth analysis. (5) Perform
analyses with enhanced computational efficiency with parallel
computing and enhanced data safety with S4-class data objects.
(6) Analyze studies with complex experimental designs, with
multiple covariates, and with interaction effects, e.g.,
testing whether pathway association with clinical phenotype is
different between male and female subjects. Citations: Chen et
al. (2008) <https://doi.org/10.1093/bioinformatics/btn458>;
Chen et al. (2010) <https://doi.org/10.1002/gepi.20532>; and
Chen (2011) <https://doi.org/10.2202/1544-6115.1697>.
biocViews: CopyNumberVariation, DNAMethylation, GeneExpression, SNP,
Transcription, GenePrediction, GeneSetEnrichment,
GeneSignaling, GeneTarget, GenomeWideAssociation,
GenomicVariation, CellBiology, Epigenetics, FunctionalGenomics,
Genetics, Lipidomics, Metabolomics, Proteomics, SystemsBiology,
Transcriptomics, Classification, DimensionReduction,
FeatureExtraction, PrincipalComponent, Regression, Survival,
MultipleComparison, Pathways
Author: Gabriel Odom [aut, cre], James Ban [aut], Lizhong Liu [aut],
Lily Wang [aut], Steven Chen [aut]
Maintainer: Gabriel Odom <gabriel.odom@med.miami.edu>
URL: <https://gabrielodom.github.io/pathwayPCA/>
VignetteBuilder: knitr
BugReports: https://github.com/gabrielodom/pathwayPCA/issues
git_url: https://git.bioconductor.org/packages/pathwayPCA
git_branch: devel
git_last_commit: c68c52f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/pathwayPCA_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pathwayPCA_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pathwayPCA_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pathwayPCA_1.23.0.tgz
vignettes:
vignettes/pathwayPCA/inst/doc/Introduction_to_pathwayPCA.html,
vignettes/pathwayPCA/inst/doc/Supplement1-Quickstart_Guide.html,
vignettes/pathwayPCA/inst/doc/Supplement2-Importing_Data.html,
vignettes/pathwayPCA/inst/doc/Supplement3-Create_Omics_Objects.html,
vignettes/pathwayPCA/inst/doc/Supplement4-Methods_Walkthrough.html,
vignettes/pathwayPCA/inst/doc/Supplement5-Analyse_Results.html
vignetteTitles: Integrative Pathway Analysis with pathwayPCA, Suppl. 1.
Quickstart Guide, Suppl. 2. Importing Data, Suppl. 3. Create
Data Objects, Suppl. 4. Test Pathway Significance, Suppl. 5.
Visualizing the Results
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pathwayPCA/inst/doc/Introduction_to_pathwayPCA.R,
vignettes/pathwayPCA/inst/doc/Supplement1-Quickstart_Guide.R,
vignettes/pathwayPCA/inst/doc/Supplement2-Importing_Data.R,
vignettes/pathwayPCA/inst/doc/Supplement3-Create_Omics_Objects.R,
vignettes/pathwayPCA/inst/doc/Supplement4-Methods_Walkthrough.R,
vignettes/pathwayPCA/inst/doc/Supplement5-Analyse_Results.R
dependencyCount: 12
Package: pcaExplorer
Version: 3.1.1
Imports: DESeq2, SummarizedExperiment, mosdef (>= 1.1.0),
GenomicRanges, IRanges, S4Vectors, genefilter, ggplot2 (>=
2.0.0), heatmaply, plotly, scales, NMF, plyr, topGO, limma,
GOstats, GO.db, AnnotationDbi, shiny (>= 0.12.0),
shinydashboard, shinyBS, ggrepel, DT, shinyAce, threejs,
biomaRt, pheatmap, knitr, rmarkdown, base64enc, tidyr,
grDevices, methods
Suggests: testthat, BiocStyle, markdown, airway, org.Hs.eg.db,
htmltools
License: MIT + file LICENSE
MD5sum: 7382d0843d09fbae858fa06aa9702cab
NeedsCompilation: no
Title: Interactive Visualization of RNA-seq Data Using a Principal
Components Approach
Description: This package provides functionality for interactive
visualization of RNA-seq datasets based on Principal Components
Analysis. The methods provided allow for quick information
extraction and effective data exploration. A Shiny application
encapsulates the whole analysis.
biocViews: ImmunoOncology, Visualization, RNASeq, DimensionReduction,
PrincipalComponent, QualityControl, GUI, ReportWriting,
ShinyApps
Author: Federico Marini [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3252-7758>)
Maintainer: Federico Marini <marinif@uni-mainz.de>
URL: https://github.com/federicomarini/pcaExplorer,
https://federicomarini.github.io/pcaExplorer/
VignetteBuilder: knitr
BugReports: https://github.com/federicomarini/pcaExplorer/issues
git_url: https://git.bioconductor.org/packages/pcaExplorer
git_branch: devel
git_last_commit: 11c744f
git_last_commit_date: 2024-12-19
Date/Publication: 2024-12-20
source.ver: src/contrib/pcaExplorer_3.1.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pcaExplorer_3.1.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pcaExplorer_3.1.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pcaExplorer_3.1.1.tgz
vignettes: vignettes/pcaExplorer/inst/doc/pcaExplorer.html,
vignettes/pcaExplorer/inst/doc/upandrunning.html
vignetteTitles: pcaExplorer User Guide, Up and running with pcaExplorer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/pcaExplorer/inst/doc/pcaExplorer.R,
vignettes/pcaExplorer/inst/doc/upandrunning.R
suggestsMe: GeDi
dependencyCount: 230
Package: pcaMethods
Version: 1.99.0
Depends: Biobase, methods
Imports: BiocGenerics, Rcpp (>= 0.11.3), MASS
LinkingTo: Rcpp
Suggests: matrixStats, lattice, ggplot2
License: GPL (>= 3)
MD5sum: 9a8028d07e3e47b07536447e63b27a1f
NeedsCompilation: yes
Title: A collection of PCA methods
Description: Provides Bayesian PCA, Probabilistic PCA, Nipals PCA,
Inverse Non-Linear PCA and the conventional SVD PCA. A cluster
based method for missing value estimation is included for
comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA
on incomplete data as well as for accurate missing value
estimation. A set of methods for printing and plotting the
results is also provided. All PCA methods make use of the same
data structure (pcaRes) to provide a common interface to the
PCA results. Initiated at the Max-Planck Institute for
Molecular Plant Physiology, Golm, Germany.
biocViews: Bayesian
Author: Wolfram Stacklies, Henning Redestig, Kevin Wright
Maintainer: Henning Redestig <henning.red@gmail.com>
URL: https://github.com/hredestig/pcamethods
SystemRequirements: Rcpp
BugReports: https://github.com/hredestig/pcamethods/issues
git_url: https://git.bioconductor.org/packages/pcaMethods
git_branch: devel
git_last_commit: 8148736
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/pcaMethods_1.99.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pcaMethods_1.99.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pcaMethods_1.99.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pcaMethods_1.99.0.tgz
vignettes: vignettes/pcaMethods/inst/doc/missingValues.pdf,
vignettes/pcaMethods/inst/doc/outliers.pdf,
vignettes/pcaMethods/inst/doc/pcaMethods.pdf
vignetteTitles: Missing value imputation, Data with outliers,
Introduction
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pcaMethods/inst/doc/missingValues.R,
vignettes/pcaMethods/inst/doc/outliers.R,
vignettes/pcaMethods/inst/doc/pcaMethods.R
dependsOnMe: DeconRNASeq, crmn, DiffCorr, imputeLCMD
importsMe: consensusDE, destiny, FRASER, MAI, MatrixQCvis, MSnbase,
MSPrep, MultiBaC, OUTRIDER, PhosR, pmp, scde,
SomaticSignatures, ADAPTS, CopSens, geneticae, lfproQC, LOST,
MetabolomicsBasics, missCompare, multiDimBio, pmartR, polyRAD,
promor, RAMClustR, santaR, scMappR
suggestsMe: autonomics, cardelino, MsCoreUtils, QFeatures, qmtools,
mtbls2, pagoda2, rsvddpd
dependencyCount: 10
Package: PCAN
Version: 1.35.0
Depends: R (>= 3.3), BiocParallel
Imports: grDevices, stats
Suggests: BiocStyle, knitr, rmarkdown, reactome.db, STRINGdb
License: CC BY-NC-ND 4.0
MD5sum: 0e41d4de64ae2d667c93486dff99f455
NeedsCompilation: no
Title: Phenotype Consensus ANalysis (PCAN)
Description: Phenotypes comparison based on a pathway consensus
approach. Assess the relationship between candidate genes and a
set of phenotypes based on additional genes related to the
candidate (e.g. Pathways or network neighbors).
biocViews: Annotation, Sequencing, Genetics, FunctionalPrediction,
VariantAnnotation, Pathways, Network
Author: Matthew Page and Patrice Godard
Maintainer: Matthew Page <matthew.page@ucb.com> and Patrice Godard
<patrice.godard@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PCAN
git_branch: devel
git_last_commit: 71c4483
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PCAN_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PCAN_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PCAN_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PCAN_1.35.0.tgz
vignettes: vignettes/PCAN/inst/doc/PCAN.html
vignetteTitles: Assessing gene relevance for a set of phenotypes
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PCAN/inst/doc/PCAN.R
dependencyCount: 14
Package: PCAtools
Version: 2.19.0
Depends: ggplot2, ggrepel
Imports: lattice, grDevices, cowplot, methods, reshape2, stats, Matrix,
DelayedMatrixStats, DelayedArray, BiocSingular, BiocParallel,
Rcpp, dqrng
LinkingTo: Rcpp, beachmat, BH, dqrng
Suggests: testthat, scran, BiocGenerics, knitr, Biobase, GEOquery,
hgu133a.db, ggplotify, beachmat, RMTstat, ggalt, DESeq2,
airway, org.Hs.eg.db, magrittr, rmarkdown
License: GPL-3
MD5sum: cc3aca422a7670cfcacc8f245832182a
NeedsCompilation: yes
Title: PCAtools: Everything Principal Components Analysis
Description: Principal Component Analysis (PCA) is a very powerful
technique that has wide applicability in data science,
bioinformatics, and further afield. It was initially developed
to analyse large volumes of data in order to tease out the
differences/relationships between the logical entities being
analysed. It extracts the fundamental structure of the data
without the need to build any model to represent it. This
'summary' of the data is arrived at through a process of
reduction that can transform the large number of variables into
a lesser number that are uncorrelated (i.e. the 'principal
components'), while at the same time being capable of easy
interpretation on the original data. PCAtools provides
functions for data exploration via PCA, and allows the user to
generate publication-ready figures. PCA is performed via
BiocSingular - users can also identify optimal number of
principal components via different metrics, such as elbow
method and Horn's parallel analysis, which has relevance for
data reduction in single-cell RNA-seq (scRNA-seq) and high
dimensional mass cytometry data.
biocViews: RNASeq, ATACSeq, GeneExpression, Transcription, SingleCell,
PrincipalComponent
Author: Kevin Blighe [aut, cre], Anna-Leigh Brown [ctb], Vincent Carey
[ctb], Guido Hooiveld [ctb], Aaron Lun [aut, ctb]
Maintainer: Kevin Blighe <kevin@clinicalbioinformatics.co.uk>
URL: https://github.com/kevinblighe/PCAtools
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PCAtools
git_branch: devel
git_last_commit: fb5bb35
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PCAtools_2.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PCAtools_2.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PCAtools_2.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PCAtools_2.19.0.tgz
vignettes: vignettes/PCAtools/inst/doc/PCAtools.html
vignetteTitles: PCAtools: everything Principal Component Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PCAtools/inst/doc/PCAtools.R
importsMe: CRISPRball, regionalpcs
suggestsMe: RNAseqCovarImpute, scDataviz
dependencyCount: 76
Package: PDATK
Version: 1.15.0
Depends: R (>= 4.1), SummarizedExperiment
Imports: data.table, MultiAssayExperiment, ConsensusClusterPlus,
igraph, ggplotify, matrixStats, RColorBrewer, clusterRepro,
CoreGx, caret, survminer, methods, S4Vectors, BiocGenerics,
survival, stats, plyr, dplyr, MatrixGenerics, BiocParallel,
rlang, piano, scales, survcomp, genefu, ggplot2, switchBox,
reportROC, pROC, verification, utils
Suggests: testthat (>= 3.0.0), msigdbr, BiocStyle, rmarkdown, knitr,
HDF5Array
License: MIT + file LICENSE
MD5sum: 49ffc2c1626f0f53262493ef0f4dc10b
NeedsCompilation: no
Title: Pancreatic Ductal Adenocarcinoma Tool-Kit
Description: Pancreatic ductal adenocarcinoma (PDA) has a relatively
poor prognosis and is one of the most lethal cancers. Molecular
classification of gene expression profiles holds the potential
to identify meaningful subtypes which can inform therapeutic
strategy in the clinical setting. The Pancreatic Cancer
Adenocarcinoma Tool-Kit (PDATK) provides an S4 class-based
interface for performing unsupervised subtype discovery,
cross-cohort meta-clustering, gene-expression-based
classification, and subsequent survival analysis to identify
prognostically useful subtypes in pancreatic cancer and beyond.
Two novel methods, Consensus Subtypes in Pancreatic Cancer
(CSPC) and Pancreatic Cancer Overall Survival Predictor (PCOSP)
are included for consensus-based meta-clustering and
overall-survival prediction, respectively. Additionally, four
published subtype classifiers and three published prognostic
gene signatures are included to allow users to easily recreate
published results, apply existing classifiers to new data, and
benchmark the relative performance of new methods. The use of
existing Bioconductor classes as input to all PDATK classes and
methods enables integration with existing Bioconductor
datasets, including the 21 pancreatic cancer patient cohorts
available in the MetaGxPancreas data package. PDATK has been
used to replicate results from Sandhu et al (2019)
[https://doi.org/10.1200/cci.18.00102] and an additional paper
is in the works using CSPC to validate subtypes from the
included published classifiers, both of which use the data
available in MetaGxPancreas. The inclusion of subtype centroids
and prognostic gene signatures from these and other
publications will enable researchers and clinicians to classify
novel patient gene expression data, allowing the direct
clinical application of the classifiers included in PDATK.
Overall, PDATK provides a rich set of tools to identify and
validate useful prognostic and molecular subtypes based on
gene-expression data, benchmark new classifiers against
existing ones, and apply discovered classifiers on novel
patient data to inform clinical decision making.
biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics,
Software, Classification, Survival, Clustering, GenePrediction
Author: Vandana Sandhu [aut], Heewon Seo [aut], Christopher Eeles
[aut], Neha Rohatgi [ctb], Benjamin Haibe-Kains [aut, cre]
Maintainer: Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>
VignetteBuilder: knitr
BugReports: https://github.com/bhklab/PDATK/issues
git_url: https://git.bioconductor.org/packages/PDATK
git_branch: devel
git_last_commit: 6c52f5f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-27
source.ver: src/contrib/PDATK_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PDATK_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PDATK_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PDATK_1.15.0.tgz
vignettes: vignettes/PDATK/inst/doc/PCOSP_model_analysis.html,
vignettes/PDATK/inst/doc/PDATK_introduction.html
vignetteTitles: PCOSP: Pancreatic Cancer Overall Survival Predictor,
PDATK_introduction.html
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PDATK/inst/doc/PCOSP_model_analysis.R,
vignettes/PDATK/inst/doc/PDATK_introduction.R
dependencyCount: 265
Package: pdInfoBuilder
Version: 1.71.0
Depends: R (>= 3.2.0), methods, Biobase (>= 2.27.3), RSQLite (>=
1.0.0), affxparser (>= 1.39.4), oligo (>= 1.31.5)
Imports: Biostrings (>= 2.35.12), BiocGenerics (>= 0.13.11), DBI (>=
0.3.1), IRanges (>= 2.1.43), oligoClasses (>= 1.29.6),
S4Vectors (>= 0.5.22)
License: Artistic-2.0
Archs: x64
MD5sum: 7bd23b8817673b3401969e8610d7e320
NeedsCompilation: yes
Title: Platform Design Information Package Builder
Description: Builds platform design information packages. These consist
of a SQLite database containing feature-level data such as x, y
position on chip and featureSet ID. The database also
incorporates featureSet-level annotation data. The products of
this packages are used by the oligo pkg.
biocViews: Annotation, Infrastructure
Author: Seth Falcon, Vince Carey, Matt Settles, Kristof de Beuf,
Benilton Carvalho
Maintainer: Benilton Carvalho <benilton@unicamp.br>
git_url: https://git.bioconductor.org/packages/pdInfoBuilder
git_branch: devel
git_last_commit: 0504fdc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/pdInfoBuilder_1.71.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pdInfoBuilder_1.71.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pdInfoBuilder_1.71.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pdInfoBuilder_1.71.0.tgz
vignettes: vignettes/pdInfoBuilder/inst/doc/BuildingPDInfoPkgs.pdf,
vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.pdf
vignetteTitles: Building Annotation Packages with pdInfoBuilder for Use
with the oligo Package, PDInfo Package Building Affymetrix
Mapping Chips
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.R
suggestsMe: maqcExpression4plex, aroma.affymetrix, maGUI
dependencyCount: 64
Package: PeacoQC
Version: 1.17.0
Depends: R (>= 4.0)
Imports: circlize, ComplexHeatmap, flowCore, flowWorkspace, ggplot2,
grDevices, grid, gridExtra, methods, plyr, stats, utils
Suggests: knitr, rmarkdown, BiocStyle
License: GPL (>=3)
MD5sum: 3fb67dc5649ee0bf34237458d98f6c0f
NeedsCompilation: no
Title: Peak-based selection of high quality cytometry data
Description: This is a package that includes pre-processing and quality
control functions that can remove margin events, compensate and
transform the data and that will use PeacoQCSignalStability for
quality control. This last function will first detect peaks in
each channel of the flowframe. It will remove anomalies based
on the IsolationTree function and the MAD outlier detection
method. This package can be used for both flow- and mass
cytometry data.
biocViews: FlowCytometry, QualityControl, Preprocessing, PeakDetection
Author: Annelies Emmaneel [aut, cre]
Maintainer: Annelies Emmaneel <annelies.emmaneel@hotmail.com>
URL: http://github.com/saeyslab/PeacoQC
VignetteBuilder: knitr
BugReports: http://github.com/saeyslab/PeacoQC/issues
git_url: https://git.bioconductor.org/packages/PeacoQC
git_branch: devel
git_last_commit: 3449ab9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PeacoQC_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PeacoQC_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PeacoQC_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PeacoQC_1.17.0.tgz
vignettes: vignettes/PeacoQC/inst/doc/PeacoQC_Vignette.pdf
vignetteTitles: PeacoQC_Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PeacoQC/inst/doc/PeacoQC_Vignette.R
importsMe: CytoPipeline
dependencyCount: 83
Package: peakPantheR
Version: 1.21.0
Depends: R (>= 4.2)
Imports: foreach (>= 1.4.4), doParallel (>= 1.0.11), ggplot2 (>=
3.5.0), gridExtra (>= 2.3), MSnbase (>= 2.4.0), mzR (>=
2.12.0), stringr (>= 1.2.0), methods (>= 3.4.0), XML (>=
3.98.1.10), minpack.lm (>= 1.2.1), scales(>= 0.5.0), shiny (>=
1.0.5), bslib, shinycssloaders (>= 1.0.0), DT (>= 0.15), pracma
(>= 2.2.3), utils, lubridate, svglite (>= 2.1.1)
Suggests: testthat, devtools, faahKO, msdata, knitr, rmarkdown, pander,
BiocStyle
License: GPL-3
Archs: x64
MD5sum: 7df6dc0f574354126eb32278ab88a15f
NeedsCompilation: no
Title: Peak Picking and Annotation of High Resolution Experiments
Description: An automated pipeline for the detection, integration and
reporting of predefined features across a large number of mass
spectrometry data files. It enables the real time annotation of
multiple compounds in a single file, or the parallel annotation
of multiple compounds in multiple files. A graphical user
interface as well as command line functions will assist in
assessing the quality of annotation and update fitting
parameters until a satisfactory result is obtained.
biocViews: MassSpectrometry, Metabolomics, PeakDetection
Author: Arnaud Wolfer [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5856-3218>), Goncalo Correia [aut]
(ORCID: <https://orcid.org/0000-0001-8271-9294>), Jake Pearce
[ctb], Caroline Sands [ctb]
Maintainer: Arnaud Wolfer <adwolfer@gmail.com>
URL: https://github.com/phenomecentre/peakPantheR
VignetteBuilder: knitr
BugReports: https://github.com/phenomecentre/peakPantheR/issues/new
git_url: https://git.bioconductor.org/packages/peakPantheR
git_branch: devel
git_last_commit: 4d6e977
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/peakPantheR_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/peakPantheR_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/peakPantheR_1.21.0.tgz
vignettes: vignettes/peakPantheR/inst/doc/getting-started.html,
vignettes/peakPantheR/inst/doc/parallel-annotation.html,
vignettes/peakPantheR/inst/doc/peakPantheR-GUI.html,
vignettes/peakPantheR/inst/doc/real-time-annotation.html
vignetteTitles: Getting Started with the peakPantheR package, Parallel
Annotation, peakPantheR Graphical User Interface, Real Time
Annotation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/peakPantheR/inst/doc/getting-started.R,
vignettes/peakPantheR/inst/doc/parallel-annotation.R,
vignettes/peakPantheR/inst/doc/peakPantheR-GUI.R,
vignettes/peakPantheR/inst/doc/real-time-annotation.R
dependencyCount: 150
Package: PECA
Version: 1.43.0
Depends: R (>= 3.3)
Imports: ROTS, limma, affy, genefilter, preprocessCore,
aroma.affymetrix, aroma.core
Suggests: SpikeIn
License: GPL (>= 2)
MD5sum: 698fe50dbcb3d8de2d42260d3fc62062
NeedsCompilation: no
Title: Probe-level Expression Change Averaging
Description: Calculates Probe-level Expression Change Averages (PECA)
to identify differential expression in Affymetrix gene
expression microarray studies or in proteomic studies using
peptide-level mesurements respectively.
biocViews: Software, Proteomics, Microarray, DifferentialExpression,
GeneExpression, ExonArray, DifferentialSplicing
Author: Tomi Suomi, Jukka Hiissa, Laura L. Elo
Maintainer: Tomi Suomi <tomi.suomi@utu.fi>
git_url: https://git.bioconductor.org/packages/PECA
git_branch: devel
git_last_commit: a4a29d4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PECA_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PECA_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PECA_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PECA_1.43.0.tgz
vignettes: vignettes/PECA/inst/doc/PECA.pdf
vignetteTitles: PECA: Probe-level Expression Change Averaging
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PECA/inst/doc/PECA.R
dependencyCount: 104
Package: peco
Version: 1.19.0
Depends: R (>= 3.5.0)
Imports: assertthat, circular, conicfit, doParallel, foreach, genlasso
(>= 1.4), graphics, methods, parallel, scater,
SingleCellExperiment, SummarizedExperiment, stats, utils
Suggests: knitr, rmarkdown
License: GPL (>= 3)
Archs: x64
MD5sum: a8432650393b347d3fe0ef977082af7b
NeedsCompilation: no
Title: A Supervised Approach for **P**r**e**dicting **c**ell Cycle
Pr**o**gression using scRNA-seq data
Description: Our approach provides a way to assign continuous cell
cycle phase using scRNA-seq data, and consequently, allows to
identify cyclic trend of gene expression levels along the cell
cycle. This package provides method and training data, which
includes scRNA-seq data collected from 6 individual cell lines
of induced pluripotent stem cells (iPSCs), and also continuous
cell cycle phase derived from FUCCI fluorescence imaging data.
biocViews: Sequencing, RNASeq, GeneExpression, Transcriptomics,
SingleCell, Software, StatisticalMethod, Classification,
Visualization
Author: Chiaowen Joyce Hsiao [aut, cre], Matthew Stephens [aut], John
Blischak [ctb], Peter Carbonetto [ctb]
Maintainer: Chiaowen Joyce Hsiao <joyce.hsiao1@gmail.com>
URL: https://github.com/jhsiao999/peco
VignetteBuilder: knitr
BugReports: https://github.com/jhsiao999/peco/issues
git_url: https://git.bioconductor.org/packages/peco
git_branch: devel
git_last_commit: a87c0cd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/peco_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/peco_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/peco_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/peco_1.19.0.tgz
vignettes: vignettes/peco/inst/doc/vignette.html
vignetteTitles: An example of predicting cell cycle phase using peco
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/peco/inst/doc/vignette.R
dependencyCount: 119
Package: Pedixplorer
Version: 1.3.4
Depends: R (>= 4.4.0)
Imports: graphics, stats, methods, ggplot2, utils, grDevices, stringr,
plyr, dplyr, tidyr, quadprog, Matrix, S4Vectors, shiny, readxl,
shinyWidgets, htmlwidgets, DT, gridExtra, data.table, plotly,
colourpicker, shinytoastr, scales, shinycssloaders
Suggests: diffviewer, testthat (>= 3.0.0), vdiffr, rmarkdown,
BiocStyle, knitr, withr, qpdf, shinytest2, devtools, R.devices,
usethis, rlang, magick, cyclocomp
License: Artistic-2.0
MD5sum: 25f83bb7b2028b70057b9d0632bc12b7
NeedsCompilation: no
Title: Pedigree Functions
Description: Routines to handle family data with a Pedigree object. The
initial purpose was to create correlation structures that
describe family relationships such as kinship and
identity-by-descent, which can be used to model family data in
mixed effects models, such as in the coxme function. Also
includes a tool for Pedigree drawing which is focused on
producing compact layouts without intervention. Recent
additions include utilities to trim the Pedigree object with
various criteria, and kinship for the X chromosome.
biocViews: Software, DataRepresentation, Genetics, GraphAndNetwork,
Visualization
Author: Louis Le Nezet [aut, cre, ctb] (ORCID:
<https://orcid.org/0009-0000-0202-2703>), Jason Sinnwell [aut],
Terry Therneau [aut], Daniel Schaid [ctb], Elizabeth Atkinson
[ctb]
Maintainer: Louis Le Nezet <louislenezet@gmail.com>
URL: https://louislenezet.github.io/Pedixplorer/
VignetteBuilder: knitr
BugReports: https://github.com/LouisLeNezet/Pedixplorer/issues
git_url: https://git.bioconductor.org/packages/Pedixplorer
git_branch: devel
git_last_commit: 7eacf67
git_last_commit_date: 2025-03-15
Date/Publication: 2025-03-17
source.ver: src/contrib/Pedixplorer_1.3.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Pedixplorer_1.3.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Pedixplorer_1.3.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Pedixplorer_1.3.4.tgz
vignettes: vignettes/Pedixplorer/inst/doc/pedigree_alignment.html,
vignettes/Pedixplorer/inst/doc/pedigree_kinship.html,
vignettes/Pedixplorer/inst/doc/pedigree_object.html,
vignettes/Pedixplorer/inst/doc/pedigree_plot.html,
vignettes/Pedixplorer/inst/doc/Pedixplorer.html
vignetteTitles: Pedigree alignment details, Pedigree kinship() details,
Pedigree object, Pedigree plotting details, Pedixplorer
tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Pedixplorer/inst/doc/pedigree_alignment.R,
vignettes/Pedixplorer/inst/doc/pedigree_kinship.R,
vignettes/Pedixplorer/inst/doc/pedigree_object.R,
vignettes/Pedixplorer/inst/doc/pedigree_plot.R,
vignettes/Pedixplorer/inst/doc/Pedixplorer.R
dependencyCount: 103
Package: pengls
Version: 1.13.0
Depends: R (>= 4.2.0)
Imports: glmnet, nlme, stats, BiocParallel
Suggests: knitr,rmarkdown,testthat
License: GPL-2
MD5sum: 9c0e1a94eae933b8a586de201837ee4c
NeedsCompilation: no
Title: Fit Penalised Generalised Least Squares models
Description: Combine generalised least squares methodology from the
nlme package for dealing with autocorrelation with penalised
least squares methods from the glmnet package to deal with high
dimensionality. This pengls packages glues them together
through an iterative loop. The resulting method is applicable
to high dimensional datasets that exhibit autocorrelation, such
as spatial or temporal data.
biocViews: Transcriptomics, Regression, TimeCourse, Spatial
Author: Stijn Hawinkel [cre, aut] (ORCID:
<https://orcid.org/0000-0002-4501-5180>)
Maintainer: Stijn Hawinkel <stijn.hawinkel@psb.ugent.be>
VignetteBuilder: knitr
BugReports: https://github.com/sthawinke/pengls
git_url: https://git.bioconductor.org/packages/pengls
git_branch: devel
git_last_commit: 1a0f003
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/pengls_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pengls_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pengls_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pengls_1.13.0.tgz
vignettes: vignettes/pengls/inst/doc/penglsVignette.html
vignetteTitles: Vignette of the pengls package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pengls/inst/doc/penglsVignette.R
dependencyCount: 27
Package: PepSetTest
Version: 1.1.0
Imports: dplyr, limma, lme4, MASS, matrixStats, reshape2, stats,
tibble, SummarizedExperiment, methods
Suggests: statmod, BiocStyle, knitr, rmarkdown, tidyr
License: GPL (>= 3)
MD5sum: 04384970b0f71ad64b08f8019d7c0043
NeedsCompilation: no
Title: Peptide Set Test
Description: Peptide Set Test (PepSetTest) is a peptide-centric
strategy to infer differentially expressed proteins in LC-MS/MS
proteomics data. This test detects coordinated changes in the
expression of peptides originating from the same protein and
compares these changes against the rest of the peptidome.
Compared to traditional aggregation-based approaches, the
peptide set test demonstrates improved statistical power, yet
controlling the Type I error rate correctly in most cases. This
test can be valuable for discovering novel biomarkers and
prioritizing drug targets, especially when the direct
application of statistical analysis to protein data fails to
provide substantial insights.
biocViews: DifferentialExpression, Regression, Proteomics,
MassSpectrometry
Author: Junmin Wang [aut, cre]
Maintainer: Junmin Wang <jmwang.bio@gmail.com>
URL: https://github.com/JmWangBio/PepSetTest
VignetteBuilder: knitr
BugReports: https://github.com/JmWangBio/PepSetTest/issues
git_url: https://git.bioconductor.org/packages/PepSetTest
git_branch: devel
git_last_commit: 7d3c75f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PepSetTest_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PepSetTest_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PepSetTest_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PepSetTest_1.1.0.tgz
vignettes: vignettes/PepSetTest/inst/doc/PepSetTest.html
vignetteTitles: A Tutorial for PepSetTest
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PepSetTest/inst/doc/PepSetTest.R
dependencyCount: 69
Package: PepsNMR
Version: 1.25.0
Depends: R (>= 3.6)
Imports: Matrix, ptw, ggplot2, gridExtra, matrixStats, reshape2,
methods, graphics, stats
Suggests: knitr, markdown, rmarkdown, BiocStyle, PepsNMRData
License: GPL-2 | file LICENSE
MD5sum: 1a9a4e00ce260319bcb0305d11d70d94
NeedsCompilation: no
Title: Pre-process 1H-NMR FID signals
Description: This package provides R functions for common pre-procssing
steps that are applied on 1H-NMR data. It also provides a
function to read the FID signals directly in the Bruker format.
biocViews: Software, Preprocessing, Visualization, Metabolomics,
DataImport
Author: Manon Martin [aut, cre], Bernadette Govaerts [aut, ths], Benoît
Legat [aut], Paul H.C. Eilers [aut], Pascal de Tullio [dtc],
Bruno Boulanger [ctb], Julien Vanwinsberghe [ctb]
Maintainer: Manon Martin <manon.martin@uclouvain.be>
URL: https://github.com/ManonMartin/PepsNMR
VignetteBuilder: knitr
BugReports: https://github.com/ManonMartin/PepsNMR/issues
git_url: https://git.bioconductor.org/packages/PepsNMR
git_branch: devel
git_last_commit: 1fcfe4b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PepsNMR_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PepsNMR_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PepsNMR_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PepsNMR_1.25.0.tgz
vignettes: vignettes/PepsNMR/inst/doc/PepsNMR_minimal_example.html
vignetteTitles: Application of PepsNMR on the Human Serum dataset
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PepsNMR/inst/doc/PepsNMR_minimal_example.R
importsMe: ASICS
dependencyCount: 46
Package: pepStat
Version: 1.41.0
Depends: R (>= 3.0.0), Biobase, IRanges
Imports: limma, fields, GenomicRanges, ggplot2, plyr, tools, methods,
data.table
Suggests: pepDat, Pviz, knitr, shiny
License: Artistic-2.0
MD5sum: d9d212c1dee6c80bd62c1a700fa15ab0
NeedsCompilation: no
Title: Statistical analysis of peptide microarrays
Description: Statistical analysis of peptide microarrays
biocViews: Microarray, Preprocessing
Author: Raphael Gottardo, Gregory C Imholte, Renan Sauteraud, Mike
Jiang
Maintainer: Gregory C Imholte <gimholte@uw.edu>
URL: https://github.com/RGLab/pepStat
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/pepStat
git_branch: devel
git_last_commit: 42e8f77
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/pepStat_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pepStat_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pepStat_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pepStat_1.41.0.tgz
vignettes: vignettes/pepStat/inst/doc/pepStat.pdf
vignetteTitles: Full peptide microarray analysis
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pepStat/inst/doc/pepStat.R
dependencyCount: 63
Package: pepXMLTab
Version: 1.41.0
Depends: R (>= 3.0.1)
Imports: XML(>= 3.98-1.1)
Suggests: RUnit, BiocGenerics
License: Artistic-2.0
MD5sum: 7f25228aa3ff2f5610b1678d1f62b31c
NeedsCompilation: no
Title: Parsing pepXML files and filter based on peptide FDR.
Description: Parsing pepXML files based one XML package. The package
tries to handle pepXML files generated from different
softwares. The output will be a peptide-spectrum-matching
tabular file. The package also provide function to filter the
PSMs based on FDR.
biocViews: ImmunoOncology, Proteomics, MassSpectrometry
Author: Xiaojing Wang
Maintainer: Xiaojing Wang <xiaojing.wang@vanderbilt.edu>
git_url: https://git.bioconductor.org/packages/pepXMLTab
git_branch: devel
git_last_commit: 6c9f5e2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/pepXMLTab_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pepXMLTab_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pepXMLTab_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pepXMLTab_1.41.0.tgz
vignettes: vignettes/pepXMLTab/inst/doc/pepXMLTab.pdf
vignetteTitles: Introduction to pepXMLTab
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pepXMLTab/inst/doc/pepXMLTab.R
dependencyCount: 3
Package: periodicDNA
Version: 1.17.0
Depends: R (>= 4.0), Biostrings, GenomicRanges, IRanges, BSgenome,
BiocParallel
Imports: S4Vectors, rtracklayer, stats, GenomeInfoDb, magrittr, zoo,
ggplot2, methods, parallel, cowplot
Suggests: BSgenome.Scerevisiae.UCSC.sacCer3,
BSgenome.Celegans.UCSC.ce11, BSgenome.Dmelanogaster.UCSC.dm6,
BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38,
BSgenome.Mmusculus.UCSC.mm10, reticulate, testthat, covr,
knitr, rmarkdown, pkgdown
License: GPL-3 + file LICENSE
MD5sum: dd5471fd6c8ba8bb2648ade9a4d836c3
NeedsCompilation: no
Title: Set of tools to identify periodic occurrences of k-mers in DNA
sequences
Description: This R package helps the user identify k-mers (e.g. di- or
tri-nucleotides) present periodically in a set of genomic loci
(typically regulatory elements). The functions of this package
provide a straightforward approach to find periodic occurrences
of k-mers in DNA sequences, such as regulatory elements. It is
not aimed at identifying motifs separated by a conserved
distance; for this type of analysis, please visit MEME website.
biocViews: SequenceMatching, MotifDiscovery, MotifAnnotation,
Sequencing, Coverage, Alignment, DataImport
Author: Jacques Serizay [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4295-0624>)
Maintainer: Jacques Serizay <jacquesserizay@gmail.com>
URL: https://github.com/js2264/periodicDNA
VignetteBuilder: knitr
BugReports: https://github.com/js2264/periodicDNA/issues
git_url: https://git.bioconductor.org/packages/periodicDNA
git_branch: devel
git_last_commit: 75b48a6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/periodicDNA_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/periodicDNA_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/periodicDNA_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/periodicDNA_1.17.0.tgz
vignettes: vignettes/periodicDNA/inst/doc/periodicDNA.html
vignetteTitles: Introduction to periodicDNA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/periodicDNA/inst/doc/periodicDNA.R
dependencyCount: 87
Package: pfamAnalyzeR
Version: 1.7.0
Depends: R (>= 4.3.0), readr, stringr, dplyr
Imports: utils, tibble, magrittr
Suggests: BiocStyle, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 61e05c3f8192fb73363aec580d3d00f3
NeedsCompilation: no
Title: Identification of domain isotypes in pfam data
Description: Protein domains is one of the most import annoation of
proteins we have with the Pfam database/tool being (by far) the
most used tool. This R package enables the user to read the
pfam prediction from both webserver and stand-alone runs into
R. We have recently shown most human protein domains exist as
multiple distinct variants termed domain isotypes. Different
domain isotypes are used in a cell, tissue, and
disease-specific manner. Accordingly, we find that domain
isotypes, compared to each other, modulate, or abolish the
functionality of a protein domain. This R package enables the
identification and classification of such domain isotypes from
Pfam data.
biocViews: AlternativeSplicing, TranscriptomeVariant,
BiomedicalInformatics, FunctionalGenomics, SystemsBiology,
Annotation, FunctionalPrediction, GenePrediction, DataImport
Author: Kristoffer Vitting-Seerup [cre, aut] (ORCID:
<https://orcid.org/0000-0002-6450-0608>)
Maintainer: Kristoffer Vitting-Seerup <k.vitting.seerup@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/kvittingseerup/pfamAnalyzeR/issues
git_url: https://git.bioconductor.org/packages/pfamAnalyzeR
git_branch: devel
git_last_commit: f426e74
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/pfamAnalyzeR_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pfamAnalyzeR_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pfamAnalyzeR_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pfamAnalyzeR_1.7.0.tgz
vignettes: vignettes/pfamAnalyzeR/inst/doc/pfamAnalyzeR.html
vignetteTitles: pfamAnalyzeR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/pfamAnalyzeR/inst/doc/pfamAnalyzeR.R
dependsOnMe: IsoformSwitchAnalyzeR
dependencyCount: 35
Package: pgca
Version: 1.31.0
Imports: utils, stats
Suggests: knitr, testthat, rmarkdown
License: GPL (>= 2)
MD5sum: ea1413ca8dd1506e6274a9720c1fb075
NeedsCompilation: no
Title: PGCA: An Algorithm to Link Protein Groups Created from MS/MS
Data
Description: Protein Group Code Algorithm (PGCA) is a computationally
inexpensive algorithm to merge protein summaries from multiple
experimental quantitative proteomics data. The algorithm
connects two or more groups with overlapping accession numbers.
In some cases, pairwise groups are mutually exclusive but they
may still be connected by another group (or set of groups) with
overlapping accession numbers. Thus, groups created by PGCA
from multiple experimental runs (i.e., global groups) are
called "connected" groups. These identified global protein
groups enable the analysis of quantitative data available for
protein groups instead of unique protein identifiers.
biocViews:
WorkflowStep,AssayDomain,Proteomics,MassSpectrometry,ImmunoOncology
Author: Gabriela Cohen-Freue <gcohen@stat.ubc.ca>
Maintainer: Gabriela Cohen-Freue <gcohen@stat.ubc.ca>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/pgca
git_branch: devel
git_last_commit: ec5b4ff
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/pgca_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pgca_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pgca_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pgca_1.31.0.tgz
vignettes: vignettes/pgca/inst/doc/intro.html
vignetteTitles: Introduction
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pgca/inst/doc/intro.R
dependencyCount: 2
Package: pgxRpi
Version: 1.3.5
Depends: R (>= 4.2)
Imports: utils, methods, grDevices, graphics, circlize, httr, dplyr,
attempt, lubridate, survival, survminer, ggplot2,
GenomicRanges, SummarizedExperiment, S4Vectors, yaml, parallel,
future, future.apply
Suggests: BiocStyle, rmarkdown, knitr, testthat
License: Artistic-2.0
MD5sum: 70c9c40a88d838cae77460789ecc284e
NeedsCompilation: no
Title: R wrapper for Progenetix
Description: The package is an R wrapper for Progenetix REST API built
upon the Beacon v2 protocol. Its purpose is to provide a
seamless way for retrieving genomic data from Progenetix
database—an open resource dedicated to curated oncogenomic
profiles. Empowered by this package, users can effortlessly
access and visualize data from Progenetix.
biocViews: CopyNumberVariation, GenomicVariation, DataImport, Software
Author: Hangjia Zhao [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8376-5751>), Michael Baudis [aut]
(ORCID: <https://orcid.org/0000-0002-9903-4248>)
Maintainer: Hangjia Zhao <hangjia.zhao@uzh.ch>
URL: https://github.com/progenetix/pgxRpi
VignetteBuilder: knitr
BugReports: https://github.com/progenetix/pgxRpi/issues
git_url: https://git.bioconductor.org/packages/pgxRpi
git_branch: devel
git_last_commit: 0392824
git_last_commit_date: 2025-03-21
Date/Publication: 2025-03-21
source.ver: src/contrib/pgxRpi_1.3.5.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pgxRpi_1.3.5.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pgxRpi_1.3.5.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pgxRpi_1.3.5.tgz
vignettes: vignettes/pgxRpi/inst/doc/Introduction_1_load_metadata.html,
vignettes/pgxRpi/inst/doc/Introduction_2_query_variants.html,
vignettes/pgxRpi/inst/doc/Introduction_3_access_cnv_frequency.html,
vignettes/pgxRpi/inst/doc/Introduction_4_process_pgxseg.html
vignetteTitles: Introduction_1_load_metadata,
Introduction_2_query_variants,
Introduction_3_access_cnv_frequency,
Introduction_4_process_pgxseg
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pgxRpi/inst/doc/Introduction_1_load_metadata.R,
vignettes/pgxRpi/inst/doc/Introduction_2_query_variants.R,
vignettes/pgxRpi/inst/doc/Introduction_3_access_cnv_frequency.R,
vignettes/pgxRpi/inst/doc/Introduction_4_process_pgxseg.R
dependencyCount: 140
Package: phantasus
Version: 1.27.0
Depends: R (>= 4.3)
Imports: ggplot2, protolite, Biobase, GEOquery, Rook, htmltools,
httpuv, jsonlite, limma, edgeR, opencpu, assertthat, methods,
httr, rhdf5, utils, parallel, stringr, fgsea (>= 1.9.4),
svglite, gtable, stats, Matrix, pheatmap, scales, ccaPP, grid,
grDevices, AnnotationDbi, DESeq2, data.table, curl, apeglm,
tidyr, config (>= 0.3.2), rhdf5client (>= 1.25.1), yaml, fs,
phantasusLite, XML
Suggests: testthat, BiocStyle, knitr, rmarkdown, org.Hs.eg.db,
org.Mm.eg.db
License: MIT + file LICENSE
MD5sum: 251ef56c68e9b599d3f5a3471ba8714a
NeedsCompilation: no
Title: Visual and interactive gene expression analysis
Description: Phantasus is a web-application for visual and interactive
gene expression analysis. Phantasus is based on Morpheus – a
web-based software for heatmap visualisation and analysis,
which was integrated with an R environment via OpenCPU API.
Aside from basic visualization and filtering methods, R-based
methods such as k-means clustering, principal component
analysis or differential expression analysis with limma package
are supported.
biocViews: GeneExpression, GUI, Visualization, DataRepresentation,
Transcriptomics, RNASeq, Microarray, Normalization, Clustering,
DifferentialExpression, PrincipalComponent, ImmunoOncology
Author: Maxim Kleverov [aut], Daria Zenkova [aut], Vladislav Kamenev
[aut], Margarita Sablina [ctb], Maxim Artyomov [aut], Alexey
Sergushichev [aut, cre]
Maintainer: Alexey Sergushichev <alsergbox@gmail.com>
URL: https://alserglab.wustl.edu/phantasus
VignetteBuilder: knitr
BugReports: https://github.com/ctlab/phantasus/issues
git_url: https://git.bioconductor.org/packages/phantasus
git_branch: devel
git_last_commit: 57b2ec9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/phantasus_1.27.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/phantasus_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/phantasus_1.27.0.tgz
vignettes: vignettes/phantasus/inst/doc/phantasus-tutorial.html
vignetteTitles: Using phantasus application
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/phantasus/inst/doc/phantasus-tutorial.R
dependencyCount: 164
Package: phantasusLite
Version: 1.5.0
Depends: R (>= 4.2)
Imports: data.table, rhdf5client(>= 1.25.1), httr, stringr, stats,
utils, Biobase, methods
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle, rhdf5,
GEOquery
License: MIT + file LICENSE
MD5sum: fd8d806e071afd34e789ec317d6eacf2
NeedsCompilation: no
Title: Loading and annotation RNA-seq counts matrices
Description: PhantasusLite – a lightweight package with helper
functions of general interest extracted from phantasus package.
In parituclar it simplifies working with public RNA-seq
datasets from GEO by providing access to the remote HSDS
repository with the precomputed gene counts from ARCHS4 and
DEE2 projects.
biocViews: GeneExpression, Transcriptomics, RNASeq
Author: Rita Sablina [aut], Maxim Kleverov [aut], Alexey Sergushichev
[aut, cre]
Maintainer: Alexey Sergushichev <alsergbox@gmail.com>
URL: https://github.com/ctlab/phantasusLite/
VignetteBuilder: knitr
BugReports: https://github.com/ctlab/phantasusLite/issues
git_url: https://git.bioconductor.org/packages/phantasusLite
git_branch: devel
git_last_commit: 13aa8ea
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/phantasusLite_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/phantasusLite_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/phantasusLite_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/phantasusLite_1.5.0.tgz
vignettes: vignettes/phantasusLite/inst/doc/phantasusLite-tutorial.html
vignetteTitles: phantasusLite tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/phantasusLite/inst/doc/phantasusLite-tutorial.R
importsMe: phantasus
dependencyCount: 42
Package: PharmacoGx
Version: 3.11.1
Depends: R (>= 3.6), CoreGx
Imports: BiocGenerics, Biobase, S4Vectors, SummarizedExperiment,
MultiAssayExperiment, BiocParallel, ggplot2, RColorBrewer,
magicaxis, parallel, caTools, methods, downloader, stats,
utils, graphics, grDevices, reshape2, jsonlite, data.table,
checkmate, boot, coop
LinkingTo: Rcpp
Suggests: pander, rmarkdown, knitr, knitcitations, crayon, testthat,
markdown, BiocStyle, R.utils
License: GPL (>= 3)
MD5sum: a9e004d7ccf5a007d883c28b18426186
NeedsCompilation: yes
Title: Analysis of Large-Scale Pharmacogenomic Data
Description: Contains a set of functions to perform large-scale
analysis of pharmaco-genomic data. These include the
PharmacoSet object for storing the results of pharmacogenomic
experiments, as well as a number of functions for computing
common summaries of drug-dose response and correlating them
with the molecular features in a cancer cell-line.
biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics,
Software, Classification
Author: Petr Smirnov [aut], Christopher Eeles [aut], Jermiah Joseph
[aut], Zhaleh Safikhani [aut], Mark Freeman [aut], Feifei Li
[aut], Benjamin Haibe-Kains [aut, cre]
Maintainer: Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>
VignetteBuilder: knitr
BugReports: https://github.com/bhklab/PharmacoGx/issues
git_url: https://git.bioconductor.org/packages/PharmacoGx
git_branch: devel
git_last_commit: d1415db
git_last_commit_date: 2025-01-07
Date/Publication: 2025-01-08
source.ver: src/contrib/PharmacoGx_3.11.1.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PharmacoGx_3.11.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PharmacoGx_3.11.1.tgz
vignettes: vignettes/PharmacoGx/inst/doc/CreatingPharmacoSet.html,
vignettes/PharmacoGx/inst/doc/DetectingDrugSynergyAndAntagonism.html,
vignettes/PharmacoGx/inst/doc/PharmacoGx.html
vignetteTitles: Creating a PharmacoSet Object, Detecting Drug Synergy
and Antagonism with PharmacoGx 3.0+, PharmacoGx: An R Package
for Analysis of Large Pharmacogenomic Datasets
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PharmacoGx/inst/doc/CreatingPharmacoSet.R,
vignettes/PharmacoGx/inst/doc/DetectingDrugSynergyAndAntagonism.R,
vignettes/PharmacoGx/inst/doc/PharmacoGx.R
importsMe: gDRimport, Xeva
suggestsMe: ToxicoGx
dependencyCount: 154
Package: PhenoGeneRanker
Version: 1.15.0
Imports: igraph, Matrix, foreach, doParallel, dplyr, stats, utils,
parallel
Suggests: knitr, rmarkdown
License: Creative Commons Attribution 4.0 International License
MD5sum: d67d1cb5eca340dadfaa929569d52ac2
NeedsCompilation: no
Title: PhenoGeneRanker: A gene and phenotype prioritization tool
Description: This package is a gene/phenotype prioritization tool that
utilizes multiplex heterogeneous gene phenotype network.
PhenoGeneRanker allows multi-layer gene and phenotype networks.
It also calculates empirical p-values of gene/phenotype ranking
using random stratified sampling of genes/phenotypes based on
their connectivity degree in the network.
https://dl.acm.org/doi/10.1145/3307339.3342155.
biocViews: BiomedicalInformatics, GenePrediction, GraphAndNetwork,
Network, NetworkInference, Pathways, Software, SystemsBiology
Author: Cagatay Dursun [aut, cre]
Maintainer: Cagatay Dursun <cagataydursun@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PhenoGeneRanker
git_branch: devel
git_last_commit: 9dbd9f0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PhenoGeneRanker_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PhenoGeneRanker_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PhenoGeneRanker_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PhenoGeneRanker_1.15.0.tgz
vignettes: vignettes/PhenoGeneRanker/inst/doc/PhenoGeneRanker.html
vignetteTitles: PhenoGeneRanker
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PhenoGeneRanker/inst/doc/PhenoGeneRanker.R
dependencyCount: 31
Package: phenomis
Version: 1.9.0
Depends: SummarizedExperiment
Imports: Biobase, biodb, biodbChebi, data.table, futile.logger,
ggplot2, ggrepel, graphics, grDevices, grid, htmlwidgets,
igraph, limma, methods, MultiAssayExperiment, MultiDataSet,
PMCMRplus, plotly, ranger, RColorBrewer, ropls, stats, tibble,
tidyr, utils, VennDiagram
Suggests: BiocGenerics, BiocStyle, biosigner, CLL, knitr, omicade4,
rmarkdown, testthat
License: CeCILL
MD5sum: 3e030601a777810746bece09d6b18892
NeedsCompilation: no
Title: Postprocessing and univariate analysis of omics data
Description: The 'phenomis' package provides methods to perform
post-processing (i.e. quality control and normalization) as
well as univariate statistical analysis of single and
multi-omics data sets. These methods include quality control
metrics, signal drift and batch effect correction, intensity
transformation, univariate hypothesis testing, but also
clustering (as well as annotation of metabolomics data). The
data are handled in the standard Bioconductor formats (i.e.
SummarizedExperiment and MultiAssayExperiment for single and
multi-omics datasets, respectively; the alternative
ExpressionSet and MultiDataSet formats are also supported for
convenience). As a result, all methods can be readily chained
as workflows. The pipeline can be further enriched by
multivariate analysis and feature selection, by using the
'ropls' and 'biosigner' packages, which support the same
formats. Data can be conveniently imported from and exported to
text files. Although the methods were initially targeted to
metabolomics data, most of the methods can be applied to other
types of omics data (e.g., transcriptomics, proteomics).
biocViews: BatchEffect, Clustering, Coverage, KEGG, MassSpectrometry,
Metabolomics, Normalization, Proteomics, QualityControl,
Sequencing, StatisticalMethod, Transcriptomics
Author: Etienne A. Thevenot [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1019-4577>), Natacha Lenuzza
[ctb], Marie Tremblay-Franco [ctb], Alyssa Imbert [ctb],
Pierrick Roger [ctb], Eric Venot [ctb], Sylvain Dechaumet [ctb]
Maintainer: Etienne A. Thevenot <etienne.thevenot@cea.fr>
URL: https://doi.org/10.1038/s41597-021-01095-3
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/phenomis
git_branch: devel
git_last_commit: b15f3f7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/phenomis_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/phenomis_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/phenomis_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/phenomis_1.9.0.tgz
vignettes: vignettes/phenomis/inst/doc/phenomis-vignette.html
vignetteTitles: phenomis-vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/phenomis/inst/doc/phenomis-vignette.R
suggestsMe: ropls
dependencyCount: 154
Package: phenopath
Version: 1.31.0
Imports: Rcpp (>= 0.12.8), SummarizedExperiment, methods, stats, dplyr,
tibble, ggplot2, tidyr
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, forcats, testthat, BiocStyle,
SingleCellExperiment
License: Apache License (== 2.0)
MD5sum: ae11f42533ed419a97c605c801e6509d
NeedsCompilation: yes
Title: Genomic trajectories with heterogeneous genetic and
environmental backgrounds
Description: PhenoPath infers genomic trajectories (pseudotimes) in the
presence of heterogeneous genetic and environmental backgrounds
and tests for interactions between them.
biocViews: ImmunoOncology, RNASeq, GeneExpression, Bayesian,
SingleCell, PrincipalComponent
Author: Kieran Campbell
Maintainer: Kieran Campbell <kieranrcampbell@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/phenopath
git_branch: devel
git_last_commit: e66e4a6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/phenopath_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/phenopath_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/phenopath_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/phenopath_1.31.0.tgz
vignettes: vignettes/phenopath/inst/doc/introduction_to_phenopath.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/phenopath/inst/doc/introduction_to_phenopath.R
suggestsMe: splatter
dependencyCount: 70
Package: phenoTest
Version: 1.55.0
Depends: R (>= 3.6.0), Biobase, methods, annotate, Heatplus, BMA,
ggplot2, Hmisc
Imports: survival, limma, gplots, Category, AnnotationDbi, hopach,
biomaRt, GSEABase, genefilter, xtable, annotate, mgcv,
hgu133a.db, ellipse
Suggests: GSEABase, GO.db
Enhances: parallel, org.Ce.eg.db, org.Mm.eg.db, org.Rn.eg.db,
org.Hs.eg.db, org.Dm.eg.db
License: GPL (>=2)
MD5sum: ad7433b2a2569ca5236d13dfdd842412
NeedsCompilation: no
Title: Tools to test association between gene expression and phenotype
in a way that is efficient, structured, fast and scalable. We
also provide tools to do GSEA (Gene set enrichment analysis)
and copy number variation.
Description: Tools to test correlation between gene expression and
phenotype in a way that is efficient, structured, fast and
scalable. GSEA is also provided.
biocViews: Microarray, DifferentialExpression, MultipleComparison,
Clustering, Classification
Author: Evarist Planet
Maintainer: Evarist Planet <evarist.planet@epfl.ch>
git_url: https://git.bioconductor.org/packages/phenoTest
git_branch: devel
git_last_commit: 689f101
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/phenoTest_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/phenoTest_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/phenoTest_1.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/phenoTest_1.55.0.tgz
vignettes: vignettes/phenoTest/inst/doc/phenoTest.pdf
vignetteTitles: Manual for the phenoTest library
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/phenoTest/inst/doc/phenoTest.R
importsMe: canceR
dependencyCount: 145
Package: PhenStat
Version: 2.43.0
Depends: R (>= 3.5.0)
Imports: SmoothWin, methods, car, nlme, nortest, MASS, msgps, logistf,
knitr, tools, pingr, ggplot2, reshape, corrplot, graph, lme4,
graphics, grDevices, utils, stats
Suggests: RUnit, BiocGenerics
License: file LICENSE
MD5sum: c670458808ee1712479274c77d7fcc45
NeedsCompilation: no
Title: Statistical analysis of phenotypic data
Description: Package contains methods for statistical analysis of
phenotypic data.
biocViews: StatisticalMethod
Author: Natalja Kurbatova, Natasha Karp, Jeremy Mason, Hamed
Haselimashhadi
Maintainer: Hamed Haselimashhadi <hamedhm@ebi.ac.uk>
git_url: https://git.bioconductor.org/packages/PhenStat
git_branch: devel
git_last_commit: 59cb12c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PhenStat_2.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PhenStat_2.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PhenStat_2.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PhenStat_2.43.0.tgz
vignettes: vignettes/PhenStat/inst/doc/PhenStat.pdf
vignetteTitles: PhenStat Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PhenStat/inst/doc/PhenStat.R
dependencyCount: 120
Package: philr
Version: 1.33.0
Imports: ape, phangorn, tidyr, ggplot2, ggtree, methods
Suggests: testthat, knitr, ecodist, rmarkdown, BiocStyle, phyloseq,
SummarizedExperiment, TreeSummarizedExperiment, glmnet, dplyr,
mia
License: GPL-3
MD5sum: ae358b71a0587942eb951bfa5aef03a1
NeedsCompilation: no
Title: Phylogenetic partitioning based ILR transform for metagenomics
data
Description: PhILR is short for Phylogenetic Isometric Log-Ratio
Transform. This package provides functions for the analysis of
compositional data (e.g., data representing proportions of
different variables/parts). Specifically this package allows
analysis of compositional data where the parts can be related
through a phylogenetic tree (as is common in microbiota survey
data) and makes available the Isometric Log Ratio transform
built from the phylogenetic tree and utilizing a weighted
reference measure.
biocViews: ImmunoOncology, Sequencing, Microbiome, Metagenomics,
Software
Author: Justin Silverman [aut, cre], Leo Lahti [ctb] (ORCID:
<https://orcid.org/0000-0001-5537-637X>)
Maintainer: Justin Silverman <jsilve24@gmail.com>
URL: https://github.com/jsilve24/philr
VignetteBuilder: knitr
BugReports: https://github.com/jsilve24/philr/issues
git_url: https://git.bioconductor.org/packages/philr
git_branch: devel
git_last_commit: 9061ebc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/philr_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/philr_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/philr_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/philr_1.33.0.tgz
vignettes: vignettes/philr/inst/doc/philr-intro.html
vignetteTitles: Introduction to PhILR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/philr/inst/doc/philr-intro.R
suggestsMe: mia
dependencyCount: 64
Package: PhIPData
Version: 1.15.0
Depends: R (>= 4.1.0), SummarizedExperiment (>= 1.3.81)
Imports: BiocFileCache, BiocGenerics, methods, GenomicRanges, IRanges,
S4Vectors, edgeR, cli, utils
Suggests: BiocStyle, testthat, knitr, rmarkdown, covr, dplyr, readr,
withr
License: MIT + file LICENSE
Archs: x64
MD5sum: 736836e503fa9bf41b12d19dcf0fe4a3
NeedsCompilation: no
Title: Container for PhIP-Seq Experiments
Description: PhIPData defines an S4 class for phage-immunoprecipitation
sequencing (PhIP-seq) experiments. Buliding upon the
RangedSummarizedExperiment class, PhIPData enables users to
coordinate metadata with experimental data in analyses.
Additionally, PhIPData provides specialized methods to subset
and identify beads-only samples, subset objects using virus
aliases, and use existing peptide libraries to populate object
parameters.
biocViews: Infrastructure, DataRepresentation, Sequencing, Coverage
Author: Athena Chen [aut, cre] (ORCID:
<https://orcid.org/0000-0001-6900-2264>), Rob Scharpf [aut],
Ingo Ruczinski [aut]
Maintainer: Athena Chen <achen70@jhu.edu>
VignetteBuilder: knitr
BugReports: https://github.com/athchen/PhIPData/issues
git_url: https://git.bioconductor.org/packages/PhIPData
git_branch: devel
git_last_commit: 3dd9454
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PhIPData_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PhIPData_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PhIPData_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PhIPData_1.15.0.tgz
vignettes: vignettes/PhIPData/inst/doc/PhIPData.html
vignetteTitles: PhIPData: A Container for PhIP-Seq Experiments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PhIPData/inst/doc/PhIPData.R
dependsOnMe: beer
dependencyCount: 71
Package: phosphonormalizer
Version: 1.31.0
Depends: R (>= 4.0)
Imports: plyr, stats, graphics, matrixStats, methods
Suggests: knitr, rmarkdown, testthat
Enhances: MSnbase
License: GPL (>= 2)
MD5sum: e953f26c9a6c2b642be2d7dea0c7c8e9
NeedsCompilation: no
Title: Compensates for the bias introduced by median normalization in
Description: It uses the overlap between enriched and non-enriched
datasets to compensate for the bias introduced in global
phosphorylation after applying median normalization.
biocViews: Software, StatisticalMethod, WorkflowStep, Normalization,
Proteomics
Author: Sohrab Saraei [aut, cre], Tomi Suomi [ctb], Otto Kauko [ctb],
Laura Elo [ths]
Maintainer: Sohrab Saraei <sohrab.saraei@blueprintgenetics.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/phosphonormalizer
git_branch: devel
git_last_commit: b1d7f27
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/phosphonormalizer_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/phosphonormalizer_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/phosphonormalizer_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/phosphonormalizer_1.31.0.tgz
vignettes: vignettes/phosphonormalizer/inst/doc/phosphonormalizer.pdf,
vignettes/phosphonormalizer/inst/doc/vignette.html
vignetteTitles: phosphonormalizer: Phosphoproteomics Normalization,
Pairwise normalization of phosphoproteomics data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/phosphonormalizer/inst/doc/phosphonormalizer.R,
vignettes/phosphonormalizer/inst/doc/vignette.R
dependencyCount: 7
Package: PhosR
Version: 1.17.0
Depends: R (>= 4.2.0)
Imports: ruv, e1071, dendextend, limma, pcaMethods, stats,
RColorBrewer, circlize, dplyr, igraph, pheatmap,
preprocessCore, tidyr, rlang, graphics, grDevices, utils,
SummarizedExperiment, methods, S4Vectors, BiocGenerics,
ggplot2, GGally, ggdendro, ggpubr, network, reshape2, ggtext,
stringi
Suggests: testthat, knitr, rgl, sna, ClueR, directPA, rmarkdown,
org.Rn.eg.db, org.Mm.eg.db, reactome.db, annotate, BiocStyle,
stringr, calibrate
License: GPL-3 + file LICENSE
MD5sum: 8c51a271394cc0f3a2baa7c6bafd887f
NeedsCompilation: no
Title: A set of methods and tools for comprehensive analysis of
phosphoproteomics data
Description: PhosR is a package for the comprenhensive analysis of
phosphoproteomic data. There are two major components to PhosR:
processing and downstream analysis. PhosR consists of various
processing tools for phosphoproteomics data including
filtering, imputation, normalisation, and functional analysis
for inferring active kinases and signalling pathways.
biocViews: Software, ResearchField, Proteomics
Author: Pengyi Yang [aut], Taiyun Kim [aut, cre], Hani Jieun Kim [aut]
Maintainer: Taiyun Kim <taiyun.kim91@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PhosR
git_branch: devel
git_last_commit: 1e4d7a4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PhosR_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PhosR_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PhosR_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PhosR_1.17.0.tgz
vignettes: vignettes/PhosR/inst/doc/PhosR.html
vignetteTitles: An introduction to PhosR package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PhosR/inst/doc/PhosR.R
dependencyCount: 142
Package: PhyloProfile
Version: 1.99.7
Depends: R (>= 4.4.0)
Imports: ape, bioDist, BiocStyle, Biostrings, colourpicker, data.table,
dplyr, DT, energy, ExperimentHub, extrafont, fastcluster,
ggplot2, gridExtra, pbapply, plotly, RColorBrewer, RCurl,
Rfast, scattermore, shiny, shinyBS, shinycssloaders,
shinyFiles, shinyjs, stringr, tsne, svglite, umap, xml2, zoo,
yaml
Suggests: knitr, rmarkdown, testthat, OmaDB
License: MIT + file LICENSE
MD5sum: 2e2c788c767dcebc177e8a7983bb8f2e
NeedsCompilation: no
Title: PhyloProfile
Description: PhyloProfile is a tool for exploring complex phylogenetic
profiles. Phylogenetic profiles, presence/absence patterns of
genes over a set of species, are commonly used to trace the
functional and evolutionary history of genes across species and
time. With PhyloProfile we can enrich regular phylogenetic
profiles with further data like sequence/structure similarity,
to make phylogenetic profiling more meaningful. Besides the
interactive visualisation powered by R-Shiny, the package
offers a set of further analysis features to gain insights like
the gene age estimation or core gene identification.
biocViews: Software, Visualization, DataRepresentation,
MultipleComparison, FunctionalPrediction, DimensionReduction
Author: Vinh Tran [aut, cre] (ORCID:
<https://orcid.org/0000-0001-6772-7595>), Bastian Greshake
Tzovaras [aut], Ingo Ebersberger [aut], Carla Mölbert [ctb]
Maintainer: Vinh Tran <tran@bio.uni-frankfurt.de>
URL: https://github.com/BIONF/PhyloProfile/
VignetteBuilder: knitr
BugReports: https://github.com/BIONF/PhyloProfile/issues
git_url: https://git.bioconductor.org/packages/PhyloProfile
git_branch: devel
git_last_commit: a894902
git_last_commit_date: 2025-03-21
Date/Publication: 2025-03-23
source.ver: src/contrib/PhyloProfile_1.99.7.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PhyloProfile_1.99.7.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PhyloProfile_1.99.7.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PhyloProfile_1.99.7.tgz
vignettes: vignettes/PhyloProfile/inst/doc/PhyloProfile-vignette.html
vignetteTitles: PhyloProfile
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PhyloProfile/inst/doc/PhyloProfile-vignette.R
dependencyCount: 151
Package: phyloseq
Version: 1.51.0
Depends: R (>= 3.3.0)
Imports: ade4 (>= 1.7-4), ape (>= 5.0), Biobase (>= 2.36.2),
BiocGenerics (>= 0.22.0), biomformat (>= 1.0.0), Biostrings (>=
2.40.0), cluster (>= 2.0.4), data.table (>= 1.10.4), foreach
(>= 1.4.3), ggplot2 (>= 2.1.0), igraph (>= 1.0.1), methods (>=
3.3.0), multtest (>= 2.28.0), plyr (>= 1.8.3), reshape2 (>=
1.4.1), scales (>= 0.4.0), vegan (>= 2.5)
Suggests: BiocStyle (>= 2.4), DESeq2 (>= 1.16.1), genefilter (>= 1.58),
knitr (>= 1.16), magrittr (>= 1.5), metagenomeSeq (>= 1.14),
rmarkdown (>= 1.6), testthat (>= 1.0.2)
Enhances: doParallel (>= 1.0.10)
License: AGPL-3
MD5sum: b02da7c28dfa5a7259cd5b7399c88b0a
NeedsCompilation: no
Title: Handling and analysis of high-throughput microbiome census data
Description: phyloseq provides a set of classes and tools to facilitate
the import, storage, analysis, and graphical display of
microbiome census data.
biocViews: ImmunoOncology, Sequencing, Microbiome, Metagenomics,
Clustering, Classification, MultipleComparison,
GeneticVariability
Author: Paul J. McMurdie <joey711@gmail.com>, Susan Holmes
<susan@stat.stanford.edu>, with contributions from Gregory
Jordan and Scott Chamberlain
Maintainer: Paul J. McMurdie <joey711@gmail.com>
URL: http://dx.plos.org/10.1371/journal.pone.0061217
VignetteBuilder: knitr
BugReports: https://github.com/joey711/phyloseq/issues
git_url: https://git.bioconductor.org/packages/phyloseq
git_branch: devel
git_last_commit: fca5959
git_last_commit_date: 2024-10-29
Date/Publication: 2025-01-23
source.ver: src/contrib/phyloseq_1.51.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/phyloseq_1.51.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/phyloseq_1.51.0.tgz
vignettes: vignettes/phyloseq/inst/doc/phyloseq-analysis.html,
vignettes/phyloseq/inst/doc/phyloseq-basics.html,
vignettes/phyloseq/inst/doc/phyloseq-FAQ.html,
vignettes/phyloseq/inst/doc/phyloseq-mixture-models.html
vignetteTitles: analysis vignette, phyloseq basics vignette, phyloseq
Frequently Asked Questions (FAQ), phyloseq and DESeq2 on
Colorectal Cancer Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/phyloseq/inst/doc/phyloseq-analysis.R,
vignettes/phyloseq/inst/doc/phyloseq-basics.R,
vignettes/phyloseq/inst/doc/phyloseq-FAQ.R,
vignettes/phyloseq/inst/doc/phyloseq-mixture-models.R
dependsOnMe: microbiome, SIAMCAT, MiscMetabar, phyloseqGraphTest
importsMe: ADAPT, benchdamic, combi, dar, MBECS, microbiomeDASim,
PathoStat, RCM, reconsi, RPA, SimBu, SPsimSeq, zitools,
HMP2Data, adaptiveGPCA, BaHZING, breakaway, chem16S, FLORAL,
holobiont, HybridMicrobiomes, microbial, mixKernel, multimedia,
SigTree, speedytax, TaxaNorm, treeDA
suggestsMe: ANCOMBC, decontam, lefser, MGnifyR, mia, MicrobiotaProcess,
MMUPHin, philr, HMP16SData, corncob, FAVA, fido, file2meco,
metacoder, parafac4microbiome, pctax, phyloregion
dependencyCount: 82
Package: piano
Version: 2.23.0
Depends: R (>= 3.5)
Imports: BiocGenerics, Biobase, gplots, igraph, relations, marray,
fgsea, shiny, DT, htmlwidgets, shinyjs, shinydashboard,
visNetwork, scales, grDevices, graphics, stats, utils, methods
Suggests: yeast2.db, rsbml, plotrix, limma, affy, plier, affyPLM,
gtools, biomaRt, snowfall, AnnotationDbi, knitr, rmarkdown,
BiocStyle
License: GPL (>=2)
MD5sum: 727d3e0215591903753a9c0a496a1b42
NeedsCompilation: no
Title: Platform for integrative analysis of omics data
Description: Piano performs gene set analysis using various statistical
methods, from different gene level statistics and a wide range
of gene-set collections. Furthermore, the Piano package
contains functions for combining the results of multiple runs
of gene set analyses.
biocViews: Microarray, Preprocessing, QualityControl,
DifferentialExpression, Visualization, GeneExpression,
GeneSetEnrichment, Pathways
Author: Leif Varemo Wigge <piano.rpkg@gmail.com> and Intawat Nookaew
<piano.rpkg@gmail.com>
Maintainer: Leif Varemo Wigge <piano.rpkg@gmail.com>
URL: http://www.sysbio.se/piano
VignetteBuilder: knitr
BugReports: https://github.com/varemo/piano/issues
git_url: https://git.bioconductor.org/packages/piano
git_branch: devel
git_last_commit: f5e29d0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/piano_2.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/piano_2.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/piano_2.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/piano_2.23.0.tgz
vignettes: vignettes/piano/inst/doc/piano-vignette.pdf,
vignettes/piano/inst/doc/Running_gene-set_analysis_with_piano.html
vignetteTitles: Piano - Platform for Integrative Analysis of Omics
data, Running gene-set anaysis with piano
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/piano/inst/doc/piano-vignette.R,
vignettes/piano/inst/doc/Running_gene-set_analysis_with_piano.R
importsMe: CoreGx, PDATK
suggestsMe: cosmosR, BloodCancerMultiOmics2017
dependencyCount: 103
Package: PICB
Version: 0.99.15
Imports: utils, GenomicRanges, GenomicAlignments, GenomeInfoDb,
Biostrings, Rsamtools, data.table, IRanges, seqinr, stats,
openxlsx, dplyr, S4Vectors, methods
Suggests: knitr, rtracklayer, testthat, BiocStyle, prettydoc, BSgenome,
BSgenome.Dmelanogaster.UCSC.dm6, BiocManager, rmarkdown,
ggplot2
License: CC0
MD5sum: 89df16a4d7c7ab45fd0303fe849dc280
NeedsCompilation: no
Title: piRNA Cluster Builder
Description: piRNAs (short for PIWI-interacting RNAs) and their PIWI
protein partners play a key role in fertility and maintaining
genome integrity by restricting mobile genetic elements
(transposons) in germ cells. piRNAs originate from genomic
regions known as piRNA clusters. The piRNA Cluster Builder
(PICB) is a versatile toolkit designed to identify genomic
regions with a high density of piRNAs. It constructs piRNA
clusters through a stepwise integration of unique and
multimapping piRNAs and offers wide-ranging parameter settings,
supported by an optimization function that allows users to test
different parameter combinations to tailor the analysis to
their specific piRNA system. The output includes extensive
metadata columns, enabling researchers to rank clusters and
extract cluster characteristics.
biocViews: Genetics, GenomeAnnotation, Sequencing,
FunctionalPrediction, Coverage, Transcriptomics
Author: Pavol Genzor [aut], Aleksandr Friman [aut], Daniel Stoyko
[aut], Parthena Konstantinidou [aut], Franziska Ahrend [aut,
cre] (ORCID: <https://orcid.org/0009-0004-7464-3444>), Zuzana
Loubalova [aut], Yuejun Wang [aut], Hernan Lorenzi [aut],
Astrid D Haase [aut]
Maintainer: Franziska Ahrend <haase-lab-bioinfo@nih.gov>
URL: https://github.com/HaaseLab/PICB
VignetteBuilder: knitr
BugReports: https://github.com/HaaseLab/PICB/issues
git_url: https://git.bioconductor.org/packages/PICB
git_branch: devel
git_last_commit: 7163d82
git_last_commit_date: 2025-02-06
Date/Publication: 2025-02-09
source.ver: src/contrib/PICB_0.99.15.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PICB_0.99.15.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PICB_0.99.15.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PICB_0.99.15.tgz
vignettes: vignettes/PICB/inst/doc/PICB.html
vignetteTitles: Introduction to the piRNA Cluster Builder (PICB)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PICB/inst/doc/PICB.R
dependencyCount: 78
Package: pickgene
Version: 1.79.0
Imports: graphics, grDevices, MASS, stats, utils
License: GPL (>= 2)
MD5sum: 6bbda0aa1e802ed3c0edea51085e4675
NeedsCompilation: no
Title: Adaptive Gene Picking for Microarray Expression Data Analysis
Description: Functions to Analyze Microarray (Gene Expression) Data.
biocViews: Microarray, DifferentialExpression
Author: Brian S. Yandell <yandell@stat.wisc.edu>
Maintainer: Brian S. Yandell <yandell@stat.wisc.edu>
URL: http://www.stat.wisc.edu/~yandell/statgen
git_url: https://git.bioconductor.org/packages/pickgene
git_branch: devel
git_last_commit: 681c91a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/pickgene_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pickgene_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pickgene_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pickgene_1.79.0.tgz
vignettes: vignettes/pickgene/inst/doc/pickgene.pdf
vignetteTitles: Adaptive Gene Picking for Microarray Expression Data
Analysis
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 6
Package: PICS
Version: 2.51.0
Depends: R (>= 3.0.0)
Imports: utils, stats, graphics, grDevices, methods, IRanges,
GenomicRanges, Rsamtools, GenomicAlignments
Suggests: rtracklayer, parallel, knitr
License: Artistic-2.0
MD5sum: b49c50d8c987aad52b362f8d3f1aaf63
NeedsCompilation: yes
Title: Probabilistic inference of ChIP-seq
Description: Probabilistic inference of ChIP-Seq using an empirical
Bayes mixture model approach.
biocViews: Clustering, Visualization, Sequencing, ChIPseq
Author: Xuekui Zhang <xzhang@stat.ubc.ca>, Raphael Gottardo
<rgottard@fhcrc.org>
Maintainer: Renan Sauteraud <renan.sauteraud@gmail.com>
URL: https://github.com/SRenan/PICS
VignetteBuilder: knitr
BugReports: https://github.com/SRenan/PICS/issues
git_url: https://git.bioconductor.org/packages/PICS
git_branch: devel
git_last_commit: 59be92f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PICS_2.51.0.tar.gz
vignettes: vignettes/PICS/inst/doc/PICS.html
vignetteTitles: The PICS users guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PICS/inst/doc/PICS.R
importsMe: PING
dependencyCount: 51
Package: Pigengene
Version: 1.33.0
Depends: R (>= 4.0.3), graph, BiocStyle (>= 2.28.0)
Imports: bnlearn (>= 4.7), C50 (>= 0.1.2), MASS, matrixStats, partykit,
Rgraphviz, WGCNA, GO.db, impute, preprocessCore, grDevices,
graphics, stats, utils, parallel, pheatmap (>= 1.0.8), dplyr,
gdata, clusterProfiler, ReactomePA, ggplot2, openxlsx, DBI,
DOSE
Suggests: org.Hs.eg.db (>= 3.7.0), org.Mm.eg.db (>= 3.7.0), biomaRt (>=
2.30.0), knitr, AnnotationDbi, energy
License: GPL (>=2)
MD5sum: 050912e566a8158668fdefa7d4e0c260
NeedsCompilation: no
Title: Infers biological signatures from gene expression data
Description: Pigengene package provides an efficient way to infer
biological signatures from gene expression profiles. The
signatures are independent from the underlying platform, e.g.,
the input can be microarray or RNA Seq data. It can even infer
the signatures using data from one platform, and evaluate them
on the other. Pigengene identifies the modules (clusters) of
highly coexpressed genes using coexpression network analysis,
summarizes the biological information of each module in an
eigengene, learns a Bayesian network that models the
probabilistic dependencies between modules, and builds a
decision tree based on the expression of eigengenes.
biocViews: GeneExpression, RNASeq, NetworkInference, Network,
GraphAndNetwork, BiomedicalInformatics, SystemsBiology,
Transcriptomics, Classification, Clustering, DecisionTree,
DimensionReduction, PrincipalComponent, Microarray,
Normalization, ImmunoOncology
Author: Habil Zare, Amir Foroushani, Rupesh Agrahari, Meghan Short,
Isha Mehta, Neda Emami, and Sogand Sajedi
Maintainer: Habil Zare <zare@u.washington.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Pigengene
git_branch: devel
git_last_commit: 25bba85
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Pigengene_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Pigengene_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Pigengene_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Pigengene_1.33.0.tgz
vignettes: vignettes/Pigengene/inst/doc/Pigengene_inference.pdf
vignetteTitles: Pigengene: Computing and using eigengenes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Pigengene/inst/doc/Pigengene_inference.R
importsMe: iNETgrate
dependencyCount: 184
Package: PING
Version: 2.51.0
Depends: R(>= 3.5.0)
Imports: methods, PICS, graphics, grDevices, stats, Gviz, fda,
BSgenome, stats4, BiocGenerics, IRanges, GenomicRanges,
S4Vectors
Suggests: parallel, ShortRead, rtracklayer
License: Artistic-2.0
MD5sum: f1657891f0d0c8e351e0c7d98c2cf868
NeedsCompilation: yes
Title: Probabilistic inference for Nucleosome Positioning with
MNase-based or Sonicated Short-read Data
Description: Probabilistic inference of ChIP-Seq using an empirical
Bayes mixture model approach.
biocViews: Clustering, StatisticalMethod, Visualization, Sequencing
Author: Xuekui Zhang <xuezhang@jhsph.edu>, Raphael Gottardo
<rgottard@fredhutch.org>, Sangsoon Woo <swoo@fhcrc.org>
Maintainer: Renan Sauteraud <renan.sauteraud@gmail.com>
git_url: https://git.bioconductor.org/packages/PING
git_branch: devel
git_last_commit: 025f8b7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PING_2.51.0.tar.gz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 173
Package: pipeComp
Version: 1.17.0
Depends: R (>= 4.1)
Imports: BiocParallel, S4Vectors, ComplexHeatmap, SingleCellExperiment,
SummarizedExperiment, Seurat, matrixStats, Matrix, cluster,
aricode, methods, utils, dplyr, grid, scales, scran,
viridisLite, clue, randomcoloR, ggplot2, cowplot,
intrinsicDimension, scater, knitr, reshape2, stats, Rtsne,
uwot, circlize, RColorBrewer
Suggests: BiocStyle, rmarkdown
License: GPL
MD5sum: 4d2a416922c1e854599a3a8e7f3d1941
NeedsCompilation: no
Title: pipeComp pipeline benchmarking framework
Description: A simple framework to facilitate the comparison of
pipelines involving various steps and parameters. The
`pipelineDefinition` class represents pipelines as, minimally,
a set of functions consecutively executed on the output of the
previous one, and optionally accompanied by step-wise
evaluation and aggregation functions. Given such an object, a
set of alternative parameters/methods, and benchmark datasets,
the `runPipeline` function then proceeds through all
combinations arguments, avoiding recomputing the same step
twice and compiling evaluations on the fly to avoid storing
potentially large intermediate data.
biocViews: GeneExpression, Transcriptomics, Clustering,
DataRepresentation
Author: Pierre-Luc Germain [cre, aut] (ORCID:
<https://orcid.org/0000-0003-3418-4218>), Anthony Sonrel [aut]
(ORCID: <https://orcid.org/0000-0002-2414-715X>), Mark D.
Robinson [aut, fnd] (ORCID:
<https://orcid.org/0000-0002-3048-5518>)
Maintainer: Pierre-Luc Germain <pierre-luc.germain@hest.ethz.ch>
URL: https://doi.org/10.1186/s13059-020-02136-7
VignetteBuilder: knitr
BugReports: https://github.com/plger/pipeComp
git_url: https://git.bioconductor.org/packages/pipeComp
git_branch: devel
git_last_commit: be750ef
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/pipeComp_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pipeComp_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pipeComp_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pipeComp_1.17.0.tgz
vignettes: vignettes/pipeComp/inst/doc/pipeComp_dea.html,
vignettes/pipeComp/inst/doc/pipeComp_scRNA.html,
vignettes/pipeComp/inst/doc/pipeComp.html
vignetteTitles: pipeComp_dea, pipeComp_scRNA, pipeComp
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/pipeComp/inst/doc/pipeComp_dea.R,
vignettes/pipeComp/inst/doc/pipeComp_scRNA.R,
vignettes/pipeComp/inst/doc/pipeComp.R
dependencyCount: 218
Package: pipeFrame
Version: 1.23.0
Depends: R (>= 4.0.0),
Imports: BSgenome, digest, visNetwork, magrittr, methods, Biostrings,
GenomeInfoDb, parallel, stats, utils, rmarkdown
Suggests: BiocManager, knitr, rtracklayer, testthat,
BSgenome.Hsapiens.UCSC.hg19
License: GPL-3
MD5sum: 78dfa5108d8ab4bec68e3689ea0e46b8
NeedsCompilation: no
Title: Pipeline framework for bioinformatics in R
Description: pipeFrame is an R package for building a componentized
bioinformatics pipeline. Each step in this pipeline is wrapped
in the framework, so the connection among steps is created
seamlessly and automatically. Users could focus more on
fine-tuning arguments rather than spending a lot of time on
transforming file format, passing task outputs to task inputs
or installing the dependencies. Componentized step elements can
be customized into other new pipelines flexibly as well. This
pipeline can be split into several important functional steps,
so it is much easier for users to understand the complex
arguments from each step rather than parameter combination from
the whole pipeline. At the same time, componentized pipeline
can restart at the breakpoint and avoid rerunning the whole
pipeline, which may save a lot of time for users on pipeline
tuning or such issues as power off or process other interrupts.
biocViews: Software, Infrastructure, WorkflowStep
Author: Zheng Wei, Shining Ma
Maintainer: Zheng Wei <wzweizheng@qq.com>
URL: https://github.com/wzthu/pipeFrame
VignetteBuilder: knitr
BugReports: https://github.com/wzthu/pipeFrame/issues
git_url: https://git.bioconductor.org/packages/pipeFrame
git_branch: devel
git_last_commit: f9587ae
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/pipeFrame_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pipeFrame_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pipeFrame_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pipeFrame_1.23.0.tgz
vignettes: vignettes/pipeFrame/inst/doc/pipeFrame.html
vignetteTitles: An Introduction to pipeFrame
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pipeFrame/inst/doc/pipeFrame.R
dependsOnMe: esATAC
dependencyCount: 84
Package: PIPETS
Version: 1.3.0
Depends: R (>= 4.4.0)
Imports: dplyr, utils, stats, GenomicRanges, BiocGenerics, methods
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0)
License: GPL-3
MD5sum: b62e90e81f9d8dfe85e90174e1923a57
NeedsCompilation: no
Title: Poisson Identification of PEaks from Term-Seq data
Description: PIPETS provides statistically robust analysis for
3'-seq/term-seq data. It utilizes a sliding window approach to
apply a Poisson Distribution test to identify genomic positions
with termination read coverage that is significantly higher
than the surrounding signal. PIPETS then condenses proximal
signal and produces strand specific results that contain all
significant termination peaks.
biocViews: Sequencing, Transcription, GeneRegulation, PeakDetection,
Genetics, Transcriptomics, Coverage
Author: Quinlan Furumo [aut, cre] (ORCID:
<https://orcid.org/0000-0003-4486-302X>)
Maintainer: Quinlan Furumo <furumo@bc.edu>
URL: https://github.com/qfurumo/PIPETS
VignetteBuilder: knitr
BugReports: https://github.com/qfurumo/PIPETS/issues
git_url: https://git.bioconductor.org/packages/PIPETS
git_branch: devel
git_last_commit: 611f676
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PIPETS_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PIPETS_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PIPETS_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PIPETS_1.3.0.tgz
vignettes: vignettes/PIPETS/inst/doc/PIPETS.html
vignetteTitles: PIPETS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PIPETS/inst/doc/PIPETS.R
dependencyCount: 38
Package: Pirat
Version: 1.1.0
Depends: R (>= 4.4.0)
Imports: basilisk, reticulate, progress, ggplot2, MASS, invgamma,
grDevices, stats, graphics, SummarizedExperiment, S4Vectors
Suggests: knitr, BiocStyle
License: GPL-2
MD5sum: e553be7913a5dec9229358fbecae7d97
NeedsCompilation: no
Title: Precursor or Peptide Imputation under Random Truncation
Description: Pirat enables the imputation of missing values (either
MNARs or MCARs) in bottom-up LC-MS/MS proteomics data using a
penalized maximum likelihood strategy. It does not require any
parameter tuning, it models the instrument censorship from the
data available. It accounts for sibling peptides correlations
and it can leverage complementary transcriptomics measurements.
biocViews: Proteomics, MassSpectrometry, Preprocessing, Software
Author: Lucas Etourneau [aut], Laura Fancello [aut], Samuel Wieczorek
[cre, aut] (ORCID: <https://orcid.org/0000-0002-5016-1203>),
Nelle Varoquaux [aut], Thomas Burger [aut]
Maintainer: Samuel Wieczorek <samuel.wieczorek@cea.fr>
URL: http://www.prostar-proteomics.org/
VignetteBuilder: knitr
BugReports: https://github.com/prostarproteomics/Pirat/issues
git_url: https://git.bioconductor.org/packages/Pirat
git_branch: devel
git_last_commit: d00d343
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Pirat_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Pirat_1.1.0.zip
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Pirat_1.1.0.tgz
vignettes: vignettes/Pirat/inst/doc/Pirat.html
vignetteTitles: Pirat-vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Pirat/inst/doc/Pirat.R
dependencyCount: 78
Package: PIUMA
Version: 1.3.0
Depends: R (>= 4.3), ggplot2
Imports: cluster, umap, tsne, kernlab, vegan, dbscan, igraph, scales,
Hmisc, patchwork, grDevices, stats, methods,
SummarizedExperiment
Suggests: BiocStyle, knitr, testthat, rmarkdown
License: GPL-3 + file LICENSE
MD5sum: 66ddb6542f3cedc09413f19990d74783
NeedsCompilation: no
Title: Phenotypes Identification Using Mapper from topological data
Analysis
Description: The PIUMA package offers a tidy pipeline of Topological
Data Analysis frameworks to identify and characterize
communities in high and heterogeneous dimensional data.
biocViews: Clustering, GraphAndNetwork, DimensionReduction, Network,
Classification
Author: Mattia Chiesa [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7427-9954>), Arianna Dagliati
[aut] (ORCID: <https://orcid.org/0000-0002-5041-0409>), Alessia
Gerbasi [aut] (ORCID: <https://orcid.org/0000-0003-4501-1777>),
Giuseppe Albi [aut], Laura Ballarini [aut], Luca Piacentini
[aut] (ORCID: <https://orcid.org/0000-0003-1022-4481>)
Maintainer: Mattia Chiesa <mattia.chiesa@cardiologicomonzino.it>
URL: https://github.com/BioinfoMonzino/PIUMA
VignetteBuilder: knitr
BugReports: https://github.com/BioinfoMonzino/PIUMA/issues
git_url: https://git.bioconductor.org/packages/PIUMA
git_branch: devel
git_last_commit: 223a516
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PIUMA_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PIUMA_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PIUMA_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PIUMA_1.3.0.tgz
vignettes: vignettes/PIUMA/inst/doc/PIUMA_vignette.html
vignetteTitles: PIUMA package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PIUMA/inst/doc/PIUMA_vignette.R
dependencyCount: 114
Package: planet
Version: 1.15.5
Depends: R (>= 4.3)
Imports: methods, tibble, magrittr, dplyr
Suggests: ExperimentHub, mixOmics, ggplot2, testthat, tidyr, scales,
minfi, EpiDISH, knitr, rmarkdown
License: GPL-2
MD5sum: 66aa4dfa697dd4f5e84fd14d3ff7d9a3
NeedsCompilation: no
Title: Placental DNA methylation analysis tools
Description: This package contains R functions to predict biological
variables to from placnetal DNA methylation data generated from
infinium arrays. This includes inferring ethnicity/ancestry,
gestational age, and cell composition from placental DNA
methylation array (450k/850k) data.
biocViews: Software, DifferentialMethylation, Epigenetics, Microarray,
MethylationArray, DNAMethylation, CpGIsland
Author: Victor Yuan [aut, cre], Wendy P. Robinson [aut, ctb], IcÃar
Fernández-Boyano [aut, ctb]
Maintainer: Victor Yuan <victor.2wy@gmail.com>
URL: http://github.com/wvictor14/planet, http://victoryuan.com/planet/
VignetteBuilder: knitr
BugReports: http://github.com/wvictor14/planet/issues
git_url: https://git.bioconductor.org/packages/planet
git_branch: devel
git_last_commit: faa2413
git_last_commit_date: 2025-02-01
Date/Publication: 2025-02-02
source.ver: src/contrib/planet_1.15.5.tar.gz
win.binary.ver: bin/windows/contrib/4.5/planet_1.15.5.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/planet_1.15.5.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/planet_1.15.5.tgz
vignettes: vignettes/planet/inst/doc/planet.html
vignetteTitles: planet
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/planet/inst/doc/planet.R
importsMe: methylclock
suggestsMe: eoPredData
dependencyCount: 20
Package: planttfhunter
Version: 1.7.0
Depends: R (>= 4.2.0)
Imports: Biostrings, SummarizedExperiment, utils, methods
Suggests: BiocStyle, covr, sessioninfo, knitr, rmarkdown, testthat (>=
3.0.0)
License: GPL-3
MD5sum: 95f64f35cbe8f43c06bb0412c0f0324f
NeedsCompilation: no
Title: Identification and classification of plant transcription factors
Description: planttfhunter is used to identify plant transcription
factors (TFs) from protein sequence data and classify them into
families and subfamilies using the classification scheme
implemented in PlantTFDB. TFs are identified using pre-built
hidden Markov model profiles for DNA-binding domains. Then,
auxiliary and forbidden domains are used with DNA-binding
domains to classify TFs into families and subfamilies (when
applicable). Currently, TFs can be classified in 58 different
TF families/subfamilies.
biocViews: Software, Transcription, FunctionalPrediction,
GenomeAnnotation, FunctionalGenomics, HiddenMarkovModel,
Sequencing, Classification
Author: FabrÃcio Almeida-Silva [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5314-2964>), Yves Van de Peer
[aut] (ORCID: <https://orcid.org/0000-0003-4327-3730>)
Maintainer: FabrÃcio Almeida-Silva <fabricio_almeidasilva@hotmail.com>
URL: https://github.com/almeidasilvaf/planttfhunter
SystemRequirements: HMMER <http://hmmer.org/>
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/planttfhunter
git_url: https://git.bioconductor.org/packages/planttfhunter
git_branch: devel
git_last_commit: b3714d5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/planttfhunter_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/planttfhunter_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/planttfhunter_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/planttfhunter_1.7.0.tgz
vignettes: vignettes/planttfhunter/inst/doc/vignette_planttfhunter.html
vignetteTitles: Genome-wide identification and classification of
transcription factors in plant genomes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/planttfhunter/inst/doc/vignette_planttfhunter.R
dependencyCount: 37
Package: plasmut
Version: 1.5.0
Depends: R (>= 4.3.0)
Imports: tibble, stats, dplyr
Suggests: knitr, rmarkdown, tidyverse, ggrepel, magrittr, qpdf,
BiocStyle, biocViews, testthat (>= 3.0.0)
License: Artistic-2.0
MD5sum: 39e0441b62e666be7c8ff0d024314f35
NeedsCompilation: no
Title: Stratifying mutations observed in cell-free DNA and white blood
cells as germline, hematopoietic, or somatic
Description: A Bayesian method for quantifying the liklihood that a
given plasma mutation arises from clonal hematopoesis or the
underlying tumor. It requires sequencing data of the mutation
in plasma and white blood cells with the number of distinct and
mutant reads in both tissues. We implement a Monte Carlo
importance sampling method to assess the likelihood that a
mutation arises from the tumor relative to non-tumor origin.
biocViews: Bayesian, SomaticMutation, GermlineMutation, Sequencing
Author: Adith Arun [aut, cre], Robert Scharpf [aut]
Maintainer: Adith Arun <adith.3.arun@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/plasmut
git_branch: devel
git_last_commit: 129bd1d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/plasmut_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/plasmut_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/plasmut_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/plasmut_1.5.0.tgz
vignettes: vignettes/plasmut/inst/doc/plasmut.html
vignetteTitles: Modeling the origin of mutations in a liquid biopsy:
cancer or clonal hematopoiesis?
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/plasmut/inst/doc/plasmut.R
dependencyCount: 21
Package: plgem
Version: 1.79.0
Depends: R (>= 2.10)
Imports: utils, Biobase (>= 2.5.5), MASS, methods
License: GPL-2
MD5sum: abbbf59b5ea8ba40f0ac9db31d87054c
NeedsCompilation: no
Title: Detect differential expression in microarray and proteomics
datasets with the Power Law Global Error Model (PLGEM)
Description: The Power Law Global Error Model (PLGEM) has been shown to
faithfully model the variance-versus-mean dependence that
exists in a variety of genome-wide datasets, including
microarray and proteomics data. The use of PLGEM has been shown
to improve the detection of differentially expressed genes or
proteins in these datasets.
biocViews: ImmunoOncology, Microarray, DifferentialExpression,
Proteomics, GeneExpression, MassSpectrometry
Author: Mattia Pelizzola <mattia.pelizzola@gmail.com> and Norman
Pavelka <normanpavelka@gmail.com>
Maintainer: Norman Pavelka <normanpavelka@gmail.com>
URL: http://www.genopolis.it
git_url: https://git.bioconductor.org/packages/plgem
git_branch: devel
git_last_commit: 4123c5b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/plgem_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/plgem_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/plgem_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/plgem_1.79.0.tgz
vignettes: vignettes/plgem/inst/doc/plgem.pdf
vignetteTitles: An introduction to PLGEM
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/plgem/inst/doc/plgem.R
importsMe: INSPEcT
dependencyCount: 9
Package: plier
Version: 1.77.0
Depends: R (>= 2.0), methods
Imports: affy, Biobase, methods
License: GPL (>= 2)
MD5sum: 9193f6732b7c7b8addfe830fcababd32
NeedsCompilation: yes
Title: Implements the Affymetrix PLIER algorithm
Description: The PLIER (Probe Logarithmic Error Intensity Estimate)
method produces an improved signal by accounting for
experimentally observed patterns in probe behavior and handling
error at the appropriately at low and high signal values.
biocViews: Software
Author: Affymetrix Inc., Crispin J Miller, PICR
Maintainer: Crispin Miller <cmiller@picr.man.ac.uk>
git_url: https://git.bioconductor.org/packages/plier
git_branch: devel
git_last_commit: f4e9849
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/plier_1.77.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/plier_1.77.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/plier_1.77.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/plier_1.77.0.tgz
hasREADME: TRUE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
suggestsMe: piano
dependencyCount: 12
Package: plotgardener
Version: 1.13.0
Depends: R (>= 4.1.0)
Imports: curl, data.table, dplyr, GenomeInfoDb, GenomicRanges, glue,
grDevices, grid, ggplotify, IRanges, methods, plyranges, purrr,
Rcpp, RColorBrewer, rhdf5, rlang, stats, strawr, tools, utils,
withr
LinkingTo: Rcpp
Suggests: AnnotationDbi, AnnotationHub, BSgenome,
BSgenome.Hsapiens.UCSC.hg19, ComplexHeatmap, GenomicFeatures,
ggplot2, InteractionSet, knitr, org.Hs.eg.db, rtracklayer,
plotgardenerData, pdftools, png, rmarkdown, scales, showtext,
testthat (>= 3.0.0), TxDb.Hsapiens.UCSC.hg19.knownGene
License: MIT + file LICENSE
MD5sum: 69bb01123c35ec6a4f6947bf3eb01465
NeedsCompilation: yes
Title: Coordinate-Based Genomic Visualization Package for R
Description: Coordinate-based genomic visualization package for R. It
grants users the ability to programmatically produce complex,
multi-paneled figures. Tailored for genomics, plotgardener
allows users to visualize large complex genomic datasets and
provides exquisite control over how plots are placed and
arranged on a page.
biocViews: Visualization, GenomeAnnotation, FunctionalGenomics,
GenomeAssembly, HiC
Author: Nicole Kramer [aut, cre], Eric S. Davis [aut], Craig Wenger
[aut], Sarah Parker [ctb], Erika Deoudes [art], Michael Love
[ctb], Douglas H. Phanstiel [aut, cre, cph]
Maintainer: Nicole Kramer <nekramer27@gmail.com>, Douglas Phanstiel
<douglas_phanstiel@med.unc.edu>
URL: https://phanstiellab.github.io/plotgardener,
https://github.com/PhanstielLab/plotgardener
VignetteBuilder: knitr
BugReports: https://github.com/PhanstielLab/plotgardener/issues
git_url: https://git.bioconductor.org/packages/plotgardener
git_branch: devel
git_last_commit: bc6f006
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/plotgardener_1.13.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/plotgardener_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/plotgardener_1.13.0.tgz
vignettes:
vignettes/plotgardener/inst/doc/introduction_to_plotgardener.html
vignetteTitles: Introduction to plotgardener
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/plotgardener/inst/doc/introduction_to_plotgardener.R
importsMe: DegCre, mariner, Ularcirc
suggestsMe: nullranges, rigvf
dependencyCount: 99
Package: plotGrouper
Version: 1.25.0
Depends: R (>= 3.5)
Imports: ggplot2 (>= 3.0.0), dplyr (>= 0.7.6), tidyr (>= 0.2.0), tibble
(>= 1.4.2), stringr (>= 1.3.1), readr (>= 1.1.1), readxl (>=
1.1.0), scales (>= 1.0.0), stats, grid, gridExtra (>= 2.3), egg
(>= 0.4.0), gtable (>= 0.2.0), ggpubr (>= 0.1.8), shiny (>=
1.1.0), shinythemes (>= 1.1.1), colourpicker (>= 1.0), magrittr
(>= 1.5), Hmisc (>= 4.1.1), rlang (>= 0.2.2)
Suggests: knitr, htmltools, BiocStyle, rmarkdown, testthat
License: GPL-3
MD5sum: adf99f4b400e11c66ba6cc495b60bd90
NeedsCompilation: no
Title: Shiny app GUI wrapper for ggplot with built-in statistical
analysis
Description: A shiny app-based GUI wrapper for ggplot with built-in
statistical analysis. Import data from file and use dropdown
menus and checkboxes to specify the plotting variables, graph
type, and look of your plots. Once created, plots can be saved
independently or stored in a report that can be saved as a pdf.
If new data are added to the file, the report can be refreshed
to include new data. Statistical tests can be selected and
added to the graphs. Analysis of flow cytometry data is
especially integrated with plotGrouper. Count data can be
transformed to return the absolute number of cells in a sample
(this feature requires inclusion of the number of beads per
sample and information about any dilution performed).
biocViews: ImmunoOncology, FlowCytometry, GraphAndNetwork,
StatisticalMethod, DataImport, GUI, MultipleComparison
Author: John D. Gagnon [aut, cre]
Maintainer: John D. Gagnon <john.gagnon.42@gmail.com>
URL: https://jdgagnon.github.io/plotGrouper/
VignetteBuilder: knitr
BugReports: https://github.com/jdgagnon/plotGrouper/issues
git_url: https://git.bioconductor.org/packages/plotGrouper
git_branch: devel
git_last_commit: 6987e2a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/plotGrouper_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/plotGrouper_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/plotGrouper_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/plotGrouper_1.25.0.tgz
vignettes: vignettes/plotGrouper/inst/doc/plotGrouper-vignette.html
vignetteTitles: plotGrouper
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/plotGrouper/inst/doc/plotGrouper-vignette.R
dependencyCount: 136
Package: PLPE
Version: 1.67.0
Depends: R (>= 2.6.2), Biobase (>= 2.5.5), LPE, MASS, methods
License: GPL (>= 2)
MD5sum: c4145d9517d53e227f50c2c4be01a6bf
NeedsCompilation: no
Title: Local Pooled Error Test for Differential Expression with Paired
High-throughput Data
Description: This package performs tests for paired high-throughput
data.
biocViews: Proteomics, Microarray, DifferentialExpression
Author: HyungJun Cho <hj4cho@korea.ac.kr> and Jae K. Lee
<jaeklee@virginia.edu>
Maintainer: Soo-heang Eo <hanansh@korea.ac.kr>
URL: http://www.korea.ac.kr/~stat2242/
git_url: https://git.bioconductor.org/packages/PLPE
git_branch: devel
git_last_commit: 014198f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PLPE_1.67.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PLPE_1.67.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PLPE_1.67.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PLPE_1.67.0.tgz
vignettes: vignettes/PLPE/inst/doc/PLPE.pdf
vignetteTitles: PLPE Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PLPE/inst/doc/PLPE.R
dependencyCount: 10
Package: PLSDAbatch
Version: 1.3.0
Depends: R (>= 4.3.0)
Imports: mixOmics, scales, Rdpack, ggplot2, gridExtra, ggpubr,
lmerTest, performance, grid, stats, pheatmap, vegan, Biobase,
BiocStyle, TreeSummarizedExperiment
Suggests: knitr, rmarkdown, testthat, badger
License: GPL-3
MD5sum: 5904dd74ec8c6f1632c6bbcca6d3a371
NeedsCompilation: no
Title: PLSDA-batch
Description: A novel framework to correct for batch effects prior to
any downstream analysis in microbiome data based on Projection
to Latent Structures Discriminant Analysis. The main method is
named “PLSDA-batchâ€. It first estimates treatment and batch
variation with latent components, then subtracts
batch-associated components from the data whilst preserving
biological variation of interest. PLSDA-batch is highly
suitable for microbiome data as it is non-parametric,
multivariate and allows for ordination and data visualisation.
Combined with centered log-ratio transformation for addressing
uneven library sizes and compositional structure, PLSDA-batch
addresses all characteristics of microbiome data that existing
correction methods have ignored so far. Two other variants are
proposed for 1/ unbalanced batch x treatment designs that are
commonly encountered in studies with small sample sizes, and
for 2/ selection of discriminative variables amongst treatment
groups to avoid overfitting in classification problems. These
two variants have widened the scope of applicability of
PLSDA-batch to different data settings.
biocViews: StatisticalMethod, DimensionReduction, PrincipalComponent,
Classification, Microbiome, BatchEffect, Normalization,
Visualization
Author: Yiwen (Eva) Wang [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7067-9093>), Kim-Anh Le Cao [aut]
Maintainer: Yiwen (Eva) Wang <anjiwangyiwen@gmail.com>
URL: https://github.com/EvaYiwenWang/PLSDAbatch
VignetteBuilder: knitr
BugReports: https://github.com/EvaYiwenWang/PLSDAbatch/issues/
git_url: https://git.bioconductor.org/packages/PLSDAbatch
git_branch: devel
git_last_commit: 758afa3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PLSDAbatch_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PLSDAbatch_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PLSDAbatch_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PLSDAbatch_1.3.0.tgz
vignettes: vignettes/PLSDAbatch/inst/doc/brief_vignette.html
vignetteTitles: PLSDA-batch Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PLSDAbatch/inst/doc/brief_vignette.R
dependencyCount: 163
Package: plyinteractions
Version: 1.5.0
Depends: R (>= 4.3.0)
Imports: InteractionSet, GenomeInfoDb, BiocGenerics, GenomicRanges,
plyranges, IRanges, S4Vectors, rlang, dplyr, tibble,
tidyselect, methods, utils
Suggests: tidyverse, BSgenome.Mmusculus.UCSC.mm10, Biostrings,
BiocParallel, scales, HiContactsData, rtracklayer, BiocStyle,
covr, knitr, rmarkdown, sessioninfo, testthat (>= 3.0.0),
RefManageR
License: Artistic-2.0
Archs: x64
MD5sum: fd2780d22463a7397c06e7ebf4790daa
NeedsCompilation: no
Title: Extending tidy verbs to genomic interactions
Description: Operate on `GInteractions` objects as tabular data using
`dplyr`-like verbs. The functions and methods in
`plyinteractions` provide a grammatical approach to manipulate
`GInteractions`, to facilitate their integration in genomic
analysis workflows.
biocViews: Software, Infrastructure
Author: Jacques Serizay [aut, cre]
Maintainer: Jacques Serizay <jacquesserizay@gmail.com>
URL: https://github.com/js2264/plyinteractions
VignetteBuilder: knitr
BugReports: https://github.com/js2264/plyinteractions/issues
git_url: https://git.bioconductor.org/packages/plyinteractions
git_branch: devel
git_last_commit: ac2f117
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/plyinteractions_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/plyinteractions_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/plyinteractions_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/plyinteractions_1.5.0.tgz
vignettes: vignettes/plyinteractions/inst/doc/plyinteractions.html,
vignettes/plyinteractions/inst/doc/process_pairs.html
vignetteTitles: plyinteractions, HiCarithmetic
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/plyinteractions/inst/doc/plyinteractions.R,
vignettes/plyinteractions/inst/doc/process_pairs.R
importsMe: OHCA
dependencyCount: 75
Package: plyranges
Version: 1.27.5
Depends: R (>= 3.5), BiocGenerics, IRanges (>= 2.12.0), GenomicRanges
(>= 1.28.4)
Imports: methods, dplyr, rlang (>= 0.2.0), magrittr, tidyselect (>=
1.0.0), rtracklayer, GenomicAlignments, GenomeInfoDb,
Rsamtools, S4Vectors (>= 0.23.10), utils
Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 2.1.0),
HelloRanges, HelloRangesData, BSgenome.Hsapiens.UCSC.hg19,
pasillaBamSubset, covr, ggplot2
License: Artistic-2.0
MD5sum: 8a895400b56b71c653d0639026dea071
NeedsCompilation: no
Title: A fluent interface for manipulating GenomicRanges
Description: A dplyr-like interface for interacting with the common
Bioconductor classes Ranges and GenomicRanges. By providing a
grammatical and consistent way of manipulating these classes
their accessiblity for new Bioconductor users is hopefully
increased.
biocViews: Infrastructure, DataRepresentation, WorkflowStep, Coverage
Author: Stuart Lee [aut] (ORCID:
<https://orcid.org/0000-0003-1179-8436>), Michael Lawrence
[aut, ctb], Dianne Cook [aut, ctb], Spencer Nystrom [ctb]
(ORCID: <https://orcid.org/0000-0003-1000-1579>), Pierre-Paul
Axisa [ctb], Michael Love [ctb, cre]
Maintainer: Michael Love <michaelisaiahlove@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/tidyomics/plyranges
git_url: https://git.bioconductor.org/packages/plyranges
git_branch: devel
git_last_commit: 051e720
git_last_commit_date: 2025-03-19
Date/Publication: 2025-03-19
source.ver: src/contrib/plyranges_1.27.5.tar.gz
win.binary.ver: bin/windows/contrib/4.5/plyranges_1.27.5.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/plyranges_1.27.5.tgz
vignettes: vignettes/plyranges/inst/doc/an-introduction.html
vignetteTitles: Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/plyranges/inst/doc/an-introduction.R
importsMe: BOBaFIT, BUSpaRse, cfDNAPro, Damsel, GenomicPlot, InPAS,
katdetectr, mariner, methylCC, multicrispr, nearBynding,
nullranges, plotgardener, plyinteractions, SARC,
SingleMoleculeFootprinting, tidyomics, fluentGenomics
suggestsMe: EpiCompare, extraChIPs, memes, rigvf, svaNUMT, svaRetro,
tidyCoverage, CTCF
dependencyCount: 72
Package: plyxp
Version: 1.1.2
Depends: R (>= 4.4.0)
Imports: dplyr, purrr, rlang, SummarizedExperiment, tidyr, tidyselect,
vctrs, tibble, pillar, cli, glue, S7, S4Vectors, utils, methods
Suggests: devtools, knitr, rmarkdown, testthat, airway, IRanges, here
License: MIT + file LICENSE
MD5sum: d364935a003e7531dd12d731f9f7e142
NeedsCompilation: no
Title: Data masks for SummarizedExperiment enabling dplyr-like
manipulation
Description: The package provides `rlang` data masks for the
SummarizedExperiment class. The enables the evaluation of
unquoted expression in different contexts of the
SummarizedExperiment object with optional access to other
contexts. The goal for `plyxp` is for evaluation to feel like a
data.frame object without ever needing to unwind to a
rectangular data.frame.
biocViews: Annotation, GenomeAnnotation, Transcriptomics
Author: Justin Landis [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5501-4934>), Michael Love [aut]
(ORCID: <https://orcid.org/0000-0001-8401-0545>)
Maintainer: Justin Landis <jtlandis314@gmail.com>
URL: https://github.com/jtlandis/plyxp,
https://jtlandis.github.io/plyxp
VignetteBuilder: knitr
BugReports: https://www.github.com/jtlandis/plyxp/issues
git_url: https://git.bioconductor.org/packages/plyxp
git_branch: devel
git_last_commit: e792a40
git_last_commit_date: 2025-03-17
Date/Publication: 2025-03-17
source.ver: src/contrib/plyxp_1.1.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/plyxp_1.1.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/plyxp_1.1.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/plyxp_1.1.2.tgz
vignettes: vignettes/plyxp/inst/doc/plyxp.html
vignetteTitles: plyxp Usage Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/plyxp/inst/doc/plyxp.R
dependencyCount: 56
Package: pmm
Version: 1.39.0
Depends: R (>= 2.10)
Imports: lme4, splines
License: GPL-3
MD5sum: e2270507c716d3e4785926617dc26ff0
NeedsCompilation: no
Title: Parallel Mixed Model
Description: The Parallel Mixed Model (PMM) approach is suitable for
hit selection and cross-comparison of RNAi screens generated in
experiments that are performed in parallel under several
conditions. For example, we could think of the measurements or
readouts from cells under RNAi knock-down, which are infected
with several pathogens or which are grown from different cell
lines.
biocViews: SystemsBiology, Regression
Author: Anna Drewek
Maintainer: Anna Drewek <adrewek@stat.math.ethz.ch>
git_url: https://git.bioconductor.org/packages/pmm
git_branch: devel
git_last_commit: 0b45c51
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/pmm_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pmm_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pmm_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pmm_1.39.0.tgz
vignettes: vignettes/pmm/inst/doc/pmm-package.pdf
vignetteTitles: User manual for R-Package PMM
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pmm/inst/doc/pmm-package.R
dependencyCount: 22
Package: pmp
Version: 1.19.0
Depends: R (>= 4.0)
Imports: stats, impute, pcaMethods, missForest, ggplot2, methods,
SummarizedExperiment, S4Vectors, matrixStats, grDevices,
reshape2, utils
Suggests: testthat, covr, knitr, rmarkdown, BiocStyle, gridExtra,
magick
License: GPL-3
MD5sum: 145f0039af37615c079899701089144f
NeedsCompilation: no
Title: Peak Matrix Processing and signal batch correction for
metabolomics datasets
Description: Methods and tools for (pre-)processing of metabolomics
datasets (i.e. peak matrices), including filtering,
normalisation, missing value imputation, scaling, and signal
drift and batch effect correction methods. Filtering methods
are based on: the fraction of missing values (across samples or
features); Relative Standard Deviation (RSD) calculated from
the Quality Control (QC) samples; the blank samples.
Normalisation methods include Probabilistic Quotient
Normalisation (PQN) and normalisation to total signal
intensity. A unified user interface for several commonly used
missing value imputation algorithms is also provided. Supported
methods are: k-nearest neighbours (knn), random forests (rf),
Bayesian PCA missing value estimator (bpca), mean or median
value of the given feature and a constant small value. The
generalised logarithm (glog) transformation algorithm is
available to stabilise the variance across low and high
intensity mass spectral features. Finally, this package
provides an implementation of the Quality Control-Robust Spline
Correction (QCRSC) algorithm for signal drift and batch effect
correction of mass spectrometry-based datasets.
biocViews: MassSpectrometry, Metabolomics, Software, QualityControl,
BatchEffect
Author: Andris Jankevics [aut], Gavin Rhys Lloyd [aut, cre], Ralf
Johannes Maria Weber [aut]
Maintainer: Gavin Rhys Lloyd <g.r.lloyd@bham.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/pmp
git_branch: devel
git_last_commit: 7b8d496
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/pmp_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pmp_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pmp_1.19.0.tgz
vignettes:
vignettes/pmp/inst/doc/pmp_vignette_peak_matrix_processing_for_metabolomics_datasets.html,
vignettes/pmp/inst/doc/pmp_vignette_sbc_spectral_quality_assessment.html,
vignettes/pmp/inst/doc/pmp_vignette_signal_batch_correction_mass_spectrometry.html
vignetteTitles: Peak Matrix Processing for metabolomics datasets,
Signal drift and batch effect correction and mass spectral
quality assessment, Signal drift and batch effect correction
for mass spectrometry
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/pmp/inst/doc/pmp_vignette_peak_matrix_processing_for_metabolomics_datasets.R,
vignettes/pmp/inst/doc/pmp_vignette_sbc_spectral_quality_assessment.R,
vignettes/pmp/inst/doc/pmp_vignette_signal_batch_correction_mass_spectrometry.R
suggestsMe: metabolomicsWorkbenchR, structToolbox
dependencyCount: 79
Package: PoDCall
Version: 1.15.2
Depends: R (>= 4.5)
Imports: ggplot2, gridExtra, mclust, diptest, rlist, shiny, DT,
LaplacesDemon, purrr, shinyjs, readr, grDevices, stats, utils
Suggests: knitr, rmarkdown, testthat, BiocStyle
License: GPL-3
MD5sum: 9dbea4b7e77846ba2ca42c8d78fa3c96
NeedsCompilation: no
Title: Positive Droplet Calling for DNA Methylation Droplet Digital PCR
Description: Reads files exported from 'QX Manager or QuantaSoft'
containing amplitude values from a run of ddPCR (96 well plate)
and robustly sets thresholds to determine positive droplets for
each channel of each individual well. Concentration and
normalized concentration in addition to other metrics is then
calculated for each well. Results are returned as a table,
optionally written to file, as well as optional plots
(scatterplot and histogram) for both channels per well written
to file. The package includes a shiny application which
provides an interactive and user-friendly interface to the full
functionality of PoDCall.
biocViews: Classification, Epigenetics, ddPCR, DifferentialMethylation,
CpGIsland, DNAMethylation,
Author: Hans Petter Brodal [aut, cre], Marine Jeanmougin [aut], Guro
Elisabeth Lind [aut]
Maintainer: Hans Petter Brodal <h.p.brodal@ous-research.no>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PoDCall
git_branch: devel
git_last_commit: df1cfc5
git_last_commit_date: 2025-01-22
Date/Publication: 2025-01-22
source.ver: src/contrib/PoDCall_1.15.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PoDCall_1.15.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PoDCall_1.15.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PoDCall_1.15.2.tgz
vignettes: vignettes/PoDCall/inst/doc/PoDCall.html
vignetteTitles: PoDCall: Positive Droplet Caller for DNA Methylation
ddPCR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PoDCall/inst/doc/PoDCall.R
dependencyCount: 91
Package: podkat
Version: 1.39.0
Depends: R (>= 3.5.0), methods, Rsamtools (>= 1.99.1), GenomicRanges
Imports: Rcpp (>= 0.11.1), parallel, stats (>= 4.3.0), graphics,
grDevices, utils, Biobase, BiocGenerics, Matrix, GenomeInfoDb,
IRanges, Biostrings, BSgenome (>= 1.32.0)
LinkingTo: Rcpp, Rhtslib (>= 1.15.3)
Suggests: BSgenome.Hsapiens.UCSC.hg38.masked,
TxDb.Hsapiens.UCSC.hg38.knownGene,
BSgenome.Mmusculus.UCSC.mm10.masked, GWASTools (>= 1.13.24),
VariantAnnotation, SummarizedExperiment, knitr
License: GPL (>= 2)
MD5sum: 01c5b204f945fe381aa8a79e6fafd662
NeedsCompilation: yes
Title: Position-Dependent Kernel Association Test
Description: This package provides an association test that is capable
of dealing with very rare and even private variants. This is
accomplished by a kernel-based approach that takes the
positions of the variants into account. The test can be used
for pre-processed matrix data, but also directly for variant
data stored in VCF files. Association testing can be performed
whole-genome, whole-exome, or restricted to pre-defined regions
of interest. The test is complemented by tools for analyzing
and visualizing the results.
biocViews: Genetics, WholeGenome, Annotation, VariantAnnotation,
Sequencing, DataImport
Author: Ulrich Bodenhofer [aut,cre]
Maintainer: Ulrich Bodenhofer <ulrich@bodenhofer.com>
URL: https://github.com/UBod/podkat
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/podkat
git_branch: devel
git_last_commit: ff139de
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/podkat_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/podkat_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/podkat_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/podkat_1.39.0.tgz
vignettes: vignettes/podkat/inst/doc/podkat.pdf
vignetteTitles: PODKAT - An R Package for Association Testing Involving
Rare and Private Variants
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/podkat/inst/doc/podkat.R
dependencyCount: 60
Package: poem
Version: 0.99.7
Depends: R (>= 4.1.0)
Imports: aricode, BiocNeighbors, BiocParallel, bluster, clevr, clue,
cluster, elsa, fclust, igraph, Matrix, mclustcomp, methods, sp,
spdep, stats, utils, SpatialExperiment, SummarizedExperiment
Suggests: testthat (>= 3.0.0), BiocStyle, knitr, DT, dplyr, kableExtra,
scico, cowplot, ggnetwork, ggplot2, tidyr, STexampleData
License: GPL (>= 3)
MD5sum: d9a5d4d9d5c4ea45820bf95523ba40bd
NeedsCompilation: no
Title: POpulation-based Evaluation Metrics
Description: This package provides a comprehensive set of external and
internal evaluation metrics. It includes metrics for assessing
partitions or fuzzy partitions derived from clustering results,
as well as for evaluating subpopulation identification results
within embeddings or graph representations. Additionally, it
provides metrics for comparing spatial domain detection results
against ground truth labels, and tools for visualizing spatial
errors.
biocViews: DimensionReduction, Clustering, GraphAndNetwork, Spatial,
ATACSeq, SingleCell, RNASeq, Software, Visualization
Author: Siyuan Luo [cre, aut] (ORCID:
<https://orcid.org/0009-0007-6404-3244>), Pierre-Luc Germain
[aut, ctb] (ORCID: <https://orcid.org/0000-0003-3418-4218>)
Maintainer: Siyuan Luo <roseluosy@gmail.com>
URL: https://roseyuan.github.io/poem/
VignetteBuilder: knitr
BugReports: https://github.com/RoseYuan/poem/issues
git_url: https://git.bioconductor.org/packages/poem
git_branch: devel
git_last_commit: 7e621fc
git_last_commit_date: 2025-01-17
Date/Publication: 2025-03-10
source.ver: src/contrib/poem_0.99.7.tar.gz
win.binary.ver: bin/windows/contrib/4.5/poem_0.99.7.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/poem_0.99.7.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/poem_0.99.7.tgz
vignettes: vignettes/poem/inst/doc/MetricsInPoem.html,
vignettes/poem/inst/doc/poem.html,
vignettes/poem/inst/doc/PoemOnSpatialExperiment.html
vignetteTitles: MetricsInPoem.html, 1_introduction, 3_SpatialExperiment
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/poem/inst/doc/MetricsInPoem.R,
vignettes/poem/inst/doc/poem.R,
vignettes/poem/inst/doc/PoemOnSpatialExperiment.R
dependencyCount: 113
Package: pogos
Version: 1.27.1
Depends: R (>= 3.5.0), rjson (>= 0.2.15), httr (>= 1.3.1)
Imports: methods, S4Vectors, utils, shiny, ontoProc, ggplot2, graphics
Suggests: knitr, DT, ontologyPlot, testthat, rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: a3e33d7d0b7a9e7cd77c8e311b59cea3
NeedsCompilation: no
Title: PharmacOGenomics Ontology Support
Description: Provide simple utilities for querying bhklab PharmacoDB,
modeling API outputs, and integrating to cell and compound
ontologies.
biocViews: Pharmacogenomics, PooledScreens, ImmunoOncology
Author: Vince Carey <stvjc@channing.harvard.edu>
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/pogos
git_branch: devel
git_last_commit: c609080
git_last_commit_date: 2025-01-15
Date/Publication: 2025-01-15
source.ver: src/contrib/pogos_1.27.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pogos_1.27.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pogos_1.27.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pogos_1.27.1.tgz
vignettes: vignettes/pogos/inst/doc/pogos.html
vignetteTitles: pogos -- simple interface to bhklab PharmacoDB with
emphasis on ontology
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pogos/inst/doc/pogos.R
dependencyCount: 135
Package: PolySTest
Version: 1.1.0
Depends: R (>= 4.4.0)
Imports: fdrtool (>= 1.2.15), limma (>= 3.61.3), matrixStats (>=
0.57.0), qvalue (>= 2.22.0), shiny (>= 1.5.0),
SummarizedExperiment (>= 1.20.0), knitr (>= 1.33), plotly (>=
4.9.4), heatmaply (>= 1.1.1), circlize (>= 0.4.12), UpSetR (>=
1.4.0), gplots (>= 3.1.1), S4Vectors (>= 0.30.0), parallel (>=
4.1.0), grDevices (>= 4.1.0), graphics (>= 4.1.0), stats (>=
4.1.0), utils (>= 4.1.0)
Suggests: testthat (>= 3.0.0), BiocStyle
License: GPL-2
MD5sum: 46542d80032d03380a3fd9f3f30e3f0b
NeedsCompilation: no
Title: PolySTest: Detection of differentially regulated features.
Combined statistical testing for data with few replicates and
missing values
Description: The complexity of high-throughput quantitative omics
experiments often leads to low replicates numbers and many
missing values. We implemented a new test to simultaneously
consider missing values and quantitative changes, which we
combined with well-performing statistical tests for high
confidence detection of differentially regulated features. The
package contains functions to run the test and to visualize the
results.
biocViews: MassSpectrometry, Proteomics, Software,
DifferentialExpression
Author: Veit Schwämmle [aut, cre] (ORCID:
<https://orcid.org/0000-0002-9708-6722>)
Maintainer: Veit Schwämmle <veits@bmb.sdu.dk>
URL: https://github.com/computproteomics/PolySTest
VignetteBuilder: knitr
BugReports: https://github.com/computproteomics/PolySTest/issues
git_url: https://git.bioconductor.org/packages/PolySTest
git_branch: devel
git_last_commit: 43fe068
git_last_commit_date: 2024-11-22
Date/Publication: 2024-11-22
source.ver: src/contrib/PolySTest_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PolySTest_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PolySTest_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PolySTest_1.1.0.tgz
vignettes: vignettes/PolySTest/inst/doc/StatisticalTest.html
vignetteTitles: PolySTest
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PolySTest/inst/doc/StatisticalTest.R
dependencyCount: 140
Package: Polytect
Version: 0.99.5
Depends: R (>= 4.4.0)
Imports: stats, utils, grDevices, mvtnorm, sn, dplyr, flowPeaks,
ggplot2, tidyverse, cowplot, mlrMBO, DiceKriging, smoof,
ParamHelpers, lhs, rgenoud, BiocManager
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, ddPCRclust
License: Artistic-2.0
Archs: x64
MD5sum: 15845f927456b0730841951a9b449996
NeedsCompilation: no
Title: An R package for digital data clustering
Description: Polytect is an advanced computational tool designed for
the analysis of multi-color digital PCR data. It provides
automatic clustering and labeling of partitions into distinct
groups based on clusters first identified by the flowPeaks
algorithm. Polytect is particularly useful for researchers in
molecular biology and bioinformatics, enabling them to gain
deeper insights into their experimental results through precise
partition classification and data visualization.
biocViews: ddPCR, Clustering, MultiChannel, Classification
Author: Yao Chen [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8172-3996>)
Maintainer: Yao Chen <emmachentar@live.com>
URL: https://github.com/emmachenlingo/Polytect
VignetteBuilder: knitr
BugReports: https://github.com/emmachenlingo/Polytect/issues
git_url: https://git.bioconductor.org/packages/Polytect
git_branch: devel
git_last_commit: ff9290d
git_last_commit_date: 2024-12-27
Date/Publication: 2025-01-08
source.ver: src/contrib/Polytect_0.99.5.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Polytect_0.99.5.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Polytect_0.99.5.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Polytect_0.99.5.tgz
vignettes: vignettes/Polytect/inst/doc/introduction.pdf
vignetteTitles: Polytect Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Polytect/inst/doc/introduction.R
dependencyCount: 141
Package: POMA
Version: 1.17.6
Depends: R (>= 4.0)
Imports: broom, caret, ComplexHeatmap, dbscan, dplyr, DESeq2, fgsea,
FSA, ggcorrplot, ggplot2, ggrepel, glmnet, grid, impute,
janitor, limma, lme4, magrittr, MASS, mixOmics, multcomp,
msigdbr, purrr, randomForest, RankProd (>= 3.14), rlang,
SummarizedExperiment, sva, tibble, tidyr, utils, uwot, vegan
Suggests: BiocStyle, covr, ggraph, ggtext, knitr, patchwork, plotly,
tidyverse, testthat (>= 2.3.2)
License: GPL-3
MD5sum: 0f8cdd096c650e05bb1cb6b130ad6372
NeedsCompilation: no
Title: Tools for Omics Data Analysis
Description: The POMA package offers a comprehensive toolkit designed
for omics data analysis, streamlining the process from initial
visualization to final statistical analysis. Its primary goal
is to simplify and unify the various steps involved in omics
data processing, making it more accessible and manageable
within a single, intuitive R package. Emphasizing on
reproducibility and user-friendliness, POMA leverages the
standardized SummarizedExperiment class from Bioconductor,
ensuring seamless integration and compatibility with a wide
array of Bioconductor tools. This approach guarantees maximum
flexibility and replicability, making POMA an essential asset
for researchers handling omics datasets. See
https://github.com/pcastellanoescuder/POMAShiny. Paper:
Castellano-Escuder et al. (2021)
<doi:10.1371/journal.pcbi.1009148> for more details.
biocViews: BatchEffect, Classification, Clustering, DecisionTree,
DimensionReduction, MultidimensionalScaling, Normalization,
Preprocessing, PrincipalComponent, Regression, RNASeq,
Software, StatisticalMethod, Visualization
Author: Pol Castellano-Escuder [aut, cre] (ORCID:
<https://orcid.org/0000-0001-6466-877X>)
Maintainer: Pol Castellano-Escuder <polcaes@gmail.com>
URL: https://github.com/pcastellanoescuder/POMA
VignetteBuilder: knitr
BugReports: https://github.com/pcastellanoescuder/POMA/issues
git_url: https://git.bioconductor.org/packages/POMA
git_branch: devel
git_last_commit: 09bb015
git_last_commit_date: 2024-11-26
Date/Publication: 2024-11-29
source.ver: src/contrib/POMA_1.17.6.tar.gz
win.binary.ver: bin/windows/contrib/4.5/POMA_1.17.6.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/POMA_1.17.6.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/POMA_1.17.6.tgz
vignettes: vignettes/POMA/inst/doc/POMA-normalization.html,
vignettes/POMA/inst/doc/POMA-workflow.html
vignetteTitles: Normalization Methods, Get Started
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/POMA/inst/doc/POMA-normalization.R,
vignettes/POMA/inst/doc/POMA-workflow.R
importsMe: PRONE
suggestsMe: fobitools
dependencyCount: 232
Package: powerTCR
Version: 1.27.0
Imports: cubature, doParallel, evmix, foreach, magrittr, methods,
parallel, purrr, stats, truncdist, vegan, VGAM
Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics
License: Artistic-2.0
MD5sum: 46d05d3c988bf7c7a5cdb8cbf17a3bc3
NeedsCompilation: no
Title: Model-Based Comparative Analysis of the TCR Repertoire
Description: This package provides a model for the clone size
distribution of the TCR repertoire. Further, it permits
comparative analysis of TCR repertoire libraries based on
theoretical model fits.
biocViews: Software, Clustering, BiomedicalInformatics
Author: Hillary Koch
Maintainer: Hillary Koch <hillary.koch01@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/powerTCR
git_branch: devel
git_last_commit: 2bb8d0f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/powerTCR_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/powerTCR_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/powerTCR_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/powerTCR_1.27.0.tgz
vignettes: vignettes/powerTCR/inst/doc/powerTCR.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/powerTCR/inst/doc/powerTCR.R
dependencyCount: 36
Package: POWSC
Version: 1.15.0
Depends: R (>= 4.1), Biobase, SingleCellExperiment, MAST
Imports: pheatmap, ggplot2, RColorBrewer, grDevices,
SummarizedExperiment, limma
Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle
License: GPL-2
MD5sum: a8cd43bbcb478fa51243052d481aab73
NeedsCompilation: no
Title: Simulation, power evaluation, and sample size recommendation for
single cell RNA-seq
Description: Determining the sample size for adequate power to detect
statistical significance is a crucial step at the design stage
for high-throughput experiments. Even though a number of
methods and tools are available for sample size calculation for
microarray and RNA-seq in the context of differential
expression (DE), this topic in the field of single-cell RNA
sequencing is understudied. Moreover, the unique data
characteristics present in scRNA-seq such as sparsity and
heterogeneity increase the challenge. We propose POWSC, a
simulation-based method, to provide power evaluation and sample
size recommendation for single-cell RNA sequencing DE analysis.
POWSC consists of a data simulator that creates realistic
expression data, and a power assessor that provides a
comprehensive evaluation and visualization of the power and
sample size relationship.
biocViews: DifferentialExpression, ImmunoOncology, SingleCell, Software
Author: Kenong Su [aut, cre], Hao Wu [aut]
Maintainer: Kenong Su <kenong.su@emory.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/POWSC
git_branch: devel
git_last_commit: ce35b26
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/POWSC_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/POWSC_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/POWSC_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/POWSC_1.15.0.tgz
vignettes: vignettes/POWSC/inst/doc/POWSC.html
vignetteTitles: The POWSC User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/POWSC/inst/doc/POWSC.R
dependencyCount: 77
Package: ppcseq
Version: 1.15.0
Depends: R (>= 4.1.0), rstan (>= 2.18.1)
Imports: benchmarkme, dplyr, edgeR, foreach, ggplot2, graphics,
lifecycle, magrittr, methods, parallel, purrr, Rcpp (>=
0.12.0), RcppParallel (>= 5.0.1), rlang, rstantools (>= 2.1.1),
stats, tibble, tidybayes, tidyr (>= 0.8.3.9000), utils
LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0),
RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>=
2.18.0)
Suggests: knitr, testthat, BiocStyle, rmarkdown
License: GPL-3
MD5sum: 382847209aa36d954475bc8f93ca0d28
NeedsCompilation: yes
Title: Probabilistic Outlier Identification for RNA Sequencing
Generalized Linear Models
Description: Relative transcript abundance has proven to be a valuable
tool for understanding the function of genes in biological
systems. For the differential analysis of transcript abundance
using RNA sequencing data, the negative binomial model is by
far the most frequently adopted. However, common methods that
are based on a negative binomial model are not robust to
extreme outliers, which we found to be abundant in public
datasets. So far, no rigorous and probabilistic methods for
detection of outliers have been developed for RNA sequencing
data, leaving the identification mostly to visual inspection.
Recent advances in Bayesian computation allow large-scale
comparison of observed data against its theoretical
distribution given in a statistical model. Here we propose
ppcseq, a key quality-control tool for identifying transcripts
that include outlier data points in differential expression
analysis, which do not follow a negative binomial distribution.
Applying ppcseq to analyse several publicly available datasets
using popular tools, we show that from 3 to 10 percent of
differentially abundant transcripts across algorithms and
datasets had statistics inflated by the presence of outliers.
biocViews: RNASeq, DifferentialExpression, GeneExpression,
Normalization, Clustering, QualityControl, Sequencing,
Transcription, Transcriptomics
Author: Stefano Mangiola [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7474-836X>)
Maintainer: Stefano Mangiola <mangiolastefano@gmail.com>
URL: https://github.com/stemangiola/ppcseq
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/stemangiola/ppcseq/issues
git_url: https://git.bioconductor.org/packages/ppcseq
git_branch: devel
git_last_commit: 403820a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ppcseq_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ppcseq_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ppcseq_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ppcseq_1.15.0.tgz
vignettes: vignettes/ppcseq/inst/doc/introduction.html
vignetteTitles: Overview of the ppcseq package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ppcseq/inst/doc/introduction.R
dependencyCount: 94
Package: PPInfer
Version: 1.33.0
Depends: biomaRt, fgsea, kernlab, ggplot2, igraph, STRINGdb,
yeastExpData
Imports: httr, grDevices, graphics, stats, utils
License: Artistic-2.0
MD5sum: aa4af44aa93dd2991cb66c45cb74faeb
NeedsCompilation: no
Title: Inferring functionally related proteins using protein
interaction networks
Description: Interactions between proteins occur in many, if not most,
biological processes. Most proteins perform their functions in
networks associated with other proteins and other biomolecules.
This fact has motivated the development of a variety of
experimental methods for the identification of protein
interactions. This variety has in turn ushered in the
development of numerous different computational approaches for
modeling and predicting protein interactions. Sometimes an
experiment is aimed at identifying proteins closely related to
some interesting proteins. A network based statistical learning
method is used to infer the putative functions of proteins from
the known functions of its neighboring proteins on a PPI
network. This package identifies such proteins often involved
in the same or similar biological functions.
biocViews: Software, StatisticalMethod, Network, GraphAndNetwork,
GeneSetEnrichment, NetworkEnrichment, Pathways
Author: Dongmin Jung, Xijin Ge
Maintainer: Dongmin Jung <dmdmjung@gmail.com>
git_url: https://git.bioconductor.org/packages/PPInfer
git_branch: devel
git_last_commit: d4d24bc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PPInfer_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PPInfer_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PPInfer_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PPInfer_1.33.0.tgz
vignettes: vignettes/PPInfer/inst/doc/PPInfer.pdf
vignetteTitles: User manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PPInfer/inst/doc/PPInfer.R
dependsOnMe: gsean
dependencyCount: 116
Package: pqsfinder
Version: 2.23.0
Depends: R (>= 3.5.0), Biostrings
Imports: Rcpp (>= 0.12.3), GenomicRanges, IRanges, S4Vectors, methods
LinkingTo: Rcpp, BH (>= 1.78.0)
Suggests: BiocStyle, knitr, rmarkdown, Gviz, rtracklayer, ggplot2,
BSgenome.Hsapiens.UCSC.hg38, testthat, stringr, stringi
License: BSD_2_clause + file LICENSE
MD5sum: 0cbb6128c492db712c55c9630d778d0f
NeedsCompilation: yes
Title: Identification of potential quadruplex forming sequences
Description: Pqsfinder detects DNA and RNA sequence patterns that are
likely to fold into an intramolecular G-quadruplex (G4). Unlike
many other approaches, pqsfinder is able to detect G4s folded
from imperfect G-runs containing bulges or mismatches or G4s
having long loops. Pqsfinder also assigns an integer score to
each hit that was fitted on G4 sequencing data and corresponds
to expected stability of the folded G4.
biocViews: MotifDiscovery, SequenceMatching, GeneRegulation
Author: Jiri Hon, Dominika Labudova, Matej Lexa and Tomas Martinek
Maintainer: Jiri Hon <jiri.hon@gmail.com>
URL: https://pqsfinder.fi.muni.cz
SystemRequirements: GNU make, C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/pqsfinder
git_branch: devel
git_last_commit: acbc114
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/pqsfinder_2.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pqsfinder_2.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pqsfinder_2.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pqsfinder_2.23.0.tgz
vignettes: vignettes/pqsfinder/inst/doc/pqsfinder.html
vignetteTitles: pqsfinder: User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/pqsfinder/inst/doc/pqsfinder.R
dependencyCount: 28
Package: pram
Version: 1.23.0
Depends: R (>= 3.6)
Imports: methods, BiocParallel, tools, utils, data.table (>= 1.11.8),
GenomicAlignments (>= 1.16.0), rtracklayer (>= 1.40.6),
BiocGenerics (>= 0.26.0), GenomeInfoDb (>= 1.16.0),
GenomicRanges (>= 1.32.0), IRanges (>= 2.14.12), Rsamtools (>=
1.32.3), S4Vectors (>= 0.18.3)
Suggests: testthat, BiocStyle, knitr, rmarkdown
License: GPL (>= 3)
MD5sum: 765ad2106222d172507d6ed779f7c844
NeedsCompilation: no
Title: Pooling RNA-seq datasets for assembling transcript models
Description: Publicly available RNA-seq data is routinely used for
retrospective analysis to elucidate new biology. Novel
transcript discovery enabled by large collections of RNA-seq
datasets has emerged as one of such analysis. To increase the
power of transcript discovery from large collections of RNA-seq
datasets, we developed a new R package named Pooling RNA-seq
and Assembling Models (PRAM), which builds transcript models in
intergenic regions from pooled RNA-seq datasets. This package
includes functions for defining intergenic regions, extracting
and pooling related RNA-seq alignments, predicting, selected,
and evaluating transcript models.
biocViews: Software, Technology, Sequencing, RNASeq,
BiologicalQuestion, GenePrediction, GenomeAnnotation,
ResearchField, Transcriptomics
Author: Peng Liu [aut, cre], Colin N. Dewey [aut], Sündüz Keleş [aut]
Maintainer: Peng Liu <pliu55.wisc+bioconductor@gmail.com>
URL: https://github.com/pliu55/pram
VignetteBuilder: knitr
BugReports: https://github.com/pliu55/pram/issues
git_url: https://git.bioconductor.org/packages/pram
git_branch: devel
git_last_commit: a4a7d7e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/pram_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pram_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pram_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pram_1.23.0.tgz
vignettes: vignettes/pram/inst/doc/pram.html
vignetteTitles: Pooling RNA-seq and Assembling Models
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pram/inst/doc/pram.R
dependencyCount: 59
Package: prebs
Version: 1.47.0
Depends: R (>= 2.14.0), GenomicAlignments, affy, RPA
Imports: parallel, methods, stats, GenomicRanges (>= 1.13.3), IRanges,
Biobase, GenomeInfoDb, S4Vectors
Suggests: prebsdata, hgu133plus2cdf, hgu133plus2probe
License: Artistic-2.0
Archs: x64
MD5sum: 6f2a0fb34ba7d28ace2810fa691295e1
NeedsCompilation: no
Title: Probe region expression estimation for RNA-seq data for improved
microarray comparability
Description: The prebs package aims at making RNA-sequencing (RNA-seq)
data more comparable to microarray data. The comparability is
achieved by summarizing sequencing-based expressions of probe
regions using a modified version of RMA algorithm. The pipeline
takes mapped reads in BAM format as an input and produces
either gene expressions or original microarray probe set
expressions as an output.
biocViews: ImmunoOncology, Microarray, RNASeq, Sequencing,
GeneExpression, Preprocessing
Author: Karolis Uziela and Antti Honkela
Maintainer: Karolis Uziela <karolis.uziela@scilifelab.se>
git_url: https://git.bioconductor.org/packages/prebs
git_branch: devel
git_last_commit: 711f895
git_last_commit_date: 2024-10-29
Date/Publication: 2025-01-23
source.ver: src/contrib/prebs_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/prebs_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/prebs_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/prebs_1.47.0.tgz
vignettes: vignettes/prebs/inst/doc/prebs.pdf
vignetteTitles: prebs User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/prebs/inst/doc/prebs.R
dependencyCount: 127
Package: preciseTAD
Version: 1.17.0
Depends: R (>= 4.1)
Imports: S4Vectors, IRanges, GenomicRanges, randomForest, ModelMetrics,
e1071, PRROC, pROC, caret, utils, cluster, dbscan, doSNOW,
foreach, pbapply, stats, parallel, gtools, rCGH
Suggests: knitr, rmarkdown, testthat, BiocCheck, BiocManager, BiocStyle
License: MIT + file LICENSE
MD5sum: ce54edc02fd0738c76318d3cf8feb551
NeedsCompilation: no
Title: preciseTAD: A machine learning framework for precise TAD
boundary prediction
Description: preciseTAD provides functions to predict the location of
boundaries of topologically associated domains (TADs) and
chromatin loops at base-level resolution. As an input, it takes
BED-formatted genomic coordinates of domain boundaries detected
from low-resolution Hi-C data, and coordinates of
high-resolution genomic annotations from ENCODE or other
consortia. preciseTAD employs several feature engineering
strategies and resampling techniques to address class
imbalance, and trains an optimized random forest model for
predicting low-resolution domain boundaries. Translated on a
base-level, preciseTAD predicts the probability for each base
to be a boundary. Density-based clustering and scalable
partitioning techniques are used to detect precise boundary
regions and summit points. Compared with low-resolution
boundaries, preciseTAD boundaries are highly enriched for CTCF,
RAD21, SMC3, and ZNF143 signal and more conserved across cell
lines. The pre-trained model can accurately predict boundaries
in another cell line using CTCF, RAD21, SMC3, and ZNF143
annotation data for this cell line.
biocViews: Software, HiC, Sequencing, Clustering, Classification,
FunctionalGenomics, FeatureExtraction
Author: Spiro Stilianoudakis [aut], Mikhail Dozmorov [aut, cre]
Maintainer: Mikhail Dozmorov <mikhail.dozmorov@gmail.com>
URL: https://github.com/dozmorovlab/preciseTAD
VignetteBuilder: knitr
BugReports: https://github.com/dozmorovlab/preciseTAD/issues
git_url: https://git.bioconductor.org/packages/preciseTAD
git_branch: devel
git_last_commit: 8d055cd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/preciseTAD_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/preciseTAD_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/preciseTAD_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/preciseTAD_1.17.0.tgz
vignettes: vignettes/preciseTAD/inst/doc/preciseTAD.html
vignetteTitles: preciseTAD
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/preciseTAD/inst/doc/preciseTAD.R
suggestsMe: preciseTADhub
dependencyCount: 179
Package: PREDA
Version: 1.53.0
Depends: R (>= 2.9.0), Biobase, lokern (>= 1.0.9), multtest, stats,
methods, annotate
Suggests: quantsmooth, qvalue, limma, caTools, affy, PREDAsampledata
Enhances: Rmpi, rsprng
License: GPL-2
Archs: x64
MD5sum: 51faf9b4e46d160c22b03c2c0a6f9411
NeedsCompilation: no
Title: Position Related Data Analysis
Description: Package for the position related analysis of quantitative
functional genomics data.
biocViews: Software, CopyNumberVariation, GeneExpression, Genetics
Author: Francesco Ferrari <francesco.ferrari@ifom.eu>
Maintainer: Francesco Ferrari <francesco.ferrari@ifom.eu>
git_url: https://git.bioconductor.org/packages/PREDA
git_branch: devel
git_last_commit: 431f3f6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PREDA_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PREDA_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PREDA_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PREDA_1.53.0.tgz
vignettes: vignettes/PREDA/inst/doc/PREDAclasses.pdf,
vignettes/PREDA/inst/doc/PREDAtutorial.pdf
vignetteTitles: PREDA S4-classes, PREDA tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PREDA/inst/doc/PREDAtutorial.R
dependsOnMe: PREDAsampledata
dependencyCount: 57
Package: preprocessCore
Version: 1.69.0
Imports: stats
License: LGPL (>= 2)
MD5sum: 67dffc12c57539b7af1c55ed1f402b4c
NeedsCompilation: yes
Title: A collection of pre-processing functions
Description: A library of core preprocessing routines.
biocViews: Infrastructure
Author: Ben Bolstad <bmb@bmbolstad.com>
Maintainer: Ben Bolstad <bmb@bmbolstad.com>
URL: https://github.com/bmbolstad/preprocessCore
git_url: https://git.bioconductor.org/packages/preprocessCore
git_branch: devel
git_last_commit: d2e67f1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/preprocessCore_1.69.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/preprocessCore_1.69.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/preprocessCore_1.69.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/preprocessCore_1.69.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: affyPLM, crlmm
importsMe: affy, BloodGen3Module, bnbc, cn.farms, CPSM, cypress,
EMDomics, ExiMiR, fastLiquidAssociation, frma, frmaTools,
hipathia, iCheck, InPAS, lumi, MADSEQ, MBCB, MBQN, MEDIPS,
methylclock, mimager, minfi, MSPrep, MSstats, NormalyzerDE,
oligo, PanomiR, PECA, PhosR, Pigengene, PRONE, qPLEXanalyzer,
quantiseqr, sesame, soGGi, tidybulk, yarn, GSE13015, ADAPTS,
bulkAnalyseR, cinaR, FARDEEP, HEMDAG, lilikoi, MiDA, noise,
noisyr, oncoPredict, retriever, SMDIC, WGCNA
suggestsMe: DAPAR, MsCoreUtils, multiClust, QFeatures, roastgsa, scp,
splatter, tidytof, wateRmelon, aroma.affymetrix, aroma.core,
SCdeconR, wrMisc, wrTopDownFrag
linksToMe: affy, affyPLM, crlmm, oligo
dependencyCount: 1
Package: primirTSS
Version: 1.25.0
Depends: R (>= 3.5.0)
Imports: GenomicRanges (>= 1.32.2), S4Vectors (>= 0.18.2), rtracklayer
(>= 1.40.3), dplyr (>= 0.7.6), stringr (>= 1.3.1), tidyr (>=
0.8.1), Biostrings (>= 2.48.0), purrr (>= 0.2.5),
BSgenome.Hsapiens.UCSC.hg38 (>= 1.4.1),
phastCons100way.UCSC.hg38 (>= 3.7.1), GenomicScores (>= 1.4.1),
shiny (>= 1.0.5), Gviz (>= 1.24.0), BiocGenerics (>= 0.26.0),
IRanges (>= 2.14.10), TFBSTools (>= 1.18.0), JASPAR2018 (>=
1.1.1), tibble (>= 1.4.2), R.utils (>= 2.6.0), stats, utils
Suggests: knitr, rmarkdown
License: GPL-2
MD5sum: abb8f9ca0b4f7415339dc9863e3154c8
NeedsCompilation: no
Title: Prediction of pri-miRNA Transcription Start Site
Description: A fast, convenient tool to identify the TSSs of miRNAs by
integrating the data of H3K4me3 and Pol II as well as combining
the conservation level and sequence feature, provided within
both command-line and graphical interfaces, which achieves a
better performance than the previous non-cell-specific methods
on miRNA TSSs.
biocViews: ImmunoOncology, Sequencing, RNASeq, Genetics, Preprocessing,
Transcription, GeneRegulation
Author: Pumin Li [aut, cre], Qi Xu [aut], Jie Li [aut], Jin Wang [aut]
Maintainer: Pumin Li <ipumin@163.com>
URL: https://github.com/ipumin/primirTSS
VignetteBuilder: knitr
BugReports: http://github.com/ipumin/primirTSS/issues
git_url: https://git.bioconductor.org/packages/primirTSS
git_branch: devel
git_last_commit: dcff06d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/primirTSS_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/primirTSS_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/primirTSS_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/primirTSS_1.25.0.tgz
vignettes: vignettes/primirTSS/inst/doc/primirTSS.html
vignetteTitles: primirTSS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/primirTSS/inst/doc/primirTSS.R
dependencyCount: 185
Package: PrInCE
Version: 1.23.0
Depends: R (>= 3.6.0)
Imports: purrr (>= 0.2.4), dplyr (>= 0.7.4), tidyr (>= 0.8.99),
forecast (>= 8.2), progress (>= 1.1.2), Hmisc (>= 4.0),
naivebayes (>= 0.9.1), robustbase (>= 0.92-7), ranger (>=
0.8.0), LiblineaR (>= 2.10-8), speedglm (>= 0.3-2), tester (>=
0.1.7), magrittr (>= 1.5), Biobase (>= 2.40.0), MSnbase (>=
2.8.3), stats, utils, methods, Rdpack (>= 0.7)
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-3 + file LICENSE
MD5sum: 1f79ec02bb0f7bc1a40dbc292c22c7c5
NeedsCompilation: no
Title: Predicting Interactomes from Co-Elution
Description: PrInCE (Predicting Interactomes from Co-Elution) uses a
naive Bayes classifier trained on dataset-derived features to
recover protein-protein interactions from co-elution
chromatogram profiles. This package contains the R
implementation of PrInCE.
biocViews: Proteomics, SystemsBiology, NetworkInference
Author: Michael Skinnider [aut, trl, cre], R. Greg Stacey [ctb],
Nichollas Scott [ctb], Anders Kristensen [ctb], Leonard Foster
[aut, led]
Maintainer: Michael Skinnider <michael.skinnider@msl.ubc.ca>
VignetteBuilder: knitr
BugReports: https://github.com/fosterlab/PrInCE/issues
git_url: https://git.bioconductor.org/packages/PrInCE
git_branch: devel
git_last_commit: 92c3d03
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PrInCE_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PrInCE_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PrInCE_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PrInCE_1.23.0.tgz
vignettes: vignettes/PrInCE/inst/doc/intro-to-prince.html
vignetteTitles: Interactome reconstruction from co-elution data with
PrInCE
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/PrInCE/inst/doc/intro-to-prince.R
dependencyCount: 174
Package: proActiv
Version: 1.17.0
Depends: R (>= 4.0.0)
Imports: AnnotationDbi, BiocParallel, data.table, dplyr, DESeq2,
IRanges, GenomicRanges, GenomicFeatures, GenomicAlignments,
GenomeInfoDb, ggplot2, gplots, graphics, methods, rlang,
scales, S4Vectors, SummarizedExperiment, stats, tibble,
txdbmaker
Suggests: testthat, rmarkdown, knitr, Rtsne, gridExtra
License: MIT + file LICENSE
MD5sum: 77db3f2e44f6cce3f3c995ca7e9f9230
NeedsCompilation: no
Title: Estimate Promoter Activity from RNA-Seq data
Description: Most human genes have multiple promoters that control the
expression of different isoforms. The use of these alternative
promoters enables the regulation of isoform expression
pre-transcriptionally. Alternative promoters have been found to
be important in a wide number of cell types and diseases.
proActiv is an R package that enables the analysis of promoters
from RNA-seq data. proActiv uses aligned reads as input, and
generates counts and normalized promoter activity estimates for
each annotated promoter. In particular, proActiv accepts
junction files from TopHat2 or STAR or BAM files as inputs.
These estimates can then be used to identify which promoter is
active, which promoter is inactive, and which promoters change
their activity across conditions. proActiv also allows
visualization of promoter activity across conditions.
biocViews: RNASeq, GeneExpression, Transcription, AlternativeSplicing,
GeneRegulation, DifferentialSplicing, FunctionalGenomics,
Epigenetics, Transcriptomics, Preprocessing
Author: Deniz Demircioglu [aut] (ORCID:
<https://orcid.org/0000-0001-7857-0407>), Jonathan Göke [aut],
Joseph Lee [cre]
Maintainer: Joseph Lee <joseph.lee@u.nus.edu>
URL: https://github.com/GoekeLab/proActiv
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/proActiv
git_branch: devel
git_last_commit: f0775da
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/proActiv_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/proActiv_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/proActiv_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/proActiv_1.17.0.tgz
vignettes: vignettes/proActiv/inst/doc/proActiv.html
vignetteTitles: Identifying Active and Alternative Promoters from
RNA-Seq data with proActiv
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/proActiv/inst/doc/proActiv.R
dependencyCount: 124
Package: proBAMr
Version: 1.41.0
Depends: R (>= 3.0.1), IRanges, AnnotationDbi
Imports: GenomicRanges, Biostrings, GenomicFeatures, txdbmaker,
rtracklayer
Suggests: RUnit, BiocGenerics
License: Artistic-2.0
Archs: x64
MD5sum: e60f0517e79ed7e8f95b3b997d570a7d
NeedsCompilation: no
Title: Generating SAM file for PSMs in shotgun proteomics data
Description: Mapping PSMs back to genome. The package builds SAM file
from shotgun proteomics data The package also provides function
to prepare annotation from GTF file.
biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Software,
Visualization
Author: Xiaojing Wang
Maintainer: Xiaojing Wang <xiaojing.wang@vanderbilt.edu>
git_url: https://git.bioconductor.org/packages/proBAMr
git_branch: devel
git_last_commit: 911ce04
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/proBAMr_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/proBAMr_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/proBAMr_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/proBAMr_1.41.0.tgz
vignettes: vignettes/proBAMr/inst/doc/proBAMr.pdf
vignetteTitles: Introduction to proBAMr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/proBAMr/inst/doc/proBAMr.R
dependencyCount: 101
Package: PROcess
Version: 1.83.0
Depends: Icens
Imports: graphics, grDevices, Icens, stats, utils
License: Artistic-2.0
MD5sum: 6aacc35c850924a9938ec4369469ebfc
NeedsCompilation: no
Title: Ciphergen SELDI-TOF Processing
Description: A package for processing protein mass spectrometry data.
biocViews: ImmunoOncology, MassSpectrometry, Proteomics
Author: Xiaochun Li
Maintainer: Xiaochun Li <xiaochun@jimmy.harvard.edu>
git_url: https://git.bioconductor.org/packages/PROcess
git_branch: devel
git_last_commit: abf0d44
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PROcess_1.83.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PROcess_1.83.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PROcess_1.83.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PROcess_1.83.0.tgz
vignettes: vignettes/PROcess/inst/doc/howtoprocess.pdf
vignetteTitles: HOWTO PROcess
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PROcess/inst/doc/howtoprocess.R
dependencyCount: 11
Package: procoil
Version: 2.35.0
Depends: R (>= 3.3.0), kebabs
Imports: methods, stats, graphics, S4Vectors, Biostrings, utils
Suggests: knitr
License: GPL (>= 2)
MD5sum: 42e2e19a611452dfcb35ad61fd4e8aef
NeedsCompilation: no
Title: Prediction of Oligomerization of Coiled Coil Proteins
Description: The package allows for predicting whether a coiled coil
sequence (amino acid sequence plus heptad register) is more
likely to form a dimer or more likely to form a trimer.
Additionally to the prediction itself, a prediction profile is
computed which allows for determining the strengths to which
the individual residues are indicative for either class.
Prediction profiles can also be visualized as curves or
heatmaps.
biocViews: Proteomics, Classification, SupportVectorMachine
Author: Ulrich Bodenhofer [aut,cre]
Maintainer: Ulrich Bodenhofer <ulrich@bodenhofer.com>
URL: https://github.com/UBod/procoil
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/procoil
git_branch: devel
git_last_commit: 57dcf7b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/procoil_2.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/procoil_2.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/procoil_2.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/procoil_2.35.0.tgz
vignettes: vignettes/procoil/inst/doc/procoil.pdf
vignetteTitles: PrOCoil - A Web Service and an R Package for Predicting
the Oligomerization of Coiled-Coil Proteins
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/procoil/inst/doc/procoil.R
dependencyCount: 37
Package: proDA
Version: 1.21.0
Imports: stats, utils, methods, BiocGenerics, SummarizedExperiment,
S4Vectors, extraDistr
Suggests: testthat (>= 2.1.0), MSnbase, dplyr, stringr, readr, tidyr,
tibble, limma, DEP, numDeriv, pheatmap, knitr, rmarkdown,
BiocStyle
License: GPL-3
MD5sum: 3bca55e262bdd78c44e90af360115493
NeedsCompilation: no
Title: Differential Abundance Analysis of Label-Free Mass Spectrometry
Data
Description: Account for missing values in label-free mass spectrometry
data without imputation. The package implements a probabilistic
dropout model that ensures that the information from observed
and missing values are properly combined. It adds empirical
Bayesian priors to increase power to detect differentially
abundant proteins.
biocViews: Proteomics, MassSpectrometry, DifferentialExpression,
Bayesian, Regression, Software, Normalization, QualityControl
Author: Constantin Ahlmann-Eltze [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3762-068X>), Simon Anders [ths]
(ORCID: <https://orcid.org/0000-0003-4868-1805>)
Maintainer: Constantin Ahlmann-Eltze <artjom31415@googlemail.com>
URL: https://github.com/const-ae/proDA
VignetteBuilder: knitr
BugReports: https://github.com/const-ae/proDA/issues
git_url: https://git.bioconductor.org/packages/proDA
git_branch: devel
git_last_commit: 217e7c7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/proDA_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/proDA_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/proDA_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/proDA_1.21.0.tgz
vignettes: vignettes/proDA/inst/doc/data-import.html,
vignettes/proDA/inst/doc/Introduction.html
vignetteTitles: Data Import, Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/proDA/inst/doc/data-import.R,
vignettes/proDA/inst/doc/Introduction.R
importsMe: MatrixQCvis
suggestsMe: protti
dependencyCount: 38
Package: profileplyr
Version: 1.23.0
Depends: R (>= 3.6), BiocGenerics, SummarizedExperiment
Imports: GenomicRanges, stats, soGGi, methods, utils, S4Vectors,
R.utils, dplyr, magrittr, tidyr, IRanges, rjson,
ChIPseeker,GenomicFeatures,TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Hsapiens.UCSC.hg38.knownGene,TxDb.Mmusculus.UCSC.mm10.knownGene,
TxDb.Mmusculus.UCSC.mm9.knownGene,org.Hs.eg.db,org.Mm.eg.db,rGREAT,
pheatmap, ggplot2, EnrichedHeatmap, ComplexHeatmap, grid,
circlize, BiocParallel, rtracklayer, GenomeInfoDb, grDevices,
rlang, tiff, Rsamtools
Suggests: BiocStyle, testthat, knitr, rmarkdown, png, Cairo
License: GPL (>= 3)
MD5sum: c72c8b046fb887b7d65a1403617f6eeb
NeedsCompilation: no
Title: Visualization and annotation of read signal over genomic ranges
with profileplyr
Description: Quick and straightforward visualization of read signal
over genomic intervals is key for generating hypotheses from
sequencing data sets (e.g. ChIP-seq, ATAC-seq,
bisulfite/methyl-seq). Many tools both inside and outside of R
and Bioconductor are available to explore these types of data,
and they typically start with a bigWig or BAM file and end with
some representation of the signal (e.g. heatmap). profileplyr
leverages many Bioconductor tools to allow for both flexibility
and additional functionality in workflows that end with
visualization of the read signal.
biocViews: ChIPSeq, DataImport, Sequencing, ChipOnChip, Coverage
Author: Tom Carroll and Doug Barrows
Maintainer: Tom Carroll <tc.infomatics@gmail.com>, Doug Barrows
<doug.barrows@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/profileplyr
git_branch: devel
git_last_commit: 580fe41
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/profileplyr_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/profileplyr_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/profileplyr_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/profileplyr_1.23.0.tgz
vignettes: vignettes/profileplyr/inst/doc/profileplyr.html
vignetteTitles: Visualization and annotation of read signal over
genomic ranges with profileplyr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/profileplyr/inst/doc/profileplyr.R
suggestsMe: DiffBind
dependencyCount: 205
Package: profileScoreDist
Version: 1.35.0
Depends: R(>= 3.3)
Imports: Rcpp, BiocGenerics, methods, graphics
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, MotifDb
License: MIT + file LICENSE
MD5sum: 57672ed4ebe5320940497f1f278bf60e
NeedsCompilation: yes
Title: Profile score distributions
Description: Regularization and score distributions for position count
matrices.
biocViews: Software, GeneRegulation, StatisticalMethod
Author: Paal O. Westermark
Maintainer: Paal O. Westermark <pal-olof.westermark@charite.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/profileScoreDist
git_branch: devel
git_last_commit: f562e3f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/profileScoreDist_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/profileScoreDist_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/profileScoreDist_1.35.0.tgz
vignettes:
vignettes/profileScoreDist/inst/doc/profileScoreDist-vignette.pdf
vignetteTitles: Using profileScoreDist
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/profileScoreDist/inst/doc/profileScoreDist-vignette.R
dependencyCount: 7
Package: progeny
Version: 1.29.0
Depends: R (>= 3.6.0)
Imports: Biobase, stats, dplyr, tidyr, ggplot2, ggrepel, gridExtra,
decoupleR, reshape2
Suggests: airway, biomaRt, BiocFileCache, broom, Seurat,
SingleCellExperiment, DESeq2, BiocStyle, knitr, readr, readxl,
pheatmap, tibble, rmarkdown, testthat (>= 2.1.0)
License: Apache License (== 2.0) | file LICENSE
MD5sum: ef34f73577f32f56e639edbd94662f35
NeedsCompilation: no
Title: Pathway RespOnsive GENes for activity inference from gene
expression
Description: PROGENy is resource that leverages a large compendium of
publicly available signaling perturbation experiments to yield
a common core of pathway responsive genes for human and mouse.
These, coupled with any statistical method, can be used to
infer pathway activities from bulk or single-cell
transcriptomics.
biocViews: SystemsBiology, GeneExpression, FunctionalPrediction,
GeneRegulation
Author: Michael Schubert [aut], Alberto Valdeolivas [ctb] (ORCID:
<https://orcid.org/0000-0001-5482-9023>), Christian H. Holland
[ctb] (ORCID: <https://orcid.org/0000-0002-3060-5786>), Igor
Bulanov [ctb], Aurélien Dugourd [cre, ctb]
Maintainer: Aurélien Dugourd
<aurelien.dugourd@bioquant.uni-heidelberg.de>
URL: https://github.com/saezlab/progeny
VignetteBuilder: knitr
BugReports: https://github.com/saezlab/progeny/issues
git_url: https://git.bioconductor.org/packages/progeny
git_branch: devel
git_last_commit: 22fe492
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/progeny_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/progeny_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/progeny_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/progeny_1.29.0.tgz
vignettes: vignettes/progeny/inst/doc/progeny.html
vignetteTitles: PROGENy pathway signatures
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/progeny/inst/doc/progeny.R
importsMe: easier
suggestsMe: autonomics
dependencyCount: 64
Package: projectR
Version: 1.23.2
Depends: R (>= 4.0.0)
Imports: SingleCellExperiment, methods, cluster, stats, limma, NMF,
ROCR, ggalluvial, RColorBrewer, dplyr, fgsea, reshape2,
viridis, scales, Matrix, MatrixModels, msigdbr, ggplot2,
cowplot, ggrepel, umap, tsne
Suggests: BiocStyle, CoGAPS, gridExtra, grid, testthat, devtools,
knitr, rmarkdown, ComplexHeatmap, gplots, SeuratObject
License: GPL (==2)
MD5sum: 39a61ceace8c46043212480fea2f99bf
NeedsCompilation: no
Title: Functions for the projection of weights from PCA, CoGAPS, NMF,
correlation, and clustering
Description: Functions for the projection of data into the spaces
defined by PCA, CoGAPS, NMF, correlation, and clustering.
biocViews: FunctionalPrediction, GeneRegulation, BiologicalQuestion,
Software
Author: Gaurav Sharma, Charles Shin, Jared Slosberg, Loyal Goff,
Genevieve Stein-O'Brien
Maintainer: Genevieve Stein-O'Brien <gsteinobrien@gmail.com>
URL: https://github.com/genesofeve/projectR/
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/projectR/
git_url: https://git.bioconductor.org/packages/projectR
git_branch: devel
git_last_commit: d3dd79e
git_last_commit_date: 2025-03-18
Date/Publication: 2025-03-18
source.ver: src/contrib/projectR_1.23.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/projectR_1.23.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/projectR_1.23.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/projectR_1.23.2.tgz
vignettes: vignettes/projectR/inst/doc/projectR.html
vignetteTitles: projectR
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/projectR/inst/doc/projectR.R
dependencyCount: 123
Package: pRoloc
Version: 1.47.4
Depends: R (>= 3.5), MSnbase (>= 1.19.20), MLInterfaces (>= 1.67.10),
methods, Rcpp (>= 0.10.3), BiocParallel
Imports: stats4, Biobase, mclust (>= 4.3), caret, e1071, sampling,
class, kernlab, lattice, nnet, randomForest, proxy, FNN,
hexbin, BiocGenerics, stats, dendextend, RColorBrewer, scales,
MASS, knitr, mvtnorm, LaplacesDemon, coda, mixtools, gtools,
plyr, ggplot2, biomaRt, utils, grDevices, graphics, colorspace
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat, rmarkdown, pRolocdata (>= 1.43.2), roxygen2,
xtable, rgl, BiocStyle (>= 2.5.19), hpar (>= 1.41.0), dplyr,
akima, fields, vegan, GO.db, AnnotationDbi, Rtsne (>= 0.13),
nipals, reshape, magick, umap
License: GPL-2
MD5sum: 8f7f671a96f6047561fe99ef6c3b607c
NeedsCompilation: yes
Title: A unifying bioinformatics framework for spatial proteomics
Description: The pRoloc package implements machine learning and
visualisation methods for the analysis and interogation of
quantitiative mass spectrometry data to reliably infer protein
sub-cellular localisation.
biocViews: ImmunoOncology, Proteomics, MassSpectrometry,
Classification, Clustering, QualityControl
Author: Laurent Gatto [aut], Lisa Breckels [aut, cre], Thomas Burger
[ctb], Samuel Wieczorek [ctb], Charlotte Hutchings [ctb],
Oliver Crook [aut]
Maintainer: Lisa Breckels <lms79@cam.ac.uk>
URL: https://github.com/lgatto/pRoloc
VignetteBuilder: knitr
Video:
https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow
BugReports: https://github.com/lgatto/pRoloc/issues
git_url: https://git.bioconductor.org/packages/pRoloc
git_branch: devel
git_last_commit: 6647c2f
git_last_commit_date: 2025-03-27
Date/Publication: 2025-03-27
source.ver: src/contrib/pRoloc_1.47.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pRoloc_1.47.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pRoloc_1.47.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pRoloc_1.47.4.tgz
vignettes: vignettes/pRoloc/inst/doc/v01-pRoloc-tutorial.html,
vignettes/pRoloc/inst/doc/v02-pRoloc-ml.html,
vignettes/pRoloc/inst/doc/v03-pRoloc-bayesian.html,
vignettes/pRoloc/inst/doc/v05-pRoloc-transfer-learning.html
vignetteTitles: Using pRoloc for spatial proteomics data analysis,
Machine learning techniques available in pRoloc, Bayesian
spatial proteomics with pRoloc, A transfer learning algorithm
for spatial proteomics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pRoloc/inst/doc/v01-pRoloc-tutorial.R,
vignettes/pRoloc/inst/doc/v02-pRoloc-ml.R,
vignettes/pRoloc/inst/doc/v03-pRoloc-bayesian.R,
vignettes/pRoloc/inst/doc/v05-pRoloc-transfer-learning.R
dependsOnMe: bandle, pRolocGUI
suggestsMe: MSnbase, pRolocdata, RforProteomics
dependencyCount: 229
Package: pRolocGUI
Version: 2.17.0
Depends: methods, R (>= 3.1.0), pRoloc (>= 1.27.6), Biobase, MSnbase
(>= 2.1.11)
Imports: shiny (>= 0.9.1), scales, dplyr, DT (>= 0.1.40), graphics,
utils, ggplot2, shinydashboardPlus (>= 2.0.0), colourpicker,
shinyhelper, shinyWidgets, shinyjs, colorspace, stats,
grDevices, grid, BiocGenerics, shinydashboard
Suggests: pRolocdata, knitr, BiocStyle (>= 2.5.19), rmarkdown, testthat
(>= 3.0.0)
License: GPL-2
Archs: x64
MD5sum: 639d599de1a6e98701f23bec3f33de2b
NeedsCompilation: no
Title: Interactive visualisation of spatial proteomics data
Description: The package pRolocGUI comprises functions to interactively
visualise spatial proteomics data on the basis of pRoloc,
pRolocdata and shiny.
biocViews: Proteomics, Visualization, GUI
Author: Lisa Breckels [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8918-7171>), Thomas Naake [aut],
Laurent Gatto [aut] (ORCID:
<https://orcid.org/0000-0002-1520-2268>)
Maintainer: Lisa Breckels <lms79@cam.ac.uk>
URL: https://github.com/lgatto/pRolocGUI
VignetteBuilder: knitr
Video:
https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow
BugReports: https://github.com/lgatto/pRolocGUI/issues
git_url: https://git.bioconductor.org/packages/pRolocGUI
git_branch: devel
git_last_commit: e4978d7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/pRolocGUI_2.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pRolocGUI_2.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pRolocGUI_2.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pRolocGUI_2.17.0.tgz
vignettes: vignettes/pRolocGUI/inst/doc/pRolocGUI.html
vignetteTitles: pRolocGUI - Interactive visualisation of spatial
proteomics data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pRolocGUI/inst/doc/pRolocGUI.R
dependencyCount: 243
Package: PROMISE
Version: 1.59.0
Depends: R (>= 3.1.0), Biobase, GSEABase
Imports: Biobase, GSEABase, stats
License: GPL (>= 2)
MD5sum: fe0812324d1bf3e89cf1caec04dde0eb
NeedsCompilation: no
Title: PRojection Onto the Most Interesting Statistical Evidence
Description: A general tool to identify genomic features with a
specific biologically interesting pattern of associations with
multiple endpoint variables as described in Pounds et. al.
(2009) Bioinformatics 25: 2013-2019
biocViews: Microarray, OneChannel, MultipleComparison, GeneExpression
Author: Stan Pounds <stanley.pounds@stjude.org>, Xueyuan Cao
<xueyuan.cao@stjude.org>
Maintainer: Stan Pounds <stanley.pounds@stjude.org>, Xueyuan Cao
<xueyuan.cao@stjude.org>
git_url: https://git.bioconductor.org/packages/PROMISE
git_branch: devel
git_last_commit: 7962d03
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PROMISE_1.59.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PROMISE_1.59.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PROMISE_1.59.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PROMISE_1.59.0.tgz
vignettes: vignettes/PROMISE/inst/doc/PROMISE.pdf
vignetteTitles: An introduction to PROMISE
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PROMISE/inst/doc/PROMISE.R
dependsOnMe: CCPROMISE
dependencyCount: 50
Package: PRONE
Version: 1.1.8
Depends: R (>= 4.4.0), SummarizedExperiment
Imports: dplyr, magrittr, data.table, RColorBrewer, ggplot2, S4Vectors,
ComplexHeatmap, stringr, NormalyzerDE, tibble, limma, MASS,
edgeR, matrixStats, preprocessCore, stats, gtools, methods,
ROTS, ComplexUpset, tidyr, purrr, circlize, gprofiler2,
plotROC, MSnbase, UpSetR, dendsort, vsn, Biobase, reshape2,
POMA, ggtext, scales, DEqMS, vegan
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle, DT
License: GPL (>= 3)
MD5sum: 509a942739de49c030d3af4ff7934531
NeedsCompilation: no
Title: The PROteomics Normalization Evaluator
Description: High-throughput omics data are often affected by
systematic biases introduced throughout all the steps of a
clinical study, from sample collection to quantification.
Normalization methods aim to adjust for these biases to make
the actual biological signal more prominent. However, selecting
an appropriate normalization method is challenging due to the
wide range of available approaches. Therefore, a comparative
evaluation of unnormalized and normalized data is essential in
identifying an appropriate normalization strategy for a
specific data set. This R package provides different functions
for preprocessing, normalizing, and evaluating different
normalization approaches. Furthermore, normalization methods
can be evaluated on downstream steps, such as differential
expression analysis and statistical enrichment analysis.
Spike-in data sets with known ground truth and real-world data
sets of biological experiments acquired by either tandem mass
tag (TMT) or label-free quantification (LFQ) can be analyzed.
biocViews: Proteomics, Preprocessing, Normalization,
DifferentialExpression, Visualization
Author: Lis Arend [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7990-8385>)
Maintainer: Lis Arend <lis.arend@tum.de>
URL: https://github.com/daisybio/PRONE
VignetteBuilder: knitr
BugReports: https://github.com/daisybio/PRONE/issues
git_url: https://git.bioconductor.org/packages/PRONE
git_branch: devel
git_last_commit: c75329b
git_last_commit_date: 2025-03-21
Date/Publication: 2025-03-21
source.ver: src/contrib/PRONE_1.1.8.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PRONE_1.1.8.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PRONE_1.1.8.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PRONE_1.1.8.tgz
vignettes: vignettes/PRONE/inst/doc/Differential_Expression.html,
vignettes/PRONE/inst/doc/Imputation.html,
vignettes/PRONE/inst/doc/Normalization.html,
vignettes/PRONE/inst/doc/Preprocessing.html,
vignettes/PRONE/inst/doc/PRONE.html,
vignettes/PRONE/inst/doc/Spike_In_Data.html
vignetteTitles: 5. Differential Expression Analysis, 4. Imputation, 3.
Normalization, 2. Preprocessing, 1. Getting started with PRONE,
6. PRONE with Spike-In Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PRONE/inst/doc/Differential_Expression.R,
vignettes/PRONE/inst/doc/Imputation.R,
vignettes/PRONE/inst/doc/Normalization.R,
vignettes/PRONE/inst/doc/Preprocessing.R,
vignettes/PRONE/inst/doc/PRONE.R,
vignettes/PRONE/inst/doc/Spike_In_Data.R
dependencyCount: 285
Package: PROPER
Version: 1.39.0
Depends: R (>= 3.3)
Imports: edgeR
Suggests: BiocStyle,DESeq2,DSS,knitr
License: GPL
Archs: x64
MD5sum: c964bd3a2f3d364a914b15dfa6ee69ed
NeedsCompilation: no
Title: PROspective Power Evaluation for RNAseq
Description: This package provide simulation based methods for
evaluating the statistical power in differential expression
analysis from RNA-seq data.
biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression
Author: Hao Wu
Maintainer: Hao Wu <hao.wu@emory.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PROPER
git_branch: devel
git_last_commit: 57f641a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PROPER_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PROPER_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PROPER_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PROPER_1.39.0.tgz
vignettes: vignettes/PROPER/inst/doc/PROPER.pdf
vignetteTitles: Power and Sample size analysis for gene expression from
RNA-seq
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PROPER/inst/doc/PROPER.R
importsMe: cypress
dependencyCount: 11
Package: PROPS
Version: 1.29.0
Imports: bnlearn, reshape2, sva, stats, utils, Biobase
Suggests: knitr, rmarkdown
License: GPL-2
MD5sum: f4f9d580fa9e71316b91aa092bdb1004
NeedsCompilation: no
Title: PRObabilistic Pathway Score (PROPS)
Description: This package calculates probabilistic pathway scores using
gene expression data. Gene expression values are aggregated
into pathway-based scores using Bayesian network
representations of biological pathways.
biocViews: Classification, Bayesian, GeneExpression
Author: Lichy Han
Maintainer: Lichy Han <lhan2@stanford.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PROPS
git_branch: devel
git_last_commit: 0490c20
git_last_commit_date: 2024-11-27
Date/Publication: 2024-11-27
source.ver: src/contrib/PROPS_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PROPS_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PROPS_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PROPS_1.29.0.tgz
vignettes: vignettes/PROPS/inst/doc/props.html
vignetteTitles: PRObabilistic Pathway Scores (PROPS)
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PROPS/inst/doc/props.R
dependencyCount: 79
Package: Prostar
Version: 1.39.0
Depends: R (>= 4.4.0)
Imports: DAPAR (>= 1.35.1), DAPARdata (>= 1.30.0), rhandsontable,
data.table, shiny, shinyBS, shinyAce, highcharter, htmlwidgets,
webshot, shinythemes, later, shinycssloaders, future, promises,
shinyjqui, tibble, ggplot2, gplots, shinyjs, vioplot, Biobase,
DT, R.utils, RColorBrewer, XML, colourpicker, gtools, markdown,
rclipboard, sass, shinyTree, shinyWidgets
Suggests: BiocStyle, BiocManager, testthat, knitr
License: Artistic-2.0
MD5sum: acac777655eb0c2e2c802244e693ebfc
NeedsCompilation: no
Title: Provides a GUI for DAPAR
Description: This package provides a GUI interface for the DAPAR
package. The package Prostar (Proteomics statistical analysis
with R) is a Bioconductor distributed R package which provides
all the necessary functions to analyze quantitative data from
label-free proteomics experiments. Contrarily to most other
similar R packages, it is endowed with rich and user-friendly
graphical interfaces, so that no programming skill is required.
biocViews: Proteomics, MassSpectrometry, Normalization, Preprocessing,
Software, GUI
Author: Thomas Burger [aut], Florence Combes [aut], Samuel Wieczorek
[cre, aut]
Maintainer: Samuel Wieczorek <samuel.wieczorek@cea.fr>
URL: http://www.prostar-proteomics.org/
VignetteBuilder: knitr
BugReports: https://github.com/edyp-lab/Prostar/issues
git_url: https://git.bioconductor.org/packages/Prostar
git_branch: devel
git_last_commit: 5e60bdd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-05
source.ver: src/contrib/Prostar_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Prostar_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Prostar_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Prostar_1.39.0.tgz
vignettes: vignettes/Prostar/inst/doc/Prostar_UserManual.html
vignetteTitles: Prostar User Manual
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Prostar/inst/doc/Prostar_UserManual.R
dependencyCount: 188
Package: proteinProfiles
Version: 1.47.0
Depends: R (>= 2.15.2)
Imports: graphics, stats
Suggests: testthat
License: GPL-3
MD5sum: 43c93507ef65d5ee526becd1b101b5cb
NeedsCompilation: no
Title: Protein Profiling
Description: Significance assessment for distance measures of
time-course protein profiles
Author: Julian Gehring
Maintainer: Julian Gehring <jg-bioc@gmx.com>
git_url: https://git.bioconductor.org/packages/proteinProfiles
git_branch: devel
git_last_commit: 7292bf6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/proteinProfiles_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/proteinProfiles_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/proteinProfiles_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/proteinProfiles_1.47.0.tgz
vignettes: vignettes/proteinProfiles/inst/doc/proteinProfiles.pdf
vignetteTitles: The proteinProfiles package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/proteinProfiles/inst/doc/proteinProfiles.R
dependencyCount: 2
Package: ProteoDisco
Version: 1.13.0
Depends: R (>= 4.1.0),
Imports: BiocGenerics (>= 0.38.0), BiocParallel (>= 1.26.0), Biostrings
(>= 2.60.1), checkmate (>= 2.0.0), cleaver (>= 1.30.0), dplyr
(>= 1.0.6), GenomeInfoDb (>= 1.28.0), GenomicFeatures (>=
1.44.0), GenomicRanges (>= 1.44.0), IRanges (>= 2.26.0),
methods (>= 4.1.0), ParallelLogger (>= 2.0.1), plyr (>= 1.8.6),
rlang (>= 0.4.11), S4Vectors (>= 0.30.0), tibble (>= 3.1.2),
tidyr (>= 1.1.3), VariantAnnotation (>= 1.36.0), XVector (>=
0.32.0),
Suggests: AnnotationDbi (>= 1.54.1), BSgenome (>= 1.60.0),
BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.3), BiocStyle (>= 2.20.1),
DelayedArray (>= 0.18.0), devtools (>= 2.4.2), knitr (>= 1.33),
matrixStats (>= 0.59.0), markdown (>= 1.1), org.Hs.eg.db (>=
3.13.0), purrr (>= 0.3.4), RCurl (>= 1.98.1.3), readr (>=
1.4.0), ggplot2 (>= 3.3.5), rmarkdown (>= 2.9), rtracklayer (>=
1.52.0), seqinr (>= 4.2.8), stringr (>= 1.4.0), reshape2 (>=
1.4.4), scales (>= 1.1.1), testthat (>= 3.0.3),
TxDb.Hsapiens.UCSC.hg19.knownGene (>= 3.2.2)
License: GPL-3
MD5sum: 3ff14a333982a899931da030ef695100
NeedsCompilation: no
Title: Generation of customized protein variant databases from genomic
variants, splice-junctions and manual sequences
Description: ProteoDisco is an R package to facilitate proteogenomics
studies. It houses functions to create customized (variant)
protein databases based on user-submitted genomic variants,
splice-junctions, fusion genes and manual transcript sequences.
The flexible workflow can be adopted to suit a myriad of
research and experimental settings.
biocViews: Software, Proteomics, RNASeq, SNP, Sequencing,
VariantAnnotation, DataImport
Author: Job van Riet [cre], Wesley van de Geer [aut], Harmen van de
Werken [ths]
Maintainer: Job van Riet <jobvriet@gmail.com>
URL: https://github.com/ErasmusMC-CCBC/ProteoDisco
VignetteBuilder: knitr
BugReports: https://github.com/ErasmusMC-CCBC/ProteoDisco/issues
git_url: https://git.bioconductor.org/packages/ProteoDisco
git_branch: devel
git_last_commit: 896da28
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ProteoDisco_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ProteoDisco_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ProteoDisco_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ProteoDisco_1.13.0.tgz
vignettes: vignettes/ProteoDisco/inst/doc/Overview_ProteoDisco.html
vignetteTitles: Overview_Proteodisco
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ProteoDisco/inst/doc/Overview_ProteoDisco.R
dependencyCount: 99
Package: ProteoMM
Version: 1.25.0
Depends: R (>= 3.5)
Imports: gdata, biomaRt, ggplot2, ggrepel, gtools, stats, matrixStats,
graphics
Suggests: BiocStyle, knitr, rmarkdown
License: MIT
MD5sum: b96b2bb4857aa4c65545fdd66ac728ff
NeedsCompilation: no
Title: Multi-Dataset Model-based Differential Expression Proteomics
Analysis Platform
Description: ProteoMM is a statistical method to perform model-based
peptide-level differential expression analysis of single or
multiple datasets. For multiple datasets ProteoMM produces a
single fold change and p-value for each protein across multiple
datasets. ProteoMM provides functionality for normalization,
missing value imputation and differential expression.
Model-based peptide-level imputation and differential
expression analysis component of package follows the analysis
described in “A statistical framework for protein quantitation
in bottom-up MS based proteomics" (Karpievitch et al.
Bioinformatics 2009). EigenMS normalisation is implemented as
described in "Normalization of peak intensities in bottom-up
MS-based proteomics using singular value decomposition."
(Karpievitch et al. Bioinformatics 2009).
biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Normalization,
DifferentialExpression
Author: Yuliya V Karpievitch, Tim Stuart and Sufyaan Mohamed
Maintainer: Yuliya V Karpievitch <yuliya.k@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ProteoMM
git_branch: devel
git_last_commit: 19b533b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ProteoMM_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ProteoMM_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ProteoMM_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ProteoMM_1.25.0.tgz
vignettes: vignettes/ProteoMM/inst/doc/ProteoMM_vignette.html
vignetteTitles: Multi-Dataset Model-based Differential Expression
Proteomics Platform
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ProteoMM/inst/doc/ProteoMM_vignette.R
suggestsMe: mi4p
dependencyCount: 90
Package: protGear
Version: 1.11.0
Depends: R (>= 4.2), dplyr (>= 0.8.0) , limma (>= 3.40.2) ,vsn (>=
3.54.0)
Imports: magrittr (>= 1.5) , stats (>= 3.6) , ggplot2 (>= 3.3.0) ,
tidyr (>= 1.1.3) , data.table (>= 1.14.0), ggpubr (>= 0.4.0),
gtools (>= 3.8.2) , tibble (>= 3.1.0) , rmarkdown (>= 2.9) ,
knitr (>= 1.33), utils (>= 3.6), genefilter (>= 1.74.0), readr
(>= 2.0.1) , Biobase (>= 2.52.0), plyr (>= 1.8.6) , Kendall (>=
2.2) , shiny (>= 1.0.0) , purrr (>= 0.3.4), plotly (>= 4.9.0) ,
MASS (>= 7.3) , htmltools (>= 0.4.0) , flexdashboard (>= 0.5.2)
, shinydashboard (>= 0.7.1) , GGally (>= 2.1.2) , pheatmap (>=
1.0.12) , grid(>= 4.1.1), styler (>= 1.6.1) , factoextra (>=
1.0.7) ,FactoMineR (>= 2.4) , rlang (>= 0.4.11), remotes (>=
2.4.0)
Suggests: gridExtra (>= 2.3), png (>= 0.1-7) , magick (>= 2.7.3) ,
ggplotify (>= 0.1.0) , scales (>= 1.1.1) , shinythemes (>=
1.2.0) , shinyjs (>= 2.0.0) , shinyWidgets (>= 0.6.2) ,
shinycssloaders (>= 1.0.0) , shinyalert (>= 3.0.0) , shinyFiles
(>= 0.9.1) , shinyFeedback (>= 0.3.0)
License: GPL-3
MD5sum: 269870311b841582528ebec7f656cfe5
NeedsCompilation: no
Title: Protein Micro Array Data Management and Interactive
Visualization
Description: A generic three-step pre-processing package for protein
microarray data. This package contains different data
pre-processing procedures to allow comparison of their
performance.These steps are background correction, the
coefficient of variation (CV) based filtering, batch correction
and normalization.
biocViews: Microarray, OneChannel, Preprocessing ,
BiomedicalInformatics , Proteomics , BatchEffect, Normalization
, Bayesian, Clustering, Regression,SystemsBiology,
ImmunoOncology
Author: Kennedy Mwai [cre, aut], James Mburu [aut], Jacqueline Waeni
[ctb]
Maintainer: Kennedy Mwai <keniajin@gmail.com>
URL: https://github.com/Keniajin/protGear
VignetteBuilder: knitr
BugReports: https://github.com/Keniajin/protGear/issues
git_url: https://git.bioconductor.org/packages/protGear
git_branch: devel
git_last_commit: 97e8c52
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/protGear_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/protGear_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/protGear_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/protGear_1.11.0.tgz
vignettes: vignettes/protGear/inst/doc/vignette.html
vignetteTitles: protGear
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/protGear/inst/doc/vignette.R
dependencyCount: 190
Package: ProtGenerics
Version: 1.39.2
Depends: methods
Suggests: testthat
License: Artistic-2.0
MD5sum: 1dbc1d278e232240a9162908857ecbd0
NeedsCompilation: no
Title: Generic infrastructure for Bioconductor mass spectrometry
packages
Description: S4 generic functions and classes needed by Bioconductor
proteomics packages.
biocViews: Infrastructure, Proteomics, MassSpectrometry
Author: Laurent Gatto <laurent.gatto@uclouvain.be>, Johannes Rainer
<johannes.rainer@eurac.edu>
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
URL: https://github.com/RforMassSpectrometry/ProtGenerics
git_url: https://git.bioconductor.org/packages/ProtGenerics
git_branch: devel
git_last_commit: 0103c31
git_last_commit_date: 2025-01-15
Date/Publication: 2025-01-15
source.ver: src/contrib/ProtGenerics_1.39.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ProtGenerics_1.39.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ProtGenerics_1.39.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ProtGenerics_1.39.2.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: Cardinal, MsExperiment, MSnbase, SpectraQL, topdownr
importsMe: CompoundDb, ensembldb, matter, MetaboAnnotation,
MsBackendMassbank, MsBackendMetaboLights, MsBackendMgf,
MsBackendMsp, MsBackendRawFileReader, MsBackendSql, MsFeatures,
MSnID, MsQuality, mzID, mzR, PSMatch, QFeatures, Spectra, xcms
dependencyCount: 1
Package: psichomics
Version: 1.33.0
Depends: R (>= 4.0), shiny (>= 1.7.0), shinyBS
Imports: AnnotationDbi, AnnotationHub, BiocFileCache, cluster,
colourpicker, data.table, digest, dplyr, DT (>= 0.2), edgeR,
fastICA, fastmatch, ggplot2, ggrepel, graphics, grDevices,
highcharter (>= 0.5.0), htmltools, httr, jsonlite, limma,
pairsD3, plyr, purrr, Rcpp (>= 0.12.14), recount, Rfast,
R.utils, reshape2, shinyjs, stringr, stats,
SummarizedExperiment, survival, tools, utils, XML, xtable,
methods
LinkingTo: Rcpp
Suggests: testthat, knitr, parallel, devtools, rmarkdown, gplots, covr,
car, rstudioapi, spelling
License: MIT + file LICENSE
MD5sum: 0ff34f114e25954b958b0a181bcd8454
NeedsCompilation: yes
Title: Graphical Interface for Alternative Splicing Quantification,
Analysis and Visualisation
Description: Interactive R package with an intuitive Shiny-based
graphical interface for alternative splicing quantification and
integrative analyses of alternative splicing and gene
expression based on The Cancer Genome Atlas (TCGA), the
Genotype-Tissue Expression project (GTEx), Sequence Read
Archive (SRA) and user-provided data. The tool interactively
performs survival, dimensionality reduction and median- and
variance-based differential splicing and gene expression
analyses that benefit from the incorporation of clinical and
molecular sample-associated features (such as tumour stage or
survival). Interactive visual access to genomic mapping and
functional annotation of selected alternative splicing events
is also included.
biocViews: Sequencing, RNASeq, AlternativeSplicing,
DifferentialSplicing, Transcription, GUI, PrincipalComponent,
Survival, BiomedicalInformatics, Transcriptomics,
ImmunoOncology, Visualization, MultipleComparison,
GeneExpression, DifferentialExpression
Author: Nuno Saraiva-Agostinho [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5549-105X>), Nuno LuÃs
Barbosa-Morais [aut, led, ths] (ORCID:
<https://orcid.org/0000-0002-1215-0538>), André Falcão [ths],
Lina Gallego Paez [ctb], Marie Bordone [ctb], Teresa Maia
[ctb], Mariana Ferreira [ctb], Ana Carolina Leote [ctb],
Bernardo de Almeida [ctb]
Maintainer: Nuno Saraiva-Agostinho <nunodanielagostinho@gmail.com>
URL: https://nuno-agostinho.github.io/psichomics/,
https://github.com/nuno-agostinho/psichomics/
VignetteBuilder: knitr
BugReports: https://github.com/nuno-agostinho/psichomics/issues
git_url: https://git.bioconductor.org/packages/psichomics
git_branch: devel
git_last_commit: 55e72bb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-12-13
source.ver: src/contrib/psichomics_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/psichomics_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/psichomics_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/psichomics_1.33.0.tgz
vignettes: vignettes/psichomics/inst/doc/AS_events_preparation.html,
vignettes/psichomics/inst/doc/CLI_tutorial.html,
vignettes/psichomics/inst/doc/custom_data.html,
vignettes/psichomics/inst/doc/GUI_tutorial.html
vignetteTitles: Preparing an Alternative Splicing Annotation for
psichomics, Case study: command-line interface (CLI) tutorial,
Loading user-provided data, Case study: visual interface
tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/psichomics/inst/doc/AS_events_preparation.R,
vignettes/psichomics/inst/doc/CLI_tutorial.R,
vignettes/psichomics/inst/doc/custom_data.R,
vignettes/psichomics/inst/doc/GUI_tutorial.R
dependencyCount: 208
Package: PSMatch
Version: 1.11.0
Depends: S4Vectors
Imports: utils, stats, igraph, methods, Matrix, BiocParallel,
BiocGenerics, ProtGenerics (>= 1.27.1), QFeatures, MsCoreUtils
Suggests: msdata, rpx, mzID, mzR, Spectra, SummarizedExperiment,
BiocStyle, rmarkdown, knitr, factoextra, testthat
License: Artistic-2.0
MD5sum: b54aaf81037c7ee6841d382974f940e8
NeedsCompilation: no
Title: Handling and Managing Peptide Spectrum Matches
Description: The PSMatch package helps proteomics practitioners to
load, handle and manage Peptide Spectrum Matches. It provides
functions to model peptide-protein relations as adjacency
matrices and connected components, visualise these as graphs
and make informed decision about shared peptide filtering. The
package also provides functions to calculate and visualise MS2
fragment ions.
biocViews: Infrastructure, Proteomics, MassSpectrometry
Author: Laurent Gatto [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1520-2268>), Johannes Rainer [aut]
(ORCID: <https://orcid.org/0000-0002-6977-7147>), Sebastian
Gibb [aut] (ORCID: <https://orcid.org/0000-0001-7406-4443>),
Samuel Wieczorek [ctb], Thomas Burger [ctb]
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
URL: https://github.com/RforMassSpectrometry/PSM
VignetteBuilder: knitr
BugReports: https://github.com/RforMassSpectrometry/PSM/issues
git_url: https://git.bioconductor.org/packages/PSMatch
git_branch: devel
git_last_commit: e8f78a1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PSMatch_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PSMatch_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PSMatch_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PSMatch_1.11.0.tgz
vignettes: vignettes/PSMatch/inst/doc/AdjacencyMatrix.html,
vignettes/PSMatch/inst/doc/Fragments.html,
vignettes/PSMatch/inst/doc/PSM.html
vignetteTitles: Understanding protein groups with adjacency matrices,
MS2 fragment ions, Working with PSM data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PSMatch/inst/doc/AdjacencyMatrix.R,
vignettes/PSMatch/inst/doc/Fragments.R,
vignettes/PSMatch/inst/doc/PSM.R
importsMe: MSnbase, omXplore, topdownr
suggestsMe: MsDataHub
dependencyCount: 116
Package: ptairMS
Version: 1.15.0
Imports: Biobase, bit64, chron, data.table, doParallel, DT, enviPat,
foreach, ggplot2, graphics, grDevices, ggpubr, gridExtra,
Hmisc, methods, minpack.lm, MSnbase, parallel, plotly, rhdf5,
rlang, Rcpp, shiny, shinyscreenshot, signal, scales, stats,
utils
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0), ptairData,
ropls
License: GPL-3
MD5sum: 8ff7a8ef9e447ce26604f01d75893990
NeedsCompilation: yes
Title: Pre-processing PTR-TOF-MS Data
Description: This package implements a suite of methods to preprocess
data from PTR-TOF-MS instruments (HDF5 format) and generates
the 'sample by features' table of peak intensities in addition
to the sample and feature metadata (as a singl<e ExpressionSet
object for subsequent statistical analysis). This package also
permit usefull tools for cohorts management as analyzing data
progressively, visualization tools and quality control. The
steps include calibration, expiration detection, peak detection
and quantification, feature alignment, missing value imputation
and feature annotation. Applications to exhaled air and cell
culture in headspace are described in the vignettes and
examples. This package was used for data analysis of Gassin
Delyle study on adults undergoing invasive mechanical
ventilation in the intensive care unit due to severe COVID-19
or non-COVID-19 acute respiratory distress syndrome (ARDS), and
permit to identfy four potentiel biomarquers of the infection.
biocViews: Software, MassSpectrometry, Preprocessing, Metabolomics,
PeakDetection, Alignment
Author: camille Roquencourt [aut, cre]
Maintainer: camille Roquencourt <camille.roquencourt@hotmail.fr>
VignetteBuilder: knitr
BugReports: https://github.com/camilleroquencourt/ptairMS/issues
git_url: https://git.bioconductor.org/packages/ptairMS
git_branch: devel
git_last_commit: 70fcc78
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ptairMS_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ptairMS_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ptairMS_1.15.0.tgz
vignettes: vignettes/ptairMS/inst/doc/ptairMS.html
vignetteTitles: ptaiMS: Processing and analysis of PTR-TOF-MS data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ptairMS/inst/doc/ptairMS.R
dependencyCount: 192
Package: puma
Version: 3.49.3
Depends: R (>= 3.2.0), oligo (>= 1.32.0),graphics,grDevices, methods,
stats, utils, mclust, oligoClasses
Imports: Biobase (>= 2.5.5), affy (>= 1.46.0), affyio, oligoClasses
Suggests: pumadata, affydata, snow, limma, ROCR,annotate
License: LGPL
MD5sum: 30862b67417001cccf08974d4b7c9868
NeedsCompilation: yes
Title: Propagating Uncertainty in Microarray Analysis(including
Affymetrix tranditional 3' arrays and exon arrays and Human
Transcriptome Array 2.0)
Description: Most analyses of Affymetrix GeneChip data (including
tranditional 3' arrays and exon arrays and Human Transcriptome
Array 2.0) are based on point estimates of expression levels
and ignore the uncertainty of such estimates. By propagating
uncertainty to downstream analyses we can improve results from
microarray analyses. For the first time, the puma package makes
a suite of uncertainty propagation methods available to a
general audience. In additon to calculte gene expression from
Affymetrix 3' arrays, puma also provides methods to process
exon arrays and produces gene and isoform expression for
alternative splicing study. puma also offers improvements in
terms of scope and speed of execution over previously available
uncertainty propagation methods. Included are summarisation,
differential expression detection, clustering and PCA methods,
together with useful plotting functions.
biocViews: Microarray, OneChannel, Preprocessing,
DifferentialExpression, Clustering, ExonArray, GeneExpression,
mRNAMicroarray, ChipOnChip, AlternativeSplicing,
DifferentialSplicing, Bayesian, TwoChannel, DataImport, HTA2.0
Author: Richard D. Pearson, Xuejun Liu, Magnus Rattray, Marta Milo,
Neil D. Lawrence, Guido Sanguinetti, Li Zhang
Maintainer: Xuejun Liu <xuejun.liu@nuaa.edu.cn>
URL: http://umber.sbs.man.ac.uk/resources/puma
git_url: https://git.bioconductor.org/packages/puma
git_branch: devel
git_last_commit: 027ca65
git_last_commit_date: 2025-03-22
Date/Publication: 2025-03-23
source.ver: src/contrib/puma_3.49.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/puma_3.49.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/puma_3.49.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/puma_3.49.3.tgz
vignettes: vignettes/puma/inst/doc/puma.pdf
vignetteTitles: puma User Guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/puma/inst/doc/puma.R
suggestsMe: tigre
dependencyCount: 66
Package: PureCN
Version: 2.13.2
Depends: R (>= 3.5.0), DNAcopy, VariantAnnotation (>= 1.14.1)
Imports: GenomicRanges (>= 1.20.3), IRanges (>= 2.2.1), RColorBrewer,
S4Vectors, data.table, grDevices, graphics, stats, utils,
SummarizedExperiment, GenomeInfoDb, GenomicFeatures, Rsamtools,
Biobase, Biostrings, BiocGenerics, rtracklayer, ggplot2,
gridExtra, futile.logger, VGAM, tools, methods, mclust, rhdf5,
Matrix
Suggests: BiocParallel, BiocStyle, PSCBS, R.utils,
TxDb.Hsapiens.UCSC.hg19.knownGene, covr, knitr, optparse,
org.Hs.eg.db, jsonlite, markdown, rmarkdown, testthat
Enhances: genomicsdb (>= 0.0.3)
License: Artistic-2.0
Archs: x64
MD5sum: 704a098dce42bbb269e7f464f2b6a979
NeedsCompilation: no
Title: Copy number calling and SNV classification using targeted short
read sequencing
Description: This package estimates tumor purity, copy number, and loss
of heterozygosity (LOH), and classifies single nucleotide
variants (SNVs) by somatic status and clonality. PureCN is
designed for targeted short read sequencing data, integrates
well with standard somatic variant detection and copy number
pipelines, and has support for tumor samples without matching
normal samples.
biocViews: CopyNumberVariation, Software, Sequencing,
VariantAnnotation, VariantDetection, Coverage, ImmunoOncology
Author: Markus Riester [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4759-8332>), Angad P. Singh [aut]
Maintainer: Markus Riester <markus.riester@novartis.com>
URL: https://github.com/lima1/PureCN
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PureCN
git_branch: devel
git_last_commit: 88754b2
git_last_commit_date: 2025-03-20
Date/Publication: 2025-03-20
source.ver: src/contrib/PureCN_2.13.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PureCN_2.13.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PureCN_2.13.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PureCN_2.13.2.tgz
vignettes: vignettes/PureCN/inst/doc/PureCN.pdf,
vignettes/PureCN/inst/doc/Quick.html
vignetteTitles: Overview of the PureCN R package, Best practices,,
quick start and command line usage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PureCN/inst/doc/PureCN.R,
vignettes/PureCN/inst/doc/Quick.R
dependencyCount: 107
Package: pvac
Version: 1.55.0
Depends: R (>= 2.8.0)
Imports: affy (>= 1.20.0), stats, Biobase
Suggests: pbapply, affydata, ALLMLL, genefilter
License: LGPL (>= 2.0)
MD5sum: 28db7ae5b3626e451570ba4bcfad4ccd
NeedsCompilation: no
Title: PCA-based gene filtering for Affymetrix arrays
Description: The package contains the function for filtering genes by
the proportion of variation accounted for by the first
principal component (PVAC).
biocViews: Microarray, OneChannel, QualityControl
Author: Jun Lu and Pierre R. Bushel
Maintainer: Jun Lu <jlu276@gmail.com>, Pierre R. Bushel
<bushel@niehs.nih.gov>
git_url: https://git.bioconductor.org/packages/pvac
git_branch: devel
git_last_commit: 372aad1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/pvac_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pvac_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pvac_1.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pvac_1.55.0.tgz
vignettes: vignettes/pvac/inst/doc/pvac.pdf
vignetteTitles: PCA-based gene filtering for Affymetrix GeneChips
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pvac/inst/doc/pvac.R
dependencyCount: 12
Package: pvca
Version: 1.47.0
Depends: R (>= 2.15.1)
Imports: Matrix, Biobase, vsn, stats, lme4
Suggests: golubEsets
License: LGPL (>= 2.0)
Archs: x64
MD5sum: 52e80e3a883f32cb79df8b0669f3e1b1
NeedsCompilation: no
Title: Principal Variance Component Analysis (PVCA)
Description: This package contains the function to assess the batch
sourcs by fitting all "sources" as random effects including
two-way interaction terms in the Mixed Model(depends on lme4
package) to selected principal components, which were obtained
from the original data correlation matrix. This package
accompanies the book "Batch Effects and Noise in Microarray
Experiements, chapter 12.
biocViews: Microarray, BatchEffect
Author: Pierre Bushel <bushel@niehs.nih.gov>
Maintainer: Jianying LI <li11@niehs.nih.gov>
git_url: https://git.bioconductor.org/packages/pvca
git_branch: devel
git_last_commit: 74c5ee9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/pvca_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pvca_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pvca_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pvca_1.47.0.tgz
vignettes: vignettes/pvca/inst/doc/pvca.pdf
vignetteTitles: Batch effect estimation in Microarray data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pvca/inst/doc/pvca.R
importsMe: ExpressionNormalizationWorkflow, statVisual
dependencyCount: 56
Package: Pviz
Version: 1.41.0
Depends: R(>= 3.0.0), Gviz(>= 1.7.10)
Imports: biovizBase, Biostrings, GenomicRanges, IRanges, data.table,
methods
Suggests: knitr, pepDat
License: Artistic-2.0
Archs: x64
MD5sum: 999aebafd833140d35394093abcf2479
NeedsCompilation: no
Title: Peptide Annotation and Data Visualization using Gviz
Description: Pviz adapts the Gviz package for protein sequences and
data.
biocViews: Visualization, Proteomics, Microarray
Author: Renan Sauteraud, Mike Jiang, Raphael Gottardo
Maintainer: Renan Sauteraud <rsautera@fhcrc.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Pviz
git_branch: devel
git_last_commit: 048901b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Pviz_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Pviz_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Pviz_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Pviz_1.41.0.tgz
vignettes: vignettes/Pviz/inst/doc/Pviz.pdf
vignetteTitles: The Pviz users guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Pviz/inst/doc/Pviz.R
suggestsMe: pepStat
dependencyCount: 156
Package: pwalign
Version: 1.3.3
Depends: BiocGenerics, S4Vectors, IRanges, Biostrings (>= 2.71.5)
Imports: methods, utils
LinkingTo: S4Vectors, IRanges, XVector, Biostrings
Suggests: RUnit
Enhances: Rmpi
License: Artistic-2.0
MD5sum: b379be7468c7a6bba3a5f7c0c19f6f70
NeedsCompilation: yes
Title: Perform pairwise sequence alignments
Description: The two main functions in the package are
pairwiseAlignment() and stringDist(). The former solves
(Needleman-Wunsch) global alignment, (Smith-Waterman) local
alignment, and (ends-free) overlap alignment problems. The
latter computes the Levenshtein edit distance or pairwise
alignment score matrix for a set of strings.
biocViews: Alignment, SequenceMatching, Sequencing, Genetics
Author: Patrick Aboyoun [aut], Robert Gentleman [aut], Hervé Pagès
[cre] (ORCID: <https://orcid.org/0009-0002-8272-4522>)
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/pwalign
BugReports: https://github.com/Bioconductor/pwalign/issues
git_url: https://git.bioconductor.org/packages/pwalign
git_branch: devel
git_last_commit: 7bd00dc
git_last_commit_date: 2025-03-18
Date/Publication: 2025-03-18
source.ver: src/contrib/pwalign_1.3.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/pwalign_1.3.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/pwalign_1.3.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/pwalign_1.3.3.tgz
vignettes: vignettes/pwalign/inst/doc/PairwiseAlignments.pdf
vignetteTitles: Pairwise Sequence Alignments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/pwalign/inst/doc/PairwiseAlignments.R
dependsOnMe: amplican, hiReadsProcessor, MethTargetedNGS, QSutils,
R453Plus1Toolbox, sangeranalyseR, sangerseqR, CleanBSequences
importsMe: ChIPpeakAnno, CNEr, crisprShiny, DominoEffect,
enhancerHomologSearch, ggseqalign, girafe, GUIDEseq, IMMAN,
IsoformSwitchAnalyzeR, LinTInd, methylscaper, motifbreakR,
MSA2dist, openPrimeR, scanMiR, ShortRead, SPLINTER,
StructuralVariantAnnotation, svaNUMT, TFBSTools, XNAString,
AntibodyForests
suggestsMe: BiocGenerics, Biostrings, idpr, msa, RSVSim, dowser,
geneviewer, seqtrie
dependencyCount: 25
Package: PWMEnrich
Version: 4.43.0
Depends: R (>= 3.5.0), methods, BiocGenerics, Biostrings
Imports: grid, seqLogo, gdata, evd, S4Vectors
Suggests: MotifDb, BSgenome, BSgenome.Dmelanogaster.UCSC.dm3,
PWMEnrich.Dmelanogaster.background, testthat, gtools, parallel,
PWMEnrich.Hsapiens.background, PWMEnrich.Mmusculus.background,
BiocStyle, knitr
License: LGPL (>= 2)
MD5sum: ff9f616f6794f12dcc06a05b9a648720
NeedsCompilation: no
Title: PWM enrichment analysis
Description: A toolkit of high-level functions for DNA motif scanning
and enrichment analysis built upon Biostrings. The main
functionality is PWM enrichment analysis of already known PWMs
(e.g. from databases such as MotifDb), but the package also
implements high-level functions for PWM scanning and
visualisation. The package does not perform "de novo" motif
discovery, but is instead focused on using motifs that are
either experimentally derived or computationally constructed by
other tools.
biocViews: MotifAnnotation, SequenceMatching, Software
Author: Robert Stojnic, Diego Diez
Maintainer: Diego Diez <diego10ruiz@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/PWMEnrich
git_branch: devel
git_last_commit: 23d8640
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/PWMEnrich_4.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/PWMEnrich_4.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/PWMEnrich_4.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/PWMEnrich_4.43.0.tgz
vignettes: vignettes/PWMEnrich/inst/doc/PWMEnrich.pdf
vignetteTitles: Overview of the 'PWMEnrich' package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/PWMEnrich/inst/doc/PWMEnrich.R
dependsOnMe: PWMEnrich.Dmelanogaster.background,
PWMEnrich.Hsapiens.background, PWMEnrich.Mmusculus.background
suggestsMe: rTRM
dependencyCount: 30
Package: qckitfastq
Version: 1.23.0
Imports: magrittr, ggplot2, dplyr, seqTools, zlibbioc, data.table,
reshape2, grDevices, graphics, stats, utils, Rcpp, rlang,
RSeqAn
LinkingTo: Rcpp, RSeqAn
Suggests: knitr, rmarkdown, kableExtra, testthat
License: Artistic-2.0
MD5sum: 6bd30344a96d2bf6504adc4b186693e5
NeedsCompilation: yes
Title: FASTQ Quality Control
Description: Assessment of FASTQ file format with multiple metrics
including quality score, sequence content, overrepresented
sequence and Kmers.
biocViews: Software,QualityControl,Sequencing
Author: Wenyue Xing [aut], August Guang [aut, cre]
Maintainer: August Guang <august.guang@gmail.com>
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/qckitfastq
git_branch: devel
git_last_commit: 6d6381d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/qckitfastq_1.23.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/qckitfastq_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/qckitfastq_1.23.0.tgz
vignettes: vignettes/qckitfastq/inst/doc/vignette-qckitfastq.pdf
vignetteTitles: Quality control analysis and visualization using
qckitfastq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/qckitfastq/inst/doc/vignette-qckitfastq.R
dependencyCount: 48
Package: qcmetrics
Version: 1.45.0
Depends: R (>= 3.3)
Imports: Biobase, methods, knitr, tools, xtable, pander, S4Vectors
Suggests: affy, MSnbase, ggplot2, lattice, mzR, BiocStyle, rmarkdown,
markdown
License: GPL-2
MD5sum: 7d66c42e45be3062eb8ce9d22a3472d8
NeedsCompilation: no
Title: A Framework for Quality Control
Description: The package provides a framework for generic quality
control of data. It permits to create, manage and visualise
individual or sets of quality control metrics and generate
quality control reports in various formats.
biocViews: ImmunoOncology, Software, QualityControl, Proteomics,
Microarray, MassSpectrometry, Visualization, ReportWriting
Author: Laurent Gatto [aut, cre]
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
URL: http://lgatto.github.io/qcmetrics/articles/qcmetrics.html
VignetteBuilder: knitr
BugReports: https://github.com/lgatto/qcmetrics/issues
git_url: https://git.bioconductor.org/packages/qcmetrics
git_branch: devel
git_last_commit: d4b8b9e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/qcmetrics_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/qcmetrics_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/qcmetrics_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/qcmetrics_1.45.0.tgz
vignettes: vignettes/qcmetrics/inst/doc/qcmetrics.html
vignetteTitles: Index file for the qcmetrics package vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/qcmetrics/inst/doc/qcmetrics.R
importsMe: MSstatsQC
dependencyCount: 20
Package: QDNAseq
Version: 1.43.0
Depends: R (>= 3.1.0)
Imports: graphics, methods, stats, utils, Biobase (>= 2.18.0), CGHbase
(>= 1.18.0), CGHcall (>= 2.18.0), DNAcopy (>= 1.32.0),
GenomicRanges (>= 1.20), IRanges (>= 2.2), matrixStats (>=
0.60.0), R.utils (>= 2.9.0), Rsamtools (>= 1.20), future.apply
(>= 1.8.1)
Suggests: BiocStyle (>= 1.8.0), BSgenome (>= 1.38.0), digest (>=
0.6.20), GenomeInfoDb (>= 1.6.0), future (>= 1.22.1),
parallelly (>= 1.28.1), R.cache (>= 0.13.0), QDNAseq.hg19,
QDNAseq.mm10
License: GPL
MD5sum: 7abc85b32baec305b2df159070546dc3
NeedsCompilation: no
Title: Quantitative DNA Sequencing for Chromosomal Aberrations
Description: Quantitative DNA sequencing for chromosomal aberrations.
The genome is divided into non-overlapping fixed-sized bins,
number of sequence reads in each counted, adjusted with a
simultaneous two-dimensional loess correction for sequence
mappability and GC content, and filtered to remove spurious
regions in the genome. Downstream steps of segmentation and
calling are also implemented via packages DNAcopy and CGHcall,
respectively.
biocViews: CopyNumberVariation, DNASeq, Genetics, GenomeAnnotation,
Preprocessing, QualityControl, Sequencing
Author: Ilari Scheinin [aut], Daoud Sie [aut, cre], Henrik Bengtsson
[aut], Erik van Dijk [ctb]
Maintainer: Daoud Sie <d.sie@vumc.nl>
URL: https://github.com/ccagc/QDNAseq
BugReports: https://github.com/ccagc/QDNAseq/issues
git_url: https://git.bioconductor.org/packages/QDNAseq
git_branch: devel
git_last_commit: 2221207
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/QDNAseq_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/QDNAseq_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/QDNAseq_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/QDNAseq_1.43.0.tgz
vignettes: vignettes/QDNAseq/inst/doc/QDNAseq.pdf
vignetteTitles: Introduction to QDNAseq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/QDNAseq/inst/doc/QDNAseq.R
dependsOnMe: GeneBreak, QDNAseq.hg19, QDNAseq.mm10
importsMe: ACE, biscuiteer, cfdnakit
dependencyCount: 58
Package: QFeatures
Version: 1.17.4
Depends: R (>= 4.0), MultiAssayExperiment
Imports: methods, stats, utils, S4Vectors, IRanges,
SummarizedExperiment, BiocGenerics, ProtGenerics (>= 1.35.1),
AnnotationFilter, lazyeval, Biobase, MsCoreUtils (>= 1.7.2),
igraph, grDevices, plotly, tidyr, tidyselect, reshape2
Suggests: SingleCellExperiment, MsDataHub (>= 1.3.3), Matrix,
HDF5Array, msdata, ggplot2, gplots, dplyr, limma, DT, shiny,
shinydashboard, testthat, knitr, BiocStyle, rmarkdown, vsn,
preprocessCore, matrixStats, imputeLCMD, pcaMethods, impute,
norm, ComplexHeatmap
License: Artistic-2.0
Archs: x64
MD5sum: f1499e5cc718ee2ed9351e93e0a51350
NeedsCompilation: no
Title: Quantitative features for mass spectrometry data
Description: The QFeatures infrastructure enables the management and
processing of quantitative features for high-throughput mass
spectrometry assays. It provides a familiar Bioconductor user
experience to manages quantitative data across different assay
levels (such as peptide spectrum matches, peptides and
proteins) in a coherent and tractable format.
biocViews: Infrastructure, MassSpectrometry, Proteomics, Metabolomics
Author: Laurent Gatto [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1520-2268>), Christophe Vanderaa
[aut] (ORCID: <https://orcid.org/0000-0001-7443-5427>), Léopold
Guyot [ctb]
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
URL: https://github.com/RforMassSpectrometry/QFeatures
VignetteBuilder: knitr
BugReports: https://github.com/RforMassSpectrometry/QFeatures/issues
git_url: https://git.bioconductor.org/packages/QFeatures
git_branch: devel
git_last_commit: 9d3c8b7
git_last_commit_date: 2025-03-04
Date/Publication: 2025-03-04
source.ver: src/contrib/QFeatures_1.17.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/QFeatures_1.17.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/QFeatures_1.17.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/QFeatures_1.17.4.tgz
vignettes: vignettes/QFeatures/inst/doc/Processing.html,
vignettes/QFeatures/inst/doc/QFeatures.html,
vignettes/QFeatures/inst/doc/read_QFeatures.html,
vignettes/QFeatures/inst/doc/Visualization.html
vignetteTitles: Processing quantitative proteomics data with QFeatures,
Quantitative features for mass spectrometry data, Load data
using readQFeatures(), Data visualization from a QFeatures
object
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/QFeatures/inst/doc/Processing.R,
vignettes/QFeatures/inst/doc/QFeatures.R,
vignettes/QFeatures/inst/doc/read_QFeatures.R,
vignettes/QFeatures/inst/doc/Visualization.R
dependsOnMe: hdxmsqc, msqrob2, scp, scpdata
importsMe: MetaboAnnotation, MsExperiment, mspms, PSMatch
suggestsMe: MsDataHub
dependencyCount: 106
Package: qmtools
Version: 1.11.0
Depends: R (>= 4.2.0), SummarizedExperiment
Imports: rlang, ggplot2, patchwork, heatmaply, methods, MsCoreUtils,
stats, igraph, VIM, scales, grDevices, graphics, limma
Suggests: Rtsne, missForest, vsn, pcaMethods, pls, MsFeatures, impute,
imputeLCMD, nlme, testthat (>= 3.0.0), BiocStyle, knitr,
rmarkdown
License: GPL-3
MD5sum: eda7ca7629203de48d56e0b34254e62b
NeedsCompilation: no
Title: Quantitative Metabolomics Data Processing Tools
Description: The qmtools (quantitative metabolomics tools) package
provides basic tools for processing quantitative metabolomics
data with the standard SummarizedExperiment class. This
includes functions for imputation, normalization, feature
filtering, feature clustering, dimension-reduction, and
visualization to help users prepare data for statistical
analysis. This package also offers a convenient way to compute
empirical Bayes statistics for which metabolic features are
different between two sets of study samples. Several functions
in this package could also be used in other types of omics
data.
biocViews: Metabolomics, Preprocessing, Normalization,
DimensionReduction, MassSpectrometry
Author: Jaehyun Joo [aut, cre], Blanca Himes [aut]
Maintainer: Jaehyun Joo <jaehyunjoo@outlook.com>
URL: https://github.com/HimesGroup/qmtools
VignetteBuilder: knitr
BugReports: https://github.com/HimesGroup/qmtools/issues
git_url: https://git.bioconductor.org/packages/qmtools
git_branch: devel
git_last_commit: d02f681
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/qmtools_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/qmtools_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/qmtools_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/qmtools_1.11.0.tgz
vignettes: vignettes/qmtools/inst/doc/qmtools.html
vignetteTitles: Quantitative metabolomics data processing
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/qmtools/inst/doc/qmtools.R
dependencyCount: 165
Package: qpcrNorm
Version: 1.65.0
Depends: methods, Biobase, limma, affy
License: LGPL (>= 2)
MD5sum: 966b13adfe8bf2a60ca8e5492a8a8051
NeedsCompilation: no
Title: Data-driven normalization strategies for high-throughput qPCR
data.
Description: The package contains functions to perform normalization of
high-throughput qPCR data. Basic functions for processing raw
Ct data plus functions to generate diagnostic plots are also
available.
biocViews: Preprocessing, GeneExpression
Author: Jessica Mar
Maintainer: Jessica Mar <jess@jimmy.harvard.edu>
git_url: https://git.bioconductor.org/packages/qpcrNorm
git_branch: devel
git_last_commit: 434cba8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/qpcrNorm_1.65.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/qpcrNorm_1.65.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/qpcrNorm_1.65.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/qpcrNorm_1.65.0.tgz
vignettes: vignettes/qpcrNorm/inst/doc/qpcrNorm.pdf
vignetteTitles: qPCR Normalization Example
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/qpcrNorm/inst/doc/qpcrNorm.R
dependencyCount: 14
Package: qpgraph
Version: 2.41.6
Depends: R (>= 3.5)
Imports: methods, parallel, Matrix (>= 1.5-0), grid, annotate, graph
(>= 1.45.1), Biobase, S4Vectors, BiocParallel, AnnotationDbi,
IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, mvtnorm,
qtl, Rgraphviz
Suggests: RUnit, BiocGenerics, BiocStyle, genefilter, org.EcK12.eg.db,
rlecuyer, snow, Category, GOstats
License: GPL (>= 2)
MD5sum: c4c10692f6f1e8f34525e48ad1741e9e
NeedsCompilation: yes
Title: Estimation of Genetic and Molecular Regulatory Networks from
High-Throughput Genomics Data
Description: Estimate gene and eQTL networks from high-throughput
expression and genotyping assays.
biocViews: Microarray, GeneExpression, Transcription, Pathways,
NetworkInference, GraphAndNetwork, GeneRegulation, Genetics,
GeneticVariability, SNP, Software
Author: Robert Castelo [aut, cre], Alberto Roverato [aut]
Maintainer: Robert Castelo <robert.castelo@upf.edu>
URL: https://github.com/rcastelo/qpgraph
BugReports: https://github.com/rcastelo/qpgraph/issues
git_url: https://git.bioconductor.org/packages/qpgraph
git_branch: devel
git_last_commit: d9808da
git_last_commit_date: 2025-03-28
Date/Publication: 2025-03-28
source.ver: src/contrib/qpgraph_2.41.6.tar.gz
win.binary.ver: bin/windows/contrib/4.5/qpgraph_2.41.6.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/qpgraph_2.41.6.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/qpgraph_2.41.6.tgz
vignettes: vignettes/qpgraph/inst/doc/BasicUsersGuide.pdf,
vignettes/qpgraph/inst/doc/eQTLnetworks.pdf,
vignettes/qpgraph/inst/doc/qpgraphSimulate.pdf,
vignettes/qpgraph/inst/doc/qpTxRegNet.pdf
vignetteTitles: BasicUsersGuide.pdf, Estimate eQTL networks using
qpgraph, Simulating molecular regulatory networks using
qpgraph, Reverse-engineer transcriptional regulatory networks
using qpgraph
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/qpgraph/inst/doc/eQTLnetworks.R,
vignettes/qpgraph/inst/doc/qpgraphSimulate.R,
vignettes/qpgraph/inst/doc/qpTxRegNet.R
importsMe: clipper, MOSClip, topologyGSA
dependencyCount: 83
Package: qPLEXanalyzer
Version: 1.25.0
Depends: R (>= 4.0), Biobase, MSnbase
Imports: assertthat, BiocGenerics, Biostrings, dplyr (>= 1.0.0),
ggdendro, ggplot2, graphics, grDevices, IRanges, limma,
magrittr, preprocessCore, purrr, RColorBrewer, readr, rlang,
scales, stats, stringr, tibble, tidyr, tidyselect, utils
Suggests: patchwork, knitr, qPLEXdata, rmarkdown, statmod, testthat,
UniProt.ws, vdiffr
License: GPL-2
MD5sum: 8727493ce796834804210d5d7c4391c2
NeedsCompilation: no
Title: Tools for quantitative proteomics data analysis
Description: Tools for TMT based quantitative proteomics data analysis.
biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Normalization,
Preprocessing, QualityControl, DataImport
Author: Matthew Eldridge [aut], Kamal Kishore [aut], Ashley Sawle [aut,
cre]
Maintainer: Ashley Sawle <ads2202cu@gmail.com>
VignetteBuilder: knitr
BugReports:
https://github.com/crukci-bioinformatics/qPLEXanalyzer/issues
git_url: https://git.bioconductor.org/packages/qPLEXanalyzer
git_branch: devel
git_last_commit: 9825be7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/qPLEXanalyzer_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/qPLEXanalyzer_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/qPLEXanalyzer_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/qPLEXanalyzer_1.25.0.tgz
vignettes: vignettes/qPLEXanalyzer/inst/doc/qPLEXanalyzer.html
vignetteTitles: qPLEXanalyzer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/qPLEXanalyzer/inst/doc/qPLEXanalyzer.R
dependsOnMe: qPLEXdata
dependencyCount: 148
Package: qsea
Version: 1.33.0
Depends: R (>= 4.3)
Imports: Biostrings, graphics, gtools, methods, stats, utils, HMMcopy,
rtracklayer, BSgenome, GenomicRanges, Rsamtools, IRanges,
limma, GenomeInfoDb, BiocGenerics, grDevices, zoo,
BiocParallel, S4Vectors
Suggests: BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, testthat, BiocStyle,
knitr, rmarkdown, BiocManager, MASS
License: GPL-2
MD5sum: b46f4621eb7c84d4bc88bf08afca86a0
NeedsCompilation: yes
Title: IP-seq data analysis and vizualization
Description: qsea (quantitative sequencing enrichment analysis) was
developed as the successor of the MEDIPS package for analyzing
data derived from methylated DNA immunoprecipitation (MeDIP)
experiments followed by sequencing (MeDIP-seq). However, qsea
provides several functionalities for the analysis of other
kinds of quantitative sequencing data (e.g. ChIP-seq, MBD-seq,
CMS-seq and others) including calculation of differential
enrichment between groups of samples.
biocViews: Sequencing, DNAMethylation, CpGIsland, ChIPSeq,
Preprocessing, Normalization, QualityControl, Visualization,
CopyNumberVariation, ChipOnChip, DifferentialMethylation
Author: Matthias Lienhard [aut, cre] (ORCID:
<https://orcid.org/0000-0002-2549-3142>), Lukas Chavez [aut]
(ORCID: <https://orcid.org/0000-0002-8718-8848>), Ralf Herwig
[aut] (ORCID: <https://orcid.org/0000-0002-9335-1760>)
Maintainer: Matthias Lienhard <lienhard@molgen.mpg.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/qsea
git_branch: devel
git_last_commit: c55425a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/qsea_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/qsea_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/qsea_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/qsea_1.33.0.tgz
vignettes: vignettes/qsea/inst/doc/qsea_tutorial.html
vignetteTitles: qsea
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/qsea/inst/doc/qsea_tutorial.R
dependencyCount: 65
Package: qsmooth
Version: 1.23.0
Depends: R (>= 4.0)
Imports: SummarizedExperiment, utils, sva, stats, methods, graphics,
Hmisc
Suggests: bodymapRat, quantro, knitr, rmarkdown, BiocStyle, testthat
License: GPL-3
MD5sum: dcb908e2b2ed0f360c8d900932e4f14a
NeedsCompilation: no
Title: Smooth quantile normalization
Description: Smooth quantile normalization is a generalization of
quantile normalization, which is average of the two types of
assumptions about the data generation process: quantile
normalization and quantile normalization between groups.
biocViews: Normalization, Preprocessing, MultipleComparison,
Microarray, Sequencing, RNASeq, BatchEffect
Author: Stephanie C. Hicks [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7858-0231>), Kwame Okrah [aut],
Koen Van den Berge [ctb], Hector Corrada Bravo [aut] (ORCID:
<https://orcid.org/0000-0002-1255-4444>), Rafael Irizarry [aut]
(ORCID: <https://orcid.org/0000-0002-3944-4309>)
Maintainer: Stephanie C. Hicks <shicks19@jhu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/qsmooth
git_branch: devel
git_last_commit: 6a7968b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/qsmooth_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/qsmooth_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/qsmooth_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/qsmooth_1.23.0.tgz
vignettes: vignettes/qsmooth/inst/doc/qsmooth.html
vignetteTitles: The qsmooth user's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/qsmooth/inst/doc/qsmooth.R
importsMe: CleanUpRNAseq
dependencyCount: 127
Package: QSutils
Version: 1.25.0
Depends: R (>= 3.5), Biostrings, pwalign, BiocGenerics, methods
Imports: ape, stats, psych
Suggests: BiocStyle, knitr, rmarkdown, ggplot2
License: GPL-2
Archs: x64
MD5sum: 12e80d52960292478c3a3ae9210bbaad
NeedsCompilation: no
Title: Quasispecies Diversity
Description: Set of utility functions for viral quasispecies analysis
with NGS data. Most functions are equally useful for
metagenomic studies. There are three main types: (1) data
manipulation and exploration—functions useful for converting
reads to haplotypes and frequencies, repairing reads,
intersecting strand haplotypes, and visualizing haplotype
alignments. (2) diversity indices—functions to compute
diversity and entropy, in which incidence, abundance, and
functional indices are considered. (3) data
simulation—functions useful for generating random viral
quasispecies data.
biocViews: Software, Genetics, DNASeq, GeneticVariability, Sequencing,
Alignment, SequenceMatching, DataImport
Author: Mercedes Guerrero-Murillo [cre, aut] (ORCID:
<https://orcid.org/0000-0002-5556-2460>), Josep Gregori i Font
[aut] (ORCID: <https://orcid.org/0000-0002-4253-8015>)
Maintainer: Mercedes Guerrero-Murillo <mergumu@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/QSutils
git_branch: devel
git_last_commit: 4274cf1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/QSutils_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/QSutils_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/QSutils_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/QSutils_1.25.0.tgz
vignettes: vignettes/QSutils/inst/doc/QSUtils-Alignment.html,
vignettes/QSutils/inst/doc/QSutils-Diversity.html,
vignettes/QSutils/inst/doc/QSutils-Simulation.html
vignetteTitles: QSUtils-Alignment, QSutils-Diversity,
QSutils-Simulation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/QSutils/inst/doc/QSUtils-Alignment.R,
vignettes/QSutils/inst/doc/QSutils-Diversity.R,
vignettes/QSutils/inst/doc/QSutils-Simulation.R
importsMe: longreadvqs
dependencyCount: 36
Package: qsvaR
Version: 1.11.1
Depends: R (>= 4.2), SummarizedExperiment
Imports: dplyr, sva, stats, ggplot2, rlang, methods
Suggests: BiocFileCache, BiocStyle, covr, knitr, limma, RefManageR,
rmarkdown, sessioninfo, testthat (>= 3.0.0)
License: Artistic-2.0
MD5sum: 3895d549969c89515fe4ffe01cbf9992
NeedsCompilation: no
Title: Generate Quality Surrogate Variable Analysis for Degradation
Correction
Description: The qsvaR package contains functions for removing the
effect of degration in rna-seq data from postmortem brain
tissue. The package is equipped to help users generate
principal components associated with degradation. The
components can be used in differential expression analysis to
remove the effects of degradation.
biocViews: Software, WorkflowStep, Normalization, BiologicalQuestion,
DifferentialExpression, Sequencing, Coverage
Author: Joshua Stolz [aut] (ORCID:
<https://orcid.org/0000-0001-5694-5247>), Hedia Tnani [ctb,
cre] (ORCID: <https://orcid.org/0000-0002-0380-9740>), Leonardo
Collado-Torres [ctb] (ORCID:
<https://orcid.org/0000-0003-2140-308X>), Nicholas J. Eagles
[aut] (ORCID: <https://orcid.org/0000-0002-9808-5254>)
Maintainer: Hedia Tnani <hediatnani0@gmail.com>
URL: https://github.com/LieberInstitute/qsvaR
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/qsvaR
git_url: https://git.bioconductor.org/packages/qsvaR
git_branch: devel
git_last_commit: 55f8513
git_last_commit_date: 2024-12-10
Date/Publication: 2024-12-11
source.ver: src/contrib/qsvaR_1.11.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/qsvaR_1.11.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/qsvaR_1.11.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/qsvaR_1.11.1.tgz
vignettes: vignettes/qsvaR/inst/doc/Intro_qsvaR.html
vignetteTitles: Introduction to qsvaR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/qsvaR/inst/doc/Intro_qsvaR.R
dependencyCount: 97
Package: QTLExperiment
Version: 1.99.1
Depends: SummarizedExperiment
Imports: methods, rlang, checkmate, dplyr, collapse, vroom, tidyr,
tibble, utils, stats, ashr, S4Vectors, BiocGenerics
Suggests: testthat, BiocStyle, knitr, rmarkdown, covr
License: GPL-3
Archs: x64
MD5sum: 0afdd1a1c42d7fe065913081e830f3ee
NeedsCompilation: no
Title: S4 classes for QTL summary statistics and metadata
Description: QLTExperiment defines an S4 class for storing and
manipulating summary statistics from QTL mapping experiments in
one or more states. It is based on the 'SummarizedExperiment'
class and contains functions for creating, merging, and
subsetting objects. 'QTLExperiment' also stores experiment
metadata and has checks in place to ensure that transformations
apply correctly.
biocViews: FunctionalGenomics, DataImport, DataRepresentation,
Infrastructure, Sequencing, SNP, Software
Author: Christina Del Azodi [aut], Davis McCarthy [ctb], Amelia
Dunstone [cre, ctb] (ORCID:
<https://orcid.org/0009-0009-6426-1529>)
Maintainer: Amelia Dunstone <amelia.dunstone@svi.edu.au>
URL: https://github.com/dunstone-a/QTLExperiment
VignetteBuilder: knitr
BugReports: https://github.com/dunstone-a/QTLExperiment/issues
git_url: https://git.bioconductor.org/packages/QTLExperiment
git_branch: devel
git_last_commit: 2ecec5f
git_last_commit_date: 2025-03-07
Date/Publication: 2025-03-07
source.ver: src/contrib/QTLExperiment_1.99.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/QTLExperiment_1.99.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/QTLExperiment_1.99.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/QTLExperiment_1.99.1.tgz
vignettes: vignettes/QTLExperiment/inst/doc/QTLExperiment.html
vignetteTitles: An introduction to the QTLExperiment class
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/QTLExperiment/inst/doc/QTLExperiment.R
dependsOnMe: multistateQTL
dependencyCount: 74
Package: Qtlizer
Version: 1.21.1
Depends: R (>= 3.6.0)
Imports: httr, curl, GenomicRanges, stringi
Suggests: BiocStyle, testthat, knitr, rmarkdown
License: GPL-3
MD5sum: 8f7e1f7969c24a9672ad1a0907aba37a
NeedsCompilation: no
Title: Comprehensive QTL annotation of GWAS results
Description: This R package provides access to the Qtlizer web server.
Qtlizer annotates lists of common small variants (mainly SNPs)
and genes in humans with associated changes in gene expression
using the most comprehensive database of published quantitative
trait loci (QTLs).
biocViews: GenomeWideAssociation, SNP, Genetics, LinkageDisequilibrium
Author: Matthias Munz [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4728-3357>), Julia Remes [aut]
Maintainer: Matthias Munz <matthias.munz@gmx.de>
VignetteBuilder: knitr
BugReports: https://github.com/matmu/Qtlizer/issues
git_url: https://git.bioconductor.org/packages/Qtlizer
git_branch: devel
git_last_commit: 84ff30d
git_last_commit_date: 2025-02-28
Date/Publication: 2025-02-28
source.ver: src/contrib/Qtlizer_1.21.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Qtlizer_1.21.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Qtlizer_1.21.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Qtlizer_1.21.1.tgz
vignettes: vignettes/Qtlizer/inst/doc/Qtlizer.html
vignetteTitles: Qtlizer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Qtlizer/inst/doc/Qtlizer.R
dependencyCount: 24
Package: quantiseqr
Version: 1.15.0
Depends: R (>= 4.1.0)
Imports: Biobase, limSolve, MASS, methods, preprocessCore, stats,
SummarizedExperiment, ggplot2, tidyr, rlang, utils
Suggests: AnnotationDbi, BiocStyle, dplyr, ExperimentHub, GEOquery,
knitr, macrophage, org.Hs.eg.db, reshape2, rmarkdown, testthat,
tibble
License: GPL-3
MD5sum: ef4bdb9123538637df083941686b3237
NeedsCompilation: no
Title: Quantification of the Tumor Immune contexture from RNA-seq data
Description: This package provides a streamlined workflow for the
quanTIseq method, developed to perform the quantification of
the Tumor Immune contexture from RNA-seq data. The
quantification is performed against the TIL10 signature
(dissecting the contributions of ten immune cell types),
carefully crafted from a collection of human RNA-seq samples.
The TIL10 signature has been extensively validated using
simulated, flow cytometry, and immunohistochemistry data.
biocViews: GeneExpression, Software, Transcription, Transcriptomics,
Sequencing, Microarray, Visualization, Annotation,
ImmunoOncology, FeatureExtraction, Classification,
StatisticalMethod, ExperimentHubSoftware, FlowCytometry
Author: Federico Marini [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3252-7758>), Francesca Finotello
[aut] (ORCID: <https://orcid.org/0000-0003-0712-4658>)
Maintainer: Federico Marini <marinif@uni-mainz.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/quantiseqr
git_branch: devel
git_last_commit: d4608cd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/quantiseqr_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/quantiseqr_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/quantiseqr_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/quantiseqr_1.15.0.tgz
vignettes: vignettes/quantiseqr/inst/doc/using_quantiseqr.html
vignetteTitles: Using quantiseqr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/quantiseqr/inst/doc/using_quantiseqr.R
importsMe: easier
dependencyCount: 73
Package: quantro
Version: 1.41.0
Depends: R (>= 4.0)
Imports: Biobase, minfi, doParallel, foreach, iterators, ggplot2,
methods, RColorBrewer
Suggests: rmarkdown, knitr, RUnit, BiocGenerics, BiocStyle
License: GPL-3
MD5sum: 46bad2499177025d4db46886efc27fcc
NeedsCompilation: no
Title: A test for when to use quantile normalization
Description: A data-driven test for the assumptions of quantile
normalization using raw data such as objects that inherit eSets
(e.g. ExpressionSet, MethylSet). Group level information about
each sample (such as Tumor / Normal status) must also be
provided because the test assesses if there are global
differences in the distributions between the user-defined
groups.
biocViews: Normalization, Preprocessing, MultipleComparison,
Microarray, Sequencing
Author: Stephanie Hicks [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7858-0231>), Rafael Irizarry [aut]
(ORCID: <https://orcid.org/0000-0002-3944-4309>)
Maintainer: Stephanie Hicks <shicks19@jhu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/quantro
git_branch: devel
git_last_commit: 7ff2ff0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/quantro_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/quantro_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/quantro_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/quantro_1.41.0.tgz
vignettes: vignettes/quantro/inst/doc/quantro.html
vignetteTitles: The quantro user's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/quantro/inst/doc/quantro.R
importsMe: yarn
suggestsMe: extraChIPs, qsmooth
dependencyCount: 156
Package: quantsmooth
Version: 1.73.0
Depends: R(>= 2.10.0), quantreg, grid
License: GPL-2
MD5sum: 1b2cf6d3435441c532c9722da025636d
NeedsCompilation: no
Title: Quantile smoothing and genomic visualization of array data
Description: Implements quantile smoothing as introduced in: Quantile
smoothing of array CGH data; Eilers PH, de Menezes RX;
Bioinformatics. 2005 Apr 1;21(7):1146-53.
biocViews: Visualization, CopyNumberVariation
Author: Jan Oosting, Paul Eilers, Renee Menezes
Maintainer: Jan Oosting <j.oosting@lumc.nl>
git_url: https://git.bioconductor.org/packages/quantsmooth
git_branch: devel
git_last_commit: eff14ec
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/quantsmooth_1.73.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/quantsmooth_1.73.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/quantsmooth_1.73.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/quantsmooth_1.73.0.tgz
vignettes: vignettes/quantsmooth/inst/doc/quantsmooth.pdf
vignetteTitles: quantsmooth
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/quantsmooth/inst/doc/quantsmooth.R
importsMe: GWASTools, SIM
suggestsMe: PREDA
dependencyCount: 14
Package: QuasR
Version: 1.47.3
Depends: R (>= 4.4), parallel, GenomicRanges, Rbowtie
Imports: methods, grDevices, graphics, utils, stats, tools,
BiocGenerics, S4Vectors, IRanges, Biobase, Biostrings,
BSgenome, Rsamtools (>= 2.13.1), GenomicFeatures, txdbmaker,
ShortRead, BiocParallel, GenomeInfoDb, rtracklayer,
GenomicFiles, AnnotationDbi
LinkingTo: Rhtslib (>= 1.99.1)
Suggests: Gviz, BiocStyle, GenomicAlignments, Rhisat2, knitr,
rmarkdown, covr, testthat
License: GPL-2
MD5sum: 179ab2d7ad9c66c2f78e960f34be5900
NeedsCompilation: yes
Title: Quantify and Annotate Short Reads in R
Description: This package provides a framework for the quantification
and analysis of Short Reads. It covers a complete workflow
starting from raw sequence reads, over creation of alignments
and quality control plots, to the quantification of genomic
regions of interest. Read alignments are either generated
through Rbowtie (data from DNA/ChIP/ATAC/Bis-seq experiments)
or Rhisat2 (data from RNA-seq experiments that require spliced
alignments), or can be provided in the form of bam files.
biocViews: Genetics, Preprocessing, Sequencing, ChIPSeq, RNASeq,
MethylSeq, Coverage, Alignment, QualityControl, ImmunoOncology
Author: Anita Lerch [aut], Adam Alexander Thil SMITH [aut] (ORCID:
<https://orcid.org/0000-0003-4593-3042>), Charlotte Soneson
[aut] (ORCID: <https://orcid.org/0000-0003-3833-2169>), Dimos
Gaidatzis [aut], Michael Stadler [aut, cre] (ORCID:
<https://orcid.org/0000-0002-2269-4934>)
Maintainer: Michael Stadler <michael.stadler@fmi.ch>
URL: https://bioconductor.org/packages/QuasR
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/fmicompbio/QuasR/issues
git_url: https://git.bioconductor.org/packages/QuasR
git_branch: devel
git_last_commit: cb5faf1
git_last_commit_date: 2025-02-21
Date/Publication: 2025-02-21
source.ver: src/contrib/QuasR_1.47.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/QuasR_1.47.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/QuasR_1.47.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/QuasR_1.47.3.tgz
vignettes: vignettes/QuasR/inst/doc/QuasR.html
vignetteTitles: An introduction to QuasR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/QuasR/inst/doc/QuasR.R
importsMe: SingleMoleculeFootprinting
suggestsMe: eisaR
dependencyCount: 116
Package: QuaternaryProd
Version: 1.41.0
Depends: R (>= 3.2.0), Rcpp (>= 0.11.3), dplyr, yaml (>= 2.1.18)
LinkingTo: Rcpp
Suggests: knitr
License: GPL (>=3)
MD5sum: 7da483446f7b4af3a2bb465eed3d3d3b
NeedsCompilation: yes
Title: Computes the Quaternary Dot Product Scoring Statistic for Signed
and Unsigned Causal Graphs
Description: QuaternaryProd is an R package that performs causal
reasoning on biological networks, including publicly available
networks such as STRINGdb. QuaternaryProd is an open-source
alternative to commercial products such as Inginuity Pathway
Analysis. For a given a set of differentially expressed genes,
QuaternaryProd computes the significance of upstream regulators
in the network by performing causal reasoning using the
Quaternary Dot Product Scoring Statistic (Quaternary
Statistic), Ternary Dot product Scoring Statistic (Ternary
Statistic) and Fisher's exact test (Enrichment test). The
Quaternary Statistic handles signed, unsigned and ambiguous
edges in the network. Ambiguity arises when the direction of
causality is unknown, or when the source node (e.g., a protein)
has edges with conflicting signs for the same target gene. On
the other hand, the Ternary Statistic provides causal reasoning
using the signed and unambiguous edges only. The Vignette
provides more details on the Quaternary Statistic and
illustrates an example of how to perform causal reasoning using
STRINGdb.
biocViews: GraphAndNetwork, GeneExpression, Transcription
Author: Carl Tony Fakhry [cre, aut], Ping Chen [ths], Kourosh
Zarringhalam [aut, ths]
Maintainer: Carl Tony Fakhry <cfakhry@cs.umb.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/QuaternaryProd
git_branch: devel
git_last_commit: 1675b8e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/QuaternaryProd_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/QuaternaryProd_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/QuaternaryProd_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/QuaternaryProd_1.41.0.tgz
vignettes: vignettes/QuaternaryProd/inst/doc/QuaternaryProdVignette.pdf
vignetteTitles: <span style="color:red">QuaternaryProdVignette</span>
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/QuaternaryProd/inst/doc/QuaternaryProdVignette.R
dependencyCount: 22
Package: QUBIC
Version: 1.35.0
Depends: R (>= 3.1), biclust
Imports: Rcpp (>= 0.11.0), methods, Matrix
LinkingTo: Rcpp, RcppArmadillo
Suggests: QUBICdata, qgraph, fields, knitr, rmarkdown
Enhances: RColorBrewer
License: CC BY-NC-ND 4.0 + file LICENSE
MD5sum: 1c08b2f83cca81766f8808e122286c78
NeedsCompilation: yes
Title: An R package for qualitative biclustering in support of gene
co-expression analyses
Description: The core function of this R package is to provide the
implementation of the well-cited and well-reviewed QUBIC
algorithm, aiming to deliver an effective and efficient
biclustering capability. This package also includes the
following related functions: (i) a qualitative representation
of the input gene expression data, through a well-designed
discretization way considering the underlying data property,
which can be directly used in other biclustering programs; (ii)
visualization of identified biclusters using heatmap in support
of overall expression pattern analysis; (iii) bicluster-based
co-expression network elucidation and visualization, where
different correlation coefficient scores between a pair of
genes are provided; and (iv) a generalize output format of
biclusters and corresponding network can be freely downloaded
so that a user can easily do following comprehensive functional
enrichment analysis (e.g. DAVID) and advanced network
visualization (e.g. Cytoscape).
biocViews: StatisticalMethod, Microarray, DifferentialExpression,
MultipleComparison, Clustering, Visualization, GeneExpression,
Network
Author: Yu Zhang [aut, cre], Qin Ma [aut]
Maintainer: Yu Zhang <zy26@jlu.edu.cn>
URL: http://github.com/zy26/QUBIC
SystemRequirements: C++11, Rtools (>= 3.1)
VignetteBuilder: knitr
BugReports: http://github.com/zy26/QUBIC/issues
git_url: https://git.bioconductor.org/packages/QUBIC
git_branch: devel
git_last_commit: 51d595d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/QUBIC_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/QUBIC_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/QUBIC_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/QUBIC_1.35.0.tgz
vignettes: vignettes/QUBIC/inst/doc/qubic_vignette.pdf
vignetteTitles: QUBIC Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/QUBIC/inst/doc/qubic_vignette.R
importsMe: mosbi
suggestsMe: runibic
dependencyCount: 53
Package: qusage
Version: 2.41.0
Depends: R (>= 2.10), limma (>= 3.14), methods
Imports: utils, Biobase, nlme, emmeans, fftw
License: GPL (>= 2)
MD5sum: fbad1cdb63b58a208be98e40403623a1
NeedsCompilation: no
Title: qusage: Quantitative Set Analysis for Gene Expression
Description: This package is an implementation the Quantitative Set
Analysis for Gene Expression (QuSAGE) method described in
(Yaari G. et al, Nucl Acids Res, 2013). This is a novel Gene
Set Enrichment-type test, which is designed to provide a
faster, more accurate, and easier to understand test for gene
expression studies. qusage accounts for inter-gene correlations
using the Variance Inflation Factor technique proposed by Wu et
al. (Nucleic Acids Res, 2012). In addition, rather than simply
evaluating the deviation from a null hypothesis with a single
number (a P value), qusage quantifies gene set activity with a
complete probability density function (PDF). From this PDF, P
values and confidence intervals can be easily extracted.
Preserving the PDF also allows for post-hoc analysis (e.g.,
pair-wise comparisons of gene set activity) while maintaining
statistical traceability. Finally, while qusage is compatible
with individual gene statistics from existing methods (e.g.,
LIMMA), a Welch-based method is implemented that is shown to
improve specificity. The QuSAGE package also includes a mixed
effects model implementation, as described in (Turner JA et al,
BMC Bioinformatics, 2015), and a meta-analysis framework as
described in (Meng H, et al. PLoS Comput Biol. 2019). For
questions, contact Chris Bolen (cbolen1@gmail.com) or Steven
Kleinstein (steven.kleinstein@yale.edu)
biocViews: GeneSetEnrichment, Microarray, RNASeq, Software,
ImmunoOncology
Author: Christopher Bolen and Gur Yaari, with contributions from Juilee
Thakar, Hailong Meng, Jacob Turner, Derek Blankenship, and
Steven Kleinstein
Maintainer: Christopher Bolen <cbolen1@gmail.com>
URL: http://clip.med.yale.edu/qusage
git_url: https://git.bioconductor.org/packages/qusage
git_branch: devel
git_last_commit: 6d6b8f8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/qusage_2.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/qusage_2.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/qusage_2.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/qusage_2.41.0.tgz
vignettes: vignettes/qusage/inst/doc/qusage.pdf
vignetteTitles: Running qusage
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/qusage/inst/doc/qusage.R
importsMe: mExplorer
suggestsMe: SigCheck
dependencyCount: 18
Package: qvalue
Version: 2.39.0
Depends: R(>= 2.10)
Imports: splines, ggplot2, grid, reshape2
Suggests: knitr
License: LGPL
MD5sum: 24c24aa852b2231ebbd3d84102874b48
NeedsCompilation: no
Title: Q-value estimation for false discovery rate control
Description: This package takes a list of p-values resulting from the
simultaneous testing of many hypotheses and estimates their
q-values and local FDR values. The q-value of a test measures
the proportion of false positives incurred (called the false
discovery rate) when that particular test is called
significant. The local FDR measures the posterior probability
the null hypothesis is true given the test's p-value. Various
plots are automatically generated, allowing one to make
sensible significance cut-offs. Several mathematical results
have recently been shown on the conservative accuracy of the
estimated q-values from this software. The software can be
applied to problems in genomics, brain imaging, astrophysics,
and data mining.
biocViews: MultipleComparisons
Author: John D. Storey [aut, cre], Andrew J. Bass [aut], Alan Dabney
[aut], David Robinson [aut], Gregory Warnes [ctb]
Maintainer: John D. Storey <jstorey@princeton.edu>, Andrew J. Bass
<ajbass@emory.edu>
URL: http://github.com/jdstorey/qvalue
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/qvalue
git_branch: devel
git_last_commit: 6891905
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/qvalue_2.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/qvalue_2.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/qvalue_2.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/qvalue_2.39.0.tgz
vignettes: vignettes/qvalue/inst/doc/qvalue.pdf
vignetteTitles: qvalue Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/qvalue/inst/doc/qvalue.R
dependsOnMe: anota, DEGseq, DrugVsDisease, r3Cseq, webbioc, BonEV,
cp4p, isva, ReAD, STAREG
importsMe: Anaquin, anota, clusterProfiler, CTSV, DegCre, derfinder,
DOSE, edge, erccdashboard, EventPointer, FindIT2, fishpond,
LimROTS, metaseqR2, methylKit, MOMA, msmsTests, MWASTools,
netresponse, normr, OPWeight, PAST, PolySTest, RiboDiPA,
RNAsense, Rnits, RolDE, SDAMS, sights, signatureSearch,
SpaceMarkers, subSeq, synapter, trigger, vsclust, webbioc,
IHWpaper, AEenrich, cancerGI, fdrDiscreteNull, glmmSeq,
groupedSurv, HDMT, jaccard, medScan, NBPSeq, qch, SeqFeatR,
sffdr, shinyExprPortal, ssizeRNA, TFactSR
suggestsMe: biobroom, LBE, PREDA, RnBeads, swfdr, BootstrapQTL, dartR,
dartR.base, dartR.popgen, DGEobj.utils, easylabel, familiar,
jackstraw, mutoss, Rediscover, seqgendiff, volcano3D, wrMisc
dependencyCount: 41
Package: R3CPET
Version: 1.39.0
Depends: R (>= 3.2), Rcpp (>= 0.10.4), methods
Imports: methods, parallel, ggplot2, pheatmap, clValid, igraph,
data.table, reshape2, Hmisc, RCurl, BiocGenerics, S4Vectors,
IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges (>= 1.31.8),
ggbio
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, TxDb.Hsapiens.UCSC.hg19.knownGene,
biovizBase, biomaRt, AnnotationDbi, org.Hs.eg.db, shiny,
ChIPpeakAnno
License: GPL (>=2)
Archs: x64
MD5sum: a45bcf5c51bec0897e1d3b42b72a14d7
NeedsCompilation: yes
Title: 3CPET: Finding Co-factor Complexes in Chia-PET experiment using
a Hierarchical Dirichlet Process
Description: The package provides a method to infer the set of proteins
that are more probably to work together to maintain chormatin
interaction given a ChIA-PET experiment results.
biocViews: NetworkInference, GenePrediction, Bayesian, GraphAndNetwork,
Network, GeneExpression, HiC
Author: Djekidel MN, Yang Chen et al.
Maintainer: Mohamed Nadhir Djekidel <djek.nad@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/sirusb/R3CPET/issues
git_url: https://git.bioconductor.org/packages/R3CPET
git_branch: devel
git_last_commit: adae88d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/R3CPET_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/R3CPET_1.39.0.zip
vignettes: vignettes/R3CPET/inst/doc/R3CPET.pdf
vignetteTitles: 3CPET: Finding Co-factor Complexes maintaining Chia-PET
interactions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/R3CPET/inst/doc/R3CPET.R
dependencyCount: 166
Package: r3Cseq
Version: 1.53.0
Depends: GenomicRanges, Rsamtools, rtracklayer, VGAM, qvalue
Imports: methods, GenomeInfoDb, IRanges, Biostrings, data.table, sqldf,
RColorBrewer
Suggests: BSgenome.Mmusculus.UCSC.mm9.masked,
BSgenome.Mmusculus.UCSC.mm10.masked,
BSgenome.Hsapiens.UCSC.hg18.masked,
BSgenome.Hsapiens.UCSC.hg19.masked,
BSgenome.Rnorvegicus.UCSC.rn5.masked
License: GPL-3
Archs: x64
MD5sum: c54addc8bbf7b49ff181e4e1d7972ed8
NeedsCompilation: no
Title: Analysis of Chromosome Conformation Capture and Next-generation
Sequencing (3C-seq)
Description: This package is used for the analysis of long-range
chromatin interactions from 3C-seq assay.
biocViews: Preprocessing, Sequencing
Author: Supat Thongjuea, MRC WIMM Centre for Computational Biology,
Weatherall Institute of Molecular Medicine, University of
Oxford, UK <supat.thongjuea@imm.ox.ac.uk>
Maintainer: Supat Thongjuea <supat.thongjuea@imm.ox.ac.uk> or
<supat.thongjuea@gmail.com>
URL: http://r3cseq.genereg.net,https://github.com/supatt-lab/r3Cseq/
git_url: https://git.bioconductor.org/packages/r3Cseq
git_branch: devel
git_last_commit: d021c8c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/r3Cseq_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/r3Cseq_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/r3Cseq_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/r3Cseq_1.53.0.tgz
vignettes: vignettes/r3Cseq/inst/doc/r3Cseq.pdf
vignetteTitles: r3Cseq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/r3Cseq/inst/doc/r3Cseq.R
dependencyCount: 105
Package: R453Plus1Toolbox
Version: 1.57.0
Depends: R (>= 2.12.0), methods, VariantAnnotation (>= 1.25.11),
Biostrings (>= 2.47.6), pwalign, Biobase
Imports: utils, grDevices, graphics, stats, tools, xtable, R2HTML,
TeachingDemos, BiocGenerics, S4Vectors (>= 0.17.25), IRanges
(>= 2.13.12), XVector, GenomicRanges (>= 1.31.8),
SummarizedExperiment, biomaRt, BSgenome (>= 1.47.3), Rsamtools,
ShortRead (>= 1.37.1)
Suggests: rtracklayer, BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Scerevisiae.UCSC.sacCer2
License: LGPL-3
Archs: x64
MD5sum: b599d16d4229f56efd20c02d53e4bd9b
NeedsCompilation: yes
Title: A package for importing and analyzing data from Roche's Genome
Sequencer System
Description: The R453Plus1 Toolbox comprises useful functions for the
analysis of data generated by Roche's 454 sequencing platform.
It adds functions for quality assurance as well as for
annotation and visualization of detected variants,
complementing the software tools shipped by Roche with their
product. Further, a pipeline for the detection of structural
variants is provided.
biocViews: Sequencing, Infrastructure, DataImport, DataRepresentation,
Visualization, QualityControl, ReportWriting
Author: Hans-Ulrich Klein, Christoph Bartenhagen, Christian Ruckert
Maintainer: Hans-Ulrich Klein <h.klein@uni-muenster.de>
git_url: https://git.bioconductor.org/packages/R453Plus1Toolbox
git_branch: devel
git_last_commit: 8d481bd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/R453Plus1Toolbox_1.57.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/R453Plus1Toolbox_1.57.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/R453Plus1Toolbox_1.57.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/R453Plus1Toolbox_1.57.0.tgz
vignettes: vignettes/R453Plus1Toolbox/inst/doc/vignette.pdf
vignetteTitles: A package for importing and analyzing data from Roche's
Genome Sequencer System
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/R453Plus1Toolbox/inst/doc/vignette.R
dependencyCount: 116
Package: R4RNA
Version: 1.35.0
Depends: R (>= 3.2.0), Biostrings (>= 2.38.0)
License: GPL-3
MD5sum: dbd7c06435c71ffe1b4045086bd7602e
NeedsCompilation: no
Title: An R package for RNA visualization and analysis
Description: A package for RNA basepair analysis, including the
visualization of basepairs as arc diagrams for easy comparison
and annotation of sequence and structure. Arc diagrams can
additionally be projected onto multiple sequence alignments to
assess basepair conservation and covariation, with numerical
methods for computing statistics for each.
biocViews: Alignment, MultipleSequenceAlignment, Preprocessing,
Visualization, DataImport, DataRepresentation,
MultipleComparison
Author: Daniel Lai, Irmtraud Meyer <irmtraud.meyer@cantab.net>
Maintainer: Daniel Lai <jujubix@cs.ubc.ca>
URL: http://www.e-rna.org/r-chie/
git_url: https://git.bioconductor.org/packages/R4RNA
git_branch: devel
git_last_commit: 50cd94f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/R4RNA_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/R4RNA_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/R4RNA_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/R4RNA_1.35.0.tgz
vignettes: vignettes/R4RNA/inst/doc/R4RNA.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/R4RNA/inst/doc/R4RNA.R
importsMe: ggmsa, rnaCrosslinkOO
suggestsMe: rfaRm
dependencyCount: 25
Package: RadioGx
Version: 2.11.0
Depends: R (>= 4.1), CoreGx
Imports: SummarizedExperiment, BiocGenerics, data.table, S4Vectors,
Biobase, parallel, BiocParallel, RColorBrewer, caTools,
magicaxis, methods, reshape2, scales, grDevices, graphics,
stats, utils, assertthat, matrixStats, downloader
Suggests: rmarkdown, BiocStyle, knitr, pander, markdown
License: GPL-3
MD5sum: cc6b89140e21713716090da1cceb9733
NeedsCompilation: no
Title: Analysis of Large-Scale Radio-Genomic Data
Description: Computational tool box for radio-genomic analysis which
integrates radio-response data, radio-biological modelling and
comprehensive cell line annotations for hundreds of cancer cell
lines. The 'RadioSet' class enables creation and manipulation
of standardized datasets including information about cancer
cells lines, radio-response assays and dose-response
indicators. Included methods allow fitting and plotting
dose-response data using established radio-biological models
along with quality control to validate results. Additional
functions related to fitting and plotting dose response curves,
quantifying statistical correlation and calculating area under
the curve (AUC) or survival fraction (SF) are included. For
more details please see the included documentation, references,
as well as: Manem, V. et al (2018) <doi:10.1101/449793>.
biocViews: Software, Pharmacogenetics, QualityControl, Survival,
Pharmacogenomics, Classification
Author: Venkata Manem [aut], Petr Smirnov [aut], Ian Smith [aut],
Meghan Lambie [aut], Christopher Eeles [aut], Scott Bratman
[aut], Jermiah Joseph [aut], Benjamin Haibe-Kains [aut, cre]
Maintainer: Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RadioGx
git_branch: devel
git_last_commit: 7921677
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RadioGx_2.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RadioGx_2.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RadioGx_2.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RadioGx_2.11.0.tgz
vignettes: vignettes/RadioGx/inst/doc/RadioGx.html
vignetteTitles: RadioGx: An R Package for Analysis of Large
Radiogenomic Datasets
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RadioGx/inst/doc/RadioGx.R
dependencyCount: 153
Package: raer
Version: 1.5.0
Imports: stats, methods, GenomicRanges, IRanges, Rsamtools, BSgenome,
Biostrings, SummarizedExperiment, SingleCellExperiment,
S4Vectors, GenomeInfoDb, GenomicAlignments, GenomicFeatures,
BiocGenerics, BiocParallel, rtracklayer, Matrix, cli
LinkingTo: Rhtslib
Suggests: testthat (>= 3.0.0), knitr, DESeq2, edgeR, limma, rmarkdown,
BiocStyle, ComplexHeatmap, TxDb.Hsapiens.UCSC.hg38.knownGene,
SNPlocs.Hsapiens.dbSNP144.GRCh38,
BSgenome.Hsapiens.NCBI.GRCh38, scater, scran, scuttle,
AnnotationHub, covr, raerdata, txdbmaker
License: MIT + file LICENSE
Archs: x64
MD5sum: 88f7ab6221a8420c8cc8ff17ba8e64fb
NeedsCompilation: yes
Title: RNA editing tools in R
Description: Toolkit for identification and statistical testing of RNA
editing signals from within R. Provides support for identifying
sites from bulk-RNA and single cell RNA-seq datasets, and
general methods for extraction of allelic read counts from
alignment files. Facilitates annotation and exploratory
analysis of editing signals using Bioconductor packages and
resources.
biocViews: MultipleComparison, RNASeq, SingleCell, Sequencing,
Coverage, Epitranscriptomics, FeatureExtraction, Annotation,
Alignment
Author: Kent Riemondy [aut, cre] (ORCID:
<https://orcid.org/0000-0003-0750-1273>), Kristen Wells-Wrasman
[aut] (ORCID: <https://orcid.org/0000-0002-7466-8164>), Ryan
Sheridan [ctb] (ORCID:
<https://orcid.org/0000-0003-4012-3147>), Jay Hesselberth [ctb]
(ORCID: <https://orcid.org/0000-0002-6299-179X>), RNA
Bioscience Initiative [cph, fnd]
Maintainer: Kent Riemondy <kent.riemondy@gmail.com>
URL: https://rnabioco.github.io/raer, https://github.com/rnabioco/raer
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/rnabioco/raer/issues
git_url: https://git.bioconductor.org/packages/raer
git_branch: devel
git_last_commit: 438c245
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/raer_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/raer_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/raer_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/raer_1.5.0.tgz
vignettes: vignettes/raer/inst/doc/raer.html
vignetteTitles: Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/raer/inst/doc/raer.R
dependencyCount: 79
Package: RaggedExperiment
Version: 1.31.1
Depends: R (>= 4.2.0), GenomicRanges (>= 1.37.17)
Imports: BiocBaseUtils, BiocGenerics, GenomeInfoDb, IRanges, Matrix,
MatrixGenerics, methods, S4Vectors, stats,
SummarizedExperiment, utils
Suggests: BiocStyle, knitr, rmarkdown, testthat, MultiAssayExperiment
License: Artistic-2.0
MD5sum: 7eee9a9bd39878cd0d4589dcff661c50
NeedsCompilation: no
Title: Representation of Sparse Experiments and Assays Across Samples
Description: This package provides a flexible representation of copy
number, mutation, and other data that fit into the ragged array
schema for genomic location data. The basic representation of
such data provides a rectangular flat table interface to the
user with range information in the rows and samples/specimen in
the columns. The RaggedExperiment class derives from a
GRangesList representation and provides a semblance of a
rectangular dataset.
biocViews: Infrastructure, DataRepresentation
Author: Martin Morgan [aut], Marcel Ramos [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3242-0582>), Lydia King [ctb]
Maintainer: Marcel Ramos <marcel.ramos@sph.cuny.edu>
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/RaggedExperiment/issues
git_url: https://git.bioconductor.org/packages/RaggedExperiment
git_branch: devel
git_last_commit: 3c81b9a
git_last_commit_date: 2024-11-22
Date/Publication: 2024-11-24
source.ver: src/contrib/RaggedExperiment_1.31.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RaggedExperiment_1.31.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RaggedExperiment_1.31.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RaggedExperiment_1.31.1.tgz
vignettes:
vignettes/RaggedExperiment/inst/doc/ASCAT_to_RaggedExperiment.html,
vignettes/RaggedExperiment/inst/doc/RaggedExperiment.html
vignetteTitles: ASCAT to RaggedExperiment, RaggedExperiment
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/RaggedExperiment/inst/doc/ASCAT_to_RaggedExperiment.R,
vignettes/RaggedExperiment/inst/doc/RaggedExperiment.R
dependsOnMe: CNVRanger, SARC, curatedPCaData
importsMe: cBioPortalData, omicsPrint, RTCGAToolbox, TCGAutils,
terraTCGAdata
suggestsMe: maftools, MultiAssayExperiment, MultiDataSet, TENxIO,
curatedTCGAData, SingleCellMultiModal
dependencyCount: 37
Package: RAIDS
Version: 1.5.0
Depends: R (>= 4.2.0), gdsfmt, SNPRelate, stats, utils, GENESIS
Imports: S4Vectors, GenomicRanges, ensembldb, BSgenome, AnnotationDbi,
methods, class, pROC, IRanges, AnnotationFilter, rlang,
VariantAnnotation, MatrixGenerics, ggplot2, stringr
Suggests: testthat, knitr, rmarkdown, BiocStyle, withr, GenomeInfoDb,
BSgenome.Hsapiens.UCSC.hg38, EnsDb.Hsapiens.v86
License: Apache License (>= 2)
Archs: x64
MD5sum: ff975c34e652687ddf26bd951d89fc39
NeedsCompilation: no
Title: Accurate Inference of Genetic Ancestry from Cancer Sequences
Description: This package implements specialized algorithms that enable
genetic ancestry inference from various cancer sequences
sources (RNA, Exome and Whole-Genome sequences). This package
also implements a simulation algorithm that generates synthetic
cancer-derived data. This code and analysis pipeline was
designed and developed for the following publication: Belleau,
P et al. Genetic Ancestry Inference from Cancer-Derived
Molecular Data across Genomic and Transcriptomic Platforms.
Cancer Res 1 January 2023; 83 (1): 49–58.
biocViews: Genetics, Software, Sequencing, WholeGenome,
PrincipalComponent, GeneticVariability, DimensionReduction,
BiocViews
Author: Pascal Belleau [cre, aut] (ORCID:
<https://orcid.org/0000-0002-0802-1071>), Astrid Deschênes
[aut] (ORCID: <https://orcid.org/0000-0001-7846-6749>), David
A. Tuveson [aut] (ORCID:
<https://orcid.org/0000-0002-8017-2712>), Alexander Krasnitz
[aut]
Maintainer: Pascal Belleau <pascal_belleau@hotmail.com>
URL: https://krasnitzlab.github.io/RAIDS/
VignetteBuilder: knitr
BugReports: https://github.com/KrasnitzLab/RAIDS/issues
git_url: https://git.bioconductor.org/packages/RAIDS
git_branch: devel
git_last_commit: bb243c9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RAIDS_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RAIDS_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RAIDS_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RAIDS_1.5.0.tgz
vignettes: vignettes/RAIDS/inst/doc/Create_Reference_GDS_File.html,
vignettes/RAIDS/inst/doc/RAIDS.html,
vignettes/RAIDS/inst/doc/Wrappers.html
vignetteTitles: Population reference dataset GDS files, Accurate
Inference of Genetic Ancestry from Cancer-derived Sequences,
Using wrappper functionss
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RAIDS/inst/doc/Create_Reference_GDS_File.R,
vignettes/RAIDS/inst/doc/RAIDS.R,
vignettes/RAIDS/inst/doc/Wrappers.R
dependencyCount: 168
Package: rain
Version: 1.41.0
Depends: R (>= 2.10), gmp, multtest
Suggests: lattice, BiocStyle
License: GPL-2
MD5sum: 6e1065268e4f94955884e1265313a2c9
NeedsCompilation: no
Title: Rhythmicity Analysis Incorporating Non-parametric Methods
Description: This package uses non-parametric methods to detect rhythms
in time series. It deals with outliers, missing values and is
optimized for time series comprising 10-100 measurements. As it
does not assume expect any distinct waveform it is optimal or
detecting oscillating behavior (e.g. circadian or cell cycle)
in e.g. genome- or proteome-wide biological measurements such
as: micro arrays, proteome mass spectrometry, or metabolome
measurements.
biocViews: TimeCourse, Genetics, SystemsBiology, Proteomics,
Microarray, MultipleComparison
Author: Paul F. Thaben, PÃ¥l O. Westermark
Maintainer: Paul F. Thaben <paul.thaben@charite.de>
git_url: https://git.bioconductor.org/packages/rain
git_branch: devel
git_last_commit: 8ce75e2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rain_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rain_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rain_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rain_1.41.0.tgz
vignettes: vignettes/rain/inst/doc/rain.pdf
vignetteTitles: Rain Usage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rain/inst/doc/rain.R
dependencyCount: 17
Package: ramr
Version: 1.15.2
Depends: R (>= 4.1)
Imports: methods, data.table, GenomeInfoDb, GenomicRanges, IRanges,
BiocGenerics, S4Vectors, Rcpp
LinkingTo: Rcpp
Suggests: RUnit, knitr, rmarkdown, ggplot2, gridExtra, annotatr, LOLA,
org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, parallel,
doParallel, foreach, doRNG, matrixStats, EnvStats, ExtDist,
gamlss, gamlss.dist
License: Artistic-2.0
MD5sum: 787bd15add751b52d1cc8b4f1f13669a
NeedsCompilation: yes
Title: Detection of Rare Aberrantly Methylated Regions in Array and NGS
Data
Description: ramr is an R package for detection of epimutations (i.e.,
infrequent aberrant DNA methylation events) in large data sets
obtained by methylation profiling using array or
high-throughput methylation sequencing. In addition, package
provides functions to visualize found aberrantly methylated
regions (AMRs), to generate sets of all possible regions to be
used as reference sets for enrichment analysis, and to generate
biologically relevant test data sets for performance evaluation
of AMR/DMR search algorithms.
biocViews: DNAMethylation, DifferentialMethylation, Epigenetics,
MethylationArray, MethylSeq
Author: Oleksii Nikolaienko [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5910-4934>)
Maintainer: Oleksii Nikolaienko <oleksii.nikolaienko@gmail.com>
URL: https://github.com/BBCG/ramr
SystemRequirements: C++20, GNU make
VignetteBuilder: knitr
BugReports: https://github.com/BBCG/ramr/issues
git_url: https://git.bioconductor.org/packages/ramr
git_branch: devel
git_last_commit: f361f7c
git_last_commit_date: 2025-03-06
Date/Publication: 2025-03-06
source.ver: src/contrib/ramr_1.15.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ramr_1.15.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ramr_1.15.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ramr_1.15.2.tgz
vignettes: vignettes/ramr/inst/doc/ramr.html
vignetteTitles: ramr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ramr/inst/doc/ramr.R
dependencyCount: 25
Package: ramwas
Version: 1.31.0
Depends: R (>= 3.3.0), methods, filematrix
Imports: graphics, stats, utils, digest, glmnet, KernSmooth, grDevices,
GenomicAlignments, Rsamtools, parallel, biomaRt, Biostrings,
BiocGenerics
Suggests: knitr, rmarkdown, pander, BiocStyle,
BSgenome.Ecoli.NCBI.20080805
License: LGPL-3
Archs: x64
MD5sum: 120929b566737999afdd30a5be91c7f3
NeedsCompilation: yes
Title: Fast Methylome-Wide Association Study Pipeline for Enrichment
Platforms
Description: A complete toolset for methylome-wide association studies
(MWAS). It is specifically designed for data from enrichment
based methylation assays, but can be applied to other data as
well. The analysis pipeline includes seven steps: (1) scanning
aligned reads from BAM files, (2) calculation of quality
control measures, (3) creation of methylation score (coverage)
matrix, (4) principal component analysis for capturing batch
effects and detection of outliers, (5) association analysis
with respect to phenotypes of interest while correcting for top
PCs and known covariates, (6) annotation of significant
findings, and (7) multi-marker analysis (methylation risk
score) using elastic net. Additionally, RaMWAS include tools
for joint analysis of methlyation and genotype data. This work
is published in Bioinformatics, Shabalin et al. (2018)
<doi:10.1093/bioinformatics/bty069>.
biocViews: DNAMethylation, Sequencing, QualityControl, Coverage,
Preprocessing, Normalization, BatchEffect, PrincipalComponent,
DifferentialMethylation, Visualization
Author: Andrey A Shabalin [aut, cre] (ORCID:
<https://orcid.org/0000-0003-0309-6821>), Shaunna L Clark
[aut], Mohammad W Hattab [aut], Karolina A Aberg [aut], Edwin J
C G van den Oord [aut]
Maintainer: Andrey A Shabalin <andrey.shabalin@gmail.com>
URL: https://bioconductor.org/packages/ramwas/
VignetteBuilder: knitr
BugReports: https://github.com/andreyshabalin/ramwas/issues
git_url: https://git.bioconductor.org/packages/ramwas
git_branch: devel
git_last_commit: d24c1d2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ramwas_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ramwas_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ramwas_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ramwas_1.31.0.tgz
vignettes: vignettes/ramwas/inst/doc/RW1_intro.html,
vignettes/ramwas/inst/doc/RW2_CpG_sets.html,
vignettes/ramwas/inst/doc/RW3_BAM_QCs.html,
vignettes/ramwas/inst/doc/RW4_SNPs.html,
vignettes/ramwas/inst/doc/RW5a_matrix.html,
vignettes/ramwas/inst/doc/RW5c_matrix.html,
vignettes/ramwas/inst/doc/RW6_param.html
vignetteTitles: 1. Overview, 2. CpG sets, 3. BAM Quality Control
Measures, 4. Joint Analysis of Methylation and Genotype Data,
5.a. Analyzing Illumina Methylation Array Data, 5.c. Analyzing
data from other sources, 6. RaMWAS parameters
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ramwas/inst/doc/RW1_intro.R,
vignettes/ramwas/inst/doc/RW2_CpG_sets.R,
vignettes/ramwas/inst/doc/RW3_BAM_QCs.R,
vignettes/ramwas/inst/doc/RW4_SNPs.R,
vignettes/ramwas/inst/doc/RW5a_matrix.R,
vignettes/ramwas/inst/doc/RW5c_matrix.R,
vignettes/ramwas/inst/doc/RW6_param.R
dependencyCount: 102
Package: randPack
Version: 1.53.0
Depends: methods
Imports: Biobase
License: Artistic 2.0
MD5sum: 0a62eb37c9989f2247a1422dc65f5f41
NeedsCompilation: no
Title: Randomization routines for Clinical Trials
Description: A suite of classes and functions for randomizing patients
in clinical trials.
biocViews: StatisticalMethod
Author: Vincent Carey <stvjc@channing.harvard.edu> and Robert Gentleman
Maintainer: Robert Gentleman <rgentlem@gmail.com>
git_url: https://git.bioconductor.org/packages/randPack
git_branch: devel
git_last_commit: 4066dc4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/randPack_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/randPack_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/randPack_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/randPack_1.53.0.tgz
vignettes: vignettes/randPack/inst/doc/randPack.pdf
vignetteTitles: Clinical trial randomization infrastructure
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/randPack/inst/doc/randPack.R
dependencyCount: 7
Package: randRotation
Version: 1.19.0
Imports: methods, graphics, utils, stats, Rdpack (>= 0.7)
Suggests: knitr, BiocParallel, lme4, nlme, rmarkdown, BiocStyle,
testthat (>= 2.1.0), limma, sva
License: GPL-3
MD5sum: 5505ca6c5cc963c25feb28af16beef76
NeedsCompilation: no
Title: Random Rotation Methods for High Dimensional Data with Batch
Structure
Description: A collection of methods for performing random rotations on
high-dimensional, normally distributed data (e.g. microarray or
RNA-seq data) with batch structure. The random rotation
approach allows exact testing of dependent test statistics with
linear models following arbitrary batch effect correction
methods.
biocViews: Software, Sequencing, BatchEffect, BiomedicalInformatics,
RNASeq, Preprocessing, Microarray, DifferentialExpression,
GeneExpression, Genetics, MicroRNAArray, Normalization,
StatisticalMethod
Author: Peter Hettegger [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8557-588X>)
Maintainer: Peter Hettegger <p.hettegger@gmail.com>
URL: https://github.com/phettegger/randRotation
VignetteBuilder: knitr
BugReports: https://github.com/phettegger/randRotation/issues
git_url: https://git.bioconductor.org/packages/randRotation
git_branch: devel
git_last_commit: f41d79e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/randRotation_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/randRotation_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/randRotation_1.19.0.tgz
vignettes: vignettes/randRotation/inst/doc/randRotationIntro.pdf
vignetteTitles: Random Rotation Package Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/randRotation/inst/doc/randRotationIntro.R
dependencyCount: 7
Package: RankProd
Version: 3.33.0
Depends: R (>= 3.2.1), stats, methods, Rmpfr, gmp
Imports: graphics
License: file LICENSE
License_restricts_use: yes
MD5sum: 2fbf3797ccfaabeab24de692df3cb738
NeedsCompilation: no
Title: Rank Product method for identifying differentially expressed
genes with application in meta-analysis
Description: Non-parametric method for identifying differentially
expressed (up- or down- regulated) genes based on the estimated
percentage of false predictions (pfp). The method can combine
data sets from different origins (meta-analysis) to increase
the power of the identification.
biocViews: DifferentialExpression, StatisticalMethod, Software,
ResearchField, Metabolomics, Lipidomics, Proteomics,
SystemsBiology, GeneExpression, Microarray, GeneSignaling
Author: Francesco Del Carratore
<francesco.delcarratore@manchester.ac.uk>, Andris Jankevics
<andris.jankevics@gmail.com> Fangxin Hong
<fxhong@jimmy.harvard.edu>, Ben Wittner
<Wittner.Ben@mgh.harvard.edu>, Rainer Breitling
<rainer.breitling@manchester.ac.uk>, and Florian Battke
<battke@informatik.uni-tuebingen.de>
Maintainer: Francesco Del Carratore <francescodc87@gmail.com>
git_url: https://git.bioconductor.org/packages/RankProd
git_branch: devel
git_last_commit: 11e9e00
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RankProd_3.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RankProd_3.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RankProd_3.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RankProd_3.33.0.tgz
vignettes: vignettes/RankProd/inst/doc/RankProd.pdf
vignetteTitles: RankProd Tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/RankProd/inst/doc/RankProd.R
dependsOnMe: tRanslatome
importsMe: mslp, POMA, synlet
suggestsMe: sigQC
dependencyCount: 6
Package: RAREsim
Version: 1.11.0
Depends: R (>= 4.1.0)
Imports: nloptr
Suggests: markdown, ggplot2, BiocStyle, rmarkdown, knitr, testthat (>=
3.0.0)
License: GPL-3
Archs: x64
MD5sum: 0657e2ed67e885c409c847612c489bbf
NeedsCompilation: no
Title: Simulation of Rare Variant Genetic Data
Description: Haplotype simulations of rare variant genetic data that
emulates real data can be performed with RAREsim. RAREsim uses
the expected number of variants in MAC bins - either as
provided by default parameters or estimated from target data -
and an abundance of rare variants as simulated HAPGEN2 to
probabilistically prune variants. RAREsim produces haplotypes
that emulate real sequencing data with respect to the total
number of variants, allele frequency spectrum, haplotype
structure, and variant annotation.
biocViews: Genetics, Software, VariantAnnotation, Sequencing
Author: Megan Null [aut], Ryan Barnard [cre]
Maintainer: Ryan Barnard <rbarnard1107@gmail.com>
URL: https://github.com/meganmichelle/RAREsim
VignetteBuilder: knitr
BugReports: https://github.com/meganmichelle/RAREsim/issues
git_url: https://git.bioconductor.org/packages/RAREsim
git_branch: devel
git_last_commit: a9cca8e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RAREsim_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RAREsim_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RAREsim_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RAREsim_1.11.0.tgz
vignettes: vignettes/RAREsim/inst/doc/RAREsim_Vignette.html
vignetteTitles: RAREsim Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RAREsim/inst/doc/RAREsim_Vignette.R
dependencyCount: 1
Package: RareVariantVis
Version: 2.35.0
Depends: BiocGenerics, VariantAnnotation, googleVis, GenomicFeatures
Imports: S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, gtools,
BSgenome, BSgenome.Hsapiens.UCSC.hg19,
TxDb.Hsapiens.UCSC.hg19.knownGene, phastCons100way.UCSC.hg19,
SummarizedExperiment, GenomicScores
Suggests: knitr
License: Artistic-2.0
Archs: x64
MD5sum: 11798cf2aafceeda262c33ba99d75019
NeedsCompilation: no
Title: A suite for analysis of rare genomic variants in whole genome
sequencing data
Description: Second version of RareVariantVis package aims to provide
comprehensive information about rare variants for your genome
data. It annotates, filters and presents genomic variants
(especially rare ones) in a global, per chromosome way. For
discovered rare variants CRISPR guide RNAs are designed, so the
user can plan further functional studies. Large structural
variants, including copy number variants are also supported.
Package accepts variants directly from variant caller - for
example GATK or Speedseq. Output of package are lists of
variants together with adequate visualization. Visualization of
variants is performed in two ways - standard that outputs png
figures and interactive that uses JavaScript d3 package.
Interactive visualization allows to analyze trio/family data,
for example in search for causative variants in rare Mendelian
diseases, in point-and-click interface. The package includes
homozygous region caller and allows to analyse whole human
genomes in less than 30 minutes on a desktop computer.
RareVariantVis disclosed novel causes of several rare monogenic
disorders, including one with non-coding causative variant -
keratolythic winter erythema.
biocViews: GenomicVariation, Sequencing, WholeGenome
Author: Adam Gudys and Tomasz Stokowy
Maintainer: Tomasz Stokowy <tomasz.stokowy@k2.uib.no>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RareVariantVis
git_branch: devel
git_last_commit: f38cd5b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RareVariantVis_2.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RareVariantVis_2.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RareVariantVis_2.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RareVariantVis_2.35.0.tgz
vignettes: vignettes/RareVariantVis/inst/doc/RareVariantsVis.pdf
vignetteTitles: RareVariantVis
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RareVariantVis/inst/doc/RareVariantsVis.R
dependencyCount: 109
Package: Rarr
Version: 1.7.2
Depends: R (>= 4.1.0), DelayedArray, BiocGenerics
Imports: jsonlite, httr, stringr, R.utils, utils, paws.storage, methods
Suggests: BiocStyle, covr, knitr, tinytest, mockery
License: MIT + file LICENSE
MD5sum: 87ae9a73cdc12ab70a27c6a6f0403895
NeedsCompilation: yes
Title: Read Zarr Files in R
Description: The Zarr specification defines a format for chunked,
compressed, N-dimensional arrays. It's design allows efficient
access to subsets of the stored array, and supports both local
and cloud storage systems. Rarr aims to implement this
specifcation in R with minimal reliance on an external tools or
libraries.
biocViews: DataImport
Author: Mike Smith [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7800-3848>)
Maintainer: Mike Smith <grimbough@gmail.com>
URL: https://github.com/grimbough/Rarr
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/grimbough/Rarr/issues
git_url: https://git.bioconductor.org/packages/Rarr
git_branch: devel
git_last_commit: e7375db
git_last_commit_date: 2025-02-27
Date/Publication: 2025-02-27
source.ver: src/contrib/Rarr_1.7.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Rarr_1.7.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Rarr_1.7.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Rarr_1.7.2.tgz
vignettes: vignettes/Rarr/inst/doc/Rarr.html
vignetteTitles: "Working with Zarr arrays in R"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Rarr/inst/doc/Rarr.R
dependencyCount: 50
Package: rawDiag
Version: 1.3.4
Depends: R (>= 4.4)
Imports: dplyr, ggplot2 (>= 3.4), grDevices, hexbin, htmltools,
BiocManager, BiocParallel, rawrr (>= 1.15.5), rlang, reshape2,
scales, shiny (>= 1.5), stats, utils
Suggests: BiocStyle (>= 2.28), ExperimentHub, tartare, knitr, testthat
License: GPL-3
MD5sum: cc1aa22c8bfa48736c59dc3895c13d5f
NeedsCompilation: no
Title: Brings Orbitrap Mass Spectrometry Data to Life; Fast and
Colorful
Description: Optimizing methods for liquid chromatography coupled to
mass spectrometry (LC-MS) poses a nontrivial challenge. The
rawDiag package facilitates rational method optimization by
generating MS operator-tailored diagnostic plots of scan-level
metadata. The package is designed for use on the R shell or as
a Shiny application on the Orbitrap instrument PC.
biocViews: MassSpectrometry, Proteomics, Metabolomics, Infrastructure,
Software, ShinyApps
Author: Christian Panse [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1975-3064>), Christian Trachsel
[aut], Tobias Kockmann [aut]
Maintainer: Christian Panse <cp@fgcz.ethz.ch>
URL: https://github.com/fgcz/rawDiag/
VignetteBuilder: knitr
BugReports: https://github.com/fgcz/rawDiag/issues
git_url: https://git.bioconductor.org/packages/rawDiag
git_branch: devel
git_last_commit: 21b3fa2
git_last_commit_date: 2024-11-12
Date/Publication: 2024-11-12
source.ver: src/contrib/rawDiag_1.3.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rawDiag_1.3.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rawDiag_1.3.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rawDiag_1.3.4.tgz
vignettes: vignettes/rawDiag/inst/doc/rawDiag.html
vignetteTitles: Brings Orbitrap Mass Spectrometry Data to Life; Fast
and Colorful
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: FALSE
Rfiles: vignettes/rawDiag/inst/doc/rawDiag.R
dependencyCount: 79
Package: rawrr
Version: 1.15.10
Depends: R (>= 4.1)
Imports: grDevices, graphics, stats, utils
Suggests: BiocStyle (>= 2.5), ExperimentHub, knitr, protViz (>= 0.7),
rmarkdown, tartare (>= 1.5), testthat
License: GPL-3
MD5sum: 04f12dd7e884f9a7158bead141243de9
NeedsCompilation: no
Title: Direct Access to Orbitrap Data and Beyond
Description: This package wraps the functionality of the Thermo Fisher
Scientic RawFileReader .NET 8.0 assembly. Within the R
environment, spectra and chromatograms are represented by S3
objects. The package provides basic functions to download and
install the required third-party libraries. The package is
developed, tested, and used at the Functional Genomics Center
Zurich, Switzerland.
biocViews: MassSpectrometry, Proteomics, Metabolomics, Infrastructure,
Software
Author: Christian Panse [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1975-3064>), Leonardo Schwarz
[ctb] (ORCID: <https://orcid.org/0009-0003-1828-6924>), Tobias
Kockmann [aut] (ORCID: <https://orcid.org/0000-0002-1847-885X>)
Maintainer: Christian Panse <cp@fgcz.ethz.ch>
URL: https://github.com/fgcz/rawrr/
SystemRequirements: .NET 8.0 (optional; required only if you want to
compile and link the C# code)
VignetteBuilder: knitr
BugReports: https://github.com/fgcz/rawrr/issues
git_url: https://git.bioconductor.org/packages/rawrr
git_branch: devel
git_last_commit: cd82c1d
git_last_commit_date: 2024-12-04
Date/Publication: 2024-12-04
source.ver: src/contrib/rawrr_1.15.10.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rawrr_1.15.10.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rawrr_1.15.10.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rawrr_1.15.10.tgz
vignettes: vignettes/rawrr/inst/doc/rawrr.html
vignetteTitles: Direct Access to Orbitrap Data and Beyond
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: FALSE
Rfiles: vignettes/rawrr/inst/doc/rawrr.R
importsMe: MsBackendRawFileReader, rawDiag
dependencyCount: 4
Package: RbcBook1
Version: 1.75.0
Depends: R (>= 2.10), Biobase, graph, rpart
License: Artistic-2.0
MD5sum: 2adbfcd1ce2f4ff11d33e1f4da3abb74
NeedsCompilation: no
Title: Support for Springer monograph on Bioconductor
Description: tools for building book
biocViews: Software
Author: Vince Carey <stvjc@channing.harvard.edu> and Wolfgang Huber
<huber@ebi.ac.uk>
Maintainer: Vince Carey <stvjc@channing.harvard.edu>
URL: http://www.biostat.harvard.edu/~carey
git_url: https://git.bioconductor.org/packages/RbcBook1
git_branch: devel
git_last_commit: fa0cc73
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RbcBook1_1.75.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RbcBook1_1.75.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RbcBook1_1.75.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RbcBook1_1.75.0.tgz
vignettes: vignettes/RbcBook1/inst/doc/RbcBook1.pdf
vignetteTitles: RbcBook1 Primer
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RbcBook1/inst/doc/RbcBook1.R
dependencyCount: 11
Package: Rbec
Version: 1.15.0
Imports: Rcpp (>= 1.0.6), dada2, ggplot2, readr, doParallel, foreach,
grDevices, stats, utils
LinkingTo: Rcpp
Suggests: knitr, rmarkdown
License: LGPL-3
MD5sum: df67d205e2c48f8210bca58fd130ec5a
NeedsCompilation: yes
Title: Rbec: a tool for analysis of amplicon sequencing data from
synthetic microbial communities
Description: Rbec is a adapted version of DADA2 for analyzing amplicon
sequencing data from synthetic communities (SynComs), where the
reference sequences for each strain exists. Rbec can not only
accurately profile the microbial compositions in SynComs, but
also predict the contaminants in SynCom samples.
biocViews: Sequencing, MicrobialStrain, Microbiome
Author: Pengfan Zhang [aut, cre]
Maintainer: Pengfan Zhang <pzhang@mpipz.mpg.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Rbec
git_branch: devel
git_last_commit: baf2f91
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Rbec_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Rbec_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Rbec_1.15.0.tgz
vignettes: vignettes/Rbec/inst/doc/Rbec.html
vignetteTitles: Rbec
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rbec/inst/doc/Rbec.R
dependencyCount: 106
Package: RBGL
Version: 1.83.0
Depends: graph, methods
Imports: methods
LinkingTo: BH
Suggests: Rgraphviz, XML, RUnit, BiocGenerics, BiocStyle, knitr
License: Artistic-2.0
Archs: x64
MD5sum: 46de6022c5b9eef95b45ba25d6f21829
NeedsCompilation: yes
Title: An interface to the BOOST graph library
Description: A fairly extensive and comprehensive interface to the
graph algorithms contained in the BOOST library.
biocViews: GraphAndNetwork, Network
Author: Vince Carey [aut], Li Long [aut], R. Gentleman [aut], Emmanuel
Taiwo [ctb] (Converted RBGL vignette from Sweave to RMarkdown /
HTML.), Bioconductor Package Maintainer [cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: http://www.bioconductor.org
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RBGL
git_branch: devel
git_last_commit: 3ba1ecb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RBGL_1.83.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RBGL_1.83.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RBGL_1.83.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RBGL_1.83.0.tgz
vignettes: vignettes/RBGL/inst/doc/RBGL.html
vignetteTitles: RBGL Overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RBGL/inst/doc/RBGL.R
dependsOnMe: apComplex, BioNet, CellNOptR, fgga, PerfMeas,
SubpathwayLNCE
importsMe: BiocPkgTools, biocViews, CAMERA, Category, ChIPpeakAnno,
CHRONOS, CytoML, DEGraph, DEsubs, EventPointer, flowWorkspace,
GenomicInteractionNodes, GOstats, NCIgraph, openCyto,
OrganismDbi, Streamer, VariantFiltering, BiDAG, clustNet, eff2,
HEMDAG, micd, pcalg, rags2ridges, RANKS, SEMgraph, SID
suggestsMe: DEGraph, G4SNVHunter, GeneNetworkBuilder, graph, gwascat,
KEGGgraph, rBiopaxParser, VariantTools, yeastExpData,
archeofrag, maGUI
dependencyCount: 9
Package: RBioFormats
Version: 1.7.0
Imports: EBImage, methods, rJava (>= 0.9-6), S4Vectors, stats
Suggests: BiocStyle, knitr, testthat, xml2
License: GPL-3
MD5sum: 113a5a60c00fad0b34f6cfa8121efcbb
NeedsCompilation: no
Title: R interface to Bio-Formats
Description: An R package which interfaces the OME Bio-Formats Java
library to allow reading of proprietary microscopy image data
and metadata.
biocViews: DataImport
Author: Andrzej OleÅ› [aut, cre] (ORCID:
<https://orcid.org/0000-0003-0285-2787>), John Lee [ctb]
(ORCID: <https://orcid.org/0000-0001-5884-4247>)
Maintainer: Andrzej OleÅ› <andrzej.oles@gmail.com>
URL: https://github.com/aoles/RBioFormats
SystemRequirements: Java (>= 1.7)
VignetteBuilder: knitr
BugReports: https://github.com/aoles/RBioFormats/issues
git_url: https://git.bioconductor.org/packages/RBioFormats
git_branch: devel
git_last_commit: f6293b2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RBioFormats_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RBioFormats_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RBioFormats_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RBioFormats_1.7.0.tgz
vignettes: vignettes/RBioFormats/inst/doc/RBioFormats.html
vignetteTitles: RBioFormats: an R interface to the Bio-Formats library
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RBioFormats/inst/doc/RBioFormats.R
importsMe: alabaster.sfe, SpatialOmicsOverlay
suggestsMe: SpatialFeatureExperiment, Voyager
dependencyCount: 49
Package: RBioinf
Version: 1.67.1
Depends: graph, methods
Suggests: Rgraphviz
License: Artistic-2.0
MD5sum: 80582903d1c3899afd93119dfbaefc97
NeedsCompilation: yes
Title: RBioinf
Description: Functions and datasets and examples to accompany the
monograph R For Bioinformatics.
biocViews: GeneExpression, Microarray, Preprocessing, QualityControl,
Classification, Clustering, MultipleComparison, Annotation
Author: Robert Gentleman
Maintainer: Robert Gentleman <rgentlem@gmail.com>
PackageStatus: Deprecated
git_url: https://git.bioconductor.org/packages/RBioinf
git_branch: devel
git_last_commit: 052ec63
git_last_commit_date: 2025-02-25
Date/Publication: 2025-02-26
source.ver: src/contrib/RBioinf_1.67.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RBioinf_1.67.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RBioinf_1.67.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RBioinf_1.67.1.tgz
vignettes: vignettes/RBioinf/inst/doc/RBioinf.pdf
vignetteTitles: RBioinf Introduction
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RBioinf/inst/doc/RBioinf.R
dependencyCount: 8
Package: rBiopaxParser
Version: 2.47.0
Depends: R (>= 4.0), data.table
Imports: XML
Suggests: Rgraphviz, RCurl, graph, RUnit, BiocGenerics, RBGL, igraph
License: GPL (>= 2)
MD5sum: a7495cf4ae42e2f93020de3a4515bb4d
NeedsCompilation: no
Title: Parses BioPax files and represents them in R
Description: Parses BioPAX files and represents them in R, at the
moment BioPAX level 2 and level 3 are supported.
biocViews: DataRepresentation
Author: Frank Kramer
Maintainer: Frank Kramer <frank.kramer@informatik.uni-augsburg.de>
URL: https://github.com/frankkramer-lab/rBiopaxParser
git_url: https://git.bioconductor.org/packages/rBiopaxParser
git_branch: devel
git_last_commit: dd61169
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rBiopaxParser_2.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rBiopaxParser_2.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rBiopaxParser_2.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rBiopaxParser_2.47.0.tgz
vignettes: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.pdf
vignetteTitles: rBiopaxParser Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.R
suggestsMe: AnnotationHub, NetPathMiner
dependencyCount: 4
Package: rBLAST
Version: 1.3.1
Depends: Biostrings (>= 2.26.2)
Imports: methods, utils, BiocFileCache
Suggests: knitr, rmarkdown, testthat
License: GPL-3
Archs: x64
MD5sum: 939aa1735c1fdc9b7652b632396879e9
NeedsCompilation: no
Title: R Interface for the Basic Local Alignment Search Tool
Description: Seamlessly interfaces the Basic Local Alignment Search
Tool (BLAST) running locally to search genetic sequence data
bases. This work was partially supported by grant no.
R21HG005912 from the National Human Genome Research Institute.
biocViews: Genetics, Sequencing, SequenceMatching, Alignment,
DataImport
Author: Michael Hahsler [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2716-1405>), Nagar Anurag [aut]
Maintainer: Michael Hahsler <mhahsler@lyle.smu.edu>
URL: https://github.com/mhahsler/rBLAST
SystemRequirements: ncbi-blast+
VignetteBuilder: knitr
BugReports: https://github.com/mhahsler/rBLAST/issues
git_url: https://git.bioconductor.org/packages/rBLAST
git_branch: devel
git_last_commit: e1044a5
git_last_commit_date: 2025-01-06
Date/Publication: 2025-01-08
source.ver: src/contrib/rBLAST_1.3.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rBLAST_1.3.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rBLAST_1.3.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rBLAST_1.3.1.tgz
vignettes: vignettes/rBLAST/inst/doc/blast.html
vignetteTitles: rBLAST: R Interface for the Basic Local Alignment
Search Tool
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: FALSE
Rfiles: vignettes/rBLAST/inst/doc/blast.R
dependencyCount: 56
Package: RBM
Version: 1.39.0
Depends: R (>= 3.2.0), limma, marray
License: GPL (>= 2)
Archs: x64
MD5sum: a9ebfb6ae5114a1fb1820269a0b7f74e
NeedsCompilation: no
Title: RBM: a R package for microarray and RNA-Seq data analysis
Description: Use A Resampling-Based Empirical Bayes Approach to Assess
Differential Expression in Two-Color Microarrays and RNA-Seq
data sets.
biocViews: Microarray, DifferentialExpression
Author: Dongmei Li and Chin-Yuan Liang
Maintainer: Dongmei Li <Dongmei_Li@urmc.rochester.edu>
git_url: https://git.bioconductor.org/packages/RBM
git_branch: devel
git_last_commit: 7acbaea
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RBM_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RBM_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RBM_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RBM_1.39.0.tgz
vignettes: vignettes/RBM/inst/doc/RBM.pdf
vignetteTitles: RBM
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RBM/inst/doc/RBM.R
dependencyCount: 8
Package: Rbowtie
Version: 1.47.0
Imports: utils
Suggests: testthat, parallel, BiocStyle, knitr, rmarkdown
License: Artistic-2.0 | file LICENSE
MD5sum: fc8f3f0bd77c816b375eafff7c4c2e5a
NeedsCompilation: yes
Title: R bowtie wrapper
Description: This package provides an R wrapper around the popular
bowtie short read aligner and around SpliceMap, a de novo
splice junction discovery and alignment tool. The package is
used by the QuasR bioconductor package. We recommend to use the
QuasR package instead of using Rbowtie directly.
biocViews: Sequencing, Alignment
Author: Florian Hahne [aut], Anita Lerch [aut], Michael Stadler [aut,
cre] (ORCID: <https://orcid.org/0000-0002-2269-4934>)
Maintainer: Michael Stadler <michael.stadler@fmi.ch>
URL: https://github.com/fmicompbio/Rbowtie
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/fmicompbio/Rbowtie/issues
git_url: https://git.bioconductor.org/packages/Rbowtie
git_branch: devel
git_last_commit: 1ccecf4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Rbowtie_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Rbowtie_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Rbowtie_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Rbowtie_1.47.0.tgz
vignettes: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.html
vignetteTitles: An introduction to Rbowtie
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.R
dependsOnMe: QuasR
importsMe: crisprBowtie, multicrispr
suggestsMe: crisprDesign, eisaR
dependencyCount: 1
Package: Rbowtie2
Version: 2.13.3
Depends: R (>= 4.1.0)
Imports: magrittr, Rsamtools
Suggests: knitr, testthat (>= 3.0.0), rmarkdown
License: GPL (>= 3)
Archs: x64
MD5sum: 72e8301f383fa1308f1b4c282bae8a7d
NeedsCompilation: yes
Title: An R Wrapper for Bowtie2 and AdapterRemoval
Description: This package provides an R wrapper of the popular bowtie2
sequencing reads aligner and AdapterRemoval, a convenient tool
for rapid adapter trimming, identification, and read merging.
The package contains wrapper functions that allow for genome
indexing and alignment to those indexes. The package also
allows for the creation of .bam files via Rsamtools.
biocViews: Sequencing, Alignment, Preprocessing
Author: Zheng Wei [aut, cre], Wei Zhang [aut]
Maintainer: Zheng Wei <wzweizheng@qq.com>
SystemRequirements: C++11, GNU make, samtools
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Rbowtie2
git_branch: devel
git_last_commit: c5f03e0
git_last_commit_date: 2025-01-19
Date/Publication: 2025-01-19
source.ver: src/contrib/Rbowtie2_2.13.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Rbowtie2_2.13.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Rbowtie2_2.13.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Rbowtie2_2.13.3.tgz
vignettes: vignettes/Rbowtie2/inst/doc/Rbowtie2-Introduction.html
vignetteTitles: An Introduction to Rbowtie2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rbowtie2/inst/doc/Rbowtie2-Introduction.R
importsMe: CircSeqAlignTk, esATAC, UMI4Cats, MetaScope
dependencyCount: 40
Package: rbsurv
Version: 2.65.0
Depends: R (>= 2.5.0), Biobase (>= 2.5.5), survival
License: GPL (>= 2)
MD5sum: ead112938961c8ddaba83960e03bbb45
NeedsCompilation: no
Title: Robust likelihood-based survival modeling with microarray data
Description: This package selects genes associated with survival.
biocViews: Microarray
Author: HyungJun Cho <hj4cho@korea.ac.kr>, Sukwoo Kim
<s4kim@korea.ac.kr>, Soo-heang Eo <hanansh@korea.ac.kr>, Jaewoo
Kang <kangj@korea.ac.kr>
Maintainer: Soo-heang Eo <hanansh@korea.ac.kr>
URL: http://www.korea.ac.kr/~stat2242/
git_url: https://git.bioconductor.org/packages/rbsurv
git_branch: devel
git_last_commit: 8f38276
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rbsurv_2.65.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rbsurv_2.65.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rbsurv_2.65.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rbsurv_2.65.0.tgz
vignettes: vignettes/rbsurv/inst/doc/rbsurv.pdf
vignetteTitles: Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rbsurv/inst/doc/rbsurv.R
dependencyCount: 13
Package: Rbwa
Version: 1.11.0
Depends: R (>= 4.1)
Suggests: testthat, BiocStyle, knitr, rmarkdown
License: MIT + file LICENSE
OS_type: unix
MD5sum: 099a86b1b6d2cce0acd52917ce040491
NeedsCompilation: yes
Title: R wrapper for BWA-backtrack and BWA-MEM aligners
Description: Provides an R wrapper for BWA alignment algorithms. Both
BWA-backtrack and BWA-MEM are available. Convenience function
to build a BWA index from a reference genome is also provided.
Currently not supported for Windows machines.
biocViews: Sequencing, Alignment
Author: Jean-Philippe Fortin [aut, cre]
Maintainer: Jean-Philippe Fortin <fortin946@gmail.com>
URL: https://github.com/Jfortin1/Rbwa
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/crisprVerse/Rbwa/issues
git_url: https://git.bioconductor.org/packages/Rbwa
git_branch: devel
git_last_commit: 441c698
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Rbwa_1.11.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Rbwa_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Rbwa_1.11.0.tgz
vignettes: vignettes/Rbwa/inst/doc/Rbwa.html
vignetteTitles: An introduction to Rbwa
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Rbwa/inst/doc/Rbwa.R
importsMe: crisprBwa
suggestsMe: crisprDesign
dependencyCount: 0
Package: RCAS
Version: 1.33.0
Depends: R (>= 3.5.0), plotly (>= 4.5.2), DT (>= 0.2), data.table
Imports: GenomicRanges, IRanges, BSgenome, BSgenome.Hsapiens.UCSC.hg19,
GenomeInfoDb (>= 1.12.0), Biostrings, rtracklayer,
GenomicFeatures, txdbmaker, rmarkdown (>= 0.9.5), genomation
(>= 1.5.5), knitr (>= 1.12.3), BiocGenerics, S4Vectors,
plotrix, pbapply, RSQLite, proxy, pheatmap, ggplot2, cowplot,
seqLogo, utils, ranger, gprofiler2
Suggests: testthat, covr, BiocManager
License: Artistic-2.0
MD5sum: 3dc884fe331821233a3e869551a129d2
NeedsCompilation: no
Title: RNA Centric Annotation System
Description: RCAS is an R/Bioconductor package designed as a generic
reporting tool for the functional analysis of
transcriptome-wide regions of interest detected by
high-throughput experiments. Such transcriptomic regions could
be, for instance, signal peaks detected by CLIP-Seq analysis
for protein-RNA interaction sites, RNA modification sites
(alias the epitranscriptome), CAGE-tag locations, or any other
collection of query regions at the level of the transcriptome.
RCAS produces in-depth annotation summaries and coverage
profiles based on the distribution of the query regions with
respect to transcript features (exons, introns, 5'/3' UTR
regions, exon-intron boundaries, promoter regions). Moreover,
RCAS can carry out functional enrichment analyses and
discriminative motif discovery.
biocViews: Software, GeneTarget, MotifAnnotation, MotifDiscovery, GO,
Transcriptomics, GenomeAnnotation, GeneSetEnrichment, Coverage
Author: Bora Uyar [aut, cre], Dilmurat Yusuf [aut], Ricardo Wurmus
[aut], Altuna Akalin [aut]
Maintainer: Bora Uyar <bora.uyar@mdc-berlin.de>
SystemRequirements: pandoc (>= 1.12.3)
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RCAS
git_branch: devel
git_last_commit: 44fd977
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/RCAS_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RCAS_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RCAS_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RCAS_1.33.0.tgz
vignettes: vignettes/RCAS/inst/doc/RCAS.metaAnalysis.vignette.html,
vignettes/RCAS/inst/doc/RCAS.vignette.html
vignetteTitles: How to do meta-analysis of multiple samples,
Introduction - single sample analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RCAS/inst/doc/RCAS.metaAnalysis.vignette.R,
vignettes/RCAS/inst/doc/RCAS.vignette.R
importsMe: GenomicPlot
dependencyCount: 161
Package: RCASPAR
Version: 1.53.0
License: GPL (>=3)
MD5sum: 83f4f3b7c15dc367660bfeac477766c0
NeedsCompilation: no
Title: A package for survival time prediction based on a piecewise
baseline hazard Cox regression model.
Description: The package is the R-version of the C-based software
\bold{CASPAR} (Kaderali,2006:
\url{http://bioinformatics.oxfordjournals.org/content/22/12/1495}).
It is meant to help predict survival times in the presence of
high-dimensional explanatory covariates. The model is a
piecewise baseline hazard Cox regression model with an Lq-norm
based prior that selects for the most important regression
coefficients, and in turn the most relevant covariates for
survival analysis. It was primarily tried on gene expression
and aCGH data, but can be used on any other type of
high-dimensional data and in disciplines other than biology and
medicine.
biocViews: aCGH, GeneExpression, Genetics, Proteomics, Visualization
Author: Douaa Mugahid, Lars Kaderali
Maintainer: Douaa Mugahid <douaa.mugahid@gmail.com>, Lars Kaderali
<lars.kaderali@uni-greifswald.de>
git_url: https://git.bioconductor.org/packages/RCASPAR
git_branch: devel
git_last_commit: 105038a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RCASPAR_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RCASPAR_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RCASPAR_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RCASPAR_1.53.0.tgz
vignettes: vignettes/RCASPAR/inst/doc/RCASPAR.pdf
vignetteTitles: RCASPAR: Software for high-dimentional-data driven
survival time prediction
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RCASPAR/inst/doc/RCASPAR.R
dependencyCount: 0
Package: rcellminer
Version: 2.29.0
Depends: R (>= 3.2), Biobase, rcellminerData (>= 2.0.0)
Imports: stringr, gplots, ggplot2, methods, stats, utils, shiny
Suggests: knitr, RColorBrewer, sqldf, BiocGenerics, testthat,
BiocStyle, jsonlite, heatmaply, glmnet, foreach, doSNOW,
parallel, rmarkdown
License: LGPL-3 + file LICENSE
Archs: x64
MD5sum: 1dcbf748fd2d10f0f7ad61e95bf7ce51
NeedsCompilation: no
Title: rcellminer: Molecular Profiles, Drug Response, and Chemical
Structures for the NCI-60 Cell Lines
Description: The NCI-60 cancer cell line panel has been used over the
course of several decades as an anti-cancer drug screen. This
panel was developed as part of the Developmental Therapeutics
Program (DTP, http://dtp.nci.nih.gov/) of the U.S. National
Cancer Institute (NCI). Thousands of compounds have been tested
on the NCI-60, which have been extensively characterized by
many platforms for gene and protein expression, copy number,
mutation, and others (Reinhold, et al., 2012). The purpose of
the CellMiner project (http://discover.nci.nih.gov/ cellminer)
has been to integrate data from multiple platforms used to
analyze the NCI-60 and to provide a powerful suite of tools for
exploration of NCI-60 data.
biocViews: aCGH, CellBasedAssays, CopyNumberVariation, GeneExpression,
Pharmacogenomics, Pharmacogenetics, miRNA, Cheminformatics,
Visualization, Software, SystemsBiology
Author: Augustin Luna, Vinodh Rajapakse, Fabricio Sousa
Maintainer: Augustin Luna <augustin.luna@nih.gov>, Vinodh Rajapakse
<vinodh.rajapakse@nih.gov>, Fathi Elloumi
<fathi.elloumi@nih.gov>
URL: http://discover.nci.nih.gov/cellminer/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rcellminer
git_branch: devel
git_last_commit: 6866a6f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rcellminer_2.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rcellminer_2.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rcellminer_2.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rcellminer_2.29.0.tgz
vignettes: vignettes/rcellminer/inst/doc/rcellminerUsage.html
vignetteTitles: Using rcellminer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/rcellminer/inst/doc/rcellminerUsage.R
suggestsMe: rcellminerData
dependencyCount: 70
Package: rCGH
Version: 1.37.0
Depends: R (>= 3.4),methods,stats,utils,graphics
Imports: plyr,DNAcopy,lattice,ggplot2,grid,shiny (>= 0.11.1),
limma,affy,mclust,TxDb.Hsapiens.UCSC.hg18.knownGene,
TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Hsapiens.UCSC.hg38.knownGene,
org.Hs.eg.db,GenomicFeatures,GenomeInfoDb,GenomicRanges,AnnotationDbi,
parallel,IRanges,grDevices,aCGH
Suggests: BiocStyle, knitr, BiocGenerics, RUnit
License: Artistic-2.0
Archs: x64
MD5sum: 0250c6588dfb60e936f14cebc9f1cde0
NeedsCompilation: no
Title: Comprehensive Pipeline for Analyzing and Visualizing Array-Based
CGH Data
Description: A comprehensive pipeline for analyzing and interactively
visualizing genomic profiles generated through commercial or
custom aCGH arrays. As inputs, rCGH supports Agilent dual-color
Feature Extraction files (.txt), from 44 to 400K, Affymetrix
SNP6.0 and cytoScanHD probeset.txt, cychp.txt, and cnchp.txt
files exported from ChAS or Affymetrix Power Tools. rCGH also
supports custom arrays, provided data complies with the
expected format. This package takes over all the steps required
for individual genomic profiles analysis, from reading files to
profiles segmentation and gene annotations. This package also
provides several visualization functions (static or
interactive) which facilitate individual profiles
interpretation. Input files can be in compressed format, e.g.
.bz2 or .gz.
biocViews: aCGH,CopyNumberVariation,Preprocessing,FeatureExtraction
Author: Frederic Commo [aut, cre]
Maintainer: Frederic Commo <fredcommo@gmail.com>
URL: https://github.com/fredcommo/rCGH
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rCGH
git_branch: devel
git_last_commit: f2bf7cc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rCGH_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rCGH_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rCGH_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rCGH_1.37.0.tgz
vignettes: vignettes/rCGH/inst/doc/rCGH.pdf
vignetteTitles: using rCGH package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rCGH/inst/doc/rCGH.R
importsMe: preciseTAD
dependencyCount: 131
Package: RcisTarget
Version: 1.27.0
Depends: R (>= 3.5.0)
Imports: AUCell (>= 1.1.6), BiocGenerics, data.table, graphics,
GenomeInfoDb, GenomicRanges, arrow (>= 2.0.0), dplyr, tibble,
GSEABase, methods, R.utils, stats, SummarizedExperiment,
S4Vectors, utils, zoo
Suggests: Biobase, BiocStyle, BiocParallel, doParallel, DT, foreach,
gplots, rtracklayer, igraph, knitr,
RcisTarget.hg19.motifDBs.cisbpOnly.500bp, rmarkdown, testthat,
visNetwork
Enhances: doMC, doRNG
License: GPL-3
MD5sum: b7e87937febf6d2eee990c0cb45107fc
NeedsCompilation: no
Title: RcisTarget Identify transcription factor binding motifs enriched
on a list of genes or genomic regions
Description: RcisTarget identifies transcription factor binding motifs
(TFBS) over-represented on a gene list. In a first step,
RcisTarget selects DNA motifs that are significantly
over-represented in the surroundings of the transcription start
site (TSS) of the genes in the gene-set. This is achieved by
using a database that contains genome-wide cross-species
rankings for each motif. The motifs that are then annotated to
TFs and those that have a high Normalized Enrichment Score
(NES) are retained. Finally, for each motif and gene-set,
RcisTarget predicts the candidate target genes (i.e. genes in
the gene-set that are ranked above the leading edge).
biocViews: GeneRegulation, MotifAnnotation, Transcriptomics,
Transcription, GeneSetEnrichment, GeneTarget
Author: Sara Aibar, Gert Hulselmans, Stein Aerts. Laboratory of
Computational Biology. VIB-KU Leuven Center for Brain & Disease
Research. Leuven, Belgium
Maintainer: Gert Hulselmans <Gert.Hulselmans@kuleuven.be>
URL: http://scenic.aertslab.org
VignetteBuilder: knitr
BugReports: https://github.com/aertslab/RcisTarget/issues
git_url: https://git.bioconductor.org/packages/RcisTarget
git_branch: devel
git_last_commit: 21c0c48
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RcisTarget_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RcisTarget_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RcisTarget_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RcisTarget_1.27.0.tgz
vignettes: vignettes/RcisTarget/inst/doc/RcisTarget_MainTutorial.html,
vignettes/RcisTarget/inst/doc/Tutorial_AnalysisOfGenomicRegions.html,
vignettes/RcisTarget/inst/doc/Tutorial_AnalysisWithBackground.html
vignetteTitles: RcisTarget: Transcription factor binding motif
enrichment, RcisTarget - on regions, RcisTarget - with
background
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RcisTarget/inst/doc/RcisTarget_MainTutorial.R,
vignettes/RcisTarget/inst/doc/Tutorial_AnalysisOfGenomicRegions.R,
vignettes/RcisTarget/inst/doc/Tutorial_AnalysisWithBackground.R
dependencyCount: 124
Package: RCM
Version: 1.23.0
Depends: R (>= 4.0), DBI
Imports: RColorBrewer, alabama, edgeR, reshape2, tseries, stats, VGAM,
ggplot2 (>= 2.2.1.9000), nleqslv, phyloseq, tensor, MASS,
grDevices, graphics, methods
Suggests: knitr, rmarkdown, testthat
License: GPL-2
MD5sum: 6811d1a50116d971b4ecaed1efe0d019
NeedsCompilation: no
Title: Fit row-column association models with the negative binomial
distribution for the microbiome
Description: Combine ideas of log-linear analysis of contingency table,
flexible response function estimation and empirical Bayes
dispersion estimation for explorative visualization of
microbiome datasets. The package includes unconstrained as well
as constrained analysis. In addition, diagnostic plot to detect
lack of fit are available.
biocViews: Metagenomics, DimensionReduction, Microbiome, Visualization
Author: Stijn Hawinkel [cre, aut] (ORCID:
<https://orcid.org/0000-0002-4501-5180>)
Maintainer: Stijn Hawinkel <stijn.hawinkel@psb.ugent.be>
URL:
https://bioconductor.org/packages/release/bioc/vignettes/RCM/inst/doc/RCMvignette.html/
VignetteBuilder: knitr
BugReports: https://github.com/CenterForStatistics-UGent/RCM/issues
git_url: https://git.bioconductor.org/packages/RCM
git_branch: devel
git_last_commit: 0cde287
git_last_commit_date: 2024-10-29
Date/Publication: 2025-01-23
source.ver: src/contrib/RCM_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RCM_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RCM_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RCM_1.23.0.tgz
vignettes: vignettes/RCM/inst/doc/RCMvignette.html
vignetteTitles: Manual for the RCM pacakage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RCM/inst/doc/RCMvignette.R
dependencyCount: 99
Package: Rcollectl
Version: 1.7.0
Imports: utils, ggplot2, lubridate, processx
Suggests: knitr, BiocStyle, knitcitations, sessioninfo, rmarkdown,
testthat, covr
License: Artistic-2.0
MD5sum: b962309dcef5a22e0410f25976e32e47
NeedsCompilation: no
Title: Help use collectl with R in Linux, to measure resource
consumption in R processes
Description: Provide functions to obtain instrumentation data on
processes in a unix environment. Parse output of a collectl
run. Vizualize aspects of system usage over time, with
annotation.
biocViews: Software, Infrastructure
Author: Vincent Carey [aut, cre] (ORCID:
<https://orcid.org/0000-0003-4046-0063>), Yubo Cheng [aut]
Maintainer: Vincent Carey <stvjc@channing.harvard.edu>
URL: https://github.com/vjcitn/Rcollectl
SystemRequirements: collectl
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/Rcollectl
git_url: https://git.bioconductor.org/packages/Rcollectl
git_branch: devel
git_last_commit: bd65277
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Rcollectl_1.7.0.tar.gz
vignettes: vignettes/Rcollectl/inst/doc/Rcollectl.html
vignetteTitles: Rcollectl
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rcollectl/inst/doc/Rcollectl.R
dependencyCount: 41
Package: Rcpi
Version: 1.43.0
Depends: R (>= 3.0.2)
Imports: Biostrings, GOSemSim, curl, doParallel, foreach, httr2,
jsonlite, methods, rlang, stats, utils
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
License: Artistic-2.0 | file LICENSE
MD5sum: 6df0ceb2cdeaeb53bdc9546643f72e1e
NeedsCompilation: no
Title: Molecular Informatics Toolkit for Compound-Protein Interaction
in Drug Discovery
Description: A molecular informatics toolkit with an integration of
bioinformatics and chemoinformatics tools for drug discovery.
biocViews: Software, DataImport, DataRepresentation, FeatureExtraction,
Cheminformatics, BiomedicalInformatics, Proteomics, GO,
SystemsBiology
Author: Nan Xiao [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0250-5673>), Dong-Sheng Cao [aut],
Qing-Song Xu [aut]
Maintainer: Nan Xiao <me@nanx.me>
URL: https://nanx.me/Rcpi/, https://github.com/nanxstats/Rcpi
VignetteBuilder: knitr
BugReports: https://github.com/nanxstats/Rcpi/issues
git_url: https://git.bioconductor.org/packages/Rcpi
git_branch: devel
git_last_commit: 88f2c1f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Rcpi_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Rcpi_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Rcpi_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Rcpi_1.43.0.tgz
vignettes: vignettes/Rcpi/inst/doc/Rcpi-quickref.html,
vignettes/Rcpi/inst/doc/Rcpi.html
vignetteTitles: Rcpi Quick Reference Card, Rcpi: R/Bioconductor Package
as an Integrated Informatics Platform for Drug Discovery
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Rcpi/inst/doc/Rcpi.R
dependencyCount: 63
Package: RCSL
Version: 1.15.0
Depends: R (>= 4.1)
Imports: RcppAnnoy, igraph, NbClust, Rtsne, ggplot2(>= 3.4.0), methods,
pracma, umap, grDevices, graphics, stats, Rcpp (>= 0.11.0),
MatrixGenerics, SingleCellExperiment
Suggests: testthat, knitr, BiocStyle, rmarkdown, mclust, tidyverse,
tinytex
License: Artistic-2.0
MD5sum: 0ca7919fd18328629a78938d14b4e130
NeedsCompilation: no
Title: Rank Constrained Similarity Learning for single cell RNA
sequencing data
Description: A novel clustering algorithm and toolkit RCSL (Rank
Constrained Similarity Learning) to accurately identify various
cell types using scRNA-seq data from a complex tissue. RCSL
considers both lo-cal similarity and global similarity among
the cells to discern the subtle differences among cells of the
same type as well as larger differences among cells of
different types. RCSL uses Spearman’s rank correlations of a
cell’s expression vector with those of other cells to measure
its global similar-ity, and adaptively learns neighbour
representation of a cell as its local similarity. The overall
similar-ity of a cell to other cells is a linear combination of
its global similarity and local similarity.
biocViews: SingleCell, Software, Clustering, DimensionReduction,
RNASeq, Visualization, Sequencing
Author: Qinglin Mei [cre, aut], Guojun Li [fnd], Zhengchang Su [fnd]
Maintainer: Qinglin Mei <meiqinglinkf@163.com>
URL: https://github.com/QinglinMei/RCSL
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RCSL
git_branch: devel
git_last_commit: 8f4cc5d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RCSL_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RCSL_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RCSL_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RCSL_1.15.0.tgz
vignettes: vignettes/RCSL/inst/doc/RCSL.html
vignetteTitles: RCSL package manual
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RCSL/inst/doc/RCSL.R
dependencyCount: 79
Package: Rcwl
Version: 1.23.0
Depends: R (>= 3.6), yaml, methods, S4Vectors
Imports: utils, stats, BiocParallel, batchtools, DiagrammeR, shiny,
R.utils, codetools, basilisk
Suggests: testthat, knitr, rmarkdown, BiocStyle
License: GPL-2 | file LICENSE
MD5sum: 25730dd8dfdde7abf262a4794eaff510
NeedsCompilation: no
Title: An R interface to the Common Workflow Language
Description: The Common Workflow Language (CWL) is an open standard for
development of data analysis workflows that is portable and
scalable across different tools and working environments. Rcwl
provides a simple way to wrap command line tools and build CWL
data analysis pipelines programmatically within R. It increases
the ease of usage, development, and maintenance of CWL
pipelines.
biocViews: Software, WorkflowStep, ImmunoOncology
Author: Qiang Hu [aut, cre], Qian Liu [aut]
Maintainer: Qiang Hu <qiang.hu@roswellpark.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Rcwl
git_branch: devel
git_last_commit: dd1c6c5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Rcwl_1.23.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Rcwl_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Rcwl_1.23.0.tgz
vignettes: vignettes/Rcwl/inst/doc/Rcwl.html
vignetteTitles: Rcwl: An R interface to the Common Workflow Language
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Rcwl/inst/doc/Rcwl.R
dependsOnMe: RcwlPipelines
importsMe: ReUseData
dependencyCount: 111
Package: RcwlPipelines
Version: 1.23.0
Depends: R (>= 3.6), Rcwl, BiocFileCache
Imports: rappdirs, methods, utils, git2r, httr, S4Vectors
Suggests: testthat, knitr, rmarkdown, BiocStyle
License: GPL-2
MD5sum: 48be1b696c6184248f04ef5c98f69a06
NeedsCompilation: no
Title: Bioinformatics pipelines based on Rcwl
Description: A collection of Bioinformatics tools and pipelines based
on R and the Common Workflow Language.
biocViews: Software, WorkflowStep, Alignment, Preprocessing,
QualityControl, DNASeq, RNASeq, DataImport, ImmunoOncology
Author: Qiang Hu [aut, cre], Qian Liu [aut], Shuang Gao [aut]
Maintainer: Qiang Hu <qiang.hu@roswellpark.org>
SystemRequirements: nodejs
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RcwlPipelines
git_branch: devel
git_last_commit: 77e9e9a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RcwlPipelines_1.23.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RcwlPipelines_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RcwlPipelines_1.23.0.tgz
vignettes: vignettes/RcwlPipelines/inst/doc/RcwlPipelines.html
vignetteTitles: RcwlPipelines
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RcwlPipelines/inst/doc/RcwlPipelines.R
importsMe: ReUseData
dependencyCount: 124
Package: RCX
Version: 1.11.0
Depends: R (>= 4.2.0)
Imports: jsonlite, plyr, igraph, methods
Suggests: BiocStyle, testthat, knitr, rmarkdown, base64enc, graph
License: MIT + file LICENSE
MD5sum: f9d2bfb0f80e6f3bdaa48057050f517d
NeedsCompilation: no
Title: R package implementing the Cytoscape Exchange (CX) format
Description: Create, handle, validate, visualize and convert networks
in the Cytoscape exchange (CX) format to standard data types
and objects. The package also provides conversion to and from
objects of iGraph and graphNEL. The CX format is also used by
the NDEx platform, a online commons for biological networks,
and the network visualization software Cytocape.
biocViews: Pathways, DataImport, Network
Author: Florian Auer [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5320-8900>)
Maintainer: Florian Auer <florian.auer@informatik.uni-augsburg.de>
URL: https://github.com/frankkramer-lab/RCX
VignetteBuilder: knitr
BugReports: https://github.com/frankkramer-lab/RCX/issues
git_url: https://git.bioconductor.org/packages/RCX
git_branch: devel
git_last_commit: c707da4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RCX_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RCX_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RCX_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RCX_1.11.0.tgz
vignettes:
vignettes/RCX/inst/doc/Appendix_The_RCX_and_CX_Data_Model.html,
vignettes/RCX/inst/doc/Creating_RCX_from_scratch.html,
vignettes/RCX/inst/doc/Extending_the_RCX_Data_Model.html,
vignettes/RCX/inst/doc/RCX_an_R_package_implementing_the_Cytoscape_Exchange_format.html
vignetteTitles: Appendix: The RCX and CX Data Model, 02. Creating RCX
from scratch, 03. Extending the RCX Data Model, 01. RCX - an R
package implementing the Cytoscape Exchange (CX) format
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/RCX/inst/doc/Appendix_The_RCX_and_CX_Data_Model.R,
vignettes/RCX/inst/doc/Creating_RCX_from_scratch.R,
vignettes/RCX/inst/doc/Extending_the_RCX_Data_Model.R,
vignettes/RCX/inst/doc/RCX_an_R_package_implementing_the_Cytoscape_Exchange_format.R
dependsOnMe: ndexr
dependencyCount: 20
Package: RCy3
Version: 2.27.1
Imports: httr, methods, RJSONIO, XML, utils, BiocGenerics, stats,
graph, fs, uuid, stringi, glue, RCurl, base64url, base64enc,
IRkernel, IRdisplay, RColorBrewer, gplots
Suggests: BiocStyle, knitr, rmarkdown, igraph, grDevices
License: MIT + file LICENSE
MD5sum: 49e7ffd3278d913bb692dbbb7b118501
NeedsCompilation: no
Title: Functions to Access and Control Cytoscape
Description: Vizualize, analyze and explore networks using Cytoscape
via R. Anything you can do using the graphical user interface
of Cytoscape, you can now do with a single RCy3 function.
biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network
Author: Alex Pico [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5706-2163>), Tanja Muetze [aut],
Paul Shannon [aut], Ruth Isserlin [ctb], Shraddha Pai [ctb],
Julia Gustavsen [ctb], Georgi Kolishovski [ctb], Yihang Xin
[ctb]
Maintainer: Alex Pico <alex.pico@gladstone.ucsf.edu>
URL: https://github.com/cytoscape/RCy3
SystemRequirements: Cytoscape (>= 3.7.1), CyREST (>= 3.8.0)
VignetteBuilder: knitr
BugReports: https://github.com/cytoscape/RCy3/issues
git_url: https://git.bioconductor.org/packages/RCy3
git_branch: devel
git_last_commit: ce5b6ca
git_last_commit_date: 2025-03-26
Date/Publication: 2025-03-27
source.ver: src/contrib/RCy3_2.27.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RCy3_2.27.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RCy3_2.27.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RCy3_2.27.1.tgz
vignettes: vignettes/RCy3/inst/doc/Cancer-networks-and-data.html,
vignettes/RCy3/inst/doc/Custom-Graphics.html,
vignettes/RCy3/inst/doc/Cytoscape-and-graphNEL.html,
vignettes/RCy3/inst/doc/Cytoscape-and-iGraph.html,
vignettes/RCy3/inst/doc/Cytoscape-and-NDEx.html,
vignettes/RCy3/inst/doc/Filtering-Networks.html,
vignettes/RCy3/inst/doc/Group-nodes.html,
vignettes/RCy3/inst/doc/Identifier-mapping.html,
vignettes/RCy3/inst/doc/Importing-data.html,
vignettes/RCy3/inst/doc/Jupyter-bridge-rcy3.html,
vignettes/RCy3/inst/doc/Network-functions-and-visualization.html,
vignettes/RCy3/inst/doc/Overview-of-RCy3.html,
vignettes/RCy3/inst/doc/Phylogenetic-trees.html,
vignettes/RCy3/inst/doc/Upgrading-existing-scripts.html
vignetteTitles: 06. Cancer networks and data ~40 min, 11. Custom
Graphics and Labels ~10 min, 03. Cytoscape and graphNEL ~5 min,
02. Cytoscape and igraph ~5 min, 09. Cytoscape and NDEx ~20
min, 12. Filtering Networks ~10 min, 10. Group nodes ~15 min,
07. Identifier mapping ~20 min, 04. Importing data ~5 min, 14.
Jupyter Bridge and RCy3 ~10 min, 05. Network functions and
visualization ~15 min, 01. Overview of RCy3 ~25 min, 13.
Phylogenetic Trees ~3 min, 08. Upgrading existing scripts ~15
min
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/RCy3/inst/doc/Cancer-networks-and-data.R,
vignettes/RCy3/inst/doc/Custom-Graphics.R,
vignettes/RCy3/inst/doc/Cytoscape-and-graphNEL.R,
vignettes/RCy3/inst/doc/Cytoscape-and-iGraph.R,
vignettes/RCy3/inst/doc/Cytoscape-and-NDEx.R,
vignettes/RCy3/inst/doc/Filtering-Networks.R,
vignettes/RCy3/inst/doc/Group-nodes.R,
vignettes/RCy3/inst/doc/Identifier-mapping.R,
vignettes/RCy3/inst/doc/Importing-data.R,
vignettes/RCy3/inst/doc/Jupyter-bridge-rcy3.R,
vignettes/RCy3/inst/doc/Network-functions-and-visualization.R,
vignettes/RCy3/inst/doc/Overview-of-RCy3.R,
vignettes/RCy3/inst/doc/Phylogenetic-trees.R,
vignettes/RCy3/inst/doc/Upgrading-existing-scripts.R
importsMe: categoryCompare, CeTF, enrichViewNet, fedup,
GeneNetworkBuilder, MetaPhOR, MOGAMUN, MSstatsBioNet, NCIgraph,
regutools, transomics2cytoscape, dendroNetwork, lilikoi,
netgsa, ScriptMapR
suggestsMe: graphite, rScudo, tidysbml, sharp
dependencyCount: 49
Package: RCyjs
Version: 2.29.0
Depends: R (>= 3.5.0), BrowserViz (>= 2.7.18), graph (>= 1.56.0)
Imports: methods, httpuv (>= 1.5.0), BiocGenerics, base64enc, utils
Suggests: RUnit, BiocStyle, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 06a52938e4d4cc13cff9b6e6f6b4a9be
NeedsCompilation: no
Title: Display and manipulate graphs in cytoscape.js
Description: Interactive viewing and exploration of graphs, connecting
R to Cytoscape.js, using websockets.
biocViews: Visualization, GraphAndNetwork, ThirdPartyClient
Author: Paul Shannon
Maintainer: Paul Shannon <paul.thurmond.shannon@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RCyjs
git_branch: devel
git_last_commit: d9fd4c8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RCyjs_2.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RCyjs_2.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RCyjs_2.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RCyjs_2.29.0.tgz
vignettes: vignettes/RCyjs/inst/doc/RCyjs.html
vignetteTitles: "RCyjs: programmatic access to the web browser-based
network viewer,, cytoscape.js"
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/RCyjs/inst/doc/RCyjs.R
dependencyCount: 19
Package: Rdisop
Version: 1.67.5
Depends: R (>= 2.10), Rcpp
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, RUnit, testthat (>= 3.0.0)
License: GPL-2
MD5sum: 21faa8621cea4f975fa2bf7bff156d1f
NeedsCompilation: yes
Title: Decomposition of Isotopic Patterns
Description: In high resolution mass spectrometry (HR-MS), the measured
masses can be decomposed into potential element combinations
(chemical sum formulas). Where additional mass/intensity
information of respective isotopic peaks is available,
decomposition can take this information into account to better
rank the potential candidate sum formulas. To compare measured
mass/intensity information with the theoretical distribution of
candidate sum formulas, the latter needs to be calculated. This
package implements fast algorithms to address both tasks, the
calculation of isotopic distributions for arbitrary sum
formulas (assuming a HR-MS resolution of roughly 30,000), and
the ranked list of sum formulas fitting an observed peak or
isotopic peak set.
biocViews: ImmunoOncology, MassSpectrometry, Metabolomics
Author: Anton Pervukhin [aut], Steffen Neumann [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7899-7192>), Jan Lisec [ctb]
(ORCID: <https://orcid.org/0000-0003-1220-2286>), Miao Yu
[ctb], Roberto Canteri [ctb]
Maintainer: Steffen Neumann <sneumann@ipb-halle.de>
URL: https://github.com/sneumann/Rdisop
SystemRequirements: None
VignetteBuilder: knitr
BugReports: https://github.com/sneumann/Rdisop/issues/new
git_url: https://git.bioconductor.org/packages/Rdisop
git_branch: devel
git_last_commit: f83aaac
git_last_commit_date: 2025-01-21
Date/Publication: 2025-01-22
source.ver: src/contrib/Rdisop_1.67.5.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Rdisop_1.67.5.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Rdisop_1.67.5.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Rdisop_1.67.5.tgz
vignettes: vignettes/Rdisop/inst/doc/Rdisop.html
vignetteTitles: Mass decomposition with the Rdisop package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: TRUE
hasLICENSE: FALSE
importsMe: enviGCMS
suggestsMe: adductomicsR, MSnbase, RforProteomics, CorMID,
InterpretMSSpectrum
dependencyCount: 3
Package: RDRToolbox
Version: 1.57.0
Depends: R (>= 2.9.0)
Imports: graphics, grDevices, methods, stats, MASS, rgl
Suggests: golubEsets
License: GPL (>= 2)
MD5sum: 6ddd29bbf96ec594036bd1a2c9533173
NeedsCompilation: no
Title: A package for nonlinear dimension reduction with Isomap and LLE.
Description: A package for nonlinear dimension reduction using the
Isomap and LLE algorithm. It also includes a routine for
computing the Davis-Bouldin-Index for cluster validation, a
plotting tool and a data generator for microarray gene
expression data and for the Swiss Roll dataset.
biocViews: DimensionReduction, FeatureExtraction, Visualization,
Clustering, Microarray
Author: Christoph Bartenhagen
Maintainer: Christoph Bartenhagen <c.bartenhagen@uni-koeln.de>
git_url: https://git.bioconductor.org/packages/RDRToolbox
git_branch: devel
git_last_commit: fa6a727
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RDRToolbox_1.57.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RDRToolbox_1.57.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RDRToolbox_1.57.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RDRToolbox_1.57.0.tgz
vignettes: vignettes/RDRToolbox/inst/doc/vignette.pdf
vignetteTitles: A package for nonlinear dimension reduction with Isomap
and LLE.
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RDRToolbox/inst/doc/vignette.R
suggestsMe: loon
dependencyCount: 36
Package: ReactomeGSA
Version: 1.21.2
Imports: Biobase, BiocSingular, dplyr, ggplot2, gplots, httr, igraph,
jsonlite, methods, progress, RColorBrewer,
SummarizedExperiment, tidyr
Suggests: devtools, knitr, ReactomeGSA.data, rmarkdown, scater, scran,
scRNAseq, scuttle, Seurat (>= 3.0), SingleCellExperiment,
testthat
License: MIT + file LICENSE
MD5sum: 771833d7894095791de20d6b68251802
NeedsCompilation: no
Title: Client for the Reactome Analysis Service for comparative
multi-omics gene set analysis
Description: The ReactomeGSA packages uses Reactome's online analysis
service to perform a multi-omics gene set analysis. The main
advantage of this package is, that the retrieved results can be
visualized using REACTOME's powerful webapplication. Since
Reactome's analysis service also uses R to perfrom the actual
gene set analysis you will get similar results when using the
same packages (such as limma and edgeR) locally. Therefore, if
you only require a gene set analysis, different packages are
more suited.
biocViews: GeneSetEnrichment, Proteomics, Transcriptomics,
SystemsBiology, GeneExpression, Reactome
Author: Johannes Griss [aut, cre]
(<https://orcid.org/0000-0003-2206-9511>)
Maintainer: Johannes Griss <johannes.griss@meduniwien.ac.at>
URL: https://github.com/reactome/ReactomeGSA
VignetteBuilder: knitr
BugReports: https://github.com/reactome/ReactomeGSA/issues
git_url: https://git.bioconductor.org/packages/ReactomeGSA
git_branch: devel
git_last_commit: 1612e12
git_last_commit_date: 2024-11-27
Date/Publication: 2024-11-29
source.ver: src/contrib/ReactomeGSA_1.21.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ReactomeGSA_1.21.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ReactomeGSA_1.21.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ReactomeGSA_1.21.2.tgz
vignettes: vignettes/ReactomeGSA/inst/doc/analysing-scRNAseq.html,
vignettes/ReactomeGSA/inst/doc/reanalysing-public-data.html,
vignettes/ReactomeGSA/inst/doc/using-reactomegsa.html
vignetteTitles: Analysing single-cell RNAseq data, Loading and
re-analysing public data through ReactomeGSA, Using the
ReactomeGSA package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ReactomeGSA/inst/doc/analysing-scRNAseq.R,
vignettes/ReactomeGSA/inst/doc/reanalysing-public-data.R,
vignettes/ReactomeGSA/inst/doc/using-reactomegsa.R
dependsOnMe: ReactomeGSA.data
importsMe: scPipeline
dependencyCount: 94
Package: ReactomePA
Version: 1.51.0
Depends: R (>= 3.4.0)
Imports: AnnotationDbi, DOSE (>= 3.5.1), enrichplot, ggplot2 (>=
3.3.5), ggraph, reactome.db, igraph, graphite, gson,
yulab.utils (>= 0.1.5)
Suggests: BiocStyle, clusterProfiler, knitr, rmarkdown, org.Hs.eg.db,
prettydoc, testthat
License: GPL-2
MD5sum: b359c61464f7447086da80538ac97176
NeedsCompilation: no
Title: Reactome Pathway Analysis
Description: This package provides functions for pathway analysis based
on REACTOME pathway database. It implements enrichment
analysis, gene set enrichment analysis and several functions
for visualization. This package is not affiliated with the
Reactome team.
biocViews: Pathways, Visualization, Annotation, MultipleComparison,
GeneSetEnrichment, Reactome
Author: Guangchuang Yu [aut, cre], Vladislav Petyuk [ctb]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
URL: https://yulab-smu.top/contribution-knowledge-mining/
VignetteBuilder: knitr
BugReports: https://github.com/GuangchuangYu/ReactomePA/issues
git_url: https://git.bioconductor.org/packages/ReactomePA
git_branch: devel
git_last_commit: e581e3e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ReactomePA_1.51.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ReactomePA_1.51.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ReactomePA_1.51.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ReactomePA_1.51.0.tgz
vignettes: vignettes/ReactomePA/inst/doc/ReactomePA.html
vignetteTitles: An R package for Reactome Pathway Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ReactomePA/inst/doc/ReactomePA.R
dependsOnMe: maEndToEnd
importsMe: bioCancer, gINTomics, miRSM, miRspongeR, Pigengene,
scTensor, ExpHunterSuite
suggestsMe: CBNplot, ChIPseeker, CINdex, cola, GeDi, GRaNIE, scGPS
dependencyCount: 132
Package: ReadqPCR
Version: 1.53.0
Depends: R(>= 2.14.0), Biobase, methods
Suggests: qpcR
License: LGPL-3
MD5sum: 58d2860c7bd23dd4c50048880883d8d8
NeedsCompilation: no
Title: Read qPCR data
Description: The package provides functions to read raw RT-qPCR data of
different platforms.
biocViews: DataImport, MicrotitrePlateAssay, GeneExpression, qPCR
Author: James Perkins, Matthias Kohl, Nor Izayu Abdul Rahman
Maintainer: James Perkins <jimrperkins@gmail.com>
URL:
http://www.bioconductor.org/packages/release/bioc/html/ReadqPCR.html
git_url: https://git.bioconductor.org/packages/ReadqPCR
git_branch: devel
git_last_commit: e70ef53
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ReadqPCR_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ReadqPCR_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ReadqPCR_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ReadqPCR_1.53.0.tgz
vignettes: vignettes/ReadqPCR/inst/doc/ReadqPCR.pdf
vignetteTitles: Functions to load RT-qPCR data into R
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ReadqPCR/inst/doc/ReadqPCR.R
dependsOnMe: NormqPCR
dependencyCount: 7
Package: REBET
Version: 1.25.0
Depends: ASSET
Imports: stats, utils
Suggests: RUnit, BiocGenerics
License: GPL-2
Archs: x64
MD5sum: 7ec2955f62ee9557e8296d465e1b8b29
NeedsCompilation: yes
Title: The subREgion-based BurdEn Test (REBET)
Description: There is an increasing focus to investigate the
association between rare variants and diseases. The REBET
package implements the subREgion-based BurdEn Test which is a
powerful burden test that simultaneously identifies
susceptibility loci and sub-regions.
biocViews: Software, VariantAnnotation, SNP
Author: Bill Wheeler [cre], Bin Zhu [aut], Lisa Mirabello [ctb],
Nilanjan Chatterjee [ctb]
Maintainer: Bill Wheeler <wheelerb@imsweb.com>
git_url: https://git.bioconductor.org/packages/REBET
git_branch: devel
git_last_commit: 28e6fc9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/REBET_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/REBET_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/REBET_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/REBET_1.25.0.tgz
vignettes: vignettes/REBET/inst/doc/vignette.pdf
vignetteTitles: REBET Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/REBET/inst/doc/vignette.R
dependencyCount: 28
Package: rebook
Version: 1.17.0
Imports: utils, methods, knitr (>= 1.32), rmarkdown, CodeDepends,
dir.expiry, filelock, BiocStyle
Suggests: testthat, igraph, XML, BiocManager, RCurl, bookdown,
rappdirs, yaml, BiocParallel, OSCA.intro, OSCA.workflows
License: GPL-3
MD5sum: 5f6a821169049a126ac6324e39538525
NeedsCompilation: no
Title: Re-using Content in Bioconductor Books
Description: Provides utilities to re-use content across chapters of a
Bioconductor book. This is mostly based on functionality
developed while writing the OSCA book, but generalized for
potential use in other large books with heavy compute. Also
contains some functions to assist book deployment.
biocViews: Software, Infrastructure, ReportWriting
Author: Aaron Lun [aut, cre, cph]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rebook
git_branch: devel
git_last_commit: 9f1bbd3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rebook_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rebook_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rebook_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rebook_1.17.0.tgz
vignettes: vignettes/rebook/inst/doc/userguide.html
vignetteTitles: Reusing book content
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rebook/inst/doc/userguide.R
dependsOnMe: csawBook, OSCA, OSCA.basic, OSCA.intro, OSCA.multisample,
OSCA.workflows, SingleRBook
dependencyCount: 44
Package: receptLoss
Version: 1.19.0
Depends: R (>= 3.6.0)
Imports: dplyr, ggplot2, magrittr, tidyr, SummarizedExperiment
Suggests: knitr, rmarkdown, testthat (>= 2.1.0), here
License: GPL-3 + file LICENSE
MD5sum: 409a2b68c935d0d01d4c568cdd94ca2f
NeedsCompilation: no
Title: Unsupervised Identification of Genes with Expression Loss in
Subsets of Tumors
Description: receptLoss identifies genes whose expression is lost in
subsets of tumors relative to normal tissue. It is particularly
well-suited in cases where the number of normal tissue samples
is small, as the distribution of gene expression in normal
tissue samples is approximated by a Gaussian. Originally
designed for identifying nuclear hormone receptor expression
loss but can be applied transcriptome wide as well.
biocViews: GeneExpression, StatisticalMethod
Author: Daniel Pique, John Greally, Jessica Mar
Maintainer: Daniel Pique <daniel.pique@med.einstein.yu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/receptLoss
git_branch: devel
git_last_commit: f57ea9f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/receptLoss_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/receptLoss_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/receptLoss_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/receptLoss_1.19.0.tgz
vignettes: vignettes/receptLoss/inst/doc/receptLoss.html
vignetteTitles: receptLoss
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/receptLoss/inst/doc/receptLoss.R
dependencyCount: 69
Package: reconsi
Version: 1.19.0
Imports: phyloseq, ks, reshape2, ggplot2, stats, methods, graphics,
grDevices, matrixStats, Matrix
Suggests: knitr, rmarkdown, testthat
License: GPL-2
MD5sum: 3503e276ff3498b962bb544975467058
NeedsCompilation: no
Title: Resampling Collapsed Null Distributions for Simultaneous
Inference
Description: Improves simultaneous inference under dependence of tests
by estimating a collapsed null distribution through resampling.
Accounting for the dependence between tests increases the power
while reducing the variability of the false discovery
proportion. This dependence is common in genomics applications,
e.g. when combining flow cytometry measurements with microbiome
sequence counts.
biocViews: Metagenomics, Microbiome, MultipleComparison, FlowCytometry
Author: Stijn Hawinkel [cre, aut] (ORCID:
<https://orcid.org/0000-0002-4501-5180>)
Maintainer: Stijn Hawinkel <stijn.hawinkel@psb.ugent.be>
VignetteBuilder: knitr
BugReports: https://github.com/CenterForStatistics-UGent/reconsi/issues
git_url: https://git.bioconductor.org/packages/reconsi
git_branch: devel
git_last_commit: ebca5ca
git_last_commit_date: 2024-10-29
Date/Publication: 2025-01-23
source.ver: src/contrib/reconsi_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/reconsi_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/reconsi_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/reconsi_1.19.0.tgz
vignettes: vignettes/reconsi/inst/doc/reconsiVignette.html
vignetteTitles: Manual for the RCM pacakage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/reconsi/inst/doc/reconsiVignette.R
dependencyCount: 92
Package: recount
Version: 1.33.0
Depends: R (>= 3.5.0), SummarizedExperiment
Imports: BiocParallel, derfinder, downloader, GEOquery, GenomeInfoDb,
GenomicRanges, IRanges, methods, RCurl, rentrez, rtracklayer
(>= 1.35.3), S4Vectors, stats, utils
Suggests: AnnotationDbi, BiocManager, BiocStyle (>= 2.5.19), DESeq2,
sessioninfo, EnsDb.Hsapiens.v79, GenomicFeatures, txdbmaker,
knitr (>= 1.6), org.Hs.eg.db, RefManageR, regionReport (>=
1.9.4), rmarkdown (>= 0.9.5), testthat (>= 2.1.0), covr,
pheatmap, DT, edgeR, ggplot2, RColorBrewer
License: Artistic-2.0
MD5sum: db31035f911b83485f7c1847cd738d5c
NeedsCompilation: no
Title: Explore and download data from the recount project
Description: Explore and download data from the recount project
available at https://jhubiostatistics.shinyapps.io/recount/.
Using the recount package you can download
RangedSummarizedExperiment objects at the gene, exon or
exon-exon junctions level, the raw counts, the phenotype
metadata used, the urls to the sample coverage bigWig files or
the mean coverage bigWig file for a particular study. The
RangedSummarizedExperiment objects can be used by different
packages for performing differential expression analysis. Using
http://bioconductor.org/packages/derfinder you can perform
annotation-agnostic differential expression analyses with the
data from the recount project as described at
http://www.nature.com/nbt/journal/v35/n4/full/nbt.3838.html.
biocViews: Coverage, DifferentialExpression, GeneExpression, RNASeq,
Sequencing, Software, DataImport, ImmunoOncology
Author: Leonardo Collado-Torres [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2140-308X>), Abhinav Nellore
[ctb], Andrew E. Jaffe [ctb] (ORCID:
<https://orcid.org/0000-0001-6886-1454>), Margaret A. Taub
[ctb], Kai Kammers [ctb], Shannon E. Ellis [ctb] (ORCID:
<https://orcid.org/0000-0002-9231-0481>), Kasper Daniel Hansen
[ctb] (ORCID: <https://orcid.org/0000-0003-0086-0687>), Ben
Langmead [ctb] (ORCID:
<https://orcid.org/0000-0003-2437-1976>), Jeffrey T. Leek [aut,
ths] (ORCID: <https://orcid.org/0000-0002-2873-2671>)
Maintainer: Leonardo Collado-Torres <lcolladotor@gmail.com>
URL: https://github.com/leekgroup/recount
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/recount/
git_url: https://git.bioconductor.org/packages/recount
git_branch: devel
git_last_commit: 7570ca3
git_last_commit_date: 2024-12-12
Date/Publication: 2024-12-13
source.ver: src/contrib/recount_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/recount_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/recount_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/recount_1.33.0.tgz
vignettes: vignettes/recount/inst/doc/recount-quickstart.html,
vignettes/recount/inst/doc/SRP009615-results.html
vignetteTitles: recount quick start guide, Basic DESeq2 results
exploration
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/recount/inst/doc/recount-quickstart.R,
vignettes/recount/inst/doc/SRP009615-results.R
importsMe: psichomics, RNAAgeCalc, recountWorkflow
suggestsMe: recount3
dependencyCount: 166
Package: recount3
Version: 1.17.0
Depends: SummarizedExperiment
Imports: BiocFileCache, methods, rtracklayer, S4Vectors, utils, httr,
data.table, R.utils, Matrix, GenomicRanges, sessioninfo, tools
Suggests: BiocStyle, covr, knitcitations, knitr, RefManageR, rmarkdown,
testthat, pryr, interactiveDisplayBase, recount
License: Artistic-2.0
Archs: x64
MD5sum: 3fc6e64f3ef68704fa4ffe6c75b8bd3c
NeedsCompilation: no
Title: Explore and download data from the recount3 project
Description: The recount3 package enables access to a large amount of
uniformly processed RNA-seq data from human and mouse. You can
download RangedSummarizedExperiment objects at the gene, exon
or exon-exon junctions level with sample metadata and QC
statistics. In addition we provide access to sample coverage
BigWig files.
biocViews: Coverage, DifferentialExpression, GeneExpression, RNASeq,
Sequencing, Software, DataImport
Author: Leonardo Collado-Torres [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2140-308X>)
Maintainer: Leonardo Collado-Torres <lcolladotor@gmail.com>
URL: https://github.com/LieberInstitute/recount3
VignetteBuilder: knitr
BugReports: https://github.com/LieberInstitute/recount3/issues
git_url: https://git.bioconductor.org/packages/recount3
git_branch: devel
git_last_commit: 4c4249b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/recount3_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/recount3_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/recount3_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/recount3_1.17.0.tgz
vignettes: vignettes/recount3/inst/doc/recount3-quickstart.html
vignetteTitles: recount3 quick start guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/recount3/inst/doc/recount3-quickstart.R
suggestsMe: geyser, RNAseqQC
dependencyCount: 93
Package: recountmethylation
Version: 1.17.0
Depends: R (>= 4.1)
Imports: minfi, HDF5Array, rhdf5, S4Vectors, utils, methods, RCurl,
R.utils, BiocFileCache, basilisk, reticulate,
DelayedMatrixStats
Suggests: minfiData, minfiDataEPIC, knitr, testthat, ggplot2,
gridExtra, rmarkdown, BiocStyle, GenomicRanges, limma,
ExperimentHub, AnnotationHub
License: Artistic-2.0
MD5sum: 6fd3852c45bccf94508f91f4c9f0afbb
NeedsCompilation: no
Title: Access and analyze public DNA methylation array data
compilations
Description: Resources for cross-study analyses of public DNAm array
data from NCBI GEO repo, produced using Illumina's Infinium
HumanMethylation450K (HM450K) and MethylationEPIC (EPIC)
platforms. Provided functions enable download, summary, and
filtering of large compilation files. Vignettes detail
background about file formats, example analyses, and more. Note
the disclaimer on package load and consult the main manuscripts
for further info.
biocViews: DNAMethylation, Epigenetics, Microarray, MethylationArray,
ExperimentHub
Author: Sean K Maden [cre, aut] (ORCID:
<https://orcid.org/0000-0002-2212-4894>), Brian Walsh [aut]
(ORCID: <https://orcid.org/0000-0003-0913-1528>), Kyle Ellrott
[aut] (ORCID: <https://orcid.org/0000-0002-6573-5900>), Kasper
D Hansen [aut] (ORCID:
<https://orcid.org/0000-0003-0086-0687>), Reid F Thompson [aut]
(ORCID: <https://orcid.org/0000-0003-3661-5296>), Abhinav
Nellore [aut] (ORCID: <https://orcid.org/0000-0001-8145-1484>)
Maintainer: Sean K Maden <maden@ohsu.edu>
URL: https://github.com/metamaden/recountmethylation
VignetteBuilder: knitr
BugReports: https://github.com/metamaden/recountmethylation/issues
git_url: https://git.bioconductor.org/packages/recountmethylation
git_branch: devel
git_last_commit: 873438e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/recountmethylation_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/recountmethylation_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/recountmethylation_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/recountmethylation_1.17.0.tgz
vignettes:
vignettes/recountmethylation/inst/doc/cpg_probe_annotations.html,
vignettes/recountmethylation/inst/doc/exporting_saving_data.html,
vignettes/recountmethylation/inst/doc/recountmethylation_data_analyses.html,
vignettes/recountmethylation/inst/doc/recountmethylation_glint.html,
vignettes/recountmethylation/inst/doc/recountmethylation_pwrewas.html,
vignettes/recountmethylation/inst/doc/recountmethylation_search_index.html,
vignettes/recountmethylation/inst/doc/recountmethylation_users_guide.html
vignetteTitles: Practical uses for CpG annotations, Working with DNAm
data types, Data Analyses, Determine population ancestry from
DNAm arrays, Power analysis for DNAm arrays, Nearest neighbors
analysis for DNAm arrays, recountmethylation User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/recountmethylation/inst/doc/cpg_probe_annotations.R,
vignettes/recountmethylation/inst/doc/exporting_saving_data.R,
vignettes/recountmethylation/inst/doc/recountmethylation_data_analyses.R,
vignettes/recountmethylation/inst/doc/recountmethylation_glint.R,
vignettes/recountmethylation/inst/doc/recountmethylation_pwrewas.R,
vignettes/recountmethylation/inst/doc/recountmethylation_search_index.R,
vignettes/recountmethylation/inst/doc/recountmethylation_users_guide.R
dependencyCount: 155
Package: recoup
Version: 1.35.0
Depends: R (>= 4.0.0), GenomicRanges, GenomicAlignments, ggplot2,
ComplexHeatmap
Imports: BiocGenerics, biomaRt, Biostrings, circlize, GenomeInfoDb,
GenomicFeatures, graphics, grDevices, httr, IRanges, methods,
parallel, RSQLite, Rsamtools, rtracklayer, S4Vectors, stats,
stringr, txdbmaker, utils
Suggests: grid, BiocStyle, knitr, rmarkdown, zoo, RUnit, BiocManager,
BSgenome, RMySQL
License: GPL (>= 3)
MD5sum: 274d288281f9edee8e6291be30b0ad12
NeedsCompilation: no
Title: An R package for the creation of complex genomic profile plots
Description: recoup calculates and plots signal profiles created from
short sequence reads derived from Next Generation Sequencing
technologies. The profiles provided are either sumarized curve
profiles or heatmap profiles. Currently, recoup supports
genomic profile plots for reads derived from ChIP-Seq and
RNA-Seq experiments. The package uses ggplot2 and
ComplexHeatmap graphics facilities for curve and heatmap
coverage profiles respectively.
biocViews: ImmunoOncology, Software, GeneExpression, Preprocessing,
QualityControl, RNASeq, ChIPSeq, Sequencing, Coverage, ATACSeq,
ChipOnChip, Alignment, DataImport
Author: Panagiotis Moulos <moulos@fleming.gr>
Maintainer: Panagiotis Moulos <moulos@fleming.gr>
URL: https://github.com/pmoulos/recoup
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/recoup
git_branch: devel
git_last_commit: 8112b7e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/recoup_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/recoup_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/recoup_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/recoup_1.35.0.tgz
vignettes: vignettes/recoup/inst/doc/recoup_intro.html
vignetteTitles: Introduction to the recoup package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/recoup/inst/doc/recoup_intro.R
dependencyCount: 125
Package: RedeR
Version: 3.3.0
Depends: R (>= 4.0), methods
Imports: scales, igraph
Suggests: knitr, rmarkdown, markdown, BiocStyle, TreeAndLeaf
License: GPL-3
MD5sum: 0d90681342f9c3de58d4510a87d3dc70
NeedsCompilation: no
Title: Interactive visualization and manipulation of nested networks
Description: RedeR is an R-based package combined with a stand-alone
Java application for interactive visualization and manipulation
of nested networks. Graph, node, and edge attributes can be
configured using either graphical or command-line methods,
following igraph syntax rules.
biocViews: GUI, GraphAndNetwork, Network, NetworkEnrichment,
NetworkInference, Software, SystemsBiology
Author: Xin Wang [ctb], Florian Markowetz [ctb], Mauro Castro [aut,
cre] (ORCID: <https://orcid.org/0000-0003-4942-8131>)
Maintainer: Mauro Castro <mauro.a.castro@gmail.com>
URL: https://doi.org/10.1186/gb-2012-13-4-r29
SystemRequirements: Java Runtime Environment (Java>= 11)
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RedeR
git_branch: devel
git_last_commit: 7abfaa7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RedeR_3.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RedeR_3.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RedeR_3.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RedeR_3.3.0.tgz
vignettes: vignettes/RedeR/inst/doc/RedeR.html
vignetteTitles: "RedeR: nested networks"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RedeR/inst/doc/RedeR.R
dependsOnMe: Fletcher2013b, dc3net
importsMe: PANR, RTN, transcriptogramer, TreeAndLeaf
suggestsMe: PathwaySpace
dependencyCount: 25
Package: RedisParam
Version: 1.9.0
Depends: R (>= 4.2.0), BiocParallel (>= 1.29.12)
Imports: methods, redux, withr, futile.logger
Suggests: rmarkdown, knitr, testthat, BiocStyle
License: Artistic-2.0
MD5sum: 332aa4a4d5d14cdf382c5259ef64b7cb
NeedsCompilation: no
Title: Provide a 'redis' back-end for BiocParallel
Description: This package provides a Redis-based back-end for
BiocParallel, enabling an alternative mechanism for distributed
computation. The The 'manager' distributes tasks to a 'worker'
pool through a central Redis server, rather than directly to
workers as with other BiocParallel implementations. This means
that the worker pool can change dynamically during job
evaluation. All features of BiocParallel are supported,
including reproducible random number streams, logging to the
manager, and alternative 'load balancing' task distributions.
biocViews: Infrastructure
Author: Martin Morgan [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5874-8148>), Jiefei Wang [aut]
Maintainer: Martin Morgan <mtmorgan.bioc@gmail.com>
SystemRequirements: hiredis
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RedisParam
git_branch: devel
git_last_commit: ce2bc74
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RedisParam_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RedisParam_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RedisParam_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RedisParam_1.9.0.tgz
vignettes: vignettes/RedisParam/inst/doc/RedisParamDeveloperGuide.html,
vignettes/RedisParam/inst/doc/RedisParamUserGuide.html
vignetteTitles: RedisParam for Developers, Using RedisParam
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: FALSE
Rfiles: vignettes/RedisParam/inst/doc/RedisParamDeveloperGuide.R,
vignettes/RedisParam/inst/doc/RedisParamUserGuide.R
dependencyCount: 20
Package: REDseq
Version: 1.53.0
Depends: R (>= 3.5.0), BiocGenerics, BSgenome.Celegans.UCSC.ce2,
multtest, Biostrings, BSgenome, ChIPpeakAnno
Imports: AnnotationDbi, graphics, IRanges (>= 1.13.5), stats, utils
License: GPL (>=2)
MD5sum: 4b27fda1c51a48ff696ce1200214f7e2
NeedsCompilation: no
Title: Analysis of high-throughput sequencing data processed by
restriction enzyme digestion
Description: The package includes functions to build restriction enzyme
cut site (RECS) map, distribute mapped sequences on the map
with five different approaches, find enriched/depleted RECSs
for a sample, and identify differentially enriched/depleted
RECSs between samples.
biocViews: Sequencing, SequenceMatching, Preprocessing
Author: Lihua Julie Zhu, Junhui Li and Thomas Fazzio
Maintainer: Lihua Julie Zhu <julie.zhu@umassmed.edu>
git_url: https://git.bioconductor.org/packages/REDseq
git_branch: devel
git_last_commit: c53b524
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/REDseq_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/REDseq_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/REDseq_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/REDseq_1.53.0.tgz
vignettes: vignettes/REDseq/inst/doc/REDseq.pdf
vignetteTitles: REDseq Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/REDseq/inst/doc/REDseq.R
dependencyCount: 133
Package: ReducedExperiment
Version: 0.99.6
Depends: R (>= 4.4.0), SummarizedExperiment, methods
Imports: WGCNA, ica, moments, clusterProfiler, msigdbr, RColorBrewer,
car, lme4, lmerTest, pheatmap, biomaRt, stats, grDevices,
BiocParallel, ggplot2, patchwork, BiocGenerics, S4Vectors
Suggests: knitr, rmarkdown, testthat, biocViews, BiocCheck, BiocStyle,
airway
License: GPL (>= 3)
MD5sum: e66d4414714871eba3f516538bbb18a3
NeedsCompilation: no
Title: Containers and tools for dimensionally-reduced -omics
representations
Description: Provides SummarizedExperiment-like containers for storing
and manipulating dimensionally-reduced assay data. The
ReducedExperiment classes allow users to simultaneously
manipulate their original dataset and their decomposed data, in
addition to other method-specific outputs like feature
loadings. Implements utilities and specialised classes for the
application of stabilised independent component analysis (sICA)
and weighted gene correlation network analysis (WGCNA).
biocViews: GeneExpression, Infrastructure, DataRepresentation,
Software, DimensionReduction, Network
Author: Jack Gisby [aut, cre] (ORCID:
<https://orcid.org/0000-0003-0511-8123>), Michael Barnes [aut]
(ORCID: <https://orcid.org/0000-0001-9097-7381>)
Maintainer: Jack Gisby <jackgisby@gmail.com>
URL: https://github.com/jackgisby/ReducedExperiment
VignetteBuilder: knitr
BugReports: https://github.com/jackgisby/ReducedExperiment/issues
git_url: https://git.bioconductor.org/packages/ReducedExperiment
git_branch: devel
git_last_commit: d7ad3c7
git_last_commit_date: 2025-01-15
Date/Publication: 2025-01-15
source.ver: src/contrib/ReducedExperiment_0.99.6.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ReducedExperiment_0.99.6.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ReducedExperiment_0.99.6.tgz
vignettes: vignettes/ReducedExperiment/inst/doc/ReducedExperiment.html
vignetteTitles: ReducedExperiment
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ReducedExperiment/inst/doc/ReducedExperiment.R
dependencyCount: 202
Package: RegEnrich
Version: 1.17.0
Depends: R (>= 4.0.0), S4Vectors, dplyr, tibble, BiocSet,
SummarizedExperiment
Imports: randomForest, fgsea, DOSE, BiocParallel, DESeq2, limma, WGCNA,
ggplot2 (>= 2.2.0), methods, reshape2, magrittr, BiocStyle
Suggests: GEOquery, rmarkdown, knitr, BiocManager, testthat
License: GPL (>= 2)
MD5sum: 41c3e81843e4f7c4376169a321cb0c4d
NeedsCompilation: no
Title: Gene regulator enrichment analysis
Description: This package is a pipeline to identify the key gene
regulators in a biological process, for example in cell
differentiation and in cell development after stimulation.
There are four major steps in this pipeline: (1) differential
expression analysis; (2) regulator-target network inference;
(3) enrichment analysis; and (4) regulators scoring and
ranking.
biocViews: GeneExpression, Transcriptomics, RNASeq, TwoChannel,
Transcription, GeneTarget, NetworkEnrichment,
DifferentialExpression, Network, NetworkInference,
GeneSetEnrichment, FunctionalPrediction
Author: Weiyang Tao [cre, aut], Aridaman Pandit [aut]
Maintainer: Weiyang Tao <weiyangtao1513@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RegEnrich
git_branch: devel
git_last_commit: 231e44e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RegEnrich_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RegEnrich_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RegEnrich_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RegEnrich_1.17.0.tgz
vignettes: vignettes/RegEnrich/inst/doc/RegEnrich.html
vignetteTitles: Gene regulator enrichment with RegEnrich
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RegEnrich/inst/doc/RegEnrich.R
dependencyCount: 156
Package: regionalpcs
Version: 1.5.0
Depends: R (>= 4.3.0)
Imports: dplyr, PCAtools, tibble, GenomicRanges
Suggests: knitr, rmarkdown, RMTstat, testthat (>= 3.0.0), BiocStyle,
tidyr, minfiData, TxDb.Hsapiens.UCSC.hg19.knownGene, IRanges
License: MIT + file LICENSE
MD5sum: e13e89ca540cfac1138f2a0d5ef242c5
NeedsCompilation: no
Title: Summarizing Regional Methylation with Regional Principal
Components Analysis
Description: Functions to summarize DNA methylation data using regional
principal components. Regional principal components are
computed using principal components analysis within genomic
regions to summarize the variability in methylation levels
across CpGs. The number of principal components is chosen using
either the Marcenko-Pasteur or Gavish-Donoho method to identify
relevant signal in the data.
biocViews: DNAMethylation, DifferentialMethylation, StatisticalMethod,
Software, MethylationArray
Author: Tiffany Eulalio [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7084-9646>)
Maintainer: Tiffany Eulalio <tyeulalio@gmail.com>
URL: https://github.com/tyeulalio/regionalpcs
VignetteBuilder: knitr
BugReports: https://github.com/tyeulalio/regionalpcs/issues
git_url: https://git.bioconductor.org/packages/regionalpcs
git_branch: devel
git_last_commit: 78178d2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/regionalpcs_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/regionalpcs_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/regionalpcs_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/regionalpcs_1.5.0.tgz
vignettes: vignettes/regionalpcs/inst/doc/regionalpcs.html
vignetteTitles: regionalpcs
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/regionalpcs/inst/doc/regionalpcs.R
dependencyCount: 90
Package: RegionalST
Version: 1.5.8
Depends: R (>= 4.3.0)
Imports: stats, grDevices, utils, ggplot2, dplyr, scater, gridExtra,
BiocStyle, BayesSpace, fgsea, magrittr, SingleCellExperiment,
RColorBrewer, Seurat, S4Vectors, tibble, TOAST, assertthat,
colorspace, shiny, SummarizedExperiment
Suggests: knitr, rmarkdown, gplots, testthat (>= 3.0.0)
License: GPL-3
MD5sum: d9794d34ef48ea7495f4c7d09ce92ff9
NeedsCompilation: no
Title: Investigating regions of interest and performing regional cell
type-specific analysis with spatial transcriptomics data
Description: This package analyze spatial transcriptomics data through
cross-regional cell type-specific analysis. It selects regions
of interest (ROIs) and identifys cross-regional cell
type-specific differential signals. The ROIs can be selected
using automatic algorithm or through manual selection. It
facilitates manual selection of ROIs using a shiny application.
biocViews: Spatial, Transcriptomics, Reactome, KEGG
Author: Ziyi Li [aut, cre]
Maintainer: Ziyi Li <zli16@mdanderson.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RegionalST
git_branch: devel
git_last_commit: 0ddee1b
git_last_commit_date: 2024-12-05
Date/Publication: 2024-12-05
source.ver: src/contrib/RegionalST_1.5.8.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RegionalST_1.5.8.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RegionalST_1.5.8.tgz
vignettes: vignettes/RegionalST/inst/doc/RegionalST.html
vignetteTitles: RegionalST
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RegionalST/inst/doc/RegionalST.R
dependencyCount: 253
Package: regioneR
Version: 1.39.0
Depends: GenomicRanges
Imports: memoise, GenomicRanges, IRanges, BSgenome, Biostrings,
rtracklayer, parallel, graphics, stats, utils, methods,
GenomeInfoDb, S4Vectors, tools
Suggests: BiocStyle, knitr, rmarkdown,
BSgenome.Hsapiens.UCSC.hg19.masked, testthat
License: Artistic-2.0
MD5sum: 4c72576e313fa0f0cab3b66ef50eb944
NeedsCompilation: no
Title: Association analysis of genomic regions based on permutation
tests
Description: regioneR offers a statistical framework based on
customizable permutation tests to assess the association
between genomic region sets and other genomic features.
biocViews: Genetics, ChIPSeq, DNASeq, MethylSeq, CopyNumberVariation
Author: Anna Diez-Villanueva <adiez@iconcologia.net>, Roberto
Malinverni <roberto.malinverni@gmail.com> and Bernat Gel
<bgel@igtp.cat>
Maintainer: Bernat Gel <bgel@imppc.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/regioneR
git_branch: devel
git_last_commit: 0c8711e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/regioneR_1.39.0.tar.gz
vignettes: vignettes/regioneR/inst/doc/regioneR.html
vignetteTitles: regioneR vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/regioneR/inst/doc/regioneR.R
dependsOnMe: karyoploteR, regioneReloaded
importsMe: annotatr, ChIPpeakAnno, CNVfilteR, CopyNumberPlots,
karyoploteR, RgnTX, UMI4Cats
suggestsMe: CNVRanger, EpiMix, UPDhmm, MitoHEAR
dependencyCount: 63
Package: regioneReloaded
Version: 1.9.0
Depends: R (>= 4.2), regioneR
Imports: stats, RColorBrewer, Rtsne, umap, ggplot2, ggrepel, reshape2,
methods, scales, cluster, grid, grDevices
Suggests: rmarkdown, BiocStyle, GenomeInfoDb, knitr, testthat (>=
3.0.0)
License: Artistic-2.0
MD5sum: 6b107eb96910a600c43ac9eccd4041b7
NeedsCompilation: no
Title: RegioneReloaded: Multiple Association for Genomic Region Sets
Description: RegioneReloaded is a package that allows simultaneous
analysis of associations between genomic region sets, enabling
clustering of data and the creation of ready-to-publish graphs.
It takes over and expands on all the features of its
predecessor regioneR. It also incorporates a strategy to
improve p-value calculations and normalize z-scores coming from
multiple analysis to allow for their direct comparison.
RegioneReloaded builds upon regioneR by adding new plotting
functions for obtaining publication-ready graphs.
biocViews: Genetics, ChIPSeq, DNASeq, MethylSeq, CopyNumberVariation,
Clustering, MultipleComparison
Author: Roberto Malinverni [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0113-3417>), David Corujo [aut],
Bernat Gel [aut]
Maintainer: Roberto Malinverni <roberto.malinverni@gmail.com>
URL: https://github.com/RMalinverni/regioneReload
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/regioneReloaded
git_branch: devel
git_last_commit: 11e1a34
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/regioneReloaded_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/regioneReloaded_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/regioneReloaded_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/regioneReloaded_1.9.0.tgz
vignettes: vignettes/regioneReloaded/inst/doc/regioneReloaded.html
vignetteTitles: regioneReloaded
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/regioneReloaded/inst/doc/regioneReloaded.R
dependencyCount: 106
Package: regionReport
Version: 1.41.1
Depends: R(>= 3.2)
Imports: BiocStyle (>= 2.5.19), derfinder (>= 1.25.3), DEFormats,
DESeq2, GenomeInfoDb, GenomicRanges, knitr (>= 1.6),
knitrBootstrap (>= 0.9.0), methods, RefManageR, rmarkdown (>=
0.9.5), S4Vectors, SummarizedExperiment, utils
Suggests: BiocManager, biovizBase, bumphunter (>= 1.7.6), derfinderPlot
(>= 1.29.1), sessioninfo, DT, edgeR, ggbio (>= 1.35.2),
ggplot2, grid, gridExtra, IRanges, mgcv, pasilla, pheatmap,
RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene, whisker
License: Artistic-2.0
MD5sum: 4a0f488d6ace3fe173d25fbbaa5e07d2
NeedsCompilation: no
Title: Generate HTML or PDF reports for a set of genomic regions or
DESeq2/edgeR results
Description: Generate HTML or PDF reports to explore a set of regions
such as the results from annotation-agnostic expression
analysis of RNA-seq data at base-pair resolution performed by
derfinder. You can also create reports for DESeq2 or edgeR
results.
biocViews: DifferentialExpression, Sequencing, RNASeq, Software,
Visualization, Transcription, Coverage, ReportWriting,
DifferentialMethylation, DifferentialPeakCalling,
ImmunoOncology, QualityControl
Author: Leonardo Collado-Torres [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2140-308X>), Andrew E. Jaffe [aut]
(ORCID: <https://orcid.org/0000-0001-6886-1454>), Jeffrey T.
Leek [aut, ths] (ORCID:
<https://orcid.org/0000-0002-2873-2671>)
Maintainer: Leonardo Collado-Torres <lcolladotor@gmail.com>
URL: https://github.com/leekgroup/regionReport
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/regionReport/
git_url: https://git.bioconductor.org/packages/regionReport
git_branch: devel
git_last_commit: f885790
git_last_commit_date: 2025-01-14
Date/Publication: 2025-01-14
source.ver: src/contrib/regionReport_1.41.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/regionReport_1.41.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/regionReport_1.41.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/regionReport_1.41.1.tgz
vignettes: vignettes/regionReport/inst/doc/bumphunterExample.html,
vignettes/regionReport/inst/doc/regionReport.html
vignetteTitles: Example report using bumphunter results, Introduction
to regionReport
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/regionReport/inst/doc/bumphunterExample.R,
vignettes/regionReport/inst/doc/regionReport.R
importsMe: recountWorkflow
suggestsMe: recount
dependencyCount: 161
Package: regsplice
Version: 1.33.0
Imports: glmnet, SummarizedExperiment, S4Vectors, limma, edgeR, stats,
pbapply, utils, methods
Suggests: testthat, BiocStyle, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 19dd2a9fa1dd90d143a674959fb38330
NeedsCompilation: no
Title: L1-regularization based methods for detection of differential
splicing
Description: Statistical methods for detection of differential splicing
(differential exon usage) in RNA-seq and exon microarray data,
using L1-regularization (lasso) to improve power.
biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression,
DifferentialSplicing, Sequencing, RNASeq, Microarray,
ExonArray, ExperimentalDesign, Software
Author: Lukas M. Weber [aut, cre]
Maintainer: Lukas M. Weber <lukas.weber.edu@gmail.com>
URL: https://github.com/lmweber/regsplice
VignetteBuilder: knitr
BugReports: https://github.com/lmweber/regsplice/issues
git_url: https://git.bioconductor.org/packages/regsplice
git_branch: devel
git_last_commit: 27b614a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/regsplice_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/regsplice_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/regsplice_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/regsplice_1.33.0.tgz
vignettes: vignettes/regsplice/inst/doc/regsplice-workflow.html
vignetteTitles: regsplice workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/regsplice/inst/doc/regsplice-workflow.R
dependencyCount: 51
Package: regutools
Version: 1.19.0
Depends: R (>= 4.0)
Imports: AnnotationDbi, AnnotationHub, Biostrings, DBI, GenomicRanges,
Gviz, IRanges, RCy3, RSQLite, S4Vectors, methods, stats, utils,
BiocFileCache
Suggests: BiocStyle, knitr, RefManageR, rmarkdown, sessioninfo,
testthat (>= 2.1.0), covr
License: Artistic-2.0
MD5sum: fb0bf5d8129d2616f1bbd74621479d21
NeedsCompilation: no
Title: regutools: an R package for data extraction from RegulonDB
Description: RegulonDB has collected, harmonized and centralized data
from hundreds of experiments for nearly two decades and is
considered a point of reference for transcriptional regulation
in Escherichia coli K12. Here, we present the regutools R
package to facilitate programmatic access to RegulonDB data in
computational biology. regutools provides researchers with the
possibility of writing reproducible workflows with automated
queries to RegulonDB. The regutools package serves as a bridge
between RegulonDB data and the Bioconductor ecosystem by
reusing the data structures and statistical methods powered by
other Bioconductor packages. We demonstrate the integration of
regutools with Bioconductor by analyzing transcription factor
DNA binding sites and transcriptional regulatory networks from
RegulonDB. We anticipate that regutools will serve as a useful
building block in our progress to further our understanding of
gene regulatory networks.
biocViews: GeneRegulation, GeneExpression, SystemsBiology,
Network,NetworkInference,Visualization, Transcription
Author: Joselyn Chavez [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4974-4591>), Carmina
Barberena-Jonas [aut] (ORCID:
<https://orcid.org/0000-0001-7413-638X>), Jesus E.
Sotelo-Fonseca [aut] (ORCID:
<https://orcid.org/0000-0003-1600-2396>), Jose
Alquicira-Hernandez [ctb] (ORCID:
<https://orcid.org/0000-0002-9049-7780>), Heladia Salgado [ctb]
(ORCID: <https://orcid.org/0000-0002-3166-5801>), Leonardo
Collado-Torres [aut] (ORCID:
<https://orcid.org/0000-0003-2140-308X>), Alejandro Reyes [aut]
(ORCID: <https://orcid.org/0000-0001-8717-6612>)
Maintainer: Joselyn Chavez <joselynchavezf@gmail.com>
URL: https://github.com/ComunidadBioInfo/regutools
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/regutools
git_url: https://git.bioconductor.org/packages/regutools
git_branch: devel
git_last_commit: 6b11fd5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/regutools_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/regutools_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/regutools_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/regutools_1.19.0.tgz
vignettes: vignettes/regutools/inst/doc/regutools.html
vignetteTitles: regutools: an R package for data extraction from
RegulonDB
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/regutools/inst/doc/regutools.R
dependencyCount: 172
Package: REMP
Version: 1.31.0
Depends: R (>= 3.6), SummarizedExperiment(>= 1.1.6), minfi (>= 1.22.0)
Imports: readr, rtracklayer, graphics, stats, utils, methods, settings,
BiocGenerics, S4Vectors, Biostrings, GenomicRanges, IRanges,
GenomeInfoDb, BiocParallel, doParallel, parallel, foreach,
caret, kernlab, ranger, BSgenome, AnnotationHub, org.Hs.eg.db,
impute, iterators
Suggests: IlluminaHumanMethylation450kanno.ilmn12.hg19,
IlluminaHumanMethylationEPICanno.ilm10b2.hg19,
BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38,
knitr, rmarkdown, minfiDataEPIC
License: GPL-3
MD5sum: 40ff58a58ae60188430d2d0c872a3678
NeedsCompilation: no
Title: Repetitive Element Methylation Prediction
Description: Machine learning-based tools to predict DNA methylation of
locus-specific repetitive elements (RE) by learning surrounding
genetic and epigenetic information. These tools provide
genomewide and single-base resolution of DNA methylation
prediction on RE that are difficult to measure using
array-based or sequencing-based platforms, which enables
epigenome-wide association study (EWAS) and differentially
methylated region (DMR) analysis on RE.
biocViews: DNAMethylation, Microarray, MethylationArray, Sequencing,
GenomeWideAssociation, Epigenetics, Preprocessing,
MultiChannel, TwoChannel, DifferentialMethylation,
QualityControl, DataImport
Author: Yinan Zheng [aut, cre], Lei Liu [aut], Wei Zhang [aut], Warren
Kibbe [aut], Lifang Hou [aut, cph]
Maintainer: Yinan Zheng <y-zheng@northwestern.edu>
URL: https://github.com/YinanZheng/REMP
BugReports: https://github.com/YinanZheng/REMP/issues
git_url: https://git.bioconductor.org/packages/REMP
git_branch: devel
git_last_commit: 6b87c46
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/REMP_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/REMP_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/REMP_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/REMP_1.31.0.tgz
vignettes: vignettes/REMP/inst/doc/REMP.pdf
vignetteTitles: An Introduction to the REMP Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/REMP/inst/doc/REMP.R
dependencyCount: 200
Package: Repitools
Version: 1.53.0
Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.8.0)
Imports: parallel, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12),
GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools,
GenomicAlignments, rtracklayer, BSgenome (>= 1.47.3), gplots,
grid, MASS, gsmoothr, edgeR (>= 3.4.0), DNAcopy, Rsolnp,
cluster
Suggests: ShortRead, BSgenome.Hsapiens.UCSC.hg18
License: LGPL (>= 2)
MD5sum: b3fc35357dcfd7dd162b464c633cf30d
NeedsCompilation: yes
Title: Epigenomic tools
Description: Tools for the analysis of enrichment-based epigenomic
data. Features include summarization and visualization of
epigenomic data across promoters according to gene expression
context, finding regions of differential methylation/binding,
BayMeth for quantifying methylation etc.
biocViews: DNAMethylation, GeneExpression, MethylSeq
Author: Mark Robinson <mark.robinson@mls.uzh.ch>, Dario Strbenac
<dario.strbenac@sydney.edu.au>, Aaron Statham
<a.statham@garvan.org.au>, Andrea Riebler
<andrea.riebler@math.ntnu.no>
Maintainer: Mark Robinson <mark.robinson@mls.uzh.ch>
git_url: https://git.bioconductor.org/packages/Repitools
git_branch: devel
git_last_commit: b707049
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Repitools_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Repitools_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Repitools_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Repitools_1.53.0.tgz
vignettes: vignettes/Repitools/inst/doc/Repitools_vignette.pdf
vignetteTitles: Using Repitools for Epigenomic Sequencing Data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Repitools/inst/doc/Repitools_vignette.R
dependencyCount: 73
Package: ReportingTools
Version: 2.47.0
Depends: methods, knitr, utils
Imports: Biobase,hwriter,Category,GOstats,limma(>=
3.17.5),lattice,AnnotationDbi,edgeR, annotate,PFAM.db,
GSEABase, BiocGenerics(>= 0.1.6), grid, XML, R.utils, DESeq2(>=
1.3.41), ggplot2, ggbio, IRanges
Suggests: RUnit, ALL, hgu95av2.db, org.Mm.eg.db, shiny, pasilla,
org.Sc.sgd.db, rmarkdown, markdown
License: Artistic-2.0
MD5sum: 4fb3117eff39bbe73f9418abaa821750
NeedsCompilation: no
Title: Tools for making reports in various formats
Description: The ReportingTools software package enables users to
easily display reports of analysis results generated from
sources such as microarray and sequencing data. The package
allows users to create HTML pages that may be viewed on a web
browser such as Safari, or in other formats readable by
programs such as Excel. Users can generate tables with
sortable and filterable columns, make and display plots, and
link table entries to other data sources such as NCBI or larger
plots within the HTML page. Using the package, users can also
produce a table of contents page to link various reports
together for a particular project that can be viewed in a web
browser. For more examples, please visit our site: http://
research-pub.gene.com/ReportingTools.
biocViews: ImmunoOncology, Software, Visualization, Microarray, RNASeq,
GO, DataRepresentation, GeneSetEnrichment
Author: Jason A. Hackney, Melanie Huntley, Jessica L. Larson, Christina
Chaivorapol, Gabriel Becker, and Josh Kaminker
Maintainer: Jason A. Hackney <hackney.jason@gene.com>, Gabriel Becker
<becker.gabe@gene.com>, Jessica L. Larson
<larson.jessica@gmail.com>
VignetteBuilder: utils, rmarkdown
git_url: https://git.bioconductor.org/packages/ReportingTools
git_branch: devel
git_last_commit: 30a7956
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ReportingTools_2.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ReportingTools_2.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ReportingTools_2.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ReportingTools_2.47.0.tgz
vignettes: vignettes/ReportingTools/inst/doc/basicReportingTools.pdf,
vignettes/ReportingTools/inst/doc/microarrayAnalysis.pdf,
vignettes/ReportingTools/inst/doc/rnaseqAnalysis.pdf,
vignettes/ReportingTools/inst/doc/shiny.pdf
vignetteTitles: ReportingTools basics, Reporting on microarray
differential expression, Reporting on RNA-seq differential
expression, ReportingTools shiny
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ReportingTools/inst/doc/basicReportingTools.R,
vignettes/ReportingTools/inst/doc/microarrayAnalysis.R,
vignettes/ReportingTools/inst/doc/rnaseqAnalysis.R,
vignettes/ReportingTools/inst/doc/shiny.R
importsMe: affycoretools
suggestsMe: cpvSNP, EnrichmentBrowser, GSEABase, npGSEA
dependencyCount: 183
Package: RepViz
Version: 1.23.0
Depends: R (>= 3.5.1), GenomicRanges (>= 1.30.0), Rsamtools (>=
1.34.1), IRanges (>= 2.14.0), biomaRt (>= 2.36.0), S4Vectors
(>= 0.18.0), graphics, grDevices, utils
Suggests: rmarkdown, knitr, testthat
License: GPL-3
MD5sum: dbc91943c40ad97646c089907d5f3a02
NeedsCompilation: no
Title: Replicate oriented Visualization of a genomic region
Description: RepViz enables the view of a genomic region in a simple
and efficient way. RepViz allows simultaneous viewing of both
intra- and intergroup variation in sequencing counts of the
studied conditions, as well as their comparison to the output
features (e.g. identified peaks) from user selected data
analysis methods.The RepViz tool is primarily designed for
chromatin data such as ChIP-seq and ATAC-seq, but can also be
used with other sequencing data such as RNA-seq, or
combinations of different types of genomic data.
biocViews: WorkflowStep, Visualization, Sequencing, ChIPSeq, ATACSeq,
Software, Coverage, GenomicVariation
Author: Thomas Faux, Kalle Rytkönen, Asta Laiho, Laura L. Elo
Maintainer: Thomas Faux, Asta Laiho <faux.thomas1@gmail.com>
<asta.laiho@utu.fi>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RepViz
git_branch: devel
git_last_commit: 76973d5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RepViz_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RepViz_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RepViz_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RepViz_1.23.0.tgz
vignettes: vignettes/RepViz/inst/doc/RepViz.html
vignetteTitles: RepViz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RepViz/inst/doc/RepViz.R
dependencyCount: 81
Package: ResidualMatrix
Version: 1.17.0
Imports: methods, Matrix, S4Vectors, DelayedArray
Suggests: testthat, BiocStyle, knitr, rmarkdown, BiocSingular
License: GPL-3
MD5sum: d73abb6733c3ebf028db4fff12602d43
NeedsCompilation: no
Title: Creating a DelayedMatrix of Regression Residuals
Description: Provides delayed computation of a matrix of residuals
after fitting a linear model to each column of an input matrix.
Also supports partial computation of residuals where selected
factors are to be preserved in the output matrix. Implements a
number of efficient methods for operating on the delayed matrix
of residuals, most notably matrix multiplication and
calculation of row/column sums or means.
biocViews: Software, DataRepresentation, Regression, BatchEffect,
ExperimentalDesign
Author: Aaron Lun [aut, cre, cph]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://github.com/LTLA/ResidualMatrix
VignetteBuilder: knitr
BugReports: https://github.com/LTLA/ResidualMatrix/issues
git_url: https://git.bioconductor.org/packages/ResidualMatrix
git_branch: devel
git_last_commit: bde62f5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ResidualMatrix_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ResidualMatrix_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ResidualMatrix_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ResidualMatrix_1.17.0.tgz
vignettes: vignettes/ResidualMatrix/inst/doc/ResidualMatrix.html
vignetteTitles: Using the ResidualMatrix
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ResidualMatrix/inst/doc/ResidualMatrix.R
importsMe: batchelor
suggestsMe: alabaster.matrix, BiocSingular, chihaya, scran
dependencyCount: 22
Package: RESOLVE
Version: 1.9.1
Depends: R (>= 4.1.0)
Imports: Biostrings, BSgenome, BSgenome.Hsapiens.1000genomes.hs37d5,
cluster, data.table, GenomeInfoDb, GenomicRanges, glmnet,
ggplot2, gridExtra, IRanges, lsa, MutationalPatterns, nnls,
parallel, reshape2, S4Vectors, RhpcBLASctl, survival
Suggests: BiocGenerics, BiocStyle, testthat, knitr
License: file LICENSE
MD5sum: 915f9849a7c5451ee5a667757b98107c
NeedsCompilation: no
Title: RESOLVE: An R package for the efficient analysis of mutational
signatures from cancer genomes
Description: Cancer is a genetic disease caused by somatic mutations in
genes controlling key biological functions such as cellular
growth and division. Such mutations may arise both through
cell-intrinsic and exogenous processes, generating
characteristic mutational patterns over the genome named
mutational signatures. The study of mutational signatures have
become a standard component of modern genomics studies, since
it can reveal which (environmental and endogenous) mutagenic
processes are active in a tumor, and may highlight markers for
therapeutic response. Mutational signatures computational
analysis presents many pitfalls. First, the task of determining
the number of signatures is very complex and depends on
heuristics. Second, several signatures have no clear etiology,
casting doubt on them being computational artifacts rather than
due to mutagenic processes. Last, approaches for signatures
assignment are greatly influenced by the set of signatures used
for the analysis. To overcome these limitations, we developed
RESOLVE (Robust EStimation Of mutationaL signatures Via
rEgularization), a framework that allows the efficient
extraction and assignment of mutational signatures. RESOLVE
implements a novel algorithm that enables (i) the efficient
extraction, (ii) exposure estimation, and (iii) confidence
assessment during the computational inference of mutational
signatures.
biocViews: BiomedicalInformatics, SomaticMutation
Author: Daniele Ramazzotti [aut] (ORCID:
<https://orcid.org/0000-0002-6087-2666>), Luca De Sano [cre,
aut] (ORCID: <https://orcid.org/0000-0002-9618-3774>)
Maintainer: Luca De Sano <luca.desano@gmail.com>
URL: https://github.com/danro9685/RESOLVE
VignetteBuilder: knitr
BugReports: https://github.com/danro9685/RESOLVE/issues
git_url: https://git.bioconductor.org/packages/RESOLVE
git_branch: devel
git_last_commit: 5cd9c4c
git_last_commit_date: 2025-03-25
Date/Publication: 2025-03-26
source.ver: src/contrib/RESOLVE_1.9.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RESOLVE_1.9.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RESOLVE_1.9.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RESOLVE_1.9.1.tgz
vignettes: vignettes/RESOLVE/inst/doc/RESOLVE.html
vignetteTitles: RESOLVE.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/RESOLVE/inst/doc/RESOLVE.R
dependencyCount: 135
Package: retrofit
Version: 1.7.0
Depends: R (>= 4.2), Rcpp
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, rmarkdown, testthat, DescTools, ggplot2,
corrplot, cowplot, grid, colorspace, png, reshape2, pals, RCurl
License: GPL-3
Archs: x64
MD5sum: a03cc8d8db67766db92067677dfb7004
NeedsCompilation: yes
Title: RETROFIT: Reference-free deconvolution of cell mixtures in
spatial transcriptomics
Description: RETROFIT is a Bayesian non-negative matrix factorization
framework to decompose cell type mixtures in ST data without
using external single-cell expression references. RETROFIT
outperforms existing reference-based methods in estimating cell
type proportions and reconstructing gene expressions in
simulations with varying spot size and sample heterogeneity,
irrespective of the quality or availability of the single-cell
reference. RETROFIT recapitulates known cell-type localization
patterns in a Slide-seq dataset of mouse cerebellum without
using any single-cell data.
biocViews: Transcriptomics, Visualization, RNASeq, Bayesian, Spatial,
Software, GeneExpression, DimensionReduction,
FeatureExtraction, SingleCell
Author: Adam Park [aut, cre], Roopali Singh [aut] (ORCID:
<https://orcid.org/0000-0001-6539-6622>), Xiang Zhu [aut]
(ORCID: <https://orcid.org/0000-0003-1134-6413>), Qunhua Li
[aut] (ORCID: <https://orcid.org/0000-0003-0675-7648>)
Maintainer: Adam Park <akp6031@psu.edu>
URL: https://github.com/qunhualilab/retrofit
VignetteBuilder: knitr
BugReports: https://github.com/qunhualilab/retrofit/issues
git_url: https://git.bioconductor.org/packages/retrofit
git_branch: devel
git_last_commit: a8142a1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/retrofit_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/retrofit_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/retrofit_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/retrofit_1.7.0.tgz
vignettes: vignettes/retrofit/inst/doc/ColonVignette.html,
vignettes/retrofit/inst/doc/SimulationVignette.html
vignetteTitles: Retrofit Colon Vignette, Retrofit Simulation Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/retrofit/inst/doc/ColonVignette.R,
vignettes/retrofit/inst/doc/SimulationVignette.R
dependencyCount: 3
Package: ReUseData
Version: 1.7.0
Imports: Rcwl, RcwlPipelines, BiocFileCache, S4Vectors, stats, tools,
utils, methods, jsonlite, yaml, basilisk
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle
License: GPL-3
MD5sum: a85e9118e03ac054c845e1cf32b79e39
NeedsCompilation: no
Title: Reusable and reproducible Data Management
Description: ReUseData is an _R/Bioconductor_ software tool to provide
a systematic and versatile approach for standardized and
reproducible data management. ReUseData facilitates
transformation of shell or other ad hoc scripts for data
preprocessing into workflow-based data recipes. Evaluation of
data recipes generate curated data files in their generic
formats (e.g., VCF, bed). Both recipes and data are cached
using database infrastructure for easy data management and
reuse. Prebuilt data recipes are available through ReUseData
portal ("https://rcwl.org/dataRecipes/") with full annotation
and user instructions. Pregenerated data are available through
ReUseData cloud bucket that is directly downloadable through
"getCloudData()".
biocViews: Software, Infrastructure, DataImport, Preprocessing,
ImmunoOncology
Author: Qian Liu [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1456-5099>)
Maintainer: Qian Liu <qian.liu@roswellpark.org>
URL: https://github.com/rworkflow/ReUseData
VignetteBuilder: knitr
BugReports: https://github.com/rworkflow/ReUseData/issues
git_url: https://git.bioconductor.org/packages/ReUseData
git_branch: devel
git_last_commit: 85fc756
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ReUseData_1.7.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ReUseData_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ReUseData_1.7.0.tgz
vignettes: vignettes/ReUseData/inst/doc/ReUseData_data.html,
vignettes/ReUseData/inst/doc/ReUseData_quickStart.html,
vignettes/ReUseData/inst/doc/ReUseData_recipe.html
vignetteTitles: ReUseDataData, ReUseDataQS, ReUseDataRecipes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ReUseData/inst/doc/ReUseData_data.R,
vignettes/ReUseData/inst/doc/ReUseData_quickStart.R,
vignettes/ReUseData/inst/doc/ReUseData_recipe.R
dependencyCount: 125
Package: rexposome
Version: 1.29.0
Depends: R (>= 3.5), Biobase
Imports: methods, utils, stats, lsr, FactoMineR, stringr, circlize,
corrplot, ggplot2, ggridges, reshape2, pryr, S4Vectors,
imputeLCMD, scatterplot3d, glmnet, gridExtra, grid, Hmisc,
gplots, gtools, scales, lme4, grDevices, graphics, ggrepel,
mice
Suggests: mclust, flexmix, testthat, BiocStyle, knitr, formatR,
rmarkdown
License: MIT + file LICENSE
MD5sum: c5ee8d0c050892cf858b155380a9e660
NeedsCompilation: no
Title: Exposome exploration and outcome data analysis
Description: Package that allows to explore the exposome and to perform
association analyses between exposures and health outcomes.
biocViews: Software, BiologicalQuestion, Infrastructure, DataImport,
DataRepresentation, BiomedicalInformatics, ExperimentalDesign,
MultipleComparison, Classification, Clustering
Author: Carles Hernandez-Ferrer [aut, cre], Juan R. Gonzalez [aut],
Xavier Escribà -Montagut [aut]
Maintainer: Xavier Escribà Montagut <xavier.escriba@isglobal.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rexposome
git_branch: devel
git_last_commit: 16d7082
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rexposome_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rexposome_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rexposome_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rexposome_1.29.0.tgz
vignettes: vignettes/rexposome/inst/doc/exposome_data_analysis.html,
vignettes/rexposome/inst/doc/mutiple_imputation_data_analysis.html
vignetteTitles: Exposome Data Analysis, Dealing with Multiple
Imputations
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/rexposome/inst/doc/exposome_data_analysis.R,
vignettes/rexposome/inst/doc/mutiple_imputation_data_analysis.R
importsMe: omicRexposome
suggestsMe: brgedata
dependencyCount: 169
Package: rfaRm
Version: 1.19.0
Imports: httr, stringi, rsvg, magick, data.table, Biostrings, utils,
rvest, xml2, IRanges, S4Vectors, jsonlite
Suggests: R4RNA, treeio, knitr, BiocStyle, rmarkdown, BiocGenerics,
RUnit
License: GPL-3
MD5sum: fcb01c2a9926079a4f36b54296e84f60
NeedsCompilation: no
Title: An R interface to the Rfam database
Description: rfaRm provides a client interface to the Rfam database of
RNA families. Data that can be retrieved include RNA families,
secondary structure images, covariance models, sequences within
each family, alignments leading to the identification of a
family and secondary structures in the dot-bracket format.
biocViews: FunctionalGenomics, DataImport, ThirdPartyClient,
Visualization, MultipleSequenceAlignment
Author: Lara Selles Vidal, Rafael Ayala, Guy-Bart Stan, Rodrigo
Ledesma-Amaro
Maintainer: Lara Selles Vidal <lara.selles@oist.jp>, Rafael Ayala
<rafael.ayala@oist.jp>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rfaRm
git_branch: devel
git_last_commit: 7c62429
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rfaRm_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rfaRm_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rfaRm_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rfaRm_1.19.0.tgz
vignettes: vignettes/rfaRm/inst/doc/rfaRm.html
vignetteTitles: rfaRm
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rfaRm/inst/doc/rfaRm.R
dependencyCount: 45
Package: Rfastp
Version: 1.17.0
Imports: Rcpp, rjson, ggplot2, reshape2
LinkingTo: Rcpp, Rhtslib, zlibbioc
Suggests: BiocStyle, testthat, knitr, rmarkdown
License: GPL-3 + file LICENSE
MD5sum: 3c15db628600bbcd771efb38f9a27416
NeedsCompilation: yes
Title: An Ultra-Fast and All-in-One Fastq Preprocessor (Quality
Control, Adapter, low quality and polyX trimming) and UMI
Sequence Parsing).
Description: Rfastp is an R wrapper of fastp developed in c++. fastp
performs quality control for fastq files. including low quality
bases trimming, polyX trimming, adapter auto-detection and
trimming, paired-end reads merging, UMI sequence/id handling.
Rfastp can concatenate multiple files into one file (like shell
command cat) and accept multiple files as input.
biocViews: QualityControl, Sequencing, Preprocessing, Software
Author: Wei Wang [aut] (ORCID:
<https://orcid.org/0000-0002-3216-7118>), Ji-Dung Luo [ctb]
(ORCID: <https://orcid.org/0000-0003-0150-1440>), Thomas
Carroll [cre, aut] (ORCID:
<https://orcid.org/0000-0002-0073-1714>)
Maintainer: Thomas Carroll <tc.infomatics@gmail.com>
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Rfastp
git_branch: devel
git_last_commit: 0411058
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Rfastp_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Rfastp_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Rfastp_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Rfastp_1.17.0.tgz
vignettes: vignettes/Rfastp/inst/doc/Rfastp.html
vignetteTitles: Rfastp
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Rfastp/inst/doc/Rfastp.R
dependencyCount: 44
Package: rfPred
Version: 1.45.0
Depends: R (>= 3.5.0), methods
Imports: utils, GenomeInfoDb, data.table, IRanges, GenomicRanges,
parallel, Rsamtools
Suggests: BiocStyle
License: GPL (>=2 )
MD5sum: c5ffc6986cbd93d12b6230ad08715a4e
NeedsCompilation: yes
Title: Assign rfPred functional prediction scores to a missense
variants list
Description: Based on external numerous data files where rfPred scores
are pre-calculated on all genomic positions of the human exome,
the package gives rfPred scores to missense variants identified
by the chromosome, the position (hg19 version), the referent
and alternative nucleotids and the uniprot identifier of the
protein. Note that for using the package, the user has to
download the TabixFile and index (approximately 3.3 Go).
biocViews: Software, Annotation, Classification
Author: Fabienne Jabot-Hanin, Hugo Varet and Jean-Philippe Jais
Maintainer: Hugo Varet <varethugo@gmail.com>
git_url: https://git.bioconductor.org/packages/rfPred
git_branch: devel
git_last_commit: 5eac6f7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rfPred_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rfPred_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rfPred_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rfPred_1.45.0.tgz
vignettes: vignettes/rfPred/inst/doc/vignette.pdf
vignetteTitles: CalculatingrfPredscoreswithpackagerfPred
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rfPred/inst/doc/vignette.R
dependencyCount: 40
Package: rGADEM
Version: 2.55.0
Depends: R (>= 2.11.0), Biostrings, IRanges, BSgenome, methods, seqLogo
Imports: Biostrings, GenomicRanges, methods, graphics, seqLogo
Suggests: BSgenome.Hsapiens.UCSC.hg19, rtracklayer
License: Artistic-2.0
Archs: x64
MD5sum: 559bc8c7899d2ab42685c6463135ac05
NeedsCompilation: yes
Title: de novo motif discovery
Description: rGADEM is an efficient de novo motif discovery tool for
large-scale genomic sequence data. It is an open-source R
package, which is based on the GADEM software.
biocViews: Microarray, ChIPchip, Sequencing, ChIPSeq, MotifDiscovery
Author: Arnaud Droit, Raphael Gottardo, Gordon Robertson and Leiping Li
Maintainer: Arnaud Droit <arnaud.droit@crchuq.ulaval.ca>
git_url: https://git.bioconductor.org/packages/rGADEM
git_branch: devel
git_last_commit: 0bdc9ac
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rGADEM_2.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rGADEM_2.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rGADEM_2.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rGADEM_2.55.0.tgz
vignettes: vignettes/rGADEM/inst/doc/rGADEM.pdf
vignetteTitles: The rGADEM users guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rGADEM/inst/doc/rGADEM.R
dependencyCount: 60
Package: rGenomeTracks
Version: 1.13.0
Depends: R (>= 4.1.0),
Imports: imager, reticulate, methods, rGenomeTracksData
Suggests: rmarkdown, knitr, testthat (>= 3.0.0)
License: GPL-3
MD5sum: 7332b173122aba65bd5433f741e5ab7e
NeedsCompilation: no
Title: Integerated visualization of epigenomic data
Description: rGenomeTracks package leverages the power of
pyGenomeTracks software with the interactivity of R.
pyGenomeTracks is a python software that offers robust method
for visualizing epigenetic data files like narrowPeak, Hic
matrix, TADs and arcs, however though, here is no way currently
to use it within R interactive session. rGenomeTracks wrapped
the whole functionality of pyGenomeTracks with additional
utilites to make to more pleasant for R users.
biocViews: Software, HiC, Visualization
Author: Omar Elashkar [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5505-778X>)
Maintainer: Omar Elashkar <omar.i.elashkar@gmail.com>
SystemRequirements: pyGenomeTracks (prefered to use
install_pyGenomeTracks())
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rGenomeTracks
git_branch: devel
git_last_commit: c6e4fb5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rGenomeTracks_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rGenomeTracks_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rGenomeTracks_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rGenomeTracks_1.13.0.tgz
vignettes: vignettes/rGenomeTracks/inst/doc/rGenomeTracks.html
vignetteTitles: rGenomeTracks
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rGenomeTracks/inst/doc/rGenomeTracks.R
dependencyCount: 82
Package: RgnTX
Version: 1.9.0
Depends: R (>= 4.2.0)
Imports: GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2,
graphics, IRanges, methods, regioneR, S4Vectors, stats,
TxDb.Hsapiens.UCSC.hg19.knownGene
Suggests: BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0)
License: Artistic-2.0
MD5sum: b09af6813a26d87f3c4880a3bf308ee3
NeedsCompilation: no
Title: Colocalization analysis of transcriptome elements in the
presence of isoform heterogeneity and ambiguity
Description: RgnTX allows the integration of transcriptome annotations
so as to model the complex alternative splicing patterns. It
supports the testing of transcriptome elements without clear
isoform association, which is often the real scenario due to
technical limitations. It involves functions that do permutaion
test for evaluating association between features and
transcriptome regions.
biocViews: AlternativeSplicing, Sequencing, RNASeq, MethylSeq,
Transcription, SplicedAlignment
Author: Yue Wang [aut, cre], Jia Meng [aut]
Maintainer: Yue Wang <yue.wang19@student.xjtlu.edu.cn>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RgnTX
git_branch: devel
git_last_commit: c0f1208
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RgnTX_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RgnTX_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RgnTX_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RgnTX_1.9.0.tgz
vignettes: vignettes/RgnTX/inst/doc/RgnTX.html
vignetteTitles: RgnTX
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RgnTX/inst/doc/RgnTX.R
dependencyCount: 100
Package: rgoslin
Version: 1.11.0
Imports: Rcpp (>= 1.0.3), dplyr
LinkingTo: Rcpp
Suggests: testthat (>= 2.1.0), BiocStyle, knitr, rmarkdown, kableExtra,
BiocManager, stringr, stringi, ggplot2, tibble, lipidr
License: MIT + file LICENSE
Archs: x64
MD5sum: 54f2c94b77e8bc4316ae8d5687a5f247
NeedsCompilation: yes
Title: Lipid Shorthand Name Parsing and Normalization
Description: The R implementation for the Grammar of Succint Lipid
Nomenclature parses different short hand notation dialects for
lipid names. It normalizes them to a standard name. It further
provides calculated monoisotopic masses and sum formulas for
each successfully parsed lipid name and supplements it with
LIPID MAPS Category and Class information. Also, the structural
level and further structural details about the head group,
fatty acyls and functional groups are returned, where
applicable.
biocViews: Software, Lipidomics, Metabolomics, Preprocessing,
Normalization, MassSpectrometry
Author: Nils Hoffmann [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6540-6875>), Dominik Kopczynski
[aut] (ORCID: <https://orcid.org/0000-0001-5885-4568>)
Maintainer: Nils Hoffmann <nils.hoffmann@cebitec.uni-bielefeld.de>
URL: https://github.com/lifs-tools/rgoslin
VignetteBuilder: knitr
BugReports: https://github.com/lifs-tools/rgoslin/issues
git_url: https://git.bioconductor.org/packages/rgoslin
git_branch: devel
git_last_commit: f32d084
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rgoslin_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rgoslin_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rgoslin_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rgoslin_1.11.0.tgz
vignettes: vignettes/rgoslin/inst/doc/introduction.html
vignetteTitles: Using R Goslin to parse and normalize lipid
nomenclature
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/rgoslin/inst/doc/introduction.R
suggestsMe: MetMashR
dependencyCount: 21
Package: RGraph2js
Version: 1.35.0
Imports: utils, whisker, rjson, digest, graph
Suggests: RUnit, BiocStyle, BiocGenerics, xtable, sna
License: GPL-2
Archs: x64
MD5sum: 795fb6d4dcc768fafa8d8c2b167403d2
NeedsCompilation: no
Title: Convert a Graph into a D3js Script
Description: Generator of web pages which display interactive
network/graph visualizations with D3js, jQuery and Raphael.
biocViews: Visualization, Network, GraphAndNetwork, ThirdPartyClient
Author: Stephane Cano [aut, cre], Sylvain Gubian [aut], Florian Martin
[aut]
Maintainer: Stephane Cano <DL.RSupport@pmi.com>
SystemRequirements: jQuery, jQueryUI, qTip2, D3js and Raphael are
required Javascript libraries made available via the online
CDNJS service (http://cdnjs.cloudflare.com).
git_url: https://git.bioconductor.org/packages/RGraph2js
git_branch: devel
git_last_commit: 2bd32df
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RGraph2js_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RGraph2js_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RGraph2js_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RGraph2js_1.35.0.tgz
vignettes: vignettes/RGraph2js/inst/doc/RGraph2js.pdf
vignetteTitles: RGraph2js
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RGraph2js/inst/doc/RGraph2js.R
dependencyCount: 11
Package: Rgraphviz
Version: 2.51.9
Depends: R (>= 2.6.0), methods, utils, graph, grid
Imports: stats4, graphics, grDevices
Suggests: RUnit, BiocGenerics, XML
License: EPL
Archs: x64
MD5sum: a07351dcff197581b617d63c6579be1f
NeedsCompilation: yes
Title: Provides plotting capabilities for R graph objects
Description: Interfaces R with the AT and T graphviz library for
plotting R graph objects from the graph package.
biocViews: GraphAndNetwork, Visualization
Author: Kasper Daniel Hansen [cre, aut], Jeff Gentry [aut], Li Long
[aut], Robert Gentleman [aut], Seth Falcon [aut], Florian Hahne
[aut], Deepayan Sarkar [aut]
Maintainer: Kasper Daniel Hansen <kasperdanielhansen@gmail.com>
SystemRequirements: optionally Graphviz (>= 2.16), USE_C17
git_url: https://git.bioconductor.org/packages/Rgraphviz
git_branch: devel
git_last_commit: b0e3e7c
git_last_commit_date: 2025-03-26
Date/Publication: 2025-03-26
source.ver: src/contrib/Rgraphviz_2.51.9.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Rgraphviz_2.51.9.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Rgraphviz_2.51.9.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Rgraphviz_2.51.9.tgz
vignettes: vignettes/Rgraphviz/inst/doc/newRgraphvizInterface.pdf,
vignettes/Rgraphviz/inst/doc/Rgraphviz.pdf
vignetteTitles: A New Interface to Plot Graphs Using Rgraphviz, How To
Plot A Graph Using Rgraphviz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rgraphviz/inst/doc/newRgraphvizInterface.R,
vignettes/Rgraphviz/inst/doc/Rgraphviz.R
dependsOnMe: biocGraph, BioMVCClass, CellNOptR, MineICA, netresponse,
paircompviz, pathRender, ROntoTools, SplicingGraphs,
maEndToEnd, dlsem, gridGraphviz, GUIProfiler
importsMe: apComplex, biocGraph, bnem, chimeraviz, CytoML, dce,
DEGraph, EnrichDO, EnrichmentBrowser, flowWorkspace,
GeneNetworkBuilder, GOstats, hyperdraw, KEGGgraph,
mirIntegrator, MIRit, mnem, OncoSimulR, ontoProc, paircompviz,
pathview, Pigengene, qpgraph, SGCP, TRONCO, abn, agena.ai,
BCDAG, BiDAG, bnpa, bnRep, ceg, CePa, classGraph, cogmapr,
ontologyPlot, SEMgraph, stablespec
suggestsMe: a4, altcdfenvs, annotate, Category, CNORfeeder, CNORfuzzy,
DEGraph, flowCore, geneplotter, GlobalAncova, globaltest,
GSEABase, MLP, NCIgraph, OmnipathR, RBGL, RBioinf,
rBiopaxParser, Rtreemix, safe, SPIA, SRAdb, Streamer, topGO,
ViSEAGO, vtpnet, NCIgraphData, SNAData, arulesViz, BayesNetBP,
bnlearn, bnstruct, bsub, ChoR, CodeDepends, gbutils, GeneNet,
HEMDAG, iTOP, kst, lava, loon, maGUI, micd, multiplex, netmeta,
ParallelPC, pcalg, pchc, pks, psych, relations, rEMM, rPref,
rSpectral, SCCI, sisal, textplot, tm, topologyGSA, tpc,
unifDAG, zenplots
dependencyCount: 10
Package: rGREAT
Version: 2.9.2
Depends: R (>= 4.0.0), GenomicRanges, IRanges, methods
Imports: graphics, rjson, GetoptLong (>= 0.0.9), RCurl, utils, stats,
GlobalOptions, shiny, DT, GenomicFeatures, digest, GO.db,
progress, circlize, AnnotationDbi,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, RColorBrewer,
S4Vectors, GenomeInfoDb, foreach, doParallel, Rcpp
LinkingTo: Rcpp
Suggests: testthat (>= 0.3), knitr, rmarkdown, BiocManager,
org.Mm.eg.db, msigdbr, KEGGREST, reactome.db
Enhances: BioMartGOGeneSets, UniProtKeywords
License: MIT + file LICENSE
MD5sum: dcbaf3f4a9098df0c62738d3d0e8896b
NeedsCompilation: yes
Title: GREAT Analysis - Functional Enrichment on Genomic Regions
Description: GREAT (Genomic Regions Enrichment of Annotations Tool) is
a type of functional enrichment analysis directly performed on
genomic regions. This package implements the GREAT algorithm
(the local GREAT analysis), also it supports directly
interacting with the GREAT web service (the online GREAT
analysis). Both analysis can be viewed by a Shiny application.
rGREAT by default supports more than 600 organisms and a large
number of gene set collections, as well as self-provided gene
sets and organisms from users. Additionally, it implements a
general method for dealing with background regions.
biocViews: GeneSetEnrichment, GO, Pathways, Software, Sequencing,
WholeGenome, GenomeAnnotation, Coverage
Author: Zuguang Gu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7395-8709>)
Maintainer: Zuguang Gu <z.gu@dkfz.de>
URL: https://github.com/jokergoo/rGREAT,
http://great.stanford.edu/public/html/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rGREAT
git_branch: devel
git_last_commit: a1b51fc
git_last_commit_date: 2025-03-13
Date/Publication: 2025-03-13
source.ver: src/contrib/rGREAT_2.9.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rGREAT_2.9.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rGREAT_2.9.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rGREAT_2.9.2.tgz
vignettes: vignettes/rGREAT/inst/doc/rGREAT.html
vignetteTitles: The rGREAT package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
dependencyCount: 122
Package: RGSEA
Version: 1.41.0
Depends: R(>= 2.10.0)
Imports: BiocGenerics
Suggests: BiocStyle, GEOquery, knitr, RUnit
License: GPL(>=3)
MD5sum: 897801f8d580ee6c83e7bda7007e39f1
NeedsCompilation: no
Title: Random Gene Set Enrichment Analysis
Description: Combining bootstrap aggregating and Gene set enrichment
analysis (GSEA), RGSEA is a classfication algorithm with high
robustness and no over-fitting problem. It performs well
especially for the data generated from different exprements.
biocViews: GeneSetEnrichment, StatisticalMethod, Classification
Author: Chengcheng Ma
Maintainer: Chengcheng Ma <ccma@sibs.ac.cn>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RGSEA
git_branch: devel
git_last_commit: f8d6d9d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RGSEA_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RGSEA_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RGSEA_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RGSEA_1.41.0.tgz
vignettes: vignettes/RGSEA/inst/doc/RGSEA.pdf
vignetteTitles: Introduction to RGSEA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RGSEA/inst/doc/RGSEA.R
dependencyCount: 6
Package: rgsepd
Version: 1.39.0
Depends: R (>= 4.2.0), DESeq2, goseq (>= 1.28)
Imports: gplots, biomaRt, org.Hs.eg.db, GO.db, SummarizedExperiment,
AnnotationDbi
Suggests: boot, tools, BiocGenerics, knitr, xtable
License: GPL-3
Archs: x64
MD5sum: 118bef87bd40e58ee0c40dfd5aa5f974
NeedsCompilation: no
Title: Gene Set Enrichment / Projection Displays
Description: R/GSEPD is a bioinformatics package for R to help
disambiguate transcriptome samples (a matrix of RNA-Seq counts
at transcript IDs) by automating differential expression (with
DESeq2), then gene set enrichment (with GOSeq), and finally a
N-dimensional projection to quantify in which ways each sample
is like either treatment group.
biocViews: ImmunoOncology, Software, DifferentialExpression,
GeneSetEnrichment, RNASeq
Author: Karl Stamm
Maintainer: Karl Stamm <karl.stamm@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rgsepd
git_branch: devel
git_last_commit: 42b2653
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rgsepd_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rgsepd_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rgsepd_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rgsepd_1.39.0.tgz
vignettes: vignettes/rgsepd/inst/doc/rgsepd.pdf
vignetteTitles: An Introduction to the rgsepd package
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rgsepd/inst/doc/rgsepd.R
dependencyCount: 128
Package: rhdf5
Version: 2.51.2
Depends: R (>= 4.0.0), methods
Imports: Rhdf5lib (>= 1.13.4), rhdf5filters (>= 1.15.5)
LinkingTo: Rhdf5lib
Suggests: bit64, BiocStyle, knitr, rmarkdown, testthat, bench, dplyr,
ggplot2, mockery, BiocParallel
License: Artistic-2.0
MD5sum: f446dc884cd9a7490a86532556e7837d
NeedsCompilation: yes
Title: R Interface to HDF5
Description: This package provides an interface between HDF5 and R.
HDF5's main features are the ability to store and access very
large and/or complex datasets and a wide variety of metadata on
mass storage (disk) through a completely portable file format.
The rhdf5 package is thus suited for the exchange of large
and/or complex datasets between R and other software package,
and for letting R applications work on datasets that are larger
than the available RAM.
biocViews: Infrastructure, DataImport
Author: Bernd Fischer [aut], Mike Smith [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7800-3848>), Gregoire Pau [aut],
Martin Morgan [ctb], Daniel van Twisk [ctb]
Maintainer: Mike Smith <mike.smith@embl.de>
URL: https://github.com/grimbough/rhdf5
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/grimbough/rhdf5/issues
git_url: https://git.bioconductor.org/packages/rhdf5
git_branch: devel
git_last_commit: 225a6fb
git_last_commit_date: 2025-01-08
Date/Publication: 2025-01-08
source.ver: src/contrib/rhdf5_2.51.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rhdf5_2.51.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rhdf5_2.51.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rhdf5_2.51.2.tgz
vignettes: vignettes/rhdf5/inst/doc/practical_tips.html,
vignettes/rhdf5/inst/doc/rhdf5_cloud_reading.html,
vignettes/rhdf5/inst/doc/rhdf5.html
vignetteTitles: rhdf5 Practical Tips, Reading HDF5 Files In The Cloud,
rhdf5 - HDF5 interface for R
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rhdf5/inst/doc/practical_tips.R,
vignettes/rhdf5/inst/doc/rhdf5_cloud_reading.R,
vignettes/rhdf5/inst/doc/rhdf5.R
dependsOnMe: GSCA, h5mread, HiCBricks, LoomExperiment, MuData, octad
importsMe: alabaster.base, alabaster.bumpy, alabaster.mae,
alabaster.matrix, alabaster.ranges, alabaster.spatial,
BayesSpace, BgeeCall, biomformat, bnbc, bsseq, chihaya,
CiteFuse, cmapR, CoGAPS, CopyNumberPlots, cTRAP, cytomapper,
diffHic, DropletUtils, epigraHMM, EventPointer, FRASER,
GenomicScores, gep2pep, h5vc, HDF5Array, HicAggR, HiCcompare,
HiCExperiment, IONiseR, mariner, methodical, MOFA2,
MoleculeExperiment, phantasus, plotgardener, ptairMS, PureCN,
recountmethylation, ribor, scCB2, scMitoMut, scone,
scRNAseqApp, signatureSearch, SpliceWiz, SpotClean, SurfR,
TENxIO, trackViewer, MafH5.gnomAD.v4.0.GRCh38, MethylSeqData,
ptairData, scMultiome, signatureSearchData, TumourMethData,
bioRad, ebvcube, file2meco, karyotapR, LOMAR, NEONiso,
rDataPipeline
suggestsMe: beachmat.hdf5, edgeR, HiCDOC, HiCParser, mia, pairedGSEA,
phantasusLite, rhdf5filters, SCArray, scviR, slalom,
SpatialFeatureExperiment, spatialHeatmap, Spectra,
SummarizedExperiment, tximport, Voyager, zellkonverter,
ClustAssess, conos, CRMetrics, io, MplusAutomation, neonstore,
neonUtilities, SignacX, SpatialDDLS
dependencyCount: 3
Package: rhdf5client
Version: 1.29.0
Depends: R (>= 3.6), methods, DelayedArray
Imports: httr, rjson, utils, data.table
Suggests: knitr, testthat, BiocStyle, DT, rmarkdown
License: Artistic-2.0
Archs: x64
MD5sum: 88bc7040834dce0d95d3b0552d408e1a
NeedsCompilation: yes
Title: Access HDF5 content from HDF Scalable Data Service
Description: This package provides functionality for reading data from
HDF Scalable Data Service from within R. The HSDSArray
function bridges from HSDS to the user via the DelayedArray
interface. Bioconductor manages an open HSDS instance
graciously provided by John Readey of the HDF Group.
biocViews: DataImport, Software, Infrastructure
Author: Samuela Pollack [aut], Shweta Gopaulakrishnan [aut], BJ Stubbs
[aut], Alexey Sergushichev [aut], Vincent Carey [cre, aut]
Maintainer: Vincent Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rhdf5client
git_branch: devel
git_last_commit: 65b8c5d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rhdf5client_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rhdf5client_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rhdf5client_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rhdf5client_1.29.0.tgz
vignettes: vignettes/rhdf5client/inst/doc/delayed-array.html
vignetteTitles: HSDSArray DelayedArray backend
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rhdf5client/inst/doc/delayed-array.R
importsMe: phantasus, phantasusLite
dependencyCount: 32
Package: rhdf5filters
Version: 1.19.2
LinkingTo: Rhdf5lib
Suggests: BiocStyle, knitr, rmarkdown, tinytest, rhdf5 (>= 2.47.7)
License: BSD_2_clause + file LICENSE
Archs: x64
MD5sum: 66e0d19c1f42d373f3fa0b121e736aca
NeedsCompilation: yes
Title: HDF5 Compression Filters
Description: Provides a collection of additional compression filters
for HDF5 datasets. The package is intended to provide seemless
integration with rhdf5, however the compiled filters can also
be used with external applications.
biocViews: Infrastructure, DataImport
Author: Mike Smith [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7800-3848>)
Maintainer: Mike Smith <grimbough@gmail.com>
URL: https://github.com/grimbough/rhdf5filters
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/grimbough/rhdf5filters
git_url: https://git.bioconductor.org/packages/rhdf5filters
git_branch: devel
git_last_commit: 4057808
git_last_commit_date: 2025-03-05
Date/Publication: 2025-03-05
source.ver: src/contrib/rhdf5filters_1.19.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rhdf5filters_1.19.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rhdf5filters_1.19.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rhdf5filters_1.19.2.tgz
vignettes: vignettes/rhdf5filters/inst/doc/rhdf5filters.html
vignetteTitles: HDF5 Compression Filters
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/rhdf5filters/inst/doc/rhdf5filters.R
importsMe: h5mread, rhdf5
dependencyCount: 1
Package: Rhdf5lib
Version: 1.29.2
Depends: R (>= 4.2.0)
Suggests: BiocStyle, knitr, rmarkdown, tinytest, mockery
License: Artistic-2.0
MD5sum: 7e080a3ca00e2c4ee8b412ef420a3784
NeedsCompilation: yes
Title: hdf5 library as an R package
Description: Provides C and C++ hdf5 libraries.
biocViews: Infrastructure
Author: Mike Smith [ctb, cre] (ORCID:
<https://orcid.org/0000-0002-7800-3848>), The HDF Group [cph]
Maintainer: Mike Smith <grimbough@gmail.com>
URL: https://github.com/Huber-group-EMBL/Rhdf5lib
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/Huber-group-EMBL/Rhdf5lib/issues
git_url: https://git.bioconductor.org/packages/Rhdf5lib
git_branch: devel
git_last_commit: 61119ec
git_last_commit_date: 2025-03-24
Date/Publication: 2025-03-24
source.ver: src/contrib/Rhdf5lib_1.29.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Rhdf5lib_1.29.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Rhdf5lib_1.29.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Rhdf5lib_1.29.2.tgz
vignettes: vignettes/Rhdf5lib/inst/doc/downloadHDF5.html,
vignettes/Rhdf5lib/inst/doc/Rhdf5lib.html
vignetteTitles: Creating this HDF5 distribution, Linking to Rhdf5lib
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rhdf5lib/inst/doc/downloadHDF5.R,
vignettes/Rhdf5lib/inst/doc/Rhdf5lib.R
importsMe: epigraHMM, rhdf5
suggestsMe: mbkmeans
linksToMe: alabaster.base, beachmat.hdf5, chihaya, CytoML,
DropletUtils, epigraHMM, h5mread, mbkmeans, mzR, ncdfFlow,
rhdf5, rhdf5filters, smer
dependencyCount: 0
Package: rhinotypeR
Version: 1.1.0
Depends: R (>= 4.4.0)
Imports: Biostrings
Suggests: knitr, rmarkdown, BiocManager, BiocStyle, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: a0e3782802b3b62aa5c8d78de2f7133f
NeedsCompilation: no
Title: Rhinovirus genotyping
Description: "rhinotypeR" is designed to automate the comparison of
sequence data against prototype strains, streamlining the
genotype assignment process. By implementing predefined
pairwise distance thresholds, this package makes genotype
assignment accessible to researchers and public health
professionals. This tool enhances our epidemiological toolkit
by enabling more efficient surveillance and analysis of
rhinoviruses (RVs) and other viral pathogens with complex
genomic landscapes. Additionally, "rhinotypeR" supports
comprehensive visualization and analysis of single nucleotide
polymorphisms (SNPs) and amino acid substitutions, facilitating
in-depth genetic and evolutionary studies.
biocViews: Sequencing, Genetics, Phylogenetics
Author: Martha Luka [aut, cre] (ORCID:
<https://orcid.org/0000-0001-6217-4426>), Ruth Nanjala [aut],
Winfred Gatua [aut], Wafaa M. Rashed [aut], Olaitan Awe [aut]
Maintainer: Martha Luka <marthaluka20@gmail.com>
URL: https://github.com/omicscodeathon/rhinotypeR
VignetteBuilder: knitr
BugReports: https://github.com/omicscodeathon/rhinotypeR/issues
git_url: https://git.bioconductor.org/packages/rhinotypeR
git_branch: devel
git_last_commit: 4fd8c88
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rhinotypeR_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rhinotypeR_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rhinotypeR_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rhinotypeR_1.1.0.tgz
vignettes: vignettes/rhinotypeR/inst/doc/rhinotypeR.html
vignetteTitles: Introduction to rhinotypeR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/rhinotypeR/inst/doc/rhinotypeR.R
dependencyCount: 25
Package: Rhisat2
Version: 1.23.0
Depends: R (>= 4.4.0)
Imports: txdbmaker, SGSeq, GenomicRanges, methods, utils
Suggests: testthat, knitr, rmarkdown, BiocStyle
License: GPL-3
Archs: x64
MD5sum: b9bf564b09b72d8b6e7fc850790693f2
NeedsCompilation: yes
Title: R Wrapper for HISAT2 Aligner
Description: An R interface to the HISAT2 spliced short-read aligner by
Kim et al. (2015). The package contains wrapper functions to
create a genome index and to perform the read alignment to the
generated index.
biocViews: Alignment, Sequencing, SplicedAlignment
Author: Charlotte Soneson [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3833-2169>)
Maintainer: Charlotte Soneson <charlottesoneson@gmail.com>
URL: https://github.com/fmicompbio/Rhisat2
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/fmicompbio/Rhisat2/issues
git_url: https://git.bioconductor.org/packages/Rhisat2
git_branch: devel
git_last_commit: 695fc7d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Rhisat2_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Rhisat2_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Rhisat2_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Rhisat2_1.23.0.tgz
vignettes: vignettes/Rhisat2/inst/doc/Rhisat2.html
vignetteTitles: Rhisat2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Rhisat2/inst/doc/Rhisat2.R
importsMe: CircSeqAlignTk
suggestsMe: eisaR, QuasR
dependencyCount: 104
Package: Rhtslib
Version: 3.3.2
Imports: tools
Suggests: knitr, rmarkdown, BiocStyle
License: LGPL (>= 2)
Archs: x64
MD5sum: 1cd7a03615b725db60ed2dbf99f8e461
NeedsCompilation: yes
Title: HTSlib high-throughput sequencing library as an R package
Description: This package provides version 1.18 of the 'HTSlib' C
library for high-throughput sequence analysis. The package is
primarily useful to developers of other R packages who wish to
make use of HTSlib. Motivation and instructions for use of this
package are in the vignette, vignette(package="Rhtslib",
"Rhtslib").
biocViews: DataImport, Sequencing
Author: Nathaniel Hayden [led, aut], Martin Morgan [aut], Hervé Pagès
[aut, cre], Tomas Kalibera [ctb], Jeroen Ooms [ctb]
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/Rhtslib, http://www.htslib.org/
SystemRequirements: libbz2 & liblzma & libcurl (with header files), GNU
make
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/Rhtslib/issues
git_url: https://git.bioconductor.org/packages/Rhtslib
git_branch: devel
git_last_commit: 90f07ae
git_last_commit_date: 2025-03-05
Date/Publication: 2025-03-05
source.ver: src/contrib/Rhtslib_3.3.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Rhtslib_3.3.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Rhtslib_3.3.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Rhtslib_3.3.2.tgz
vignettes: vignettes/Rhtslib/inst/doc/Rhtslib.html
vignetteTitles: Motivation and use of Rhtslib
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rhtslib/inst/doc/Rhtslib.R
importsMe: deepSNV, diffHic, maftools, mitoClone2, scPipe
linksToMe: bamsignals, csaw, deepSNV, DiffBind, diffHic, epialleleR,
FLAMES, h5vc, maftools, methylKit, mitoClone2, podkat, QuasR,
raer, Rfastp, Rsamtools, scPipe, ShortRead, VariantAnnotation,
jackalope
dependencyCount: 1
Package: RiboCrypt
Version: 1.13.0
Depends: R (>= 3.6.0), ORFik (>= 1.13.12)
Imports: bslib, BiocGenerics, BiocParallel, Biostrings, data.table,
dplyr, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2,
htmlwidgets, httr, IRanges, jsonlite, knitr, markdown,
NGLVieweR, plotly, rlang, RCurl, shiny, shinycssloaders,
shinyhelper, shinyjqui, stringr
Suggests: testthat, rmarkdown, BiocStyle, BSgenome,
BSgenome.Hsapiens.UCSC.hg19
License: MIT + file LICENSE
MD5sum: 0acaf3184ace6789b54d50969d6bc811
NeedsCompilation: no
Title: Interactive visualization in genomics
Description: R Package for interactive visualization and browsing NGS
data. It contains a browser for both transcript and genomic
coordinate view. In addition a QC and general metaplots are
included, among others differential translation plots and gene
expression plots. The package is still under development.
biocViews: Software, Sequencing, RiboSeq, RNASeq,
Author: Michal Swirski [aut, cre, cph], Haakon Tjeldnes [aut, ctb],
Kornel Labun [ctb]
Maintainer: Michal Swirski <michal.swirski@uw.edu.pl>
URL: https://github.com/m-swirski/RiboCrypt
VignetteBuilder: knitr
BugReports: https://github.com/m-swirski/RiboCrypt/issues
git_url: https://git.bioconductor.org/packages/RiboCrypt
git_branch: devel
git_last_commit: 148e091
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RiboCrypt_1.13.0.tar.gz
vignettes: vignettes/RiboCrypt/inst/doc/RiboCrypt_app_tutorial.html,
vignettes/RiboCrypt/inst/doc/RiboCrypt_overview.html
vignetteTitles: RiboCrypt_app_tutorial.html, RiboCrypt_overview.html
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/RiboCrypt/inst/doc/RiboCrypt_app_tutorial.R,
vignettes/RiboCrypt/inst/doc/RiboCrypt_overview.R
dependencyCount: 168
Package: RiboDiPA
Version: 1.15.1
Depends: R (>= 4.1), Rsamtools, GenomicFeatures, GenomicAlignments
Imports: Rcpp (>= 1.0.2), graphics, stats, data.table, elitism,
methods, S4Vectors, IRanges, GenomicRanges, matrixStats,
reldist, doParallel, foreach, parallel, qvalue, DESeq2,
ggplot2, BiocFileCache, BiocGenerics, txdbmaker
LinkingTo: Rcpp
Suggests: knitr, rmarkdown
License: LGPL (>= 3)
Archs: x64
MD5sum: 2805394c91d7c8f3c7cabce394a0bbdb
NeedsCompilation: yes
Title: Differential pattern analysis for Ribo-seq data
Description: This package performs differential pattern analysis for
Ribo-seq data. It identifies genes with significantly different
patterns in the ribosome footprint between two conditions.
RiboDiPA contains five major components including bam file
processing, P-site mapping, data binning, differential pattern
analysis and footprint visualization.
biocViews: RiboSeq, GeneExpression, GeneRegulation,
DifferentialExpression, Sequencing, Coverage, Alignment,
RNASeq, ImmunoOncology, QualityControl, DataImport, Software,
Normalization
Author: Keren Li [aut], Matt Hope [aut], Xiaozhong Wang [aut], Ji-Ping
Wang [aut, cre]
Maintainer: Ji-Ping Wang <jzwang@northwestern.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RiboDiPA
git_branch: devel
git_last_commit: 921e372
git_last_commit_date: 2024-12-05
Date/Publication: 2024-12-06
source.ver: src/contrib/RiboDiPA_1.15.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RiboDiPA_1.15.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RiboDiPA_1.15.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RiboDiPA_1.15.1.tgz
vignettes: vignettes/RiboDiPA/inst/doc/RiboDiPA.html
vignetteTitles: RiboDiPA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RiboDiPA/inst/doc/RiboDiPA.R
dependencyCount: 149
Package: RiboProfiling
Version: 1.37.0
Depends: R (>= 3.5.0), Biostrings
Imports: BiocGenerics, GenomeInfoDb, GenomicRanges, IRanges, reshape2,
GenomicFeatures, grid, plyr, S4Vectors, GenomicAlignments,
ggplot2, ggbio, Rsamtools, rtracklayer, data.table, sqldf
Suggests: knitr, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene,
BSgenome.Hsapiens.UCSC.hg19, testthat, SummarizedExperiment
License: GPL-3
Archs: x64
MD5sum: 2400ff8ebc8097aeb28e2b1c4438c231
NeedsCompilation: no
Title: Ribosome Profiling Data Analysis: from BAM to Data
Representation and Interpretation
Description: Starting with a BAM file, this package provides the
necessary functions for quality assessment, read start position
recalibration, the counting of reads on CDS, 3'UTR, and 5'UTR,
plotting of count data: pairs, log fold-change, codon frequency
and coverage assessment, principal component analysis on codon
coverage.
biocViews: RiboSeq, Sequencing, Coverage, Alignment, QualityControl,
Software, PrincipalComponent
Author: Alexandra Popa
Maintainer: A. Popa <alexandra.mariela.popa@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RiboProfiling
git_branch: devel
git_last_commit: c7c7bd6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RiboProfiling_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RiboProfiling_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RiboProfiling_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RiboProfiling_1.37.0.tgz
vignettes: vignettes/RiboProfiling/inst/doc/RiboProfiling.pdf
vignetteTitles: Analysing Ribo-Seq data with the "RiboProfiling"
package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RiboProfiling/inst/doc/RiboProfiling.R
dependencyCount: 166
Package: ribor
Version: 1.19.0
Depends: R (>= 3.6.0)
Imports: dplyr, ggplot2, hash, methods, rhdf5, rlang, stats, S4Vectors,
tidyr, tools, yaml
Suggests: testthat, knitr, rmarkdown
License: GPL-3
MD5sum: 5c9de7c95e2b68ac4858a2d37dc12b42
NeedsCompilation: no
Title: An R Interface for Ribo Files
Description: The ribor package provides an R Interface for .ribo files.
It provides functionality to read the .ribo file, which is of
HDF5 format, and performs common analyses on its contents.
biocViews: Software, Infrastructure
Author: Michael Geng [cre, aut], Hakan Ozadam [aut], Can Cenik [aut]
Maintainer: Michael Geng <michaelgeng@utexas.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ribor
git_branch: devel
git_last_commit: 390801c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ribor_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ribor_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ribor_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ribor_1.19.0.tgz
vignettes: vignettes/ribor/inst/doc/ribor.html
vignetteTitles: A Walkthrough of RiboR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ribor/inst/doc/ribor.R
dependencyCount: 52
Package: riboSeqR
Version: 1.41.0
Depends: R (>= 3.0.2), methods, GenomicRanges, abind
Imports: Rsamtools, IRanges, S4Vectors, baySeq, GenomeInfoDb, seqLogo
Suggests: BiocStyle, RUnit, BiocGenerics
License: GPL-3
Archs: x64
MD5sum: 17e9dc88d0eff23073dc20bf77b89957
NeedsCompilation: no
Title: Analysis of sequencing data from ribosome profiling experiments
Description: Plotting functions, frameshift detection and parsing of
sequencing data from ribosome profiling experiments.
biocViews: Sequencing,Genetics,Visualization,RiboSeq
Author: Thomas J. Hardcastle [aut], Samuel Granjeaud [cre] (ORCID:
<https://orcid.org/0000-0001-9245-1535>)
Maintainer: Samuel Granjeaud <samuel.granjeaud@inserm.fr>
URL: https://github.com/samgg/riboSeqR
BugReports: https://github.com/samgg/riboSeqR/issues
git_url: https://git.bioconductor.org/packages/riboSeqR
git_branch: devel
git_last_commit: f585003
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/riboSeqR_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/riboSeqR_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/riboSeqR_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/riboSeqR_1.41.0.tgz
vignettes: vignettes/riboSeqR/inst/doc/riboSeqR.pdf
vignetteTitles: riboSeqR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/riboSeqR/inst/doc/riboSeqR.R
dependencyCount: 48
Package: ribosomeProfilingQC
Version: 1.19.2
Depends: R (>= 4.0), GenomicRanges
Imports: AnnotationDbi, BiocGenerics, Biostrings, BSgenome, EDASeq,
GenomicAlignments, GenomicFeatures, GenomeInfoDb, IRanges,
methods, motifStack, rtracklayer, Rsamtools, RUVSeq, Rsubread,
S4Vectors, XVector, ggplot2, ggfittext, scales, ggrepel, utils,
cluster, stats, graphics, grid, txdbmaker, ggExtra
Suggests: RUnit, BiocStyle, knitr, BSgenome.Drerio.UCSC.danRer10,
edgeR, DESeq2, limma, ashr, testthat, rmarkdown, vsn, Biobase
License: GPL (>=3) + file LICENSE
Archs: x64
MD5sum: c643667acb9b3a8fbf3841fbb2cc954f
NeedsCompilation: no
Title: Ribosome Profiling Quality Control
Description: Ribo-Seq (also named ribosome profiling or footprinting)
measures translatome (unlike RNA-Seq, which sequences the
transcriptome) by direct quantification of the
ribosome-protected fragments (RPFs). This package provides the
tools for quality assessment of ribosome profiling. In
addition, it can preprocess Ribo-Seq data for subsequent
differential analysis.
biocViews: RiboSeq, Sequencing, GeneRegulation, QualityControl,
Visualization, Coverage
Author: Jianhong Ou [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8652-2488>), Mariah Hoye [aut]
Maintainer: Jianhong Ou <jou@morgridge.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ribosomeProfilingQC
git_branch: devel
git_last_commit: 520ccf1
git_last_commit_date: 2025-02-06
Date/Publication: 2025-02-09
source.ver: src/contrib/ribosomeProfilingQC_1.19.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ribosomeProfilingQC_1.19.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ribosomeProfilingQC_1.19.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ribosomeProfilingQC_1.19.2.tgz
vignettes:
vignettes/ribosomeProfilingQC/inst/doc/ribosomeProfilingQC.html
vignetteTitles: ribosomeProfilingQC Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ribosomeProfilingQC/inst/doc/ribosomeProfilingQC.R
dependencyCount: 180
Package: rifi
Version: 1.11.0
Depends: R (>= 4.2)
Imports: car, cowplot, doMC, parallel, dplyr, egg, foreach, ggplot2,
graphics, grDevices, grid, methods, nls2, nnet, rlang,
S4Vectors, scales, stats, stringr, SummarizedExperiment,
tibble, rtracklayer, reshape2, utils
Suggests: DescTools, devtools, knitr, rmarkdown, BiocStyle
License: GPL-3 + file LICENSE
MD5sum: eac8aac73731b331e011e45a080c0a33
NeedsCompilation: no
Title: 'rifi' analyses data from rifampicin time series created by
microarray or RNAseq
Description: 'rifi' analyses data from rifampicin time series created
by microarray or RNAseq. 'rifi' is a transcriptome data
analysis tool for the holistic identification of transcription
and decay associated processes. The decay constants and the
delay of the onset of decay is fitted for each probe/bin.
Subsequently, probes/bins of equal properties are combined into
segments by dynamic programming, independent of a existing
genome annotation. This allows to detect transcript segments of
different stability or transcriptional events within one
annotated gene. In addition to the classic decay
constant/half-life analysis, 'rifi' detects processing sites,
transcription pausing sites, internal transcription start sites
in operons, sites of partial transcription termination in
operons, identifies areas of likely transcriptional
interference by the collision mechanism and gives an estimate
of the transcription velocity. All data are integrated to give
an estimate of continous transcriptional units, i.e. operons.
Comprehensive output tables and visualizations of the full
genome result and the individual fits for all probes/bins are
produced.
biocViews: RNASeq, DifferentialExpression, GeneRegulation,
Transcriptomics, Regression, Microarray, Software
Author: Loubna Youssar [aut, ctb], Walja Wanney [aut, ctb], Jens Georg
[aut, cre]
Maintainer: Jens Georg <jens.georg@biologie.uni-freiburg.de>
VignetteBuilder: knitr
BugReports: https://github.com/CyanolabFreiburg/rifi
git_url: https://git.bioconductor.org/packages/rifi
git_branch: devel
git_last_commit: 73780f2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rifi_1.11.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rifi_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rifi_1.11.0.tgz
vignettes: vignettes/rifi/inst/doc/vignette.html
vignetteTitles: Rifi for decay estimation,, based on high resolution
microarray or RNA-seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/rifi/inst/doc/vignette.R
dependencyCount: 125
Package: rifiComparative
Version: 1.7.0
Depends: R (>= 4.2)
Imports: cowplot, doMC, parallel, dplyr, egg, foreach, ggplot2,
ggrepel, graphics, grDevices, grid, methods, nnet, rlang,
S4Vectors, scales, stats, stringr, tibble, rtracklayer, utils,
writexl, DTA, LSD, reshape2, devtools, SummarizedExperiment
Suggests: DescTools, knitr, rmarkdown, BiocStyle
License: GPL-3 + file LICENSE
MD5sum: 08644eebb0005e8118f6539e571fa90e
NeedsCompilation: no
Title: 'rifiComparative' compares the output of rifi from two different
conditions.
Description: 'rifiComparative' is a continuation of rifi package. It
compares two conditions output of rifi using half-life and mRNA
at time 0 segments. As an input for the segmentation, the
difference between half-life of both condtions and log2FC of
the mRNA at time 0 are used. The package provides segmentation,
statistics, summary table, fragments visualization and some
additional useful plots for further anaylsis.
biocViews: RNASeq, DifferentialExpression, GeneRegulation,
Transcriptomics, Microarray, Software
Author: Loubna Youssar [aut, cre], Jens cre Georg [aut]
Maintainer: Loubna Youssar <lyoussar@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/CyanolabFreiburg/rifiComparative
git_url: https://git.bioconductor.org/packages/rifiComparative
git_branch: devel
git_last_commit: 0e250a5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rifiComparative_1.7.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rifiComparative_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rifiComparative_1.7.0.tgz
vignettes: vignettes/rifiComparative/inst/doc/rifiComparative.html
vignetteTitles: rifiComparative
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/rifiComparative/inst/doc/rifiComparative.R
dependencyCount: 172
Package: Rigraphlib
Version: 0.99.5
LinkingTo: biocmake
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: GPL-3
Archs: x64
MD5sum: d5112c29fe27491d64d399e28f0a87c6
NeedsCompilation: yes
Title: igraph library as an R package
Description: Vendors the igraph C source code and builds it into a
static library. Other Bioconductor packages can link to
libigraph.a in their own C/C++ code. This is intended for
packages wrapping C/C++ libraries that depend on the igraph C
library and cannot be easily adapted to use the igraph R
package.
biocViews: Clustering, GraphAndNetwork
Author: Aaron Lun [cre, aut]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://github.com/libscran/Rigraphlib
VignetteBuilder: knitr
BugReports: https://github.com/libscran/Rigraphlib/issues
git_url: https://git.bioconductor.org/packages/Rigraphlib
git_branch: devel
git_last_commit: 5d1c434
git_last_commit_date: 2025-03-24
Date/Publication: 2025-03-25
source.ver: src/contrib/Rigraphlib_0.99.5.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Rigraphlib_0.99.5.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Rigraphlib_0.99.5.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Rigraphlib_0.99.5.tgz
vignettes: vignettes/Rigraphlib/inst/doc/userguide.html
vignetteTitles: Using the igraph C library
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rigraphlib/inst/doc/userguide.R
importsMe: scrapper
dependencyCount: 5
Package: rigvf
Version: 0.99.6
Depends: R (>= 4.1.0)
Imports: methods, httr2, rjsoncons, dplyr, tidyr, rlang, memoise,
cachem, whisker, jsonlite, GenomicRanges, IRanges, GenomeInfoDb
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), plyranges,
plotgardener, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene
License: MIT + file LICENSE
MD5sum: 31ba571e115bca445e15b67b535ae229
NeedsCompilation: no
Title: R interface to the IGVF Catalog
Description: The IGVF Catalog provides data on the impact of genomic
variants on function. The `rigvf` package provides an interface
to the IGVF Catalog, allowing easy integration with
Bioconductor resources.
biocViews: ThirdPartyClient, Annotation, VariantAnnotation,
FunctionalGenomics, GeneRegulation, GenomicVariation,
GeneTarget
Author: Martin Morgan [aut] (ORCID:
<https://orcid.org/0000-0002-5874-8148>), Michael Love [aut,
cre] (ORCID: <https://orcid.org/0000-0001-8401-0545>), NIH
NHGRI UM1HG012003 [fnd]
Maintainer: Michael Love <michaelisaiahlove@gmail.com>
URL: https://IGVF.github.io/rigvf
VignetteBuilder: knitr
BugReports: https://github.com/IGVF/rigvf/issues
git_url: https://git.bioconductor.org/packages/rigvf
git_branch: devel
git_last_commit: d61b381
git_last_commit_date: 2025-03-19
Date/Publication: 2025-03-24
source.ver: src/contrib/rigvf_0.99.6.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rigvf_0.99.6.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rigvf_0.99.6.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rigvf_0.99.6.tgz
vignettes: vignettes/rigvf/inst/doc/rigvf.html
vignetteTitles: Accessing data from the IGVF Catalog
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/rigvf/inst/doc/rigvf.R
dependencyCount: 50
Package: RImmPort
Version: 1.35.0
Imports: plyr, dplyr, DBI, data.table, reshape2, methods, sqldf, tools,
utils, RSQLite
Suggests: knitr
License: GPL-3
MD5sum: 029d1a61780f961d46c7d4f2cf3112b6
NeedsCompilation: no
Title: RImmPort: Enabling Ready-for-analysis Immunology Research Data
Description: The RImmPort package simplifies access to ImmPort data for
analysis in the R environment. It provides a standards-based
interface to the ImmPort study data that is in a proprietary
format.
biocViews: BiomedicalInformatics, DataImport, DataRepresentation
Author: Ravi Shankar <rshankar@stanford.edu>
Maintainer: Zicheng Hu <Zicheng.Hu@ucsf.edu>, Ravi Shankar
<rshankar@stanford.edu>
URL: http://bioconductor.org/packages/RImmPort/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RImmPort
git_branch: devel
git_last_commit: 804fce5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RImmPort_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RImmPort_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RImmPort_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RImmPort_1.35.0.tgz
vignettes: vignettes/RImmPort/inst/doc/RImmPort_Article.pdf,
vignettes/RImmPort/inst/doc/RImmPort_QuickStart.pdf
vignetteTitles: RImmPort: Enabling ready-for-analysis immunology
research data, RImmPort: Quick Start Guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RImmPort/inst/doc/RImmPort_Article.R,
vignettes/RImmPort/inst/doc/RImmPort_QuickStart.R
dependencyCount: 42
Package: RITAN
Version: 1.31.0
Depends: R (>= 4.0),
Imports: graphics, methods, stats, utils, grid, gridExtra, reshape2,
gplots, ggplot2, plotrix, RColorBrewer, STRINGdb, MCL,
linkcomm, dynamicTreeCut, gsubfn, hash, png, sqldf, igraph,
BgeeDB, knitr, RITANdata, GenomicFeatures, ensembldb,
AnnotationFilter, EnsDb.Hsapiens.v86
Suggests: rmarkdown, BgeeDB
License: file LICENSE
MD5sum: aa61109d4ea33ca850cf01276973ce46
NeedsCompilation: no
Title: Rapid Integration of Term Annotation and Network resources
Description: Tools for comprehensive gene set enrichment and extraction
of multi-resource high confidence subnetworks. RITAN
facilitates bioinformatic tasks for enabling network biology
research.
biocViews: QualityControl, Network, NetworkEnrichment,
NetworkInference, GeneSetEnrichment, FunctionalGenomics,
GraphAndNetwork
Author: Michael Zimmermann [aut, cre]
Maintainer: Michael Zimmermann <mtzimmermann@mcw.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RITAN
git_branch: devel
git_last_commit: e6e8dc4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RITAN_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RITAN_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RITAN_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RITAN_1.31.0.tgz
vignettes: vignettes/RITAN/inst/doc/choosing_resources.html,
vignettes/RITAN/inst/doc/enrichment.html,
vignettes/RITAN/inst/doc/multi_tissue_analysis.html,
vignettes/RITAN/inst/doc/resource_relationships.html,
vignettes/RITAN/inst/doc/subnetworks.html
vignetteTitles: Choosing Resources, Enrichment Vignette, Multi-Tissue
Analysis, Relationships Among Resources, Network Biology
Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/RITAN/inst/doc/choosing_resources.R,
vignettes/RITAN/inst/doc/enrichment.R,
vignettes/RITAN/inst/doc/multi_tissue_analysis.R,
vignettes/RITAN/inst/doc/resource_relationships.R,
vignettes/RITAN/inst/doc/subnetworks.R
dependencyCount: 162
Package: RIVER
Version: 1.31.0
Depends: R (>= 3.3.2)
Imports: glmnet, pROC, ggplot2, graphics, stats, Biobase, methods,
utils
Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools
License: GPL (>= 2)
MD5sum: e455c047833de584732e5c246f2763b2
NeedsCompilation: no
Title: R package for RIVER (RNA-Informed Variant Effect on Regulation)
Description: An implementation of a probabilistic modeling framework
that jointly analyzes personal genome and transcriptome data to
estimate the probability that a variant has regulatory impact
in that individual. It is based on a generative model that
assumes that genomic annotations, such as the location of a
variant with respect to regulatory elements, determine the
prior probability that variant is a functional regulatory
variant, which is an unobserved variable. The functional
regulatory variant status then influences whether nearby genes
are likely to display outlier levels of gene expression in that
person. See the RIVER website for more information,
documentation and examples.
biocViews: GeneExpression, GeneticVariability, SNP, Transcription,
FunctionalPrediction, GeneRegulation, GenomicVariation,
BiomedicalInformatics, FunctionalGenomics, Genetics,
SystemsBiology, Transcriptomics, Bayesian, Clustering,
TranscriptomeVariant, Regression
Author: Yungil Kim [aut, cre], Alexis Battle [aut]
Maintainer: Yungil Kim <ipw012@gmail.com>
URL: https://github.com/ipw012/RIVER
VignetteBuilder: knitr
BugReports: https://github.com/ipw012/RIVER/issues
git_url: https://git.bioconductor.org/packages/RIVER
git_branch: devel
git_last_commit: 6afdc8c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RIVER_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RIVER_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RIVER_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RIVER_1.31.0.tgz
vignettes: vignettes/RIVER/inst/doc/RIVER.html
vignetteTitles: RIVER
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RIVER/inst/doc/RIVER.R
dependencyCount: 48
Package: RJMCMCNucleosomes
Version: 1.31.0
Depends: R (>= 3.5.0), IRanges, GenomicRanges
Imports: Rcpp (>= 0.12.5), consensusSeekeR, BiocGenerics, GenomeInfoDb,
S4Vectors (>= 0.23.10), BiocParallel, stats, graphics, methods,
grDevices
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, rmarkdown, nucleoSim, RUnit
License: Artistic-2.0
MD5sum: ab53922049ec8e02e25204f9f518a852
NeedsCompilation: yes
Title: Bayesian hierarchical model for genome-wide nucleosome
positioning with high-throughput short-read data (MNase-Seq)
Description: This package does nucleosome positioning using informative
Multinomial-Dirichlet prior in a t-mixture with reversible jump
estimation of nucleosome positions for genome-wide profiling.
biocViews: BiologicalQuestion, ChIPSeq, NucleosomePositioning,
Software, StatisticalMethod, Bayesian, Sequencing, Coverage
Author: Pascal Belleau [aut], Rawane Samb [aut], Astrid Deschênes [cre,
aut], Khader Khadraoui [aut], Lajmi Lakhal-Chaieb [aut], Arnaud
Droit [aut]
Maintainer: Astrid Deschênes <adeschen@hotmail.com>
URL: https://github.com/ArnaudDroitLab/RJMCMCNucleosomes
SystemRequirements: Rcpp
VignetteBuilder: knitr
BugReports: https://github.com/ArnaudDroitLab/RJMCMCNucleosomes/issues
git_url: https://git.bioconductor.org/packages/RJMCMCNucleosomes
git_branch: devel
git_last_commit: aa06559
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RJMCMCNucleosomes_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RJMCMCNucleosomes_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RJMCMCNucleosomes_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RJMCMCNucleosomes_1.31.0.tgz
vignettes: vignettes/RJMCMCNucleosomes/inst/doc/RJMCMCNucleosomes.html
vignetteTitles: Nucleosome Positioning
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RJMCMCNucleosomes/inst/doc/RJMCMCNucleosomes.R
dependencyCount: 68
Package: RLassoCox
Version: 1.15.0
Depends: R (>= 4.1), glmnet
Imports: Matrix, igraph, survival, stats
Suggests: knitr
License: Artistic-2.0
MD5sum: 3d6e3bbc9bd11855e67f99224cf2736f
NeedsCompilation: no
Title: A reweighted Lasso-Cox by integrating gene interaction
information
Description: RLassoCox is a package that implements the RLasso-Cox
model proposed by Wei Liu. The RLasso-Cox model integrates gene
interaction information into the Lasso-Cox model for accurate
survival prediction and survival biomarker discovery. It is
based on the hypothesis that topologically important genes in
the gene interaction network tend to have stable expression
changes. The RLasso-Cox model uses random walk to evaluate the
topological weight of genes, and then highlights topologically
important genes to improve the generalization ability of the
Lasso-Cox model. The RLasso-Cox model has the advantage of
identifying small gene sets with high prognostic performance on
independent datasets, which may play an important role in
identifying robust survival biomarkers for various cancer
types.
biocViews: Survival, Regression, GeneExpression, GenePrediction,
Network
Author: Wei Liu [cre, aut] (ORCID:
<https://orcid.org/0000-0002-5496-3641>)
Maintainer: Wei Liu <freelw@qq.com>
VignetteBuilder: knitr
BugReports: https://github.com/weiliu123/RLassoCox/issues
git_url: https://git.bioconductor.org/packages/RLassoCox
git_branch: devel
git_last_commit: 9f0003e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RLassoCox_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RLassoCox_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RLassoCox_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RLassoCox_1.15.0.tgz
vignettes: vignettes/RLassoCox/inst/doc/RLassoCox.pdf
vignetteTitles: RLassoCox
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RLassoCox/inst/doc/RLassoCox.R
dependencyCount: 26
Package: RLMM
Version: 1.69.0
Depends: R (>= 2.1.0)
Imports: graphics, grDevices, MASS, stats, utils
License: LGPL (>= 2)
MD5sum: 4697d703ea82a89c35496b149a7ccfa3
NeedsCompilation: no
Title: A Genotype Calling Algorithm for Affymetrix SNP Arrays
Description: A classification algorithm, based on a multi-chip,
multi-SNP approach for Affymetrix SNP arrays. Using a large
training sample where the genotype labels are known, this
aglorithm will obtain more accurate classification results on
new data. RLMM is based on a robust, linear model and uses the
Mahalanobis distance for classification. The chip-to-chip
non-biological variation is removed through normalization. This
model-based algorithm captures the similarities across genotype
groups and probes, as well as thousands other SNPs for accurate
classification. NOTE: 100K-Xba only at for now.
biocViews: Microarray, OneChannel, SNP, GeneticVariability
Author: Nusrat Rabbee <nrabbee@post.harvard.edu>, Gary Wong
<wongg62@berkeley.edu>
Maintainer: Nusrat Rabbee <nrabbee@post.harvard.edu>
URL: http://www.stat.berkeley.edu/users/nrabbee/RLMM
SystemRequirements: Internal files Xba.CQV, Xba.regions (or other
regions file)
git_url: https://git.bioconductor.org/packages/RLMM
git_branch: devel
git_last_commit: b3e0ae7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RLMM_1.69.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RLMM_1.69.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RLMM_1.69.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RLMM_1.69.0.tgz
vignettes: vignettes/RLMM/inst/doc/RLMM.pdf
vignetteTitles: RLMM Doc
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RLMM/inst/doc/RLMM.R
dependencyCount: 6
Package: Rmagpie
Version: 1.63.0
Depends: R (>= 2.6.1), Biobase (>= 2.5.5)
Imports: Biobase (>= 2.5.5), e1071, graphics, grDevices, kernlab,
methods, pamr, stats, utils
Suggests: xtable
License: GPL (>= 3)
MD5sum: 755e0cc6baa62303eb2f9d03a0026ecd
NeedsCompilation: no
Title: MicroArray Gene-expression-based Program In Error rate
estimation
Description: Microarray Classification is designed for both biologists
and statisticians. It offers the ability to train a classifier
on a labelled microarray dataset and to then use that
classifier to predict the class of new observations. A range of
modern classifiers are available, including support vector
machines (SVMs), nearest shrunken centroids (NSCs)... Advanced
methods are provided to estimate the predictive error rate and
to report the subset of genes which appear essential in
discriminating between classes.
biocViews: Microarray, Classification
Author: Camille Maumet <Rmagpie@gmail.com>, with contributions from C.
Ambroise J. Zhu
Maintainer: Camille Maumet <Rmagpie@gmail.com>
URL: http://www.bioconductor.org/
git_url: https://git.bioconductor.org/packages/Rmagpie
git_branch: devel
git_last_commit: cc7cbb8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Rmagpie_1.63.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Rmagpie_1.63.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Rmagpie_1.63.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Rmagpie_1.63.0.tgz
vignettes: vignettes/Rmagpie/inst/doc/Magpie_examples.pdf
vignetteTitles: Rmagpie Examples
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rmagpie/inst/doc/Magpie_examples.R
dependencyCount: 20
Package: RMassBank
Version: 3.17.0
Depends: Rcpp
Imports: assertthat, Biobase, ChemmineR, data.table, digest, dplyr,
enviPat, glue, httr, httr2, logger, methods, MSnbase, mzR,
purrr, R.utils, rcdk, readJDX, readr, rjson, S4Vectors, tibble,
tidyselect, webchem, XML, yaml
Suggests: BiocStyle, CAMERA, gplots, knitr, magick, rmarkdown,
RMassBankData (>= 1.33.1), RUnit, xcms (>= 1.37.1)
License: Artistic-2.0
MD5sum: 1196508cd910156327ea198e8060680c
NeedsCompilation: no
Title: Workflow to process tandem MS files and build MassBank records
Description: Workflow to process tandem MS files and build MassBank
records. Functions include automated extraction of tandem MS
spectra, formula assignment to tandem MS fragments,
recalibration of tandem MS spectra with assigned fragments,
spectrum cleanup, automated retrieval of compound information
from Internet databases, and export to MassBank records.
biocViews: ImmunoOncology, Bioinformatics, MassSpectrometry,
Metabolomics, Software
Author: Michael Stravs, Emma Schymanski, Steffen Neumann, Erik Mueller,
Paul Stahlhofen, Tobias Schulze with contributions of Hendrik
Treutler
Maintainer: RMassBank at Eawag <massbank@eawag.ch>
SystemRequirements: OpenBabel
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RMassBank
git_branch: devel
git_last_commit: 8e633a1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RMassBank_3.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RMassBank_3.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RMassBank_3.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RMassBank_3.17.0.tgz
vignettes: vignettes/RMassBank/inst/doc/RMassBank.html,
vignettes/RMassBank/inst/doc/RMassBankNonstandard.html
vignetteTitles: RMassBank: The workflow by example, RMassBank:
Non-standard usage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RMassBank/inst/doc/RMassBank.R,
vignettes/RMassBank/inst/doc/RMassBankNonstandard.R
suggestsMe: RMassBankData
dependencyCount: 173
Package: rmelting
Version: 1.23.0
Depends: R (>= 3.6)
Imports: Rdpack, rJava (>= 0.9-8)
Suggests: readxl, knitr, rmarkdown, reshape2, pander, testthat
License: GPL-2 | GPL-3
Archs: x64
MD5sum: 54b5cca8150dea68ab242948cb25c8e8
NeedsCompilation: no
Title: R Interface to MELTING 5
Description: R interface to the MELTING 5 program
(https://www.ebi.ac.uk/biomodels/tools/melting/) to compute
melting temperatures of nucleic acid duplexes along with other
thermodynamic parameters.
biocViews: BiomedicalInformatics, Cheminformatics,
Author: J. Aravind [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4791-442X>), G. K. Krishna [aut],
Bob Rudis [ctb] (melting5jars), Nicolas Le Novère [ctb]
(MELTING 5 Java Library), Marine Dumousseau [ctb] (MELTING 5
Java Library), William John Gowers [ctb] (MELTING 5 Java
Library)
Maintainer: J. Aravind <j.aravind@icar.gov.in>
URL: https://github.com/aravind-j/rmelting,
https://aravind-j.github.io/rmelting/
SystemRequirements: Java
VignetteBuilder: knitr
BugReports: https://github.com/aravind-j/rmelting/issues
git_url: https://git.bioconductor.org/packages/rmelting
git_branch: devel
git_last_commit: ae2bfcf
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rmelting_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rmelting_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rmelting_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rmelting_1.23.0.tgz
vignettes: vignettes/rmelting/inst/doc/Tutorial.pdf
vignetteTitles: Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 6
Package: Rmmquant
Version: 1.25.0
Depends: R (>= 3.6)
Imports: Rcpp (>= 0.12.8), methods, S4Vectors, GenomicRanges,
SummarizedExperiment, devtools, TBX20BamSubset,
TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, DESeq2,
apeglm, BiocStyle
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, testthat
License: GPL-3
MD5sum: e305b16a5953d79a876f5b5862583606
NeedsCompilation: yes
Title: RNA-Seq multi-mapping Reads Quantification Tool
Description: RNA-Seq is currently used routinely, and it provides
accurate information on gene transcription. However, the method
cannot accurately estimate duplicated genes expression. Several
strategies have been previously used, but all of them provide
biased results. With Rmmquant, if a read maps at different
positions, the tool detects that the corresponding genes are
duplicated; it merges the genes and creates a merged gene. The
counts of ambiguous reads is then based on the input genes and
the merged genes. Rmmquant is a drop-in replacement of the
widely used tools findOverlaps and featureCounts that handles
multi-mapping reads in an unabiased way.
biocViews: GeneExpression, Transcription
Author: Zytnicki Matthias [aut, cre]
Maintainer: Zytnicki Matthias <matthias.zytnicki@inra.fr>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Rmmquant
git_branch: devel
git_last_commit: 46d2936
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Rmmquant_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Rmmquant_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Rmmquant_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Rmmquant_1.25.0.tgz
vignettes: vignettes/Rmmquant/inst/doc/Rmmquant.html
vignetteTitles: The Rmmquant package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rmmquant/inst/doc/Rmmquant.R
dependencyCount: 185
Package: rmspc
Version: 1.13.2
Imports: processx, BiocManager, rtracklayer, stats, tools, methods,
GenomicRanges, stringr
Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0)
License: GPL-3
MD5sum: 74135b08f927c2bab7da7227aa96dc12
NeedsCompilation: no
Title: Multiple Sample Peak Calling
Description: The rmspc package runs MSPC (Multiple Sample Peak Calling)
software using R. The analysis of ChIP-seq samples outputs a
number of enriched regions (commonly known as "peaks"), each
indicating a protein-DNA interaction or a specific chromatin
modification. When replicate samples are analyzed, overlapping
peaks are expected. This repeated evidence can therefore be
used to locally lower the minimum significance required to
accept a peak. MSPC uses combined evidence from replicated
experiments to evaluate peak calling output, rescuing peaks,
and reduce false positives. It takes any number of replicates
as input and improves sensitivity and specificity of peak
calling on each, and identifies consensus regions between the
input samples.
biocViews: ChIPSeq, Sequencing, ChipOnChip, DataImport, RNASeq
Author: Vahid Jalili [aut], Marzia Angela Cremona [aut], Fernando
Palluzzi [aut], Meriem Bahda [aut, cre]
Maintainer: Meriem Bahda <meriembahda@gmail.com>
URL: https://genometric.github.io/MSPC/
SystemRequirements: .NET 9.0
VignetteBuilder: knitr
BugReports: https://github.com/Genometric/MSPC/issues
git_url: https://git.bioconductor.org/packages/rmspc
git_branch: devel
git_last_commit: f18aa13
git_last_commit_date: 2025-03-23
Date/Publication: 2025-03-24
source.ver: src/contrib/rmspc_1.13.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rmspc_1.13.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rmspc_1.13.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rmspc_1.13.2.tgz
vignettes: vignettes/rmspc/inst/doc/rmpsc.html
vignetteTitles: User guide to the rmspc package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rmspc/inst/doc/rmpsc.R
dependencyCount: 69
Package: RNAAgeCalc
Version: 1.19.0
Depends: R (>= 3.6)
Imports: ggplot2, recount, impute, AnnotationDbi, org.Hs.eg.db, stats,
SummarizedExperiment, methods
Suggests: knitr, rmarkdown, testthat
License: GPL-2
MD5sum: b4c6ea7739e6d0bb926896ac37bc554d
NeedsCompilation: no
Title: A multi-tissue transcriptional age calculator
Description: It has been shown that both DNA methylation and RNA
transcription are linked to chronological age and age related
diseases. Several estimators have been developed to predict
human aging from DNA level and RNA level. Most of the human
transcriptional age predictor are based on microarray data and
limited to only a few tissues. To date, transcriptional studies
on aging using RNASeq data from different human tissues is
limited. The aim of this package is to provide a tool for
across-tissue and tissue-specific transcriptional age
calculation based on GTEx RNASeq data.
biocViews: RNASeq,GeneExpression
Author: Xu Ren [aut, cre], Pei Fen Kuan [aut]
Maintainer: Xu Ren <xuren2120@gmail.com>
URL: https://github.com/reese3928/RNAAgeCalc
VignetteBuilder: knitr
BugReports: https://github.com/reese3928/RNAAgeCalc/issues
git_url: https://git.bioconductor.org/packages/RNAAgeCalc
git_branch: devel
git_last_commit: d15a7d3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-12-13
source.ver: src/contrib/RNAAgeCalc_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RNAAgeCalc_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RNAAgeCalc_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RNAAgeCalc_1.19.0.tgz
vignettes: vignettes/RNAAgeCalc/inst/doc/RNAAge-vignette.html
vignetteTitles: RNAAgeCalc
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RNAAgeCalc/inst/doc/RNAAge-vignette.R
dependencyCount: 169
Package: RNAdecay
Version: 1.27.0
Depends: R (>= 3.5)
Imports: stats, grDevices, grid, ggplot2, gplots, utils, TMB, nloptr,
scales
Suggests: parallel, knitr, reshape2, rmarkdown
License: GPL-2
MD5sum: e535ef177169ff43cabf060201d21261
NeedsCompilation: yes
Title: Maximum Likelihood Decay Modeling of RNA Degradation Data
Description: RNA degradation is monitored through measurement of RNA
abundance after inhibiting RNA synthesis. This package has
functions and example scripts to facilitate (1) data
normalization, (2) data modeling using constant decay rate or
time-dependent decay rate models, (3) the evaluation of
treatment or genotype effects, and (4) plotting of the data and
models. Data Normalization: functions and scripts make easy the
normalization to the initial (T0) RNA abundance, as well as a
method to correct for artificial inflation of Reads per Million
(RPM) abundance in global assessments as the total size of the
RNA pool decreases. Modeling: Normalized data is then modeled
using maximum likelihood to fit parameters. For making
treatment or genotype comparisons (up to four), the modeling
step models all possible treatment effects on each gene by
repeating the modeling with constraints on the model parameters
(i.e., the decay rate of treatments A and B are modeled once
with them being equal and again allowing them to both vary
independently). Model Selection: The AICc value is calculated
for each model, and the model with the lowest AICc is chosen.
Modeling results of selected models are then compiled into a
single data frame. Graphical Plotting: functions are provided
to easily visualize decay data model, or half-life
distributions using ggplot2 package functions.
biocViews: ImmunoOncology, Software, GeneExpression, GeneRegulation,
DifferentialExpression, Transcription, Transcriptomics,
TimeCourse, Regression, RNASeq, Normalization, WorkflowStep
Author: Reed Sorenson [aut, cre], Katrina Johnson [aut], Frederick
Adler [aut], Leslie Sieburth [aut]
Maintainer: Reed Sorenson <reedssorenson@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RNAdecay
git_branch: devel
git_last_commit: 42841c3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RNAdecay_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RNAdecay_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RNAdecay_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RNAdecay_1.27.0.tgz
vignettes: vignettes/RNAdecay/inst/doc/RNAdecay_workflow.html
vignetteTitles: RNAdecay
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RNAdecay/inst/doc/RNAdecay_workflow.R
dependencyCount: 44
Package: rnaEditr
Version: 1.17.0
Depends: R (>= 4.0)
Imports: GenomicRanges, IRanges, BiocGenerics, GenomeInfoDb,
bumphunter, S4Vectors, stats, survival, logistf, plyr, corrplot
Suggests: knitr, rmarkdown, testthat
License: GPL-3
MD5sum: ca3e9611b6bdad96750076d5e6d19ba9
NeedsCompilation: no
Title: Statistical analysis of RNA editing sites and hyper-editing
regions
Description: RNAeditr analyzes site-specific RNA editing events, as
well as hyper-editing regions. The editing frequencies can be
tested against binary, continuous or survival outcomes.
Multiple covariate variables as well as interaction effects can
also be incorporated in the statistical models.
biocViews: GeneTarget, Epigenetics, DimensionReduction,
FeatureExtraction, Regression, Survival, RNASeq
Author: Lanyu Zhang [aut, cre], Gabriel Odom [aut], Tiago Silva [aut],
Lissette Gomez [aut], Lily Wang [aut]
Maintainer: Lanyu Zhang <jennyzly2016@gmail.com>
URL: https://github.com/TransBioInfoLab/rnaEditr
VignetteBuilder: knitr
BugReports: https://github.com/TransBioInfoLab/rnaEditr/issues
git_url: https://git.bioconductor.org/packages/rnaEditr
git_branch: devel
git_last_commit: a074827
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rnaEditr_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rnaEditr_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rnaEditr_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rnaEditr_1.17.0.tgz
vignettes: vignettes/rnaEditr/inst/doc/introduction_to_rnaEditr.html
vignetteTitles: Introduction to rnaEditr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rnaEditr/inst/doc/introduction_to_rnaEditr.R
dependencyCount: 139
Package: RNAmodR
Version: 1.21.0
Depends: R (>= 4.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9),
GenomicRanges, Modstrings
Imports: methods, stats, grDevices, matrixStats, BiocGenerics,
Biostrings (>= 2.57.2), BiocParallel, txdbmaker,
GenomicFeatures, GenomicAlignments, GenomeInfoDb, rtracklayer,
Rsamtools, BSgenome, RColorBrewer, colorRamps, ggplot2, Gviz
(>= 1.31.0), reshape2, graphics, ROCR
Suggests: BiocStyle, knitr, rmarkdown, testthat, RNAmodR.Data
License: Artistic-2.0
Archs: x64
MD5sum: e09dee3b914edbe5c7d6a1f6a017a2b3
NeedsCompilation: no
Title: Detection of post-transcriptional modifications in high
throughput sequencing data
Description: RNAmodR provides classes and workflows for
loading/aggregation data from high througput sequencing aimed
at detecting post-transcriptional modifications through
analysis of specific patterns. In addition, utilities are
provided to validate and visualize the results. The RNAmodR
package provides a core functionality from which specific
analysis strategies can be easily implemented as a seperate
package.
biocViews: Software, Infrastructure, WorkflowStep, Visualization,
Sequencing
Author: Felix G.M. Ernst [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5064-0928>), Denis L.J. Lafontaine
[ctb, fnd]
Maintainer: Felix G.M. Ernst <felix.gm.ernst@outlook.com>
URL: https://github.com/FelixErnst/RNAmodR
VignetteBuilder: knitr
BugReports: https://github.com/FelixErnst/RNAmodR/issues
git_url: https://git.bioconductor.org/packages/RNAmodR
git_branch: devel
git_last_commit: 83a9c61
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RNAmodR_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RNAmodR_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RNAmodR_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RNAmodR_1.21.0.tgz
vignettes: vignettes/RNAmodR/inst/doc/RNAmodR.creation.html,
vignettes/RNAmodR/inst/doc/RNAmodR.html
vignetteTitles: RNAmodR - creating new classes for a new detection
strategy, RNAmodR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RNAmodR/inst/doc/RNAmodR.creation.R,
vignettes/RNAmodR/inst/doc/RNAmodR.R
dependsOnMe: RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq
dependencyCount: 166
Package: RNAmodR.AlkAnilineSeq
Version: 1.21.0
Depends: R (>= 4.0), RNAmodR (>= 1.5.3)
Imports: methods, S4Vectors, IRanges, BiocGenerics, GenomicRanges, Gviz
Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer,
Biostrings, RNAmodR.Data
License: Artistic-2.0
MD5sum: cd8ef38e9f6a7595b7680f8171b885d9
NeedsCompilation: no
Title: Detection of m7G, m3C and D modification by AlkAnilineSeq
Description: RNAmodR.AlkAnilineSeq implements the detection of m7G, m3C
and D modifications on RNA from experimental data generated
with the AlkAnilineSeq protocol. The package builds on the core
functionality of the RNAmodR package to detect specific
patterns of the modifications in high throughput sequencing
data.
biocViews: Software, WorkflowStep, Visualization, Sequencing
Author: Felix G.M. Ernst [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5064-0928>), Denis L.J. Lafontaine
[ctb, fnd]
Maintainer: Felix G.M. Ernst <felix.gm.ernst@outlook.com>
URL: https://github.com/FelixErnst/RNAmodR.AlkAnilineSeq
VignetteBuilder: knitr
BugReports: https://github.com/FelixErnst/RNAmodR.AlkAnilineSeq/issues
git_url: https://git.bioconductor.org/packages/RNAmodR.AlkAnilineSeq
git_branch: devel
git_last_commit: 4d75977
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RNAmodR.AlkAnilineSeq_1.21.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.5/RNAmodR.AlkAnilineSeq_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RNAmodR.AlkAnilineSeq_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RNAmodR.AlkAnilineSeq_1.21.0.tgz
vignettes:
vignettes/RNAmodR.AlkAnilineSeq/inst/doc/RNAmodR.AlkAnilineSeq.html
vignetteTitles: RNAmodR.AlkAnilineSeq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/RNAmodR.AlkAnilineSeq/inst/doc/RNAmodR.AlkAnilineSeq.R
suggestsMe: RNAmodR.ML
dependencyCount: 167
Package: RNAmodR.ML
Version: 1.21.0
Depends: R (>= 3.6), RNAmodR
Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges,
stats, ranger
Suggests: BiocStyle, knitr, rmarkdown, testthat, RNAmodR.Data,
RNAmodR.AlkAnilineSeq, GenomicFeatures, Rsamtools, rtracklayer,
keras
License: Artistic-2.0
MD5sum: 5ebadd980bd152ed8cf31f668f6c6d45
NeedsCompilation: no
Title: Detecting patterns of post-transcriptional modifications using
machine learning
Description: RNAmodR.ML extend the functionality of the RNAmodR package
and classical detection strategies towards detection through
machine learning models. RNAmodR.ML provides classes, functions
and an example workflow to establish a detection stratedy,
which can be packaged.
biocViews: Software, Infrastructure, WorkflowStep, Visualization,
Sequencing
Author: Felix G.M. Ernst [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5064-0928>), Denis L.J. Lafontaine
[ctb]
Maintainer: Felix G.M. Ernst <felix.gm.ernst@outlook.com>
URL: https://github.com/FelixErnst/RNAmodR.ML
VignetteBuilder: knitr
BugReports: https://github.com/FelixErnst/RNAmodR.ML/issues
git_url: https://git.bioconductor.org/packages/RNAmodR.ML
git_branch: devel
git_last_commit: d85435c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RNAmodR.ML_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RNAmodR.ML_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RNAmodR.ML_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RNAmodR.ML_1.21.0.tgz
vignettes: vignettes/RNAmodR.ML/inst/doc/RNAmodR.ML.html
vignetteTitles: RNAmodR.ML
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RNAmodR.ML/inst/doc/RNAmodR.ML.R
dependencyCount: 168
Package: RNAmodR.RiboMethSeq
Version: 1.21.0
Depends: R (>= 4.0), RNAmodR (>= 1.5.3)
Imports: methods, S4Vectors, BiocGenerics, IRanges, GenomicRanges, Gviz
Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer,
RNAmodR.Data
License: Artistic-2.0
Archs: x64
MD5sum: ae5b931ff08580629739d5a72824f888
NeedsCompilation: no
Title: Detection of 2'-O methylations by RiboMethSeq
Description: RNAmodR.RiboMethSeq implements the detection of 2'-O
methylations on RNA from experimental data generated with the
RiboMethSeq protocol. The package builds on the core
functionality of the RNAmodR package to detect specific
patterns of the modifications in high throughput sequencing
data.
biocViews: Software, WorkflowStep, Visualization, Sequencing
Author: Felix G.M. Ernst [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5064-0928>), Denis L.J. Lafontaine
[ctb, fnd]
Maintainer: Felix G.M. Ernst <felix.gm.ernst@outlook.com>
URL: https://github.com/FelixErnst/RNAmodR.RiboMethSeq
VignetteBuilder: knitr
BugReports: https://github.com/FelixErnst/RNAmodR.RiboMethSeq/issues
git_url: https://git.bioconductor.org/packages/RNAmodR.RiboMethSeq
git_branch: devel
git_last_commit: e2f22fe
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RNAmodR.RiboMethSeq_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RNAmodR.RiboMethSeq_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RNAmodR.RiboMethSeq_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RNAmodR.RiboMethSeq_1.21.0.tgz
vignettes:
vignettes/RNAmodR.RiboMethSeq/inst/doc/RNAmodR.RiboMethSeq.html
vignetteTitles: RNAmodR.RiboMethSeq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RNAmodR.RiboMethSeq/inst/doc/RNAmodR.RiboMethSeq.R
dependencyCount: 167
Package: RNAsense
Version: 1.21.0
Depends: R (>= 3.6)
Imports: ggplot2, parallel, NBPSeq, qvalue, SummarizedExperiment,
stats, utils, methods
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: c328859a40ff2f7be5ea695524eda791
NeedsCompilation: no
Title: Analysis of Time-Resolved RNA-Seq Data
Description: RNA-sense tool compares RNA-seq time curves in two
experimental conditions, i.e. wild-type and mutant, and works
in three steps. At Step 1, it builds expression profile for
each transcript in one condition (i.e. wild-type) and tests if
the transcript abundance grows or decays significantly.
Dynamic transcripts are then sorted to non-overlapping groups
(time profiles) by the time point of switch up or down. At Step
2, RNA-sense outputs the groups of differentially expressed
transcripts, which are up- or downregulated in the mutant
compared to the wild-type at each time point. At Step 3,
Correlations (Fisher's exact test) between the outputs of Step
1 (switch up- and switch down- time profile groups) and the
outputs of Step2 (differentially expressed transcript groups)
are calculated. The results of the correlation analysis are
printed as two-dimensional color plot, with time profiles and
differential expression groups at y- and x-axis, respectively,
and facilitates the biological interpretation of the data.
biocViews: RNASeq, GeneExpression, DifferentialExpression
Author: Marcus Rosenblatt [cre], Gao Meijang [aut], Helge Hass [aut],
Daria Onichtchouk [aut]
Maintainer: Marcus Rosenblatt <marcus.rosenblatt@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/marcusrosenblatt/RNAsense
git_url: https://git.bioconductor.org/packages/RNAsense
git_branch: devel
git_last_commit: e99ce3a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RNAsense_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RNAsense_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RNAsense_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RNAsense_1.21.0.tgz
vignettes: vignettes/RNAsense/inst/doc/example.html
vignetteTitles: Put the title of your vignette here
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RNAsense/inst/doc/example.R
dependencyCount: 70
Package: rnaseqcomp
Version: 1.37.0
Depends: R (>= 3.2.0)
Imports: RColorBrewer, methods
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-3
MD5sum: f4e5509d8f416be7fe43d4d167c8f60e
NeedsCompilation: no
Title: Benchmarks for RNA-seq Quantification Pipelines
Description: Several quantitative and visualized benchmarks for RNA-seq
quantification pipelines. Two-condition quantifications for
genes, transcripts, junctions or exons by each pipeline with
necessary meta information should be organized into numeric
matrices in order to proceed the evaluation.
biocViews: RNASeq, Visualization, QualityControl
Author: Mingxiang Teng and Rafael A. Irizarry
Maintainer: Mingxiang Teng <tengmx@gmail.com>
URL: https://github.com/tengmx/rnaseqcomp
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rnaseqcomp
git_branch: devel
git_last_commit: b6cc833
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rnaseqcomp_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rnaseqcomp_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rnaseqcomp_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rnaseqcomp_1.37.0.tgz
vignettes: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.html
vignetteTitles: The rnaseqcomp user's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.R
dependencyCount: 2
Package: RNAseqCovarImpute
Version: 1.5.0
Depends: R (>= 4.3.0)
Imports: Biobase, BiocGenerics, BiocParallel, stats, limma, dplyr,
magrittr, rlang, edgeR, foreach, mice
Suggests: BiocStyle, knitr, PCAtools, rmarkdown, tidyr, stringr,
testthat (>= 3.0.0)
License: GPL-3
MD5sum: 88d8a6ca5855aad7053fd1394019e3e9
NeedsCompilation: no
Title: Impute Covariate Data in RNA Sequencing Studies
Description: The RNAseqCovarImpute package makes linear model analysis
for RNA sequencing read counts compatible with multiple
imputation (MI) of missing covariates. A major problem with
implementing MI in RNA sequencing studies is that the outcome
data must be included in the imputation prediction models to
avoid bias. This is difficult in omics studies with
high-dimensional data. The first method we developed in the
RNAseqCovarImpute package surmounts the problem of
high-dimensional outcome data by binning genes into smaller
groups to analyze pseudo-independently. This method implements
covariate MI in gene expression studies by 1) randomly binning
genes into smaller groups, 2) creating M imputed datasets
separately within each bin, where the imputation predictor
matrix includes all covariates and the log counts per million
(CPM) for the genes within each bin, 3) estimating gene
expression changes using `limma::voom` followed by
`limma::lmFit` functions, separately on each M imputed dataset
within each gene bin, 4) un-binning the gene sets and stacking
the M sets of model results before applying the
`limma::squeezeVar` function to apply a variance shrinking
Bayesian procedure to each M set of model results, 5) pooling
the results with Rubins’ rules to produce combined
coefficients, standard errors, and P-values, and 6) adjusting
P-values for multiplicity to account for false discovery rate
(FDR). A faster method uses principal component analysis (PCA)
to avoid binning genes while still retaining outcome
information in the MI models. Binning genes into smaller groups
requires that the MI and limma-voom analysis is run many times
(typically hundreds). The more computationally efficient MI PCA
method implements covariate MI in gene expression studies by 1)
performing PCA on the log CPM values for all genes using the
Bioconductor `PCAtools` package, 2) creating M imputed datasets
where the imputation predictor matrix includes all covariates
and the optimum number of PCs to retain (e.g., based on Horn’s
parallel analysis or the number of PCs that account for >80%
explained variation), 3) conducting the standard limma-voom
pipeline with the `voom` followed by `lmFit` followed by
`eBayes` functions on each M imputed dataset, 4) pooling the
results with Rubins’ rules to produce combined coefficients,
standard errors, and P-values, and 5) adjusting P-values for
multiplicity to account for false discovery rate (FDR).
biocViews: RNASeq, GeneExpression, DifferentialExpression, Sequencing
Author: Brennan Baker [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5459-9141>), Sheela Sathyanarayana
[aut], Adam Szpiro [aut], James MacDonald [aut], Alison
Paquette [aut]
Maintainer: Brennan Baker <brennanhilton@gmail.com>
URL: https://github.com/brennanhilton/RNAseqCovarImpute
VignetteBuilder: knitr
BugReports: https://github.com/brennanhilton/RNAseqCovarImpute/issues
git_url: https://git.bioconductor.org/packages/RNAseqCovarImpute
git_branch: devel
git_last_commit: f490cdd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RNAseqCovarImpute_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RNAseqCovarImpute_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RNAseqCovarImpute_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RNAseqCovarImpute_1.5.0.tgz
vignettes:
vignettes/RNAseqCovarImpute/inst/doc/Example_Data_for_RNAseqCovarImpute.html,
vignettes/RNAseqCovarImpute/inst/doc/Impute_Covariate_Data_in_RNA_sequencing_Studies.html
vignetteTitles: Example Data for RNAseqCovarImpute, Impute Covariate
Data in RNA-sequencing Studies
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/RNAseqCovarImpute/inst/doc/Example_Data_for_RNAseqCovarImpute.R,
vignettes/RNAseqCovarImpute/inst/doc/Impute_Covariate_Data_in_RNA_sequencing_Studies.R
dependencyCount: 85
Package: RNASeqPower
Version: 1.47.0
License: LGPL (>=2)
MD5sum: 6f2c7179a3708f6443a8567b04c92a63
NeedsCompilation: no
Title: Sample size for RNAseq studies
Description: RNA-seq, sample size
biocViews: ImmunoOncology, RNASeq
Author: Terry M Therneau [aut, cre], Hart Stephen [ctb]
Maintainer: Terry M Therneau <therneau.terry@mayo.edu>
git_url: https://git.bioconductor.org/packages/RNASeqPower
git_branch: devel
git_last_commit: 82e3a96
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RNASeqPower_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RNASeqPower_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RNASeqPower_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RNASeqPower_1.47.0.tgz
vignettes: vignettes/RNASeqPower/inst/doc/samplesize.pdf
vignetteTitles: RNAseq samplesize
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RNASeqPower/inst/doc/samplesize.R
suggestsMe: DGEobj.utils
dependencyCount: 0
Package: RnaSeqSampleSize
Version: 2.17.0
Depends: R (>= 4.0.0), ggplot2, RnaSeqSampleSizeData
Imports: biomaRt,edgeR,heatmap3,matlab,KEGGREST,methods,grDevices,
graphics, stats, Rcpp (>=
0.11.2),recount,ggpubr,SummarizedExperiment,tidyr,dplyr,tidyselect,utils
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, testthat
License: GPL (>= 2)
MD5sum: 34c58dfc73b93fc753e81ffdac678954
NeedsCompilation: yes
Title: RnaSeqSampleSize
Description: RnaSeqSampleSize package provides a sample size
calculation method based on negative binomial model and the
exact test for assessing differential expression analysis of
RNA-seq data. It controls FDR for multiple testing and utilizes
the average read count and dispersion distributions from real
data to estimate a more reliable sample size. It is also
equipped with several unique features, including estimation for
interested genes or pathway, power curve visualization, and
parameter optimization.
biocViews: ImmunoOncology, ExperimentalDesign, Sequencing, RNASeq,
GeneExpression, DifferentialExpression
Author: Shilin Zhao Developer [aut, cre], Chung-I Li [aut], Yan Guo
[aut], Quanhu Sheng [aut], Yu Shyr [aut]
Maintainer: Shilin Zhao Developer <zhaoshilin@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RnaSeqSampleSize
git_branch: devel
git_last_commit: 9a5f28d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-12-13
source.ver: src/contrib/RnaSeqSampleSize_2.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RnaSeqSampleSize_2.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RnaSeqSampleSize_2.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RnaSeqSampleSize_2.17.0.tgz
vignettes: vignettes/RnaSeqSampleSize/inst/doc/RnaSeqSampleSize.pdf
vignetteTitles: RnaSeqSampleSize: Sample size estimation by real data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RnaSeqSampleSize/inst/doc/RnaSeqSampleSize.R
dependencyCount: 205
Package: RnBeads
Version: 2.25.1
Depends: R (>= 3.0.0), BiocGenerics, S4Vectors (>= 0.9.25),
GenomicRanges, MASS, cluster, ff, fields, ggplot2 (>= 0.9.2),
gplots, grid, gridExtra, limma, matrixStats, methods,
illuminaio, methylumi, plyr
Imports: IRanges
Suggests: Category, GOstats, Gviz,
IlluminaHumanMethylation450kmanifest, RPMM, RnBeads.hg19,
RnBeads.mm9, RnBeads.hg38, XML, annotate, biomaRt, foreach,
doParallel, ggbio, isva, mclust, mgcv, minfi, nlme,
org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, quadprog,
rtracklayer, qvalue, sva, wateRmelon, wordcloud, qvalue,
argparse, glmnet, IlluminaHumanMethylation450kanno.ilmn12.hg19,
scales, missMethyl, impute, shiny, shinyjs, plotrix, hexbin,
RUnit, MethylSeekR, sesame
License: GPL-3
MD5sum: 6adc63f5e1a663d01816ad0ca52f5881
NeedsCompilation: no
Title: RnBeads
Description: RnBeads facilitates comprehensive analysis of various
types of DNA methylation data at the genome scale.
biocViews: DNAMethylation, MethylationArray, MethylSeq, Epigenetics,
QualityControl, Preprocessing, BatchEffect,
DifferentialMethylation, Sequencing, CpGIsland, ImmunoOncology,
TwoChannel, DataImport
Author: Yassen Assenov [aut], Christoph Bock [aut], Pavlo Lutsik [aut],
Michael Scherer [aut], Fabian Mueller [aut, cre]
Maintainer: Fabian Mueller <team@rnbeads.org>
git_url: https://git.bioconductor.org/packages/RnBeads
git_branch: devel
git_last_commit: 460c356
git_last_commit_date: 2025-02-06
Date/Publication: 2025-03-13
source.ver: src/contrib/RnBeads_2.25.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RnBeads_2.25.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RnBeads_2.25.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RnBeads_2.25.1.tgz
vignettes: vignettes/RnBeads/inst/doc/RnBeads_Annotations.pdf,
vignettes/RnBeads/inst/doc/RnBeads.pdf
vignetteTitles: RnBeads Annotation, Comprehensive DNA Methylation
Analysis with RnBeads
hasREADME: TRUE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RnBeads/inst/doc/RnBeads_Annotations.R,
vignettes/RnBeads/inst/doc/RnBeads.R
dependsOnMe: MAGAR
suggestsMe: RnBeads.hg19, RnBeads.hg38, RnBeads.mm10, RnBeads.mm9,
RnBeads.rn5
dependencyCount: 171
Package: Rnits
Version: 1.41.0
Depends: R (>= 3.6.0), Biobase, ggplot2, limma, methods
Imports: affy, boot, impute, splines, graphics, qvalue, reshape2
Suggests: BiocStyle, knitr, GEOquery, stringr
License: GPL-3
MD5sum: 65ac011aeb41bd6e6a3538aa48c9de4a
NeedsCompilation: no
Title: R Normalization and Inference of Time Series data
Description: R/Bioconductor package for normalization, curve
registration and inference in time course gene expression data.
biocViews: GeneExpression, Microarray, TimeCourse,
DifferentialExpression, Normalization
Author: Dipen P. Sangurdekar <dipen.sangurdekar@gmail.com>
Maintainer: Dipen P. Sangurdekar <dipen.sangurdekar@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Rnits
git_branch: devel
git_last_commit: 4df67b1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Rnits_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Rnits_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Rnits_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Rnits_1.41.0.tgz
vignettes: vignettes/Rnits/inst/doc/Rnits-vignette.pdf
vignetteTitles: R/Bioconductor package for normalization and
differential expression inference in time series gene
expression microarray data.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rnits/inst/doc/Rnits-vignette.R
dependencyCount: 53
Package: roar
Version: 1.43.0
Depends: R (>= 3.0.1)
Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges,
SummarizedExperiment, GenomicAlignments (>= 0.99.4),
rtracklayer, GenomeInfoDb
Suggests: RNAseqData.HNRNPC.bam.chr14, testthat
License: GPL-3
MD5sum: e92d2d421e1561343cc66b1136ac2965
NeedsCompilation: no
Title: Identify differential APA usage from RNA-seq alignments
Description: Identify preferential usage of APA sites, comparing two
biological conditions, starting from known alternative sites
and alignments obtained from standard RNA-seq experiments.
biocViews: Sequencing, HighThroughputSequencing, RNAseq, Transcription
Author: Elena Grassi
Maintainer: Elena Grassi <grassi.e@gmail.com>
URL: https://github.com/vodkatad/roar/
git_url: https://git.bioconductor.org/packages/roar
git_branch: devel
git_last_commit: 9a977ed
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/roar_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/roar_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/roar_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/roar_1.43.0.tgz
vignettes: vignettes/roar/inst/doc/roar.pdf
vignetteTitles: Identify differential APA usage from RNA-seq alignments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/roar/inst/doc/roar.R
dependencyCount: 58
Package: roastgsa
Version: 1.5.0
Depends: R (>= 4.3.0)
Imports: parallel, grDevices, graphics, utils, stats, methods, grid,
RColorBrewer, gplots, ggplot2, limma, Biobase
Suggests: BiocStyle, knitr, rmarkdown, GSEABenchmarkeR,
EnrichmentBrowser, preprocessCore, DESeq2
License: GPL-3
MD5sum: 61d52cacb7adcf9d9ec99d90125ac5e0
NeedsCompilation: no
Title: Rotation based gene set analysis
Description: This package implements a variety of functions useful for
gene set analysis using rotations to approximate the null
distribution. It contributes with the implementation of seven
test statistic scores that can be used with different goals and
interpretations. Several functions are available to complement
the statistical results with graphical representations.
biocViews: Microarray, Preprocessing, Normalization, GeneExpression,
Survival, Transcription, Sequencing, Transcriptomics, Bayesian,
Clustering, Regression, RNASeq, MicroRNAArray, mRNAMicroarray,
FunctionalGenomics, SystemsBiology, ImmunoOncology,
DifferentialExpression, GeneSetEnrichment, BatchEffect,
MultipleComparison, QualityControl, TimeCourse, Metabolomics,
Proteomics, Epigenetics, Cheminformatics, ExonArray,
OneChannel, TwoChannel, ProprietaryPlatforms, CellBiology,
BiomedicalInformatics, AlternativeSplicing,
DifferentialSplicing, DataImport, Pathways
Author: Adria Caballe [aut, cre] (ORCID:
<https://orcid.org/0000-0002-2388-4713>)
Maintainer: Adria Caballe <adria.caballe@irbbarcelona.org>
VignetteBuilder: knitr
BugReports: https://github.com/adricaba/roastgsa/issues
git_url: https://git.bioconductor.org/packages/roastgsa
git_branch: devel
git_last_commit: 5f94c99
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/roastgsa_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/roastgsa_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/roastgsa_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/roastgsa_1.5.0.tgz
vignettes:
vignettes/roastgsa/inst/doc/roastgsaExample_genesetcollections.html,
vignettes/roastgsa/inst/doc/roastgsaExample_main.html,
vignettes/roastgsa/inst/doc/roastgsaExample_RNAseq.html
vignetteTitles: roastgsa vignette (gene set collections), roastgsa
vignette (main), roastgsa vignette (RNAseq)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/roastgsa/inst/doc/roastgsaExample_genesetcollections.R,
vignettes/roastgsa/inst/doc/roastgsaExample_main.R,
vignettes/roastgsa/inst/doc/roastgsaExample_RNAseq.R
dependencyCount: 46
Package: ROC
Version: 1.83.0
Depends: R (>= 1.9.0), utils, methods
Imports: knitr
Suggests: rmarkdown, Biobase, BiocStyle
License: Artistic-2.0
Archs: x64
MD5sum: f65d1a4b2534cba1235dde3172ba2dfe
NeedsCompilation: yes
Title: utilities for ROC, with microarray focus
Description: Provide utilities for ROC, with microarray focus.
biocViews: DifferentialExpression
Author: Vince Carey <stvjc@channing.harvard.edu>, Henning Redestig for
C++ language enhancements
Maintainer: Vince Carey <stvjc@channing.harvard.edu>
URL: http://www.bioconductor.org
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ROC
git_branch: devel
git_last_commit: 1857a19
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ROC_1.83.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ROC_1.83.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ROC_1.83.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ROC_1.83.0.tgz
vignettes: vignettes/ROC/inst/doc/ROCnotes.html
vignetteTitles: Notes on ROC package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: TCC, wateRmelon
importsMe: clst
suggestsMe: genefilter
dependencyCount: 10
Package: ROCpAI
Version: 1.19.0
Depends: boot, SummarizedExperiment, fission, knitr, methods
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-3
MD5sum: 6519a4feae05766b493b437a7c950d01
NeedsCompilation: no
Title: Receiver Operating Characteristic Partial Area Indexes for
evaluating classifiers
Description: The package analyzes the Curve ROC, identificates it among
different types of Curve ROC and calculates the area under de
curve through the method that is most accuracy. This package is
able to standarizate proper and improper pAUC.
biocViews: Software, StatisticalMethod, Classification
Author: Juan-Pedro Garcia [aut, cre], Manuel Franco [aut], Juana-MarÃa
Vivo [aut]
Maintainer: Juan-Pedro Garcia <juanpedro.garcia4@um.es>
VignetteBuilder: knitr
BugReports: https://github.com/juanpegarcia/ROCpAI/tree/master/issues
git_url: https://git.bioconductor.org/packages/ROCpAI
git_branch: devel
git_last_commit: 15f6984
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-08
source.ver: src/contrib/ROCpAI_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ROCpAI_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ROCpAI_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ROCpAI_1.19.0.tgz
vignettes: vignettes/ROCpAI/inst/doc/vignettes.html
vignetteTitles: ROC Partial Area Indexes for evaluating classifiers
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ROCpAI/inst/doc/vignettes.R
dependencyCount: 43
Package: RolDE
Version: 1.11.0
Depends: R (>= 4.2.0)
Imports: stats, methods, ROTS, matrixStats, foreach, parallel,
doParallel, doRNG, rngtools, SummarizedExperiment, nlme,
qvalue, grDevices, graphics, utils
Suggests: knitr, printr, rmarkdown, testthat
License: GPL-3
MD5sum: 9b9d0bf581c0cfc4165d86d6bcd26aa0
NeedsCompilation: no
Title: RolDE: Robust longitudinal Differential Expression
Description: RolDE detects longitudinal differential expression between
two conditions in noisy high-troughput data. Suitable even for
data with a moderate amount of missing values.RolDE is a
composite method, consisting of three independent modules with
different approaches to detecting longitudinal differential
expression. The combination of these diverse modules allows
RolDE to robustly detect varying differences in longitudinal
trends and expression levels in diverse data types and
experimental settings.
biocViews: StatisticalMethod, Software, TimeCourse, Regression,
Proteomics, DifferentialExpression
Author: Tommi Valikangas [aut], Medical Bioinformatics Centre [cre]
Maintainer: Medical Bioinformatics Centre <mbc@utu.fi>
URL: https://github.com/elolab/RolDE
VignetteBuilder: knitr
BugReports: https://github.com/elolab/RolDE/issues
git_url: https://git.bioconductor.org/packages/RolDE
git_branch: devel
git_last_commit: d2ff8d1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RolDE_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RolDE_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RolDE_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RolDE_1.11.0.tgz
vignettes: vignettes/RolDE/inst/doc/Introduction.html
vignetteTitles: Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RolDE/inst/doc/Introduction.R
dependencyCount: 93
Package: rols
Version: 3.3.0
Depends: methods
Imports: httr2, jsonlite, utils, Biobase, BiocGenerics (>= 0.23.1)
Suggests: GO.db, knitr (>= 1.1.0), BiocStyle (>= 2.5.19), testthat,
lubridate, DT, rmarkdown,
License: GPL-2
MD5sum: aacd3dea9724f1cb0875b06ccab1e760
NeedsCompilation: no
Title: An R interface to the Ontology Lookup Service
Description: The rols package is an interface to the Ontology Lookup
Service (OLS) to access and query hundred of ontolgies directly
from R.
biocViews: ImmunoOncology, Software, Annotation, MassSpectrometry, GO
Author: Laurent Gatto [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1520-2268>), Tiage Chedraoui Silva
[ctb], Andrew Clugston [ctb]
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
URL: http://lgatto.github.io/rols/
VignetteBuilder: knitr
BugReports: https://github.com/lgatto/rols/issues
git_url: https://git.bioconductor.org/packages/rols
git_branch: devel
git_last_commit: 7d005fb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rols_3.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rols_3.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rols_3.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rols_3.3.0.tgz
vignettes: vignettes/rols/inst/doc/rols.html
vignetteTitles: An R interface to the Ontology Lookup Service
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rols/inst/doc/rols.R
importsMe: OmicsMLRepoR, struct
suggestsMe: MSnbase, spatialHeatmap, RforProteomics
dependencyCount: 23
Package: ROntoTools
Version: 2.35.0
Depends: methods, graph, boot, KEGGREST, KEGGgraph, Rgraphviz
Suggests: RUnit, BiocGenerics
License: CC BY-NC-ND 4.0 + file LICENSE
MD5sum: ec394cb63a389cef7779b508f9ade8f4
NeedsCompilation: no
Title: R Onto-Tools suite
Description: Suite of tools for functional analysis.
biocViews: NetworkAnalysis, Microarray, GraphsAndNetworks
Author: Calin Voichita <calin@wayne.edu> and Sahar Ansari
<saharansari@wayne.edu> and Sorin Draghici
<sorin@advaitabio.com>
Maintainer: Sorin Draghici <sorin@advaitabio.com>
git_url: https://git.bioconductor.org/packages/ROntoTools
git_branch: devel
git_last_commit: d2b826d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ROntoTools_2.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ROntoTools_2.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ROntoTools_2.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ROntoTools_2.35.0.tgz
vignettes: vignettes/ROntoTools/inst/doc/rontotools.pdf
vignetteTitles: ROntoTools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ROntoTools/inst/doc/rontotools.R
dependsOnMe: BLMA
suggestsMe: RCPA
dependencyCount: 35
Package: ropls
Version: 1.39.0
Depends: R (>= 3.5.0)
Imports: Biobase, ggplot2, graphics, grDevices, methods, plotly, stats,
MultiAssayExperiment, MultiDataSet, SummarizedExperiment, utils
Suggests: BiocGenerics, BiocStyle, knitr, multtest, omicade4, phenomis,
rmarkdown, testthat
License: CeCILL
Archs: x64
MD5sum: 4d289e5493420191b812db27ce914b28
NeedsCompilation: no
Title: PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and
feature selection of omics data
Description: Latent variable modeling with Principal Component Analysis
(PCA) and Partial Least Squares (PLS) are powerful methods for
visualization, regression, classification, and feature
selection of omics data where the number of variables exceeds
the number of samples and with multicollinearity among
variables. Orthogonal Partial Least Squares (OPLS) enables to
separately model the variation correlated (predictive) to the
factor of interest and the uncorrelated (orthogonal) variation.
While performing similarly to PLS, OPLS facilitates
interpretation. Successful applications of these chemometrics
techniques include spectroscopic data such as Raman
spectroscopy, nuclear magnetic resonance (NMR), mass
spectrometry (MS) in metabolomics and proteomics, but also
transcriptomics data. In addition to scores, loadings and
weights plots, the package provides metrics and graphics to
determine the optimal number of components (e.g. with the R2
and Q2 coefficients), check the validity of the model by
permutation testing, detect outliers, and perform feature
selection (e.g. with Variable Importance in Projection or
regression coefficients). The package can be accessed via a
user interface on the Workflow4Metabolomics.org online resource
for computational metabolomics (built upon the Galaxy
environment).
biocViews: Regression, Classification, PrincipalComponent,
Transcriptomics, Proteomics, Metabolomics, Lipidomics,
MassSpectrometry, ImmunoOncology
Author: Etienne A. Thevenot [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1019-4577>)
Maintainer: Etienne A. Thevenot <etienne.thevenot@cea.fr>
URL: https://doi.org/10.1021/acs.jproteome.5b00354
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ropls
git_branch: devel
git_last_commit: e7c2554
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ropls_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ropls_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ropls_1.39.0.tgz
vignettes: vignettes/ropls/inst/doc/ropls-vignette.html
vignetteTitles: ropls-vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ropls/inst/doc/ropls-vignette.R
importsMe: ASICS, biosigner, lipidr, MultiBaC, phenomis
suggestsMe: autonomics, ptairMS, structToolbox, MetabolomicsBasics
dependencyCount: 104
Package: ROSeq
Version: 1.19.0
Depends: R (>= 4.0)
Imports: pbmcapply, edgeR, limma
Suggests: knitr, rmarkdown, testthat, RUnit, BiocGenerics
License: GPL-3
MD5sum: 7e4fca2a827f8717c1cb9c7dd5451621
NeedsCompilation: no
Title: Modeling expression ranks for noise-tolerant differential
expression analysis of scRNA-Seq data
Description: ROSeq - A rank based approach to modeling gene expression
with filtered and normalized read count matrix. ROSeq takes
filtered and normalized read matrix and
cell-annotation/condition as input and determines the
differentially expressed genes between the contrasting groups
of single cells. One of the input parameters is the number of
cores to be used.
biocViews: GeneExpression, DifferentialExpression, SingleCell
Author: Krishan Gupta [aut, cre], Manan Lalit [aut], Aditya Biswas
[aut], Abhik Ghosh [aut], Debarka Sengupta [aut]
Maintainer: Krishan Gupta <krishang@iiitd.ac.in>
URL: https://github.com/krishan57gupta/ROSeq
VignetteBuilder: knitr
BugReports: https://github.com/krishan57gupta/ROSeq/issues
git_url: https://git.bioconductor.org/packages/ROSeq
git_branch: devel
git_last_commit: f033b59
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ROSeq_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ROSeq_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ROSeq_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ROSeq_1.19.0.tgz
vignettes: vignettes/ROSeq/inst/doc/ROSeq.html
vignetteTitles: ROSeq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ROSeq/inst/doc/ROSeq.R
dependencyCount: 13
Package: ROTS
Version: 1.99.11
Depends: R (>= 3.6)
Imports: Rcpp, stats, Biobase, methods, BiocParallel, lme4
LinkingTo: Rcpp
Suggests: testthat
License: GPL (>= 2)
MD5sum: 746891b30cae47945d13ec3b64b4c35b
NeedsCompilation: yes
Title: Reproducibility-Optimized Test Statistic
Description: Calculates the Reproducibility-Optimized Test Statistic
(ROTS) for differential testing in omics data.
biocViews: Software, GeneExpression, DifferentialExpression,
Microarray, RNASeq, Proteomics, ImmunoOncology
Author: Fatemeh Seyednasrollah, Tomi Suomi, Laura L. Elo
Maintainer: Tomi Suomi <tomi.suomi@utu.fi>
git_url: https://git.bioconductor.org/packages/ROTS
git_branch: devel
git_last_commit: 2d56249
git_last_commit_date: 2025-01-24
Date/Publication: 2025-01-24
source.ver: src/contrib/ROTS_1.99.11.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ROTS_1.99.11.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ROTS_1.99.11.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ROTS_1.99.11.tgz
vignettes: vignettes/ROTS/inst/doc/ROTS.pdf
vignetteTitles: ROTS: Reproducibility Optimized Test Statistic
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ROTS/inst/doc/ROTS.R
importsMe: PECA, PRONE, RolDE
suggestsMe: LimROTS, wrProteo
dependencyCount: 34
Package: RPA
Version: 1.63.0
Depends: R (>= 3.1.1), affy, BiocGenerics, BiocStyle, methods,
rmarkdown
Imports: phyloseq
Suggests: knitr, parallel
License: BSD_2_clause + file LICENSE
MD5sum: 1f85c09ce479b9b7cc81781c0a006aef
NeedsCompilation: no
Title: RPA: Robust Probabilistic Averaging for probe-level analysis
Description: Probabilistic analysis of probe reliability and
differential gene expression on short oligonucleotide arrays.
biocViews: GeneExpression, Microarray, Preprocessing, QualityControl
Author: Leo Lahti [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5537-637X>)
Maintainer: Leo Lahti <leo.lahti@iki.fi>
URL: https://github.com/antagomir/RPA
VignetteBuilder: knitr
BugReports: https://github.com/antagomir/RPA
git_url: https://git.bioconductor.org/packages/RPA
git_branch: devel
git_last_commit: b608015
git_last_commit_date: 2024-10-29
Date/Publication: 2025-01-23
source.ver: src/contrib/RPA_1.63.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RPA_1.63.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RPA_1.63.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RPA_1.63.0.tgz
vignettes: vignettes/RPA/inst/doc/RPA.html
vignetteTitles: RPA R package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
dependsOnMe: prebs
dependencyCount: 107
Package: rprimer
Version: 1.11.0
Depends: R (>= 4.1)
Imports: Biostrings, bslib, DT, ggplot2, IRanges, mathjaxr, methods,
patchwork, reshape2, S4Vectors, shiny, shinycssloaders,
shinyFeedback
Suggests: BiocStyle, covr, kableExtra, knitr, rmarkdown, styler,
testthat (>= 3.0.0)
License: GPL-3
MD5sum: a97ca648e8ffff1a4ca43e7a979a6a77
NeedsCompilation: no
Title: Design Degenerate Oligos from a Multiple DNA Sequence Alignment
Description: Functions, workflow, and a Shiny application for
visualizing sequence conservation and designing degenerate
primers, probes, and (RT)-(q/d)PCR assays from a multiple DNA
sequence alignment. The results can be presented in data frame
format and visualized as dashboard-like plots. For more
information, please see the package vignette.
biocViews: Alignment, ddPCR, Coverage, MultipleSequenceAlignment,
SequenceMatching, qPCR
Author: Sofia Persson [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2611-3030>)
Maintainer: Sofia Persson <sofiapersson27@gmail.com>
URL: https://github.com/sofpn/rprimer
VignetteBuilder: knitr
BugReports: https://github.com/sofpn/rprimer/issues
git_url: https://git.bioconductor.org/packages/rprimer
git_branch: devel
git_last_commit: 8d5946a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rprimer_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rprimer_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rprimer_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rprimer_1.11.0.tgz
vignettes: vignettes/rprimer/inst/doc/getting-started-with-rprimer.html
vignetteTitles: Instructions for use
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rprimer/inst/doc/getting-started-with-rprimer.R
dependencyCount: 93
Package: RProtoBufLib
Version: 2.19.0
Suggests: knitr, rmarkdown
License: BSD_3_clause
MD5sum: 8f0335d0f6de6903b2d03a073cbb9439
NeedsCompilation: yes
Title: C++ headers and static libraries of Protocol buffers
Description: This package provides the headers and static library of
Protocol buffers for other R packages to compile and link
against.
biocViews: Infrastructure
Author: Mike Jiang
Maintainer: Mike Jiang <mike@ozette.ai>
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RProtoBufLib
git_branch: devel
git_last_commit: 1d936fb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RProtoBufLib_2.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RProtoBufLib_2.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RProtoBufLib_2.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RProtoBufLib_2.19.0.tgz
vignettes: vignettes/RProtoBufLib/inst/doc/UsingRProtoBufLib.html
vignetteTitles: Using RProtoBufLib
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: TRUE
Rfiles: vignettes/RProtoBufLib/inst/doc/UsingRProtoBufLib.R
importsMe: cytolib, flowWorkspace
linksToMe: cytolib, CytoML, flowCore, flowWorkspace
dependencyCount: 0
Package: rpx
Version: 2.15.1
Depends: R (>= 3.5.0), methods
Imports: BiocFileCache, jsonlite, xml2, RCurl, curl, utils
Suggests: Biostrings, BiocStyle, testthat, knitr, tibble, rmarkdown
License: GPL-2
MD5sum: afb60d8236f80e9c8512988c305e5c48
NeedsCompilation: no
Title: R Interface to the ProteomeXchange Repository
Description: The rpx package implements an interface to proteomics data
submitted to the ProteomeXchange consortium.
biocViews: ImmunoOncology, Proteomics, MassSpectrometry, DataImport,
ThirdPartyClient
Author: Laurent Gatto
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be>
URL: https://github.com/lgatto/rpx
VignetteBuilder: knitr
BugReports: https://github.com/lgatto/rpx/issues
git_url: https://git.bioconductor.org/packages/rpx
git_branch: devel
git_last_commit: ca86c88
git_last_commit_date: 2025-02-07
Date/Publication: 2025-02-09
source.ver: src/contrib/rpx_2.15.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rpx_2.15.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rpx_2.15.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rpx_2.15.1.tgz
vignettes: vignettes/rpx/inst/doc/rpx.html
vignetteTitles: An R interface to the ProteomeXchange repository
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rpx/inst/doc/rpx.R
suggestsMe: MsExperiment, MSnbase, PSMatch, RforProteomics
dependencyCount: 49
Package: Rqc
Version: 1.41.0
Depends: BiocParallel, ShortRead, ggplot2
Imports: BiocGenerics (>= 0.25.1), Biostrings, IRanges, methods,
S4Vectors, knitr (>= 1.7), BiocStyle, plyr, markdown, grid,
reshape2, Rcpp (>= 0.11.6), biovizBase, shiny, Rsamtools,
GenomicAlignments, GenomicFiles
LinkingTo: Rcpp
Suggests: rmarkdown, testthat
License: GPL (>= 2)
Archs: x64
MD5sum: 63bfd0476d416c03ea7594937c3d2dd5
NeedsCompilation: yes
Title: Quality Control Tool for High-Throughput Sequencing Data
Description: Rqc is an optimised tool designed for quality control and
assessment of high-throughput sequencing data. It performs
parallel processing of entire files and produces a report which
contains a set of high-resolution graphics.
biocViews: Sequencing, QualityControl, DataImport
Author: Welliton Souza, Benilton Carvalho <beniltoncarvalho@gmail.com>
Maintainer: Welliton Souza <well309@gmail.com>
URL: https://github.com/labbcb/Rqc
VignetteBuilder: knitr
BugReports: https://github.com/labbcb/Rqc/issues
git_url: https://git.bioconductor.org/packages/Rqc
git_branch: devel
git_last_commit: beb1258
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Rqc_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Rqc_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Rqc_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Rqc_1.41.0.tgz
vignettes: vignettes/Rqc/inst/doc/Rqc.html
vignetteTitles: Using Rqc
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rqc/inst/doc/Rqc.R
dependencyCount: 160
Package: rqt
Version: 1.33.0
Depends: R (>= 3.4), SummarizedExperiment
Imports:
stats,Matrix,ropls,methods,car,RUnit,metap,CompQuadForm,glmnet,utils,pls
Suggests: BiocStyle, knitr, rmarkdown
License: GPL
MD5sum: 9b1294cd5c100666fddf564946205c66
NeedsCompilation: no
Title: rqt: utilities for gene-level meta-analysis
Description: Despite the recent advances of modern GWAS methods, it
still remains an important problem of addressing calculation an
effect size and corresponding p-value for the whole gene rather
than for single variant. The R- package rqt offers gene-level
GWAS meta-analysis. For more information, see: "Gene-set
association tests for next-generation sequencing data" by Lee
et al (2016), Bioinformatics, 32(17), i611-i619,
<doi:10.1093/bioinformatics/btw429>.
biocViews: GenomeWideAssociation, Regression, Survival,
PrincipalComponent, StatisticalMethod, Sequencing
Author: Ilya Zhbannikov [aut, cre], Konstantin Arbeev [aut], Anatoliy
Yashin [aut]
Maintainer: Ilya Zhbannikov <ilya.zhbannikov@duke.edu>
URL: https://github.com/izhbannikov/rqt
VignetteBuilder: knitr
BugReports: https://github.com/izhbannikov/rqt/issues
git_url: https://git.bioconductor.org/packages/rqt
git_branch: devel
git_last_commit: 4366457
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rqt_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rqt_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rqt_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rqt_1.33.0.tgz
vignettes: vignettes/rqt/inst/doc/rqt-vignette.html
vignetteTitles: Tutorial for rqt package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rqt/inst/doc/rqt-vignette.R
dependencyCount: 155
Package: rqubic
Version: 1.53.0
Imports: methods, Biobase, BiocGenerics, biclust
Suggests: RColorBrewer
License: GPL-2
MD5sum: 59a86d704299c0aa8db365f120c6637b
NeedsCompilation: yes
Title: Qualitative biclustering algorithm for expression data analysis
in R
Description: This package implements the QUBIC algorithm introduced by
Li et al. for the qualitative biclustering with gene expression
data.
biocViews: Clustering
Author: Jitao David Zhang [aut, cre, ctb] (ORCID:
<https://orcid.org/0000-0002-3085-0909>)
Maintainer: Jitao David Zhang <jitao_david.zhang@roche.com>
git_url: https://git.bioconductor.org/packages/rqubic
git_branch: devel
git_last_commit: 0a9a982
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rqubic_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rqubic_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rqubic_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rqubic_1.53.0.tgz
vignettes: vignettes/rqubic/inst/doc/rqubic.pdf
vignetteTitles: Qualitative Biclustering with Bioconductor Package
rqubic
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rqubic/inst/doc/rqubic.R
importsMe: miRSM
suggestsMe: RcmdrPlugin.BiclustGUI
dependencyCount: 53
Package: rRDP
Version: 1.41.0
Depends: Biostrings (>= 2.26.2)
Imports: rJava, utils
Suggests: rRDPData, knitr, rmarkdown
License: GPL-2 + file LICENSE
Archs: x64
MD5sum: 23c31bcb96d9c306c2d25e10710ffd95
NeedsCompilation: no
Title: Interface to the RDP Classifier
Description: This package installs and interfaces the naive Bayesian
classifier for 16S rRNA sequences developed by the Ribosomal
Database Project (RDP). With this package the classifier
trained with the standard training set can be used or a custom
classifier can be trained.
biocViews: Genetics, Sequencing, Infrastructure, Classification,
Microbiome, ImmunoOncology, Alignment, SequenceMatching,
DataImport, Bayesian
Author: Michael Hahsler [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2716-1405>), Nagar Anurag [aut]
Maintainer: Michael Hahsler <mhahsler@lyle.smu.edu>
URL: https://github.com/mhahsler/rRDP/
SystemRequirements: Java JDK 1.4 or higher
VignetteBuilder: knitr
BugReports: https://github.com/mhahsler/rRDP/issues
git_url: https://git.bioconductor.org/packages/rRDP
git_branch: devel
git_last_commit: 8b3502c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rRDP_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rRDP_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rRDP_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rRDP_1.41.0.tgz
vignettes: vignettes/rRDP/inst/doc/rRDP.html
vignetteTitles: rRDP: Interface to the RDP Classifier
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/rRDP/inst/doc/rRDP.R
dependsOnMe: rRDPData
dependencyCount: 26
Package: RRHO
Version: 1.47.0
Depends: R (>= 2.10), grid
Imports: VennDiagram
Suggests: lattice
License: GPL-2
MD5sum: afeddd5993538e91afdddffe4833cbc6
NeedsCompilation: no
Title: Inference on agreement between ordered lists
Description: The package is aimed at inference on the amount of
agreement in two sorted lists using the Rank-Rank
Hypergeometric Overlap test.
biocViews: Genetics, SequenceMatching, Microarray, Transcription
Author: Jonathan Rosenblatt and Jason Stein
Maintainer: Jonathan Rosenblatt <john.ros.work@gmail.com>
git_url: https://git.bioconductor.org/packages/RRHO
git_branch: devel
git_last_commit: 5fc347a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RRHO_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RRHO_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RRHO_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RRHO_1.47.0.tgz
vignettes: vignettes/RRHO/inst/doc/RRHO.pdf
vignetteTitles: RRHO
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RRHO/inst/doc/RRHO.R
dependencyCount: 8
Package: rrvgo
Version: 1.19.0
Imports: GOSemSim, AnnotationDbi, GO.db, pheatmap, ggplot2, ggrepel,
treemap, tm, wordcloud, shiny, grDevices, grid, stats, methods,
umap
Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0),
shinydashboard, DT, plotly, heatmaply, magrittr, utils,
clusterProfiler, DOSE, slam, org.Ag.eg.db, org.At.tair.db,
org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db,
org.Dr.eg.db, org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db,
org.Hs.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Pt.eg.db,
org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db
License: GPL-3
Archs: x64
MD5sum: d2f69ff215e2f36060a5cdb4185f159d
NeedsCompilation: no
Title: Reduce + Visualize GO
Description: Reduce and visualize lists of Gene Ontology terms by
identifying redudance based on semantic similarity.
biocViews: Annotation, Clustering, GO, Network, Pathways, Software
Author: Sergi Sayols [aut, cre], Sara Elmeligy [ctb]
Maintainer: Sergi Sayols <sergisayolspuig@gmail.com>
URL: https://www.bioconductor.org/packages/rrvgo,
https://ssayols.github.io/rrvgo/index.html
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rrvgo
git_branch: devel
git_last_commit: b7ae543
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rrvgo_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rrvgo_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rrvgo_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rrvgo_1.19.0.tgz
vignettes: vignettes/rrvgo/inst/doc/rrvgo.html
vignetteTitles: Using rrvgo
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/rrvgo/inst/doc/rrvgo.R
suggestsMe: genekitr, scDiffCom
dependencyCount: 111
Package: Rsamtools
Version: 2.23.1
Depends: methods, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.31.8),
Biostrings (>= 2.47.6), R (>= 3.5.0)
Imports: utils, BiocGenerics (>= 0.25.1), S4Vectors (>= 0.17.25),
IRanges (>= 2.13.12), XVector (>= 0.19.7), bitops,
BiocParallel, stats
LinkingTo: Rhtslib (>= 3.3.1), S4Vectors, IRanges, XVector, Biostrings
Suggests: GenomicAlignments, ShortRead (>= 1.19.10), GenomicFeatures,
TxDb.Dmelanogaster.UCSC.dm3.ensGene,
TxDb.Hsapiens.UCSC.hg18.knownGene, RNAseqData.HNRNPC.bam.chr14,
BSgenome.Hsapiens.UCSC.hg19, RUnit, BiocStyle, knitr
License: Artistic-2.0 | file LICENSE
MD5sum: 3e389a59ca230f6e8438a5e054252c84
NeedsCompilation: yes
Title: Binary alignment (BAM), FASTA, variant call (BCF), and tabix
file import
Description: This package provides an interface to the 'samtools',
'bcftools', and 'tabix' utilities for manipulating SAM
(Sequence Alignment / Map), FASTA, binary variant call (BCF)
and compressed indexed tab-delimited (tabix) files.
biocViews: DataImport, Sequencing, Coverage, Alignment, QualityControl
Author: Martin Morgan [aut], Hervé Pagès [aut], Valerie Obenchain
[aut], Nathaniel Hayden [aut], Busayo Samuel [ctb] (Converted
Rsamtools vignette from Sweave to RMarkdown / HTML.),
Bioconductor Package Maintainer [cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://bioconductor.org/packages/Rsamtools
SystemRequirements: GNU make
VignetteBuilder: knitr
Video:
https://www.youtube.com/watch?v=Rfon-DQYbWA&list=UUqaMSQd_h-2EDGsU6WDiX0Q
BugReports: https://github.com/Bioconductor/Rsamtools/issues
git_url: https://git.bioconductor.org/packages/Rsamtools
git_branch: devel
git_last_commit: 21a5945
git_last_commit_date: 2024-11-26
Date/Publication: 2024-11-27
source.ver: src/contrib/Rsamtools_2.23.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Rsamtools_2.23.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Rsamtools_2.23.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Rsamtools_2.23.1.tgz
vignettes: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.html
vignetteTitles: An Introduction to Rsamtools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.R
dependsOnMe: CODEX, CoverageView, esATAC, FRASER, GenomicAlignments,
GenomicFiles, girafe, gmapR, HelloRanges, IntEREst, MEDIPS,
methylPipe, MMDiff2, podkat, r3Cseq, RepViz, RiboDiPA, SCOPE,
SGSeq, ShortRead, SICtools, SNPhood, spiky, ssviz, systemPipeR,
TEQC, VariantAnnotation, wavClusteR, leeBamViews,
TBX20BamSubset, sequencing, csawBook
importsMe: alabaster.files, alabaster.vcf, AllelicImbalance,
AneuFinder, annmap, AnnotationHubData, APAlyzer, appreci8R,
ASpli, ATACseqQC, ATACseqTFEA, atena, BadRegionFinder, bambu,
BBCAnalyzer, biovizBase, biscuiteer, breakpointR, BSgenome,
CAGEr, casper, CellBarcode, cellbaseR, CexoR, cfdnakit,
cfDNAPro, chimeraviz, ChIPexoQual, ChIPpeakAnno, ChIPQC,
ChromSCape, chromVAR, CircSeqAlignTk, CleanUpRNAseq, cn.mops,
CNVfilteR, CNVPanelizer, CNVrd2, compEpiTools, consensusDE,
CopyNumberPlots, CrispRVariants, csaw, CSSQ, customProDB,
DAMEfinder, Damsel, DegNorm, derfinder, DEXSeq, DiffBind,
diffHic, DNAfusion, easyRNASeq, EDASeq, ensembldb, epigenomix,
epigraHMM, eudysbiome, extraChIPs, FilterFFPE, FLAMES, gcapc,
gDNAx, GeneGeneInteR, genomation, GenomicAlignments,
GenomicInteractions, GenomicPlot, GenVisR, ggbio, gmoviz,
GOTHiC, GreyListChIP, GUIDEseq, Gviz, h5vc, icetea, IMAS,
INSPEcT, karyoploteR, MADSEQ, magpie, MDTS, metagene2,
metaseqR2, methylKit, mosaics, motifmatchr, MotifPeeker,
msgbsR, NADfinder, NanoMethViz, nearBynding, nucleR, ORFik,
panelcn.mops, PICB, PICS, plyranges, pram, profileplyr, PureCN,
QDNAseq, qsea, QuasR, R453Plus1Toolbox, raer, ramwas, Rbowtie2,
recoup, Repitools, rfPred, RiboProfiling, riboSeqR,
ribosomeProfilingQC, RNAmodR, Rqc, rtracklayer, scDblFinder,
scPipe, scRNAseqApp, scruff, segmentSeq, seqsetvis, SimFFPE,
sitadela, soGGi, SplicingGraphs, srnadiff, strandCheckR, tadar,
TCseq, TFutils, tracktables, trackViewer, transcriptR, TRESS,
tRNAscanImport, TVTB, UMI4Cats, uncoverappLib,
VariantFiltering, VariantTools, VaSP, VCFArray, VplotR,
ZygosityPredictor, chipseqDBData, gDNAinRNAseqData,
LungCancerLines, MetaScope, raerdata, GenoPop, hoardeR, iimi,
kibior, MAAPER, NIPTeR, noisyr, PlasmaMutationDetector, revert,
scPloidy, Signac, umiAnalyzer, VALERIE
suggestsMe: AnnotationHub, bamsignals, BaseSpaceR, BiocGenerics,
BiocParallel, biomvRCNS, BSgenomeForge, Chicago, ELViS,
epivizrChart, gage, GenomeInfoDb, GenomicDataCommons,
GenomicFeatures, GenomicRanges, gwascat, HIBAG, igvShiny,
IRanges, ldblock, MOSim, MungeSumstats, omicsPrint, RNAmodR.ML,
SeqArray, SigFuge, similaRpeak, Streamer, TENxIO,
GeuvadisTranscriptExpr, NanoporeRNASeq, parathyroidSE,
systemPipeRdata, chipseqDB, karyotapR, polyRAD, seqmagick
dependencyCount: 38
Package: rsbml
Version: 2.65.0
Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), methods, utils
Imports: BiocGenerics, graph, utils
License: Artistic-2.0
Archs: x64
MD5sum: 891ce47c563fb5033c04d64a6ce882e4
NeedsCompilation: yes
Title: R support for SBML, using libsbml
Description: Links R to libsbml for SBML parsing, validating output,
provides an S4 SBML DOM, converts SBML to R graph objects.
Optionally links to the SBML ODE Solver Library (SOSLib) for
simulating models.
biocViews: GraphAndNetwork, Pathways, Network
Author: Michael Lawrence <michafla@gene.com>
Maintainer: Michael Lawrence <michafla@gene.com>
URL: http://www.sbml.org
SystemRequirements: libsbml (==5.10.2)
git_url: https://git.bioconductor.org/packages/rsbml
git_branch: devel
git_last_commit: b523cb6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rsbml_2.65.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rsbml_2.65.0.zip
vignettes: vignettes/rsbml/inst/doc/quick-start.pdf
vignetteTitles: Quick start for rsbml
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: FALSE
Rfiles: vignettes/rsbml/inst/doc/quick-start.R
dependsOnMe: BiGGR
suggestsMe: piano, SBMLR, seeds
dependencyCount: 8
Package: rScudo
Version: 1.23.0
Depends: R (>= 3.6)
Imports: methods, stats, igraph, stringr, grDevices, Biobase,
S4Vectors, SummarizedExperiment, BiocGenerics
Suggests: testthat, BiocStyle, knitr, rmarkdown, ALL, RCy3, caret,
e1071, parallel, doParallel
License: GPL-3
MD5sum: fb0aeb530b3312b3d1b485b3c9f5c10e
NeedsCompilation: no
Title: Signature-based Clustering for Diagnostic Purposes
Description: SCUDO (Signature-based Clustering for Diagnostic Purposes)
is a rank-based method for the analysis of gene expression
profiles for diagnostic and classification purposes. It is
based on the identification of sample-specific gene signatures
composed of the most up- and down-regulated genes for that
sample. Starting from gene expression data, functions in this
package identify sample-specific gene signatures and use them
to build a graph of samples. In this graph samples are joined
by edges if they have a similar expression profile, according
to a pre-computed similarity matrix. The similarity between the
expression profiles of two samples is computed using a method
similar to GSEA. The graph of samples can then be used to
perform community clustering or to perform supervised
classification of samples in a testing set.
biocViews: GeneExpression, DifferentialExpression,
BiomedicalInformatics, Classification, Clustering,
GraphAndNetwork, Network, Proteomics, Transcriptomics,
SystemsBiology, FeatureExtraction
Author: Matteo Ciciani [aut, cre], Thomas Cantore [aut], Enrica
Colasurdo [ctb], Mario Lauria [ctb]
Maintainer: Matteo Ciciani <matteo.ciciani@gmail.com>
URL: https://github.com/Matteo-Ciciani/scudo
VignetteBuilder: knitr
BugReports: https://github.com/Matteo-Ciciani/scudo/issues
git_url: https://git.bioconductor.org/packages/rScudo
git_branch: devel
git_last_commit: 7918e62
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rScudo_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rScudo_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rScudo_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rScudo_1.23.0.tgz
vignettes: vignettes/rScudo/inst/doc/rScudo-vignette.html
vignetteTitles: Signature-based Clustering for Diagnostic Purposes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rScudo/inst/doc/rScudo-vignette.R
dependencyCount: 47
Package: rsemmed
Version: 1.17.0
Depends: R (>= 4.0), igraph
Imports: methods, magrittr, stringr, dplyr
Suggests: testthat, knitr, BiocStyle, rmarkdown
License: Artistic-2.0
MD5sum: 6ca8be5762f62ea6d410090d226b4422
NeedsCompilation: no
Title: An interface to the Semantic MEDLINE database
Description: A programmatic interface to the Semantic MEDLINE database.
It provides functions for searching the database for concepts
and finding paths between concepts. Path searching can also be
tailored to user specifications, such as placing restrictions
on concept types and the type of link between concepts. It also
provides functions for summarizing and visualizing those paths.
biocViews: Software, Annotation, Pathways, SystemsBiology
Author: Leslie Myint [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2478-0331>)
Maintainer: Leslie Myint <leslie.myint@gmail.com>
URL: https://github.com/lmyint/rsemmed
VignetteBuilder: knitr
BugReports: https://github.com/lmyint/rsemmed/issues
git_url: https://git.bioconductor.org/packages/rsemmed
git_branch: devel
git_last_commit: 7e5464b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rsemmed_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rsemmed_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rsemmed_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rsemmed_1.17.0.tgz
vignettes: vignettes/rsemmed/inst/doc/rsemmed_user_guide.html
vignetteTitles: rsemmed User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rsemmed/inst/doc/rsemmed_user_guide.R
dependencyCount: 29
Package: RSeqAn
Version: 1.27.0
Imports: Rcpp
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, testthat
License: BSD_3_clause + file LICENSE
MD5sum: ff90931ef09984e6a7fb2742085eee49
NeedsCompilation: yes
Title: R SeqAn
Description: Headers and some wrapper functions from the SeqAn C++
library for ease of usage in R.
biocViews: Infrastructure, Software
Author: August Guang [aut, cre]
Maintainer: August Guang <august.guang@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/compbiocore/RSeqAn/issues
git_url: https://git.bioconductor.org/packages/RSeqAn
git_branch: devel
git_last_commit: 65879b2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RSeqAn_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RSeqAn_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RSeqAn_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RSeqAn_1.27.0.tgz
vignettes: vignettes/RSeqAn/inst/doc/first_example.html
vignetteTitles: Introduction to Using RSeqAn
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/RSeqAn/inst/doc/first_example.R
importsMe: qckitfastq
linksToMe: qckitfastq
dependencyCount: 3
Package: Rsubread
Version: 2.21.3
Imports: grDevices, stats, utils, Matrix
License: GPL (>=3)
Archs: x64
MD5sum: 8fad1d6d43f54f5ce96958c269594c6f
NeedsCompilation: yes
Title: Mapping, quantification and variant analysis of sequencing data
Description: Alignment, quantification and analysis of RNA sequencing
data (including both bulk RNA-seq and scRNA-seq) and DNA
sequenicng data (including ATAC-seq, ChIP-seq, WGS, WES etc).
Includes functionality for read mapping, read counting, SNP
calling, structural variant detection and gene fusion
discovery. Can be applied to all major sequencing techologies
and to both short and long sequence reads.
biocViews: Sequencing, Alignment, SequenceMatching, RNASeq, ChIPSeq,
SingleCell, GeneExpression, GeneRegulation, Genetics,
ImmunoOncology, SNP, GeneticVariability, Preprocessing,
QualityControl, GenomeAnnotation, GeneFusionDetection,
IndelDetection, VariantAnnotation, VariantDetection,
MultipleSequenceAlignment
Author: Wei Shi, Yang Liao and Gordon K Smyth with contributions from
Jenny Dai
Maintainer: Wei Shi <wei.shi2@monash.edu>, Yang Liao
<yang.liao@monash.edu> and Gordon K Smyth <smyth@wehi.edu.au>
URL: http://bioconductor.org/packages/Rsubread
git_url: https://git.bioconductor.org/packages/Rsubread
git_branch: devel
git_last_commit: 9eee9f0
git_last_commit_date: 2025-03-21
Date/Publication: 2025-03-21
source.ver: src/contrib/Rsubread_2.21.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Rsubread_2.21.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Rsubread_2.21.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Rsubread_2.21.3.tgz
vignettes: vignettes/Rsubread/inst/doc/Rsubread.pdf,
vignettes/Rsubread/inst/doc/SubreadUsersGuide.pdf
vignetteTitles: Rsubread Vignette, SubreadUsersGuide.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rsubread/inst/doc/Rsubread.R
dependsOnMe: ExCluster
importsMe: APAlyzer, CleanUpRNAseq, Damsel, diffUTR, dupRadar, FRASER,
ribosomeProfilingQC, scPipe, scruff
suggestsMe: autonomics, icetea, singleCellTK, SpliceWiz, tidybulk,
MetaScope
dependencyCount: 8
Package: RSVSim
Version: 1.47.0
Depends: R (>= 3.5.0), Biostrings, GenomicRanges
Imports: methods, IRanges, ShortRead
Suggests: BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Hsapiens.UCSC.hg19.masked, MASS, rtracklayer, pwalign
License: LGPL-3
Archs: x64
MD5sum: d077a175a5a9c2d40e5faf851a330845
NeedsCompilation: no
Title: RSVSim: an R/Bioconductor package for the simulation of
structural variations
Description: RSVSim is a package for the simulation of deletions,
insertions, inversion, tandem-duplications and translocations
of various sizes in any genome available as FASTA-file or
BSgenome data package. SV breakpoints can be placed uniformly
accross the whole genome, with a bias towards repeat regions
and regions of high homology (for hg19) or at user-supplied
coordinates.
biocViews: Sequencing
Author: Christoph Bartenhagen
Maintainer: Christoph Bartenhagen <c.bartenhagen@uni-koeln.de>
git_url: https://git.bioconductor.org/packages/RSVSim
git_branch: devel
git_last_commit: ad6b2ec
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RSVSim_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RSVSim_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RSVSim_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RSVSim_1.47.0.tgz
vignettes: vignettes/RSVSim/inst/doc/vignette.pdf
vignetteTitles: RSVSim: an R/Bioconductor package for the simulation of
structural variations
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RSVSim/inst/doc/vignette.R
dependencyCount: 63
Package: rSWeeP
Version: 1.19.1
Depends: foreach, doParallel, parallel, Biostrings, methods, utils
Imports: tools, stringi,
Suggests: Rtsne, ape, Seurat, knitr, rmarkdown, tictoc, BiocStyle,
testthat (>= 3.0.0)
License: GPL (>= 2)
MD5sum: d66f0e4fb04ed1f5785fdad8d2850277
NeedsCompilation: no
Title: Spaced Words Projection (SWeeP)
Description: "Spaced Words Projection (SWeeP)" is a method for
representing biological sequences using vectors preserving
inter-sequence comparability.
Author: Camila Pereira Perico [com, cre, aut, cph] (ORCID:
<https://orcid.org/0000-0003-0186-4022>), Danrley Rafael
Fernandes [aut], Mariane Gonçalves Kulik [aut] (ORCID:
<https://orcid.org/0009-0000-5432-3524>), Júlia Formighieri
Varaschin [aut], Camilla Reginatto de Pierri [aut] (ORCID:
<https://orcid.org/0000-0002-6114-1734>), Ricardo Assunção
Vialle [aut] (ORCID: <https://orcid.org/0000-0003-3311-4197>),
Roberto Tadeu Raittz [aut, cph] (ORCID:
<https://orcid.org/0000-0002-5271-991X>)
Maintainer: Camila P Perico <camilapp94@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/rSWeeP
git_branch: devel
git_last_commit: 51429a6
git_last_commit_date: 2024-11-25
Date/Publication: 2024-11-26
source.ver: src/contrib/rSWeeP_1.19.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rSWeeP_1.19.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rSWeeP_1.19.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rSWeeP_1.19.1.tgz
vignettes: vignettes/rSWeeP/inst/doc/rSWeeP.html
vignetteTitles: rSWeeP
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rSWeeP/inst/doc/rSWeeP.R
dependencyCount: 31
Package: RTCA
Version: 1.59.0
Depends: methods,stats,graphics,Biobase,RColorBrewer, gtools
Suggests: xtable
License: LGPL-3
MD5sum: 148b27482fa6435e9a34a6cee40bdbd9
NeedsCompilation: no
Title: Open-source toolkit to analyse data from xCELLigence System
(RTCA)
Description: Import, analyze and visualize data from Roche(R)
xCELLigence RTCA systems. The package imports real-time cell
electrical impedance data into R. As an alternative to
commercial software shipped along the system, the Bioconductor
package RTCA provides several unique transformation
(normalization) strategies and various visualization tools.
biocViews: ImmunoOncology, CellBasedAssays, Infrastructure,
Visualization, TimeCourse
Author: Jitao David Zhang
Maintainer: Jitao David Zhang <davidvonpku@gmail.com>
URL:
http://code.google.com/p/xcelligence/,http://www.xcelligence.roche.com/,http://www.nextbiomotif.com/Home/scientific-programming
git_url: https://git.bioconductor.org/packages/RTCA
git_branch: devel
git_last_commit: 1c8301c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RTCA_1.59.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RTCA_1.59.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RTCA_1.59.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RTCA_1.59.0.tgz
vignettes: vignettes/RTCA/inst/doc/aboutRTCA.pdf,
vignettes/RTCA/inst/doc/RTCAtransformation.pdf
vignetteTitles: Introduction to Data Analysis of the Roche xCELLigence
System with RTCA Package, RTCAtransformation: Discussion of
transformation methods of RTCA data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RTCA/inst/doc/aboutRTCA.R,
vignettes/RTCA/inst/doc/RTCAtransformation.R
dependencyCount: 9
Package: RTCGA
Version: 1.37.0
Depends: R (>= 3.3.0)
Imports: XML, RCurl, assertthat, stringi, rvest, data.table, xml2,
dplyr, purrr, survival, survminer, ggplot2, ggthemes, viridis,
knitr, scales, rmarkdown, htmltools
Suggests: devtools, testthat, pander, Biobase, GenomicRanges, IRanges,
S4Vectors, RTCGA.rnaseq, RTCGA.clinical, RTCGA.mutations,
RTCGA.RPPA, RTCGA.mRNA, RTCGA.miRNASeq, RTCGA.methylation,
RTCGA.CNV, magrittr, tidyr
License: GPL-2
MD5sum: 29b7a979de36840d494a516e147d816f
NeedsCompilation: no
Title: The Cancer Genome Atlas Data Integration
Description: The Cancer Genome Atlas (TCGA) Data Portal provides a
platform for researchers to search, download, and analyze data
sets generated by TCGA. It contains clinical information,
genomic characterization data, and high level sequence analysis
of the tumor genomes. The key is to understand genomics to
improve cancer care. RTCGA package offers download and
integration of the variety and volume of TCGA data using
patient barcode key, what enables easier data possession. This
may have an benefcial infuence on impact on development of
science and improvement of patients' treatment. Furthermore,
RTCGA package transforms TCGA data to tidy form which is
convenient to use.
biocViews: ImmunoOncology, Software, DataImport, DataRepresentation,
Preprocessing, RNASeq, Survival, DNAMethylation,
PrincipalComponent, Visualization
Author: Marcin Kosinski [aut, cre], Przemyslaw Biecek [ctb], Witold
Chodor [ctb]
Maintainer: Marcin Kosinski <m.p.kosinski@gmail.com>
URL: https://rtcga.github.io/RTCGA
VignetteBuilder: knitr
BugReports: https://github.com/RTCGA/RTCGA/issues
git_url: https://git.bioconductor.org/packages/RTCGA
git_branch: devel
git_last_commit: e6e4091
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RTCGA_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RTCGA_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RTCGA_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RTCGA_1.37.0.tgz
vignettes: vignettes/RTCGA/inst/doc/RTCGA_Workflow.html
vignetteTitles: Integrating TCGA Data - RTCGA Workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RTCGA/inst/doc/RTCGA_Workflow.R
dependsOnMe: RTCGA.clinical, RTCGA.CNV, RTCGA.methylation,
RTCGA.miRNASeq, RTCGA.mRNA, RTCGA.mutations, RTCGA.PANCAN12,
RTCGA.rnaseq, RTCGA.RPPA
importsMe: TDbasedUFEadv
dependencyCount: 132
Package: RTCGAToolbox
Version: 2.37.2
Depends: R (>= 4.3.0)
Imports: BiocGenerics, data.table, DelayedArray, GenomicRanges,
GenomeInfoDb, httr, methods, RaggedExperiment, RCurl, RJSONIO,
rvest, S4Vectors, stats, stringr, SummarizedExperiment,
TCGAutils, utils
Suggests: BiocStyle, Homo.sapiens, knitr, readr, rmarkdown
License: GPL-2
Archs: x64
MD5sum: 51d174604abfe5d4a9e1b7c9a9457782
NeedsCompilation: no
Title: A new tool for exporting TCGA Firehose data
Description: Managing data from large scale projects such as The Cancer
Genome Atlas (TCGA) for further analysis is an important and
time consuming step for research projects. Several efforts,
such as Firehose project, make TCGA pre-processed data publicly
available via web services and data portals but it requires
managing, downloading and preparing the data for following
steps. We developed an open source and extensible R based data
client for Firehose pre-processed data and demonstrated its use
with sample case studies. Results showed that RTCGAToolbox
could improve data management for researchers who are
interested with TCGA data. In addition, it can be integrated
with other analysis pipelines for following data analysis.
biocViews: DifferentialExpression, GeneExpression, Sequencing
Author: Mehmet Samur [aut], Marcel Ramos [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3242-0582>), Ludwig Geistlinger
[ctb]
Maintainer: Marcel Ramos <marcel.ramos@sph.cuny.edu>
URL: http://mksamur.github.io/RTCGAToolbox/
VignetteBuilder: knitr
BugReports: https://github.com/mksamur/RTCGAToolbox/issues
git_url: https://git.bioconductor.org/packages/RTCGAToolbox
git_branch: devel
git_last_commit: 010a548
git_last_commit_date: 2024-12-18
Date/Publication: 2024-12-19
source.ver: src/contrib/RTCGAToolbox_2.37.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RTCGAToolbox_2.37.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RTCGAToolbox_2.37.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RTCGAToolbox_2.37.2.tgz
vignettes: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.html
vignetteTitles: RTCGAToolbox Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.R
importsMe: cBioPortalData
suggestsMe: TCGAutils
dependencyCount: 107
Package: RTN
Version: 2.31.0
Depends: R (>= 3.6.3), methods,
Imports: RedeR, minet, viper, mixtools, snow, stats, limma, data.table,
IRanges, igraph, S4Vectors, SummarizedExperiment, car, pwr,
pheatmap, grDevices, graphics, utils
Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown
License: Artistic-2.0
MD5sum: ebcaf079780459340d47c45752df3fa0
NeedsCompilation: no
Title: RTN: Reconstruction of Transcriptional regulatory Networks and
analysis of regulons
Description: A transcriptional regulatory network (TRN) consists of a
collection of transcription factors (TFs) and the regulated
target genes. TFs are regulators that recognize specific DNA
sequences and guide the expression of the genome, either
activating or repressing the expression the target genes. The
set of genes controlled by the same TF forms a regulon. This
package provides classes and methods for the reconstruction of
TRNs and analysis of regulons.
biocViews: Transcription, Network, NetworkInference, NetworkEnrichment,
GeneRegulation, GeneExpression, GraphAndNetwork,
GeneSetEnrichment, GeneticVariability
Author: Clarice Groeneveld [ctb], Gordon Robertson [ctb], Xin Wang
[aut], Michael Fletcher [aut], Florian Markowetz [aut], Kerstin
Meyer [aut], and Mauro Castro [aut]
Maintainer: Mauro Castro <mauro.a.castro@gmail.com>
URL: http://dx.doi.org/10.1038/ncomms3464
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RTN
git_branch: devel
git_last_commit: 84b09ed
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RTN_2.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RTN_2.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RTN_2.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RTN_2.31.0.tgz
vignettes: vignettes/RTN/inst/doc/RTN.html
vignetteTitles: "RTN: reconstruction of transcriptional regulatory
networks and analysis of regulons.""
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RTN/inst/doc/RTN.R
dependsOnMe: RTNduals, RTNsurvival, Fletcher2013b
suggestsMe: geneplast
dependencyCount: 139
Package: RTNduals
Version: 1.31.0
Depends: R(>= 3.6.3), RTN(>= 2.14.1), methods
Imports: graphics, grDevices, stats, utils
Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics
License: Artistic-2.0
MD5sum: c883c1a7ddb2f04ec02154efc7b6d3c1
NeedsCompilation: no
Title: Analysis of co-regulation and inference of 'dual regulons'
Description: RTNduals is a tool that searches for possible
co-regulatory loops between regulon pairs generated by the RTN
package. It compares the shared targets in order to infer 'dual
regulons', a new concept that tests whether regulators can
co-operate or compete in influencing targets.
biocViews: GeneRegulation, GeneExpression, NetworkEnrichment,
NetworkInference, GraphAndNetwork
Author: Vinicius S. Chagas, Clarice S. Groeneveld, Gordon Robertson,
Kerstin B. Meyer, Mauro A. A. Castro
Maintainer: Mauro Castro <mauro.a.castro@gmail.com>, Clarice Groeneveld
<clari.groeneveld@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RTNduals
git_branch: devel
git_last_commit: 2df8a76
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RTNduals_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RTNduals_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RTNduals_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RTNduals_1.31.0.tgz
vignettes: vignettes/RTNduals/inst/doc/RTNduals.html
vignetteTitles: "RTNduals: analysis of co-regulation and inference of
dual regulons."
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RTNduals/inst/doc/RTNduals.R
dependsOnMe: RTNsurvival
dependencyCount: 140
Package: RTNsurvival
Version: 1.31.0
Depends: R(>= 3.6.3), RTN(>= 2.14.1), RTNduals(>= 1.14.1), methods
Imports: survival, RColorBrewer, grDevices, graphics, stats, utils,
scales, data.table, egg, ggplot2, pheatmap, dunn.test
Suggests: Fletcher2013b, knitr, rmarkdown, BiocStyle, RUnit,
BiocGenerics
License: Artistic-2.0
MD5sum: 9bbe8d4a04eedea402bce7ca0f3b63f9
NeedsCompilation: no
Title: Survival analysis using transcriptional networks inferred by the
RTN package
Description: RTNsurvival is a tool for integrating regulons generated
by the RTN package with survival information. For a given
regulon, the 2-tailed GSEA approach computes a differential
Enrichment Score (dES) for each individual sample, and the dES
distribution of all samples is then used to assess the survival
statistics for the cohort. There are two main survival analysis
workflows: a Cox Proportional Hazards approach used to model
regulons as predictors of survival time, and a Kaplan-Meier
analysis assessing the stratification of a cohort based on the
regulon activity. All plots can be fine-tuned to the user's
specifications.
biocViews: NetworkEnrichment, Survival, GeneRegulation,
GeneSetEnrichment, NetworkInference, GraphAndNetwork
Author: Clarice S. Groeneveld, Vinicius S. Chagas, Mauro A. A. Castro
Maintainer: Clarice Groeneveld <clari.groeneveld@gmail.com>, Mauro A.
A. Castro <mauro.a.castro@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RTNsurvival
git_branch: devel
git_last_commit: d6d7932
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RTNsurvival_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RTNsurvival_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RTNsurvival_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RTNsurvival_1.31.0.tgz
vignettes: vignettes/RTNsurvival/inst/doc/RTNsurvival.html
vignetteTitles: "RTNsurvival: multivariate survival analysis using
transcriptional networks and regulons."
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RTNsurvival/inst/doc/RTNsurvival.R
dependencyCount: 144
Package: RTopper
Version: 1.53.0
Depends: R (>= 2.12.0), Biobase
Imports: limma, multtest
Suggests: org.Hs.eg.db, KEGGREST, GO.db
License: GPL (>= 3) + file LICENSE
MD5sum: 4078c0d792cb94cdada8f03f4c254d0d
NeedsCompilation: no
Title: This package is designed to perform Gene Set Analysis across
multiple genomic platforms
Description: the RTopper package is designed to perform and integrate
gene set enrichment results across multiple genomic platforms.
biocViews: Microarray
Author: Luigi Marchionni <marchion@jhu.edu>, Svitlana Tyekucheva
<svitlana@jimmy.harvard.edu>
Maintainer: Luigi Marchionni <marchion@jhu.edu>
git_url: https://git.bioconductor.org/packages/RTopper
git_branch: devel
git_last_commit: 3a32b4a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RTopper_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RTopper_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RTopper_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RTopper_1.53.0.tgz
vignettes: vignettes/RTopper/inst/doc/RTopper.pdf
vignetteTitles: RTopper user's manual
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/RTopper/inst/doc/RTopper.R
dependencyCount: 18
Package: Rtpca
Version: 1.17.0
Depends: R (>= 4.0.0), stats, dplyr, tidyr
Imports: Biobase, methods, ggplot2, pROC, fdrtool, splines, utils,
tibble
Suggests: knitr, BiocStyle, TPP, testthat, rmarkdown
License: GPL-3
MD5sum: b3b2845f8e664bcd06dd2528fc418222
NeedsCompilation: no
Title: Thermal proximity co-aggregation with R
Description: R package for performing thermal proximity co-aggregation
analysis with thermal proteome profiling datasets to analyse
protein complex assembly and (differential) protein-protein
interactions across conditions.
biocViews: Software, Proteomics, DataImport
Author: Nils Kurzawa [aut, cre], André Mateus [aut], Mikhail M.
Savitski [aut]
Maintainer: Nils Kurzawa <nilskurzawa@gmail.com>
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/
git_url: https://git.bioconductor.org/packages/Rtpca
git_branch: devel
git_last_commit: 28c7a10
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Rtpca_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Rtpca_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Rtpca_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Rtpca_1.17.0.tgz
vignettes: vignettes/Rtpca/inst/doc/Rtpca.html
vignetteTitles: Introduction to Rtpca
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rtpca/inst/doc/Rtpca.R
dependencyCount: 50
Package: rtracklayer
Version: 1.67.1
Depends: R (>= 3.5.0), methods, GenomicRanges (>= 1.37.2)
Imports: XML (>= 1.98-0), BiocGenerics (>= 0.35.3), S4Vectors (>=
0.23.18), IRanges (>= 2.13.13), XVector (>= 0.19.7),
GenomeInfoDb (>= 1.15.2), Biostrings (>= 2.47.6), curl, httr,
Rsamtools (>= 1.31.2), GenomicAlignments (>= 1.15.6), BiocIO,
tools, restfulr (>= 0.0.13)
LinkingTo: S4Vectors, IRanges, XVector
Suggests: BSgenome (>= 1.33.4), humanStemCell, microRNA (>= 1.1.1),
genefilter, limma, org.Hs.eg.db, hgu133plus2.db,
GenomicFeatures, BSgenome.Hsapiens.UCSC.hg19,
TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit
License: Artistic-2.0 + file LICENSE
MD5sum: 36bc7966222630d55499224b9bc700ad
NeedsCompilation: yes
Title: R interface to genome annotation files and the UCSC genome
browser
Description: Extensible framework for interacting with multiple genome
browsers (currently UCSC built-in) and manipulating annotation
tracks in various formats (currently GFF, BED, bedGraph, BED15,
WIG, BigWig and 2bit built-in). The user may export/import
tracks to/from the supported browsers, as well as query and
modify the browser state, such as the current viewport.
biocViews: Annotation,Visualization,DataImport
Author: Michael Lawrence, Vince Carey, Robert Gentleman
Maintainer: Michael Lawrence <michafla@gene.com>
git_url: https://git.bioconductor.org/packages/rtracklayer
git_branch: devel
git_last_commit: 8d9ecd7
git_last_commit_date: 2025-02-18
Date/Publication: 2025-02-19
source.ver: src/contrib/rtracklayer_1.67.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rtracklayer_1.67.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rtracklayer_1.67.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rtracklayer_1.67.1.tgz
vignettes: vignettes/rtracklayer/inst/doc/rtracklayer.pdf
vignetteTitles: rtracklayer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: TRUE
hasLICENSE: TRUE
Rfiles: vignettes/rtracklayer/inst/doc/rtracklayer.R
dependsOnMe: BSgenome, CAGEfightR, CoverageView, CSSQ, cummeRbund,
ExCluster, geneXtendeR, GenomicFiles, HelloRanges, IdeoViz,
MethylSeekR, ORFhunteR, r3Cseq, StructuralVariantAnnotation,
svaNUMT, svaRetro, EatonEtAlChIPseq, liftOver, sequencing,
csawBook, OSCA.intro
importsMe: AnnotationHubData, annotatr, APAlyzer, ATACseqQC,
ATACseqTFEA, ballgown, bedbaser, BgeeCall, BindingSiteFinder,
biscuiteer, BiSeq, branchpointer, BSgenomeForge, CAGEr, casper,
CexoR, chipenrich, ChIPpeakAnno, ChIPseeker, ChromHeatMap,
ChromSCape, circRNAprofiler, cliProfiler, CNEr,
consensusSeekeR, conumee, crisprDesign, customProDB, derfinder,
DEScan2, diffHic, diffUTR, DMCFB, DMCHMM, dmrseq,
DuplexDiscovereR, easylift, ELMER, enhancerHomologSearch,
ensembldb, EpiCompare, epidecodeR, epigraHMM, epimutacions,
erma, esATAC, extraChIPs, factR, fcScan, FindIT2, FLAMES,
geneAttribution, genomation, GenomicFeatures,
GenomicInteractions, GenomicPlot, ggbio, gmapR, gmoviz, goseq,
GOTHiC, GreyListChIP, Gviz, hiAnnotator, HicAggR,
hicVennDiagram, HiTC, icetea, igvR, INSPEcT,
IsoformSwitchAnalyzeR, karyoploteR, m6Aboost, MADSEQ, magpie,
maser, MEDIPS, metagene2, metaseqR2, methodical, methrix,
methylKit, mist, mobileRNA, Moonlight2R, motifbreakR, MotifDb,
MotifPeeker, multicrispr, MungeSumstats, NADfinder,
nearBynding, normr, OGRE, OMICsPCA, ORFik, PAST, periodicDNA,
plyranges, pram, primirTSS, proBAMr, profileplyr, PureCN, qsea,
QuasR, raer, RCAS, recount, recount3, recoup, regioneR, REMP,
Repitools, RiboProfiling, ribosomeProfilingQC, rifi,
rifiComparative, rmspc, RNAmodR, roar, scanMiRApp, scDblFinder,
scPipe, scRNAseqApp, scruff, seqCAT, seqsetvis, sevenC, SGSeq,
shinyepico, signeR, SigsPack, sitadela, soGGi, SOMNiBUS,
SpliceWiz, srnadiff, syntenet, TEKRABber, TENET, TFBSTools,
tidyCoverage, trackViewer, transcriptR, TRESS, tRNAscanImport,
txcutr, txdbmaker, VariantAnnotation, VariantTools, wavClusteR,
wiggleplotr, GenomicState, chipenrich.data, DMRcatedata,
geneLenDataBase, NxtIRFdata, raerdata, spatialLIBD,
SingscoreAMLMutations, crispRdesignR, GALLO, geneHapR, kibior,
locuszoomr, PlasmaMutationDetector
suggestsMe: alabaster.files, AnnotationHub, autonomics, BiocFileCache,
biovizBase, BREW3R.r, bsseq, cicero, CINdex, compEpiTools,
CrispRVariants, crisprViz, DAMEfinder, DiffBind, eisaR,
epistack, epivizrChart, epivizrData, FRASER, G4SNVHunter,
geneXtendeR, GenomicAlignments, GenomicDistributions,
GenomicInteractionNodes, GenomicRanges, gwascat, HiCExperiment,
HiContacts, igvShiny, InPAS, interactiveDisplay, megadepth,
methylumi, miRBaseConverter, motifTestR, MutationalPatterns,
NanoMethViz, OrganismDbi, PICB, PICS, PING, pipeFrame,
plotgardener, plyinteractions, pqsfinder, ProteoDisco,
R453Plus1Toolbox, RcisTarget, rGADEM, RNAmodR.AlkAnilineSeq,
RNAmodR.ML, RNAmodR.RiboMethSeq, RnBeads, RSVSim, similaRpeak,
systemPipeR, TAPseq, TCGAutils, transmogR, triplex,
tRNAdbImport, TVTB, xcore, EpiTxDb.Hs.hg38, EpiTxDb.Sc.sacCer3,
excluderanges, FDb.FANTOM4.promoters.hg19, fourDNData,
GeuvadisTranscriptExpr, nanotubes, PasillaTranscriptExpr,
systemPipeRdata, chipseqDB, gkmSVM, Rgff, RTIGER, Seurat,
Signac
dependencyCount: 57
Package: Rtreemix
Version: 1.69.0
Depends: R (>= 2.5.0)
Imports: methods, graph, Biobase, Hmisc
Suggests: Rgraphviz
License: LGPL
MD5sum: d87f0f499a0f0d4fb0a4b561ecd79408
NeedsCompilation: yes
Title: Rtreemix: Mutagenetic trees mixture models.
Description: Rtreemix is a package that offers an environment for
estimating the mutagenetic trees mixture models from
cross-sectional data and using them for various predictions. It
includes functions for fitting the trees mixture models,
likelihood computations, model comparisons, waiting time
estimations, stability analysis, etc.
biocViews: StatisticalMethod
Author: Jasmina Bogojeska
Maintainer: Jasmina Bogojeska <jasmina.bogojeska@gmail.com>
git_url: https://git.bioconductor.org/packages/Rtreemix
git_branch: devel
git_last_commit: 45c317f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Rtreemix_1.69.0.tar.gz
vignettes: vignettes/Rtreemix/inst/doc/Rtreemix.pdf
vignetteTitles: Rtreemix
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rtreemix/inst/doc/Rtreemix.R
dependencyCount: 78
Package: rTRM
Version: 1.45.0
Depends: R (>= 2.10), igraph (>= 1.0)
Imports: methods, AnnotationDbi, DBI, RSQLite
Suggests: RUnit, BiocGenerics, MotifDb, graph, PWMEnrich, biomaRt,
Biostrings, BSgenome.Mmusculus.UCSC.mm8.masked, org.Hs.eg.db,
org.Mm.eg.db, ggplot2, BiocStyle, knitr, rmarkdown
License: GPL-3
MD5sum: 9029f178dc4dca2ca2be81736537e9e1
NeedsCompilation: no
Title: Identification of Transcriptional Regulatory Modules from
Protein-Protein Interaction Networks
Description: rTRM identifies transcriptional regulatory modules (TRMs)
from protein-protein interaction networks.
biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork
Author: Diego Diez
Maintainer: Diego Diez <diego10ruiz@gmail.com>
URL: https://github.com/ddiez/rTRM
VignetteBuilder: knitr
BugReports: https://github.com/ddiez/rTRM/issues
git_url: https://git.bioconductor.org/packages/rTRM
git_branch: devel
git_last_commit: 241c6db
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rTRM_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rTRM_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rTRM_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rTRM_1.45.0.tgz
vignettes: vignettes/rTRM/inst/doc/Introduction.html
vignetteTitles: Introduction to rTRM
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rTRM/inst/doc/Introduction.R
importsMe: rTRMui
dependencyCount: 50
Package: rTRMui
Version: 1.45.0
Imports: shiny (>= 0.9), rTRM, MotifDb, org.Hs.eg.db, org.Mm.eg.db
License: GPL-3
MD5sum: 8261be29e8701fd4cecb0b6fead07a2f
NeedsCompilation: no
Title: A shiny user interface for rTRM
Description: This package provides a web interface to compute
transcriptional regulatory modules with rTRM.
biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork, GUI
Author: Diego Diez
Maintainer: Diego Diez <diego10ruiz@gmail.com>
URL: https://github.com/ddiez/rTRMui
BugReports: https://github.com/ddiez/rTRMui/issues
git_url: https://git.bioconductor.org/packages/rTRMui
git_branch: devel
git_last_commit: e21de74
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rTRMui_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rTRMui_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rTRMui_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rTRMui_1.45.0.tgz
vignettes: vignettes/rTRMui/inst/doc/rTRMui.pdf
vignetteTitles: Introduction to rTRMui
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/rTRMui/inst/doc/rTRMui.R
dependencyCount: 102
Package: runibic
Version: 1.29.0
Depends: R (>= 3.4.0), biclust, SummarizedExperiment
Imports: Rcpp (>= 0.12.12), testthat, methods
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, GEOquery, affy, airway, QUBIC
License: MIT + file LICENSE
MD5sum: 3bb6297d3f146fd1827d0c4743862522
NeedsCompilation: yes
Title: runibic: row-based biclustering algorithm for analysis of gene
expression data in R
Description: This package implements UbiBic algorithm in R. This
biclustering algorithm for analysis of gene expression data was
introduced by Zhenjia Wang et al. in 2016. It is currently
considered the most promising biclustering method for
identification of meaningful structures in complex and noisy
data.
biocViews: Microarray, Clustering, GeneExpression, Sequencing, Coverage
Author: Patryk Orzechowski, Artur Pańszczyk
Maintainer: Patryk Orzechowski <patryk.orzechowski@gmail.com>
URL: http://github.com/athril/runibic
SystemRequirements: C++11, GNU make
VignetteBuilder: knitr
BugReports: http://github.com/athril/runibic/issues
git_url: https://git.bioconductor.org/packages/runibic
git_branch: devel
git_last_commit: c55baab
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/runibic_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/runibic_1.29.0.zip
vignettes: vignettes/runibic/inst/doc/runibic.html
vignetteTitles: runibic: UniBic in R Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
suggestsMe: mosbi
dependencyCount: 91
Package: RUVcorr
Version: 1.39.0
Imports: corrplot, MASS, stats, lattice, grDevices, gridExtra,
snowfall, psych, BiocParallel, grid, bladderbatch, reshape2,
graphics
Suggests: knitr, hgu133a2.db, rmarkdown
License: GPL-2
MD5sum: 71c7c69f04bfe8c160f8a295abbcdb83
NeedsCompilation: no
Title: Removal of unwanted variation for gene-gene correlations and
related analysis
Description: RUVcorr allows to apply global removal of unwanted
variation (ridged version of RUV) to real and simulated gene
expression data.
biocViews: GeneExpression, Normalization
Author: Saskia Freytag
Maintainer: Saskia Freytag <saskia.freytag@perkins.uwa.edu.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RUVcorr
git_branch: devel
git_last_commit: 1f72b15
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RUVcorr_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RUVcorr_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RUVcorr_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RUVcorr_1.39.0.tgz
vignettes: vignettes/RUVcorr/inst/doc/Vignette.html
vignetteTitles: Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RUVcorr/inst/doc/Vignette.R
dependencyCount: 42
Package: RUVnormalize
Version: 1.41.0
Depends: R (>= 2.10.0)
Imports: RUVnormalizeData, Biobase
Enhances: spams
License: GPL-3
Archs: x64
MD5sum: 4322c131c11e0c9eb066821553c7e0c1
NeedsCompilation: no
Title: RUV for normalization of expression array data
Description: RUVnormalize is meant to remove unwanted variation from
gene expression data when the factor of interest is not
defined, e.g., to clean up a dataset for general use or to do
any kind of unsupervised analysis.
biocViews: StatisticalMethod, Normalization
Author: Laurent Jacob
Maintainer: Laurent Jacob <laurent.jacob@univ-lyon1.fr>
git_url: https://git.bioconductor.org/packages/RUVnormalize
git_branch: devel
git_last_commit: 83c9967
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RUVnormalize_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RUVnormalize_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RUVnormalize_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RUVnormalize_1.41.0.tgz
vignettes: vignettes/RUVnormalize/inst/doc/RUVnormalize.pdf
vignetteTitles: RUVnormalize
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RUVnormalize/inst/doc/RUVnormalize.R
dependencyCount: 8
Package: RUVSeq
Version: 1.41.0
Depends: Biobase, EDASeq (>= 1.99.1), edgeR
Imports: methods, MASS
Suggests: BiocStyle, knitr, RColorBrewer, zebrafishRNASeq, DESeq2
License: Artistic-2.0
MD5sum: c419115f4bb03e3c06fe33497e7aa8ae
NeedsCompilation: no
Title: Remove Unwanted Variation from RNA-Seq Data
Description: This package implements the remove unwanted variation
(RUV) methods of Risso et al. (2014) for the normalization of
RNA-Seq read counts between samples.
biocViews: ImmunoOncology, DifferentialExpression, Preprocessing,
RNASeq, Software
Author: Davide Risso [aut, cre, cph], Sandrine Dudoit [aut], Lorena
Pantano [ctb], Kamil Slowikowski [ctb]
Maintainer: Davide Risso <risso.davide@gmail.com>
URL: https://github.com/drisso/RUVSeq
VignetteBuilder: knitr
BugReports: https://github.com/drisso/RUVSeq/issues
git_url: https://git.bioconductor.org/packages/RUVSeq
git_branch: devel
git_last_commit: bb9c230
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RUVSeq_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RUVSeq_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RUVSeq_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RUVSeq_1.41.0.tgz
vignettes: vignettes/RUVSeq/inst/doc/RUVSeq.html
vignetteTitles: RUVSeq: Remove Unwanted Variation from RNA-Seq Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RUVSeq/inst/doc/RUVSeq.R
dependsOnMe: octad, rnaseqGene
importsMe: consensusDE, ribosomeProfilingQC, scone, standR
suggestsMe: DEScan2, NanoTube
dependencyCount: 121
Package: Rvisdiff
Version: 1.5.0
Depends: R (>= 4.3.0)
Imports: edgeR, utils
Suggests: knitr, rmarkdown, DESeq2, limma, SummarizedExperiment,
airway, BiocStyle, matrixTests, BiocManager
License: GPL-2 | GPL-3
MD5sum: 91f4e65d23330fd180eaad907f9486a5
NeedsCompilation: no
Title: Interactive Graphs for Differential Expression
Description: Creates a muti-graph web page which allows the interactive
exploration of differential expression results. The graphical
web interface presents results as a table which is integrated
with five interactive graphs: MA-plot, volcano plot, box plot,
lines plot and cluster heatmap. Graphical aspect and
information represented in the graphs can be customized by
means of user controls. Final graphics can be exported as PNG
format.
biocViews: Software, Visualization, RNASeq, DataRepresentation,
DifferentialExpression
Author: Carlos Prieto [aut] (ORCID:
<https://orcid.org/0000-0003-2064-4842>), David Barrios [cre,
aut] (ORCID: <https://orcid.org/0000-0003-4465-0200>)
Maintainer: David Barrios <metal@usal.es>
URL: https://github.com/BioinfoUSAL/Rvisdiff/
VignetteBuilder: knitr
BugReports: https://github.com/BioinfoUSAL/Rvisdiff/issues/
git_url: https://git.bioconductor.org/packages/Rvisdiff
git_branch: devel
git_last_commit: 5134d02
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Rvisdiff_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Rvisdiff_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Rvisdiff_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Rvisdiff_1.5.0.tgz
vignettes: vignettes/Rvisdiff/inst/doc/Rvisdiff.html
vignetteTitles: Visualize Differential Expression results
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Rvisdiff/inst/doc/Rvisdiff.R
dependencyCount: 11
Package: RVS
Version: 1.29.0
Depends: R (>= 3.5.0)
Imports: GENLIB, gRain, snpStats, kinship2, methods, stats, utils,
R.utils
Suggests: knitr, testthat, rmarkdown, BiocStyle, VariantAnnotation
License: GPL-2
Archs: x64
MD5sum: 2071741707b26df490c2cb738fdd824c
NeedsCompilation: no
Title: Computes estimates of the probability of related individuals
sharing a rare variant
Description: Rare Variant Sharing (RVS) implements tests of association
and linkage between rare genetic variant genotypes and a
dichotomous phenotype, e.g. a disease status, in family
samples. The tests are based on probabilities of rare variant
sharing by relatives under the null hypothesis of absence of
linkage and association between the rare variants and the
phenotype and apply to single variants or multiple variants in
a region (e.g. gene-based test).
biocViews: ImmunoOncology, Genetics, GenomeWideAssociation,
VariantDetection, ExomeSeq, WholeGenome
Author: Alexandre Bureau, Ingo Ruczinski, Samuel Younkin, Thomas
Sherman
Maintainer: Alexandre Bureau <alexandre.bureau@fmed.ulaval.ca>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/RVS
git_branch: devel
git_last_commit: b6690b7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/RVS_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/RVS_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/RVS_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/RVS_1.29.0.tgz
vignettes: vignettes/RVS/inst/doc/RVS.html
vignetteTitles: The RVS Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/RVS/inst/doc/RVS.R
dependencyCount: 62
Package: rWikiPathways
Version: 1.27.0
Imports: httr, utils, XML, rjson, data.table, RCurl, dplyr, tidyr,
readr, stringr, purrr, lubridate
Suggests: testthat, BiocStyle, knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 0ab514c1aae8ccbe3bd371efb305a7d2
NeedsCompilation: no
Title: rWikiPathways - R client library for the WikiPathways API
Description: Use this package to interface with the WikiPathways API.
It provides programmatic access to WikiPathways content in
multiple data and image formats, including official monthly
release files and convenient GMT read/write functions.
biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network,
Metabolomics
Author: Egon Willighagen [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7542-0286>), Alex Pico [aut]
(ORCID: <https://orcid.org/0000-0001-5706-2163>)
Maintainer: Egon Willighagen <egon.willighagen@gmail.com>
URL: https://github.com/wikipathways/rwikipathways
VignetteBuilder: knitr
BugReports: https://github.com/wikipathways/rwikipathways/issues
git_url: https://git.bioconductor.org/packages/rWikiPathways
git_branch: devel
git_last_commit: 75875b0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/rWikiPathways_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/rWikiPathways_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/rWikiPathways_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/rWikiPathways_1.27.0.tgz
vignettes: vignettes/rWikiPathways/inst/doc/Overview.html,
vignettes/rWikiPathways/inst/doc/Pathway-Analysis.html,
vignettes/rWikiPathways/inst/doc/rWikiPathways-and-BridgeDbR.html,
vignettes/rWikiPathways/inst/doc/rWikiPathways-and-RCy3.html
vignetteTitles: 1. Overview, 4. Pathway Analysis, 2. rWikiPathways and
BridgeDbR, 3. rWikiPathways and RCy3
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/rWikiPathways/inst/doc/Overview.R,
vignettes/rWikiPathways/inst/doc/Pathway-Analysis.R,
vignettes/rWikiPathways/inst/doc/rWikiPathways-and-BridgeDbR.R,
vignettes/rWikiPathways/inst/doc/rWikiPathways-and-RCy3.R
importsMe: famat, RVA
suggestsMe: TRONCO
dependencyCount: 51
Package: S4Arrays
Version: 1.7.3
Depends: R (>= 4.3.0), methods, Matrix, abind, BiocGenerics (>=
0.45.2), S4Vectors, IRanges
Imports: stats, crayon
LinkingTo: S4Vectors
Suggests: BiocParallel, SparseArray (>= 0.0.4), DelayedArray,
HDF5Array, testthat, knitr, rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: 1cb2606aeb72d138fe83b5886279dd39
NeedsCompilation: yes
Title: Foundation of array-like containers in Bioconductor
Description: The S4Arrays package defines the Array virtual class to be
extended by other S4 classes that wish to implement a container
with an array-like semantic. It also provides: (1) low-level
functionality meant to help the developer of such container to
implement basic operations like display, subsetting, or
coercion of their array-like objects to an ordinary matrix or
array, and (2) a framework that facilitates block processing of
array-like objects (typically on-disk objects).
biocViews: Infrastructure, DataRepresentation
Author: Hervé Pagès [aut, cre] (ORCID:
<https://orcid.org/0009-0002-8272-4522>), Jacques Serizay [ctb]
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/S4Arrays
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/S4Arrays/issues
git_url: https://git.bioconductor.org/packages/S4Arrays
git_branch: devel
git_last_commit: 41a64ff
git_last_commit_date: 2025-02-07
Date/Publication: 2025-02-09
source.ver: src/contrib/S4Arrays_1.7.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/S4Arrays_1.7.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/S4Arrays_1.7.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/S4Arrays_1.7.2.tgz
vignettes: vignettes/S4Arrays/inst/doc/S4Arrays_quick_overview.html
vignetteTitles: A quick overview of the S4Arrays package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/S4Arrays/inst/doc/S4Arrays_quick_overview.R
dependsOnMe: DelayedArray, SparseArray
importsMe: alabaster.matrix, DelayedTensor, dreamlet, h5mread,
HDF5Array, scran, scuttle, SummarizedExperiment
suggestsMe: BiocGenerics
dependencyCount: 15
Package: S4Vectors
Version: 0.45.4
Depends: R (>= 4.0.0), methods, utils, stats, stats4, BiocGenerics (>=
0.53.2)
Suggests: IRanges, GenomicRanges, SummarizedExperiment, Matrix,
DelayedArray, ShortRead, graph, data.table, RUnit, BiocStyle,
knitr
License: Artistic-2.0
MD5sum: b94359355e69eb1b49add4ed18c5e953
NeedsCompilation: yes
Title: Foundation of vector-like and list-like containers in
Bioconductor
Description: The S4Vectors package defines the Vector and List virtual
classes and a set of generic functions that extend the semantic
of ordinary vectors and lists in R. Package developers can
easily implement vector-like or list-like objects as concrete
subclasses of Vector or List. In addition, a few low-level
concrete subclasses of general interest (e.g. DataFrame, Rle,
Factor, and Hits) are implemented in the S4Vectors package
itself (many more are implemented in the IRanges package and in
other Bioconductor infrastructure packages).
biocViews: Infrastructure, DataRepresentation
Author: Hervé Pagès [aut, cre], Michael Lawrence [aut], Patrick Aboyoun
[aut], Aaron Lun [ctb], Beryl Kanali [ctb] (Converted vignettes
from Sweave to RMarkdown)
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/S4Vectors
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/S4Vectors/issues
git_url: https://git.bioconductor.org/packages/S4Vectors
git_branch: devel
git_last_commit: 659194b
git_last_commit_date: 2025-02-10
Date/Publication: 2025-02-11
source.ver: src/contrib/S4Vectors_0.45.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/S4Vectors_0.45.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/S4Vectors_0.45.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/S4Vectors_0.45.4.tgz
vignettes: vignettes/S4Vectors/inst/doc/S4QuickOverview.pdf,
vignettes/S4Vectors/inst/doc/RleTricks.html,
vignettes/S4Vectors/inst/doc/S4VectorsOverview.html
vignetteTitles: A quick overview of the S4 class system, Rle Tips and
Tricks, An Overview of the S4Vectors package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/S4Vectors/inst/doc/RleTricks.R,
vignettes/S4Vectors/inst/doc/S4QuickOverview.R,
vignettes/S4Vectors/inst/doc/S4VectorsOverview.R
dependsOnMe: altcdfenvs, AnnotationHubData, ATACseqQC, bambu, bandle,
betaHMM, Biostrings, BiSeq, BSgenome, bumphunter, Cardinal,
CellMapper, CexoR, chimeraviz, ChIPpeakAnno, chipseq, ChIPseqR,
ClassifyR, cliProfiler, CODEX, CompoundDb, coseq, CSAR, CSSQ,
DelayedArray, DelayedDataFrame, DESeq2, DEXSeq,
DirichletMultinomial, DMCFB, DMCHMM, DMRcaller, epigenomix,
ExperimentHubData, ExpressionAtlas, fCCAC, GA4GHclient,
GenomeInfoDb, GenomicAlignments, GenomicFeatures,
GenomicRanges, GenomicScores, GenomicTuples, GeomxTools,
girafe, Gviz, hdxmsqc, HelloRanges, HERON, InTAD, IntEREst,
IRanges, LinTInd, LoomExperiment, m6Aboost, MetNet, MotifDb,
MSnbase, MuData, MultimodalExperiment, NADfinder,
NanoStringNCTools, NBAMSeq, octad, OGRE, OTUbase, padma,
PSMatch, pwalign, Rcwl, RegEnrich, RepViz, RNAmodR, RnBeads,
S4Arrays, scDataviz, screenCounter, segmentSeq, seqArchRplus,
SeqGate, SparseArray, Spectra, SQLDataFrame, Structstrings,
topdownr, TreeSummarizedExperiment, TRESS, triplex, txdbmaker,
updateObject, VariantExperiment, VariantTools, vulcan, XVector,
pd.ag, pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501,
pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine,
pd.bsubtilis, pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine,
pd.canine.2, pd.celegans, pd.chicken, pd.chigene.1.0.st,
pd.chigene.1.1.st, pd.chogene.2.0.st, pd.chogene.2.1.st,
pd.citrus, pd.clariom.d.human, pd.clariom.s.human,
pd.clariom.s.human.ht, pd.clariom.s.mouse,
pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht,
pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st,
pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array,
pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1,
pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2,
pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st,
pd.equgene.1.1.st, pd.felgene.1.0.st, pd.felgene.1.1.st,
pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5,
pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st,
pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a,
pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219,
pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d,
pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm,
pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800,
pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1,
pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize,
pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240,
pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st,
pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a,
pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c,
pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0,
pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1,
pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st,
pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0,
pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180,
pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st,
pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine,
pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st,
pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1,
pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st,
pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st,
pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c,
pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice,
pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0,
pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean,
pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato,
pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2,
pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98,
pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish,
curatedPCaData, scATAC.Explorer, generegulation
importsMe: ADImpute, adverSCarial, affycoretools, aggregateBioVar,
airpart, alabaster.base, alabaster.bumpy, alabaster.files,
alabaster.mae, alabaster.matrix, alabaster.ranges,
alabaster.se, alabaster.sfe, alabaster.spatial,
alabaster.string, alabaster.vcf, ALDEx2, AllelicImbalance,
amplican, AneuFinder, animalcules, AnnotationDbi,
AnnotationForge, AnnotationHub, annotatr, appreci8R, ASpli,
ASURAT, ATACseqTFEA, atena, autonomics, BadRegionFinder,
ballgown, Banksy, barcodetrackR, BASiCS, batchelor, BatchQC,
BayesSpace, bettr, BindingSiteFinder, BiocHubsShiny, BiocIO,
BiocSet, BiocSingular, biotmle, biovizBase, biscuiteer, BiSeq,
bluster, bnbc, BPRMeth, branchpointer, breakpointR, BREW3R.r,
BSgenomeForge, bsseq, BumpyMatrix, BUSpaRse, BUSseq,
CAGEfightR, CAGEr, cardelino, CardinalIO, CARDspa, casper,
CATALYST, CatsCradle, cBioPortalData, ccfindR, celaref, celda,
CellBarcode, censcyt, Cepo, CeTF, cfdnakit, CHETAH,
chevreulPlot, chevreulProcess, chevreulShiny, chipenrich,
ChIPexoQual, ChIPQC, ChIPseeker, ChromSCape, chromVAR, cicero,
circRNAprofiler, CircSeqAlignTk, CiteFuse, cleanUpdTSeq,
cleaver, CluMSID, clusterExperiment, clustifyr, cn.mops, CNEr,
CNVMetrics, CNVPanelizer, CNVRanger, COCOA, CoGAPS, Cogito,
comapr, compEpiTools, consensusDE, consensusSeekeR, CoreGx,
COTAN, CoverageView, crisprBase, crisprDesign, CRISPRseek,
crisprShiny, CrispRVariants, crisprViz, csaw, CTDquerier,
cummeRbund, CuratedAtlasQueryR, customProDB, cydar, cytofQC,
cytoKernel, cytomapper, cytoviewer, DAMEfinder, dandelionR,
debrowser, DECIPHER, decompTumor2Sig, decontX, deconvR,
DEFormats, DegCre, DegNorm, DEGreport, DelayedMatrixStats,
derfinder, derfinderHelper, derfinderPlot, DEScan2, DESpace,
DEWSeq, DFplyr, DiffBind, diffcyt, diffHic, diffUTR, Dino,
DiscoRhythm, dittoSeq, DMRcate, dmrseq, DNAfusion, doseR,
dreamlet, DRIMSeq, DropletUtils, drugTargetInteractions,
dStruct, easyRNASeq, eisaR, ELMER, enhancerHomologSearch,
EnrichDO, EnrichmentBrowser, ensembldb, epigraHMM, EpiMix,
epimutacions, epiregulon, epistack, EpiTxDb, epivizr,
epivizrData, epivizrStandalone, erma, esATAC, EventPointer,
ExperimentHub, ExperimentSubset, ExploreModelMatrix,
extraChIPs, factR, FastqCleaner, fastseg, FilterFFPE, FindIT2,
fishpond, FLAMES, flowCore, flowWorkspace, FRASER, FuseSOM,
G4SNVHunter, GA4GHshiny, gcapc, gDNAx, gDRcore, gDRimport,
gDRutils, GDSArray, gemma.R, GeneRegionScan, GENESIS,
GeneTonic, genomation, GenomAutomorphism, genomeIntervals,
GenomicAlignments, GenomicFiles, GenomicInteractionNodes,
GenomicInteractions, GenomicOZone, GenomicSuperSignature,
geomeTriD, GEOquery, ggbio, Glimma, glmGamPoi, gmapR, gmoviz,
GOpro, GOTHiC, GRaNIE, GRmetrics, GSEABenchmarkeR, GSVA,
GUIDEseq, gwascat, h5mread, h5vc, HDF5Array, hermes, HicAggR,
HiCBricks, HiCcompare, HiCDOC, HiCExperiment, HiContacts,
HiCool, HiCParser, hicVennDiagram, HiLDA, hipathia, hmdbQuery,
HoloFoodR, icetea, ideal, IFAA, ILoReg, IMAS, imcRtools,
INSPEcT, InteractionSet, InteractiveComplexHeatmap, iSEE,
iSEEde, iSEEhub, iSEEpathways, iSEEtree, iSEEu, IsoBayes,
IsoformSwitchAnalyzeR, isomiRs, IVAS, ivygapSE, karyoploteR,
katdetectr, kebabs, kmcut, knowYourCG, lefser, lemur, limpca,
LimROTS, lionessR, lipidr, lisaClust, loci2path, LOLA, lute,
MACSr, MADSEQ, magpie, MAI, mariner, marr, MAST, mbkmeans,
mCSEA, MEAL, meshr, MesKit, metabCombiner, MetaboAnnotation,
MetaboDynamics, metaseqR2, MetCirc, methInheritSim, methodical,
MethReg, methrix, methylCC, methylInheritance, methylKit,
methylPipe, methylSig, methylumi, MGnifyR, mia, miaSim, miaViz,
MICSQTL, midasHLA, miloR, mimager, minfi, MinimumDistance,
MIRA, MiRaGE, missMethyl, missRows, mist, mitoClone2, MMDiff2,
moanin, mobileRNA, Modstrings, MoleculeExperiment, monaLisa,
mosaics, MOSClip, mosdef, MOSim, Motif2Site, motifbreakR,
motifmatchr, MotifPeeker, motifTestR, MPAC, mpra, msa,
MsBackendMassbank, MsBackendMetaboLights, MsBackendMgf,
MsBackendMsp, MsBackendRawFileReader, MsBackendSql,
MsCoreUtils, MsExperiment, msgbsR, MSPrep, MuData,
MultiAssayExperiment, MultiDataSet, MultiRNAflow,
multistateQTL, mumosa, muscat, musicatk, MutationalPatterns,
mygene, myvariant, NanoMethViz, ncRNAtools, nearBynding,
nucleoSim, nucleR, nullranges, oligoClasses, omicsViewer,
oncoscanR, ontoProc, openPrimeR, ORFik, Organism.dplyr,
OrganismDbi, orthos, OUTRIDER, OutSplice, packFinder,
PAIRADISE, pairedGSEA, panelcn.mops, PAST, pcaExplorer, PDATK,
pdInfoBuilder, Pedixplorer, periodicDNA, pgxRpi, PharmacoGx,
PhIPData, PhosR, PICB, PING, pipeComp, Pirat, plyinteractions,
plyranges, plyxp, pmp, pogos, PolySTest, pqsfinder, pram,
prebs, preciseTAD, primirTSS, proActiv, procoil, proDA,
profileplyr, PRONE, ProteoDisco, PureCN, PWMEnrich, qcmetrics,
QFeatures, qpgraph, qsea, QTLExperiment, QuasR, R3CPET,
R453Plus1Toolbox, RadioGx, raer, RaggedExperiment, RAIDS, ramr,
RareVariantVis, RBioFormats, RCAS, RcisTarget, RcwlPipelines,
recount, recount3, recountmethylation, recoup,
ReducedExperiment, RegionalST, regioneR, regionReport,
regsplice, regutools, REMP, Repitools, ResidualMatrix, RESOLVE,
ReUseData, rexposome, rfaRm, RgnTX, rGREAT, RiboDiPA,
RiboProfiling, ribor, riboSeqR, ribosomeProfilingQC, rifi,
rifiComparative, RJMCMCNucleosomes, RMassBank, Rmmquant,
rnaEditr, RNAmodR.AlkAnilineSeq, RNAmodR.ML,
RNAmodR.RiboMethSeq, roar, rprimer, Rqc, Rsamtools, rScudo,
RTCGAToolbox, RTN, rtracklayer, saseR, SC3, ScaledMatrix,
scanMiR, scanMiRApp, SCArray, SCArray.sat, scater, scClassify,
scDblFinder, scDD, scds, scHOT, scider, scmap, scMerge, scMET,
SCnorm, SCOPE, scp, scPipe, scran, scRepertoire, scRNAseqApp,
scruff, scTensor, scTGIF, scTreeViz, scuttle, scviR, sechm,
segmenter, SeqArray, seqCAT, seqsetvis, SeqSQC, SeqVarTools,
sesame, SEtools, sevenbridges, sevenC, SGSeq, ShortRead,
simona, simPIC, simpleSeg, SingleCellAlleleExperiment,
SingleCellExperiment, singleCellTK, SingleMoleculeFootprinting,
SingleR, singscore, sitadela, skewr, slingshot, SMITE, SNPhood,
soGGi, SomaticSignatures, SOMNiBUS, Spaniel, SpaNorm,
SpatialExperiment, SpatialExperimentIO,
SpatialFeatureExperiment, spatialHeatmap, SpatialOmicsOverlay,
spatzie, spicyR, spiky, spillR, splatter, SpliceWiz,
SplicingGraphs, SplineDV, SPLINTER, SpotClean, sRACIPE,
srnadiff, standR, Statial, strandCheckR, struct,
StructuralVariantAnnotation, SummarizedExperiment, svaNUMT,
svaRetro, SVP, SynExtend, systemPipeR, tadar, TAPseq,
TBSignatureProfiler, TCGAbiolinks, TCGAutils, TENET, TENxIO,
terraTCGAdata, TFBSTools, TFHAZ, tidybulk, tidyCoverage,
tidySingleCellExperiment, tidySpatialExperiment,
tidySummarizedExperiment, TileDBArray, TnT, ToxicoGx,
trackViewer, tradeSeq, TrajectoryUtils, transcriptR, transmogR,
treeclimbR, Trendy, tricycle, tRNA, tRNAdbImport,
tRNAscanImport, TSCAN, TVTB, twoddpcr, txcutr, tximeta,
UCSC.utils, Ularcirc, UMI4Cats, universalmotif, UPDhmm,
VanillaICE, VariantAnnotation, VariantFiltering, VaSP,
VCFArray, VDJdive, velociraptor, VisiumIO, visiumStitched,
Voyager, VplotR, wavClusteR, weitrix, wiggleplotr, xcms, xcore,
XeniumIO, xenLite, XNAString, XVector, yamss, zellkonverter,
BioMartGOGeneSets, fitCons.UCSC.hg19,
MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5,
MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5,
MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5,
MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5,
MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5,
MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5,
MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38,
MafH5.gnomAD.v4.0.GRCh38, phastCons100way.UCSC.hg19,
phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38,
SNPlocs.Hsapiens.dbSNP144.GRCh37,
SNPlocs.Hsapiens.dbSNP144.GRCh38,
SNPlocs.Hsapiens.dbSNP149.GRCh38,
SNPlocs.Hsapiens.dbSNP150.GRCh38,
SNPlocs.Hsapiens.dbSNP155.GRCh37,
SNPlocs.Hsapiens.dbSNP155.GRCh38,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh37,
XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, bugphyzz, celldex,
chipenrich.data, chipseqDBData, curatedMetagenomicData,
curatedTCGAData, DNAZooData, DropletTestFiles,
FlowSorted.Blood.EPIC, fourDNData, HCATonsilData,
HighlyReplicatedRNASeq, HMP16SData, HMP2Data,
homosapienDEE2CellScore, imcdatasets, leeBamViews, LegATo,
MerfishData, MetaGxPancreas, MetaScope, MethylSeqData,
MicrobiomeBenchmarkData, MouseGastrulationData,
MouseThymusAgeing, pd.atdschip.tiling, scMultiome, scpdata,
scRNAseq, sesameData, SimBenchData, SingleCellMultiModal,
SomaticCancerAlterations, spatialLIBD, TransOmicsData,
tuberculosis, GeoMxWorkflows, crispRdesignR, DR.SC, driveR,
genBaRcode, geno2proteo, hoardeR, imcExperiment, karyotapR,
LoopRig, MetAlyzer, microbial, multimedia, NIPTeR, oncoPredict,
PlasmaMutationDetector, restfulr, rliger, rnaCrosslinkOO,
rsolr, SC.MEB, SCRIP, scROSHI, Signac, SpatialDDLS, TaxaNorm,
toxpiR
suggestsMe: AlpsNMR, ANCOMBC, BiocGenerics, chihaya, dearseq,
epiregulon.extra, epivizrChart, GeoTcgaData, globalSeq,
GWASTools, GWENA, gypsum, hca, koinar, maftools, martini,
MicrobiotaProcess, MsQuality, MungeSumstats, RTCGA, scFeatures,
scrapper, SpectraQL, SPOTlight, TFEA.ChIP, TFutils, XAItest,
alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38,
BioPlex, curatedAdipoChIP, curatedAdipoRNA, ObMiTi, xcoredata,
gkmSVM, grandR, LorMe, MARVEL, pmartR, polyRAD, pQTLdata, RCPA,
Rgff, Seurat, SNPassoc, updog, valr
linksToMe: Biostrings, CNEr, DECIPHER, DelayedArray, GenomicAlignments,
GenomicRanges, h5mread, IRanges, kebabs, MatrixRider, pwalign,
Rsamtools, rtracklayer, S4Arrays, ShortRead, SparseArray,
Structstrings, triplex, VariantAnnotation, VariantFiltering,
XVector
dependencyCount: 7
Package: safe
Version: 3.47.0
Depends: R (>= 2.4.0), AnnotationDbi, Biobase, methods, SparseM
Suggests: GO.db, PFAM.db, reactome.db, hgu133a.db, breastCancerUPP,
survival, foreach, doRNG, Rgraphviz, GOstats
License: GPL (>= 2)
MD5sum: 0c8ab09aca4cf66fe15fa1133cc07479
NeedsCompilation: no
Title: Significance Analysis of Function and Expression
Description: SAFE is a resampling-based method for testing functional
categories in gene expression experiments. SAFE can be applied
to 2-sample and multi-class comparisons, or simple linear
regressions. Other experimental designs can also be
accommodated through user-defined functions.
biocViews: DifferentialExpression, Pathways, GeneSetEnrichment,
StatisticalMethod, Software
Author: William T. Barry
Maintainer: Ludwig Geistlinger <ludwig.geistlinger@gmail.com>
git_url: https://git.bioconductor.org/packages/safe
git_branch: devel
git_last_commit: 540d0be
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/safe_3.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/safe_3.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/safe_3.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/safe_3.47.0.tgz
vignettes: vignettes/safe/inst/doc/SAFEmanual3.pdf
vignetteTitles: SAFE manual
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/safe/inst/doc/SAFEmanual3.R
importsMe: EGSEA, EnrichmentBrowser
suggestsMe: ReporterScore
dependencyCount: 46
Package: sagenhaft
Version: 1.77.0
Depends: R (>= 2.10), SparseM (>= 0.73), methods
Imports: graphics, stats, utils
License: GPL (>= 2)
MD5sum: 2ff10079b2ff6cd3fd84649f68c06ab0
NeedsCompilation: no
Title: Collection of functions for reading and comparing SAGE libraries
Description: This package implements several functions useful for
analysis of gene expression data by sequencing tags as done in
SAGE (Serial Analysis of Gene Expressen) data, i.e. extraction
of a SAGE library from sequence files, sequence error
correction, library comparison. Sequencing error correction is
implementing using an Expectation Maximization Algorithm based
on a Mixture Model of tag counts.
biocViews: SAGE
Author: Tim Beissbarth <tim.beissbarth@bioinf.med.uni-goettingen.de>,
with contributions from Gordon Smyth <smyth@wehi.edu.au>
Maintainer: Tim Beissbarth
<tim.beissbarth@bioinf.med.uni-goettingen.de>
URL: http://www.bioinf.med.uni-goettingen.de
git_url: https://git.bioconductor.org/packages/sagenhaft
git_branch: devel
git_last_commit: ac3f6cc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/sagenhaft_1.77.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sagenhaft_1.77.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sagenhaft_1.77.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sagenhaft_1.77.0.tgz
vignettes: vignettes/sagenhaft/inst/doc/SAGEnhaft.pdf
vignetteTitles: SAGEnhaft
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sagenhaft/inst/doc/SAGEnhaft.R
dependencyCount: 5
Package: SAIGEgds
Version: 2.7.1
Depends: R (>= 3.5.0), gdsfmt (>= 1.28.0), SeqArray (>= 1.43.7), Rcpp
Imports: methods, stats, utils, Matrix, RcppParallel, CompQuadForm,
survey
LinkingTo: Rcpp, RcppArmadillo, RcppParallel (>= 5.0.0)
Suggests: parallel, markdown, rmarkdown, crayon, SNPRelate, RUnit,
knitr, ggmanh, BiocGenerics
License: GPL-3
MD5sum: 4aab8529d6eff7422953064aca35cf20
NeedsCompilation: yes
Title: Scalable Implementation of Generalized mixed models using GDS
files in Phenome-Wide Association Studies
Description: Scalable implementation of generalized mixed models with
highly optimized C++ implementation and integration with
Genomic Data Structure (GDS) files. It is designed for single
variant tests and set-based aggregate tests in large-scale
Phenome-wide Association Studies (PheWAS) with millions of
variants and samples, controlling for sample structure and
case-control imbalance. The implementation is based on the
SAIGE R package (v0.45, Zhou et al. 2018 and Zhou et al. 2020),
and it is extended to include the state-of-the-art ACAT-O
set-based tests. Benchmarks show that SAIGEgds is significantly
faster than the SAIGE R package.
biocViews: Software, Genetics, StatisticalMethod, GenomeWideAssociation
Author: Xiuwen Zheng [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1390-0708>), Wei Zhou [ctb] (the
original author of the SAIGE R package), J. Wade Davis [ctb]
Maintainer: Xiuwen Zheng <xiuwen.zheng@abbvie.com>
URL: https://github.com/AbbVie-ComputationalGenomics/SAIGEgds
SystemRequirements: C++11, GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SAIGEgds
git_branch: devel
git_last_commit: 1457cda
git_last_commit_date: 2024-11-19
Date/Publication: 2024-11-20
source.ver: src/contrib/SAIGEgds_2.7.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SAIGEgds_2.7.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SAIGEgds_2.7.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SAIGEgds_2.7.1.tgz
vignettes: vignettes/SAIGEgds/inst/doc/SAIGEgds.html
vignetteTitles: SAIGEgds Tutorial (single variant tests)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SAIGEgds/inst/doc/SAIGEgds.R
dependencyCount: 44
Package: sampleClassifier
Version: 1.31.0
Depends: R (>= 4.0), MGFM, MGFR, annotate
Imports: e1071, ggplot2, stats, utils
Suggests: sampleClassifierData, BiocStyle, hgu133a.db, hgu133plus2.db
License: Artistic-2.0
Archs: x64
MD5sum: 83e50c291d4ab2de44617d8eccfab3d6
NeedsCompilation: no
Title: Sample Classifier
Description: The package is designed to classify microarray RNA-seq
gene expression profiles.
biocViews: ImmunoOncology, Classification, Microarray, RNASeq,
GeneExpression
Author: Khadija El Amrani [aut, cre]
Maintainer: Khadija El Amrani <a.khadija@gmx.de>
git_url: https://git.bioconductor.org/packages/sampleClassifier
git_branch: devel
git_last_commit: 22a67e8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/sampleClassifier_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sampleClassifier_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sampleClassifier_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sampleClassifier_1.31.0.tgz
vignettes: vignettes/sampleClassifier/inst/doc/sampleClassifier.pdf
vignetteTitles: sampleClassifier Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sampleClassifier/inst/doc/sampleClassifier.R
dependencyCount: 93
Package: SamSPECTRAL
Version: 1.61.0
Depends: R (>= 3.3.3)
Imports: methods
License: GPL (>= 2)
Archs: x64
MD5sum: 5d40547650b7b01fdcd313ba7c2a32f4
NeedsCompilation: yes
Title: Identifies cell population in flow cytometry data
Description: Samples large data such that spectral clustering is
possible while preserving density information in edge weights.
More specifically, given a matrix of coordinates as input,
SamSPECTRAL first builds the communities to sample the data
points. Then, it builds a graph and after weighting the edges
by conductance computation, the graph is passed to a classic
spectral clustering algorithm to find the spectral clusters.
The last stage of SamSPECTRAL is to combine the spectral
clusters. The resulting "connected components" estimate
biological cell populations in the data. See the vignette for
more details on how to use this package, some illustrations,
and simple examples.
biocViews: FlowCytometry, CellBiology, Clustering, Cancer,
FlowCytometry, StemCells, HIV, ImmunoOncology
Author: Habil Zare and Parisa Shooshtari
Maintainer: Habil <zare@u.washington.edu>
git_url: https://git.bioconductor.org/packages/SamSPECTRAL
git_branch: devel
git_last_commit: 3146807
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SamSPECTRAL_1.61.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SamSPECTRAL_1.61.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SamSPECTRAL_1.61.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SamSPECTRAL_1.61.0.tgz
vignettes: vignettes/SamSPECTRAL/inst/doc/Clustering_by_SamSPECTRAL.pdf
vignetteTitles: A modified spectral clustering method for clustering
Flow Cytometry Data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SamSPECTRAL/inst/doc/Clustering_by_SamSPECTRAL.R
importsMe: ddPCRclust
dependencyCount: 1
Package: sangeranalyseR
Version: 1.17.0
Depends: R (>= 4.0.0), stringr, ape, Biostrings, pwalign, DECIPHER,
parallel, reshape2, sangerseqR, gridExtra, shiny,
shinydashboard, shinyjs, data.table, plotly, DT, zeallot,
excelR, shinycssloaders, ggdendro, shinyWidgets, openxlsx,
tools, rmarkdown (>= 2.9), knitr (>= 1.33), seqinr, BiocStyle,
logger
Suggests: testthat (>= 2.1.0)
License: GPL-2
MD5sum: 7f682a2696f551bd26b301e5c190e362
NeedsCompilation: no
Title: sangeranalyseR: a suite of functions for the analysis of Sanger
sequence data in R
Description: This package builds on sangerseqR to allow users to create
contigs from collections of Sanger sequencing reads. It
provides a wide range of options for a number of
commonly-performed actions including read trimming, detecting
secondary peaks, and detecting indels using a reference
sequence. All parameters can be adjusted interactively either
in R or in the associated Shiny applications. There is
extensive online documentation, and the package can outputs
detailed HTML reports, including chromatograms.
biocViews: Genetics, Alignment, Sequencing, SangerSeq, Preprocessing,
QualityControl, Visualization, GUI
Author: Rob Lanfear <rob.lanfear@gmail.com>, Kuan-Hao Chao
<ntueeb05howard@gmail.com>
Maintainer: Kuan-Hao Chao <ntueeb05howard@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/sangeranalyseR
git_branch: devel
git_last_commit: dca5e3f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/sangeranalyseR_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sangeranalyseR_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sangeranalyseR_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sangeranalyseR_1.17.0.tgz
vignettes: vignettes/sangeranalyseR/inst/doc/sangeranalyseR.html
vignetteTitles: sangeranalyseR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/sangeranalyseR/inst/doc/sangeranalyseR.R
dependencyCount: 122
Package: sangerseqR
Version: 1.43.0
Depends: R (>= 3.5.0), Biostrings, pwalign, stringr
Imports: methods, shiny
Suggests: BiocStyle, knitr, RUnit, BiocGenerics
License: GPL-2
MD5sum: 23890fe8f11aeea3bce8d904a614c929
NeedsCompilation: no
Title: Tools for Sanger Sequencing Data in R
Description: This package contains several tools for analyzing Sanger
Sequencing data files in R, including reading .scf and .ab1
files, making basecalls and plotting chromatograms.
biocViews: Sequencing, SNP, Visualization
Author: Jonathon T. Hill, Bradley Demarest
Maintainer: Jonathon Hill <jhill@byu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/sangerseqR
git_branch: devel
git_last_commit: 1d424d9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/sangerseqR_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sangerseqR_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sangerseqR_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sangerseqR_1.43.0.tgz
vignettes: vignettes/sangerseqR/inst/doc/sangerseqRWalkthrough.html
vignetteTitles: Using the sangerseqR package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sangerseqR/inst/doc/sangerseqRWalkthrough.R
dependsOnMe: sangeranalyseR
importsMe: scifer
suggestsMe: CrispRVariants
dependencyCount: 55
Package: SANTA
Version: 2.43.0
Depends: R (>= 4.1), igraph
Imports: graphics, Matrix, methods, stats
Suggests: BiocGenerics, BioNet, formatR, knitr, msm, org.Sc.sgd.db,
markdown, rmarkdown, RUnit
License: GPL (>= 2)
Archs: x64
MD5sum: 46356e3b0def6b3bf686f2188e26abe8
NeedsCompilation: yes
Title: Spatial Analysis of Network Associations
Description: This package provides methods for measuring the strength
of association between a network and a phenotype. It does this
by measuring clustering of the phenotype across the network
(Knet). Vertices can also be individually ranked by their
strength of association with high-weight vertices (Knode).
biocViews: Network, NetworkEnrichment, Clustering
Author: Alex Cornish [cre, aut]
Maintainer: Alex Cornish <alexcornish88@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/alexjcornish/SANTA
git_url: https://git.bioconductor.org/packages/SANTA
git_branch: devel
git_last_commit: 95b5b44
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SANTA_2.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SANTA_2.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SANTA_2.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SANTA_2.43.0.tgz
vignettes: vignettes/SANTA/inst/doc/SANTA-vignette.html
vignetteTitles: Introduction to SANTA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SANTA/inst/doc/SANTA-vignette.R
dependencyCount: 17
Package: SARC
Version: 1.5.0
Depends: R (>= 4.3), RaggedExperiment, GenomicRanges
Imports: tidyverse, utils, reshape2, DescTools, metap, multtest,
plyranges, data.table, scales, RColorBrewer, grid, gtable,
gridExtra, GenomicFeatures, stats, ggplot2, plotly, IRanges
Suggests: knitr, kableExtra, testthat,
TxDb.Hsapiens.UCSC.hg38.knownGene, Homo.sapiens,
TxDb.Mmusculus.UCSC.mm10.knownGene, Mus.musculus,
GenomicAlignments
License: GPL-3
MD5sum: fa87be9e1a66c1c40d0cc98443573eb9
NeedsCompilation: no
Title: Statistical Analysis of Regions with CNVs
Description: Imports a cov/coverage file (normalised read coverages
from BAM files) and a cnv file (list of CNVs - similiar to a
BED file) from WES/ WGS CNV (copy number variation) detection
pipelines and utilises several metrics to weigh the likelihood
of a sample containing a detected CNV being a true CNV or a
false positive. Highly useful for diagnostic testing to filter
out false positives to provide clinicians with fewer variants
to interpret. SARC uniquely only used cov and csv (similiar to
BED file) files which are the common CNV pipeline calling
filetypes, and can be used as to supplement the Interactive
Genome Browser (IGV) to generate many figures automatedly,
which can be especially helpful in large cohorts with
100s-1000s of patients.
biocViews: Software, CopyNumberVariation, Visualization, DNASeq,
Sequencing
Author: Krutik Patel [aut, cre] (ORCID:
<https://orcid.org/0000-0001-6806-8675>)
Maintainer: Krutik Patel <nkp68@newcastle.ac.uk>
URL: https://github.com/Krutik6/SARC/
VignetteBuilder: knitr
BugReports: https://github.com/Krutik6/SARC/issues
git_url: https://git.bioconductor.org/packages/SARC
git_branch: devel
git_last_commit: 4be56bd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SARC_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SARC_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SARC_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SARC_1.5.0.tgz
vignettes: vignettes/SARC/inst/doc/SARC_guide.html
vignetteTitles: SARC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SARC/inst/doc/SARC_guide.R
dependencyCount: 204
Package: sarks
Version: 1.19.0
Depends: R (>= 4.0)
Imports: rJava, Biostrings, IRanges, utils, stats, cluster, binom
Suggests: RUnit, BiocGenerics, ggplot2
License: BSD_3_clause + file LICENSE
Archs: x64
MD5sum: ec8b6b869c4c55806f16bae283cffbf5
NeedsCompilation: no
Title: Suffix Array Kernel Smoothing for discovery of correlative
sequence motifs and multi-motif domains
Description: Suffix Array Kernel Smoothing (see
https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797),
or SArKS, identifies sequence motifs whose presence correlates
with numeric scores (such as differential expression
statistics) assigned to the sequences (such as gene promoters).
SArKS smooths over sequence similarity, quantified by location
within a suffix array based on the full set of input sequences.
A second round of smoothing over spatial proximity within
sequences reveals multi-motif domains. Discovered motifs can
then be merged or extended based on adjacency within MMDs.
False positive rates are estimated and controlled by
permutation testing.
biocViews: MotifDiscovery, GeneRegulation, GeneExpression,
Transcriptomics, RNASeq, DifferentialExpression,
FeatureExtraction
Author: Dennis Wylie [aut, cre] (ORCID:
<https://orcid.org/0000-0003-0380-3549>)
Maintainer: Dennis Wylie <denniscwylie@gmail.com>
URL:
https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797,
https://github.com/denniscwylie/sarks
SystemRequirements: Java (>= 1.8)
BugReports: https://github.com/denniscwylie/sarks/issues
git_url: https://git.bioconductor.org/packages/sarks
git_branch: devel
git_last_commit: 8b05f9a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/sarks_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sarks_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sarks_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sarks_1.19.0.tgz
vignettes: vignettes/sarks/inst/doc/sarks-vignette.pdf
vignetteTitles: sarks-vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/sarks/inst/doc/sarks-vignette.R
dependencyCount: 28
Package: saseR
Version: 1.3.0
Depends: R (>= 4.3.0)
Imports: ASpli, S4Vectors, BiocGenerics, GenomicFeatures, MASS, PRROC,
SummarizedExperiment, edgeR, pracma, precrec, BiocParallel,
DESeq2, DEXSeq, data.table, limma, methods, GenomicRanges,
GenomicAlignments, rrcov, MatrixGenerics, stats, IRanges,
knitr, dplyr, igraph, parallel
License: Artistic-2.0
MD5sum: 69b4ae155cee07a069b8e10b175af155
NeedsCompilation: no
Title: Scalable Aberrant Splicing and Expression Retrieval
Description: saseR is a highly performant and fast framework for
aberrant expression and splicing analyses. The main functions
are: \itemize{ \item \code{\link{BamtoAspliCounts}} - Process
BAM files to ASpli counts \item \code{\link{convertASpli}} -
Get gene, bin or junction counts from ASpli
SummarizedExperiment \item \code{\link{calculateOffsets}} -
Create an offsets assays for aberrant expression or splicing
analysis \item \code{\link{saseRfindEncodingDim}} - Estimate
the optimal number of latent factors to include when estimating
the mean expression \item \code{\link{saseRfit}} - Parameter
estimation of the negative binomial distribution and compute
p-values for aberrant expression and splicing } For information
upon how to use these functions, check out our vignette at
\url{https://github.com/statOmics/saseR/blob/main/vignettes/Vignette.Rmd}
and the saseR paper: Segers, A. et al. (2023). Juggling offsets
unlocks RNA-seq tools for fast scalable differential usage,
aberrant splicing and expression analyses. bioRxiv.
\url{https://doi.org/10.1101/2023.06.29.547014}.
biocViews: DifferentialExpression, DifferentialSplicing, Regression,
GeneExpression, AlternativeSplicing, RNASeq, Sequencing,
Software
Author: Alexandre Segers [aut, cre], Jeroen Gilis [ctb], Mattias Van
Heetvelde [ctb], Elfride De Baere [ctb], Lieven Clement [ctb]
Maintainer: Alexandre Segers <Alexandre.segers@ugent.be>
URL: https://github.com/statOmics/saseR,
https://doi.org/10.1101/2023.06.29.547014
VignetteBuilder: knitr
BugReports: https://github.com/statOmics/saseR/issues
git_url: https://git.bioconductor.org/packages/saseR
git_branch: devel
git_last_commit: f1d9b6b
git_last_commit_date: 2024-10-29
Date/Publication: 2025-03-06
source.ver: src/contrib/saseR_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/saseR_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/saseR_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/saseR_1.3.0.tgz
vignettes: vignettes/saseR/inst/doc/saseR-vignette.html
vignetteTitles: Main vignette: saseR analyses
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/saseR/inst/doc/saseR-vignette.R
dependencyCount: 192
Package: satuRn
Version: 1.15.0
Depends: R (>= 4.1)
Imports: locfdr, SummarizedExperiment, BiocParallel, limma, pbapply,
ggplot2, boot, Matrix, stats, methods, graphics
Suggests: knitr, rmarkdown, testthat, covr, BiocStyle, AnnotationHub,
ensembldb, edgeR, DEXSeq, stageR, DelayedArray
License: Artistic-2.0
MD5sum: c538b068466c14b0f421b4a907a18d89
NeedsCompilation: no
Title: Scalable Analysis of Differential Transcript Usage for Bulk and
Single-Cell RNA-sequencing Applications
Description: satuRn provides a higly performant and scalable framework
for performing differential transcript usage analyses. The
package consists of three main functions. The first function,
fitDTU, fits quasi-binomial generalized linear models that
model transcript usage in different groups of interest. The
second function, testDTU, tests for differential usage of
transcripts between groups of interest. Finally, plotDTU
visualizes the usage profiles of transcripts in groups of
interest.
biocViews: Regression, ExperimentalDesign, DifferentialExpression,
GeneExpression, RNASeq, Sequencing, Software, SingleCell,
Transcriptomics, MultipleComparison, Visualization
Author: Jeroen Gilis [aut, cre], Kristoffer Vitting-Seerup [ctb], Koen
Van den Berge [ctb], Lieven Clement [ctb]
Maintainer: Jeroen Gilis <jeroen.gilis@ugent.be>
URL: https://github.com/statOmics/satuRn
VignetteBuilder: knitr
BugReports: https://github.com/statOmics/satuRn/issues
git_url: https://git.bioconductor.org/packages/satuRn
git_branch: devel
git_last_commit: 6656389
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/satuRn_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/satuRn_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/satuRn_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/satuRn_1.15.0.tgz
vignettes: vignettes/satuRn/inst/doc/Vignette.html
vignetteTitles: satuRn - vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/satuRn/inst/doc/Vignette.R
dependsOnMe: IsoformSwitchAnalyzeR
dependencyCount: 77
Package: SBGNview
Version: 1.21.0
Depends: R (>= 3.6), pathview, SBGNview.data
Imports: Rdpack, grDevices, methods, stats, utils, xml2, rsvg, igraph,
rmarkdown, knitr, SummarizedExperiment, AnnotationDbi, httr,
KEGGREST, bookdown
Suggests: testthat, gage
License: AGPL-3
Archs: x64
MD5sum: e80d51f565493e454e073c268e3db930
NeedsCompilation: no
Title: "SBGNview: Data Analysis, Integration and Visualization on SBGN
Pathways"
Description: SBGNview is a tool set for pathway based data
visalization, integration and analysis. SBGNview is similar and
complementary to the widely used Pathview, with the following
key features: 1. Pathway definition by the widely adopted
Systems Biology Graphical Notation (SBGN); 2. Supports multiple
major pathway databases beyond KEGG (Reactome, MetaCyc, SMPDB,
PANTHER, METACROP) and user defined pathways; 3. Covers 5,200
reference pathways and over 3,000 species by default; 4.
Extensive graphics controls, including glyph and edge
attributes, graph layout and sub-pathway highlight; 5. SBGN
pathway data manipulation, processing, extraction and analysis.
biocViews: GeneTarget, Pathways, GraphAndNetwork, Visualization,
GeneSetEnrichment, DifferentialExpression, GeneExpression,
Microarray, RNASeq, Genetics, Metabolomics, Proteomics,
SystemsBiology, Sequencing, GeneTarget
Author: Xiaoxi Dong*, Kovidh Vegesna*, Weijun Luo
Maintainer: Weijun Luo <luo_weijun@yahoo.com>
URL: https://github.com/datapplab/SBGNview
VignetteBuilder: knitr
BugReports: https://github.com/datapplab/SBGNview/issues
git_url: https://git.bioconductor.org/packages/SBGNview
git_branch: devel
git_last_commit: 248ac8b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SBGNview_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SBGNview_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SBGNview_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SBGNview_1.21.0.tgz
vignettes:
vignettes/SBGNview/inst/doc/pathway.enrichment.analysis.html,
vignettes/SBGNview/inst/doc/SBGNview.quick.start.html,
vignettes/SBGNview/inst/doc/SBGNview.Vignette.html
vignetteTitles: Pathway analysis using SBGNview gene set, Quick start
SBGNview, SBGNview functions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SBGNview/inst/doc/pathway.enrichment.analysis.R,
vignettes/SBGNview/inst/doc/SBGNview.quick.start.R,
vignettes/SBGNview/inst/doc/SBGNview.Vignette.R
dependencyCount: 88
Package: SBMLR
Version: 2.3.0
Depends: XML, deSolve
Suggests: rsbml
License: GPL-2
MD5sum: a56487b538dee1225e21dd31a3355e7a
NeedsCompilation: no
Title: SBML-R Interface and Analysis Tools
Description: This package contains a systems biology markup language
(SBML) interface to R.
biocViews: GraphAndNetwork, Pathways, Network
Author: Tomas Radivoyevitch, Vishak Venkateswaran
Maintainer: Tomas Radivoyevitch <radivot@gmail.com>
URL: http://epbi-radivot.cwru.edu/SBMLR/SBMLR.html
git_url: https://git.bioconductor.org/packages/SBMLR
git_branch: devel
git_last_commit: 7e71177
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SBMLR_2.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SBMLR_2.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SBMLR_2.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SBMLR_2.3.0.tgz
vignettes: vignettes/SBMLR/inst/doc/quick-start.pdf
vignetteTitles: Quick intro to SBMLR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SBMLR/inst/doc/quick-start.R
dependencyCount: 7
Package: SC3
Version: 1.35.0
Depends: R(>= 3.3)
Imports: graphics, stats, utils, methods, e1071, parallel, foreach,
doParallel, doRNG, shiny, ggplot2, pheatmap (>= 1.0.8), ROCR,
robustbase, rrcov, cluster, WriteXLS, Rcpp (>= 0.11.1),
SummarizedExperiment, SingleCellExperiment, BiocGenerics,
S4Vectors
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, mclust, scater, BiocStyle
License: GPL-3
MD5sum: 5bc336f780e90a61635688a015b222a1
NeedsCompilation: yes
Title: Single-Cell Consensus Clustering
Description: A tool for unsupervised clustering and analysis of single
cell RNA-Seq data.
biocViews: ImmunoOncology, SingleCell, Software, Classification,
Clustering, DimensionReduction, SupportVectorMachine, RNASeq,
Visualization, Transcriptomics, DataRepresentation, GUI,
DifferentialExpression, Transcription
Author: Vladimir Kiselev
Maintainer: Vladimir Kiselev <vladimir.yu.kiselev@gmail.com>
URL: https://github.com/hemberg-lab/SC3
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/sc3/
git_url: https://git.bioconductor.org/packages/SC3
git_branch: devel
git_last_commit: 9b5ac20
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SC3_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SC3_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SC3_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SC3_1.35.0.tgz
vignettes: vignettes/SC3/inst/doc/SC3.html
vignetteTitles: SC3 package manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SC3/inst/doc/SC3.R
importsMe: FEAST
suggestsMe: InteractiveComplexHeatmap, scTreeViz, VAExprs
dependencyCount: 108
Package: Scale4C
Version: 1.29.0
Depends: R (>= 3.4), smoothie, GenomicRanges, IRanges,
SummarizedExperiment
Imports: methods, grDevices, graphics, utils
License: LGPL-3
MD5sum: feeca4ff7235ffecec6226e825efccc1
NeedsCompilation: no
Title: Scale4C: an R/Bioconductor package for scale-space
transformation of 4C-seq data
Description: Scale4C is an R/Bioconductor package for scale-space
transformation and visualization of 4C-seq data. The
scale-space transformation is a multi-scale visualization
technique to transform a 2D signal (e.g. 4C-seq reads on a
genomic interval of choice) into a tesselation in the scale
space (2D, genomic position x scale factor) by applying
different smoothing kernels (Gauss, with increasing sigma).
This transformation allows for explorative analysis and
comparisons of the data's structure with other samples.
biocViews: Visualization, QualityControl, DataImport, Sequencing,
Coverage
Author: Carolin Walter
Maintainer: Carolin Walter <carolin.walter@uni-muenster.de>
git_url: https://git.bioconductor.org/packages/Scale4C
git_branch: devel
git_last_commit: c88a0fa
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Scale4C_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Scale4C_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Scale4C_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Scale4C_1.29.0.tgz
vignettes: vignettes/Scale4C/inst/doc/vignette.pdf
vignetteTitles: Scale4C: an R/Bioconductor package for scale-space
transformation of 4C-seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Scale4C/inst/doc/vignette.R
dependencyCount: 37
Package: ScaledMatrix
Version: 1.15.0
Imports: methods, Matrix, S4Vectors, DelayedArray
Suggests: testthat, BiocStyle, knitr, rmarkdown, BiocSingular,
DelayedMatrixStats
License: GPL-3
MD5sum: ce1828b0aa19967ba5db44f3e28a5da2
NeedsCompilation: no
Title: Creating a DelayedMatrix of Scaled and Centered Values
Description: Provides delayed computation of a matrix of scaled and
centered values. The result is equivalent to using the scale()
function but avoids explicit realization of a dense matrix
during block processing. This permits greater efficiency in
common operations, most notably matrix multiplication.
biocViews: Software, DataRepresentation
Author: Aaron Lun [aut, cre, cph]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://github.com/LTLA/ScaledMatrix
VignetteBuilder: knitr
BugReports: https://github.com/LTLA/ScaledMatrix/issues
git_url: https://git.bioconductor.org/packages/ScaledMatrix
git_branch: devel
git_last_commit: 5ea7d67
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ScaledMatrix_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ScaledMatrix_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ScaledMatrix_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ScaledMatrix_1.15.0.tgz
vignettes: vignettes/ScaledMatrix/inst/doc/ScaledMatrix.html
vignetteTitles: Using the ScaledMatrix
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ScaledMatrix/inst/doc/ScaledMatrix.R
importsMe: batchelor, BiocSingular, mumosa, scPCA
suggestsMe: scran
dependencyCount: 22
Package: SCAN.UPC
Version: 2.49.0
Depends: R (>= 2.14.0), Biobase (>= 2.6.0), oligo, Biostrings,
GEOquery, affy, affyio, foreach, sva
Imports: utils, methods, MASS, tools, IRanges
Suggests: pd.hg.u95a
License: MIT
MD5sum: 28452d3f033b4e90e8fc17dcfefb1a67
NeedsCompilation: no
Title: Single-channel array normalization (SCAN) and Universal
exPression Codes (UPC)
Description: SCAN is a microarray normalization method to facilitate
personalized-medicine workflows. Rather than processing
microarray samples as groups, which can introduce biases and
present logistical challenges, SCAN normalizes each sample
individually by modeling and removing probe- and array-specific
background noise using only data from within each array. SCAN
can be applied to one-channel (e.g., Affymetrix) or two-channel
(e.g., Agilent) microarrays. The Universal exPression Codes
(UPC) method is an extension of SCAN that estimates whether a
given gene/transcript is active above background levels in a
given sample. The UPC method can be applied to one-channel or
two-channel microarrays as well as to RNA-Seq read counts.
Because UPC values are represented on the same scale and have
an identical interpretation for each platform, they can be used
for cross-platform data integration.
biocViews: ImmunoOncology, Software, Microarray, Preprocessing, RNASeq,
TwoChannel, OneChannel
Author: Stephen R. Piccolo and Andrea H. Bild and W. Evan Johnson
Maintainer: Stephen R. Piccolo <stephen_piccolo@byu.edu>
URL: http://bioconductor.org, http://jlab.bu.edu/software/scan-upc
git_url: https://git.bioconductor.org/packages/SCAN.UPC
git_branch: devel
git_last_commit: 5f6ff63
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SCAN.UPC_2.49.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SCAN.UPC_2.49.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SCAN.UPC_2.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SCAN.UPC_2.49.0.tgz
vignettes: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.pdf
vignetteTitles: Primer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.R
dependencyCount: 119
Package: scanMiR
Version: 1.13.0
Depends: R (>= 4.0)
Imports: Biostrings, pwalign, GenomicRanges, IRanges, data.table,
BiocParallel, methods, GenomeInfoDb, S4Vectors, ggplot2, stats,
stringi, utils, graphics, grid, seqLogo, cowplot
Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0)
License: GPL-3
MD5sum: 7c15a0c7ef7e45612fa26ac6b60c9a41
NeedsCompilation: no
Title: scanMiR
Description: A set of tools for working with miRNA affinity models
(KdModels), efficiently scanning for miRNA binding sites, and
predicting target repression. It supports scanning using miRNA
seeds, full miRNA sequences (enabling 3' alignment) and
KdModels, and includes the prediction of slicing and TDMD
sites. Finally, it includes utility and plotting functions
(e.g. for the visual representation of miRNA-target alignment).
biocViews: miRNA, SequenceMatching, Alignment
Author: Pierre-Luc Germain [cre, aut] (ORCID:
<https://orcid.org/0000-0003-3418-4218>), Michael Soutschek
[aut], Fridolin Gross [aut]
Maintainer: Pierre-Luc Germain <pierre-luc.germain@hest.ethz.ch>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scanMiR
git_branch: devel
git_last_commit: 9306dd6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/scanMiR_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scanMiR_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scanMiR_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scanMiR_1.13.0.tgz
vignettes: vignettes/scanMiR/inst/doc/Kdmodels.html,
vignettes/scanMiR/inst/doc/scanning.html
vignetteTitles: 2_Kdmodels, 1_scanning
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scanMiR/inst/doc/Kdmodels.R,
vignettes/scanMiR/inst/doc/scanning.R
dependsOnMe: scanMiRApp
importsMe: scanMiRData
dependencyCount: 70
Package: scanMiRApp
Version: 1.13.0
Depends: R (>= 4.0), scanMiR
Imports: AnnotationDbi, AnnotationFilter, AnnotationHub, BiocParallel,
Biostrings, data.table, digest, DT, ensembldb, fst,
GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2,
htmlwidgets, IRanges, Matrix, methods, plotly, rintrojs,
rtracklayer, S4Vectors, scanMiRData, shiny, shinycssloaders,
shinydashboard, shinyjqui, stats, utils, txdbmaker, waiter
Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0), shinytest,
BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10,
BSgenome.Mmusculus.UCSC.mm39, BSgenome.Rnorvegicus.UCSC.rn6
License: GPL-3
Archs: x64
MD5sum: fc63d657b2d1a08345045f3d5c62fa03
NeedsCompilation: no
Title: scanMiR shiny application
Description: A shiny interface to the scanMiR package. The application
enables the scanning of transcripts and custom sequences for
miRNA binding sites, the visualization of KdModels and binding
results, as well as browsing predicted repression data. In
addition contains the IndexedFst class for fast indexed reading
of large GenomicRanges or data.frames, and some utilities for
facilitating scans and identifying enriched miRNA-target pairs.
biocViews: miRNA, SequenceMatching, GUI, ShinyApps
Author: Pierre-Luc Germain [cre, aut] (ORCID:
<https://orcid.org/0000-0003-3418-4218>), Michael Soutschek
[aut], Fridolin Gross [ctb]
Maintainer: Pierre-Luc Germain <pierre-luc.germain@hest.ethz.ch>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scanMiRApp
git_branch: devel
git_last_commit: 09a6f91
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scanMiRApp_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scanMiRApp_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scanMiRApp_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scanMiRApp_1.13.0.tgz
vignettes: vignettes/scanMiRApp/inst/doc/IndexedFST.html,
vignettes/scanMiRApp/inst/doc/scanMiRApp.html
vignetteTitles: IndexedFst, scanMiRApp
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scanMiRApp/inst/doc/IndexedFST.R,
vignettes/scanMiRApp/inst/doc/scanMiRApp.R
dependencyCount: 160
Package: scAnnotatR
Version: 1.13.0
Depends: R (>= 4.1), Seurat, SingleCellExperiment, SummarizedExperiment
Imports: dplyr, ggplot2, caret, ROCR, pROC, data.tree, methods, stats,
e1071, ape, kernlab, AnnotationHub, utils
Suggests: knitr, rmarkdown, scRNAseq, testthat
License: MIT + file LICENSE
MD5sum: e462b9df07b0b2f50a12071af007c17a
NeedsCompilation: no
Title: Pretrained learning models for cell type prediction on single
cell RNA-sequencing data
Description: The package comprises a set of pretrained machine learning
models to predict basic immune cell types. This enables all
users to quickly get a first annotation of the cell types
present in their dataset without requiring prior knowledge.
scAnnotatR also allows users to train their own models to
predict new cell types based on specific research needs.
biocViews: SingleCell, Transcriptomics, GeneExpression,
SupportVectorMachine, Classification, Software
Author: Vy Nguyen [aut] (ORCID:
<https://orcid.org/0000-0003-3436-3662>), Johannes Griss [cre]
(ORCID: <https://orcid.org/0000-0003-2206-9511>)
Maintainer: Johannes Griss <johannes.griss@meduniwien.ac.at>
URL: https://github.com/grisslab/scAnnotatR
VignetteBuilder: knitr
BugReports: https://github.com/grisslab/scAnnotatR/issues/new
git_url: https://git.bioconductor.org/packages/scAnnotatR
git_branch: devel
git_last_commit: 108d23b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scAnnotatR_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scAnnotatR_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scAnnotatR_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scAnnotatR_1.13.0.tgz
vignettes: vignettes/scAnnotatR/inst/doc/classifying-cells.html,
vignettes/scAnnotatR/inst/doc/training-basic-model.html,
vignettes/scAnnotatR/inst/doc/training-child-model.html
vignetteTitles: 1. Introduction to scAnnotatR, 2. Training basic model,
3. Training child model
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scAnnotatR/inst/doc/classifying-cells.R,
vignettes/scAnnotatR/inst/doc/training-basic-model.R,
vignettes/scAnnotatR/inst/doc/training-child-model.R
suggestsMe: scAnnotatR.models
dependencyCount: 215
Package: SCANVIS
Version: 1.21.0
Depends: R (>= 3.6)
Imports: IRanges,plotrix,RCurl,rtracklayer
Suggests: knitr, rmarkdown
License: file LICENSE
MD5sum: 6084e5a2a753e55a9132919ecf8cb73d
NeedsCompilation: no
Title: SCANVIS - a tool for SCoring, ANnotating and VISualizing splice
junctions
Description: SCANVIS is a set of annotation-dependent tools for
analyzing splice junctions and their read support as
predetermined by an alignment tool of choice (for example, STAR
aligner). SCANVIS assesses each junction's relative read
support (RRS) by relating to the context of local split reads
aligning to annotated transcripts. SCANVIS also annotates each
splice junction by indicating whether the junction is supported
by annotation or not, and if not, what type of junction it is
(e.g. exon skipping, alternative 5' or 3' events, Novel Exons).
Unannotated junctions are also futher annotated by indicating
whether it induces a frame shift or not. SCANVIS includes a
visualization function to generate static sashimi-style plots
depicting relative read support and number of split reads using
arc thickness and arc heights, making it easy for users to spot
well-supported junctions. These plots also clearly delineate
unannotated junctions from annotated ones using designated
color schemes, and users can also highlight splice junctions of
choice. Variants and/or a read profile are also incoroporated
into the plot if the user supplies variants in bed format
and/or the BAM file. One further feature of the visualization
function is that users can submit multiple samples of a certain
disease or cohort to generate a single plot - this occurs via a
"merge" function wherein junction details over multiple samples
are merged to generate a single sashimi plot, which is useful
when contrasting cohorots (eg. disease vs control).
biocViews:
Software,ResearchField,Transcriptomics,WorkflowStep,Annotation,Visualization
Author: Phaedra Agius <pagius@nygenome.org>
Maintainer: Phaedra Agius <pagius@nygenome.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SCANVIS
git_branch: devel
git_last_commit: 2740017
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SCANVIS_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SCANVIS_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SCANVIS_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SCANVIS_1.21.0.tgz
vignettes: vignettes/SCANVIS/inst/doc/runningSCANVIS.pdf
vignetteTitles: SCANVIS
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SCANVIS/inst/doc/runningSCANVIS.R
dependencyCount: 59
Package: SCArray
Version: 1.15.2
Depends: R (>= 3.5.0), gdsfmt (>= 1.36.0), methods, DelayedArray (>=
0.31.5)
Imports: S4Vectors, utils, Matrix, SparseArray (>= 1.5.13),
BiocParallel, DelayedMatrixStats, SummarizedExperiment,
SingleCellExperiment, BiocSingular
Suggests: BiocGenerics, scater, scuttle, uwot, RUnit, knitr, markdown,
rmarkdown, rhdf5, HDF5Array
License: GPL-3
MD5sum: f6b3e67d6d3e207b26bff3f7d9d27fdf
NeedsCompilation: yes
Title: Large-scale single-cell omics data manipulation with GDS files
Description: Provides large-scale single-cell omics data manipulation
using Genomic Data Structure (GDS) files. It combines dense and
sparse matrices stored in GDS files and the Bioconductor
infrastructure framework (SingleCellExperiment and
DelayedArray) to provide out-of-memory data storage and
large-scale manipulation using the R programming language.
biocViews: Infrastructure, DataRepresentation, DataImport, SingleCell,
RNASeq
Author: Xiuwen Zheng [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1390-0708>)
Maintainer: Xiuwen Zheng <xiuwen.zheng@abbvie.com>
URL: https://github.com/AbbVie-ComputationalGenomics/SCArray
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SCArray
git_branch: devel
git_last_commit: f3b9e23
git_last_commit_date: 2025-03-24
Date/Publication: 2025-03-25
source.ver: src/contrib/SCArray_1.15.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SCArray_1.15.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SCArray_1.15.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SCArray_1.15.2.tgz
vignettes: vignettes/SCArray/inst/doc/Overview.html,
vignettes/SCArray/inst/doc/SCArray.html
vignetteTitles: Overview, Single-cell RNA-seq data manipulation using
GDS files
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SCArray/inst/doc/SCArray.R
dependsOnMe: SCArray.sat
dependencyCount: 57
Package: SCArray.sat
Version: 1.7.1
Depends: methods, SCArray (>= 1.13.1), SeuratObject (>= 5.0), Seurat
(>= 5.0)
Imports: S4Vectors, utils, stats, BiocGenerics, BiocParallel, gdsfmt,
DelayedArray, BiocSingular, SummarizedExperiment, Matrix
Suggests: future, RUnit, knitr, markdown, rmarkdown, BiocStyle
License: GPL-3
MD5sum: 2c4abd9789fd677fab51d3e98f04c916
NeedsCompilation: no
Title: Large-scale single-cell RNA-seq data analysis using GDS files
and Seurat
Description: Extends the Seurat classes and functions to support
Genomic Data Structure (GDS) files as a DelayedArray backend
for data representation. It relies on the implementation of
GDS-based DelayedMatrix in the SCArray package to represent
single cell RNA-seq data. The common optimized algorithms
leveraging GDS-based and single cell-specific DelayedMatrix
(SC_GDSMatrix) are implemented in the SCArray package.
SCArray.sat introduces a new SCArrayAssay class (derived from
the Seurat Assay), which wraps raw counts, normalized
expressions and scaled data matrix based on GDS-specific
DelayedMatrix. It is designed to integrate seamlessly with the
Seurat package to provide common data analysis in the
SeuratObject-based workflow. Compared with Seurat, SCArray.sat
significantly reduces the memory usage without downsampling and
can be applied to very large datasets.
biocViews: DataRepresentation, DataImport, SingleCell, RNASeq
Author: Xiuwen Zheng [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1390-0708>), Seurat contributors
[ctb] (for the classes and methods defined in Seurat)
Maintainer: Xiuwen Zheng <xiuwen.zheng@abbvie.com>
VignetteBuilder: knitr
BugReports:
https://github.com/AbbVie-ComputationalGenomics/SCArray/issues
git_url: https://git.bioconductor.org/packages/SCArray.sat
git_branch: devel
git_last_commit: 2158011
git_last_commit_date: 2025-03-24
Date/Publication: 2025-03-25
source.ver: src/contrib/SCArray.sat_1.7.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SCArray.sat_1.7.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SCArray.sat_1.7.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SCArray.sat_1.7.1.tgz
vignettes: vignettes/SCArray.sat/inst/doc/SCArray.sat.html
vignetteTitles: scRNA-seq data analysis with GDS files and Seurat
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SCArray.sat/inst/doc/SCArray.sat.R
dependencyCount: 186
Package: scater
Version: 1.35.4
Depends: SingleCellExperiment, scuttle, ggplot2
Imports: stats, utils, methods, Matrix, BiocGenerics, S4Vectors,
SummarizedExperiment, MatrixGenerics, SparseArray,
DelayedArray, beachmat, BiocNeighbors, BiocSingular,
BiocParallel, rlang, ggbeeswarm, viridis, Rtsne, RColorBrewer,
RcppML, uwot, pheatmap, ggrepel, ggrastr
Suggests: BiocStyle, DelayedMatrixStats, snifter, densvis, cowplot,
biomaRt, knitr, scRNAseq, robustbase, rmarkdown, testthat,
Biobase, scattermore
License: GPL-3
MD5sum: a39b8a07df6a08ba8301a0192b7bf348
NeedsCompilation: no
Title: Single-Cell Analysis Toolkit for Gene Expression Data in R
Description: A collection of tools for doing various analyses of
single-cell RNA-seq gene expression data, with a focus on
quality control and visualization.
biocViews: ImmunoOncology, SingleCell, RNASeq, QualityControl,
Preprocessing, Normalization, Visualization,
DimensionReduction, Transcriptomics, GeneExpression,
Sequencing, Software, DataImport, DataRepresentation,
Infrastructure, Coverage
Author: Davis McCarthy [aut], Kieran Campbell [aut], Aaron Lun [aut,
ctb], Quin Wills [aut], Vladimir Kiselev [ctb], Felix G.M.
Ernst [ctb], Alan O'Callaghan [ctb, cre], Yun Peng [ctb], Leo
Lahti [ctb] (ORCID: <https://orcid.org/0000-0001-5537-637X>),
Tuomas Borman [ctb] (ORCID:
<https://orcid.org/0000-0002-8563-8884>)
Maintainer: Alan O'Callaghan <alan.ocallaghan@outlook.com>
URL: http://bioconductor.org/packages/scater/
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/
git_url: https://git.bioconductor.org/packages/scater
git_branch: devel
git_last_commit: 68425da
git_last_commit_date: 2025-03-07
Date/Publication: 2025-03-07
source.ver: src/contrib/scater_1.35.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scater_1.35.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scater_1.35.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scater_1.35.0.tgz
vignettes: vignettes/scater/inst/doc/overview.html
vignetteTitles: Overview of scater functionality
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scater/inst/doc/overview.R
dependsOnMe: chevreulProcess, netSmooth, OSCA.basic, OSCA.intro,
OSCA.multisample, OSCA.workflows, SingleRBook
importsMe: airpart, BayesSpace, CATALYST, celda, CelliD, CellMixS,
chevreulPlot, ChromSCape, decontX, distinct, epiregulon.extra,
FLAMES, M3Drop, MEB, mia, miaViz, muscat, peco, pipeComp,
RegionalST, scDblFinder, scDotPlot, scMerge, scTreeViz, scviR,
singleCellTK, Spaniel, tricycle, VAExprs, spatialLIBD,
CAESAR.Suite, PRECAST, SC.MEB
suggestsMe: alabaster.sfe, APL, Banksy, batchelor, bluster, ccImpute,
CellTrails, Cepo, CiteFuse, concordexR, corral, dandelionR,
dittoSeq, dreamlet, epiregulon, escheR, ExperimentSubset, ggsc,
ggspavis, Glimma, HoloFoodR, InteractiveComplexHeatmap, iSEE,
iSEEfier, iSEEhex, iSEEpathways, iSEEtree, iSEEu, jazzPanda,
MAST, mbkmeans, MGnifyR, miloR, miQC, monocle, MOSim, MuData,
mumosa, Nebulosa, raer, ReactomeGSA, SC3, SCArray,
scDiagnostics, scds, schex, scHOT, scone, scp, scPipe, scran,
scRepertoire, SingleCellAlleleExperiment, sketchR, slalom,
smartid, smoothclust, SpatialFeatureExperiment, spatialHeatmap,
speckle, splatter, SPOTlight, StabMap, standR, SVP,
tidySingleCellExperiment, tidySpatialExperiment, UCell,
velociraptor, Voyager, waddR, curatedMetagenomicData,
DuoClustering2018, HCAData, HCATonsilData, MerfishData,
MouseAgingData, muscData, SingleCellMultiModal,
TabulaMurisData, tuberculosis, simpleSingleCell, Canek,
ProFAST, SCdeconR, scellpam, SuperCell
dependencyCount: 106
Package: scatterHatch
Version: 1.13.0
Depends: R (>= 4.1)
Imports: grid, ggplot2, plyr, spatstat.geom, stats, grDevices
Suggests: knitr, rmarkdown, testthat
License: MIT + file LICENSE
MD5sum: 7c64fffdb26739df415c44844c248892
NeedsCompilation: no
Title: Creates hatched patterns for scatterplots
Description: The objective of this package is to efficiently create
scatterplots where groups can be distinguished by color and
texture. Visualizations in computational biology tend to have
many groups making it difficult to distinguish between groups
solely on color. Thus, this package is useful for increasing
the accessibility of scatterplot visualizations to those with
visual impairments such as color blindness.
biocViews: Visualization, SingleCell, CellBiology, Software, Spatial
Author: Atul Deshpande [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5144-6924>)
Maintainer: Atul Deshpande <adeshpande@jhu.edu>
URL: https://github.com/FertigLab/scatterHatch
VignetteBuilder: knitr
BugReports: https://github.com/FertigLab/scatterHatch/issues
git_url: https://git.bioconductor.org/packages/scatterHatch
git_branch: devel
git_last_commit: f2d495c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scatterHatch_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scatterHatch_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scatterHatch_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scatterHatch_1.13.0.tgz
vignettes: vignettes/scatterHatch/inst/doc/vignette.html
vignetteTitles: Creating a Scatterplot with Texture
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scatterHatch/inst/doc/vignette.R
dependencyCount: 43
Package: scBFA
Version: 1.21.0
Depends: R (>= 3.6)
Imports: SingleCellExperiment, SummarizedExperiment, Seurat, MASS,
zinbwave, stats, copula, ggplot2, DESeq2, utils, grid, methods,
Matrix
Suggests: knitr, rmarkdown, testthat, Rtsne
License: GPL-3 + file LICENSE
MD5sum: d8f6cc5045e79cced845b1bef9f3deae
NeedsCompilation: no
Title: A dimensionality reduction tool using gene detection pattern to
mitigate noisy expression profile of scRNA-seq
Description: This package is designed to model gene detection pattern
of scRNA-seq through a binary factor analysis model. This model
allows user to pass into a cell level covariate matrix X and
gene level covariate matrix Q to account for nuisance
variance(e.g batch effect), and it will output a low
dimensional embedding matrix for downstream analysis.
biocViews: SingleCell, Transcriptomics,
DimensionReduction,GeneExpression, ATACSeq, BatchEffect, KEGG,
QualityControl
Author: Ruoxin Li [aut, cre], Gerald Quon [aut]
Maintainer: Ruoxin Li <uskli@ucdavis.edu>
URL: https://github.com/ucdavis/quon-titative-biology/BFA
VignetteBuilder: knitr
BugReports: https://github.com/ucdavis/quon-titative-biology/BFA/issues
git_url: https://git.bioconductor.org/packages/scBFA
git_branch: devel
git_last_commit: 37ab3ec
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scBFA_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scBFA_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scBFA_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scBFA_1.21.0.tgz
vignettes: vignettes/scBFA/inst/doc/vignette.html
vignetteTitles: Gene Detection Analysis for scRNA-seq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scBFA/inst/doc/vignette.R
dependencyCount: 204
Package: SCBN
Version: 1.25.0
Depends: R (>= 3.5.0)
Imports: stats
Suggests: knitr,rmarkdown,BiocStyle,BiocManager
License: GPL-2
Archs: x64
MD5sum: aefc94cf587de91eeb7c5f7172e2beb2
NeedsCompilation: no
Title: A statistical normalization method and differential expression
analysis for RNA-seq data between different species
Description: This package provides a scale based normalization (SCBN)
method to identify genes with differential expression between
different species. It takes into account the available
knowledge of conserved orthologous genes and the hypothesis
testing framework to detect differentially expressed
orthologous genes. The method on this package are described in
the article 'A statistical normalization method and
differential expression analysis for RNA-seq data between
different species' by Yan Zhou, Jiadi Zhu, Tiejun Tong, Junhui
Wang, Bingqing Lin, Jun Zhang (2018, pending publication).
biocViews: DifferentialExpression, GeneExpression, Normalization
Author: Yan Zhou
Maintainer: Yan Zhou <2160090406@email.szu.edu.cn>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SCBN
git_branch: devel
git_last_commit: 4d4f8cc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SCBN_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SCBN_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SCBN_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SCBN_1.25.0.tgz
vignettes: vignettes/SCBN/inst/doc/SCBN.html
vignetteTitles: SCBN Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SCBN/inst/doc/SCBN.R
importsMe: TEKRABber
dependencyCount: 1
Package: scBubbletree
Version: 1.9.0
Depends: R (>= 4.2.0)
Imports: reshape2, BiocParallel, ape, scales, Seurat, ggplot2, ggtree,
patchwork, proxy, methods, stats, base, utils, dplyr
Suggests: BiocStyle, knitr, testthat, cluster, SingleCellExperiment
License: GPL-3 + file LICENSE
MD5sum: cbadcf538e531597a80351398a7046d9
NeedsCompilation: no
Title: Quantitative visual exploration of scRNA-seq data
Description: scBubbletree is a quantitative method for the visual
exploration of scRNA-seq data, preserving key biological
properties such as local and global cell distances and cell
density distributions across samples. It effectively resolves
overplotting and enables the visualization of diverse cell
attributes from multiomic single-cell experiments.
Additionally, scBubbletree is user-friendly and integrates
seamlessly with popular scRNA-seq analysis tools, facilitating
comprehensive and intuitive data interpretation.
biocViews: Visualization,Clustering, SingleCell,Transcriptomics,RNASeq
Author: Simo Kitanovski [aut, cre]
Maintainer: Simo Kitanovski <simokitanovski@gmail.com>
URL: https://github.com/snaketron/scBubbletree
SystemRequirements: Python (>= 3.6), leidenalg (>= 0.8.2)
VignetteBuilder: knitr
BugReports: https://github.com/snaketron/scBubbletree/issues
git_url: https://git.bioconductor.org/packages/scBubbletree
git_branch: devel
git_last_commit: 2950075
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scBubbletree_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scBubbletree_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scBubbletree_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scBubbletree_1.9.0.tgz
vignettes: vignettes/scBubbletree/inst/doc/User_manual.html
vignetteTitles: User Manual: scBubbletree
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scBubbletree/inst/doc/User_manual.R
dependencyCount: 172
Package: scCB2
Version: 1.17.0
Depends: R (>= 3.6.0)
Imports: SingleCellExperiment, SummarizedExperiment, Matrix, methods,
utils, stats, edgeR, rhdf5, parallel, DropletUtils, doParallel,
iterators, foreach, Seurat
Suggests: testthat (>= 2.1.0), KernSmooth, beachmat, knitr, BiocStyle,
rmarkdown
License: GPL-3
MD5sum: f95ec46c474c5963b0e81da3426ae1d7
NeedsCompilation: yes
Title: CB2 improves power of cell detection in droplet-based
single-cell RNA sequencing data
Description: scCB2 is an R package implementing CB2 for distinguishing
real cells from empty droplets in droplet-based single cell
RNA-seq experiments (especially for 10x Chromium). It is based
on clustering similar barcodes and calculating Monte-Carlo
p-value for each cluster to test against background
distribution. This cluster-level test outperforms
single-barcode-level tests in dealing with low count barcodes
and homogeneous sequencing library, while keeping FDR well
controlled.
biocViews: DataImport, RNASeq, SingleCell, Sequencing, GeneExpression,
Transcriptomics, Preprocessing, Clustering
Author: Zijian Ni [aut, cre], Shuyang Chen [ctb], Christina Kendziorski
[ctb]
Maintainer: Zijian Ni <zni25@wisc.edu>
URL: https://github.com/zijianni/scCB2
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/zijianni/scCB2/issues
git_url: https://git.bioconductor.org/packages/scCB2
git_branch: devel
git_last_commit: 1e577d4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scCB2_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scCB2_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scCB2_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scCB2_1.17.0.tgz
vignettes: vignettes/scCB2/inst/doc/scCB2.html
vignetteTitles: CB2 improves power of cell detection in droplet-based
single-cell RNA sequencing data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scCB2/inst/doc/scCB2.R
dependencyCount: 198
Package: scClassify
Version: 1.19.0
Depends: R (>= 4.0)
Imports: S4Vectors, limma, ggraph, igraph, methods, cluster,
minpack.lm, mixtools, BiocParallel, proxy, proxyC, Matrix,
ggplot2, hopach, diptest, mgcv, stats, graphics, statmod, Cepo
Suggests: knitr, rmarkdown, BiocStyle, pkgdown
License: GPL-3
MD5sum: 22338f860870f7ed4e4dc74d794265a3
NeedsCompilation: no
Title: scClassify: single-cell Hierarchical Classification
Description: scClassify is a multiscale classification framework for
single-cell RNA-seq data based on ensemble learning and cell
type hierarchies, enabling sample size estimation required for
accurate cell type classification and joint classification of
cells using multiple references.
biocViews: SingleCell, GeneExpression, Classification
Author: Yingxin Lin
Maintainer: Yingxin Lin <yingxin.lin@sydney.edu.au>
VignetteBuilder: knitr
BugReports: https://github.com/SydneyBioX/scClassify/issues
git_url: https://git.bioconductor.org/packages/scClassify
git_branch: devel
git_last_commit: e235107
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scClassify_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scClassify_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scClassify_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scClassify_1.19.0.tgz
vignettes: vignettes/scClassify/inst/doc/pretrainedModel.html,
vignettes/scClassify/inst/doc/scClassify.html
vignetteTitles: pretrainedModel, scClassify
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scClassify/inst/doc/pretrainedModel.R,
vignettes/scClassify/inst/doc/scClassify.R
dependencyCount: 157
Package: sccomp
Version: 1.99.18
Depends: R (>= 4.3.0)
Imports: instantiate (>= 0.2.3), stats, SingleCellExperiment, parallel,
dplyr, tidyr, purrr, magrittr, rlang, tibble, boot, lifecycle,
tidyselect, utils, ggplot2, ggrepel, patchwork, forcats, readr,
scales, stringr, glue, crayon
Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0), markdown,
loo, prettydoc, tidyseurat, tidySingleCellExperiment,
bayesplot, posterior
License: GPL-3
MD5sum: e65c72e58bdc5f11c6ec72e803aea8e1
NeedsCompilation: no
Title: Tests differences in cell-type proportion for single-cell data,
robust to outliers
Description: A robust and outlier-aware method for testing differences
in cell-type proportion in single-cell data. This model can
infer changes in tissue composition and heterogeneity, and can
produce realistic data simulations based on any existing
dataset. This model can also transfer knowledge from a large
set of integrated datasets to increase accuracy further.
biocViews: Bayesian, Regression, DifferentialExpression, SingleCell,
Metagenomics, FlowCytometry, Spatial
Author: Stefano Mangiola [aut, cre]
Maintainer: Stefano Mangiola <mangiolastefano@gmail.com>
URL: https://github.com/MangiolaLaboratory/sccomp
SystemRequirements: CmdStan
(https://mc-stan.org/users/interfaces/cmdstan)
VignetteBuilder: knitr
BugReports: https://github.com/MangiolaLaboratory/sccomp/issues
git_url: https://git.bioconductor.org/packages/sccomp
git_branch: devel
git_last_commit: 0b9ad80
git_last_commit_date: 2025-03-15
Date/Publication: 2025-03-17
source.ver: src/contrib/sccomp_1.99.18.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sccomp_1.99.18.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sccomp_1.99.18.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sccomp_1.99.18.tgz
vignettes: vignettes/sccomp/inst/doc/introduction.html
vignetteTitles: sccomp
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/sccomp/inst/doc/introduction.R
dependencyCount: 90
Package: scDataviz
Version: 1.17.0
Depends: R (>= 4.0), S4Vectors, SingleCellExperiment,
Imports: ggplot2, ggrepel, flowCore, umap, Seurat, reshape2, scales,
RColorBrewer, corrplot, stats, grDevices, graphics, utils,
MASS, matrixStats, methods
Suggests: PCAtools, cowplot, BiocGenerics, RUnit, knitr, kableExtra,
rmarkdown
License: GPL-3
MD5sum: 87777491c89c144781e80d08c1ec3379
NeedsCompilation: no
Title: scDataviz: single cell dataviz and downstream analyses
Description: In the single cell World, which includes flow cytometry,
mass cytometry, single-cell RNA-seq (scRNA-seq), and others,
there is a need to improve data visualisation and to bring
analysis capabilities to researchers even from non-technical
backgrounds. scDataviz attempts to fit into this space, while
also catering for advanced users. Additonally, due to the way
that scDataviz is designed, which is based on
SingleCellExperiment, it has a 'plug and play' feel, and
immediately lends itself as flexibile and compatibile with
studies that go beyond scDataviz. Finally, the graphics in
scDataviz are generated via the ggplot engine, which means that
users can 'add on' features to these with ease.
biocViews: SingleCell, ImmunoOncology, RNASeq, GeneExpression,
Transcription, FlowCytometry, MassSpectrometry, DataImport
Author: Kevin Blighe [aut, cre]
Maintainer: Kevin Blighe <kevin@clinicalbioinformatics.co.uk>
URL: https://github.com/kevinblighe/scDataviz
VignetteBuilder: knitr
BugReports: https://github.com/kevinblighe/scDataviz/issues
git_url: https://git.bioconductor.org/packages/scDataviz
git_branch: devel
git_last_commit: e17aeed
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scDataviz_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scDataviz_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scDataviz_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scDataviz_1.17.0.tgz
vignettes: vignettes/scDataviz/inst/doc/scDataviz.html
vignetteTitles: scDataviz: single cell dataviz and downstream analyses
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scDataviz/inst/doc/scDataviz.R
dependencyCount: 177
Package: scDblFinder
Version: 1.21.2
Depends: R (>= 4.0), SingleCellExperiment
Imports: igraph, Matrix, BiocGenerics, BiocParallel, BiocNeighbors,
BiocSingular, S4Vectors, SummarizedExperiment, scran, scater,
scuttle, bluster, methods, DelayedArray, xgboost, stats, utils,
MASS, IRanges, GenomicRanges, GenomeInfoDb, Rsamtools,
rtracklayer
Suggests: BiocStyle, knitr, rmarkdown, testthat, scRNAseq, circlize,
ComplexHeatmap, ggplot2, dplyr, viridisLite, mbkmeans
License: GPL-3 + file LICENSE
MD5sum: 9d490ae7856eff7b1f7ee69269cd542b
NeedsCompilation: no
Title: scDblFinder
Description: The scDblFinder package gathers various methods for the
detection and handling of doublets/multiplets in single-cell
sequencing data (i.e. multiple cells captured within the same
droplet or reaction volume). It includes methods formerly found
in the scran package, the new fast and comprehensive
scDblFinder method, and a reimplementation of the Amulet
detection method for single-cell ATAC-seq.
biocViews: Preprocessing, SingleCell, RNASeq, ATACSeq
Author: Pierre-Luc Germain [cre, aut] (ORCID:
<https://orcid.org/0000-0003-3418-4218>), Aaron Lun [ctb]
Maintainer: Pierre-Luc Germain <pierre-luc.germain@hest.ethz.ch>
URL: https://github.com/plger/scDblFinder,
https://plger.github.io/scDblFinder/
VignetteBuilder: knitr
BugReports: https://github.com/plger/scDblFinder/issues
git_url: https://git.bioconductor.org/packages/scDblFinder
git_branch: devel
git_last_commit: b223a7d
git_last_commit_date: 2025-03-20
Date/Publication: 2025-03-20
source.ver: src/contrib/scDblFinder_1.21.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scDblFinder_1.21.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scDblFinder_1.21.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scDblFinder_1.21.2.tgz
vignettes: vignettes/scDblFinder/inst/doc/computeDoubletDensity.html,
vignettes/scDblFinder/inst/doc/findDoubletClusters.html,
vignettes/scDblFinder/inst/doc/introduction.html,
vignettes/scDblFinder/inst/doc/recoverDoublets.html,
vignettes/scDblFinder/inst/doc/scATAC.html,
vignettes/scDblFinder/inst/doc/scDblFinder.html
vignetteTitles: 4_computeDoubletDensity, 3_findDoubletClusters,
1_introduction, 5_recoverDoublets, 6_scATAC, 2_scDblFinder
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scDblFinder/inst/doc/computeDoubletDensity.R,
vignettes/scDblFinder/inst/doc/findDoubletClusters.R,
vignettes/scDblFinder/inst/doc/introduction.R,
vignettes/scDblFinder/inst/doc/recoverDoublets.R,
vignettes/scDblFinder/inst/doc/scATAC.R,
vignettes/scDblFinder/inst/doc/scDblFinder.R
importsMe: singleCellTK
dependencyCount: 130
Package: scDD
Version: 1.31.0
Depends: R (>= 3.5.0)
Imports: fields, mclust, BiocParallel, outliers, ggplot2, EBSeq, arm,
SingleCellExperiment, SummarizedExperiment, grDevices,
graphics, stats, S4Vectors, scran
Suggests: BiocStyle, knitr, gridExtra
License: GPL-2
MD5sum: da18e9ffd54ba302df3666210dfd93b6
NeedsCompilation: yes
Title: Mixture modeling of single-cell RNA-seq data to identify genes
with differential distributions
Description: This package implements a method to analyze single-cell
RNA- seq Data utilizing flexible Dirichlet Process mixture
models. Genes with differential distributions of expression are
classified into several interesting patterns of differences
between two conditions. The package also includes functions for
simulating data with these patterns from negative binomial
distributions.
biocViews: ImmunoOncology, Bayesian, Clustering, RNASeq, SingleCell,
MultipleComparison, Visualization, DifferentialExpression
Author: Keegan Korthauer [cre, aut] (ORCID:
<https://orcid.org/0000-0002-4565-1654>)
Maintainer: Keegan Korthauer <keegan@stat.ubc.ca>
URL: https://github.com/kdkorthauer/scDD
VignetteBuilder: knitr
BugReports: https://github.com/kdkorthauer/scDD/issues
git_url: https://git.bioconductor.org/packages/scDD
git_branch: devel
git_last_commit: 11f58e6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scDD_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scDD_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scDD_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scDD_1.31.0.tgz
vignettes: vignettes/scDD/inst/doc/scDD.pdf
vignetteTitles: scDD Quickstart
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scDD/inst/doc/scDD.R
suggestsMe: splatter
dependencyCount: 131
Package: scDDboost
Version: 1.9.3
Depends: R (>= 4.2), ggplot2
Imports: Rcpp (>= 0.12.11), RcppEigen (>= 0.3.2.9.0),EBSeq,
BiocParallel, mclust, SingleCellExperiment, cluster, Oscope,
SummarizedExperiment, stats, methods
LinkingTo: Rcpp, RcppEigen, BH
Suggests: knitr, rmarkdown, BiocStyle, testthat
License: GPL (>= 2)
MD5sum: 086a90c2a880979b6dd13a592a698021
NeedsCompilation: yes
Title: A compositional model to assess expression changes from
single-cell rna-seq data
Description: scDDboost is an R package to analyze changes in the
distribution of single-cell expression data between two
experimental conditions. Compared to other methods that assess
differential expression, scDDboost benefits uniquely from
information conveyed by the clustering of cells into cellular
subtypes. Through a novel empirical Bayesian formulation it
calculates gene-specific posterior probabilities that the
marginal expression distribution is the same (or different)
between the two conditions. The implementation in scDDboost
treats gene-level expression data within each condition as a
mixture of negative binomial distributions.
biocViews: SingleCell, Software, Clustering, Sequencing,
GeneExpression, DifferentialExpression, Bayesian
Author: Xiuyu Ma [cre, aut], Michael A. Newton [ctb]
Maintainer: Xiuyu Ma <watsonforfun@gmail.com>
URL: https://github.com/wiscstatman/scDDboost
SystemRequirements: c++14
VignetteBuilder: knitr
BugReports: https://github.com/wiscstatman/scDDboost/issues
git_url: https://git.bioconductor.org/packages/scDDboost
git_branch: devel
git_last_commit: b733832
git_last_commit_date: 2025-03-16
Date/Publication: 2025-03-17
source.ver: src/contrib/scDDboost_1.9.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scDDboost_1.9.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scDDboost_1.9.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scDDboost_1.9.3.tgz
vignettes: vignettes/scDDboost/inst/doc/scDDboost.html
vignetteTitles: scDDboost Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scDDboost/inst/doc/scDDboost.R
dependencyCount: 100
Package: scde
Version: 2.35.0
Depends: R (>= 3.0.0), flexmix
Imports: Rcpp (>= 0.10.4), RcppArmadillo (>= 0.5.400.2.0), mgcv, Rook,
rjson, MASS, Cairo, RColorBrewer, edgeR, quantreg, methods,
nnet, RMTstat, extRemes, pcaMethods, BiocParallel, parallel
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, cba, fastcluster, WGCNA, GO.db, org.Hs.eg.db,
rmarkdown
License: GPL-2
MD5sum: 4554a1c26ddfeb2ab117fe5c3368f14f
NeedsCompilation: yes
Title: Single Cell Differential Expression
Description: The scde package implements a set of statistical methods
for analyzing single-cell RNA-seq data. scde fits individual
error models for single-cell RNA-seq measurements. These models
can then be used for assessment of differential expression
between groups of cells, as well as other types of analysis.
The scde package also contains the pagoda framework which
applies pathway and gene set overdispersion analysis to
identify and characterize putative cell subpopulations based on
transcriptional signatures. The overall approach to the
differential expression analysis is detailed in the following
publication: "Bayesian approach to single-cell differential
expression analysis" (Kharchenko PV, Silberstein L, Scadden DT,
Nature Methods, doi: 10.1038/nmeth.2967). The overall approach
to subpopulation identification and characterization is
detailed in the following pre-print: "Characterizing
transcriptional heterogeneity through pathway and gene set
overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G,
Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and
Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734).
biocViews: ImmunoOncology, RNASeq, StatisticalMethod,
DifferentialExpression, Bayesian, Transcription, Software
Author: Peter Kharchenko [aut, cre], Jean Fan [aut], Evan Biederstedt
[aut]
Maintainer: Evan Biederstedt <evan.biederstedt@gmail.com>
URL: http://pklab.med.harvard.edu/scde
VignetteBuilder: knitr
BugReports: https://github.com/hms-dbmi/scde/issues
git_url: https://git.bioconductor.org/packages/scde
git_branch: devel
git_last_commit: 9d12a81
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scde_2.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scde_2.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scde_2.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scde_2.35.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
suggestsMe: pagoda2
dependencyCount: 50
Package: scDesign3
Version: 1.5.0
Depends: R (>= 4.3.0)
Imports: dplyr, tibble, stats, methods, mgcv, gamlss, gamlss.dist,
SummarizedExperiment, SingleCellExperiment, mclust, mvtnorm,
parallel, pbmcapply, rvinecopulib, umap, ggplot2, irlba,
viridis, BiocParallel, matrixStats, Matrix, sparseMVN, coop
Suggests: mvnfast, igraph, knitr, rmarkdown, testthat (>= 3.0.0),
RefManageR, sessioninfo, BiocStyle
License: MIT + file LICENSE
MD5sum: 6b9a176ba9c22956d7fd65f45711ea53
NeedsCompilation: no
Title: A unified framework of realistic in silico data generation and
statistical model inference for single-cell and spatial omics
Description: We present a statistical simulator, scDesign3, to generate
realistic single-cell and spatial omics data, including various
cell states, experimental designs, and feature modalities, by
learning interpretable parameters from real data. Using a
unified probabilistic model for single-cell and spatial omics
data, scDesign3 infers biologically meaningful parameters;
assesses the goodness-of-fit of inferred cell clusters,
trajectories, and spatial locations; and generates in silico
negative and positive controls for benchmarking computational
tools.
biocViews: Software, SingleCell, Sequencing, GeneExpression, Spatial
Author: Dongyuan Song [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1114-1215>), Qingyang Wang [aut]
(ORCID: <https://orcid.org/0000-0002-1051-609X>)
Maintainer: Dongyuan Song <dongyuansong@ucla.edu>
URL: https://github.com/SONGDONGYUAN1994/scDesign3
VignetteBuilder: knitr
BugReports: https://github.com/SONGDONGYUAN1994/scDesign3/issues
git_url: https://git.bioconductor.org/packages/scDesign3
git_branch: devel
git_last_commit: 27eedaa
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scDesign3_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scDesign3_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scDesign3_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scDesign3_1.5.0.tgz
vignettes: vignettes/scDesign3/inst/doc/scDesign3.html
vignetteTitles: scDesign3-quickstart-vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scDesign3/inst/doc/scDesign3.R
dependencyCount: 104
Package: scDiagnostics
Version: 1.1.0
Depends: R (>= 4.4.0)
Imports: SingleCellExperiment, methods, isotree, ggplot2, ggridges,
SummarizedExperiment, ranger, transport, speedglm, cramer,
rlang, bluster, patchwork
Suggests: AUCell, BiocStyle, knitr, Matrix, rmarkdown, scran, scRNAseq,
SingleR, celldex, scuttle, scater, dplyr, testthat (>= 3.0.0)
License: Artistic-2.0
MD5sum: 7f1c17da6149872ceb83a8bcfb876ab7
NeedsCompilation: no
Title: Cell type annotation diagnostics
Description: The scDiagnostics package provides diagnostic plots to
assess the quality of cell type assignments from single cell
gene expression profiles. The implemented functionality allows
to assess the reliability of cell type annotations, investigate
gene expression patterns, and explore relationships between
different cell types in query and reference datasets allowing
users to detect potential misalignments between reference and
query datasets. The package also provides visualization
capabilities for diagnostics purposes.
biocViews: Annotation, Classification, Clustering, GeneExpression,
RNASeq, SingleCell, Software, Transcriptomics
Author: Anthony Christidis [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4565-6279>), Andrew Ghazi [aut],
Smriti Chawla [aut], Nitesh Turaga [ctb], Ludwig Geistlinger
[aut], Robert Gentleman [aut]
Maintainer: Anthony Christidis
<anthony-alexander_christidis@hms.harvard.edu>
URL: https://github.com/ccb-hms/scDiagnostics
VignetteBuilder: knitr
BugReports: https://github.com/ccb-hms/scDiagnostics/issues
git_url: https://git.bioconductor.org/packages/scDiagnostics
git_branch: devel
git_last_commit: 1b71ee2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scDiagnostics_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scDiagnostics_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scDiagnostics_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scDiagnostics_1.1.0.tgz
vignettes: vignettes/scDiagnostics/inst/doc/AnnotationAnomalies.html,
vignettes/scDiagnostics/inst/doc/DatasetMarkerGeneAlignment.html,
vignettes/scDiagnostics/inst/doc/scDiagnostics.html,
vignettes/scDiagnostics/inst/doc/VisualizationTools.html
vignetteTitles: 4. Detection and Analysis of Annotation Anomalies, 3.
Evaluation of Dataset and Marker Gene Alignment, 1. Getting
Started with scDiagnostics, 2. Visualization of Cell Type
Annotations
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scDiagnostics/inst/doc/AnnotationAnomalies.R,
vignettes/scDiagnostics/inst/doc/DatasetMarkerGeneAlignment.R,
vignettes/scDiagnostics/inst/doc/scDiagnostics.R,
vignettes/scDiagnostics/inst/doc/VisualizationTools.R
dependencyCount: 92
Package: scDotPlot
Version: 1.1.1
Depends: R (>= 4.4.0)
Imports: aplot, BiocGenerics, cli, dplyr, ggplot2, ggsci, ggtree,
grDevices, magrittr, purrr, rlang, scales, scater, Seurat,
SingleCellExperiment, stats, stringr, tibble, tidyr
Suggests: AnnotationDbi, BiocStyle, knitr, rmarkdown, scran, scRNAseq,
scuttle, SeuratObject, testthat, vdiffr
License: Artistic-2.0
MD5sum: 23cf43ed4d1cf522ba2e116be49e7039
NeedsCompilation: no
Title: Cluster a Single-cell RNA-seq Dot Plot
Description: Dot plots of single-cell RNA-seq data allow for an
examination of the relationships between cell groupings (e.g.
clusters) and marker gene expression. The scDotPlot package
offers a unified approach to perform a hierarchical clustering
analysis and add annotations to the columns and/or rows of a
scRNA-seq dot plot. It works with SingleCellExperiment and
Seurat objects as well as data frames.
biocViews: Software, Visualization, DifferentialExpression,
GeneExpression, Transcription, RNASeq, SingleCell, Sequencing,
Clustering
Author: Benjamin I Laufer [aut, cre], Brad A Friedman [aut]
Maintainer: Benjamin I Laufer <blaufer@gmail.com>
URL: https://github.com/ben-laufer/scDotPlot
VignetteBuilder: knitr
BugReports: https://github.com/ben-laufer/scDotPlot/issues
git_url: https://git.bioconductor.org/packages/scDotPlot
git_branch: devel
git_last_commit: 25ac347
git_last_commit_date: 2025-03-20
Date/Publication: 2025-03-20
source.ver: src/contrib/scDotPlot_1.1.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scDotPlot_1.1.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scDotPlot_1.1.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scDotPlot_1.1.1.tgz
vignettes: vignettes/scDotPlot/inst/doc/scDotPlot.html
vignetteTitles: scDotPlot
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scDotPlot/inst/doc/scDotPlot.R
dependencyCount: 206
Package: scds
Version: 1.23.0
Depends: R (>= 3.6.0)
Imports: Matrix, S4Vectors, SingleCellExperiment, SummarizedExperiment,
xgboost, methods, stats, dplyr, pROC
Suggests: BiocStyle, knitr, rsvd, Rtsne, scater, cowplot, rmarkdown
License: MIT + file LICENSE
Archs: x64
MD5sum: 1408862544c35f219290f2da5bd6c6bc
NeedsCompilation: no
Title: In-Silico Annotation of Doublets for Single Cell RNA Sequencing
Data
Description: In single cell RNA sequencing (scRNA-seq) data
combinations of cells are sometimes considered a single cell
(doublets). The scds package provides methods to annotate
doublets in scRNA-seq data computationally.
biocViews: SingleCell, RNASeq, QualityControl, Preprocessing,
Transcriptomics, GeneExpression, Sequencing, Software,
Classification
Author: Dennis Kostka [aut, cre], Bais Abha [aut]
Maintainer: Dennis Kostka <kostka@pitt.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scds
git_branch: devel
git_last_commit: f0cf1fa
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scds_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scds_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scds_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scds_1.23.0.tgz
vignettes: vignettes/scds/inst/doc/scds.html
vignetteTitles: Introduction to the scds package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scds/inst/doc/scds.R
importsMe: singleCellTK
suggestsMe: ExperimentSubset, muscData
dependencyCount: 56
Package: SCFA
Version: 1.17.0
Depends: R (>= 4.0)
Imports: matrixStats, BiocParallel, torch (>= 0.3.0), coro, igraph,
Matrix, cluster, psych, glmnet, RhpcBLASctl, stats, utils,
methods, survival
Suggests: knitr, rmarkdown, BiocStyle
License: LGPL
Archs: x64
MD5sum: a425b1187a41420d936c4cdf685db605
NeedsCompilation: no
Title: SCFA: Subtyping via Consensus Factor Analysis
Description: Subtyping via Consensus Factor Analysis (SCFA) can
efficiently remove noisy signals from consistent molecular
patterns in multi-omics data. SCFA first uses an autoencoder to
select only important features and then repeatedly performs
factor analysis to represent the data with different numbers of
factors. Using these representations, it can reliably identify
cancer subtypes and accurately predict risk scores of patients.
biocViews: Survival, Clustering, Classification
Author: Duc Tran [aut, cre], Hung Nguyen [aut], Tin Nguyen [fnd]
Maintainer: Duc Tran <duct@nevada.unr.edu>
URL: https://github.com/duct317/SCFA
VignetteBuilder: knitr
BugReports: https://github.com/duct317/SCFA/issues
git_url: https://git.bioconductor.org/packages/SCFA
git_branch: devel
git_last_commit: 694c1c8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SCFA_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SCFA_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SCFA_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SCFA_1.17.0.tgz
vignettes: vignettes/SCFA/inst/doc/Example.html
vignetteTitles: SCFA package manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SCFA/inst/doc/Example.R
dependencyCount: 61
Package: scFeatureFilter
Version: 1.27.0
Depends: R (>= 3.6)
Imports: dplyr (>= 0.7.3), ggplot2 (>= 2.1.0), magrittr (>= 1.5), rlang
(>= 0.1.2), tibble (>= 1.3.4), stats, methods
Suggests: testthat, knitr, rmarkdown, BiocStyle, SingleCellExperiment,
SummarizedExperiment, scRNAseq, cowplot
License: MIT + file LICENSE
MD5sum: 978810e068f76bc355055e1f50fc9b2e
NeedsCompilation: no
Title: A correlation-based method for quality filtering of single-cell
RNAseq data
Description: An R implementation of the correlation-based method
developed in the Joshi laboratory to analyse and filter
processed single-cell RNAseq data. It returns a filtered
version of the data containing only genes expression values
unaffected by systematic noise.
biocViews: ImmunoOncology, SingleCell, RNASeq, Preprocessing,
GeneExpression
Author: Angeles Arzalluz-Luque [aut], Guillaume Devailly [aut, cre],
Anagha Joshi [aut]
Maintainer: Guillaume Devailly <gdevailly@hotmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scFeatureFilter
git_branch: devel
git_last_commit: ef82e3e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scFeatureFilter_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scFeatureFilter_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scFeatureFilter_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scFeatureFilter_1.27.0.tgz
vignettes: vignettes/scFeatureFilter/inst/doc/Introduction.html
vignetteTitles: Introduction to scFeatureFilter
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scFeatureFilter/inst/doc/Introduction.R
dependencyCount: 38
Package: scFeatures
Version: 1.7.0
Depends: R (>= 4.2.0)
Imports: DelayedArray, DelayedMatrixStats, EnsDb.Hsapiens.v79,
EnsDb.Mmusculus.v79, GSVA, ape, glue, dplyr, ensembldb, gtools,
msigdbr, proxyC, reshape2, spatstat.explore, spatstat.geom,
tidyr, AUCell, BiocParallel, rmarkdown, methods, stats, cli,
SingleCellSignalR, MatrixGenerics, Seurat, DT
Suggests: knitr, S4Vectors, survival, survminer, BiocStyle, ClassifyR,
org.Hs.eg.db, clusterProfiler
License: GPL-3
MD5sum: 2c6d43d9dff58491e83780ce49a5f167
NeedsCompilation: no
Title: scFeatures: Multi-view representations of single-cell and
spatial data for disease outcome prediction
Description: scFeatures constructs multi-view representations of
single-cell and spatial data. scFeatures is a tool that
generates multi-view representations of single-cell and spatial
data through the construction of a total of 17 feature types.
These features can then be used for a variety of analyses using
other software in Biocondutor.
biocViews: CellBasedAssays, SingleCell, Spatial, Software,
Transcriptomics
Author: Yue Cao [aut, cre], Yingxin Lin [aut], Ellis Patrick [aut],
Pengyi Yang [aut], Jean Yee Hwa Yang [aut]
Maintainer: Yue Cao <yue.cao@sydney.edu.au>
URL: https://sydneybiox.github.io/scFeatures/
https://github.com/SydneyBioX/scFeatures/
VignetteBuilder: knitr
BugReports: https://github.com/SydneyBioX/scFeatures/issues
git_url: https://git.bioconductor.org/packages/scFeatures
git_branch: devel
git_last_commit: e9d6bd3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scFeatures_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scFeatures_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scFeatures_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scFeatures_1.7.0.tgz
vignettes: vignettes/scFeatures/inst/doc/scFeatures_overview.html
vignetteTitles: Overview of scFeatures with case studies
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scFeatures/inst/doc/scFeatures_overview.R
dependencyCount: 253
Package: scGPS
Version: 1.21.0
Depends: R (>= 3.6), SummarizedExperiment, dynamicTreeCut,
SingleCellExperiment
Imports: glmnet (> 2.0), caret (>= 6.0), ggplot2 (>= 2.2.1),
fastcluster, dplyr, Rcpp, RcppArmadillo, RcppParallel,
grDevices, graphics, stats, utils, DESeq2, locfit
LinkingTo: Rcpp, RcppArmadillo, RcppParallel
Suggests: Matrix (>= 1.2), testthat, knitr, parallel, rmarkdown,
RColorBrewer, ReactomePA, clusterProfiler, cowplot,
org.Hs.eg.db, reshape2, xlsx, dendextend, networkD3, Rtsne,
BiocParallel, e1071, WGCNA, devtools, DOSE
License: GPL-3
MD5sum: 6d8d86cde9b2ee1ed82819967ccbf37b
NeedsCompilation: yes
Title: A complete analysis of single cell subpopulations, from
identifying subpopulations to analysing their relationship
(scGPS = single cell Global Predictions of Subpopulation)
Description: The package implements two main algorithms to answer two
key questions: a SCORE (Stable Clustering at Optimal
REsolution) to find subpopulations, followed by scGPS to
investigate the relationships between subpopulations.
biocViews: SingleCell, Clustering, DataImport, Sequencing, Coverage
Author: Quan Nguyen [aut, cre], Michael Thompson [aut], Anne Senabouth
[aut]
Maintainer: Quan Nguyen <quan.nguyen@uq.edu.au>
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports:
https://github.com/IMB-Computational-Genomics-Lab/scGPS/issues
git_url: https://git.bioconductor.org/packages/scGPS
git_branch: devel
git_last_commit: 03c5e17
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scGPS_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scGPS_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scGPS_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scGPS_1.21.0.tgz
vignettes: vignettes/scGPS/inst/doc/vignette.html
vignetteTitles: single cell Global fate Potential of Subpopulations
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scGPS/inst/doc/vignette.R
dependencyCount: 126
Package: schex
Version: 1.21.0
Depends: SingleCellExperiment (>= 1.7.4), ggplot2 (>= 3.2.1)
Imports: hexbin, stats, methods, cluster, dplyr, entropy, ggforce,
grid, rlang, concaveman
Suggests: ggrepel, knitr, rmarkdown, testthat (>= 2.1.0), covr,
TENxPBMCData, scater, Seurat, shinydashboard, iSEE, igraph,
scran, tibble, scuttle
License: GPL-3
MD5sum: 044df0cb0bbe69fe40429dd858b2238d
NeedsCompilation: no
Title: Hexbin plots for single cell omics data
Description: Builds hexbin plots for variables and dimension reduction
stored in single cell omics data such as SingleCellExperiment.
The ideas used in this package are based on the excellent work
of Dan Carr, Nicholas Lewin-Koh, Martin Maechler and Thomas
Lumley.
biocViews: Software, Sequencing, SingleCell, DimensionReduction,
Visualization, ImmunoOncology, DataImport
Author: Saskia Freytag [aut, cre], Wancheng Tang [ctb], Zimo Peng
[ctb], Jingxiu Huang [ctb]
Maintainer: Saskia Freytag <freytag.s@wehi.edu.au>
URL: https://github.com/SaskiaFreytag/schex
VignetteBuilder: knitr
BugReports: https://github.com/SaskiaFreytag/schex/issues
git_url: https://git.bioconductor.org/packages/schex
git_branch: devel
git_last_commit: 6af9fd5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-05
source.ver: src/contrib/schex_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/schex_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/schex_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/schex_1.21.0.tgz
vignettes: vignettes/schex/inst/doc/Seurat_to_SCE.html,
vignettes/schex/inst/doc/using_schex.html
vignetteTitles: Seurat_to_SCE, using_schex
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/schex/inst/doc/Seurat_to_SCE.R,
vignettes/schex/inst/doc/using_schex.R
importsMe: scTensor, scTGIF
dependencyCount: 87
Package: scHOT
Version: 1.19.0
Depends: R (>= 4.0)
Imports: S4Vectors (>= 0.24.3), SingleCellExperiment, Matrix,
SummarizedExperiment, IRanges, methods, stats, BiocParallel,
reshape, ggplot2, igraph, grDevices, ggforce, graphics
Suggests: knitr, markdown, rmarkdown, scater, scattermore, scales,
matrixStats, deldir
License: GPL-3
MD5sum: 49f5b07b0f96b076772af584a8a02ce2
NeedsCompilation: no
Title: single-cell higher order testing
Description: Single cell Higher Order Testing (scHOT) is an R package
that facilitates testing changes in higher order structure of
gene expression along either a developmental trajectory or
across space. scHOT is general and modular in nature, can be
run in multiple data contexts such as along a continuous
trajectory, between discrete groups, and over spatial
orientations; as well as accommodate any higher order
measurement such as variability or correlation. scHOT
meaningfully adds to first order effect testing, such as
differential expression, and provides a framework for
interrogating higher order interactions from single cell data.
biocViews: GeneExpression, RNASeq, Sequencing, SingleCell, Software,
Transcriptomics
Author: Shila Ghazanfar [aut, cre], Yingxin Lin [aut]
Maintainer: Shila Ghazanfar <shazanfar@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scHOT
git_branch: devel
git_last_commit: 07071f6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scHOT_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scHOT_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scHOT_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scHOT_1.19.0.tgz
vignettes: vignettes/scHOT/inst/doc/scHOT.html
vignetteTitles: Getting started: scHOT
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scHOT/inst/doc/scHOT.R
dependencyCount: 83
Package: scider
Version: 1.5.0
Depends: R (>= 4.3)
Imports: SpatialExperiment, SummarizedExperiment, spatstat.geom,
spatstat.explore, sf, lwgeom, ggplot2, stats, pheatmap, plotly,
shiny, igraph, janitor, knitr, methods, utils, rlang, isoband,
S4Vectors, grDevices
Suggests: edgeR, testthat (>= 3.0.0)
License: GPL-3 + file LICENSE
MD5sum: f58ec649c8e2dadf2c182fac32cf39aa
NeedsCompilation: no
Title: Spatial cell-type inter-correlation by density in R
Description: scider is a user-friendly R package providing functions to
model the global density of cells in a slide of spatial
transcriptomics data. All functions in the package are built
based on the SpatialExperiment object, allowing integration
into various spatial transcriptomics-related packages from
Bioconductor. After modelling density, the package allows for
serveral downstream analysis, including colocalization
analysis, boundary detection analysis and differential density
analysis.
biocViews: Spatial, Transcriptomics
Author: Ning Liu [aut] (ORCID:
<https://orcid.org/0000-0002-9487-9305>), Mengbo Li [aut]
(ORCID: <https://orcid.org/0000-0002-9666-5810>), Yunshun Chen
[aut, cre] (ORCID: <https://orcid.org/0000-0003-4911-5653>)
Maintainer: Yunshun Chen <yuchen@wehi.edu.au>
URL: https://github.com/ChenLaboratory/scider,
https://chenlaboratory.github.io/scider/
VignetteBuilder: knitr
BugReports: https://github.com/ChenLaboratory/scider/issues
git_url: https://git.bioconductor.org/packages/scider
git_branch: devel
git_last_commit: 5152c7e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scider_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scider_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scider_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scider_1.5.0.tgz
vignettes: vignettes/scider/inst/doc/scider_userGuide.html
vignetteTitles: scider_introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scider/inst/doc/scider_userGuide.R
dependencyCount: 142
Package: scifer
Version: 1.9.0
Imports: dplyr, rmarkdown, data.table, Biostrings, stats, plyr, knitr,
ggplot2, gridExtra, DECIPHER, stringr, sangerseqR, kableExtra,
tibble, scales, rlang, flowCore, methods, basilisk, reticulate,
here, utils, basilisk.utils
Suggests: BiocBaseUtils, fs, BiocStyle, testthat (>= 3.0.0)
Enhances: parallel
License: MIT + file LICENSE
MD5sum: 6a43fd8241705a2b7a5dc8c4a66c255e
NeedsCompilation: no
Title: Scifer: Single-Cell Immunoglobulin Filtering of Sanger Sequences
Description: Have you ever index sorted cells in a 96 or 384-well plate
and then sequenced using Sanger sequencing? If so, you probably
had some struggles to either check the electropherogram of each
cell sequenced manually, or when you tried to identify which
cell was sorted where after sequencing the plate. Scifer was
developed to solve this issue by performing basic quality
control of Sanger sequences and merging flow cytometry data
from probed single-cell sorted B cells with sequencing data.
scifer can export summary tables, 'fasta' files,
electropherograms for visual inspection, and generate reports.
biocViews: Preprocessing, QualityControl, SangerSeq, Sequencing,
Software, FlowCytometry, SingleCell
Author: Rodrigo Arcoverde Cerveira [aut, cre, cph] (ORCID:
<https://orcid.org/0000-0002-1145-2534>), Marcel Martin [ctb],
Matthew James Hinchcliff [ctb], Sebastian Ols [aut, dtc]
(ORCID: <https://orcid.org/0000-0001-9784-7176>), Karin Loré
[dtc, ths, fnd] (ORCID:
<https://orcid.org/0000-0001-7679-9494>)
Maintainer: Rodrigo Arcoverde Cerveira <rodrigo.arcoverdi@gmail.com>
URL: https://github.com/rodrigarc/scifer
VignetteBuilder: knitr
BugReports: https://github.com/rodrigarc/scifer/issues
git_url: https://git.bioconductor.org/packages/scifer
git_branch: devel
git_last_commit: 5aa0572
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scifer_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scifer_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scifer_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scifer_1.9.0.tgz
vignettes: vignettes/scifer/inst/doc/scifer_walkthrough.html
vignetteTitles: Using scifer to filter single-cell sorted B cell
receptor (BCR) sanger sequences
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scifer/inst/doc/scifer_walkthrough.R
dependencyCount: 115
Package: scmap
Version: 1.29.0
Depends: R(>= 3.4)
Imports: Biobase, SingleCellExperiment, SummarizedExperiment,
BiocGenerics, S4Vectors, dplyr, reshape2, matrixStats, proxy,
utils, googleVis, ggplot2, methods, stats, e1071, randomForest,
Rcpp (>= 0.12.12)
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, BiocStyle
License: GPL-3
Archs: x64
MD5sum: 9a8d6bb902f0fbbfd400e2b8ee5af2d0
NeedsCompilation: yes
Title: A tool for unsupervised projection of single cell RNA-seq data
Description: Single-cell RNA-seq (scRNA-seq) is widely used to
investigate the composition of complex tissues since the
technology allows researchers to define cell-types using
unsupervised clustering of the transcriptome. However, due to
differences in experimental methods and computational analyses,
it is often challenging to directly compare the cells
identified in two different experiments. scmap is a method for
projecting cells from a scRNA-seq experiment on to the
cell-types or individual cells identified in a different
experiment.
biocViews: ImmunoOncology, SingleCell, Software, Classification,
SupportVectorMachine, RNASeq, Visualization, Transcriptomics,
DataRepresentation, Transcription, Sequencing, Preprocessing,
GeneExpression, DataImport
Author: Vladimir Kiselev
Maintainer: Vladimir Kiselev <vladimir.yu.kiselev@gmail.com>
URL: https://github.com/hemberg-lab/scmap
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/scmap/
git_url: https://git.bioconductor.org/packages/scmap
git_branch: devel
git_last_commit: 4a84aef
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scmap_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scmap_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scmap_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scmap_1.29.0.tgz
vignettes: vignettes/scmap/inst/doc/scmap.html
vignetteTitles: `scmap` package vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scmap/inst/doc/scmap.R
dependencyCount: 76
Package: scMerge
Version: 1.23.0
Depends: R (>= 3.6.0)
Imports: BiocParallel, BiocSingular, BiocNeighbors, cluster,
DelayedArray, DelayedMatrixStats, distr, igraph, M3Drop (>=
1.9.4), proxyC, ruv, cvTools, scater, batchelor, scran,
methods, S4Vectors (>= 0.23.19), SingleCellExperiment (>=
1.7.3), SummarizedExperiment
Suggests: BiocStyle, covr, HDF5Array, knitr, Matrix, rmarkdown, scales,
proxy, testthat, badger
License: GPL-3
MD5sum: e7a0023da7a2fbb704ed0d162d419a6c
NeedsCompilation: no
Title: scMerge: Merging multiple batches of scRNA-seq data
Description: Like all gene expression data, single-cell data suffers
from batch effects and other unwanted variations that makes
accurate biological interpretations difficult. The scMerge
method leverages factor analysis, stably expressed genes (SEGs)
and (pseudo-) replicates to remove unwanted variations and
merge multiple single-cell data. This package contains all the
necessary functions in the scMerge pipeline, including the
identification of SEGs, replication-identification methods, and
merging of single-cell data.
biocViews: BatchEffect, GeneExpression, Normalization, RNASeq,
Sequencing, SingleCell, Software, Transcriptomics
Author: Yingxin Lin [aut, cre], Kevin Wang [aut], Sydney Bioinformatics
and Biometrics Group [fnd]
Maintainer: Yingxin Lin <yingxin.lin@sydney.edu.au>
URL: https://github.com/SydneyBioX/scMerge
VignetteBuilder: knitr
BugReports: https://github.com/SydneyBioX/scMerge/issues
git_url: https://git.bioconductor.org/packages/scMerge
git_branch: devel
git_last_commit: d99a8f2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scMerge_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scMerge_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scMerge_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scMerge_1.23.0.tgz
vignettes: vignettes/scMerge/inst/doc/scMerge.html,
vignettes/scMerge/inst/doc/scMerge2.html
vignetteTitles: scMerge, scMerge2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scMerge/inst/doc/scMerge.R,
vignettes/scMerge/inst/doc/scMerge2.R
importsMe: singleCellTK
suggestsMe: Cepo
dependencyCount: 187
Package: scMET
Version: 1.9.0
Depends: R (>= 4.2.0)
Imports: methods, Rcpp (>= 1.0.0), RcppParallel (>= 5.0.1), rstan (>=
2.21.3), rstantools (>= 2.1.0), VGAM, data.table, MASS,
logitnorm, ggplot2, matrixStats, assertthat, viridis, coda,
BiocStyle, cowplot, stats, SummarizedExperiment,
SingleCellExperiment, Matrix, dplyr, S4Vectors
LinkingTo: BH (>= 1.66.0), Rcpp (>= 1.0.0), RcppEigen (>= 0.3.3.3.0),
RcppParallel (>= 5.0.1), rstan (>= 2.21.3), StanHeaders (>=
2.21.0.7)
Suggests: testthat, knitr, rmarkdown
License: GPL-3
MD5sum: a833076ef534b5fe2f6aa4dd17ee2263
NeedsCompilation: yes
Title: Bayesian modelling of cell-to-cell DNA methylation heterogeneity
Description: High-throughput single-cell measurements of DNA methylomes
can quantify methylation heterogeneity and uncover its role in
gene regulation. However, technical limitations and sparse
coverage can preclude this task. scMET is a hierarchical
Bayesian model which overcomes sparsity, sharing information
across cells and genomic features to robustly quantify genuine
biological heterogeneity. scMET can identify highly variable
features that drive epigenetic heterogeneity, and perform
differential methylation and variability analyses. We
illustrate how scMET facilitates the characterization of
epigenetically distinct cell populations and how it enables the
formulation of novel hypotheses on the epigenetic regulation of
gene expression.
biocViews: ImmunoOncology, DNAMethylation, DifferentialMethylation,
DifferentialExpression, GeneExpression, GeneRegulation,
Epigenetics, Genetics, Clustering, FeatureExtraction,
Regression, Bayesian, Sequencing, Coverage, SingleCell
Author: Andreas C. Kapourani [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2303-1953>), John Riddell [ctb]
Maintainer: Andreas C. Kapourani <kapouranis.andreas@gmail.com>
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/andreaskapou/scMET/issues
git_url: https://git.bioconductor.org/packages/scMET
git_branch: devel
git_last_commit: e01d7d4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scMET_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scMET_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scMET_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scMET_1.9.0.tgz
vignettes: vignettes/scMET/inst/doc/scMET_vignette.html
vignetteTitles: scMET analysis using synthetic data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scMET/inst/doc/scMET_vignette.R
dependencyCount: 117
Package: scmeth
Version: 1.27.0
Depends: R (>= 3.5.0)
Imports: knitr, rmarkdown, bsseq, AnnotationHub, GenomicRanges,
reshape2, stats, utils, BSgenome, DelayedArray (>= 0.5.15),
annotatr, SummarizedExperiment (>= 1.5.6), GenomeInfoDb,
Biostrings, DT, HDF5Array (>= 1.7.5)
Suggests: BSgenome.Mmusculus.UCSC.mm10, BSgenome.Hsapiens.NCBI.GRCh38,
TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, Biobase,
BiocGenerics, ggplot2, ggthemes
License: GPL-2
MD5sum: 7cb1722a8f19208aa152b7c7f596625c
NeedsCompilation: no
Title: Functions to conduct quality control analysis in methylation
data
Description: Functions to analyze methylation data can be found here.
Some functions are relevant for single cell methylation data
but most other functions can be used for any methylation data.
Highlight of this workflow is the comprehensive quality control
report.
biocViews: DNAMethylation, QualityControl, Preprocessing, SingleCell,
ImmunoOncology
Author: Divy Kangeyan <divyswar01@g.harvard.edu>
Maintainer: Divy Kangeyan <divyswar01@g.harvard.edu>
VignetteBuilder: knitr
BugReports: https://github.com/aryeelab/scmeth/issues
git_url: https://git.bioconductor.org/packages/scmeth
git_branch: devel
git_last_commit: 6017084
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scmeth_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scmeth_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scmeth_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scmeth_1.27.0.tgz
vignettes: vignettes/scmeth/inst/doc/my-vignette.html
vignetteTitles: Vignette Title
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scmeth/inst/doc/my-vignette.R
suggestsMe: biscuiteer
dependencyCount: 163
Package: scMitoMut
Version: 1.3.1
Depends: R (>= 4.3.0)
Imports: data.table, Rcpp, magrittr, plyr, stringr, utils, stats,
methods, ggplot2, pheatmap, RColorBrewer, rhdf5, readr,
parallel, grDevices
LinkingTo: Rcpp, RcppArmadillo
Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown, VGAM,
R.utils
License: Artistic-2.0
MD5sum: a4ba77792586a892ce8c9394eb286dfe
NeedsCompilation: yes
Title: Single-cell Mitochondrial Mutation Analysis Tool
Description: This package is designed for calling lineage-informative
mitochondrial mutations using single-cell sequencing data, such
as scRNASeq and scATACSeq (preferably the latter due to RNA
editing issues). It includes functions for mutation calling and
visualization. Mutation calling is done using beta-binomial
distribution.
biocViews: Preprocessing, Sequencing, SingleCell
Author: Wenjie Sun [cre, aut] (ORCID:
<https://orcid.org/0000-0002-3100-2346>), Leila Perie [ctb]
Maintainer: Wenjie Sun <sunwjie@gmail.com>
URL: http://github.com/wenjie1991/scMitoMut
VignetteBuilder: knitr
BugReports: https://github.com/wenjie1991/scMitoMut/issues
git_url: https://git.bioconductor.org/packages/scMitoMut
git_branch: devel
git_last_commit: 1dd76eb
git_last_commit_date: 2024-12-16
Date/Publication: 2024-12-16
source.ver: src/contrib/scMitoMut_1.3.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scMitoMut_1.3.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scMitoMut_1.3.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scMitoMut_1.3.1.tgz
vignettes:
vignettes/scMitoMut/inst/doc/Analysis_colon_cancer_dataset.html
vignetteTitles: CRC_dataset_demo
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scMitoMut/inst/doc/Analysis_colon_cancer_dataset.R
dependencyCount: 59
Package: scMultiSim
Version: 1.3.0
Depends: R (>= 4.4.0)
Imports: foreach, rlang, dplyr, ggplot2, Rtsne, ape, MASS, matrixStats,
phytools, KernelKnn, gplots, zeallot, crayon, assertthat,
igraph, methods, grDevices, graphics, stats, utils, markdown,
SummarizedExperiment, BiocParallel
Suggests: knitr, rmarkdown, roxygen2, shiny, testthat (>= 3.0.0)
License: Artistic-2.0
Archs: x64
MD5sum: 5b8532b29e531eb8ea1bbc1068baeda0
NeedsCompilation: no
Title: Simulation of Multi-Modality Single Cell Data Guided By Gene
Regulatory Networks and Cell-Cell Interactions
Description: scMultiSim simulates paired single cell RNA-seq, single
cell ATAC-seq and RNA velocity data, while incorporating
mechanisms of gene regulatory networks, chromatin accessibility
and cell-cell interactions. It allows users to tune various
parameters controlling the amount of each biological factor,
variation of gene-expression levels, the influence of chromatin
accessibility on RNA sequence data, and so on. It can be used
to benchmark various computational methods for single cell
multi-omics data, and to assist in experimental design of
wet-lab experiments.
biocViews: SingleCell, Transcriptomics, GeneExpression, Sequencing,
ExperimentalDesign
Author: Hechen Li [aut, cre] (ORCID:
<https://orcid.org/0000-0003-4907-429X>), Xiuwei Zhang [aut],
Ziqi Zhang [aut], Michael Squires [aut]
Maintainer: Hechen Li <hli691@gatech.edu>
URL: https://zhanglabgt.github.io/scMultiSim/
VignetteBuilder: knitr
BugReports: https://github.com/ZhangLabGT/scMultiSim/issues
git_url: https://git.bioconductor.org/packages/scMultiSim
git_branch: devel
git_last_commit: 2e6a4fc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scMultiSim_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scMultiSim_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scMultiSim_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scMultiSim_1.3.0.tgz
vignettes: vignettes/scMultiSim/inst/doc/basics.html,
vignettes/scMultiSim/inst/doc/options.html,
vignettes/scMultiSim/inst/doc/spatialCCI.html,
vignettes/scMultiSim/inst/doc/workflow.html
vignetteTitles: 2. Simulating Multimodal Single-cell Datasets, 4.
Parameter Guide, 3. Simulating Spatial Cell-Cell Interactions,
1. Getting Started
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scMultiSim/inst/doc/basics.R,
vignettes/scMultiSim/inst/doc/options.R,
vignettes/scMultiSim/inst/doc/spatialCCI.R,
vignettes/scMultiSim/inst/doc/workflow.R
dependencyCount: 109
Package: SCnorm
Version: 1.29.0
Depends: R (>= 3.4.0),
Imports: SingleCellExperiment, SummarizedExperiment, stats, methods,
graphics, grDevices, parallel, quantreg, cluster, moments,
data.table, BiocParallel, S4Vectors, ggplot2, forcats,
BiocGenerics
Suggests: BiocStyle, knitr, rmarkdown, devtools
License: GPL (>= 2)
MD5sum: 91701ccf6ba680496222e68c17368ddc
NeedsCompilation: no
Title: Normalization of single cell RNA-seq data
Description: This package implements SCnorm — a method to normalize
single-cell RNA-seq data.
biocViews: Normalization, RNASeq, SingleCell, ImmunoOncology
Author: Rhonda Bacher
Maintainer: Rhonda Bacher <rbacher@ufl.edu>
URL: https://github.com/rhondabacher/SCnorm
VignetteBuilder: knitr
BugReports: https://github.com/rhondabacher/SCnorm/issues
git_url: https://git.bioconductor.org/packages/SCnorm
git_branch: devel
git_last_commit: d1d79f2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SCnorm_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SCnorm_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SCnorm_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SCnorm_1.29.0.tgz
vignettes: vignettes/SCnorm/inst/doc/SCnorm.pdf
vignetteTitles: SCnorm Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SCnorm/inst/doc/SCnorm.R
dependencyCount: 81
Package: scone
Version: 1.31.1
Depends: R (>= 3.4), methods, SummarizedExperiment
Imports: graphics, stats, utils, aroma.light, BiocParallel, class,
cluster, compositions, diptest, edgeR, fpc, gplots, grDevices,
hexbin, limma, matrixStats, mixtools, RColorBrewer, boot,
rhdf5, RUVSeq, rARPACK, MatrixGenerics, SingleCellExperiment,
DelayedMatrixStats, sparseMatrixStats, SparseArray (>= 1.7.6)
Suggests: BiocStyle, DT, ggplot2, knitr, miniUI, NMF, plotly, reshape2,
rmarkdown, scran, scRNAseq, shiny, testthat, DelayedArray,
visNetwork, doParallel, batchtools, splatter, scater,
kableExtra, mclust, TENxPBMCData
License: Artistic-2.0
Archs: x64
MD5sum: e5a7c0fe049674c257f6bdc3082e50f2
NeedsCompilation: no
Title: Single Cell Overview of Normalized Expression data
Description: SCONE is an R package for comparing and ranking the
performance of different normalization schemes for single-cell
RNA-seq and other high-throughput analyses.
biocViews: ImmunoOncology, Normalization, Preprocessing,
QualityControl, GeneExpression, RNASeq, Software,
Transcriptomics, Sequencing, SingleCell, Coverage
Author: Michael Cole [aut, cph], Davide Risso [aut, cre, cph], Matteo
Borella [ctb], Chiara Romualdi [ctb]
Maintainer: Davide Risso <risso.davide@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/YosefLab/scone/issues
git_url: https://git.bioconductor.org/packages/scone
git_branch: devel
git_last_commit: 120e114
git_last_commit_date: 2025-02-19
Date/Publication: 2025-02-19
source.ver: src/contrib/scone_1.31.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scone_1.31.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scone_1.31.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scone_1.31.1.tgz
vignettes: vignettes/scone/inst/doc/PsiNorm.html,
vignettes/scone/inst/doc/sconeTutorial.html
vignetteTitles: PsiNorm normalization, Introduction to SCONE
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scone/inst/doc/PsiNorm.R,
vignettes/scone/inst/doc/sconeTutorial.R
dependencyCount: 187
Package: Sconify
Version: 1.27.0
Depends: R (>= 3.5)
Imports: tibble, dplyr, FNN, flowCore, Rtsne, ggplot2, magrittr, utils,
stats, readr
Suggests: knitr, rmarkdown, testthat
License: Artistic-2.0
MD5sum: 3d10923ef2e5d24dfbbaa1bb70ab47b6
NeedsCompilation: no
Title: A toolkit for performing KNN-based statistics for flow and mass
cytometry data
Description: This package does k-nearest neighbor based statistics and
visualizations with flow and mass cytometery data. This gives
tSNE maps"fold change" functionality and provides a data
quality metric by assessing manifold overlap between fcs files
expected to be the same. Other applications using this package
include imputation, marker redundancy, and testing the relative
information loss of lower dimension embeddings compared to the
original manifold.
biocViews: ImmunoOncology, SingleCell, FlowCytometry, Software,
MultipleComparison, Visualization
Author: Tyler J Burns
Maintainer: Tyler J Burns <burns.tyler@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Sconify
git_branch: devel
git_last_commit: e6f83c2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Sconify_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Sconify_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Sconify_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Sconify_1.27.0.tgz
vignettes: vignettes/Sconify/inst/doc/DataQuality.html,
vignettes/Sconify/inst/doc/FindingIdealK.html,
vignettes/Sconify/inst/doc/Step1.PreProcessing.html,
vignettes/Sconify/inst/doc/Step2.TheSconeWorkflow.html,
vignettes/Sconify/inst/doc/Step3.PostProcessing.html
vignetteTitles: Data Quality, Finding Ideal K, How to process FCS files
for downstream use in R, General Scone Analysis, Final
Post-Processing Steps for Scone
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Sconify/inst/doc/DataQuality.R,
vignettes/Sconify/inst/doc/FindingIdealK.R,
vignettes/Sconify/inst/doc/Step1.PreProcessing.R,
vignettes/Sconify/inst/doc/Step2.TheSconeWorkflow.R,
vignettes/Sconify/inst/doc/Step3.PostProcessing.R
dependencyCount: 62
Package: SCOPE
Version: 1.19.0
Depends: R (>= 3.6.0), GenomicRanges, IRanges, Rsamtools, GenomeInfoDb,
BSgenome.Hsapiens.UCSC.hg19
Imports: stats, grDevices, graphics, utils, DescTools, RColorBrewer,
gplots, foreach, parallel, doParallel, DNAcopy, BSgenome,
Biostrings, BiocGenerics, S4Vectors
Suggests: knitr, rmarkdown, WGSmapp, BSgenome.Hsapiens.UCSC.hg38,
BSgenome.Mmusculus.UCSC.mm10, testthat (>= 2.1.0)
License: GPL-2
Archs: x64
MD5sum: 410561e9ddb7d42d90f04fcc30497657
NeedsCompilation: no
Title: A normalization and copy number estimation method for
single-cell DNA sequencing
Description: Whole genome single-cell DNA sequencing (scDNA-seq)
enables characterization of copy number profiles at the
cellular level. This circumvents the averaging effects
associated with bulk-tissue sequencing and has increased
resolution yet decreased ambiguity in deconvolving cancer
subclones and elucidating cancer evolutionary history.
ScDNA-seq data is, however, sparse, noisy, and highly variable
even within a homogeneous cell population, due to the biases
and artifacts that are introduced during the library
preparation and sequencing procedure. Here, we propose SCOPE, a
normalization and copy number estimation method for scDNA-seq
data. The distinguishing features of SCOPE include: (i)
utilization of cell-specific Gini coefficients for quality
controls and for identification of normal/diploid cells, which
are further used as negative control samples in a Poisson
latent factor model for normalization; (ii) modeling of GC
content bias using an expectation-maximization algorithm
embedded in the Poisson generalized linear models, which
accounts for the different copy number states along the genome;
(iii) a cross-sample iterative segmentation procedure to
identify breakpoints that are shared across cells from the same
genetic background.
biocViews: SingleCell, Normalization, CopyNumberVariation, Sequencing,
WholeGenome, Coverage, Alignment, QualityControl, DataImport,
DNASeq
Author: Rujin Wang, Danyu Lin, Yuchao Jiang
Maintainer: Rujin Wang <rujin@email.unc.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SCOPE
git_branch: devel
git_last_commit: 8ca1afd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SCOPE_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SCOPE_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SCOPE_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SCOPE_1.19.0.tgz
vignettes: vignettes/SCOPE/inst/doc/SCOPE_vignette.html
vignetteTitles: SCOPE: Single-cell Copy Number Estimation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SCOPE/inst/doc/SCOPE_vignette.R
dependencyCount: 113
Package: scoreInvHap
Version: 1.29.0
Depends: R (>= 3.6.0)
Imports: Biostrings, methods, snpStats, VariantAnnotation,
GenomicRanges, BiocParallel, graphics, SummarizedExperiment
Suggests: testthat, knitr, BiocStyle, rmarkdown
License: file LICENSE
MD5sum: 6ac61ec8a2f2613d2bb579579d06104e
NeedsCompilation: no
Title: Get inversion status in predefined regions
Description: scoreInvHap can get the samples' inversion status of known
inversions. scoreInvHap uses SNP data as input and requires the
following information about the inversion: genotype frequencies
in the different haplotypes, R2 between the region SNPs and
inversion status and heterozygote genotypes in the reference.
The package include this data for 21 inversions.
biocViews: SNP, Genetics, GenomicVariation
Author: Carlos Ruiz [aut], Dolors Pelegrà [aut], Juan R. Gonzalez [aut,
cre]
Maintainer: Dolors Pelegri-Siso <dolors.pelegri@isglobal.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scoreInvHap
git_branch: devel
git_last_commit: a171cc8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scoreInvHap_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scoreInvHap_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scoreInvHap_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scoreInvHap_1.29.0.tgz
vignettes: vignettes/scoreInvHap/inst/doc/scoreInvHap.html
vignetteTitles: Inversion genotyping with scoreInvHap
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scoreInvHap/inst/doc/scoreInvHap.R
dependencyCount: 83
Package: scoup
Version: 1.1.4
Depends: R (>= 4.4), Matrix
Imports: Biostrings, methods
Suggests: BiocManager, BiocStyle, bookdown, htmltools, knitr, testthat
(>= 3.0.0), yaml
License: GPL (>= 2)
MD5sum: 6d3820c5dbb17ffcd55f581a1789b129
NeedsCompilation: no
Title: Simulate Codons with Darwinian Selection Modelled as an OU
Process
Description: An elaborate molecular evolutionary framework that
facilitates straightforward simulation of codon genetic
sequences subjected to different degrees and/or patterns of
Darwinian selection. The model is built upon the fitness
landscape paradigm of Sewall Wright, as popularised by the
mutation-selection model of Halpern and Bruno. This enables
realistic evolutionary process of living organisms to be
reproducible seamlessly. For example, an Ornstein-Uhlenbeck
fitness update algorithm is incorporated herein. Consequently,
otherwise complex biological processes, such as the effect of
the interplay between genetic drift and fitness landscape
fluctuations on the inference of diversifying selection, may
now be investigated with minimal effort. Frequency-dependent
and stochastic fitness landscape update techniques are
available.
biocViews: Alignment, Classification, ComparativeGenomics, DataImport,
Genetics, MathematicalBiology, ResearchField, Sequencing,
SequenceMatching, Software, StatisticalMethod, WorkflowStep
Author: Hassan Sadiq [aut, cre, cph] (ORCID:
<https://orcid.org/0000-0003-0192-7134>)
Maintainer: Hassan Sadiq <hassan.t.sadiq@gmail.com>
URL: https://github.com/thsadiq/scoup
VignetteBuilder: knitr
BugReports: https://github.com/thsadiq/scoup/issues
git_url: https://git.bioconductor.org/packages/scoup
git_branch: devel
git_last_commit: 7bef057
git_last_commit_date: 2025-01-08
Date/Publication: 2025-01-08
source.ver: src/contrib/scoup_1.1.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scoup_1.1.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scoup_1.1.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scoup_1.1.4.tgz
vignettes: vignettes/scoup/inst/doc/scoup.html
vignetteTitles: scoup Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scoup/inst/doc/scoup.R
dependencyCount: 28
Package: scp
Version: 1.17.0
Depends: R (>= 4.3.0), QFeatures (>= 1.13.5)
Imports: IHW, ggplot2, ggrepel, matrixStats, metapod, methods,
MsCoreUtils, MultiAssayExperiment, nipals, RColorBrewer,
S4Vectors, SingleCellExperiment, SummarizedExperiment, stats,
utils
Suggests: BiocStyle, BiocGenerics, MsDataHub (>= 1.3.3), impute, knitr,
patchwork, preprocessCore, rmarkdown, scater, scpdata, sva,
testthat, vdiffr, vsn, uwot
License: Artistic-2.0
MD5sum: 8f7e74352660b480831a9106adc12405
NeedsCompilation: no
Title: Mass Spectrometry-Based Single-Cell Proteomics Data Analysis
Description: Utility functions for manipulating, processing, and
analyzing mass spectrometry-based single-cell proteomics data.
The package is an extension to the 'QFeatures' package and
relies on 'SingleCellExpirement' to enable single-cell
proteomics analyses. The package offers the user the
functionality to process quantitative table (as generated by
MaxQuant, Proteome Discoverer, and more) into data tables ready
for downstream analysis and data visualization.
biocViews: GeneExpression, Proteomics, SingleCell, MassSpectrometry,
Preprocessing, CellBasedAssays
Author: Christophe Vanderaa [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7443-5427>), Laurent Gatto [aut]
(ORCID: <https://orcid.org/0000-0002-1520-2268>)
Maintainer: Christophe Vanderaa <christophe.vanderaa@ugent.be>
URL: https://UCLouvain-CBIO.github.io/scp
VignetteBuilder: knitr
BugReports: https://github.com/UCLouvain-CBIO/scp/issues
git_url: https://git.bioconductor.org/packages/scp
git_branch: devel
git_last_commit: aa22264
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scp_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scp_1.17.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scp_1.17.1.tgz
vignettes: vignettes/scp/inst/doc/advanced.html,
vignettes/scp/inst/doc/QFeatures_nutshell.html,
vignettes/scp/inst/doc/read_scp.html,
vignettes/scp/inst/doc/reporting_missing_values.html,
vignettes/scp/inst/doc/scp_data_modelling.html,
vignettes/scp/inst/doc/scp.html
vignetteTitles: Advanced usage of `scp`, QFeatures in a nutshell, Load
data using readSCP, Reporting missing values, Single Cell
Proteomics data modelling, Single Cell Proteomics data
processing and analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scp/inst/doc/advanced.R,
vignettes/scp/inst/doc/QFeatures_nutshell.R,
vignettes/scp/inst/doc/read_scp.R,
vignettes/scp/inst/doc/reporting_missing_values.R,
vignettes/scp/inst/doc/scp_data_modelling.R,
vignettes/scp/inst/doc/scp.R
suggestsMe: scpdata
dependencyCount: 115
Package: scPCA
Version: 1.21.1
Depends: R (>= 4.0.0)
Imports: stats, methods, assertthat, tibble, dplyr, purrr, stringr,
Rdpack, matrixStats, BiocParallel, elasticnet, sparsepca,
cluster, kernlab, origami, RSpectra, coop, Matrix,
DelayedArray, ScaledMatrix, MatrixGenerics
Suggests: DelayedMatrixStats, sparseMatrixStats, testthat (>= 2.1.0),
covr, knitr, rmarkdown, BiocStyle, ggplot2, ggpubr, splatter,
SingleCellExperiment, microbenchmark
License: MIT + file LICENSE
MD5sum: 8ec2aa1706d585b9b4e16aed3500447f
NeedsCompilation: no
Title: Sparse Contrastive Principal Component Analysis
Description: A toolbox for sparse contrastive principal component
analysis (scPCA) of high-dimensional biological data. scPCA
combines the stability and interpretability of sparse PCA with
contrastive PCA's ability to disentangle biological signal from
unwanted variation through the use of control data. Also
implements and extends cPCA.
biocViews: PrincipalComponent, GeneExpression, DifferentialExpression,
Sequencing, Microarray, RNASeq
Author: Philippe Boileau [aut, cre, cph] (ORCID:
<https://orcid.org/0000-0002-4850-2507>), Nima Hejazi [aut]
(ORCID: <https://orcid.org/0000-0002-7127-2789>), Sandrine
Dudoit [ctb, ths] (ORCID:
<https://orcid.org/0000-0002-6069-8629>)
Maintainer: Philippe Boileau <philippe_boileau@berkeley.edu>
URL: https://github.com/PhilBoileau/scPCA
VignetteBuilder: knitr
BugReports: https://github.com/PhilBoileau/scPCA/issues
git_url: https://git.bioconductor.org/packages/scPCA
git_branch: devel
git_last_commit: 4329b9a
git_last_commit_date: 2025-02-10
Date/Publication: 2025-02-10
source.ver: src/contrib/scPCA_1.21.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scPCA_1.21.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scPCA_1.21.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scPCA_1.21.1.tgz
vignettes: vignettes/scPCA/inst/doc/scpca_intro.html
vignetteTitles: Sparse contrastive principal component analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scPCA/inst/doc/scpca_intro.R
dependsOnMe: OSCA.workflows
dependencyCount: 72
Package: scPipe
Version: 2.7.1
Depends: R (>= 4.2.0), SingleCellExperiment
Imports: AnnotationDbi, basilisk, BiocGenerics, biomaRt, Biostrings,
data.table, dplyr, DropletUtils, flexmix, GenomicRanges,
GenomicAlignments, GGally, ggplot2, glue (>= 1.3.0), grDevices,
graphics, hash, IRanges, magrittr, MASS, Matrix (>= 1.5.0),
mclust, methods, MultiAssayExperiment, org.Hs.eg.db,
org.Mm.eg.db, purrr, Rcpp (>= 0.11.3), reshape, reticulate,
Rhtslib, rlang, robustbase, Rsamtools, Rsubread, rtracklayer,
SummarizedExperiment, S4Vectors, scales, stats, stringr,
tibble, tidyr, tools, utils, vctrs (>= 0.5.2)
LinkingTo: Rcpp, Rhtslib (>= 1.13.1), testthat
Suggests: BiocStyle, DT, GenomicFeatures, grid, igraph, kableExtra,
knitr, locStra, plotly, rmarkdown, RColorBrewer, readr,
reshape2, RANN, shiny, scater (>= 1.11.0), testthat, xml2, umap
License: GPL (>= 2)
MD5sum: 408c9f014761019b16875f53782b86d0
NeedsCompilation: yes
Title: Pipeline for single cell multi-omic data pre-processing
Description: A preprocessing pipeline for single cell RNA-seq/ATAC-seq
data that starts from the fastq files and produces a feature
count matrix with associated quality control information. It
can process fastq data generated by CEL-seq, MARS-seq,
Drop-seq, Chromium 10x and SMART-seq protocols.
biocViews: ImmunoOncology, Software, Sequencing, RNASeq,
GeneExpression, SingleCell, Visualization, SequenceMatching,
Preprocessing, QualityControl, GenomeAnnotation, DataImport
Author: Luyi Tian [aut], Shian Su [aut, cre], Shalin Naik [ctb], Shani
Amarasinghe [aut], Oliver Voogd [aut], Phil Yang [aut], Matthew
Ritchie [ctb]
Maintainer: Shian Su <su.s@wehi.edu.au>
URL: https://github.com/LuyiTian/scPipe
SystemRequirements: C++11, GNU make
VignetteBuilder: knitr
BugReports: https://github.com/LuyiTian/scPipe
git_url: https://git.bioconductor.org/packages/scPipe
git_branch: devel
git_last_commit: 53eacce
git_last_commit_date: 2024-12-17
Date/Publication: 2024-12-18
source.ver: src/contrib/scPipe_2.7.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scPipe_2.7.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scPipe_2.7.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scPipe_2.7.1.tgz
vignettes: vignettes/scPipe/inst/doc/scPipe_atac_tutorial.html,
vignettes/scPipe/inst/doc/scPipe_tutorial.html
vignetteTitles: scPipe: a flexible data preprocessing pipeline for
scATAC-seq data, scPipe: a flexible data preprocessing pipeline
for 3' end scRNA-seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scPipe/inst/doc/scPipe_atac_tutorial.R,
vignettes/scPipe/inst/doc/scPipe_tutorial.R
dependencyCount: 174
Package: scQTLtools
Version: 0.99.12
Depends: R (>= 4.4.1.0)
Imports: ggplot2(>= 3.5.1), Matrix (>= 1.7-0), stats (>= 4.4.1),
progress(>= 1.2.3), stringr(>= 1.5.1), dplyr(>= 1.1.4),
SeuratObject(>= 5.0.2), methods(>= 4.4.1), magrittr(>= 2.0.3),
patchwork(>= 1.2.0), DESeq2 (>= 1.45.3), VGAM (>= 1.1-11),
limma (>= 3.61.9), biomaRt(>= 2.61.3), gamlss (>= 5.4-22),
SingleCellExperiment(>= 1.27.2), SummarizedExperiment(>=
1.32.0), GOSemSim(>= 2.31.2)
Suggests: BiocStyle, knitr, rmarkdown, org.Hs.eg.db, org.Mm.eg.db,
org.Ce.eg.db, org.At.tair.db, testthat (>= 3.2.1.1)
License: MIT + file LICENSE
MD5sum: 4d1f7c10a2f09eea5ff76b2d0a40d66e
NeedsCompilation: no
Title: An R package for single-cell eQTL analysis and visualization
Description: This package specializes in analyzing and visualizing eQTL
at the single-cell level. It can read gene expression matrices
or Seurat data, or SingleCellExperiment object along with
genotype data. It offers a function for cis-eQTL analysis to
detect eQTL within a given range, and another function to fit
models with three methods. Using this package, users can also
generate single-cell level visualization result.
biocViews: Software,GeneExpression,GeneticVariability,SNP,
DifferentialExpression,GenomicVariation,VariantDetection,Genetics,
FunctionalGenomics,SystemsBiology,Regression,SingleCell,Normalization,
Visualization
Author: Xiaofeng Wu [aut, cre, cph] (ORCID:
<https://orcid.org/0009-0003-6254-5575>), Xin Huang [aut, cph],
Jingtong Kang [com], Siwen Xu [aut, cph]
Maintainer: Xiaofeng Wu <1427972815@qq.com>
URL: https://github.com/XFWuCN/scQTLtools
VignetteBuilder: knitr
BugReports: https://github.com/XFWuCN/scQTLtools/issues
git_url: https://git.bioconductor.org/packages/scQTLtools
git_branch: devel
git_last_commit: 58f5b0a
git_last_commit_date: 2025-03-26
Date/Publication: 2025-03-26
source.ver: src/contrib/scQTLtools_0.99.12.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scQTLtools_0.99.12.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scQTLtools_0.99.12.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scQTLtools_0.99.12.tgz
vignettes: vignettes/scQTLtools/inst/doc/scQTLtools.html
vignetteTitles: scQTLtools: An R package for single-cell eQTL analysis.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scQTLtools/inst/doc/scQTLtools.R
dependencyCount: 133
Package: scran
Version: 1.35.0
Depends: SingleCellExperiment, scuttle
Imports: SummarizedExperiment, S4Vectors, BiocGenerics, BiocParallel,
Rcpp, stats, methods, utils, Matrix, edgeR, limma, igraph,
statmod, MatrixGenerics, S4Arrays, DelayedArray, BiocSingular,
bluster, metapod, dqrng, beachmat
LinkingTo: Rcpp, beachmat, BH, dqrng, scuttle
Suggests: testthat, BiocStyle, knitr, rmarkdown, DelayedMatrixStats,
HDF5Array, scRNAseq, dynamicTreeCut, ResidualMatrix,
ScaledMatrix, DESeq2, pheatmap, scater
License: GPL-3
MD5sum: b50399d58c2d37c06c5ac5788f8fe733
NeedsCompilation: yes
Title: Methods for Single-Cell RNA-Seq Data Analysis
Description: Implements miscellaneous functions for interpretation of
single-cell RNA-seq data. Methods are provided for assignment
of cell cycle phase, detection of highly variable and
significantly correlated genes, identification of marker genes,
and other common tasks in routine single-cell analysis
workflows.
biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software,
GeneExpression, Transcriptomics, SingleCell, Clustering
Author: Aaron Lun [aut, cre], Karsten Bach [aut], Jong Kyoung Kim
[ctb], Antonio Scialdone [ctb]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://github.com/MarioniLab/scran/
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/MarioniLab/scran/issues
git_url: https://git.bioconductor.org/packages/scran
git_branch: devel
git_last_commit: f18e781
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scran_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scran_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scran_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scran_1.35.0.tgz
vignettes: vignettes/scran/inst/doc/scran.html
vignetteTitles: Using scran to analyze scRNA-seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scran/inst/doc/scran.R
dependsOnMe: OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows,
SingleRBook
importsMe: BASiCS, BASiCStan, BatchQC, BayesSpace, BioTIP, celda,
chevreulPlot, chevreulProcess, ChromSCape, CiteFuse, Dino,
epiregulon, epiregulon.extra, FLAMES, lute, MOSim, MPAC,
msImpute, mumosa, pipeComp, scDblFinder, scDD, scMerge,
scTreeViz, SingleCellSignalR, singleCellTK, Spaniel, SpaNorm,
mixhvg, SC.MEB, SpatialDDLS
suggestsMe: APL, Banksy, batchelor, bluster, CellTrails,
clusterExperiment, decontX, destiny, dittoSeq, escape, escheR,
ExperimentSubset, ggsc, ggspavis, Glimma, glmGamPoi, iSEEu,
jazzPanda, miloR, Nebulosa, nnSVG, PCAtools, raer, ReactomeGSA,
scDiagnostics, scDotPlot, schex, scone, scuttle,
SingleCellAlleleExperiment, sketchR, smoothclust,
spatialHeatmap, splatter, SPOTlight, StabMap, SVP,
tidySingleCellExperiment, tpSVG, transformGamPoi, TSCAN,
velociraptor, Voyager, HCAData, SingleCellMultiModal,
TabulaMurisData, simpleSingleCell, Canek, SCdeconR
dependencyCount: 73
Package: scrapper
Version: 1.1.14
Imports: methods, Rcpp, beachmat (>= 2.21.6), DelayedArray,
BiocNeighbors (>= 1.99.0), Rigraphlib, parallel
LinkingTo: Rcpp, assorthead, beachmat, BiocNeighbors
Suggests: testthat, knitr, rmarkdown, BiocStyle, MatrixGenerics,
sparseMatrixStats, Matrix, S4Vectors, SummarizedExperiment,
SingleCellExperiment, scRNAseq, igraph
License: MIT + file LICENSE
MD5sum: 583cda916e7df473de6bad7cf29ab8b5
NeedsCompilation: yes
Title: Bindings to C++ Libraries for Single-Cell Analysis
Description: Implements R bindings to C++ code for analyzing
single-cell (expression) data, mostly from various libscran
libraries. Each function performs an individual step in the
single-cell analysis workflow, ranging from quality control to
clustering and marker detection. It is mostly intended for
other Bioconductor package developers to build more
user-friendly end-to-end workflows.
biocViews: Normalization, RNASeq, Software, GeneExpression,
Transcriptomics, SingleCell, BatchEffect, QualityControl,
DifferentialExpression, FeatureExtraction, PrincipalComponent,
Clustering
Author: Aaron Lun [cre, aut]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
SystemRequirements: C++17, GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scrapper
git_branch: devel
git_last_commit: a8642ec
git_last_commit_date: 2025-03-17
Date/Publication: 2025-03-17
source.ver: src/contrib/scrapper_1.1.14.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scrapper_1.1.14.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scrapper_1.1.14.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scrapper_1.1.14.tgz
vignettes: vignettes/scrapper/inst/doc/userguide.html
vignetteTitles: Using scrapper to analyze single-cell data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scrapper/inst/doc/userguide.R
suggestsMe: SingleR
dependencyCount: 31
Package: scReClassify
Version: 1.13.0
Depends: R (>= 4.1)
Imports: randomForest, e1071, stats, SummarizedExperiment,
SingleCellExperiment, methods
Suggests: testthat, knitr, BiocStyle, rmarkdown, DT, mclust, dplyr
License: GPL-3 + file LICENSE
MD5sum: 2f647d2eaabd77bb0c240072065d3dc6
NeedsCompilation: no
Title: scReClassify: post hoc cell type classification of single-cell
RNA-seq data
Description: A post hoc cell type classification tool to fine-tune cell
type annotations generated by any cell type classification
procedure with semi-supervised learning algorithm AdaSampling
technique. The current version of scReClassify supports Support
Vector Machine and Random Forest as a base classifier.
biocViews: Software, Transcriptomics, SingleCell, Classification,
SupportVectorMachine
Author: Pengyi Yang [aut] (ORCID:
<https://orcid.org/0000-0003-1098-3138>), Taiyun Kim [aut, cre]
(ORCID: <https://orcid.org/0000-0002-5028-836X>)
Maintainer: Taiyun Kim <taiyun.kim91@gmail.com>
URL: https://github.com/SydneyBioX/scReClassify,
http://www.bioconductor.org/packages/release/bioc/html/scReClassify.html
VignetteBuilder: knitr
BugReports: https://github.com/SydneyBioX/scReClassify/issues
git_url: https://git.bioconductor.org/packages/scReClassify
git_branch: devel
git_last_commit: 5cefdbe
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scReClassify_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scReClassify_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scReClassify_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scReClassify_1.13.0.tgz
vignettes: vignettes/scReClassify/inst/doc/scReClassify.html
vignetteTitles: An introduction to scReClassify package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scReClassify/inst/doc/scReClassify.R
dependencyCount: 42
Package: scRecover
Version: 1.23.0
Depends: R (>= 3.4.0)
Imports: stats, utils, methods, graphics, doParallel, foreach,
parallel, penalized, kernlab, rsvd, Matrix (>= 1.2-14), MASS
(>= 7.3-45), pscl (>= 1.4.9), bbmle (>= 1.0.18), gamlss (>=
4.4-0), preseqR (>= 4.0.0), SAVER (>= 1.1.1), BiocParallel (>=
1.12.0)
Suggests: knitr, rmarkdown, SingleCellExperiment, testthat
License: GPL
Archs: x64
MD5sum: 80b84472e0274c37e7684ff512c635b1
NeedsCompilation: no
Title: scRecover for imputation of single-cell RNA-seq data
Description: scRecover is an R package for imputation of single-cell
RNA-seq (scRNA-seq) data. It will detect and impute dropout
values in a scRNA-seq raw read counts matrix while keeping the
real zeros unchanged, since there are both dropout zeros and
real zeros in scRNA-seq data. By combination with scImpute,
SAVER and MAGIC, scRecover not only detects dropout and real
zeros at higher accuracy, but also improve the downstream
clustering and visualization results.
biocViews: GeneExpression, SingleCell, RNASeq, Transcriptomics,
Sequencing, Preprocessing, Software
Author: Zhun Miao, Xuegong Zhang <zhangxg@tsinghua.edu.cn>
Maintainer: Zhun Miao <miaoz13@tsinghua.org.cn>
URL: https://miaozhun.github.io/scRecover
VignetteBuilder: knitr
BugReports: https://github.com/miaozhun/scRecover/issues
git_url: https://git.bioconductor.org/packages/scRecover
git_branch: devel
git_last_commit: b5db975
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scRecover_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scRecover_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scRecover_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scRecover_1.23.0.tgz
vignettes: vignettes/scRecover/inst/doc/scRecover.html
vignetteTitles: scRecover
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scRecover/inst/doc/scRecover.R
dependencyCount: 46
Package: screenCounter
Version: 1.7.1
Depends: S4Vectors, SummarizedExperiment
Imports: Rcpp, BiocParallel
LinkingTo: Rcpp
Suggests: BiocGenerics, Biostrings, BiocStyle, knitr, rmarkdown,
testthat
License: MIT + file LICENSE
MD5sum: cbd9ea1f8565dce67b0815fb53c43af4
NeedsCompilation: yes
Title: Counting Reads in High-Throughput Sequencing Screens
Description: Provides functions for counting reads from high-throughput
sequencing screen data (e.g., CRISPR, shRNA) to quantify
barcode abundance. Currently supports single barcodes in
single- or paired-end data, and combinatorial barcodes in
paired-end data.
biocViews: CRISPR, Alignment, FunctionalGenomics, FunctionalPrediction
Author: Aaron Lun [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3564-4813>)
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://github.com/crisprVerse/screenCounter
SystemRequirements: C++17, GNU make
VignetteBuilder: knitr
BugReports: https://github.com/crisprVerse/screenCounter/issues
git_url: https://git.bioconductor.org/packages/screenCounter
git_branch: devel
git_last_commit: d647762
git_last_commit_date: 2024-12-11
Date/Publication: 2024-12-12
source.ver: src/contrib/screenCounter_1.7.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/screenCounter_1.7.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/screenCounter_1.7.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/screenCounter_1.7.1.tgz
vignettes: vignettes/screenCounter/inst/doc/counting.html
vignetteTitles: Counting barcodes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/screenCounter/inst/doc/counting.R
dependencyCount: 47
Package: ScreenR
Version: 1.9.0
Depends: R (>= 4.2)
Imports: methods (>= 4.0), rlang (>= 0.4), stringr (>= 1.4), limma (>=
3.46), patchwork (>= 1.1), tibble (>= 3.1.6), scales (>=
1.1.1), ggvenn (>= 0.1.9), purrr (>= 0.3.4), ggplot2 (>= 3.3),
stats, tidyr (>= 1.2), magrittr (>= 1.0), dplyr (>= 1.0), edgeR
(>= 3.32), tidyselect (>= 1.1.2)
Suggests: rmarkdown (>= 2.11), knitr (>= 1.37), testthat (>= 3.0.0),
BiocStyle (>= 2.22.0), covr (>= 3.5)
License: MIT + file LICENSE
Archs: x64
MD5sum: ca3886ae9ad5f3978ba359129d8341ee
NeedsCompilation: no
Title: Package to Perform High Throughput Biological Screening
Description: ScreenR is a package suitable to perform hit
identification in loss of function High Throughput Biological
Screenings performed using barcoded shRNA-based libraries.
ScreenR combines the computing power of software such as edgeR
with the simplicity of use of the Tidyverse metapackage.
ScreenR executes a pipeline able to find candidate hits from
barcode counts, and integrates a wide range of visualization
modes for each step of the analysis.
biocViews: Software, AssayDomain, GeneExpression
Author: Emanuel Michele Soda [aut, cre] (ORICD: 0000-0002-2301-6465),
Elena Ceccacci [aut] (ORICD: 0000-0002-2285-8994), Saverio
Minucci [fnd, ths] (ORICD: 0000-0001-5678-536X)
Maintainer: Emanuel Michele Soda <emanuelsoda@gmail.com>
URL: https://emanuelsoda.github.io/ScreenR/
VignetteBuilder: knitr
BugReports: https://github.com/EmanuelSoda/ScreenR/issues
git_url: https://git.bioconductor.org/packages/ScreenR
git_branch: devel
git_last_commit: 9ed4aad
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ScreenR_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ScreenR_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ScreenR_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ScreenR_1.9.0.tgz
vignettes: vignettes/ScreenR/inst/doc/Analysis_Example.html
vignetteTitles: ScreenR Example Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ScreenR/inst/doc/Analysis_Example.R
dependencyCount: 50
Package: scRepertoire
Version: 2.3.2
Depends: ggplot2, R (>= 4.0)
Imports: assertthat, cubature, dplyr, evmix, ggalluvial, ggdendro,
ggraph, grDevices, igraph, iNEXT, plyr, quantreg, Rcpp,
reshape2, rjson, rlang, S4Vectors, SeuratObject,
SingleCellExperiment, stringr, stringdist,
SummarizedExperiment, tidygraph, truncdist, VGAM, purrr,
lifecycle, methods
LinkingTo: Rcpp
Suggests: BiocManager, BiocStyle, circlize, knitr, rmarkdown, scales,
scater, Seurat, spelling, testthat (>= 3.0.0), vdiffr, withr
License: MIT + file LICENSE
Archs: x64
MD5sum: d01c2e2715974f4c4132ee964128fc89
NeedsCompilation: yes
Title: A toolkit for single-cell immune receptor profiling
Description: scRepertoire is a toolkit for processing and analyzing
single-cell T-cell receptor (TCR) and immunoglobulin (Ig). The
scRepertoire framework supports use of 10x, AIRR, BD, MiXCR,
Omniscope, TRUST4, and WAT3R single-cell formats. The
functionality includes basic clonal analyses, repertoire
summaries, distance-based clustering and interaction with the
popular Seurat and SingleCellExperiment/Bioconductor R
workflows.
biocViews: Software, ImmunoOncology, SingleCell, Classification,
Annotation, Sequencing
Author: Nick Borcherding [aut, cre], Qile Yang [aut], Ksenia Safina
[aut]
Maintainer: Nick Borcherding <ncborch@gmail.com>
URL: https://www.borch.dev/uploads/scRepertoire/
VignetteBuilder: knitr
BugReports: https://github.com/BorchLab/scRepertoire/issues
git_url: https://git.bioconductor.org/packages/scRepertoire
git_branch: devel
git_last_commit: 8ff52b7
git_last_commit_date: 2025-01-29
Date/Publication: 2025-01-29
source.ver: src/contrib/scRepertoire_2.3.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scRepertoire_2.3.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scRepertoire_2.3.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scRepertoire_2.3.2.tgz
vignettes: vignettes/scRepertoire/inst/doc/vignette.html
vignetteTitles: Using scRepertoire
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scRepertoire/inst/doc/vignette.R
suggestsMe: dandelionR, immApex
dependencyCount: 119
Package: scRNAseqApp
Version: 1.7.14
Depends: R (>= 4.3.0)
Imports: bibtex, bslib, circlize, ComplexHeatmap, colourpicker,
data.table, DBI, DT, fs, GenomicRanges, GenomeInfoDb, ggdendro,
ggforce, ggplot2, ggrepel, ggridges, grDevices, grid,
gridExtra, htmltools, IRanges, jsonlite, Matrix, magrittr,
methods, patchwork, plotly, RColorBrewer, RefManageR, rhdf5,
Rsamtools, RSQLite, rtracklayer, S4Vectors, scales, scrypt,
Seurat, SeuratObject, shiny, shinyhelper, shinymanager,
slingshot, SingleCellExperiment, sortable, stats, tools, xfun,
xml2, utils
Suggests: rmarkdown, knitr, testthat, BiocStyle
Enhances: celldex, future, SingleR, SummarizedExperiment, tricycle
License: GPL-3
MD5sum: be38ddfad60e5138ad666aec920dd384
NeedsCompilation: no
Title: A single-cell RNAseq Shiny app-package
Description: The scRNAseqApp is a Shiny app package designed for
interactive visualization of single-cell data. It is an
enhanced version derived from the ShinyCell, repackaged to
accommodate multiple datasets. The app enables users to
visualize data containing various types of information
simultaneously, facilitating comprehensive analysis.
Additionally, it includes a user management system to regulate
database accessibility for different users.
biocViews: Visualization, SingleCell, RNASeq
Author: Jianhong Ou [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8652-2488>)
Maintainer: Jianhong Ou <jou@morgridge.org>
URL: https://github.com/jianhong/scRNAseqApp
VignetteBuilder: knitr
BugReports: https://github.com/jianhong/scRNAseqApp/issues
git_url: https://git.bioconductor.org/packages/scRNAseqApp
git_branch: devel
git_last_commit: 52bd8a4
git_last_commit_date: 2025-03-13
Date/Publication: 2025-03-17
source.ver: src/contrib/scRNAseqApp_1.7.14.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scRNAseqApp_1.7.14.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scRNAseqApp_1.7.14.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scRNAseqApp_1.7.14.tgz
vignettes: vignettes/scRNAseqApp/inst/doc/scRNAseqApp.html
vignetteTitles: scRNAseqApp Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scRNAseqApp/inst/doc/scRNAseqApp.R
dependencyCount: 236
Package: scruff
Version: 1.25.0
Depends: R (>= 4.0)
Imports: data.table, GenomicAlignments, GenomicFeatures, txdbmaker,
GenomicRanges, Rsamtools, ShortRead, parallel, plyr,
BiocGenerics, BiocParallel, S4Vectors, AnnotationDbi,
Biostrings, methods, ggplot2, ggthemes, scales, GenomeInfoDb,
stringdist, ggbio, rtracklayer, SingleCellExperiment,
SummarizedExperiment, Rsubread, parallelly
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: MIT + file LICENSE
MD5sum: 5afa0d01b10845165684f0755423a600
NeedsCompilation: no
Title: Single Cell RNA-Seq UMI Filtering Facilitator (scruff)
Description: A pipeline which processes single cell RNA-seq (scRNA-seq)
reads from CEL-seq and CEL-seq2 protocols. Demultiplex
scRNA-seq FASTQ files, align reads to reference genome using
Rsubread, and generate UMI filtered count matrix. Also provide
visualizations of read alignments and pre- and post-alignment
QC metrics.
biocViews: Software, Technology, Sequencing, Alignment, RNASeq,
SingleCell, WorkflowStep, Preprocessing, QualityControl,
Visualization, ImmunoOncology
Author: Zhe Wang [aut, cre], Junming Hu [aut], Joshua Campbell [aut]
Maintainer: Zhe Wang <zhe@bu.edu>
VignetteBuilder: knitr
BugReports: https://github.com/campbio/scruff/issues
git_url: https://git.bioconductor.org/packages/scruff
git_branch: devel
git_last_commit: 21462a6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scruff_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scruff_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scruff_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scruff_1.25.0.tgz
vignettes: vignettes/scruff/inst/doc/scruff.html
vignetteTitles: Process Single Cell RNA-Seq reads using scruff
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/scruff/inst/doc/scruff.R
dependencyCount: 175
Package: scry
Version: 1.19.0
Depends: R (>= 4.0), stats, methods
Imports: DelayedArray, glmpca (>= 0.2.0), Matrix, SingleCellExperiment,
SummarizedExperiment, BiocSingular
Suggests: BiocGenerics, covr, DuoClustering2018, ggplot2, HDF5Array,
knitr, markdown, rmarkdown, TENxPBMCData, testthat
License: Artistic-2.0
Archs: x64
MD5sum: 82cc98ef235ccf53dabe0f6189b93e0c
NeedsCompilation: no
Title: Small-Count Analysis Methods for High-Dimensional Data
Description: Many modern biological datasets consist of small counts
that are not well fit by standard linear-Gaussian methods such
as principal component analysis. This package provides
implementations of count-based feature selection and dimension
reduction algorithms. These methods can be used to facilitate
unsupervised analysis of any high-dimensional data such as
single-cell RNA-seq.
biocViews: DimensionReduction, GeneExpression, Normalization,
PrincipalComponent, RNASeq, Software, Sequencing, SingleCell,
Transcriptomics
Author: Kelly Street [aut, cre], F. William Townes [aut, cph], Davide
Risso [aut], Stephanie Hicks [aut]
Maintainer: Kelly Street <street.kelly@gmail.com>
URL: https://bioconductor.org/packages/scry.html
VignetteBuilder: knitr
BugReports: https://github.com/kstreet13/scry/issues
git_url: https://git.bioconductor.org/packages/scry
git_branch: devel
git_last_commit: ae09b7d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scry_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scry_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scry_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scry_1.19.0.tgz
vignettes: vignettes/scry/inst/doc/bigdata.html,
vignettes/scry/inst/doc/scry.html
vignetteTitles: Scry Methods For Larger Datasets, Overview of Scry
Methods
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scry/inst/doc/bigdata.R,
vignettes/scry/inst/doc/scry.R
dependencyCount: 56
Package: scShapes
Version: 1.13.0
Depends: R (>= 4.1)
Imports: Matrix, stats, methods, pscl, VGAM, dgof, BiocParallel, MASS,
emdbook, magrittr, utils
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
License: GPL-3
MD5sum: 5b2f3c03832d37f3879b3cbf60f502cf
NeedsCompilation: yes
Title: A Statistical Framework for Modeling and Identifying
Differential Distributions in Single-cell RNA-sequencing Data
Description: We present a novel statistical framework for identifying
differential distributions in single-cell RNA-sequencing
(scRNA-seq) data between treatment conditions by modeling gene
expression read counts using generalized linear models (GLMs).
We model each gene independently under each treatment condition
using error distributions Poisson (P), Negative Binomial (NB),
Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial
(ZINB) with log link function and model based normalization for
differences in sequencing depth. Since all four distributions
considered in our framework belong to the same family of
distributions, we first perform a Kolmogorov-Smirnov (KS) test
to select genes belonging to the family of ZINB distributions.
Genes passing the KS test will be then modeled using GLMs.
Model selection is done by calculating the Bayesian Information
Criterion (BIC) and likelihood ratio test (LRT) statistic.
biocViews: RNASeq, SingleCell, MultipleComparison, GeneExpression
Author: Malindrie Dharmaratne [cre, aut] (ORCID:
<https://orcid.org/0000-0002-1694-6496>)
Maintainer: Malindrie Dharmaratne <malindrie@gmail.com>
URL: https://github.com/Malindrie/scShapes
VignetteBuilder: knitr
BugReports: https://github.com/Malindrie/scShapes/issues
git_url: https://git.bioconductor.org/packages/scShapes
git_branch: devel
git_last_commit: f5a9606
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scShapes_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scShapes_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scShapes_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scShapes_1.13.0.tgz
vignettes: vignettes/scShapes/inst/doc/vignette_scShapes.html
vignetteTitles: The vignette for running scShapes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scShapes/inst/doc/vignette_scShapes.R
dependencyCount: 34
Package: scTensor
Version: 2.17.0
Depends: R (>= 4.1.0)
Imports: methods, RSQLite, igraph, S4Vectors, plotly, reactome.db,
AnnotationDbi, SummarizedExperiment, SingleCellExperiment,
nnTensor (>= 1.1.5), ccTensor (>= 1.0.2), rTensor (>= 1.4.8),
abind, plotrix, heatmaply, tagcloud, rmarkdown, BiocStyle,
knitr, AnnotationHub, MeSHDbi (>= 1.29.2), grDevices, graphics,
stats, utils, outliers, Category, meshr (>= 1.99.1), GOstats,
ReactomePA, DOSE, crayon, checkmate, BiocManager, visNetwork,
schex, ggplot2
Suggests: testthat, LRBaseDbi, Seurat, scTGIF, Homo.sapiens,
AnnotationHub
License: Artistic-2.0
Archs: x64
MD5sum: 79d13a34d33f8d6455b536946d971186
NeedsCompilation: no
Title: Detection of cell-cell interaction from single-cell RNA-seq
dataset by tensor decomposition
Description: The algorithm is based on the non-negative tucker
decomposition (NTD2) of nnTensor.
biocViews: DimensionReduction, SingleCell, Software, GeneExpression
Author: Koki Tsuyuzaki [aut, cre], Kozo Nishida [aut]
Maintainer: Koki Tsuyuzaki <k.t.the-answer@hotmail.co.jp>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scTensor
git_branch: devel
git_last_commit: dceaf02
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scTensor_2.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scTensor_2.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scTensor_2.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scTensor_2.17.0.tgz
vignettes:
vignettes/scTensor/inst/doc/scTensor_1_Data_format_ID_Conversion.html,
vignettes/scTensor/inst/doc/scTensor_2_Report_Interpretation.html,
vignettes/scTensor/inst/doc/scTensor_3_CCI_Simulation.html,
vignettes/scTensor/inst/doc/scTensor_4_Reanalysis.html,
vignettes/scTensor/inst/doc/scTensor.html
vignetteTitles: scTensor: 1. Data format and ID conversion, scTensor:
2. Interpretation of HTML report, scTensor: 3. Simulation of
CCI, scTensor: 4. Reanalysis of the results of scTensor,
scTensor
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/scTensor/inst/doc/scTensor_1_Data_format_ID_Conversion.R,
vignettes/scTensor/inst/doc/scTensor_2_Report_Interpretation.R,
vignettes/scTensor/inst/doc/scTensor_3_CCI_Simulation.R,
vignettes/scTensor/inst/doc/scTensor_4_Reanalysis.R,
vignettes/scTensor/inst/doc/scTensor.R
dependencyCount: 237
Package: scTGIF
Version: 1.21.0
Depends: R (>= 3.6.0)
Imports: GSEABase, Biobase, SingleCellExperiment, BiocStyle, plotly,
tagcloud, rmarkdown, Rcpp, grDevices, graphics, utils, knitr,
S4Vectors, SummarizedExperiment, RColorBrewer, nnTensor,
methods, scales, msigdbr, schex, tibble, ggplot2, igraph
Suggests: testthat
License: Artistic-2.0
MD5sum: a794200679da0fe705499d3c4633d611
NeedsCompilation: no
Title: Cell type annotation for unannotated single-cell RNA-Seq data
Description: scTGIF connects the cells and the related gene functions
without cell type label.
biocViews: DimensionReduction, QualityControl, SingleCell, Software,
GeneExpression
Author: Koki Tsuyuzaki [aut, cre]
Maintainer: Koki Tsuyuzaki <k.t.the-answer@hotmail.co.jp>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scTGIF
git_branch: devel
git_last_commit: 90a5f4e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scTGIF_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scTGIF_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scTGIF_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scTGIF_1.21.0.tgz
vignettes: vignettes/scTGIF/inst/doc/scTGIF.html
vignetteTitles: scTGIF
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scTGIF/inst/doc/scTGIF.R
suggestsMe: scTensor
dependencyCount: 149
Package: scTHI
Version: 1.19.0
Depends: R (>= 4.0)
Imports: BiocParallel, Rtsne, grDevices, graphics, stats
Suggests: scTHI.data, knitr, rmarkdown, BiocStyle
License: GPL-2
MD5sum: 3957fc1b46aa3af828fbf80836c9fba8
NeedsCompilation: no
Title: Indentification of significantly activated ligand-receptor
interactions across clusters of cells from single-cell RNA
sequencing data
Description: scTHI is an R package to identify active pairs of
ligand-receptors from single cells in order to study,among
others, tumor-host interactions. scTHI contains a set of
signatures to classify cells from the tumor microenvironment.
biocViews: Software,SingleCell
Author: Francesca Pia Caruso [aut], Michele Ceccarelli [aut, cre]
Maintainer: Michele Ceccarelli <m.ceccarelli@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/miccec/scTHI/issues
git_url: https://git.bioconductor.org/packages/scTHI
git_branch: devel
git_last_commit: f1906f2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scTHI_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scTHI_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scTHI_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scTHI_1.19.0.tgz
vignettes: vignettes/scTHI/inst/doc/vignette.html
vignetteTitles: Using scTHI
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scTHI/inst/doc/vignette.R
dependencyCount: 17
Package: scTreeViz
Version: 1.13.0
Depends: R (>= 4.0), methods, epivizr, SummarizedExperiment
Imports: data.table, S4Vectors, digest, Matrix, Rtsne, httr, igraph,
clustree, scran, sys, epivizrData, epivizrServer, ggraph,
scater, Seurat, SingleCellExperiment, ggplot2, stats, utils
Suggests: knitr, BiocStyle, testthat, SC3, scRNAseq, rmarkdown, msd16s,
metagenomeSeq, epivizrStandalone, GenomeInfoDb
License: Artistic-2.0
MD5sum: 4e6ac436529856199dbeffdfd5d2d189
NeedsCompilation: no
Title: R/Bioconductor package to interactively explore and visualize
single cell RNA-seq datasets with hierarhical annotations
Description: scTreeViz provides classes to support interactive data
aggregation and visualization of single cell RNA-seq datasets
with hierarchies for e.g. cell clusters at different
resolutions. The `TreeIndex` class provides methods to manage
hierarchy and split the tree at a given resolution or across
resolutions. The `TreeViz` class extends `SummarizedExperiment`
and can performs quick aggregations on the count matrix defined
by clusters.
biocViews: Visualization, Infrastructure, GUI, SingleCell
Author: Jayaram Kancherla [aut, cre], Hector Corrada Bravo [aut], Kazi
Tasnim Zinat [aut], Stephanie Hicks [aut]
Maintainer: Jayaram Kancherla <jayaram.kancherla@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scTreeViz
git_branch: devel
git_last_commit: 9eebcab
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scTreeViz_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scTreeViz_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scTreeViz_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scTreeViz_1.13.0.tgz
vignettes: vignettes/scTreeViz/inst/doc/ExploreTreeViz.html
vignetteTitles: Explore Data using scTreeViz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scTreeViz/inst/doc/ExploreTreeViz.R
dependencyCount: 255
Package: scuttle
Version: 1.17.0
Depends: SingleCellExperiment
Imports: methods, utils, stats, Matrix, Rcpp, BiocGenerics, S4Vectors,
BiocParallel, GenomicRanges, SummarizedExperiment, S4Arrays,
MatrixGenerics, SparseArray, DelayedArray, beachmat
LinkingTo: Rcpp, beachmat
Suggests: BiocStyle, knitr, scRNAseq, rmarkdown, testthat,
sparseMatrixStats, DelayedMatrixStats, scran
License: GPL-3
MD5sum: f5b04790204d31085cd289d03cb2f93d
NeedsCompilation: yes
Title: Single-Cell RNA-Seq Analysis Utilities
Description: Provides basic utility functions for performing
single-cell analyses, focusing on simple normalization, quality
control and data transformations. Also provides some helper
functions to assist development of other packages.
biocViews: ImmunoOncology, SingleCell, RNASeq, QualityControl,
Preprocessing, Normalization, Transcriptomics, GeneExpression,
Sequencing, Software, DataImport
Author: Aaron Lun [aut, cre], Davis McCarthy [aut]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/scuttle
git_branch: devel
git_last_commit: 5930665
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scuttle_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/scuttle_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/scuttle_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scuttle_1.17.0.tgz
vignettes: vignettes/scuttle/inst/doc/misc.html,
vignettes/scuttle/inst/doc/norm.html,
vignettes/scuttle/inst/doc/qc.html
vignetteTitles: 3. Other functions, 2. Normalization, 1. Quality
control
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scuttle/inst/doc/misc.R,
vignettes/scuttle/inst/doc/norm.R,
vignettes/scuttle/inst/doc/qc.R
dependsOnMe: scater, scran, OSCA.basic, OSCA.intro, OSCA.multisample,
OSCA.workflows, SingleRBook
importsMe: BASiCS, BASiCStan, batchelor, chevreulPlot, chevreulProcess,
DropletUtils, epiregulon, FLAMES, imcRtools, mia, mumosa,
muscat, scDblFinder, simPIC, singleCellTK, splatter, SplineDV,
spoon, velociraptor, spatialLIBD, mixhvg, SpatialDDLS
suggestsMe: Banksy, bluster, DESpace, dreamlet, epiregulon.extra,
escheR, ggsc, iSEEde, iSEEfier, iSEEpathways, mastR, miloR,
raer, ReactomeGSA, SCArray, scDiagnostics, scDotPlot, schex,
SingleCellAlleleExperiment, SingleR, sketchR, smoothclust,
spatialHeatmap, SpotSweeper, SVP, tpSVG, TSCAN, HCAData,
MouseThymusAgeing, scCustomize
linksToMe: DropletUtils, scran
dependencyCount: 50
Package: scviR
Version: 1.7.0
Depends: R (>= 4.3), basilisk, shiny, SingleCellExperiment
Imports: reticulate, BiocFileCache, utils, pheatmap,
SummarizedExperiment, S4Vectors, limma, scater, stats,
MatrixGenerics
Suggests: knitr, testthat, reshape2, ggplot2, rhdf5, BiocStyle
License: Artistic-2.0
MD5sum: 28a66b85bb8f3fa4521e6f4e5779c95a
NeedsCompilation: no
Title: experimental inferface from R to scvi-tools
Description: This package defines interfaces from R to scvi-tools. A
vignette works through the totalVI tutorial for analyzing
CITE-seq data. Another vignette compares outputs of Chapter 12
of the OSCA book with analogous outputs based on totalVI
quantifications. Future work will address other components of
scvi-tools, with a focus on building understanding of
probabilistic methods based on variational autoencoders.
biocViews: Infrastructure, SingleCell, DataImport
Author: Vincent Carey [aut, cre] (ORCID:
<https://orcid.org/0000-0003-4046-0063>)
Maintainer: Vincent Carey <stvjc@channing.harvard.edu>
URL: https://github.com/vjcitn/scviR
VignetteBuilder: knitr
BugReports: https://github.com/vjcitn/scviR/issues
git_url: https://git.bioconductor.org/packages/scviR
git_branch: devel
git_last_commit: 97c87b8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/scviR_1.7.0.tar.gz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/scviR_1.7.0.tgz
vignettes: vignettes/scviR/inst/doc/citeseq_tut.html,
vignettes/scviR/inst/doc/compch12.html,
vignettes/scviR/inst/doc/scviR.html
vignetteTitles: scvi-tools CITE-seq tutorial in R,, using serialized
tutorial components, Comparing totalVI and OSCA book CITE-seq
analyses, scviR: an R package interfacing Bioconductor and
scvi-tools
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/scviR/inst/doc/citeseq_tut.R,
vignettes/scviR/inst/doc/compch12.R,
vignettes/scviR/inst/doc/scviR.R
dependencyCount: 149
Package: SDAMS
Version: 1.27.0
Depends: R(>= 3.5), SummarizedExperiment
Imports: trust, qvalue, methods, stats, utils
Suggests: testthat
License: GPL
MD5sum: 562c478a75b98fe3f327b642e0c5be20
NeedsCompilation: no
Title: Differential Abundant/Expression Analysis for Metabolomics,
Proteomics and single-cell RNA sequencing Data
Description: This Package utilizes a Semi-parametric Differential
Abundance/expression analysis (SDA) method for metabolomics and
proteomics data from mass spectrometry as well as single-cell
RNA sequencing data. SDA is able to robustly handle
non-normally distributed data and provides a clear
quantification of the effect size.
biocViews: ImmunoOncology, DifferentialExpression, Metabolomics,
Proteomics, MassSpectrometry, SingleCell
Author: Yuntong Li <liyuntong0704@gmail.com>, Chi Wang
<chi.wang@uky.edu>, Li Chen <lichenuky@uky.edu>
Maintainer: Yuntong Li <liyuntong0704@gmail.com>
git_url: https://git.bioconductor.org/packages/SDAMS
git_branch: devel
git_last_commit: faef9bc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SDAMS_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SDAMS_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SDAMS_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SDAMS_1.27.0.tgz
vignettes: vignettes/SDAMS/inst/doc/SDAMS.pdf
vignetteTitles: SDAMS Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SDAMS/inst/doc/SDAMS.R
dependencyCount: 69
Package: seahtrue
Version: 1.1.0
Depends: R (>= 4.2.0)
Imports: dplyr (>= 1.1.2), readxl (>= 1.4.1), logger (>= 0.2.2), tidyxl
(>= 1.0.8), purrr (>= 0.3.5), tidyr (>= 1.3.0), lubridate (>=
1.8.0), stringr (>= 1.4.1), tibble (>= 3.1.8), validate (>=
1.1.1), rlang (>= 1.0.0), glue (>= 1.6.2), cli (>= 3.4.1),
janitor (>= 2.2.0), ggplot2 (>= 3.5.0), RColorBrewer (>=
1.1.3), colorspace (>= 2.1.0), forcats (>= 1.0.0), ggridges (>=
0.5.6), readr (>= 2.1.5), scales (>= 1.3.0)
Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle
License: Artistic-2.0
MD5sum: 8d7556c07580ebc40e1523c38ec3d421
NeedsCompilation: no
Title: Seahtrue revives XF data for structured data analysis
Description: Seahtrue organizes oxygen consumption and extracellular
acidification analysis data from experiments performed on an XF
analyzer into structured nested tibbles.This allows for
detailed processing of raw data and advanced data visualization
and statistics. Seahtrue introduces an open and reproducible
way to analyze these XF experiments. It uses file paths to
.xlsx files. These .xlsx files are supplied by the userand are
generated by the user in the Wave software from Agilent from
the assay result files (.asyr). The .xlsx file contains
different sheets of important data for the experiment; 1. Assay
Information - Details about how the experiment was set up. 2.
Rate Data - Information about the OCR and ECAR rates. 3. Raw
Data - The original raw data collected during the experiment.
4. Calibration Data - Data related to calibrating the
instrument. Seahtrue focuses on getting the specific data
needed for analysis. Once this data is extracted, it is
prepared for calculations through preprocessing. To make sure
everything is accurate, both the initial data and the
preprocessed data go through thorough checks.
biocViews: CellBasedAssays, FunctionalPrediction, DataRepresentation,
DataImport, CellBiology, Cheminformatics, Metabolomics,
MicrotitrePlateAssay, Visualization, QualityControl,
BatchEffect, ExperimentalDesign, Preprocessing, GO
Author: Vincent de Boer [cre, aut] (ORCID:
<https://orcid.org/0000-0001-9928-1698>), Gerwin Smits [aut],
Xiang Zhang [aut]
Maintainer: Vincent de Boer <vincent.deboer@wur.nl>
URL: https://vcjdeboer.github.io/seahtrue/
VignetteBuilder: knitr
BugReports: https://vcjdeboer.github.io/seahtrue/issues
git_url: https://git.bioconductor.org/packages/seahtrue
git_branch: devel
git_last_commit: c5c897f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/seahtrue_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/seahtrue_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/seahtrue_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/seahtrue_1.1.0.tgz
vignettes: vignettes/seahtrue/inst/doc/seahtrue.html
vignetteTitles: Introduction to Seahtrue
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/seahtrue/inst/doc/seahtrue.R
dependencyCount: 70
Package: sechm
Version: 1.15.1
Depends: R (>= 4.0), SummarizedExperiment, ComplexHeatmap
Imports: S4Vectors, seriation, circlize, methods, randomcoloR, stats,
grid, grDevices, matrixStats
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-3
MD5sum: 13106fe2c38e6f458e9d48dad377abbb
NeedsCompilation: no
Title: sechm: Complex Heatmaps from a SummarizedExperiment
Description: sechm provides a simple interface between
SummarizedExperiment objects and the ComplexHeatmap package. It
enables plotting annotated heatmaps from SE objects, with easy
access to rowData and colData columns, and implements a number
of features to make the generation of heatmaps easier and more
flexible. These functionalities used to be part of the SEtools
package.
biocViews: GeneExpression, Visualization
Author: Pierre-Luc Germain [cre, aut] (ORCID:
<https://orcid.org/0000-0003-3418-4218>)
Maintainer: Pierre-Luc Germain <pierre-luc.germain@hest.ethz.ch>
VignetteBuilder: knitr
BugReports: https://github.com/plger/sechm
git_url: https://git.bioconductor.org/packages/sechm
git_branch: devel
git_last_commit: df0b049
git_last_commit_date: 2025-02-20
Date/Publication: 2025-02-20
source.ver: src/contrib/sechm_1.15.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sechm_1.15.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sechm_1.15.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sechm_1.15.1.tgz
vignettes: vignettes/sechm/inst/doc/sechm.html
vignetteTitles: sechm
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sechm/inst/doc/sechm.R
dependsOnMe: SEtools
importsMe: broadSeq
dependencyCount: 82
Package: segmenter
Version: 1.13.0
Depends: R (>= 4.1)
Imports: ChIPseeker, GenomicRanges, SummarizedExperiment, IRanges,
S4Vectors, bamsignals, ComplexHeatmap, graphics, stats, utils,
methods, chromhmmData
Suggests: testthat, knitr, rmarkdown,
TxDb.Hsapiens.UCSC.hg18.knownGene, Gviz
License: GPL-3
MD5sum: 8360274193ca52b65361b6f02c558240
NeedsCompilation: no
Title: Perform Chromatin Segmentation Analysis in R by Calling ChromHMM
Description: Chromatin segmentation analysis transforms ChIP-seq data
into signals over the genome. The latter represents the
observed states in a multivariate Markov model to predict the
chromatin's underlying states. ChromHMM, written in Java,
integrates histone modification datasets to learn the chromatin
states de-novo. The goal of this package is to call chromHMM
from within R, capture the output files in an S4 object and
interface to other relevant Bioconductor analysis tools. In
addition, segmenter provides functions to test, select and
visualize the output of the segmentation.
biocViews: Software, HistoneModification
Author: Mahmoud Ahmed [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4377-6541>)
Maintainer: Mahmoud Ahmed <mahmoud.s.fahmy@students.kasralainy.edu.eg>
VignetteBuilder: knitr
BugReports: https://github.com/MahShaaban/segmenter/issues
git_url: https://git.bioconductor.org/packages/segmenter
git_branch: devel
git_last_commit: 2c80157
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/segmenter_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/segmenter_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/segmenter_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/segmenter_1.13.0.tgz
vignettes: vignettes/segmenter/inst/doc/segmenter.html
vignetteTitles: Chromatin Segmentation Analysis Using segmenter
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/segmenter/inst/doc/segmenter.R
dependencyCount: 162
Package: segmentSeq
Version: 2.41.1
Depends: R (>= 3.5.0), methods, baySeq (>= 2.9.0), S4Vectors, parallel,
GenomicRanges, ShortRead, stats
Imports: Rsamtools, IRanges, GenomeInfoDb, graphics, grDevices, utils,
abind
Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown
License: GPL-3
Archs: x64
MD5sum: c10b73b9722f5fd243ad1cf8a95ed55a
NeedsCompilation: no
Title: Methods for identifying small RNA loci from high-throughput
sequencing data
Description: High-throughput sequencing technologies allow the
production of large volumes of short sequences, which can be
aligned to the genome to create a set of matches to the genome.
By looking for regions of the genome which to which there are
high densities of matches, we can infer a segmentation of the
genome into regions of biological significance. The methods in
this package allow the simultaneous segmentation of data from
multiple samples, taking into account replicate data, in order
to create a consensus segmentation. This has obvious
applications in a number of classes of sequencing experiments,
particularly in the discovery of small RNA loci and novel mRNA
transcriptome discovery.
biocViews: MultipleComparison, Sequencing, Alignment,
DifferentialExpression, QualityControl, DataImport
Author: Thomas J. Hardcastle [aut], Samuel Granjeaud [cre] (ORCID:
<https://orcid.org/0000-0001-9245-1535>)
Maintainer: Samuel Granjeaud <samuel.granjeaud@inserm.fr>
URL: https://github.com/samgg/segmentSeq
VignetteBuilder: knitr
BugReports: https://github.com/samgg/segmentSeq/issues
git_url: https://git.bioconductor.org/packages/segmentSeq
git_branch: devel
git_last_commit: ce8078c
git_last_commit_date: 2024-11-04
Date/Publication: 2024-11-05
source.ver: src/contrib/segmentSeq_2.41.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/segmentSeq_2.41.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/segmentSeq_2.41.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/segmentSeq_2.41.1.tgz
vignettes: vignettes/segmentSeq/inst/doc/methylationAnalysis.html,
vignettes/segmentSeq/inst/doc/segmentSeq.html
vignetteTitles: segmentsSeq: Methylation locus identification,
segmentSeq: small RNA locus detection
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/segmentSeq/inst/doc/methylationAnalysis.R,
vignettes/segmentSeq/inst/doc/segmentSeq.R
dependencyCount: 68
Package: selectKSigs
Version: 1.19.0
Depends: R(>= 3.6)
Imports: HiLDA, magrittr, gtools, methods, Rcpp
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, dplyr, tidyr
License: GPL-3
MD5sum: 550c61356561b577f67aeebeb6dc1093
NeedsCompilation: yes
Title: Selecting the number of mutational signatures using a
perplexity-based measure and cross-validation
Description: A package to suggest the number of mutational signatures
in a collection of somatic mutations using calculating the
cross-validated perplexity score.
biocViews: Software, SomaticMutation, Sequencing, StatisticalMethod,
Clustering
Author: Zhi Yang [aut, cre], Yuichi Shiraishi [ctb]
Maintainer: Zhi Yang <zyang895@gmail.com>
URL: https://github.com/USCbiostats/selectKSigs
VignetteBuilder: knitr
BugReports: https://github.com/USCbiostats/HiLDA/selectKSigs
git_url: https://git.bioconductor.org/packages/selectKSigs
git_branch: devel
git_last_commit: 9e6184b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/selectKSigs_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/selectKSigs_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/selectKSigs_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/selectKSigs_1.19.0.tgz
vignettes: vignettes/selectKSigs/inst/doc/selectKSigs.html
vignetteTitles: An introduction to HiLDA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/selectKSigs/inst/doc/selectKSigs.R
dependencyCount: 116
Package: SELEX
Version: 1.39.0
Depends: rJava (>= 0.5-0), Biostrings (>= 2.26.0)
Imports: stats, utils
License: GPL (>=2)
MD5sum: 5f46a821d9c2b15aee5fb216a3fdabc2
NeedsCompilation: no
Title: Functions for analyzing SELEX-seq data
Description: Tools for quantifying DNA binding specificities based on
SELEX-seq data.
biocViews: Software, MotifDiscovery, MotifAnnotation, GeneRegulation,
Transcription
Author: Chaitanya Rastogi, Dahong Liu, Lucas Melo, and Harmen J.
Bussemaker
Maintainer: Harmen J. Bussemaker <hjb2004@columbia.edu>
URL: https://bussemakerlab.org/site/software/
SystemRequirements: Java (>= 1.5)
git_url: https://git.bioconductor.org/packages/SELEX
git_branch: devel
git_last_commit: 6a0fd9f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SELEX_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SELEX_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SELEX_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SELEX_1.39.0.tgz
vignettes: vignettes/SELEX/inst/doc/SELEX.pdf
vignetteTitles: Motif Discovery with SELEX-seq
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SELEX/inst/doc/SELEX.R
dependencyCount: 26
Package: SemDist
Version: 1.41.0
Depends: R (>= 3.1), AnnotationDbi, GO.db, annotate
Suggests: GOSemSim
License: GPL (>= 2)
MD5sum: f6b436acfa2ac70606e7d8a933d4ca1f
NeedsCompilation: no
Title: Information Accretion-based Function Predictor Evaluation
Description: This package implements methods to calculate information
accretion for a given version of the gene ontology and uses
this data to calculate remaining uncertainty, misinformation,
and semantic similarity for given sets of predicted annotations
and true annotations from a protein function predictor.
biocViews: Classification, Annotation, GO, Software
Author: Ian Gonzalez and Wyatt Clark
Maintainer: Ian Gonzalez <gonzalez.isv@gmail.com>
URL: http://github.com/iangonzalez/SemDist
git_url: https://git.bioconductor.org/packages/SemDist
git_branch: devel
git_last_commit: 062d1fa
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SemDist_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SemDist_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SemDist_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SemDist_1.41.0.tgz
vignettes: vignettes/SemDist/inst/doc/introduction.pdf
vignetteTitles: introduction.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SemDist/inst/doc/introduction.R
dependencyCount: 49
Package: semisup
Version: 1.31.0
Depends: R (>= 3.0.0)
Imports: VGAM
Suggests: knitr, testthat, SummarizedExperiment
License: GPL-3
MD5sum: c19663363615957ac9454466f6455c49
NeedsCompilation: no
Title: Semi-Supervised Mixture Model
Description: Implements a parametric semi-supervised mixture model. The
permutation test detects markers with main or interactive
effects, without distinguishing them. Possible applications
include genome-wide association analysis and differential
expression analysis.
biocViews: SNP, GenomicVariation, SomaticMutation, Genetics,
Classification, Clustering, DNASeq, Microarray,
MultipleComparison
Author: Armin Rauschenberger [aut, cre]
Maintainer: Armin Rauschenberger <armin.rauschenberger@uni.lu>
URL: https://github.com/rauschenberger/semisup
VignetteBuilder: knitr
BugReports: https://github.com/rauschenberger/semisup/issues
git_url: https://git.bioconductor.org/packages/semisup
git_branch: devel
git_last_commit: 5bd4eff
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/semisup_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/semisup_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/semisup_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/semisup_1.31.0.tgz
vignettes: vignettes/semisup/inst/doc/semisup.pdf,
vignettes/semisup/inst/doc/article.html,
vignettes/semisup/inst/doc/vignette.html
vignetteTitles: vignette source, article frame, vignette frame
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/semisup/inst/doc/semisup.R
dependencyCount: 5
Package: seq.hotSPOT
Version: 1.7.0
Depends: R (>= 3.5.0)
Imports: R.utils, hash, stats, base, utils
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0)
License: Artistic-2.0
MD5sum: 1247b1d404059a9076ee006810ec34cb
NeedsCompilation: no
Title: Targeted sequencing panel design based on mutation hotspots
Description: seq.hotSPOT provides a resource for designing effective
sequencing panels to help improve mutation capture efficacy for
ultradeep sequencing projects. Using SNV datasets, this package
designs custom panels for any tissue of interest and identify
the genomic regions likely to contain the most mutations.
Establishing efficient targeted sequencing panels can allow
researchers to study mutation burden in tissues at high depth
without the economic burden of whole-exome or whole-genome
sequencing. This tool was developed to make high-depth
sequencing panels to study low-frequency clonal mutations in
clinically normal and cancerous tissues.
biocViews: Software, Technology, Sequencing, DNASeq, WholeGenome
Author: Sydney Grant [aut, cre], Lei Wei [aut], Gyorgy Paragh [aut]
Maintainer: Sydney Grant <sydney.grant@roswellpark.org>
URL: https://github.com/sydney-grant/seq.hotSPOT
VignetteBuilder: knitr
BugReports: https://github.com/sydney-grant/seq.hotSPOT/issues
git_url: https://git.bioconductor.org/packages/seq.hotSPOT
git_branch: devel
git_last_commit: 8b8cba4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/seq.hotSPOT_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/seq.hotSPOT_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/seq.hotSPOT_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/seq.hotSPOT_1.7.0.tgz
vignettes: vignettes/seq.hotSPOT/inst/doc/hotSPOT-vignette.html
vignetteTitles: hotSPOT-vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/seq.hotSPOT/inst/doc/hotSPOT-vignette.R
dependencyCount: 9
Package: seq2pathway
Version: 1.39.0
Depends: R (>= 3.6.2)
Imports: nnet, WGCNA, GSA, biomaRt, GenomicRanges, seq2pathway.data
License: GPL-2
Archs: x64
MD5sum: a938073f69a98f112e9ae0725208e9c6
NeedsCompilation: no
Title: a novel tool for functional gene-set (or termed as pathway)
analysis of next-generation sequencing data
Description: Seq2pathway is a novel tool for functional gene-set (or
termed as pathway) analysis of next-generation sequencing data,
consisting of "seq2gene" and "gene2path" components. The
seq2gene links sequence-level measurements of genomic regions
(including SNPs or point mutation coordinates) to gene-level
scores, and the gene2pathway summarizes gene scores to
pathway-scores for each sample. The seq2gene has the
feasibility to assign both coding and non-exon regions to a
broader range of neighboring genes than only the nearest one,
thus facilitating the study of functional non-coding regions.
The gene2pathway takes into account the quantity of
significance for gene members within a pathway compared those
outside a pathway. The output of seq2pathway is a general
structure of quantitative pathway-level scores, thus allowing
one to functional interpret such datasets as RNA-seq, ChIP-seq,
GWAS, and derived from other next generational sequencing
experiments.
biocViews: Software
Author: Xinan Yang <xyang2@uchicago.edu>; Bin Wang <binw@uchicago.edu>
Maintainer: Arjun Kinstlick <akinstlick@uchicago.edu>
git_url: https://git.bioconductor.org/packages/seq2pathway
git_branch: devel
git_last_commit: f56684f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/seq2pathway_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/seq2pathway_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/seq2pathway_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/seq2pathway_1.39.0.tgz
vignettes: vignettes/seq2pathway/inst/doc/seq2pathwaypackage.pdf
vignetteTitles: An R package for sequence
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/seq2pathway/inst/doc/seq2pathwaypackage.R
dependencyCount: 130
Package: seqArchR
Version: 1.11.0
Depends: R (>= 4.2.0)
Imports: utils, graphics, cvTools (>= 0.3.2), MASS, Matrix, methods,
stats, cluster, matrixStats, fpc, cli, prettyunits, reshape2
(>= 1.4.3), reticulate (>= 1.22), BiocParallel, Biostrings,
grDevices, ggplot2 (>= 3.1.1), ggseqlogo (>= 0.1)
Suggests: cowplot, hopach (>= 2.42.0), BiocStyle, knitr (>= 1.22),
rmarkdown (>= 1.12), testthat (>= 3.0.2), covr, vdiffr (>=
0.3.0)
License: GPL-3 | file LICENSE
MD5sum: 7c1b75cba0ac7e74e9cb7654030b1e27
NeedsCompilation: no
Title: Identify Different Architectures of Sequence Elements
Description: seqArchR enables unsupervised discovery of _de novo_
clusters with characteristic sequence architectures
characterized by position-specific motifs or composition of
stretches of nucleotides, e.g., CG-richness. seqArchR does
_not_ require any specifications w.r.t. the number of clusters,
the length of any individual motifs, or the distance between
motifs if and when they occur in pairs/groups; it directly
detects them from the data. seqArchR uses non-negative matrix
factorization (NMF) as its backbone, and employs a
chunking-based iterative procedure that enables processing of
large sequence collections efficiently. Wrapper functions are
provided for visualizing cluster architectures as sequence
logos.
biocViews: MotifDiscovery, GeneRegulation, MathematicalBiology,
SystemsBiology, Transcriptomics, Genetics, Clustering,
DimensionReduction, FeatureExtraction, DNASeq
Author: Sarvesh Nikumbh [aut, cre, cph] (ORCID:
<https://orcid.org/0000-0003-3163-4447>)
Maintainer: Sarvesh Nikumbh <sarvesh.nikumbh@gmail.com>
URL: https://snikumbh.github.io/seqArchR/,
https://github.com/snikumbh/seqArchR
SystemRequirements: Python (>= 3.5), scikit-learn (>= 0.21.2),
packaging
VignetteBuilder: knitr
BugReports: https://github.com/snikumbh/seqArchR/issues
git_url: https://git.bioconductor.org/packages/seqArchR
git_branch: devel
git_last_commit: 2981ab5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-12-23
source.ver: src/contrib/seqArchR_1.11.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/seqArchR_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/seqArchR_1.11.0.tgz
vignettes: vignettes/seqArchR/inst/doc/seqArchR.html
vignetteTitles: Example usage of _seqArchR_ on simulated DNA sequences
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/seqArchR/inst/doc/seqArchR.R
importsMe: seqArchRplus
dependencyCount: 91
Package: seqArchRplus
Version: 1.7.0
Depends: R (>= 4.2), GenomicRanges, IRanges, S4Vectors
Imports: BiocParallel, Biostrings, BSgenome, ChIPseeker, cli,
clusterProfiler, cowplot, factoextra, GenomeInfoDb, ggplot2,
ggimage, graphics, grDevices, gridExtra, heatmaps, magick,
methods, RColorBrewer, scales, seqArchR, seqPattern, stats,
utils
Suggests: BSgenome.Dmelanogaster.UCSC.dm6, BiocStyle, CAGEr (>= 2.0.2),
covr, knitr (>= 1.22), org.Dm.eg.db, pdftools, rmarkdown (>=
1.12), slickR, TxDb.Dmelanogaster.UCSC.dm6.ensGene, xfun
License: GPL-3
MD5sum: 93d9cc723c9851b54aa0d315bb760e00
NeedsCompilation: no
Title: Downstream analyses of promoter sequence architectures and HTML
report generation
Description: seqArchRplus facilitates downstream analyses of promoter
sequence architectures/clusters identified by seqArchR (or any
other tool/method). With additional available information such
as the TPM values and interquantile widths (IQWs) of the CAGE
tag clusters, seqArchRplus can order the input promoter
clusters by their shape (IQWs), and write the cluster
information as browser/IGV track files. Provided visualizations
are of two kind: per sample/stage and per cluster
visualizations. Those of the first kind include: plot panels
for each sample showing per cluster shape, TPM and other score
distributions, sequence logos, and peak annotations. The second
include per cluster chromosome-wise and strand distributions,
motif occurrence heatmaps and GO term enrichments.
Additionally, seqArchRplus can also generate HTML reports for
easy viewing and comparison of promoter architectures between
samples/stages.
biocViews: Annotation, Visualization, ReportWriting, GO,
MotifAnnotation, Clustering
Author: Sarvesh Nikumbh [aut, cre, cph] (ORCID:
<https://orcid.org/0000-0003-3163-4447>)
Maintainer: Sarvesh Nikumbh <sarvesh.nikumbh@gmail.com>
URL: https://github.com/snikumbh/seqArchRplus
VignetteBuilder: knitr
BugReports: https://github.com/snikumbh/seqArchRplus/issues
git_url: https://git.bioconductor.org/packages/seqArchRplus
git_branch: devel
git_last_commit: 782da3e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-12-23
source.ver: src/contrib/seqArchRplus_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/seqArchRplus_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/seqArchRplus_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/seqArchRplus_1.7.0.tgz
vignettes: vignettes/seqArchRplus/inst/doc/seqArchRplus.html
vignetteTitles: seqArchRplus facilitates downstream analysis of
clusters of promoter sequence architectures
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/seqArchRplus/inst/doc/seqArchRplus.R
dependencyCount: 241
Package: SeqArray
Version: 1.47.6
Depends: R (>= 3.5.0), gdsfmt (>= 1.31.1)
Imports: methods, parallel, digest, IRanges, GenomicRanges,
GenomeInfoDb, Biostrings, S4Vectors
LinkingTo: gdsfmt
Suggests: Biobase, BiocGenerics, BiocParallel, RUnit, Rcpp, SNPRelate,
crayon, knitr, markdown, rmarkdown, Rsamtools,
VariantAnnotation
License: GPL-3
MD5sum: 255fc115d02843feb566f13a55697547
NeedsCompilation: yes
Title: Data management of large-scale whole-genome sequence variant
calls using GDS files
Description: Data management of large-scale whole-genome sequencing
variant calls with thousands of individuals: genotypic data
(e.g., SNVs, indels and structural variation calls) and
annotations in SeqArray GDS files are stored in an
array-oriented and compressed manner, with efficient data
access using the R programming language.
biocViews: Infrastructure, DataRepresentation, Sequencing, Genetics
Author: Xiuwen Zheng [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1390-0708>), Stephanie Gogarten
[aut], David Levine [ctb], Cathy Laurie [ctb]
Maintainer: Xiuwen Zheng <zhengx@u.washington.edu>
URL: https://github.com/zhengxwen/SeqArray
VignetteBuilder: knitr
BugReports: https://github.com/zhengxwen/SeqArray/issues
git_url: https://git.bioconductor.org/packages/SeqArray
git_branch: devel
git_last_commit: aed0b3e
git_last_commit_date: 2025-03-23
Date/Publication: 2025-03-24
source.ver: src/contrib/SeqArray_1.47.6.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SeqArray_1.47.6.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SeqArray_1.47.6.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SeqArray_1.47.6.tgz
vignettes: vignettes/SeqArray/inst/doc/OverviewSlides.html,
vignettes/SeqArray/inst/doc/SeqArray.html,
vignettes/SeqArray/inst/doc/SeqArrayTutorial.html
vignetteTitles: SeqArray Overview, R Integration, SeqArray Data Format
and Access
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SeqArray/inst/doc/SeqArray.R,
vignettes/SeqArray/inst/doc/SeqArrayTutorial.R
dependsOnMe: GBScleanR, SAIGEgds, SeqVarTools
importsMe: GDSArray, GENESIS, ggmanh, VariantExperiment
suggestsMe: DelayedDataFrame, HIBAG, VCFArray, GMMAT, MAGEE
dependencyCount: 29
Package: seqCAT
Version: 1.29.0
Depends: R (>= 3.6), GenomicRanges (>= 1.26.4), VariantAnnotation(>=
1.20.3)
Imports: dplyr (>= 0.5.0), GenomeInfoDb (>= 1.13.4), ggplot2 (>=
2.2.1), grid (>= 3.5.0), IRanges (>= 2.8.2), methods,
rtracklayer, rlang, scales (>= 0.4.1), S4Vectors (>= 0.12.2),
stats, SummarizedExperiment (>= 1.4.0), tidyr (>= 0.6.1), utils
Suggests: knitr, BiocStyle, rmarkdown, testthat, BiocManager
License: MIT + file LICENCE
MD5sum: da150be4ad9db80deba7f975dbb1f4c4
NeedsCompilation: no
Title: High Throughput Sequencing Cell Authentication Toolkit
Description: The seqCAT package uses variant calling data (in the form
of VCF files) from high throughput sequencing technologies to
authenticate and validate the source, function and
characteristics of biological samples used in scientific
endeavours.
biocViews: Coverage, GenomicVariation, Sequencing, VariantAnnotation
Author: Erik Fasterius [aut, cre]
Maintainer: Erik Fasterius <erik.fasterius@outlook.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/seqCAT
git_branch: devel
git_last_commit: ecf25e0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/seqCAT_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/seqCAT_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/seqCAT_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/seqCAT_1.29.0.tgz
vignettes: vignettes/seqCAT/inst/doc/seqCAT.html
vignetteTitles: seqCAT: The High Throughput Sequencing Cell
Authentication Toolkit
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/seqCAT/inst/doc/seqCAT.R
dependencyCount: 105
Package: seqcombo
Version: 1.29.0
Depends: R (>= 3.4.0)
Imports: ggplot2, grid, igraph, utils, yulab.utils
Suggests: emojifont, knitr, rmarkdown, prettydoc, tibble
License: Artistic-2.0
MD5sum: 29bb57fcd4d81e833cff1140b87d6711
NeedsCompilation: no
Title: Visualization Tool for Genetic Reassortment
Description: Provides useful functions for visualizing virus
reassortment events.
biocViews: Alignment, Software, Visualization
Author: Guangchuang Yu [aut, cre]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/GuangchuangYu/seqcombo/issues
git_url: https://git.bioconductor.org/packages/seqcombo
git_branch: devel
git_last_commit: 10d9bfc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/seqcombo_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/seqcombo_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/seqcombo_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/seqcombo_1.29.0.tgz
vignettes: vignettes/seqcombo/inst/doc/seqcombo.html
vignetteTitles: Reassortment
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/seqcombo/inst/doc/seqcombo.R
dependencyCount: 41
Package: SeqGate
Version: 1.17.0
Depends: S4Vectors, SummarizedExperiment, GenomicRanges
Imports: stats, methods, BiocManager
Suggests: testthat (>= 3.0.0), edgeR, BiocStyle, knitr, rmarkdown
License: GPL (>= 2.0)
MD5sum: 04377df3f7f609246fba239f9062e3f4
NeedsCompilation: no
Title: Filtering of Lowly Expressed Features
Description: Filtering of lowly expressed features (e.g. genes) is a
common step before performing statistical analysis, but an
arbitrary threshold is generally chosen. SeqGate implements a
method that rationalize this step by the analysis of the
distibution of counts in replicate samples. The gate is the
threshold above which sequenced features can be considered as
confidently quantified.
biocViews: DifferentialExpression, GeneExpression, Transcriptomics,
Sequencing, RNASeq
Author: Christelle Reynès [aut], Stéphanie Rialle [aut, cre]
Maintainer: Stéphanie Rialle <stephanie.rialle@mgx.cnrs.fr>
VignetteBuilder: knitr
BugReports: https://github.com/srialle/SeqGate/issues
git_url: https://git.bioconductor.org/packages/SeqGate
git_branch: devel
git_last_commit: 9990f13
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/SeqGate_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SeqGate_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SeqGate_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SeqGate_1.17.0.tgz
vignettes: vignettes/SeqGate/inst/doc/Seqgate-html-vignette.html
vignetteTitles: SeqGate: Filter lowly expressed features
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SeqGate/inst/doc/Seqgate-html-vignette.R
dependencyCount: 37
Package: SeqGSEA
Version: 1.47.0
Depends: Biobase, doParallel, DESeq2
Imports: methods, biomaRt
Suggests: GenomicRanges
License: GPL (>= 3)
MD5sum: 7b814061c3a0a89ed58ae58f949240d4
NeedsCompilation: no
Title: Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data: integrating
differential expression and splicing
Description: The package generally provides methods for gene set
enrichment analysis of high-throughput RNA-Seq data by
integrating differential expression and splicing. It uses
negative binomial distribution to model read count data, which
accounts for sequencing biases and biological variation. Based
on permutation tests, statistical significance can also be
achieved regarding each gene's differential expression and
splicing, respectively.
biocViews: Sequencing, RNASeq, GeneSetEnrichment, GeneExpression,
DifferentialExpression, DifferentialSplicing, ImmunoOncology
Author: Xi Wang <Xi.Wang@newcastle.edu.au>
Maintainer: Xi Wang <Xi.Wang@dkfz.de>
git_url: https://git.bioconductor.org/packages/SeqGSEA
git_branch: devel
git_last_commit: eb9808f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SeqGSEA_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SeqGSEA_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SeqGSEA_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SeqGSEA_1.47.0.tgz
vignettes: vignettes/SeqGSEA/inst/doc/SeqGSEA.pdf
vignetteTitles: Gene set enrichment analysis of RNA-Seq data with the
SeqGSEA package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SeqGSEA/inst/doc/SeqGSEA.R
dependencyCount: 109
Package: seqLogo
Version: 1.73.0
Depends: R (>= 4.2), methods, grid
Imports: stats4, grDevices
Suggests: knitr, BiocStyle, rmarkdown, testthat
License: LGPL (>= 2)
Archs: x64
MD5sum: 8e7af1d9bc6c80e36f3e47203be24ee2
NeedsCompilation: no
Title: Sequence logos for DNA sequence alignments
Description: seqLogo takes the position weight matrix of a DNA sequence
motif and plots the corresponding sequence logo as introduced
by Schneider and Stephens (1990).
biocViews: SequenceMatching
Author: Oliver Bembom [aut], Robert Ivanek [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8403-056X>)
Maintainer: Robert Ivanek <robert.ivanek@unibas.ch>
VignetteBuilder: knitr
BugReports: https://github.com/ivanek/seqLogo/issues
git_url: https://git.bioconductor.org/packages/seqLogo
git_branch: devel
git_last_commit: 118d640
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/seqLogo_1.73.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/seqLogo_1.73.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/seqLogo_1.73.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/seqLogo_1.73.0.tgz
vignettes: vignettes/seqLogo/inst/doc/seqLogo.html
vignetteTitles: Sequence logos for DNA sequence alignments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/seqLogo/inst/doc/seqLogo.R
dependsOnMe: rGADEM, generegulation
importsMe: IntEREst, PWMEnrich, RCAS, rGADEM, riboSeqR, scanMiR,
SPLINTER, TENET, TFBSTools, kmeRtone
suggestsMe: BCRANK, DiffLogo, igvR, MAGAR, motifcounter, MotifDb,
universalmotif
dependencyCount: 4
Package: seqPattern
Version: 1.39.0
Depends: methods, R (>= 2.15.0)
Imports: Biostrings, GenomicRanges, IRanges, KernSmooth, plotrix
Suggests: BSgenome.Drerio.UCSC.danRer7, CAGEr, RUnit, BiocGenerics,
BiocStyle
Enhances: parallel
License: GPL-3
MD5sum: 065b4d39407d437f5f5f4640bc897a80
NeedsCompilation: no
Title: Visualising oligonucleotide patterns and motif occurrences
across a set of sorted sequences
Description: Visualising oligonucleotide patterns and sequence motifs
occurrences across a large set of sequences centred at a common
reference point and sorted by a user defined feature.
biocViews: Visualization, SequenceMatching
Author: Vanja Haberle <vanja.haberle@gmail.com>
Maintainer: Vanja Haberle <vanja.haberle@gmail.com>
git_url: https://git.bioconductor.org/packages/seqPattern
git_branch: devel
git_last_commit: f37ec96
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/seqPattern_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/seqPattern_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/seqPattern_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/seqPattern_1.39.0.tgz
vignettes: vignettes/seqPattern/inst/doc/seqPattern.pdf
vignetteTitles: seqPattern
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/seqPattern/inst/doc/seqPattern.R
importsMe: genomation, seqArchRplus
dependencyCount: 28
Package: seqsetvis
Version: 1.27.2
Depends: R (>= 4.3), ggplot2
Imports: cowplot, data.table, eulerr, GenomeInfoDb, GenomicAlignments,
GenomicRanges, ggplotify, grDevices, grid, IRanges, limma,
methods, pbapply, pbmcapply, png, RColorBrewer, Rsamtools,
rtracklayer, S4Vectors, scales, stats, UpSetR
Suggests: BiocFileCache, BiocManager, BiocStyle, ChIPpeakAnno, covr,
knitr, rmarkdown, testthat
License: MIT + file LICENSE
MD5sum: 491b2f20d99f5cfdc7f180c86a525fd5
NeedsCompilation: no
Title: Set Based Visualizations for Next-Gen Sequencing Data
Description: seqsetvis enables the visualization and analysis of sets
of genomic sites in next gen sequencing data. Although
seqsetvis was designed for the comparison of mulitple ChIP-seq
samples, this package is domain-agnostic and allows the
processing of multiple genomic coordinate files (bed-like
files) and signal files (bigwig files pileups from bam file).
seqsetvis has multiple functions for fetching data from regions
into a tidy format for analysis in data.table or tidyverse and
visualization via ggplot2.
biocViews: Software, ChIPSeq, MultipleComparison, Sequencing,
Visualization
Author: Joseph R Boyd [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8969-9676>)
Maintainer: Joseph R Boyd <jrboyd@uvm.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/seqsetvis
git_branch: devel
git_last_commit: ae10577
git_last_commit_date: 2024-12-09
Date/Publication: 2024-12-10
source.ver: src/contrib/seqsetvis_1.27.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/seqsetvis_1.27.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/seqsetvis_1.27.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/seqsetvis_1.27.2.tgz
vignettes: vignettes/seqsetvis/inst/doc/seqsetvis_overview.html
vignetteTitles: Overview and Use Cases
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/seqsetvis/inst/doc/seqsetvis_overview.R
dependencyCount: 105
Package: SeqSQC
Version: 1.29.0
Depends: R (>= 3.4), ExperimentHub (>= 1.3.7), SNPRelate (>= 1.10.2)
Imports: e1071, GenomicRanges, gdsfmt, ggplot2, GGally, IRanges,
methods, plotly, RColorBrewer, reshape2, rmarkdown, S4Vectors,
stats, utils
Suggests: BiocStyle, knitr, testthat
License: GPL-3
MD5sum: 1452849542e040121d65c6518543b47f
NeedsCompilation: no
Title: A bioconductor package for sample quality check with next
generation sequencing data
Description: The SeqSQC is designed to identify problematic samples in
NGS data, including samples with gender mismatch,
contamination, cryptic relatedness, and population outlier.
biocViews: Experiment Data, Homo_sapiens_Data, Sequencing Data,
Project1000genomes, Genome
Author: Qian Liu [aut, cre]
Maintainer: Qian Liu <qliu7@buffalo.edu>
URL: https://github.com/Liubuntu/SeqSQC
VignetteBuilder: knitr
BugReports: https://github.com/Liubuntu/SeqSQC/issues
git_url: https://git.bioconductor.org/packages/SeqSQC
git_branch: devel
git_last_commit: 42fdfb6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SeqSQC_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SeqSQC_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SeqSQC_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SeqSQC_1.29.0.tgz
vignettes: vignettes/SeqSQC/inst/doc/vignette.html
vignetteTitles: Sample Quality Check for Next-Generation Sequencing
Data with SeqSQC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SeqSQC/inst/doc/vignette.R
dependencyCount: 120
Package: seqTools
Version: 1.41.0
Depends: methods,utils,zlibbioc
LinkingTo: zlibbioc
Suggests: RUnit, BiocGenerics
License: Artistic-2.0
MD5sum: c24c4d4c06682e30adb4e7e8e68e50a4
NeedsCompilation: yes
Title: Analysis of nucleotide, sequence and quality content on fastq
files
Description: Analyze read length, phred scores and alphabet frequency
and DNA k-mers on uncompressed and compressed fastq files.
biocViews: QualityControl,Sequencing
Author: Wolfgang Kaisers
Maintainer: Wolfgang Kaisers <kaisers@med.uni-duesseldorf.de>
git_url: https://git.bioconductor.org/packages/seqTools
git_branch: devel
git_last_commit: a203e78
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/seqTools_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/seqTools_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/seqTools_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/seqTools_1.41.0.tgz
vignettes: vignettes/seqTools/inst/doc/seqTools_qual_report.pdf,
vignettes/seqTools/inst/doc/seqTools.pdf
vignetteTitles: seqTools_qual_report, Introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/seqTools/inst/doc/seqTools_qual_report.R,
vignettes/seqTools/inst/doc/seqTools.R
importsMe: qckitfastq
dependencyCount: 3
Package: SeqVarTools
Version: 1.45.0
Depends: SeqArray
Imports: grDevices, graphics, stats, methods, Biobase, BiocGenerics,
gdsfmt, GenomicRanges, IRanges, S4Vectors, GWASExactHW,
logistf, Matrix, data.table,
Suggests: BiocStyle, RUnit, stringr
License: GPL-3
MD5sum: b9c7c7877ce8dd569f3955d70eb4327f
NeedsCompilation: no
Title: Tools for variant data
Description: An interface to the fast-access storage format for VCF
data provided in SeqArray, with tools for common operations and
analysis.
biocViews: SNP, GeneticVariability, Sequencing, Genetics
Author: Stephanie M. Gogarten, Xiuwen Zheng, Adrienne Stilp
Maintainer: Stephanie M. Gogarten <sdmorris@uw.edu>
URL: https://github.com/smgogarten/SeqVarTools
git_url: https://git.bioconductor.org/packages/SeqVarTools
git_branch: devel
git_last_commit: 6162bf6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SeqVarTools_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SeqVarTools_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SeqVarTools_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SeqVarTools_1.45.0.tgz
vignettes: vignettes/SeqVarTools/inst/doc/Iterators.pdf,
vignettes/SeqVarTools/inst/doc/SeqVarTools.pdf
vignetteTitles: Iterators in SeqVarTools, Introduction to SeqVarTools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SeqVarTools/inst/doc/Iterators.R,
vignettes/SeqVarTools/inst/doc/SeqVarTools.R
importsMe: GENESIS
suggestsMe: GMMAT, MAGEE
dependencyCount: 99
Package: sesame
Version: 1.25.3
Depends: R (>= 4.5.0), sesameData
Imports: graphics, BiocParallel, utils, methods, stringr, readr,
tibble, MASS, wheatmap (>= 0.2.0), GenomicRanges, IRanges,
grid, preprocessCore, S4Vectors, ggplot2, BiocFileCache,
GenomeInfoDb, stats, SummarizedExperiment, dplyr, reshape2
Suggests: scales, BiocManager, knitr, DNAcopy, e1071, randomForest,
RPMM, rmarkdown, testthat, tidyr, BiocStyle, ggrepel,
grDevices, KernSmooth, pals
License: MIT + file LICENSE
MD5sum: 552f600c7e30598aeebf86d5bcebc5a8
NeedsCompilation: no
Title: SEnsible Step-wise Analysis of DNA MEthylation BeadChips
Description: Tools For analyzing Illumina Infinium DNA methylation
arrays. SeSAMe provides utilities to support analyses of
multiple generations of Infinium DNA methylation BeadChips,
including preprocessing, quality control, visualization and
inference. SeSAMe features accurate detection calling,
intelligent inference of ethnicity, sex and advanced quality
control routines.
biocViews: DNAMethylation, MethylationArray, Preprocessing,
QualityControl
Author: Wanding Zhou [aut, cre] (ORCID:
<https://orcid.org/0000-0001-9126-1932>), Wubin Ding [ctb],
David Goldberg [ctb], Ethan Moyer [ctb], Bret Barnes [ctb],
Timothy Triche [ctb], Hui Shen [aut]
Maintainer: Wanding Zhou <zhouwanding@gmail.com>
URL: https://github.com/zwdzwd/sesame
VignetteBuilder: knitr
BugReports: https://github.com/zwdzwd/sesame/issues
git_url: https://git.bioconductor.org/packages/sesame
git_branch: devel
git_last_commit: 715da26
git_last_commit_date: 2025-01-04
Date/Publication: 2025-01-08
source.ver: src/contrib/sesame_1.25.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sesame_1.25.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sesame_1.25.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sesame_1.25.3.tgz
vignettes: vignettes/sesame/inst/doc/inferences.html,
vignettes/sesame/inst/doc/modeling.html,
vignettes/sesame/inst/doc/nonhuman.html,
vignettes/sesame/inst/doc/QC.html,
vignettes/sesame/inst/doc/sesame.html
vignetteTitles: "4. Data Inference", 3. Modeling, 2. Non-human Array,
1. Quality Control, "0. Basic Usage"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/sesame/inst/doc/inferences.R,
vignettes/sesame/inst/doc/modeling.R,
vignettes/sesame/inst/doc/nonhuman.R,
vignettes/sesame/inst/doc/QC.R,
vignettes/sesame/inst/doc/sesame.R
importsMe: MethReg, TENET, CytoMethIC
suggestsMe: knowYourCG, RnBeads, TCGAbiolinks, sesameData
dependencyCount: 113
Package: SEtools
Version: 1.21.0
Depends: R (>= 4.0), SummarizedExperiment, sechm
Imports: BiocParallel, Matrix, DESeq2, S4Vectors, data.table, edgeR,
openxlsx, pheatmap, stats, circlize, methods, sva
Suggests: BiocStyle, knitr, rmarkdown, ggplot2
License: GPL
MD5sum: ab1af374a7e8f5a154a1006c244cb769
NeedsCompilation: no
Title: SEtools: tools for working with SummarizedExperiment
Description: This includes a set of convenience functions for working
with the SummarizedExperiment class. Note that plotting
functions historically in this package have been moved to the
sechm package (see vignette for details).
biocViews: GeneExpression
Author: Pierre-Luc Germain [cre, aut] (ORCID:
<https://orcid.org/0000-0003-3418-4218>)
Maintainer: Pierre-Luc Germain <pierre-luc.germain@hest.ethz.ch>
VignetteBuilder: knitr
BugReports: https://github.com/plger/SEtools
git_url: https://git.bioconductor.org/packages/SEtools
git_branch: devel
git_last_commit: 17a9530
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SEtools_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SEtools_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SEtools_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SEtools_1.21.0.tgz
vignettes: vignettes/SEtools/inst/doc/SEtools.html
vignetteTitles: SEtools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SEtools/inst/doc/SEtools.R
dependencyCount: 128
Package: sevenbridges
Version: 1.37.0
Depends: methods, utils, stats
Imports: httr, jsonlite, yaml, objectProperties, stringr, S4Vectors,
docopt, curl, uuid, data.table
Suggests: knitr, rmarkdown, testthat, readr
License: Apache License 2.0 | file LICENSE
MD5sum: 429379eb4b52517590bd4f634231172a
NeedsCompilation: no
Title: Seven Bridges Platform API Client and Common Workflow Language
Tool Builder in R
Description: R client and utilities for Seven Bridges platform API,
from Cancer Genomics Cloud to other Seven Bridges supported
platforms.
biocViews: Software, DataImport, ThirdPartyClient
Author: Phil Webster [aut, cre], Soner Koc [aut] (ORCID:
<https://orcid.org/0000-0002-0772-6600>), Nan Xiao [aut],
Tengfei Yin [aut], Dusan Randjelovic [ctb], Emile Young [ctb],
Velsera [cph, fnd]
Maintainer: Phil Webster <phil.webster@velsera.com>
URL: https://www.sevenbridges.com,
https://sbg.github.io/sevenbridges-r/,
https://github.com/sbg/sevenbridges-r
VignetteBuilder: knitr
BugReports: https://github.com/sbg/sevenbridges-r/issues
git_url: https://git.bioconductor.org/packages/sevenbridges
git_branch: devel
git_last_commit: 265369e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/sevenbridges_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sevenbridges_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sevenbridges_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sevenbridges_1.37.0.tgz
vignettes: vignettes/sevenbridges/inst/doc/api.html,
vignettes/sevenbridges/inst/doc/apps.html,
vignettes/sevenbridges/inst/doc/bioc-workflow.html,
vignettes/sevenbridges/inst/doc/cgc-datasets.html,
vignettes/sevenbridges/inst/doc/docker.html,
vignettes/sevenbridges/inst/doc/rstudio.html
vignetteTitles: Complete Guide for Seven Bridges API R Client, Describe
and Execute CWL Tools/Workflows in R, Master Tutorial: Use R
for Cancer Genomics Cloud, Find Data on CGC via Data Browser
and Datasets API, Creating Your Docker Container and Command
Line Interface (with docopt), IDE Container: RStudio Server,,
Shiny Server,, and More
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/sevenbridges/inst/doc/api.R,
vignettes/sevenbridges/inst/doc/apps.R,
vignettes/sevenbridges/inst/doc/bioc-workflow.R,
vignettes/sevenbridges/inst/doc/cgc-datasets.R,
vignettes/sevenbridges/inst/doc/docker.R,
vignettes/sevenbridges/inst/doc/rstudio.R
dependencyCount: 31
Package: sevenC
Version: 1.27.0
Depends: R (>= 3.5), InteractionSet (>= 1.2.0)
Imports: rtracklayer (>= 1.34.1), BiocGenerics (>= 0.22.0),
GenomeInfoDb (>= 1.12.2), GenomicRanges (>= 1.28.5), IRanges
(>= 2.10.3), S4Vectors (>= 0.14.4), readr (>= 1.1.0), purrr (>=
0.2.2), data.table (>= 1.10.4), boot (>= 1.3-20), methods (>=
3.4.1)
Suggests: testthat, BiocStyle, knitr, rmarkdown, GenomicInteractions,
covr
License: GPL-3
Archs: x64
MD5sum: f12d0ee46257e854bcee6b2f72a0f1a5
NeedsCompilation: no
Title: Computational Chromosome Conformation Capture by Correlation of
ChIP-seq at CTCF motifs
Description: Chromatin looping is an essential feature of eukaryotic
genomes and can bring regulatory sequences, such as enhancers
or transcription factor binding sites, in the close physical
proximity of regulated target genes. Here, we provide sevenC,
an R package that uses protein binding signals from ChIP-seq
and sequence motif information to predict chromatin looping
events. Cross-linking of proteins that bind close to loop
anchors result in ChIP-seq signals at both anchor loci. These
signals are used at CTCF motif pairs together with their
distance and orientation to each other to predict whether they
interact or not. The resulting chromatin loops might be used to
associate enhancers or transcription factor binding sites
(e.g., ChIP-seq peaks) to regulated target genes.
biocViews: DNA3DStructure, ChIPchip, Coverage, DataImport, Epigenetics,
FunctionalGenomics, Classification, Regression, ChIPSeq, HiC,
Annotation
Author: Jonas Ibn-Salem [aut, cre]
Maintainer: Jonas Ibn-Salem <jonas.ibn-salem@tron-mainz.de>
URL: https://github.com/ibn-salem/sevenC
VignetteBuilder: knitr
BugReports: https://github.com/ibn-salem/sevenC/issues
git_url: https://git.bioconductor.org/packages/sevenC
git_branch: devel
git_last_commit: d2926f6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/sevenC_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sevenC_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sevenC_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sevenC_1.27.0.tgz
vignettes: vignettes/sevenC/inst/doc/sevenC.html
vignetteTitles: Introduction to sevenC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sevenC/inst/doc/sevenC.R
dependencyCount: 85
Package: SGCP
Version: 1.7.0
Depends: R (>= 4.2.0)
Imports: ggplot2, expm, caret, plyr, dplyr, GO.db, annotate,
SummarizedExperiment, genefilter, GOstats, RColorBrewer,
xtable, Rgraphviz, reshape2, openxlsx, ggridges, DescTools,
org.Hs.eg.db, methods, grDevices, stats, RSpectra, graph
Suggests: knitr, rmarkdown, BiocManager, devtools, BiocStyle
License: GPL-3
MD5sum: 3d5827493aa07ea1ebb580e3f210b77d
NeedsCompilation: no
Title: SGCP: A semi-supervised pipeline for gene clustering using
self-training approach in gene co-expression networks
Description: SGC is a semi-supervised pipeline for gene clustering in
gene co-expression networks. SGC consists of multiple novel
steps that enable the computation of highly enriched modules in
an unsupervised manner. But unlike all existing frameworks, it
further incorporates a novel step that leverages Gene Ontology
information in a semi-supervised clustering method that further
improves the quality of the computed modules.
biocViews: GeneExpression, GeneSetEnrichment, NetworkEnrichment,
SystemsBiology, Classification, Clustering, DimensionReduction,
GraphAndNetwork, NeuralNetwork, Network, mRNAMicroarray,
RNASeq, Visualization
Author: Niloofar AghaieAbiane [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1096-7592>), Ioannis Koutis [aut]
Maintainer: Niloofar AghaieAbiane <niloofar.abiane@gmail.com>
URL: https://github.com/na396/SGCP
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SGCP
git_branch: devel
git_last_commit: ba0ddcc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SGCP_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SGCP_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SGCP_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SGCP_1.7.0.tgz
vignettes: vignettes/SGCP/inst/doc/SGCP.html
vignetteTitles: SGCP package manual
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SGCP/inst/doc/SGCP.R
dependencyCount: 166
Package: SGSeq
Version: 1.41.0
Depends: R (>= 4.0), IRanges (>= 2.13.15), GenomicRanges (>= 1.31.10),
Rsamtools (>= 1.31.2), SummarizedExperiment, methods
Imports: AnnotationDbi, BiocGenerics (>= 0.31.5), Biostrings (>=
2.47.6), GenomicAlignments (>= 1.15.7), GenomicFeatures (>=
1.31.5), GenomeInfoDb, RUnit, S4Vectors (>= 0.23.19),
grDevices, graphics, igraph, parallel, rtracklayer (>= 1.39.7),
stats
Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19,
TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, rmarkdown
License: Artistic-2.0
MD5sum: 2ca6dac8c6a3f2daf36e7e520161aac7
NeedsCompilation: no
Title: Splice event prediction and quantification from RNA-seq data
Description: SGSeq is a software package for analyzing splice events
from RNA-seq data. Input data are RNA-seq reads mapped to a
reference genome in BAM format. Genes are represented as a
splice graph, which can be obtained from existing annotation or
predicted from the mapped sequence reads. Splice events are
identified from the graph and are quantified locally using
structurally compatible reads at the start or end of each
splice variant. The software includes functions for splice
event prediction, quantification, visualization and
interpretation.
biocViews: AlternativeSplicing, ImmunoOncology, RNASeq, Transcription
Author: Leonard Goldstein [cre, aut]
Maintainer: Leonard Goldstein <ldgoldstein@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SGSeq
git_branch: devel
git_last_commit: 193e8a3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SGSeq_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SGSeq_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SGSeq_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SGSeq_1.41.0.tgz
vignettes: vignettes/SGSeq/inst/doc/SGSeq.html
vignetteTitles: SGSeq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SGSeq/inst/doc/SGSeq.R
dependsOnMe: EventPointer
importsMe: Rhisat2
suggestsMe: FRASER
dependencyCount: 80
Package: SharedObject
Version: 1.21.0
Depends: R (>= 3.6.0)
Imports: Rcpp, methods, stats, BiocGenerics
LinkingTo: BH, Rcpp
Suggests: testthat, parallel, knitr, rmarkdown, BiocStyle
License: GPL-3
MD5sum: 4ba71128da2350a82321ba87216e5564
NeedsCompilation: yes
Title: Sharing R objects across multiple R processes without memory
duplication
Description: This package is developed for facilitating parallel
computing in R. It is capable to create an R object in the
shared memory space and share the data across multiple R
processes. It avoids the overhead of memory dulplication and
data transfer, which make sharing big data object across many
clusters possible.
biocViews: Infrastructure
Author: Jiefei Wang [aut, cre], Martin Morgan [aut]
Maintainer: Jiefei Wang <szwjf08@gmail.com>
SystemRequirements: GNU make, C++11
VignetteBuilder: knitr
BugReports: https://github.com/Jiefei-Wang/SharedObject/issues
git_url: https://git.bioconductor.org/packages/SharedObject
git_branch: devel
git_last_commit: b1ad203
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SharedObject_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SharedObject_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SharedObject_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SharedObject_1.21.0.tgz
vignettes:
vignettes/SharedObject/inst/doc/quick_start_guide_Chinese.html,
vignettes/SharedObject/inst/doc/quick_start_guide.html
vignetteTitles: quickStartChinese, quickStart
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SharedObject/inst/doc/quick_start_guide_Chinese.R,
vignettes/SharedObject/inst/doc/quick_start_guide.R
importsMe: NewWave
suggestsMe: ClustAssess
dependencyCount: 8
Package: shiny.gosling
Version: 1.3.0
Imports: htmltools, jsonlite, rlang, shiny, shiny.react, fs, digest,
rjson
Suggests: config, covr, knitr, lintr, mockery (>= 0.4.3), rcmdcheck,
rmarkdown, sessioninfo, spelling, testthat (>= 3.0.0),
GenomicRanges, VariantAnnotation, StructuralVariantAnnotation,
biovizBase, ggbio
License: LGPL-3
MD5sum: dc5dce6416549e37921a4d52f9385377
NeedsCompilation: no
Title: A Grammar-based Toolkit for Scalable and Interactive Genomics
Data Visualization for R and Shiny
Description: A Grammar-based Toolkit for Scalable and Interactive
Genomics Data Visualization. http://gosling-lang.org/. This R
package is based on gosling.js. It uses R functions to create
gosling plots that could be embedded onto R Shiny apps.
biocViews: ShinyApps, Genetics, Visualization
Author: Appsilon [aut, cre], Anirban Shaw [aut] (ORCID:
<https://orcid.org/0000-0003-4021-513X>), Federico Rivadeneira
[aut] (ORCID: <https://orcid.org/0000-0001-7818-1225>), Vedha
Viyash [aut]
Maintainer: Appsilon <opensource@appsilon.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/shiny.gosling
git_branch: devel
git_last_commit: 606472a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/shiny.gosling_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/shiny.gosling_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/shiny.gosling_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/shiny.gosling_1.3.0.tgz
vignettes: vignettes/shiny.gosling/inst/doc/GRanges.html,
vignettes/shiny.gosling/inst/doc/intro.html,
vignettes/shiny.gosling/inst/doc/lineChart.html,
vignettes/shiny.gosling/inst/doc/textAnnotation.html,
vignettes/shiny.gosling/inst/doc/VCF.html
vignetteTitles: 2. Using a GRanges object in shiny.gosling, 1.
Introduction to shiny.gosling, 4. Creating an Interactive Line
Chart with shiny.gosling, 5. Creating a Multi-Scale Sequence
Track, 3. Creating a Circos Plot with VCF Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/shiny.gosling/inst/doc/GRanges.R,
vignettes/shiny.gosling/inst/doc/intro.R,
vignettes/shiny.gosling/inst/doc/lineChart.R,
vignettes/shiny.gosling/inst/doc/textAnnotation.R,
vignettes/shiny.gosling/inst/doc/VCF.R
importsMe: gINTomics
suggestsMe: AlphaMissenseR
dependencyCount: 42
Package: shinyepico
Version: 1.15.0
Depends: R (>= 4.3.0)
Imports: DT (>= 0.15.0), data.table (>= 1.13.0), doParallel (>= 1.0.0),
dplyr (>= 1.0.9), foreach (>= 1.5.0), GenomicRanges (>=
1.38.0), ggplot2 (>= 3.3.0), gplots (>= 3.0.0), heatmaply (>=
1.1.0), limma (>= 3.42.0), minfi (>= 1.32.0), plotly (>=
4.9.2), reshape2 (>= 1.4.0), rlang (>= 1.0.2), rmarkdown (>=
2.3.0), rtracklayer (>= 1.46.0), shiny (>= 1.5.0), shinyWidgets
(>= 0.5.0), shinycssloaders (>= 0.3.0), shinyjs (>= 1.1.0),
shinythemes (>= 1.1.0), statmod (>= 1.4.0), tidyr (>= 1.2.0),
zip (>= 2.1.0)
Suggests: knitr (>= 1.30.0), mCSEA (>= 1.10.0),
IlluminaHumanMethylation450kanno.ilmn12.hg19,
IlluminaHumanMethylation450kmanifest,
IlluminaHumanMethylationEPICanno.ilm10b4.hg19,
IlluminaHumanMethylationEPICmanifest, testthat, minfiData,
BiocStyle
License: AGPL-3 + file LICENSE
MD5sum: c1674a9ccf730dcfc90f57b82aadb430
NeedsCompilation: no
Title: ShinyÉPICo
Description: ShinyÉPICo is a graphical pipeline to analyze Illumina DNA
methylation arrays (450k or EPIC). It allows to calculate
differentially methylated positions and differentially
methylated regions in a user-friendly interface. Moreover, it
includes several options to export the results and obtain files
to perform downstream analysis.
biocViews:
DifferentialMethylation,DNAMethylation,Microarray,Preprocessing,QualityControl
Author: Octavio Morante-Palacios [cre, aut]
Maintainer: Octavio Morante-Palacios <octaviompa@gmail.com>
URL: https://github.com/omorante/shiny_epico
VignetteBuilder: knitr
BugReports: https://github.com/omorante/shiny_epico/issues
git_url: https://git.bioconductor.org/packages/shinyepico
git_branch: devel
git_last_commit: 072d986
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/shinyepico_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/shinyepico_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/shinyepico_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/shinyepico_1.15.0.tgz
vignettes: vignettes/shinyepico/inst/doc/shiny_epico.html
vignetteTitles: shinyepico
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/shinyepico/inst/doc/shiny_epico.R
dependencyCount: 210
Package: shinyMethyl
Version: 1.43.0
Imports: Biobase, BiocGenerics, graphics, grDevices, htmltools,
MatrixGenerics, methods, minfi, RColorBrewer, shiny, stats,
utils
Suggests: shinyMethylData, minfiData, BiocStyle, knitr, testthat
License: Artistic-2.0
MD5sum: 0d184057a18ba5a353abf33a73a564c0
NeedsCompilation: no
Title: Interactive visualization for Illumina methylation arrays
Description: Interactive tool for visualizing Illumina methylation
array data. Both the 450k and EPIC array are supported.
biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing,
QualityControl, MethylationArray
Author: Jean-Philippe Fortin [cre, aut], Kasper Daniel Hansen [aut]
Maintainer: Jean-Philippe Fortin <fortin946@gmail.com>
URL: https://github.com/Jfortin1/shinyMethyl
VignetteBuilder: knitr
BugReports: https://github.com/Jfortin1/shinyMethyl
git_url: https://git.bioconductor.org/packages/shinyMethyl
git_branch: devel
git_last_commit: 9722e53
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/shinyMethyl_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/shinyMethyl_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/shinyMethyl_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/shinyMethyl_1.43.0.tgz
vignettes: vignettes/shinyMethyl/inst/doc/shinyMethyl.html
vignetteTitles: shinyMethyl: interactive visualization of Illumina 450K
methylation arrays
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/shinyMethyl/inst/doc/shinyMethyl.R
dependencyCount: 158
Package: ShortRead
Version: 1.65.0
Depends: BiocGenerics (>= 0.23.3), BiocParallel, Biostrings (>=
2.47.6), Rsamtools (>= 1.31.2), GenomicAlignments (>= 1.15.6)
Imports: Biobase, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12),
GenomeInfoDb (>= 1.15.2), GenomicRanges (>= 1.31.8), pwalign,
hwriter, methods, lattice, latticeExtra,
LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rhtslib
Suggests: BiocStyle, RUnit, biomaRt, GenomicFeatures, yeastNagalakshmi,
knitr
License: Artistic-2.0
MD5sum: f9f54bc4872f13981aeb6aac8fceb014
NeedsCompilation: yes
Title: FASTQ input and manipulation
Description: This package implements sampling, iteration, and input of
FASTQ files. The package includes functions for filtering and
trimming reads, and for generating a quality assessment report.
Data are represented as DNAStringSet-derived objects, and
easily manipulated for a diversity of purposes. The package
also contains legacy support for early single-end, ungapped
alignment formats.
biocViews: DataImport, Sequencing, QualityControl
Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut],
Michael Lawrence [ctb], Simon Anders [ctb], Rohit Satyam [ctb]
(Converted Overview.Rnw vignette from Sweave to RMarkdown /
HTML.), J Wokaty [ctb]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://bioconductor.org/packages/ShortRead,
https://github.com/Bioconductor/ShortRead,
https://support.bioconductor.org/tag/ShortRead
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/ShortRead/issues
git_url: https://git.bioconductor.org/packages/ShortRead
git_branch: devel
git_last_commit: ad4e1df
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ShortRead_1.65.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ShortRead_1.65.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ShortRead_1.65.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ShortRead_1.65.0.tgz
vignettes: vignettes/ShortRead/inst/doc/Overview.html
vignetteTitles: An introduction to ShortRead
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ShortRead/inst/doc/Overview.R
dependsOnMe: chipseq, EDASeq, esATAC, girafe, OTUbase, Rqc, segmentSeq,
systemPipeR, EatonEtAlChIPseq, NestLink, sequencing
importsMe: amplican, basecallQC, BEAT, CellBarcode, chipseq, ChIPseqR,
ChIPsim, CircSeqAlignTk, dada2, easyRNASeq, FastqCleaner,
GOTHiC, icetea, IONiseR, nucleR, QuasR, R453Plus1Toolbox,
RSVSim, scruff, UMI4Cats, DBTC, genBaRcode, rsahmi
suggestsMe: BiocParallel, CSAR, FLAMES, GenomicAlignments, PING,
Repitools, Rsamtools, S4Vectors, HiCDataLymphoblast,
systemPipeRdata, yeastRNASeq
dependencyCount: 62
Package: SIAMCAT
Version: 2.11.0
Depends: R (>= 4.2.0), mlr3, phyloseq
Imports: beanplot, glmnet, graphics, grDevices, grid, gridBase,
gridExtra, LiblineaR, matrixStats, methods, pROC, PRROC,
RColorBrewer, scales, stats, stringr, utils, infotheo,
progress, corrplot, lmerTest, mlr3learners, mlr3tuning,
paradox, lgr
Suggests: BiocStyle, testthat, knitr, rmarkdown, tidyverse, ggpubr
License: GPL-3
Archs: x64
MD5sum: a08285edf714abca885da70d325248a7
NeedsCompilation: no
Title: Statistical Inference of Associations between Microbial
Communities And host phenoTypes
Description: Pipeline for Statistical Inference of Associations between
Microbial Communities And host phenoTypes (SIAMCAT). A primary
goal of analyzing microbiome data is to determine changes in
community composition that are associated with environmental
factors. In particular, linking human microbiome composition to
host phenotypes such as diseases has become an area of intense
research. For this, robust statistical modeling and biomarker
extraction toolkits are crucially needed. SIAMCAT provides a
full pipeline supporting data preprocessing, statistical
association testing, statistical modeling (LASSO logistic
regression) including tools for evaluation and interpretation
of these models (such as cross validation, parameter selection,
ROC analysis and diagnostic model plots).
biocViews: ImmunoOncology, Metagenomics, Classification, Microbiome,
Sequencing, Preprocessing, Clustering, FeatureExtraction,
GeneticVariability, MultipleComparison,Regression
Author: Konrad Zych [aut] (ORCID:
<https://orcid.org/0000-0001-7426-0516>), Jakob Wirbel [aut,
cre] (ORCID: <https://orcid.org/0000-0002-4073-3562>), Georg
Zeller [aut] (ORCID: <https://orcid.org/0000-0003-1429-7485>),
Morgan Essex [ctb], Nicolai Karcher [ctb], Kersten Breuer [ctb]
Maintainer: Jakob Wirbel <jakob.wirbel@embl.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SIAMCAT
git_branch: devel
git_last_commit: 56df787
git_last_commit_date: 2024-10-29
Date/Publication: 2025-01-23
source.ver: src/contrib/SIAMCAT_2.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SIAMCAT_2.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SIAMCAT_2.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SIAMCAT_2.11.0.tgz
vignettes: vignettes/SIAMCAT/inst/doc/SIAMCAT_confounder.html,
vignettes/SIAMCAT/inst/doc/SIAMCAT_holdout.html,
vignettes/SIAMCAT/inst/doc/SIAMCAT_meta.html,
vignettes/SIAMCAT/inst/doc/SIAMCAT_ml_pitfalls.html,
vignettes/SIAMCAT/inst/doc/SIAMCAT_read-in.html,
vignettes/SIAMCAT/inst/doc/SIAMCAT_vignette.html
vignetteTitles: SIAMCAT confounder example, SIAMCAT holdout testing,
SIAMCAT meta-analysis, SIAMCAT ML pitfalls, SIAMCAT input,
SIAMCAT basic vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SIAMCAT/inst/doc/SIAMCAT_confounder.R,
vignettes/SIAMCAT/inst/doc/SIAMCAT_holdout.R,
vignettes/SIAMCAT/inst/doc/SIAMCAT_meta.R,
vignettes/SIAMCAT/inst/doc/SIAMCAT_ml_pitfalls.R,
vignettes/SIAMCAT/inst/doc/SIAMCAT_read-in.R,
vignettes/SIAMCAT/inst/doc/SIAMCAT_vignette.R
dependencyCount: 126
Package: SICtools
Version: 1.37.0
Depends: R (>= 3.0.0), methods, Rsamtools (>= 1.18.1), doParallel (>=
1.0.8), Biostrings (>= 2.32.1), stringr (>= 0.6.2), matrixStats
(>= 0.10.0), plyr (>= 1.8.3), GenomicRanges (>= 1.22.4),
IRanges (>= 2.4.8)
Suggests: knitr, RUnit, BiocGenerics
License: GPL (>=2)
MD5sum: e1fa16720baaa4a664c2da761243b20d
NeedsCompilation: yes
Title: Find SNV/Indel differences between two bam files with near
relationship
Description: This package is to find SNV/Indel differences between two
bam files with near relationship in a way of pairwise
comparison thourgh each base position across the genome region
of interest. The difference is inferred by fisher test and
euclidean distance, the input of which is the base count
(A,T,G,C) in a given position and read counts for indels that
span no less than 2bp on both sides of indel region.
biocViews: Alignment, Sequencing, Coverage, SequenceMatching,
QualityControl, DataImport, Software, SNP, VariantDetection
Author: Xiaobin Xing, Wu Wei
Maintainer: Xiaobin Xing <xiaobinxing0316@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SICtools
git_branch: devel
git_last_commit: e95cf5c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SICtools_1.37.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SICtools_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SICtools_1.37.0.tgz
vignettes: vignettes/SICtools/inst/doc/SICtools.pdf
vignetteTitles: Using SICtools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SICtools/inst/doc/SICtools.R
dependencyCount: 53
Package: SigCheck
Version: 2.39.1
Depends: R (>= 4.0.0), MLInterfaces, Biobase, e1071, BiocParallel,
survival
Imports: graphics, stats, utils, methods
Suggests: BiocStyle, breastCancerNKI, qusage
License: Artistic-2.0
MD5sum: 91db5adc3f4d3eb2c1c80da3af68b327
NeedsCompilation: no
Title: Check a gene signature's prognostic performance against random
signatures, known signatures, and permuted data/metadata
Description: While gene signatures are frequently used to predict
phenotypes (e.g. predict prognosis of cancer patients), it it
not always clear how optimal or meaningful they are (cf David
Venet, Jacques E. Dumont, and Vincent Detours' paper "Most
Random Gene Expression Signatures Are Significantly Associated
with Breast Cancer Outcome"). Based on suggestions in that
paper, SigCheck accepts a data set (as an ExpressionSet) and a
gene signature, and compares its performance on survival and/or
classification tasks against a) random gene signatures of the
same length; b) known, related and unrelated gene signatures;
and c) permuted data and/or metadata.
biocViews: GeneExpression, Classification, GeneSetEnrichment
Author: Rory Stark <bioconductor@starkhome.com> and Justin Norden
Maintainer: Rory Stark <bioconductor@starkhome.com>
git_url: https://git.bioconductor.org/packages/SigCheck
git_branch: devel
git_last_commit: 8c08261
git_last_commit_date: 2025-02-14
Date/Publication: 2025-02-14
source.ver: src/contrib/SigCheck_2.39.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SigCheck_2.39.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SigCheck_2.39.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SigCheck_2.39.1.tgz
vignettes: vignettes/SigCheck/inst/doc/SigCheck.pdf
vignetteTitles: Checking gene expression signatures against random and
known signatures with SigCheck
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SigCheck/inst/doc/SigCheck.R
dependencyCount: 134
Package: sigFeature
Version: 1.25.0
Depends: R (>= 3.5.0)
Imports: biocViews, nlme, e1071, openxlsx, pheatmap, RColorBrewer,
Matrix, SparseM, graphics, stats, utils, SummarizedExperiment,
BiocParallel, methods
Suggests: RUnit, BiocGenerics, knitr, rmarkdown
License: GPL (>= 2)
MD5sum: 76621a4696c3605ab898e5d3e8c3cc69
NeedsCompilation: no
Title: sigFeature: Significant feature selection using SVM-RFE &
t-statistic
Description: This package provides a novel feature selection algorithm
for binary classification using support vector machine
recursive feature elimination SVM-RFE and t-statistic. In this
feature selection process, the selected features are
differentially significant between the two classes and also
they are good classifier with higher degree of classification
accuracy.
biocViews: FeatureExtraction, GeneExpression, Microarray,
Transcription, mRNAMicroarray, GenePrediction, Normalization,
Classification, SupportVectorMachine
Author: Pijush Das Developer [aut, cre], Dr. Susanta Roychudhury User
[ctb], Dr. Sucheta Tripathy User [ctb]
Maintainer: Pijush Das Developer <topijush@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/sigFeature
git_branch: devel
git_last_commit: ac23f1c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/sigFeature_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sigFeature_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sigFeature_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sigFeature_1.25.0.tgz
vignettes: vignettes/sigFeature/inst/doc/vignettes.html
vignetteTitles: sigFeature
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sigFeature/inst/doc/vignettes.R
dependencyCount: 77
Package: SigFuge
Version: 1.45.0
Depends: R (>= 3.5.0), GenomicRanges
Imports: ggplot2, matlab, reshape, sigclust
Suggests: org.Hs.eg.db, prebsdata, Rsamtools (>= 1.17.0),
TxDb.Hsapiens.UCSC.hg19.knownGene, BiocStyle
License: GPL-3
MD5sum: 15239dd76df577d36c5fb139b40c8d80
NeedsCompilation: no
Title: SigFuge
Description: Algorithm for testing significance of clustering in
RNA-seq data.
biocViews: Clustering, Visualization, RNASeq, ImmunoOncology
Author: Patrick Kimes, Christopher Cabanski
Maintainer: Patrick Kimes <patrick.kimes@gmail.com>
git_url: https://git.bioconductor.org/packages/SigFuge
git_branch: devel
git_last_commit: 3cd33ce
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SigFuge_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SigFuge_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SigFuge_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SigFuge_1.45.0.tgz
vignettes: vignettes/SigFuge/inst/doc/SigFuge.pdf
vignetteTitles: SigFuge Tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SigFuge/inst/doc/SigFuge.R
dependencyCount: 58
Package: siggenes
Version: 1.81.0
Depends: Biobase, multtest, splines, methods
Imports: stats4, grDevices, graphics, stats, scrime (>= 1.2.5)
Suggests: affy, annotate, genefilter, KernSmooth
License: LGPL (>= 2)
Archs: x64
MD5sum: 79ef511a2742356a579626b0fa3ef781
NeedsCompilation: no
Title: Multiple Testing using SAM and Efron's Empirical Bayes
Approaches
Description: Identification of differentially expressed genes and
estimation of the False Discovery Rate (FDR) using both the
Significance Analysis of Microarrays (SAM) and the Empirical
Bayes Analyses of Microarrays (EBAM).
biocViews: MultipleComparison, Microarray, GeneExpression, SNP,
ExonArray, DifferentialExpression
Author: Holger Schwender
Maintainer: Holger Schwender <holger.schw@gmx.de>
git_url: https://git.bioconductor.org/packages/siggenes
git_branch: devel
git_last_commit: 60ae981
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/siggenes_1.81.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/siggenes_1.81.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/siggenes_1.81.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/siggenes_1.81.0.tgz
vignettes: vignettes/siggenes/inst/doc/siggenes.pdf,
vignettes/siggenes/inst/doc/siggenesRnews.pdf,
vignettes/siggenes/inst/doc/identify.sam.html,
vignettes/siggenes/inst/doc/plot.ebam.html,
vignettes/siggenes/inst/doc/plot.finda0.html,
vignettes/siggenes/inst/doc/plot.sam.html,
vignettes/siggenes/inst/doc/print.ebam.html,
vignettes/siggenes/inst/doc/print.finda0.html,
vignettes/siggenes/inst/doc/print.sam.html,
vignettes/siggenes/inst/doc/summary.ebam.html,
vignettes/siggenes/inst/doc/summary.sam.html
vignetteTitles: siggenes Manual, siggenesRnews.pdf, identify.sam.html,
plot.ebam.html, plot.finda0.html, plot.sam.html,
print.ebam.html, print.finda0.html, print.sam.html,
summary.ebam.html, summary.sam.html
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/siggenes/inst/doc/siggenes.R
dependsOnMe: KCsmart
importsMe: minfi, trio, XDE, DeSousa2013, NPFD
suggestsMe: logicFS
dependencyCount: 17
Package: sights
Version: 1.33.0
Depends: R(>= 3.3)
Imports: MASS(>= 7.3), qvalue(>= 2.2), ggplot2(>= 2.0), reshape2(>=
1.4), lattice(>= 0.2), stats(>= 3.3)
Suggests: testthat, knitr, rmarkdown, ggthemes, gridExtra, xlsx
License: GPL-3 | file LICENSE
MD5sum: 88ffe576205054ad34274ab0ce6e87ac
NeedsCompilation: no
Title: Statistics and dIagnostic Graphs for HTS
Description: SIGHTS is a suite of normalization methods, statistical
tests, and diagnostic graphical tools for high throughput
screening (HTS) assays. HTS assays use microtitre plates to
screen large libraries of compounds for their biological,
chemical, or biochemical activity.
biocViews: ImmunoOncology, CellBasedAssays, MicrotitrePlateAssay,
Normalization, MultipleComparison, Preprocessing,
QualityControl, BatchEffect, Visualization
Author: Elika Garg [aut, cre], Carl Murie [aut], Heydar Ensha [ctb],
Robert Nadon [aut]
Maintainer: Elika Garg <elika.garg@mail.mcgill.ca>
URL: https://eg-r.github.io/sights/
VignetteBuilder: knitr
BugReports: https://github.com/eg-r/sights/issues
git_url: https://git.bioconductor.org/packages/sights
git_branch: devel
git_last_commit: 7b43510
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/sights_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sights_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sights_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sights_1.33.0.tgz
vignettes: vignettes/sights/inst/doc/sights.html
vignetteTitles: Using **SIGHTS** R-package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/sights/inst/doc/sights.R
dependencyCount: 42
Package: signatureSearch
Version: 1.21.0
Depends: R(>= 4.2.0), Rcpp, SummarizedExperiment, org.Hs.eg.db
Imports: AnnotationDbi, ggplot2, data.table, ExperimentHub, HDF5Array,
magrittr, RSQLite, dplyr, fgsea, scales, methods, qvalue,
stats, utils, reshape2, visNetwork, BiocParallel, fastmatch,
reactome.db, Matrix, clusterProfiler, readr, DOSE, rhdf5,
GSEABase, DelayedArray, BiocGenerics, tibble
LinkingTo: Rcpp
Suggests: knitr, testthat, rmarkdown, BiocStyle, signatureSearchData,
DT
License: Artistic-2.0
MD5sum: aa73bd93f9f8ced2122944da404fc2fc
NeedsCompilation: yes
Title: Environment for Gene Expression Searching Combined with
Functional Enrichment Analysis
Description: This package implements algorithms and data structures for
performing gene expression signature (GES) searches, and
subsequently interpreting the results functionally with
specialized enrichment methods.
biocViews: Software, GeneExpression, GO, KEGG, NetworkEnrichment,
Sequencing, Coverage, DifferentialExpression
Author: Yuzhu Duan [aut], Brendan Gongol [cre, aut], Thomas Girke [aut]
Maintainer: Brendan Gongol <bgong001@ucr.edu>
URL: https://github.com/yduan004/signatureSearch/
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/yduan004/signatureSearch/issues
git_url: https://git.bioconductor.org/packages/signatureSearch
git_branch: devel
git_last_commit: eef2cb2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/signatureSearch_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/signatureSearch_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/signatureSearch_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/signatureSearch_1.21.0.tgz
vignettes: vignettes/signatureSearch/inst/doc/signatureSearch.html
vignetteTitles: signatureSearch
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/signatureSearch/inst/doc/signatureSearch.R
dependsOnMe: DFD
dependencyCount: 173
Package: signeR
Version: 2.9.0
Depends: R (>= 3.0.2), VariantAnnotation, NMF
Imports: BiocGenerics, Biostrings, class, grDevices, GenomeInfoDb,
GenomicRanges, IRanges, nloptr, methods, stats, utils,
PMCMRplus, parallel, pvclust, ppclust, clue, survival, maxstat,
survivalAnalysis, future, VGAM, MASS, kknn, glmnet, e1071,
randomForest, ada, future.apply, ggplot2, pROC, pheatmap,
RColorBrewer, listenv, reshape2, scales, survminer, dplyr,
ggpubr, cowplot, tibble, readr, shiny, shinydashboard,
shinycssloaders, shinyWidgets, bsplus, DT, magrittr, tidyr,
BiocFileCache, proxy, rtracklayer, BSgenome
LinkingTo: Rcpp, RcppArmadillo (>= 0.7.100)
Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19,
BSgenome.Hsapiens.UCSC.hg38, rmarkdown
License: GPL-3
MD5sum: f13426636d125c443aef56b1c11b2c2b
NeedsCompilation: yes
Title: Empirical Bayesian approach to mutational signature discovery
Description: The signeR package provides an empirical Bayesian approach
to mutational signature discovery. It is designed to analyze
single nucleotide variation (SNV) counts in cancer genomes, but
can also be applied to other features as well. Functionalities
to characterize signatures or genome samples according to
exposure patterns are also provided.
biocViews: GenomicVariation, SomaticMutation, StatisticalMethod,
Visualization
Author: Rafael Rosales, Rodrigo Drummond, Renan Valieris, Alexandre
Defelicibus, Israel Tojal da Silva
Maintainer: Renan Valieris <renan.valieris@accamargo.org.br>
URL: https://github.com/TojalLab/signeR
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/signeR
git_branch: devel
git_last_commit: 1531a16
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/signeR_2.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/signeR_2.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/signeR_2.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/signeR_2.9.0.tgz
vignettes: vignettes/signeR/inst/doc/signeR-vignette.html,
vignettes/signeR/inst/doc/signeRFlow.html
vignetteTitles: signeR, signeRFlow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/signeR/inst/doc/signeR-vignette.R,
vignettes/signeR/inst/doc/signeRFlow.R
dependencyCount: 245
Package: signifinder
Version: 1.9.3
Depends: R (>= 4.3.0)
Imports: AnnotationDbi, BiocGenerics, ComplexHeatmap, consensusOV,
cowplot, DGEobj.utils, dplyr, ensembldb, ggplot2, ggridges,
GSVA, IRanges, magrittr, matrixStats, maxstat, methods,
openair, org.Hs.eg.db, patchwork, RColorBrewer,
TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Hsapiens.UCSC.hg38.knownGene, SpatialExperiment, stats,
scales, SummarizedExperiment, survival, survminer, viridis
Suggests: BiocStyle, edgeR, grid, kableExtra, knitr, limma, testthat
(>= 3.0.0)
License: AGPL-3
MD5sum: 6d2f8e8b01ee7f0084d1d9e3e678e8f7
NeedsCompilation: no
Title: Collection and implementation of public transcriptional cancer
signatures
Description: signifinder is an R package for computing and exploring a
compendium of tumor signatures. It allows to compute a variety
of signatures, based on gene expression values, and return
single-sample scores. Currently, signifinder contains more than
60 distinct signatures collected from the literature, relating
to multiple tumors and multiple cancer processes.
biocViews: GeneExpression, GeneTarget, ImmunoOncology,
BiomedicalInformatics, RNASeq, Microarray, ReportWriting,
Visualization, SingleCell, Spatial, GeneSignaling
Author: Stefania Pirrotta [cre, aut] (ORCID:
<https://orcid.org/0009-0004-0030-217X>), Enrica Calura [aut]
(ORCID: <https://orcid.org/0000-0001-8463-2432>)
Maintainer: Stefania Pirrotta <stefania.pirrotta@phd.unipd.it>
URL: https://github.com/CaluraLab/signifinder
VignetteBuilder: knitr
BugReports: https://github.com/CaluraLab/signifinder/issues
git_url: https://git.bioconductor.org/packages/signifinder
git_branch: devel
git_last_commit: c426fdd
git_last_commit_date: 2025-01-13
Date/Publication: 2025-01-13
source.ver: src/contrib/signifinder_1.9.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/signifinder_1.9.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/signifinder_1.9.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/signifinder_1.9.3.tgz
vignettes: vignettes/signifinder/inst/doc/signifinder.html
vignetteTitles: signifinder vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/signifinder/inst/doc/signifinder.R
dependencyCount: 259
Package: SigsPack
Version: 1.21.0
Depends: R (>= 3.6)
Imports: quadprog (>= 1.5-5), methods, Biobase, BSgenome (>= 1.46.0),
VariantAnnotation (>= 1.24.5), Biostrings, GenomeInfoDb,
GenomicRanges, rtracklayer, SummarizedExperiment, graphics,
stats, utils
Suggests: IRanges, BSgenome.Hsapiens.UCSC.hg19, BiocStyle, knitr,
rmarkdown
License: GPL-3
MD5sum: d6c995d1c3a199501e7407fd1c8a2f2e
NeedsCompilation: no
Title: Mutational Signature Estimation for Single Samples
Description: Single sample estimation of exposure to mutational
signatures. Exposures to known mutational signatures are
estimated for single samples, based on quadratic programming
algorithms. Bootstrapping the input mutational catalogues
provides estimations on the stability of these exposures. The
effect of the sequence composition of mutational context can be
taken into account by normalising the catalogues.
biocViews: SomaticMutation, SNP, VariantAnnotation,
BiomedicalInformatics, DNASeq
Author: Franziska Schumann <franziska.schumann@student.hpi.de>
Maintainer: Franziska Schumann <franziska.schumann@student.hpi.de>
URL: https://github.com/bihealth/SigsPack
VignetteBuilder: knitr
BugReports: https://github.com/bihealth/SigsPack/issues
git_url: https://git.bioconductor.org/packages/SigsPack
git_branch: devel
git_last_commit: 7943dfb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SigsPack_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SigsPack_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SigsPack_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SigsPack_1.21.0.tgz
vignettes: vignettes/SigsPack/inst/doc/SigsPack.html
vignetteTitles: Introduction to SigsPack
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SigsPack/inst/doc/SigsPack.R
dependencyCount: 80
Package: sigsquared
Version: 1.39.0
Depends: R (>= 3.2.0), methods
Imports: Biobase, survival
Suggests: RUnit, BiocGenerics
License: GPL version 3
MD5sum: 969d6031d4182d7acdf7db957b0c7b28
NeedsCompilation: no
Title: Gene signature generation for functionally validated signaling
pathways
Description: By leveraging statistical properties (log-rank test for
survival) of patient cohorts defined by binary thresholds,
poor-prognosis patients are identified by the sigsquared
package via optimization over a cost function reducing type I
and II error.
Author: UnJin Lee
Maintainer: UnJin Lee <unjin@uchicago.edu>
git_url: https://git.bioconductor.org/packages/sigsquared
git_branch: devel
git_last_commit: 1fece7e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/sigsquared_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sigsquared_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sigsquared_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sigsquared_1.39.0.tgz
vignettes: vignettes/sigsquared/inst/doc/sigsquared.pdf
vignetteTitles: SigSquared
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sigsquared/inst/doc/sigsquared.R
dependencyCount: 13
Package: SIM
Version: 1.77.0
Depends: R (>= 3.5), quantreg
Imports: graphics, stats, globaltest, quantsmooth
Suggests: biomaRt, RColorBrewer
License: GPL (>= 2)
Archs: x64
MD5sum: d3388641da50862f2f7679f211038346
NeedsCompilation: yes
Title: Integrated Analysis on two human genomic datasets
Description: Finds associations between two human genomic datasets.
biocViews: Microarray, Visualization
Author: Renee X. de Menezes and Judith M. Boer
Maintainer: Renee X. de Menezes <r.menezes@nki.nl>
git_url: https://git.bioconductor.org/packages/SIM
git_branch: devel
git_last_commit: 6065456
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SIM_1.77.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SIM_1.77.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SIM_1.77.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SIM_1.77.0.tgz
vignettes: vignettes/SIM/inst/doc/SIM.pdf
vignetteTitles: SIM vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SIM/inst/doc/SIM.R
dependencyCount: 59
Package: SIMAT
Version: 1.39.0
Depends: R (>= 3.5.0), Rcpp (>= 0.11.3)
Imports: mzR, ggplot2, grid, reshape2, grDevices, stats, utils
Suggests: RUnit, BiocGenerics
License: GPL-2
Archs: x64
MD5sum: 9ff628b3a07b5b04a9c697fa5238ab32
NeedsCompilation: no
Title: GC-SIM-MS data processing and alaysis tool
Description: This package provides a pipeline for analysis of GC-MS
data acquired in selected ion monitoring (SIM) mode. The tool
also provides a guidance in choosing appropriate fragments for
the targets of interest by using an optimization algorithm.
This is done by considering overlapping peaks from a provided
library by the user.
biocViews: ImmunoOncology, Software, Metabolomics, MassSpectrometry
Author: M. R. Nezami Ranjbar <nranjbar@vt.edu>
Maintainer: M. R. Nezami Ranjbar <nranjbar@vt.edu>
URL: http://omics.georgetown.edu/SIMAT.html
git_url: https://git.bioconductor.org/packages/SIMAT
git_branch: devel
git_last_commit: bb9e288
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SIMAT_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SIMAT_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SIMAT_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SIMAT_1.39.0.tgz
vignettes: vignettes/SIMAT/inst/doc/SIMAT-vignette.pdf
vignetteTitles: SIMAT Usage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SIMAT/inst/doc/SIMAT-vignette.R
dependencyCount: 48
Package: SimBu
Version: 1.9.0
Imports: basilisk, BiocParallel, data.table, dplyr, ggplot2, tools,
Matrix (>= 1.3.3), methods, phyloseq, proxyC, RColorBrewer,
RCurl, reticulate, sparseMatrixStats, SummarizedExperiment,
tidyr
Suggests: curl, knitr, matrixStats, rmarkdown, Seurat (>= 5.0.0),
SeuratObject (>= 5.0.0), testthat (>= 3.0.0)
License: GPL-3 + file LICENSE
MD5sum: c2adfe6d5ae4b68c2f7a44ab620ce600
NeedsCompilation: no
Title: Simulate Bulk RNA-seq Datasets from Single-Cell Datasets
Description: SimBu can be used to simulate bulk RNA-seq datasets with
known cell type fractions. You can either use your own
single-cell study for the simulation or the sfaira database.
Different pre-defined simulation scenarios exist, as are
options to run custom simulations. Additionally, expression
values can be adapted by adding an mRNA bias, which produces
more biologically relevant simulations.
biocViews: Software, RNASeq, SingleCell
Author: Alexander Dietrich [aut, cre]
Maintainer: Alexander Dietrich <alex.dietrich@tum.de>
URL: https://github.com/omnideconv/SimBu
VignetteBuilder: knitr
BugReports: https://github.com/omnideconv/SimBu/issues
git_url: https://git.bioconductor.org/packages/SimBu
git_branch: devel
git_last_commit: 3836594
git_last_commit_date: 2024-10-29
Date/Publication: 2025-01-23
source.ver: src/contrib/SimBu_1.9.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SimBu_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SimBu_1.9.0.tgz
vignettes: vignettes/SimBu/inst/doc/sfaira_vignette.html,
vignettes/SimBu/inst/doc/SimBu.html,
vignettes/SimBu/inst/doc/simulator_input_output.html,
vignettes/SimBu/inst/doc/simulator_scaling_factors.html
vignetteTitles: Public Data Integration using Sfaira, Getting started,
Inputs and Outputs, Introducing mRNA bias into simulations with
scaling factors
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SimBu/inst/doc/sfaira_vignette.R,
vignettes/SimBu/inst/doc/SimBu.R,
vignettes/SimBu/inst/doc/simulator_input_output.R,
vignettes/SimBu/inst/doc/simulator_scaling_factors.R
dependencyCount: 116
Package: SIMD
Version: 1.25.0
Depends: R (>= 3.5.0)
Imports: edgeR, statmod, methylMnM, stats, utils
Suggests: BiocStyle, knitr,rmarkdown
License: GPL-3
MD5sum: 86057a073f9048360ed7c703fdc36657
NeedsCompilation: yes
Title: Statistical Inferences with MeDIP-seq Data (SIMD) to infer the
methylation level for each CpG site
Description: This package provides a inferential analysis method for
detecting differentially expressed CpG sites in MeDIP-seq data.
It uses statistical framework and EM algorithm, to identify
differentially expressed CpG sites. The methods on this package
are described in the article 'Methylation-level Inferences and
Detection of Differential Methylation with Medip-seq Data' by
Yan Zhou, Jiadi Zhu, Mingtao Zhao, Baoxue Zhang, Chunfu Jiang
and Xiyan Yang (2018, pending publication).
biocViews: ImmunoOncology, DifferentialMethylation,SingleCell,
DifferentialExpression
Author: Yan Zhou
Maintainer: Jiadi Zhu <2160090406@email.szu.edu.cn>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SIMD
git_branch: devel
git_last_commit: 6259f02
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SIMD_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SIMD_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SIMD_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SIMD_1.25.0.tgz
vignettes: vignettes/SIMD/inst/doc/SIMD.html
vignetteTitles: SIMD Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SIMD/inst/doc/SIMD.R
dependencyCount: 12
Package: SimFFPE
Version: 1.19.0
Depends: Biostrings
Imports: dplyr, foreach, doParallel, truncnorm, GenomicRanges, IRanges,
Rsamtools, parallel, graphics, stats, utils, methods
Suggests: BiocStyle
License: LGPL-3
MD5sum: 973c41b699285abed9b8be8afd7c9846
NeedsCompilation: no
Title: NGS Read Simulator for FFPE Tissue
Description: The NGS (Next-Generation Sequencing) reads from FFPE
(Formalin-Fixed Paraffin-Embedded) samples contain numerous
artifact chimeric reads (ACRS), which can lead to false
positive structural variant calls. These ACRs are derived from
the combination of two single-stranded DNA (ss-DNA) fragments
with short reverse complementary regions (SRCRs). This package
simulates these artifact chimeric reads as well as normal reads
for FFPE samples on the whole genome / several chromosomes /
large regions.
biocViews: Sequencing, Alignment, MultipleComparison, SequenceMatching,
DataImport
Author: Lanying Wei [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4281-8017>)
Maintainer: Lanying Wei <lanying.wei@uni-muenster.de>
git_url: https://git.bioconductor.org/packages/SimFFPE
git_branch: devel
git_last_commit: 77724cf
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SimFFPE_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SimFFPE_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SimFFPE_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SimFFPE_1.19.0.tgz
vignettes: vignettes/SimFFPE/inst/doc/SimFFPE.pdf
vignetteTitles: An introduction to SimFFPE
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SimFFPE/inst/doc/SimFFPE.R
dependencyCount: 57
Package: similaRpeak
Version: 1.39.0
Depends: R6 (>= 2.0)
Imports: stats
Suggests: RUnit, BiocGenerics, knitr, Rsamtools, GenomicAlignments,
rtracklayer, rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: 055293d24db39fa59c1ce8ddb237ed42
NeedsCompilation: no
Title: Metrics to estimate a level of similarity between two ChIP-Seq
profiles
Description: This package calculates metrics which quantify the level
of similarity between ChIP-Seq profiles. More specifically, the
package implements six pseudometrics specialized in pattern
similarity detection in ChIP-Seq profiles.
biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison,
DifferentialExpression
Author: Astrid Deschênes [cre, aut], Elsa Bernatchez [aut], Charles
Joly Beauparlant [aut], Fabien Claude Lamaze [aut], Rawane Samb
[aut], Pascal Belleau [aut], Arnaud Droit [aut]
Maintainer: Astrid Deschênes <adeschen@hotmail.com>
URL: https://github.com/adeschen/similaRpeak
VignetteBuilder: knitr
BugReports: https://github.com/adeschen/similaRpeak/issues
git_url: https://git.bioconductor.org/packages/similaRpeak
git_branch: devel
git_last_commit: e8beccd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/similaRpeak_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/similaRpeak_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/similaRpeak_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/similaRpeak_1.39.0.tgz
vignettes: vignettes/similaRpeak/inst/doc/similaRpeak.html
vignetteTitles: Similarity between two ChIP-Seq profiles
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/similaRpeak/inst/doc/similaRpeak.R
dependencyCount: 2
Package: SIMLR
Version: 1.33.1
Depends: R (>= 4.1.0),
Imports: parallel, Matrix, stats, methods, Rcpp, pracma, RcppAnnoy,
RSpectra
LinkingTo: Rcpp
Suggests: BiocGenerics, BiocStyle, testthat, knitr, igraph
License: file LICENSE
Archs: x64
MD5sum: ea9ae7cbbfd3908c94f6326f6003902f
NeedsCompilation: yes
Title: Single-cell Interpretation via Multi-kernel LeaRning (SIMLR)
Description: Single-cell RNA-seq technologies enable high throughput
gene expression measurement of individual cells, and allow the
discovery of heterogeneity within cell populations. Measurement
of cell-to-cell gene expression similarity is critical for the
identification, visualization and analysis of cell populations.
However, single-cell data introduce challenges to conventional
measures of gene expression similarity because of the high
level of noise, outliers and dropouts. We develop a novel
similarity-learning framework, SIMLR (Single-cell
Interpretation via Multi-kernel LeaRning), which learns an
appropriate distance metric from the data for dimension
reduction, clustering and visualization.
biocViews: ImmunoOncology, Clustering, GeneExpression, Sequencing,
SingleCell
Author: Daniele Ramazzotti [aut] (ORCID:
<https://orcid.org/0000-0002-6087-2666>), Bo Wang [aut], Luca
De Sano [cre, aut] (ORCID:
<https://orcid.org/0000-0002-9618-3774>), Serafim Batzoglou
[ctb]
Maintainer: Luca De Sano <luca.desano@gmail.com>
URL: https://github.com/BatzoglouLabSU/SIMLR
VignetteBuilder: knitr
BugReports: https://github.com/BatzoglouLabSU/SIMLR
git_url: https://git.bioconductor.org/packages/SIMLR
git_branch: devel
git_last_commit: 662830a
git_last_commit_date: 2025-03-25
Date/Publication: 2025-03-26
source.ver: src/contrib/SIMLR_1.33.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SIMLR_1.33.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SIMLR_1.33.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SIMLR_1.33.1.tgz
vignettes: vignettes/SIMLR/inst/doc/v1_introduction.html,
vignettes/SIMLR/inst/doc/v2_running_SIMLR.html
vignetteTitles: Introduction, Running SIMLR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SIMLR/inst/doc/v1_introduction.R,
vignettes/SIMLR/inst/doc/v2_running_SIMLR.R
dependencyCount: 14
Package: simona
Version: 1.5.0
Depends: R (>= 4.1.0)
Imports: methods, Rcpp, matrixStats, GetoptLong, grid, GlobalOptions,
igraph, Polychrome, S4Vectors, xml2 (>= 1.3.3), circlize,
ComplexHeatmap, grDevices, stats, utils, shiny
LinkingTo: Rcpp
Suggests: knitr, testthat, BiocManager, GO.db, org.Hs.eg.db, proxyC,
AnnotationDbi, Matrix, DiagrammeR, ragg, png,
InteractiveComplexHeatmap, UniProtKeywords, simplifyEnrichment,
AnnotationHub, jsonlite
License: MIT + file LICENSE
MD5sum: b4028707a6654c3481f40aa4e510674d
NeedsCompilation: yes
Title: Semantic Similarity on Bio-Ontologies
Description: This package implements infrastructures for ontology
analysis by offering efficient data structures, fast ontology
traversal methods, and elegant visualizations. It provides a
robust toolbox supporting over 70 methods for semantic
similarity analysis.
biocViews: Software, Annotation, GO, BiomedicalInformatics
Author: Zuguang Gu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7395-8709>)
Maintainer: Zuguang Gu <z.gu@dkfz.de>
URL: https://github.com/jokergoo/simona
SystemRequirements: Perl, Java
VignetteBuilder: knitr
BugReports: https://github.com/jokergoo/simona/issues
git_url: https://git.bioconductor.org/packages/simona
git_branch: devel
git_last_commit: 40a2e14
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/simona_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/simona_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/simona_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/simona_1.5.0.tgz
vignettes: vignettes/simona/inst/doc/simona.html
vignetteTitles: The simona package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
importsMe: simplifyEnrichment
dependencyCount: 68
Package: simPIC
Version: 1.3.0
Depends: R (>= 4.4.0), SingleCellExperiment
Imports: BiocGenerics, checkmate (>= 2.0.0), fitdistrplus, matrixStats,
Matrix, stats, SummarizedExperiment, actuar, rlang, S4Vectors,
methods, scales, scuttle
Suggests: ggplot2 (>= 3.4.0), knitr, rmarkdown, BiocStyle, testthat (>=
3.0.0)
License: GPL-3
Archs: x64
MD5sum: f517575958847c5d84aed00102610578
NeedsCompilation: no
Title: simPIC: flexible simulation of paired-insertion counts for
single-cell ATAC-sequencing data
Description: simPIC is a package for simulating single-cell ATAC-seq
count data. It provides a user-friendly, well documented
interface for data simulation. Functions are provided for
parameter estimation, realistic scATAC-seq data simulation, and
comparing real and simulated datasets.
biocViews: SingleCell, ATACSeq, Software, Sequencing, ImmunoOncology,
DataImport
Author: Sagrika Chugh [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8050-5214>), Davis McCarthy [aut],
Heejung Shim [aut]
Maintainer: Sagrika Chugh <sagrika.chugh@gmail.com>
URL: https://github.com/sagrikachugh/simPIC
VignetteBuilder: knitr
BugReports: https://github.com/sagrikachugh/simPIC/issues
git_url: https://git.bioconductor.org/packages/simPIC
git_branch: devel
git_last_commit: 17e7089
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/simPIC_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/simPIC_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/simPIC_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/simPIC_1.3.0.tgz
vignettes: vignettes/simPIC/inst/doc/vignette.html
vignetteTitles: simPIC: simulating single-cell ATAC-seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/simPIC/inst/doc/vignette.R
dependencyCount: 70
Package: simpleSeg
Version: 1.9.0
Depends: R (>= 3.5.0)
Imports: BiocParallel, EBImage, terra, stats, spatstat.geom, S4Vectors,
grDevices, SummarizedExperiment, methods, cytomapper
Suggests: BiocStyle, testthat (>= 3.0.0), knitr, ggplot2
License: GPL-3
MD5sum: 9cec74dec8117ed31fcd419c9b16df5a
NeedsCompilation: no
Title: A package to perform simple cell segmentation
Description: Image segmentation is the process of identifying the
borders of individual objects (in this case cells) within an
image. This allows for the features of cells such as marker
expression and morphology to be extracted, stored and analysed.
simpleSeg provides functionality for user friendly, watershed
based segmentation on multiplexed cellular images in R based on
the intensity of user specified protein marker channels.
simpleSeg can also be used for the normalization of single cell
data obtained from multiple images.
biocViews: Classification, Survival, SingleCell, Normalization, Spatial
Author: Nicolas Canete [aut], Alexander Nicholls [aut], Ellis Patrick
[aut, cre]
Maintainer: Ellis Patrick <ellis.patrick@sydney.edu.au>
URL: https://sydneybiox.github.io/simpleSeg/
https://github.com/SydneyBioX/simpleSeg
VignetteBuilder: knitr
BugReports: https://github.com/SydneyBioX/simpleSeg/issues
git_url: https://git.bioconductor.org/packages/simpleSeg
git_branch: devel
git_last_commit: 4649541
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/simpleSeg_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/simpleSeg_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/simpleSeg_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/simpleSeg_1.9.0.tgz
vignettes: vignettes/simpleSeg/inst/doc/simpleSeg.html
vignetteTitles: "Introduction to simpleSeg"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/simpleSeg/inst/doc/simpleSeg.R
importsMe: lisaClust, spicyR
dependencyCount: 152
Package: simplifyEnrichment
Version: 2.1.0
Depends: R (>= 4.0.0)
Imports: simona, ComplexHeatmap (>= 2.7.4), grid, circlize, GetoptLong,
digest, tm, GO.db, AnnotationDbi, slam, methods, clue,
grDevices, stats, utils, cluster (>= 1.14.2), colorspace,
GlobalOptions (>= 0.1.0)
Suggests: knitr, ggplot2, cowplot, mclust, apcluster, MCL, dbscan,
igraph, gridExtra, dynamicTreeCut, testthat, gridGraphics,
flexclust, BiocManager, InteractiveComplexHeatmap (>= 0.99.11),
shiny, shinydashboard, cola, hu6800.db, rmarkdown, genefilter,
gridtext, fpc
License: MIT + file LICENSE
MD5sum: 1ae5932059a0afe6322991afe755ed6a
NeedsCompilation: no
Title: Simplify Functional Enrichment Results
Description: A new clustering algorithm, "binary cut", for clustering
similarity matrices of functional terms is implemeted in this
package. It also provides functions for visualizing,
summarizing and comparing the clusterings.
biocViews: Software, Visualization, GO, Clustering, GeneSetEnrichment
Author: Zuguang Gu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7395-8709>)
Maintainer: Zuguang Gu <z.gu@dkfz.de>
URL: https://github.com/jokergoo/simplifyEnrichment,
https://simplifyEnrichment.github.io
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/simplifyEnrichment
git_branch: devel
git_last_commit: 5590aab
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/simplifyEnrichment_2.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/simplifyEnrichment_2.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/simplifyEnrichment_2.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/simplifyEnrichment_2.1.0.tgz
vignettes:
vignettes/simplifyEnrichment/inst/doc/simplifyEnrichment.html
vignetteTitles: The simplifyEnrichment package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
suggestsMe: cola, InteractiveComplexHeatmap, simona, scITD
dependencyCount: 93
Package: sincell
Version: 1.39.0
Depends: R (>= 3.0.2), igraph
Imports: Rcpp (>= 0.11.2), entropy, scatterplot3d, MASS, TSP, ggplot2,
reshape2, fields, proxy, parallel, Rtsne, fastICA, cluster,
statmod
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, biomaRt, stringr, monocle
License: GPL (>= 2)
Archs: x64
MD5sum: 99fc53dbaa9cb5aced6c634bb0354f91
NeedsCompilation: yes
Title: R package for the statistical assessment of cell state
hierarchies from single-cell RNA-seq data
Description: Cell differentiation processes are achieved through a
continuum of hierarchical intermediate cell-states that might
be captured by single-cell RNA seq. Existing computational
approaches for the assessment of cell-state hierarchies from
single-cell data might be formalized under a general workflow
composed of i) a metric to assess cell-to-cell similarities
(combined or not with a dimensionality reduction step), and ii)
a graph-building algorithm (optionally making use of a
cells-clustering step). Sincell R package implements a
methodological toolbox allowing flexible workflows under such
framework. Furthermore, Sincell contributes new algorithms to
provide cell-state hierarchies with statistical support while
accounting for stochastic factors in single-cell RNA seq.
Graphical representations and functional association tests are
provided to interpret hierarchies.
biocViews: ImmunoOncology, Sequencing, RNASeq, Clustering,
GraphAndNetwork, Visualization, GeneExpression,
GeneSetEnrichment, BiomedicalInformatics, CellBiology,
FunctionalGenomics, SystemsBiology
Author: Miguel Julia <migueljuliamolina@gmail.com>, Amalio Telenti
<atelenti@jcvi.org>, Antonio Rausell
<antonio.rausell@institutimagine.org>
Maintainer: Miguel Julia <migueljuliamolina@gmail.com>, Antonio
Rausell<antonio.rausell@institutimagine.org>
URL: http://bioconductor.org/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/sincell
git_branch: devel
git_last_commit: e455929
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/sincell_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sincell_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sincell_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sincell_1.39.0.tgz
vignettes: vignettes/sincell/inst/doc/sincell-vignette.pdf
vignetteTitles: Sincell: Analysis of cell state hierarchies from
single-cell RNA-seq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sincell/inst/doc/sincell-vignette.R
dependencyCount: 59
Package: SingleCellAlleleExperiment
Version: 1.3.2
Depends: R (>= 4.4.0), SingleCellExperiment
Imports: SummarizedExperiment, BiocParallel, DelayedArray, methods,
utils, Matrix, S4Vectors, stats
Suggests: scaeData, knitr, rmarkdown, BiocStyle, scran, scater,
scuttle, ggplot2, patchwork, org.Hs.eg.db, AnnotationDbi,
DropletUtils, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: ccaaed06db0b7283e846300080658721
NeedsCompilation: no
Title: S4 Class for Single Cell Data with Allele and Functional Levels
for Immune Genes
Description: Defines a S4 class that is based on SingleCellExperiment.
In addition to the usual gene layer the object can also store
data for immune genes such as HLAs, Igs and KIRs at allele and
functional level. The package is part of a workflow named
single-cell ImmunoGenomic Diversity (scIGD), that firstly
incorporates allele-aware quantification data for immune genes.
This new data can then be used with the here implemented data
structure and functionalities for further data handling and
data analysis.
biocViews: DataRepresentation, Infrastructure, SingleCell,
Transcriptomics, GeneExpression, Genetics, ImmunoOncology,
DataImport
Author: Jonas Schuck [aut, cre] (ORCID:
<https://orcid.org/0009-0003-5705-4579>), Ahmad Al Ajami [aut]
(ORCID: <https://orcid.org/0009-0006-5615-7447>), Federico
Marini [aut] (ORCID: <https://orcid.org/0000-0003-3252-7758>),
Katharina Imkeller [aut] (ORCID:
<https://orcid.org/0000-0002-5177-0852>)
Maintainer: Jonas Schuck <jschuckdev@gmail.com>
URL: https://github.com/AGImkeller/SingleCellAlleleExperiment
VignetteBuilder: knitr
BugReports:
https://github.com/AGImkeller/SingleCellAlleleExperiment/issues
git_url:
https://git.bioconductor.org/packages/SingleCellAlleleExperiment
git_branch: devel
git_last_commit: 8b0584d
git_last_commit_date: 2025-01-27
Date/Publication: 2025-01-27
source.ver: src/contrib/SingleCellAlleleExperiment_1.3.2.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SingleCellAlleleExperiment_1.3.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SingleCellAlleleExperiment_1.3.2.tgz
vignettes:
vignettes/SingleCellAlleleExperiment/inst/doc/scae_intro.html
vignetteTitles: An introduction to the SingleCellAlleleExperiment class
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SingleCellAlleleExperiment/inst/doc/scae_intro.R
suggestsMe: scaeData
dependencyCount: 47
Package: SingleCellExperiment
Version: 1.29.2
Depends: SummarizedExperiment
Imports: methods, utils, stats, S4Vectors, BiocGenerics, GenomicRanges,
DelayedArray
Suggests: testthat, BiocStyle, knitr, rmarkdown, Matrix, scRNAseq (>=
2.9.1), Rtsne
License: GPL-3
Archs: x64
MD5sum: cccda5cded96a70e7890b05555dad76a
NeedsCompilation: no
Title: S4 Classes for Single Cell Data
Description: Defines a S4 class for storing data from single-cell
experiments. This includes specialized methods to store and
retrieve spike-in information, dimensionality reduction
coordinates and size factors for each cell, along with the
usual metadata for genes and libraries.
biocViews: ImmunoOncology, DataRepresentation, DataImport,
Infrastructure, SingleCell
Author: Aaron Lun [aut, cph], Davide Risso [aut, cre, cph], Keegan
Korthauer [ctb], Kevin Rue-Albrecht [ctb], Luke Zappia [ctb]
(ORCID: <https://orcid.org/0000-0001-7744-8565>, github:
lazappi)
Maintainer: Davide Risso <risso.davide@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SingleCellExperiment
git_branch: devel
git_last_commit: 4151771
git_last_commit_date: 2025-03-07
Date/Publication: 2025-03-07
source.ver: src/contrib/SingleCellExperiment_1.29.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SingleCellExperiment_1.29.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SingleCellExperiment_1.29.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SingleCellExperiment_1.29.2.tgz
vignettes: vignettes/SingleCellExperiment/inst/doc/apply.html,
vignettes/SingleCellExperiment/inst/doc/devel.html,
vignettes/SingleCellExperiment/inst/doc/intro.html
vignetteTitles: 2. Applying over a SingleCellExperiment object, 3.
Developing around the SingleCellExperiment class, 1. An
introduction to the SingleCellExperiment class
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SingleCellExperiment/inst/doc/apply.R,
vignettes/SingleCellExperiment/inst/doc/devel.R,
vignettes/SingleCellExperiment/inst/doc/intro.R
dependsOnMe: alabaster.sce, BASiCS, batchelor, BayesSpace, CATALYST,
celda, CellBench, CelliD, CellTrails, CHETAH, chevreulPlot,
chevreulProcess, chevreulShiny, clusterExperiment, cydar,
cytomapper, demuxSNP, dreamlet, DropletUtils, epiregulon,
epiregulon.extra, ExperimentSubset, iSEE, iSEEhub, iSEEindex,
LoomExperiment, lute, MAST, mia, mumosa, POWSC, scAnnotatR,
scater, scDblFinder, scGPS, schex, scPipe, scran, scuttle,
scviR, simPIC, SingleCellAlleleExperiment, singleCellTK,
SiPSiC, SpatialExperiment, splatter, switchde, TENxIO,
tidySingleCellExperiment, TrajectoryUtils,
TreeSummarizedExperiment, tricycle, TSCAN, zinbwave, HCAData,
imcdatasets, MouseAgingData, MouseGastrulationData,
MouseThymusAgeing, muscData, scATAC.Explorer, scMultiome,
scRNAseq, STexampleData, TENxBrainData, TENxPBMCData,
TMExplorer, WeberDivechaLCdata, OSCA.intro, DIscBIO,
imcExperiment, karyotapR
importsMe: ADImpute, aggregateBioVar, airpart, alabaster.sfe, APL,
ASURAT, Banksy, BASiCStan, bayNorm, BUSseq, CARDspa,
CatsCradle, ccfindR, ccImpute, CDI, CellMixS, Cepo, ChromSCape,
CiteFuse, ClusterFoldSimilarity, clustifyr, CoGAPS, concordexR,
condiments, corral, COTAN, CTexploreR, CuratedAtlasQueryR,
cytofQC, cytoviewer, dandelionR, decontX, destiny,
DifferentialRegulation, Dino, distinct, dittoSeq, escape,
escheR, EWCE, FEAST, fishpond, FLAMES, ggsc, ggspavis,
glmGamPoi, GloScope, GSVA, HIPPO, ILoReg, imcRtools, immApex,
infercnv, iSEEfier, iSEEtree, iSEEu, lemur, lisaClust, mastR,
mbkmeans, MEB, MetaNeighbor, miaViz, miloR, miQC, mist, MPAC,
MuData, muscat, Nebulosa, netSmooth, NewWave, nnSVG, partCNV,
peco, pipeComp, projectR, raer, RCSL, RegionalST, SC3, SCArray,
scBFA, scCB2, sccomp, scDD, scDDboost, scDesign3,
scDiagnostics, scDotPlot, scds, scHOT, scmap, scMerge, scMET,
SCnorm, scone, scp, scQTLtools, scReClassify, scRepertoire,
scRNAseqApp, scruff, scry, scTensor, scTGIF, scTreeViz, slalom,
slingshot, sosta, Spaniel, SpaNorm, SpatialExperimentIO,
SpatialFeatureExperiment, spatialHeatmap, speckle, spicyR,
SplineDV, SPOTlight, SpotSweeper, SPsimSeq, standR, Statial,
SVP, tidySpatialExperiment, tpSVG, tradeSeq, treekoR, UCell,
VAExprs, VDJdive, velociraptor, VisiumIO, visiumStitched,
Voyager, waddR, xCell2, XeniumIO, xenLite, zellkonverter,
HCATonsilData, MerfishData, raerdata, scpdata,
SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData,
mixhvg, nebula, SC.MEB, SCIntRuler, SCRIP, scROSHI, SpatialDDLS
suggestsMe: ANCOMBC, cellxgenedp, CTdata, DEsingle, dominoSignal,
FuseSOM, genomicInstability, hca, HDF5Array,
InteractiveComplexHeatmap, jazzPanda, M3Drop, microSTASIS,
MOFA2, MOSim, ontoProc, phenopath, progeny, QFeatures,
ReactomeGSA, scBubbletree, scFeatureFilter, scPCA, scrapper,
scRecover, SingleR, sketchR, SummarizedExperiment, tidytof,
TREG, updateObject, dorothea, DuoClustering2018, GSE103322,
microbiomeDataSets, TabulaMurisData, simpleSingleCell, Canek,
clustree, CytoSimplex, dyngen, FLASHMM, harmony, Platypus,
RaceID, rliger, SCdeconR, SCORPIUS, Seurat, singleCellHaystack,
SuperCell, tidydr
dependencyCount: 36
Package: SingleCellSignalR
Version: 1.19.0
Depends: R (>= 4.0)
Imports: BiocManager, circlize, limma, igraph, gplots, grDevices,
edgeR, data.table, pheatmap, stats, Rtsne, graphics, stringr,
foreach, multtest, scran, utils,
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: 0300a9dd57604bec47533b81ae2ddcda
NeedsCompilation: no
Title: Cell Signalling Using Single Cell RNAseq Data Analysis
Description: Allows single cell RNA seq data analysis, clustering,
creates internal network and infers cell-cell interactions.
biocViews: SingleCell, Network, Clustering, RNASeq, Classification
Author: Simon Cabello-Aguilar Developer [aut], Jacques Colinge
Developer [aut, cre]
Maintainer: Jacques Colinge Developer <jacques.colinge@umontpellier.fr>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SingleCellSignalR
git_branch: devel
git_last_commit: f0f49f1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SingleCellSignalR_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SingleCellSignalR_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SingleCellSignalR_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SingleCellSignalR_1.19.0.tgz
vignettes: vignettes/SingleCellSignalR/inst/doc/UsersGuide.html
vignetteTitles: my-vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SingleCellSignalR/inst/doc/UsersGuide.R
importsMe: scFeatures
suggestsMe: tidySingleCellExperiment, scDiffCom
dependencyCount: 102
Package: singleCellTK
Version: 2.17.2
Depends: R (>= 4.0), SummarizedExperiment, SingleCellExperiment,
DelayedArray, Biobase
Imports: ape, anndata, AnnotationHub, batchelor, BiocParallel, celldex,
colourpicker, colorspace, cowplot, cluster, ComplexHeatmap,
data.table, DelayedMatrixStats, DESeq2, dplyr, DT,
ExperimentHub, ensembldb, fields, ggplot2, ggplotify, ggrepel,
ggtree, gridExtra, grid, GSVA (>= 1.50.0), GSVAdata, igraph,
KernSmooth, limma, MAST, Matrix (>= 1.6-1), matrixStats,
methods, msigdbr, multtest, plotly, plyr, ROCR, Rtsne,
S4Vectors, scater, scMerge (>= 1.2.0), scran, Seurat (>=
3.1.3), shiny, shinyjs, SingleR, stringr, SoupX, sva, reshape2,
shinyalert, circlize, enrichR (>= 3.2), celda, shinycssloaders,
DropletUtils, scds (>= 1.2.0), reticulate (>= 1.14), tools,
tximport, tidyr, eds, withr, GSEABase, R.utils, zinbwave,
scRNAseq (>= 2.0.2), TENxPBMCData, yaml, rmarkdown, magrittr,
scDblFinder, metap, VAM (>= 0.5.3), tibble, rlang, TSCAN,
TrajectoryUtils, scuttle, utils, stats, zellkonverter
Suggests: testthat, Rsubread, BiocStyle, knitr, lintr, spelling,
org.Mm.eg.db, kableExtra, shinythemes, shinyBS, shinyjqui,
shinyWidgets, shinyFiles, BiocGenerics, RColorBrewer, fastmap
(>= 1.1.0), harmony, SeuratObject, optparse
License: MIT + file LICENSE
MD5sum: 5626aeb407dcab946e27deb6a88f3902
NeedsCompilation: no
Title: Comprehensive and Interactive Analysis of Single Cell RNA-Seq
Data
Description: The Single Cell Toolkit (SCTK) in the singleCellTK package
provides an interface to popular tools for importing, quality
control, analysis, and visualization of single cell RNA-seq
data. SCTK allows users to seamlessly integrate tools from
various packages at different stages of the analysis workflow.
A general "a la carte" workflow gives users the ability access
to multiple methods for data importing, calculation of general
QC metrics, doublet detection, ambient RNA estimation and
removal, filtering, normalization, batch correction or
integration, dimensionality reduction, 2-D embedding,
clustering, marker detection, differential expression, cell
type labeling, pathway analysis, and data exporting. Curated
workflows can be used to run Seurat and Celda. Streamlined
quality control can be performed on the command line using the
SCTK-QC pipeline. Users can analyze their data using commands
in the R console or by using an interactive Shiny Graphical
User Interface (GUI). Specific analyses or entire workflows can
be summarized and shared with comprehensive HTML reports
generated by Rmarkdown. Additional documentation and vignettes
can be found at camplab.net/sctk.
biocViews: SingleCell, GeneExpression, DifferentialExpression,
Alignment, Clustering, ImmunoOncology, BatchEffect,
Normalization, QualityControl, DataImport, GUI
Author: Yichen Wang [aut] (ORCID:
<https://orcid.org/0000-0003-4347-5199>), Irzam Sarfraz [aut]
(ORCID: <https://orcid.org/0000-0001-8121-792X>), Rui Hong
[aut], Yusuke Koga [aut], Salam Alabdullatif [aut], Nida
Pervaiz [aut], David Jenkins [aut] (ORCID:
<https://orcid.org/0000-0002-7451-4288>), Vidya Akavoor [aut],
Xinyun Cao [aut], Shruthi Bandyadka [aut], Anastasia Leshchyk
[aut], Tyler Faits [aut], Mohammed Muzamil Khan [aut], Zhe Wang
[aut], W. Evan Johnson [aut] (ORCID:
<https://orcid.org/0000-0002-6247-6595>), Ming Liu [aut],
Joshua David Campbell [aut, cre] (ORCID:
<https://orcid.org/0000-0003-0780-8662>)
Maintainer: Joshua David Campbell <camp@bu.edu>
URL: https://www.camplab.net/sctk/
VignetteBuilder: knitr
BugReports: https://github.com/compbiomed/singleCellTK/issues
git_url: https://git.bioconductor.org/packages/singleCellTK
git_branch: devel
git_last_commit: b30b317e
git_last_commit_date: 2025-02-20
Date/Publication: 2025-02-21
source.ver: src/contrib/singleCellTK_2.17.2.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/singleCellTK_2.17.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/singleCellTK_2.17.2.tgz
vignettes: vignettes/singleCellTK/inst/doc/singleCellTK.html
vignetteTitles: 1. Introduction to singleCellTK
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/singleCellTK/inst/doc/singleCellTK.R
suggestsMe: celda
dependencyCount: 389
Package: SingleMoleculeFootprinting
Version: 2.1.6
Depends: R (>= 4.4.0)
Imports: BiocGenerics, Biostrings, BSgenome, cluster, dplyr,
GenomeInfoDb, GenomicRanges, ggpointdensity, ggplot2, ggrepel,
grDevices, IRanges, magrittr, Matrix, methods, miscTools,
parallel, parallelDist, patchwork, plyranges, QuasR,
RColorBrewer, rlang, S4Vectors, stats, stringr, tibble, tidyr,
utils, viridis
Suggests: BSgenome.Mmusculus.UCSC.mm10, devtools, ExperimentHub, knitr,
qs, rmarkdown, readr, rrapply, SingleMoleculeFootprintingData,
testthat (>= 3.0.0), tidyverse
License: GPL-3
MD5sum: 469548188b31bbe24479980a6d61b85a
NeedsCompilation: no
Title: Analysis tools for Single Molecule Footprinting (SMF) data
Description: SingleMoleculeFootprinting provides functions to analyze
Single Molecule Footprinting (SMF) data. Following the workflow
exemplified in its vignette, the user will be able to perform
basic data analysis of SMF data with minimal coding effort.
Starting from an aligned bam file, we show how to perform
quality controls over sequencing libraries, extract methylation
information at the single molecule level accounting for the two
possible kind of SMF experiments (single enzyme or double
enzyme), classify single molecules based on their patterns of
molecular occupancy, plot SMF information at a given genomic
location.
biocViews: DNAMethylation, Coverage, NucleosomePositioning,
DataRepresentation, Epigenetics, MethylSeq, QualityControl,
Sequencing
Author: Guido Barzaghi [aut, cre] (ORCID:
<https://orcid.org/0000-0001-6066-3920>), Arnaud Krebs [aut]
(ORCID: <https://orcid.org/0000-0001-7999-6127>), Mike Smith
[ctb] (ORCID: <https://orcid.org/0000-0002-7800-3848>)
Maintainer: Guido Barzaghi <guido.barzaghi@embl.de>
URL:
https://www.bioconductor.org/packages/release/bioc/html/SingleMoleculeFootprinting.html
VignetteBuilder: knitr
BugReports:
https://github.com/Krebslabrep/SingleMoleculeFootprinting/issues
git_url:
https://git.bioconductor.org/packages/SingleMoleculeFootprinting
git_branch: devel
git_last_commit: 47b5c67
git_last_commit_date: 2025-03-25
Date/Publication: 2025-03-26
source.ver: src/contrib/SingleMoleculeFootprinting_2.1.6.tar.gz
win.binary.ver:
bin/windows/contrib/4.5/SingleMoleculeFootprinting_2.1.6.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SingleMoleculeFootprinting_2.1.6.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SingleMoleculeFootprinting_2.1.6.tgz
vignettes:
vignettes/SingleMoleculeFootprinting/inst/doc/FootprintCharter.html,
vignettes/SingleMoleculeFootprinting/inst/doc/methylation_calling_and_QCs.html,
vignettes/SingleMoleculeFootprinting/inst/doc/single_molecule_sorting_by_TF.html
vignetteTitles: FootprintCharter.html,
methylation_calling_and_QCs.html,
single_molecule_sorting_by_TF.html
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/SingleMoleculeFootprinting/inst/doc/FootprintCharter.R,
vignettes/SingleMoleculeFootprinting/inst/doc/methylation_calling_and_QCs.R,
vignettes/SingleMoleculeFootprinting/inst/doc/single_molecule_sorting_by_TF.R
dependencyCount: 140
Package: SingleR
Version: 2.9.6
Depends: SummarizedExperiment
Imports: methods, Matrix, S4Vectors, DelayedArray, DelayedMatrixStats,
BiocParallel, BiocNeighbors, stats, utils, Rcpp, beachmat (>=
2.23.5)
LinkingTo: Rcpp, beachmat, assorthead, BiocNeighbors
Suggests: testthat, knitr, rmarkdown, BiocStyle, BiocGenerics,
SingleCellExperiment, scuttle, scrapper, scRNAseq, ggplot2,
pheatmap, grDevices, gridExtra, viridis, celldex
License: GPL-3
MD5sum: 0fec2697abc1a1adff8129b51d54f469
NeedsCompilation: yes
Title: Reference-Based Single-Cell RNA-Seq Annotation
Description: Performs unbiased cell type recognition from single-cell
RNA sequencing data, by leveraging reference transcriptomic
datasets of pure cell types to infer the cell of origin of each
single cell independently.
biocViews: Software, SingleCell, GeneExpression, Transcriptomics,
Classification, Clustering, Annotation
Author: Dvir Aran [aut, cph], Aaron Lun [ctb, cre], Daniel Bunis [ctb],
Jared Andrews [ctb], Friederike Dündar [ctb]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://github.com/SingleR-inc/SingleR
SystemRequirements: C++17
VignetteBuilder: knitr
BugReports: https://github.com/SingleR-inc/SingleR/issues
git_url: https://git.bioconductor.org/packages/SingleR
git_branch: devel
git_last_commit: 96c5041
git_last_commit_date: 2025-02-16
Date/Publication: 2025-02-16
source.ver: src/contrib/SingleR_2.9.6.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SingleR_2.9.6.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SingleR_2.9.6.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SingleR_2.9.6.tgz
vignettes: vignettes/SingleR/inst/doc/SingleR.html
vignetteTitles: Annotating scRNA-seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SingleR/inst/doc/SingleR.R
dependsOnMe: OSCA.basic, OSCA.multisample, OSCA.workflows, SingleRBook
importsMe: singleCellTK, scPipeline
suggestsMe: scDiagnostics, sketchR, tidySingleCellExperiment,
tidyseurat
dependencyCount: 52
Package: singscore
Version: 1.27.0
Depends: R (>= 3.6)
Imports: methods, stats, graphics, ggplot2, grDevices, ggrepel,
GSEABase, plotly, tidyr, plyr, magrittr, reshape, edgeR,
RColorBrewer, Biobase, BiocParallel, SummarizedExperiment,
matrixStats, reshape2, S4Vectors
Suggests: pkgdown, BiocStyle, hexbin, knitr, rmarkdown, testthat, covr
License: GPL-3
Archs: x64
MD5sum: e9c29b198a42e63996f7164215957607
NeedsCompilation: no
Title: Rank-based single-sample gene set scoring method
Description: A simple single-sample gene signature scoring method that
uses rank-based statistics to analyze the sample's gene
expression profile. It scores the expression activities of gene
sets at a single-sample level.
biocViews: Software, GeneExpression, GeneSetEnrichment
Author: Dharmesh D. Bhuva [aut] (ORCID:
<https://orcid.org/0000-0002-6398-9157>), Ruqian Lyu [aut,
ctb], Momeneh Foroutan [aut, ctb] (ORCID:
<https://orcid.org/0000-0002-1440-0457>), Malvika Kharbanda
[aut, cre] (ORCID: <https://orcid.org/0000-0001-9726-3023>)
Maintainer: Malvika Kharbanda <kharbanda.m@wehi.edu.au>
URL: https://davislaboratory.github.io/singscore
VignetteBuilder: knitr
BugReports: https://github.com/DavisLaboratory/singscore/issues
git_url: https://git.bioconductor.org/packages/singscore
git_branch: devel
git_last_commit: b18b6f0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/singscore_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/singscore_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/singscore_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/singscore_1.27.0.tgz
vignettes: vignettes/singscore/inst/doc/singscore.html
vignetteTitles: Single sample scoring
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/singscore/inst/doc/singscore.R
importsMe: TBSignatureProfiler, xCell2, SingscoreAMLMutations,
clustermole, GSEMA
suggestsMe: mastR, vissE, msigdb
dependencyCount: 128
Package: SiPSiC
Version: 1.7.0
Depends: Matrix, SingleCellExperiment
Suggests: knitr, rmarkdown, BiocStyle
License: file LICENSE
MD5sum: 9adda7dd2b14f02bf11f1d6afe01755f
NeedsCompilation: no
Title: Calculate Pathway Scores for Each Cell in scRNA-Seq Data
Description: Infer biological pathway activity of cells from
single-cell RNA-sequencing data by calculating a pathway score
for each cell (pathway genes are specified by the user). It is
recommended to have the data in Transcripts-Per-Million (TPM)
or Counts-Per-Million (CPM) units for best results. Scores may
change when adding cells to or removing cells off the data.
SiPSiC stands for Single Pathway analysis in Single Cells.
biocViews: Software, DifferentialExpression, GeneSetEnrichment,
BiomedicalInformatics, CellBiology, Transcriptomics, RNASeq,
SingleCell, Transcription, Sequencing, ImmunoOncology,
DataImport
Author: Daniel Davis [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7863-1521>), Yotam Drier [aut]
Maintainer: Daniel Davis <DanielDavis000@gmail.com>
URL: https://www.genome.org/cgi/doi/10.1101/gr.278431.123
VignetteBuilder: knitr
BugReports: https://github.com/DanielDavis12/SiPSiC/issues
git_url: https://git.bioconductor.org/packages/SiPSiC
git_branch: devel
git_last_commit: 688dfb8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SiPSiC_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SiPSiC_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SiPSiC_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SiPSiC_1.7.0.tgz
vignettes: vignettes/SiPSiC/inst/doc/SiPSiC.html
vignetteTitles: Infer Biological Pathway Activity from Single-Cell
RNA-Seq Data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SiPSiC/inst/doc/SiPSiC.R
dependencyCount: 37
Package: sitadela
Version: 1.15.0
Depends: R (>= 4.1.0)
Imports: Biobase, BiocGenerics, biomaRt, Biostrings, GenomeInfoDb,
GenomicFeatures, GenomicRanges, IRanges, methods, parallel,
Rsamtools, RSQLite, rtracklayer, S4Vectors, tools, txdbmaker,
utils
Suggests: BiocStyle, BSgenome, knitr, rmarkdown, RMySQL, RUnit
License: Artistic-2.0
MD5sum: f4c0bc1261f66e06b1f6880fd914ca1a
NeedsCompilation: no
Title: An R package for the easy provision of simple but complete
tab-delimited genomic annotation from a variety of sources and
organisms
Description: Provides an interface to build a unified database of
genomic annotations and their coordinates (gene, transcript and
exon levels). It is aimed to be used when simple tab-delimited
annotations (or simple GRanges objects) are required instead of
the more complex annotation Bioconductor packages. Also useful
when combinatorial annotation elements are reuired, such as
RefSeq coordinates with Ensembl biotypes. Finally, it can
download, construct and handle annotations with versioned genes
and transcripts (where available, e.g. RefSeq and latest
Ensembl). This is particularly useful in precision medicine
applications where the latter must be reported.
biocViews: Software, WorkflowStep, RNASeq, Transcription, Sequencing,
Transcriptomics, BiomedicalInformatics, FunctionalGenomics,
SystemsBiology, AlternativeSplicing, DataImport, ChIPSeq
Author: Panagiotis Moulos [aut, cre]
Maintainer: Panagiotis Moulos <moulos@fleming.gr>
URL: https://github.com/pmoulos/sitadela
VignetteBuilder: knitr
BugReports: https://github.com/pmoulos/sitadela/issues
git_url: https://git.bioconductor.org/packages/sitadela
git_branch: devel
git_last_commit: 76eef2f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/sitadela_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sitadela_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sitadela_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sitadela_1.15.0.tgz
vignettes: vignettes/sitadela/inst/doc/sitadela.html
vignetteTitles: Building a simple annotation database
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sitadela/inst/doc/sitadela.R
dependencyCount: 101
Package: sitePath
Version: 1.23.0
Depends: R (>= 4.1)
Imports: RColorBrewer, Rcpp, ape, aplot, ggplot2, ggrepel, ggtree,
graphics, grDevices, gridExtra, methods, parallel, seqinr,
stats, tidytree, utils
LinkingTo: Rcpp
Suggests: BiocStyle, devtools, knitr, magick, rmarkdown, testthat
License: MIT + file LICENSE
MD5sum: ac7e259bcb769c7e4356ea79149acfa4
NeedsCompilation: yes
Title: Phylogeny-based sequence clustering with site polymorphism
Description: Using site polymorphism is one of the ways to cluster
DNA/protein sequences but it is possible for the sequences with
the same polymorphism on a single site to be genetically
distant. This package is aimed at clustering sequences using
site polymorphism and their corresponding phylogenetic trees.
By considering their location on the tree, only the
structurally adjacent sequences will be clustered. However, the
adjacent sequences may not necessarily have the same
polymorphism. So a branch-and-bound like algorithm is used to
minimize the entropy representing the purity of site
polymorphism of each cluster.
biocViews: Alignment, MultipleSequenceAlignment, Phylogenetics, SNP,
Software
Author: Chengyang Ji [aut, cre, cph] (ORCID:
<https://orcid.org/0000-0001-9258-5453>), Hangyu Zhou [ths],
Aiping Wu [ths]
Maintainer: Chengyang Ji <chengyang.ji12@alumni.xjtlu.edu.cn>
URL: https://wuaipinglab.github.io/sitePath/
VignetteBuilder: knitr
BugReports: https://github.com/wuaipinglab/sitePath/issues
git_url: https://git.bioconductor.org/packages/sitePath
git_branch: devel
git_last_commit: abc9ced
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/sitePath_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sitePath_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sitePath_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sitePath_1.23.0.tgz
vignettes: vignettes/sitePath/inst/doc/sitePath.html
vignetteTitles: An introduction to sitePath
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/sitePath/inst/doc/sitePath.R
dependencyCount: 68
Package: sizepower
Version: 1.77.0
Depends: stats
License: LGPL
Archs: x64
MD5sum: c879d4604ceb21dc257087acb1be0708
NeedsCompilation: no
Title: Sample Size and Power Calculation in Micorarray Studies
Description: This package has been prepared to assist users in
computing either a sample size or power value for a microarray
experimental study. The user is referred to the cited
references for technical background on the methodology
underpinning these calculations. This package provides support
for five types of sample size and power calculations. These
five types can be adapted in various ways to encompass many of
the standard designs encountered in practice.
biocViews: Microarray
Author: Weiliang Qiu <weiliang.qiu@gmail.com> and Mei-Ling Ting Lee
<meilinglee@sph.osu.edu> and George Alex Whitmore
<george.whitmore@mcgill.ca>
Maintainer: Weiliang Qiu <weiliang.qiu@gmail.com>
git_url: https://git.bioconductor.org/packages/sizepower
git_branch: devel
git_last_commit: 4adad42
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/sizepower_1.77.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sizepower_1.77.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sizepower_1.77.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sizepower_1.77.0.tgz
vignettes: vignettes/sizepower/inst/doc/sizepower.pdf
vignetteTitles: Sample Size and Power Calculation in Microarray Studies
Using the \Rpackage{sizepower} package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sizepower/inst/doc/sizepower.R
dependencyCount: 1
Package: sketchR
Version: 1.3.0
Imports: basilisk, Biobase, DelayedArray, dplyr, ggplot2, methods,
reticulate, rlang, scales, stats
Suggests: rmarkdown, knitr, testthat (>= 3.0.0), TENxPBMCData, scuttle,
scran, scater, SingleR, celldex, cowplot, SummarizedExperiment,
beachmat.hdf5, BiocStyle, BiocManager, SingleCellExperiment
License: MIT + file LICENSE
MD5sum: b608ebbd307fd2b9bbb42f9d80457fa0
NeedsCompilation: no
Title: An R interface for python subsampling/sketching algorithms
Description: Provides an R interface for various subsampling algorithms
implemented in python packages. Currently, interfaces to the
geosketch and scSampler python packages are implemented. In
addition it also provides diagnostic plots to evaluate the
subsampling.
biocViews: SingleCell
Author: Charlotte Soneson [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3833-2169>), Michael Stadler [aut]
(ORCID: <https://orcid.org/0000-0002-2269-4934>), Friedrich
Miescher Institute for Biomedical Research [cph]
Maintainer: Charlotte Soneson <charlottesoneson@gmail.com>
URL: https://github.com/fmicompbio/sketchR
VignetteBuilder: knitr
BugReports: https://github.com/fmicompbio/sketchR/issues
git_url: https://git.bioconductor.org/packages/sketchR
git_branch: devel
git_last_commit: 87da4c7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-05
source.ver: src/contrib/sketchR_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sketchR_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sketchR_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sketchR_1.3.0.tgz
vignettes: vignettes/sketchR/inst/doc/sketchR.html
vignetteTitles: sketchR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/sketchR/inst/doc/sketchR.R
dependencyCount: 65
Package: skewr
Version: 1.39.0
Depends: R (>= 3.1.1), methylumi, wateRmelon, mixsmsn,
IlluminaHumanMethylation450kmanifest
Imports: minfi, S4Vectors (>= 0.19.1), RColorBrewer
Suggests: GEOquery, knitr, minfiData
License: GPL-2
MD5sum: 87b97a07eaadb02ccb66b9641203b06c
NeedsCompilation: no
Title: Visualize Intensities Produced by Illumina's Human Methylation
450k BeadChip
Description: The skewr package is a tool for visualizing the output of
the Illumina Human Methylation 450k BeadChip to aid in quality
control. It creates a panel of nine plots. Six of the plots
represent the density of either the methylated intensity or the
unmethylated intensity given by one of three subsets of the
485,577 total probes. These subsets include Type I-red, Type
I-green, and Type II.The remaining three distributions give the
density of the Beta-values for these same three subsets. Each
of the nine plots optionally displays the distributions of the
"rs" SNP probes and the probes associated with imprinted genes
as series of 'tick' marks located above the x-axis.
biocViews: DNAMethylation, TwoChannel, Preprocessing, QualityControl
Author: Ryan Putney [cre, aut], Steven Eschrich [aut], Anders Berglund
[aut]
Maintainer: Ryan Putney <ryanputney@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/skewr
git_branch: devel
git_last_commit: d7cf1ed
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/skewr_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/skewr_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/skewr_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/skewr_1.39.0.tgz
vignettes: vignettes/skewr/inst/doc/skewr.pdf
vignetteTitles: An Introduction to the skewr Package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/skewr/inst/doc/skewr.R
dependencyCount: 176
Package: slalom
Version: 1.29.0
Depends: R (>= 4.0)
Imports: Rcpp (>= 0.12.8), RcppArmadillo, BH, ggplot2, grid, GSEABase,
methods, rsvd, SingleCellExperiment, SummarizedExperiment,
stats
LinkingTo: Rcpp, RcppArmadillo, BH
Suggests: BiocStyle, knitr, rhdf5, rmarkdown, scater, testthat
License: GPL-2
MD5sum: 557dfff3a3b82fe42987791b96217610
NeedsCompilation: yes
Title: Factorial Latent Variable Modeling of Single-Cell RNA-Seq Data
Description: slalom is a scalable modelling framework for single-cell
RNA-seq data that uses gene set annotations to dissect
single-cell transcriptome heterogeneity, thereby allowing to
identify biological drivers of cell-to-cell variability and
model confounding factors. The method uses Bayesian factor
analysis with a latent variable model to identify active
pathways (selected by the user, e.g. KEGG pathways) that
explain variation in a single-cell RNA-seq dataset. This an
R/C++ implementation of the f-scLVM Python package. See the
publication describing the method at
https://doi.org/10.1186/s13059-017-1334-8.
biocViews: ImmunoOncology, SingleCell, RNASeq, Normalization,
Visualization, DimensionReduction, Transcriptomics,
GeneExpression, Sequencing, Software, Reactome, KEGG
Author: Florian Buettner [aut], Naruemon Pratanwanich [aut], Davis
McCarthy [aut, cre], John Marioni [aut], Oliver Stegle [aut]
Maintainer: Davis McCarthy <davis@ebi.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/slalom
git_branch: devel
git_last_commit: 9d5e3d4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/slalom_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/slalom_1.29.0.zip
vignettes: vignettes/slalom/inst/doc/vignette.html
vignetteTitles: Introduction to slalom
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/slalom/inst/doc/vignette.R
dependencyCount: 86
Package: slingshot
Version: 2.15.0
Depends: R (>= 4.0), princurve (>= 2.0.4), stats, TrajectoryUtils
Imports: graphics, grDevices, igraph, matrixStats, methods, S4Vectors,
SingleCellExperiment, SummarizedExperiment
Suggests: BiocGenerics, BiocStyle, clusterExperiment,
DelayedMatrixStats, knitr, mclust, mgcv, RColorBrewer, rgl,
rmarkdown, testthat, uwot, covr
License: Artistic-2.0
Archs: x64
MD5sum: 38908959724cbd9de3a2f88ce401522d
NeedsCompilation: no
Title: Tools for ordering single-cell sequencing
Description: Provides functions for inferring continuous, branching
lineage structures in low-dimensional data. Slingshot was
designed to model developmental trajectories in single-cell RNA
sequencing data and serve as a component in an analysis
pipeline after dimensionality reduction and clustering. It is
flexible enough to handle arbitrarily many branching events and
allows for the incorporation of prior knowledge through
supervised graph construction.
biocViews: Clustering, DifferentialExpression, GeneExpression, RNASeq,
Sequencing, Software, Sequencing, SingleCell, Transcriptomics,
Visualization
Author: Kelly Street [aut, cre, cph], Davide Risso [aut], Diya Das
[aut], Sandrine Dudoit [ths], Koen Van den Berge [ctb],
Robrecht Cannoodt [ctb] (ORCID:
<https://orcid.org/0000-0003-3641-729X>, github: rcannood)
Maintainer: Kelly Street <street.kelly@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/kstreet13/slingshot/issues
git_url: https://git.bioconductor.org/packages/slingshot
git_branch: devel
git_last_commit: cf41eec
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/slingshot_2.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/slingshot_2.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/slingshot_2.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/slingshot_2.15.0.tgz
vignettes: vignettes/slingshot/inst/doc/conditionsVignette.html,
vignettes/slingshot/inst/doc/vignette.html
vignetteTitles: Differential Topology: Comparing Conditions along a
Trajectory, Slingshot: Trajectory Inference for Single-Cell
Data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/slingshot/inst/doc/conditionsVignette.R,
vignettes/slingshot/inst/doc/vignette.R
importsMe: condiments, scRNAseqApp, tradeSeq
suggestsMe: Platypus, RaceID
dependencyCount: 49
Package: SLqPCR
Version: 1.73.0
Depends: R(>= 2.4.0)
Imports: stats
Suggests: RColorBrewer
License: GPL (>= 2)
MD5sum: c3be6921c898cb49024a14a1fb115b73
NeedsCompilation: no
Title: Functions for analysis of real-time quantitative PCR data at
SIRS-Lab GmbH
Description: Functions for analysis of real-time quantitative PCR data
at SIRS-Lab GmbH
biocViews: MicrotitrePlateAssay, qPCR
Author: Matthias Kohl
Maintainer: Matthias Kohl <kohl@sirs-lab.com>
git_url: https://git.bioconductor.org/packages/SLqPCR
git_branch: devel
git_last_commit: 988a269
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SLqPCR_1.73.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SLqPCR_1.73.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SLqPCR_1.73.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SLqPCR_1.73.0.tgz
vignettes: vignettes/SLqPCR/inst/doc/SLqPCR.pdf
vignetteTitles: SLqPCR
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SLqPCR/inst/doc/SLqPCR.R
dependencyCount: 1
Package: SMAD
Version: 1.23.0
Depends: R (>= 3.6.0), RcppAlgos
Imports: magrittr (>= 1.5), dplyr, stats, tidyr, utils, Rcpp (>= 1.0.0)
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, testthat, BiocStyle
License: MIT + file LICENSE
MD5sum: 336445097ef61aab2f372cb230df60f3
NeedsCompilation: yes
Title: Statistical Modelling of AP-MS Data (SMAD)
Description: Assigning probability scores to protein interactions
captured in affinity purification mass spectrometry (AP-MS)
expriments to infer protein-protein interactions. The output
would facilitate non-specific background removal as
contaminants are commonly found in AP-MS data.
biocViews: MassSpectrometry, Proteomics, Software
Author: Qingzhou Zhang [aut, cre]
Maintainer: Qingzhou Zhang <zqzneptune@hotmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SMAD
git_branch: devel
git_last_commit: 78f48cf
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SMAD_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SMAD_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SMAD_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SMAD_1.23.0.tgz
vignettes: vignettes/SMAD/inst/doc/quickstart.html
vignetteTitles: SMAD Quick Start
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SMAD/inst/doc/quickstart.R
dependencyCount: 30
Package: smartid
Version: 1.3.2
Depends: R (>= 4.4)
Imports: dplyr, ggplot2, graphics, Matrix, mclust, methods, mixtools,
sparseMatrixStats, stats, SummarizedExperiment, tidyr, utils
Suggests: BiocStyle, dbscan, ggpubr, knitr, rmarkdown, scater,
splatter, testthat (>= 3.0.0), tidytext, UpSetR
License: MIT + file LICENSE
MD5sum: fe28a5e585f9e568d17bf326e5d3b5e0
NeedsCompilation: no
Title: Scoring and Marker Selection Method Based on Modified TF-IDF
Description: This package enables automated selection of group specific
signature, especially for rare population. The package is
developed for generating specifc lists of signature genes based
on Term Frequency-Inverse Document Frequency (TF-IDF) modified
methods. It can also be used as a new gene-set scoring method
or data transformation method. Multiple visualization functions
are implemented in this package.
biocViews: Software, GeneExpression, Transcriptomics
Author: Jinjin Chen [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7923-5723>)
Maintainer: Jinjin Chen <chen.j@wehi.edu.au>
URL: https://davislaboratory.github.io/smartid
VignetteBuilder: knitr
BugReports: https://github.com/DavisLaboratory/smartid/issues
git_url: https://git.bioconductor.org/packages/smartid
git_branch: devel
git_last_commit: a939065
git_last_commit_date: 2024-11-12
Date/Publication: 2024-11-13
source.ver: src/contrib/smartid_1.3.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/smartid_1.3.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/smartid_1.3.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/smartid_1.3.2.tgz
vignettes: vignettes/smartid/inst/doc/smartid_Demo.html
vignetteTitles: smartid: Scoring and MARker selection method based on
modified Tf-IDf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/smartid/inst/doc/smartid_Demo.R
dependencyCount: 102
Package: SMITE
Version: 1.35.0
Depends: R (>= 3.5), GenomicRanges
Imports: scales, plyr, Hmisc, AnnotationDbi, org.Hs.eg.db, ggplot2,
reactome.db, KEGGREST, BioNet, goseq, methods, IRanges, igraph,
Biobase,tools, S4Vectors, geneLenDataBase, grDevices, graphics,
stats, utils
Suggests: knitr, rmarkdown
License: GPL (>=2)
Archs: x64
MD5sum: 38100371870105e480ea68dde5b61bdb
NeedsCompilation: no
Title: Significance-based Modules Integrating the Transcriptome and
Epigenome
Description: This package builds on the Epimods framework which
facilitates finding weighted subnetworks ("modules") on
Illumina Infinium 27k arrays using the SpinGlass algorithm, as
implemented in the iGraph package. We have created a class of
gene centric annotations associated with p-values and effect
sizes and scores from any researchers prior statistical results
to find functional modules.
biocViews: ImmunoOncology, DifferentialMethylation,
DifferentialExpression, SystemsBiology,
NetworkEnrichment,GenomeAnnotation,Network, Sequencing, RNASeq,
Coverage
Author: Neil Ari Wijetunga, Andrew Damon Johnston, John Murray Greally
Maintainer: Neil Ari Wijetunga <nawijet@gmail.com>, Andrew Damon
Johnston <Andrew.Johnston@med.einstein.yu.edu>
URL: https://github.com/GreallyLab/SMITE
VignetteBuilder: knitr
BugReports: https://github.com/GreallyLab/SMITE/issues
git_url: https://git.bioconductor.org/packages/SMITE
git_branch: devel
git_last_commit: 92d4d6f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-05
source.ver: src/contrib/SMITE_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SMITE_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SMITE_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SMITE_1.35.0.tgz
vignettes: vignettes/SMITE/inst/doc/SMITE.pdf
vignetteTitles: SMITE Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SMITE/inst/doc/SMITE.R
dependencyCount: 154
Package: smoothclust
Version: 1.3.4
Depends: R (>= 4.4.0)
Imports: SpatialExperiment, SummarizedExperiment, sparseMatrixStats,
spdep, methods, utils
Suggests: BiocStyle, knitr, STexampleData, scuttle, scran, scater,
ggspavis, testthat
License: MIT + file LICENSE
MD5sum: 98108d48e06ff4bcb4582bf945d50194
NeedsCompilation: no
Title: smoothclust
Description: Method for segmentation of spatial domains and
spatially-aware clustering in spatial transcriptomics data. The
method generates spatial domains with smooth boundaries by
smoothing gene expression profiles across neighboring spatial
locations, followed by unsupervised clustering. Spatial domains
consisting of consistent mixtures of cell types may then be
further investigated by applying cell type compositional
analyses or differential analyses.
biocViews: Spatial, SingleCell, Transcriptomics, GeneExpression,
Clustering
Author: Lukas M. Weber [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3282-1730>)
Maintainer: Lukas M. Weber <lmweb012@gmail.com>
URL: https://github.com/lmweber/smoothclust
VignetteBuilder: knitr
BugReports: https://github.com/lmweber/smoothclust/issues
git_url: https://git.bioconductor.org/packages/smoothclust
git_branch: devel
git_last_commit: ae897d0
git_last_commit_date: 2025-03-23
Date/Publication: 2025-03-24
source.ver: src/contrib/smoothclust_1.3.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/smoothclust_1.3.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/smoothclust_1.3.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/smoothclust_1.3.4.tgz
vignettes: vignettes/smoothclust/inst/doc/smoothclust.html
vignetteTitles: Smoothclust Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/smoothclust/inst/doc/smoothclust.R
dependencyCount: 88
Package: smoppix
Version: 0.99.44
Depends: R (>= 4.4.0)
Imports: spatstat.geom(>=
3.2.0),spatstat.random,methods,BiocParallel,SummarizedExperiment,SpatialExperiment,scam,Rdpack,stats,utils,extraDistr,lmerTest,lme4,ggplot2,graphics,grDevices,Rcpp
(>= 1.0.11),Matrix,spatstat.model,openxlsx
LinkingTo: Rcpp
Suggests:
testthat,rmarkdown,knitr,DropletUtils,polyCub,RImageJROI,sp,ape,htmltools,funkycells,glmnet,doParallel
License: GPL-2
MD5sum: 80f950ff2e21a89694e42839ad93d176
NeedsCompilation: yes
Title: Analyze Single Molecule Spatial Omics Data Using the
Probabilistic Index
Description: Test for univariate and bivariate spatial patterns in
spatial omics data with single-molecule resolution. The tests
implemented allow for analysis of nested designs and are
automatically calibrated to different biological specimens.
Tests for aggregation, colocalization, gradients and vicinity
to cell edge or centroid are provided.
biocViews: Transcriptomics, Spatial, SingleCell
Author: Stijn Hawinkel [cre, aut] (ORCID:
<https://orcid.org/0000-0002-4501-5180>)
Maintainer: Stijn Hawinkel <stijn.hawinkel@psb.ugent.be>
URL: https://github.com/sthawinke/smoppix
VignetteBuilder: knitr
BugReports: https://github.com/sthawinke/smoppix/issues
git_url: https://git.bioconductor.org/packages/smoppix
git_branch: devel
git_last_commit: db6aad0
git_last_commit_date: 2025-02-13
Date/Publication: 2025-02-13
source.ver: src/contrib/smoppix_0.99.44.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/smoppix_0.99.44.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/smoppix_0.99.44.tgz
vignettes: vignettes/smoppix/inst/doc/smoppixVignette.html
vignetteTitles: Vignette of the smoppix package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/smoppix/inst/doc/smoppixVignette.R
dependencyCount: 122
Package: SNAGEE
Version: 1.47.0
Depends: R (>= 2.6.0), SNAGEEdata
Suggests: ALL, hgu95av2.db
Enhances: parallel
License: Artistic-2.0
MD5sum: 083aa3b4ac8d16912ea6481368ae4d99
NeedsCompilation: no
Title: Signal-to-Noise applied to Gene Expression Experiments
Description: Signal-to-Noise applied to Gene Expression Experiments.
Signal-to-noise ratios can be used as a proxy for quality of
gene expression studies and samples. The SNRs can be calculated
on any gene expression data set as long as gene IDs are
available, no access to the raw data files is necessary. This
allows to flag problematic studies and samples in any public
data set.
biocViews: Microarray, OneChannel, TwoChannel, QualityControl
Author: David Venet <davenet@ulb.ac.be>
Maintainer: David Venet <davenet@ulb.ac.be>
URL: http://bioconductor.org/
git_url: https://git.bioconductor.org/packages/SNAGEE
git_branch: devel
git_last_commit: 0372fc0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SNAGEE_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SNAGEE_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SNAGEE_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SNAGEE_1.47.0.tgz
vignettes: vignettes/SNAGEE/inst/doc/SNAGEE.pdf
vignetteTitles: SNAGEE Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SNAGEE/inst/doc/SNAGEE.R
suggestsMe: SNAGEEdata
dependencyCount: 1
Package: snapcount
Version: 1.19.0
Depends: R (>= 4.0.0)
Imports: R6, httr, rlang, purrr, jsonlite, assertthat, data.table,
Matrix, magrittr, methods, stringr, stats, IRanges,
GenomicRanges, SummarizedExperiment
Suggests: BiocManager, bit64, covr, knitcitations, knitr (>= 1.6),
devtools, BiocStyle (>= 2.5.19), rmarkdown (>= 0.9.5), testthat
(>= 2.1.0)
License: MIT + file LICENSE
Archs: x64
MD5sum: 3762fb8b748da42deed55778f803d2aa
NeedsCompilation: no
Title: R/Bioconductor Package for interfacing with Snaptron for rapid
querying of expression counts
Description: snapcount is a client interface to the Snaptron
webservices which support querying by gene name or genomic
region. Results include raw expression counts derived from
alignment of RNA-seq samples and/or various summarized measures
of expression across one or more regions/genes per-sample (e.g.
percent spliced in).
biocViews: Coverage, GeneExpression, RNASeq, Sequencing, Software,
DataImport
Author: Rone Charles [aut, cre]
Maintainer: Rone Charles <rcharle8@jh.edu>
URL: https://github.com/langmead-lab/snapcount
VignetteBuilder: knitr
BugReports: https://github.com/langmead-lab/snapcount/issues
git_url: https://git.bioconductor.org/packages/snapcount
git_branch: devel
git_last_commit: 9751037
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/snapcount_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/snapcount_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/snapcount_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/snapcount_1.19.0.tgz
vignettes: vignettes/snapcount/inst/doc/snapcount_vignette.html
vignetteTitles: snapcount quick start guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/snapcount/inst/doc/snapcount_vignette.R
dependencyCount: 47
Package: snifter
Version: 1.17.0
Depends: R (>= 4.0.0)
Imports: basilisk, reticulate, irlba, stats, assertthat
Suggests: knitr, rmarkdown, BiocStyle, ggplot2, testthat (>= 3.0.0)
License: GPL-3
MD5sum: 19b8495fececeb77b9146fd6fd99f08a
NeedsCompilation: no
Title: R wrapper for the python openTSNE library
Description: Provides an R wrapper for the implementation of FI-tSNE
from the python package openTNSE. See PoliÄar et al. (2019)
<doi:10.1101/731877> and the algorithm described by Linderman
et al. (2018) <doi:10.1038/s41592-018-0308-4>.
biocViews: DimensionReduction, Visualization, Software, SingleCell,
Sequencing
Author: Alan O'Callaghan [aut, cre], Aaron Lun [aut]
Maintainer: Alan O'Callaghan <alan.ocallaghan@outlook.com>
URL: https://bioconductor.org/packages/snifter
VignetteBuilder: knitr
BugReports: https://github.com/Alanocallaghan/snifter/issues
git_url: https://git.bioconductor.org/packages/snifter
git_branch: devel
git_last_commit: 4fd5628
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/snifter_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/snifter_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/snifter_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/snifter_1.17.0.tgz
vignettes: vignettes/snifter/inst/doc/snifter.html
vignetteTitles: Introduction to snifter
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/snifter/inst/doc/snifter.R
suggestsMe: scater
dependencyCount: 26
Package: snm
Version: 1.55.0
Depends: R (>= 2.12.0)
Imports: corpcor, lme4 (>= 1.0), splines
License: LGPL
MD5sum: bcdb31a1913dcb3914c5037bbb546208
NeedsCompilation: no
Title: Supervised Normalization of Microarrays
Description: SNM is a modeling strategy especially designed for
normalizing high-throughput genomic data. The underlying
premise of our approach is that your data is a function of what
we refer to as study-specific variables. These variables are
either biological variables that represent the target of the
statistical analysis, or adjustment variables that represent
factors arising from the experimental or biological setting the
data is drawn from. The SNM approach aims to simultaneously
model all study-specific variables in order to more accurately
characterize the biological or clinical variables of interest.
biocViews: Microarray, OneChannel, TwoChannel, MultiChannel,
DifferentialExpression, ExonArray, GeneExpression,
Transcription, MultipleComparison, Preprocessing,
QualityControl
Author: Brig Mecham and John D. Storey <jstorey@princeton.edu>
Maintainer: John D. Storey <jstorey@princeton.edu>
git_url: https://git.bioconductor.org/packages/snm
git_branch: devel
git_last_commit: 1e313ea
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/snm_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/snm_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/snm_1.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/snm_1.55.0.tgz
vignettes: vignettes/snm/inst/doc/snm.pdf
vignetteTitles: snm Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/snm/inst/doc/snm.R
importsMe: ExpressionNormalizationWorkflow
dependencyCount: 23
Package: SNPediaR
Version: 1.33.0
Depends: R (>= 3.0.0)
Imports: RCurl, jsonlite
Suggests: BiocStyle, knitr, rmarkdown, testthat
License: GPL-2
MD5sum: c3d005be6b134b56ffd3b5d31c88ae01
NeedsCompilation: no
Title: Query data from SNPedia
Description: SNPediaR provides some tools for downloading and parsing
data from the SNPedia web site <http://www.snpedia.com>. The
implemented functions allow users to import the wiki text
available in SNPedia pages and to extract the most relevant
information out of them. If some information in the downloaded
pages is not automatically processed by the library functions,
users can easily implement their own parsers to access it in an
efficient way.
biocViews: SNP, VariantAnnotation
Author: David Montaner [aut, cre]
Maintainer: David Montaner <david.montaner@gmail.com>
URL: https://github.com/genometra/SNPediaR
VignetteBuilder: knitr
BugReports: https://github.com/genometra/SNPediaR/issues
git_url: https://git.bioconductor.org/packages/SNPediaR
git_branch: devel
git_last_commit: 33c74c4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SNPediaR_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SNPediaR_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SNPediaR_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SNPediaR_1.33.0.tgz
vignettes: vignettes/SNPediaR/inst/doc/SNPediaR.html
vignetteTitles: SNPediaR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SNPediaR/inst/doc/SNPediaR.R
dependencyCount: 4
Package: SNPhood
Version: 1.37.0
Depends: R (>= 3.5.0), GenomicRanges, Rsamtools, data.table, checkmate
Imports: DESeq2, cluster, ggplot2, lattice, GenomeInfoDb (>= 1.34.8),
BiocParallel, VariantAnnotation, BiocGenerics, IRanges,
methods, SummarizedExperiment, RColorBrewer, Biostrings,
grDevices, gridExtra, stats, grid, utils, reshape2, scales,
S4Vectors
Suggests: BiocStyle, knitr, pryr, rmarkdown, SNPhoodData, corrplot
License: LGPL (>= 3)
MD5sum: a923772d4b9a033039294750edf85c3b
NeedsCompilation: no
Title: SNPhood: Investigate, quantify and visualise the epigenomic
neighbourhood of SNPs using NGS data
Description: To date, thousands of single nucleotide polymorphisms
(SNPs) have been found to be associated with complex traits and
diseases. However, the vast majority of these
disease-associated SNPs lie in the non-coding part of the
genome, and are likely to affect regulatory elements, such as
enhancers and promoters, rather than function of a protein.
Thus, to understand the molecular mechanisms underlying genetic
traits and diseases, it becomes increasingly important to study
the effect of a SNP on nearby molecular traits such as
chromatin environment or transcription factor (TF) binding.
Towards this aim, we developed SNPhood, a user-friendly
*Bioconductor* R package to investigate and visualize the local
neighborhood of a set of SNPs of interest for NGS data such as
chromatin marks or transcription factor binding sites from
ChIP-Seq or RNA- Seq experiments. SNPhood comprises a set of
easy-to-use functions to extract, normalize and summarize reads
for a genomic region, perform various data quality checks,
normalize read counts using additional input files, and to
cluster and visualize the regions according to the binding
pattern. The regions around each SNP can be binned in a
user-defined fashion to allow for analysis of very broad
patterns as well as a detailed investigation of specific
binding shapes. Furthermore, SNPhood supports the integration
with genotype information to investigate and visualize
genotype-specific binding patterns. Finally, SNPhood can be
employed for determining, investigating, and visualizing
allele-specific binding patterns around the SNPs of interest.
biocViews: Software
Author: Christian Arnold [aut, cre], Pooja Bhat [aut], Judith Zaugg
[aut]
Maintainer: Christian Arnold <christian.arnold@embl.de>
URL: https://bioconductor.org/packages/SNPhood
VignetteBuilder: knitr
BugReports: mailto:<christian.arnold@embl.de>
git_url: https://git.bioconductor.org/packages/SNPhood
git_branch: devel
git_last_commit: 5ebb343
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SNPhood_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SNPhood_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SNPhood_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SNPhood_1.37.0.tgz
vignettes: vignettes/SNPhood/inst/doc/IntroductionToSNPhood.html,
vignettes/SNPhood/inst/doc/workflow.html
vignetteTitles: Introduction and Methodological Details, Workflow
example
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SNPhood/inst/doc/IntroductionToSNPhood.R,
vignettes/SNPhood/inst/doc/workflow.R
dependencyCount: 112
Package: SNPRelate
Version: 1.41.0
Depends: R (>= 2.15), gdsfmt (>= 1.8.3)
Imports: methods
LinkingTo: gdsfmt
Suggests: parallel, Matrix, RUnit, knitr, markdown, rmarkdown, MASS,
BiocGenerics
Enhances: SeqArray (>= 1.12.0)
License: GPL-3
MD5sum: 803cd26d54adab0f6280b48aaa46ad0a
NeedsCompilation: yes
Title: Parallel Computing Toolset for Relatedness and Principal
Component Analysis of SNP Data
Description: Genome-wide association studies (GWAS) are widely used to
investigate the genetic basis of diseases and traits, but they
pose many computational challenges. We developed an R package
SNPRelate to provide a binary format for single-nucleotide
polymorphism (SNP) data in GWAS utilizing CoreArray Genomic
Data Structure (GDS) data files. The GDS format offers the
efficient operations specifically designed for integers with
two bits, since a SNP could occupy only two bits. SNPRelate is
also designed to accelerate two key computations on SNP data
using parallel computing for multi-core symmetric
multiprocessing computer architectures: Principal Component
Analysis (PCA) and relatedness analysis using
Identity-By-Descent measures. The SNP GDS format is also used
by the GWASTools package with the support of S4 classes and
generic functions. The extended GDS format is implemented in
the SeqArray package to support the storage of single
nucleotide variations (SNVs), insertion/deletion polymorphism
(indel) and structural variation calls in whole-genome and
whole-exome variant data.
biocViews: Infrastructure, Genetics, StatisticalMethod,
PrincipalComponent
Author: Xiuwen Zheng [aut, cre, cph] (ORCID:
<https://orcid.org/0000-0002-1390-0708>), Stephanie Gogarten
[ctb], Cathy Laurie [ctb], Bruce Weir [ctb, ths] (ORCID:
<https://orcid.org/0000-0002-4883-1247>)
Maintainer: Xiuwen Zheng <zhengx@u.washington.edu>
URL: https://github.com/zhengxwen/SNPRelate
VignetteBuilder: knitr
BugReports: https://github.com/zhengxwen/SNPRelate/issues
git_url: https://git.bioconductor.org/packages/SNPRelate
git_branch: devel
git_last_commit: 5e409c6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SNPRelate_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SNPRelate_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SNPRelate_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SNPRelate_1.41.0.tgz
vignettes: vignettes/SNPRelate/inst/doc/SNPRelate.html
vignetteTitles: SNPRelate Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SNPRelate/inst/doc/SNPRelate.R
dependsOnMe: RAIDS, SeqSQC
importsMe: CNVRanger, GDSArray, GENESIS, gwasurvivr, VariantExperiment,
EthSEQ, gwid, simplePHENOTYPES, snplinkage
suggestsMe: GWASTools, HIBAG, SAIGEgds, SeqArray
dependencyCount: 2
Package: snpStats
Version: 1.57.1
Depends: R(>= 2.10.0), survival, Matrix, methods
Imports: graphics, grDevices, stats, utils, BiocGenerics, zlibbioc
Suggests: hexbin
License: GPL-3
MD5sum: 878dbc3d927cbafae7e51fc020139c64
NeedsCompilation: yes
Title: SnpMatrix and XSnpMatrix classes and methods
Description: Classes and statistical methods for large SNP association
studies. This extends the earlier snpMatrix package, allowing
for uncertainty in genotypes.
biocViews: Microarray, SNP, GeneticVariability
Author: David Clayton <dc208@cam.ac.uk>
Maintainer: David Clayton <dc208@cam.ac.uk>
git_url: https://git.bioconductor.org/packages/snpStats
git_branch: devel
git_last_commit: c8d8719
git_last_commit_date: 2025-03-27
Date/Publication: 2025-03-28
source.ver: src/contrib/snpStats_1.57.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/snpStats_1.57.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/snpStats_1.57.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/snpStats_1.57.1.tgz
vignettes: vignettes/snpStats/inst/doc/data-input-vignette.pdf,
vignettes/snpStats/inst/doc/differences.pdf,
vignettes/snpStats/inst/doc/Fst-vignette.pdf,
vignettes/snpStats/inst/doc/imputation-vignette.pdf,
vignettes/snpStats/inst/doc/ld-vignette.pdf,
vignettes/snpStats/inst/doc/pca-vignette.pdf,
vignettes/snpStats/inst/doc/snpStats-vignette.pdf,
vignettes/snpStats/inst/doc/tdt-vignette.pdf
vignetteTitles: Data input, snpMatrix-differences, Fst, Imputation and
meta-analysis, LD statistics, Principal components analysis,
snpStats introduction, TDT tests
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/snpStats/inst/doc/data-input-vignette.R,
vignettes/snpStats/inst/doc/Fst-vignette.R,
vignettes/snpStats/inst/doc/imputation-vignette.R,
vignettes/snpStats/inst/doc/ld-vignette.R,
vignettes/snpStats/inst/doc/pca-vignette.R,
vignettes/snpStats/inst/doc/snpStats-vignette.R,
vignettes/snpStats/inst/doc/tdt-vignette.R
dependsOnMe: MAGAR
importsMe: cardelino, DExMA, GeneGeneInteR, gwascat, martini, RVS,
scoreInvHap, dartR.base, GenomicTools.fileHandler, gpcp,
GWASbyCluster, PhenotypeSimulator, TriadSim
suggestsMe: crlmm, GenomicFiles, GWASTools, ldblock, omicRexposome,
omicsPrint, VariantAnnotation, adjclust, dartR, dartR.popgen,
genio, pegas, RcppDPR, statgenGWAS
dependencyCount: 13
Package: soGGi
Version: 1.39.0
Depends: R (>= 3.5.0), BiocGenerics, SummarizedExperiment
Imports: methods, reshape2, ggplot2, S4Vectors, IRanges, GenomeInfoDb,
GenomicRanges, Biostrings, Rsamtools, GenomicAlignments,
rtracklayer, preprocessCore, chipseq, BiocParallel
Suggests: testthat, BiocStyle, knitr
License: GPL (>= 3)
MD5sum: acc79d4ade6046652b0a9afc0d9d3474
NeedsCompilation: no
Title: Visualise ChIP-seq, MNase-seq and motif occurrence as aggregate
plots Summarised Over Grouped Genomic Intervals
Description: The soGGi package provides a toolset to create genomic
interval aggregate/summary plots of signal or motif occurence
from BAM and bigWig files as well as PWM, rlelist, GRanges and
GAlignments Bioconductor objects. soGGi allows for
normalisation, transformation and arithmetic operation on and
between summary plot objects as well as grouping and subsetting
of plots by GRanges objects and user supplied metadata. Plots
are created using the GGplot2 libary to allow user defined
manipulation of the returned plot object. Coupled together,
soGGi features a broad set of methods to visualise genomics
data in the context of groups of genomic intervals such as
genes, superenhancers and transcription factor binding events.
biocViews: Sequencing, ChIPSeq, Coverage
Author: Gopuraja Dharmalingam, Doug Barrows, Tom Carroll
Maintainer: Tom Carroll <tc.infomatics@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/soGGi
git_branch: devel
git_last_commit: 192a969
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/soGGi_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/soGGi_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/soGGi_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/soGGi_1.39.0.tgz
vignettes: vignettes/soGGi/inst/doc/soggi.pdf
vignetteTitles: soggi
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/soGGi/inst/doc/soggi.R
importsMe: profileplyr
dependencyCount: 100
Package: SomaticSignatures
Version: 2.43.0
Depends: R (>= 3.5.0), VariantAnnotation, GenomicRanges, NMF
Imports: S4Vectors, IRanges, GenomeInfoDb, Biostrings, ggplot2, ggbio,
reshape2, NMF, pcaMethods, Biobase, methods, proxy
Suggests: testthat, knitr, parallel,
BSgenome.Hsapiens.1000genomes.hs37d5, SomaticCancerAlterations,
ggdendro, fastICA, sva
License: MIT + file LICENSE
MD5sum: 5fb7b437eb808e4de069b2695f7e2c5a
NeedsCompilation: no
Title: Somatic Signatures
Description: The SomaticSignatures package identifies mutational
signatures of single nucleotide variants (SNVs). It provides a
infrastructure related to the methodology described in
Nik-Zainal (2012, Cell), with flexibility in the matrix
decomposition algorithms.
biocViews: Sequencing, SomaticMutation, Visualization, Clustering,
GenomicVariation, StatisticalMethod
Author: Julian Gehring
Maintainer: Julian Gehring <jg-bioc@gmx.com>
URL: https://github.com/juliangehring/SomaticSignatures
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org
git_url: https://git.bioconductor.org/packages/SomaticSignatures
git_branch: devel
git_last_commit: f592f53
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SomaticSignatures_2.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SomaticSignatures_2.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SomaticSignatures_2.43.0.tgz
vignettes:
vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.html
vignetteTitles: SomaticSignatures
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles:
vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.R
importsMe: YAPSA
dependencyCount: 171
Package: SOMNiBUS
Version: 1.15.1
Depends: R (>= 4.1.0)
Imports: Matrix, mgcv, stats, VGAM, IRanges, GenomeInfoDb,
GenomicRanges, rtracklayer, S4Vectors, BiocManager, annotatr,
yaml, utils, bsseq, reshape2, data.table, ggplot2, tidyr,
Suggests: BiocStyle, covr, devtools, dplyr, knitr, magick, rmarkdown,
testthat, TxDb.Hsapiens.UCSC.hg38.knownGene,
TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db,
License: MIT + file LICENSE
MD5sum: 19cda90faa3568452354d3e89119e3f1
NeedsCompilation: no
Title: Smooth modeling of bisulfite sequencing
Description: This package aims to analyse count-based methylation data
on predefined genomic regions, such as those obtained by
targeted sequencing, and thus to identify differentially
methylated regions (DMRs) that are associated with phenotypes
or traits. The method is built a rich flexible model that
allows for the effects, on the methylation levels, of multiple
covariates to vary smoothly along genomic regions. At the same
time, this method also allows for sequencing errors and can
adjust for variability in cell type mixture.
biocViews: DNAMethylation, Regression, Epigenetics,
DifferentialMethylation, Sequencing, FunctionalPrediction
Author: Kaiqiong Zhao [aut], Kathleen Klein [cre], Audrey Lemaçon [ctb,
ctr], Simon Laurin-Lemay [ctb, ctr], My Intelligent Machines
Inc. [ctr], Celia Greenwood [ths, aut]
Maintainer: Kathleen Klein <kathleen.klein@mail.mcgill.ca>
URL: https://github.com/kaiqiong/SOMNiBUS
VignetteBuilder: knitr
BugReports: https://github.com/kaiqiong/SOMNiBUS/issues
git_url: https://git.bioconductor.org/packages/SOMNiBUS
git_branch: devel
git_last_commit: 8d2582e
git_last_commit_date: 2024-12-31
Date/Publication: 2024-12-31
source.ver: src/contrib/SOMNiBUS_1.15.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SOMNiBUS_1.15.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SOMNiBUS_1.15.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SOMNiBUS_1.15.1.tgz
vignettes: vignettes/SOMNiBUS/inst/doc/SOMNiBUS.html
vignetteTitles: Analyzing Targeted Bisulfite Sequencing data with
SOMNiBUS
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SOMNiBUS/inst/doc/SOMNiBUS.R
dependencyCount: 143
Package: sosta
Version: 0.99.5
Depends: R (>= 4.4.0)
Imports: terra, sf, smoothr, spatstat.explore, spatstat.geom,
SpatialExperiment, SingleCellExperiment, dplyr, ggplot2,
patchwork, SummarizedExperiment, stats, rlang, parallel,
EBImage, spatstat.random
Suggests: knitr, rmarkdown, BiocStyle, ExperimentHub, lme4, lmerTest,
ggfortify, tidyr, testthat (>= 3.0.0)
License: GPL (>= 3) + file LICENSE
Archs: x64
MD5sum: 0a800463e9b37a8736ba491ed2f4c7f7
NeedsCompilation: no
Title: A package for the analysis of anatomical tissue structures in
spatial omics data
Description: sosta (Spatial Omics STructure Analysis) is a package for
analyzing spatial omics data to explore tissue organization at
the anatomical structure level. It reconstructs morphologically
relevant structures based on molecular features or cell types.
It further calculates a range of structural and shape metrics
to quantitatively describe tissue architecture. The package is
designed to integrate with other packages for the analysis of
spatial (omics) data.
biocViews: Software, Spatial, Transcriptomics, Visualization
Author: Samuel Gunz [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8909-0932>), Mark D. Robinson
[aut, fnd]
Maintainer: Samuel Gunz <samuel.gunz@uzh.ch>
URL: https://github.com/sgunz/sosta, https://sgunz.github.io/sosta/
VignetteBuilder: knitr
BugReports: https://github.com/sgunz/sosta/issues
git_url: https://git.bioconductor.org/packages/sosta
git_branch: devel
git_last_commit: 3e9e98c
git_last_commit_date: 2025-03-21
Date/Publication: 2025-03-21
source.ver: src/contrib/sosta_0.99.5.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sosta_0.99.5.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sosta_0.99.5.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sosta_0.99.5.tgz
vignettes: vignettes/sosta/inst/doc/ImcDiabetesIsletsVignette.html,
vignettes/sosta/inst/doc/StructureReconstructionVignette.html
vignetteTitles: Reconstruction and analysis of pancreatic islets from
IMC data, Overview of sosta
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/sosta/inst/doc/ImcDiabetesIsletsVignette.R,
vignettes/sosta/inst/doc/StructureReconstructionVignette.R
importsMe: DESpace
dependencyCount: 135
Package: SpaceMarkers
Version: 1.3.5
Depends: R (>= 4.4.0)
Imports: matrixStats, matrixTests, rstatix, spatstat.explore,
spatstat.geom, ape, hdf5r, nanoparquet, jsonlite, Matrix,
qvalue, stats, utils, methods, ggplot2, reshape2
Suggests: data.table, devtools, dplyr, hrbrthemes, knitr, RColorBrewer,
cowplot, readbitmap, rjson, rmarkdown, BiocStyle, testthat (>=
3.0.0), viridis, CoGAPS
Enhances: BiocParallel
License: MIT + file LICENSE
Archs: x64
MD5sum: 974a11dc4933addc4749aedf28040d0c
NeedsCompilation: no
Title: Spatial Interaction Markers
Description: Spatial transcriptomic technologies have helped to resolve
the connection between gene expression and the 2D orientation
of tissues relative to each other. However, the limited
single-cell resolution makes it difficult to highlight the most
important molecular interactions in these tissues.
SpaceMarkers, R/Bioconductor software, can help to find
molecular interactions, by identifying genes associated with
latent space interactions in spatial transcriptomics.
biocViews: SingleCell, GeneExpression, Software, Spatial,
Transcriptomics
Author: Atul Deshpande [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5144-6924>), Ludmila Danilova
[ctb], Dmitrijs Lvovs [ctb] (ORCID:
<https://orcid.org/0009-0003-2152-6853>)
Maintainer: Atul Deshpande <adeshpande@jhu.edu>
URL: https://github.com/DeshpandeLab/SpaceMarkers
VignetteBuilder: knitr
BugReports: https://github.com/DeshpandeLab/SpaceMarkers/issues
git_url: https://git.bioconductor.org/packages/SpaceMarkers
git_branch: devel
git_last_commit: d33ec2f
git_last_commit_date: 2025-03-17
Date/Publication: 2025-03-17
source.ver: src/contrib/SpaceMarkers_1.3.5.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SpaceMarkers_1.3.5.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SpaceMarkers_1.3.5.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SpaceMarkers_1.3.5.tgz
vignettes: vignettes/SpaceMarkers/inst/doc/SpaceMarkers_vignette.html
vignetteTitles: Inferring Immune Interactions in Breast Cancer
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SpaceMarkers/inst/doc/SpaceMarkers_vignette.R
dependencyCount: 97
Package: Spaniel
Version: 1.21.0
Depends: R (>= 4.0)
Imports: Seurat, SingleCellExperiment, SummarizedExperiment, dplyr,
methods, ggplot2, scater (>= 1.13), scran, igraph, shiny, jpeg,
magrittr, utils, S4Vectors, DropletUtils, jsonlite, png
Suggests: knitr, rmarkdown, testthat, devtools
License: MIT + file LICENSE
MD5sum: caa1ea076f80c49871752307047730fb
NeedsCompilation: no
Title: Spatial Transcriptomics Analysis
Description: Spaniel includes a series of tools to aid the quality
control and analysis of Spatial Transcriptomics data. Spaniel
can import data from either the original Spatial
Transcriptomics system or 10X Visium technology. The package
contains functions to create a SingleCellExperiment Seurat
object and provides a method of loading a histologial image
into R. The spanielPlot function allows visualisation of
metrics contained within the S4 object overlaid onto the image
of the tissue.
biocViews: SingleCell, RNASeq, QualityControl, Preprocessing,
Normalization, Visualization, Transcriptomics, GeneExpression,
Sequencing, Software, DataImport, DataRepresentation,
Infrastructure, Coverage, Clustering
Author: Rachel Queen [aut, cre]
Maintainer: Rachel Queen <rachel.queen@newcastle.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Spaniel
git_branch: devel
git_last_commit: b316159
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Spaniel_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Spaniel_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Spaniel_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Spaniel_1.21.0.tgz
vignettes: vignettes/Spaniel/inst/doc/spaniel-vignette-tenX-import.html
vignetteTitles: Spaniel 10X Visium
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Spaniel/inst/doc/spaniel-vignette-tenX-import.R
dependencyCount: 215
Package: SpaNorm
Version: 1.1.0
Depends: R (>= 4.4)
Imports: edgeR, ggplot2, Matrix, matrixStats, methods, rlang, scran,
SeuratObject, SingleCellExperiment, SpatialExperiment, stats,
SummarizedExperiment, S4Vectors, utils
Suggests: testthat (>= 3.0.0), knitr, rmarkdown, prettydoc, pkgdown,
covr, BiocStyle, Seurat, patchwork, ggforce, ggnewscale
License: GPL (>= 3)
MD5sum: 2e7f2b25be5f4d78bca359f666ff04c7
NeedsCompilation: no
Title: Spatially-aware normalisation for spatial transcriptomics data
Description: This package implements the spatially aware library size
normalisation algorithm, SpaNorm. SpaNorm normalises out
library size effects while retaining biology through the
modelling of smooth functions for each effect. Normalisation is
performed in a gene- and cell-/spot- specific manner, yielding
library size adjusted data.
biocViews: Software, GeneExpression, Transcriptomics, Spatial,
CellBiology
Author: Dharmesh D. Bhuva [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6398-9157>), Agus Salim [aut]
(ORCID: <https://orcid.org/0000-0003-3999-7701>), Ahmed Mohamed
[aut] (ORCID: <https://orcid.org/0000-0001-6507-5300>)
Maintainer: Dharmesh D. Bhuva <dharmesh.bhuva@adelaide.edu.au>
URL: https://bhuvad.github.io/SpaNorm
VignetteBuilder: knitr
BugReports: https://github.com/bhuvad/SpaNorm/issues
git_url: https://git.bioconductor.org/packages/SpaNorm
git_branch: devel
git_last_commit: 6bf7b58
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SpaNorm_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SpaNorm_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SpaNorm_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SpaNorm_1.1.0.tgz
vignettes: vignettes/SpaNorm/inst/doc/SpaNorm.html
vignetteTitles: SpaNorm
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SpaNorm/inst/doc/SpaNorm.R
dependencyCount: 126
Package: sparrow
Version: 1.13.4
Depends: R (>= 4.0)
Imports: babelgene (>= 21.4), BiocGenerics, BiocParallel, BiocSet,
checkmate, circlize, ComplexHeatmap (>= 2.0), data.table (>=
1.10.4), DelayedMatrixStats, edgeR (>= 3.18.1), ggplot2 (>=
2.2.0), graphics, grDevices, GSEABase, irlba, limma, Matrix,
methods, plotly (>= 4.9.0), stats, utils, viridis
Suggests: AnnotationDbi, BiasedUrn, Biobase (>= 2.24.0), BiocStyle,
DESeq2, dplyr, dtplyr, fgsea, GSVA, GO.db, goseq, hexbin,
magrittr, matrixStats, msigdbr (>= 7.4.1), KernSmooth, knitr,
PANTHER.db (>= 1.0.3), R.utils, reactome.db, rmarkdown,
SummarizedExperiment, statmod, stringr, testthat, webshot
License: MIT + file LICENSE
MD5sum: 11d635582f8b019e95a9d1b5bb3cd6ae
NeedsCompilation: no
Title: Take command of set enrichment analyses through a unified
interface
Description: Provides a unified interface to a variety of GSEA
techniques from different bioconductor packages. Results are
harmonized into a single object and can be interrogated
uniformly for quick exploration and interpretation of results.
Interactive exploration of GSEA results is enabled through a
shiny app provided by a sparrow.shiny sibling package.
biocViews: GeneSetEnrichment, Pathways
Author: Steve Lianoglou [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0924-1754>), Arkadiusz Gladki
[ctb], Aratus Informatics, LLC [fnd] (2023+), Denali
Therapeutics [fnd] (2018-2022), Genentech [fnd] (2014 - 2017)
Maintainer: Steve Lianoglou <slianoglou@gmail.com>
URL: https://github.com/lianos/sparrow
VignetteBuilder: knitr
BugReports: https://github.com/lianos/sparrow/issues
git_url: https://git.bioconductor.org/packages/sparrow
git_branch: devel
git_last_commit: 960335c
git_last_commit_date: 2024-12-03
Date/Publication: 2024-12-03
source.ver: src/contrib/sparrow_1.13.4.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sparrow_1.13.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sparrow_1.13.4.tgz
vignettes: vignettes/sparrow/inst/doc/sparrow.html
vignetteTitles: Performing gene set enrichment analyses with sparrow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/sparrow/inst/doc/sparrow.R
suggestsMe: gCrisprTools
dependencyCount: 145
Package: SparseArray
Version: 1.7.7
Depends: R (>= 4.3.0), methods, Matrix, BiocGenerics (>= 0.43.1),
MatrixGenerics (>= 1.11.1), S4Vectors (>= 0.43.2), S4Arrays (>=
1.5.11)
Imports: utils, stats, matrixStats, IRanges, XVector
LinkingTo: S4Vectors, IRanges, XVector
Suggests: HDF5Array, ExperimentHub, testthat, knitr, rmarkdown,
BiocStyle
License: Artistic-2.0
MD5sum: 5892bf80aeb29060c668b205ad121091
NeedsCompilation: yes
Title: High-performance sparse data representation and manipulation in
R
Description: The SparseArray package provides array-like containers for
efficient in-memory representation of multidimensional sparse
data in R (arrays and matrices). The package defines the
SparseArray virtual class and two concrete subclasses:
COO_SparseArray and SVT_SparseArray. Each subclass uses its own
internal representation of the nonzero multidimensional data:
the "COO layout" and the "SVT layout", respectively.
SVT_SparseArray objects mimic as much as possible the behavior
of ordinary matrix and array objects in base R. In particular,
they suppport most of the "standard matrix and array API"
defined in base R and in the matrixStats package from CRAN.
biocViews: Infrastructure, DataRepresentation
Author: Hervé Pagès [aut, cre] (ORCID:
<https://orcid.org/0009-0002-8272-4522>), Vince Carey [fnd]
(ORCID: <https://orcid.org/0000-0003-4046-0063>), Rafael A.
Irizarry [fnd] (ORCID:
<https://orcid.org/0000-0002-3944-4309>), Jacques Serizay [ctb]
(ORCID: <https://orcid.org/0000-0002-4295-0624>)
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/SparseArray
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/SparseArray/issues
git_url: https://git.bioconductor.org/packages/SparseArray
git_branch: devel
git_last_commit: 3770dcb
git_last_commit_date: 2025-03-18
Date/Publication: 2025-03-19
source.ver: src/contrib/SparseArray_1.7.7.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SparseArray_1.7.7.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SparseArray_1.7.7.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SparseArray_1.7.7.tgz
vignettes: vignettes/SparseArray/inst/doc/SparseArray_objects.html
vignetteTitles: SparseArray objects
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SparseArray/inst/doc/SparseArray_objects.R
dependsOnMe: DelayedArray, DelayedRandomArray, h5mread, HDF5Array,
TileDBArray
importsMe: alabaster.matrix, batchelor, beachmat, DelayedMatrixStats,
DelayedTensor, dreamlet, DropletUtils, glmGamPoi, SCArray,
scater, scone, scuttle, TSCAN, scRNAseq, IDLFM
suggestsMe: BiocGenerics, MatrixGenerics, S4Arrays,
SummarizedExperiment
dependencyCount: 20
Package: sparseMatrixStats
Version: 1.19.0
Depends: MatrixGenerics (>= 1.5.3)
Imports: Rcpp, Matrix, matrixStats (>= 0.60.0), methods
LinkingTo: Rcpp
Suggests: testthat (>= 2.1.0), knitr, bench, rmarkdown, BiocStyle
License: MIT + file LICENSE
MD5sum: cf632a40ed06cbe93386a44d4cd2d6ca
NeedsCompilation: yes
Title: Summary Statistics for Rows and Columns of Sparse Matrices
Description: High performance functions for row and column operations
on sparse matrices. For example: col / rowMeans2, col /
rowMedians, col / rowVars etc. Currently, the optimizations are
limited to data in the column sparse format. This package is
inspired by the matrixStats package by Henrik Bengtsson.
biocViews: Infrastructure, Software, DataRepresentation
Author: Constantin Ahlmann-Eltze [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3762-068X>)
Maintainer: Constantin Ahlmann-Eltze <artjom31415@googlemail.com>
URL: https://github.com/const-ae/sparseMatrixStats
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/const-ae/sparseMatrixStats/issues
git_url: https://git.bioconductor.org/packages/sparseMatrixStats
git_branch: devel
git_last_commit: 4a46ab4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/sparseMatrixStats_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sparseMatrixStats_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sparseMatrixStats_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sparseMatrixStats_1.19.0.tgz
vignettes: vignettes/sparseMatrixStats/inst/doc/sparseMatrixStats.html
vignetteTitles: sparseMatrixStats
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/sparseMatrixStats/inst/doc/sparseMatrixStats.R
importsMe: atena, ccImpute, concordexR, DelayedMatrixStats, dreamlet,
GSVA, scone, SimBu, smartid, smoothclust, SplineDV, SPOTlight,
adjclust, CRMetrics, GrabSVG, mombf, scBSP
suggestsMe: APL, MatrixGenerics, miloR, scPCA, scrapper, scuttle,
SpatialFeatureExperiment, StabMap, zinbwave, singleCellHaystack
dependencyCount: 11
Package: sparsenetgls
Version: 1.25.0
Depends: R (>= 4.0.0), Matrix, MASS
Imports: methods, glmnet, huge, stats, graphics, utils
Suggests: testthat, lme4, BiocStyle, knitr, rmarkdown, roxygen2 (>=
5.0.0)
License: GPL-3
MD5sum: 255ee95a1d0ccfd3b6ca9968f9465803
NeedsCompilation: no
Title: Using Gaussian graphical structue learning estimation in
generalized least squared regression for multivariate normal
regression
Description: The package provides methods of combining the graph
structure learning and generalized least squares regression to
improve the regression estimation. The main function
sparsenetgls() provides solutions for multivariate regression
with Gaussian distributed dependant variables and explanatory
variables utlizing multiple well-known graph structure learning
approaches to estimating the precision matrix, and uses a
penalized variance covariance matrix with a distance tuning
parameter of the graph structure in deriving the sandwich
estimators in generalized least squares (gls) regression. This
package also provides functions for assessing a Gaussian
graphical model which uses the penalized approach. It uses
Receiver Operative Characteristics curve as a visualization
tool in the assessment.
biocViews: ImmunoOncology,
GraphAndNetwork,Regression,Metabolomics,CopyNumberVariation,MassSpectrometry,Proteomics,Software,Visualization
Author: Irene Zeng [aut, cre], Thomas Lumley [ctb]
Maintainer: Irene Zeng <szen003@aucklanduni.ac.nz>
SystemRequirements: GNU make
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/sparsenetgls
git_branch: devel
git_last_commit: 76afb55
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/sparsenetgls_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sparsenetgls_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sparsenetgls_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sparsenetgls_1.25.0.tgz
vignettes: vignettes/sparsenetgls/inst/doc/vignettes_sparsenetgls.html
vignetteTitles: Introduction to sparsenetgls
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sparsenetgls/inst/doc/vignettes_sparsenetgls.R
dependencyCount: 28
Package: SparseSignatures
Version: 2.17.1
Depends: R (>= 4.1.0), NMF
Imports: nnlasso, nnls, parallel, data.table, Biostrings,
GenomicRanges, IRanges, BSgenome, GenomeInfoDb, ggplot2,
gridExtra, reshape2, RhpcBLASctl
Suggests: BiocGenerics, BSgenome.Hsapiens.1000genomes.hs37d5,
BiocStyle, testthat, knitr,
License: file LICENSE
MD5sum: dcea4b3044bddf24c2fe8f19d9d71f25
NeedsCompilation: no
Title: SparseSignatures
Description: Point mutations occurring in a genome can be divided into
96 categories based on the base being mutated, the base it is
mutated into and its two flanking bases. Therefore, for any
patient, it is possible to represent all the point mutations
occurring in that patient's tumor as a vector of length 96,
where each element represents the count of mutations for a
given category in the patient. A mutational signature
represents the pattern of mutations produced by a mutagen or
mutagenic process inside the cell. Each signature can also be
represented by a vector of length 96, where each element
represents the probability that this particular mutagenic
process generates a mutation of the 96 above mentioned
categories. In this R package, we provide a set of functions to
extract and visualize the mutational signatures that best
explain the mutation counts of a large number of patients.
biocViews: BiomedicalInformatics, SomaticMutation
Author: Daniele Ramazzotti [aut] (ORCID:
<https://orcid.org/0000-0002-6087-2666>), Avantika Lal [aut],
Keli Liu [ctb], Luca De Sano [cre, aut] (ORCID:
<https://orcid.org/0000-0002-9618-3774>), Robert Tibshirani
[ctb], Arend Sidow [aut]
Maintainer: Luca De Sano <luca.desano@gmail.com>
URL: https://github.com/danro9685/SparseSignatures
VignetteBuilder: knitr
BugReports: https://github.com/danro9685/SparseSignatures
git_url: https://git.bioconductor.org/packages/SparseSignatures
git_branch: devel
git_last_commit: 47eda2c
git_last_commit_date: 2025-03-25
Date/Publication: 2025-03-26
source.ver: src/contrib/SparseSignatures_2.17.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SparseSignatures_2.17.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SparseSignatures_2.17.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SparseSignatures_2.17.1.tgz
vignettes: vignettes/SparseSignatures/inst/doc/v1_introduction.html,
vignettes/SparseSignatures/inst/doc/v2_using_the_package.html
vignetteTitles: v1_introduction.html, v2_using_the_package.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SparseSignatures/inst/doc/v2_using_the_package.R
dependencyCount: 105
Package: spaSim
Version: 1.9.0
Depends: R (>= 4.2.0)
Imports: ggplot2, methods, stats, dplyr, spatstat.geom,
spatstat.random, SpatialExperiment, SummarizedExperiment, RANN
Suggests: RefManageR, BiocStyle, knitr, testthat (>= 3.0.0),
sessioninfo, rmarkdown, markdown
License: Artistic-2.0
MD5sum: d5a55fb013a35697d9205e21baf716cf
NeedsCompilation: no
Title: Spatial point data simulator for tissue images
Description: A suite of functions for simulating spatial patterns of
cells in tissue images. Output images are multitype point data
in SingleCellExperiment format. Each point represents a cell,
with its 2D locations and cell type. Potential cell patterns
include background cells, tumour/immune cell clusters, immune
rings, and blood/lymphatic vessels.
biocViews: StatisticalMethod, Spatial, BiomedicalInformatics
Author: Yuzhou Feng [aut, cre] (ORCID:
<https://orcid.org/0000-0002-2955-4053>), Anna Trigos [aut]
(ORCID: <https://orcid.org/0000-0002-5915-2952>)
Maintainer: Yuzhou Feng <yuzhou.feng@petermac.org>
URL: https://trigosteam.github.io/spaSim/
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/spaSim
git_url: https://git.bioconductor.org/packages/spaSim
git_branch: devel
git_last_commit: 27c44e5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/spaSim_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/spaSim_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/spaSim_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/spaSim_1.9.0.tgz
vignettes: vignettes/spaSim/inst/doc/vignette.html
vignetteTitles: vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/spaSim/inst/doc/vignette.R
dependencyCount: 94
Package: SpatialCPie
Version: 1.23.0
Depends: R (>= 3.6)
Imports: colorspace (>= 1.3-2), data.table (>= 1.12.2), digest (>=
0.6.21), dplyr (>= 0.7.6), ggforce (>= 0.3.0), ggiraph (>=
0.5.0), ggplot2 (>= 3.0.0), ggrepel (>= 0.8.0), grid (>=
3.5.1), igraph (>= 1.2.2), lpSolve (>= 5.6.13), methods (>=
3.5.0), purrr (>= 0.2.5), readr (>= 1.1.1), rlang (>= 0.2.2),
shiny (>= 1.1.0), shinycssloaders (>= 0.2.0), shinyjs (>= 1.0),
shinyWidgets (>= 0.4.8), stats (>= 3.6.0), SummarizedExperiment
(>= 1.10.1), tibble (>= 1.4.2), tidyr (>= 0.8.1), tidyselect
(>= 0.2.4), tools (>= 3.6.0), utils (>= 3.5.0), zeallot (>=
0.1.0)
Suggests: BiocStyle (>= 2.8.2), jpeg (>= 0.1-8), knitr (>= 1.20),
rmarkdown (>= 1.10), testthat (>= 2.0.0)
License: MIT + file LICENSE
MD5sum: 36320e0455530a9b02cd5fa40e77dce4
NeedsCompilation: no
Title: Cluster analysis of Spatial Transcriptomics data
Description: SpatialCPie is an R package designed to facilitate cluster
evaluation for spatial transcriptomics data by providing
intuitive visualizations that display the relationships between
clusters in order to guide the user during cluster
identification and other downstream applications. The package
is built around a shiny "gadget" to allow the exploration of
the data with multiple plots in parallel and an interactive UI.
The user can easily toggle between different cluster
resolutions in order to choose the most appropriate visual
cues.
biocViews: Transcriptomics, Clustering, RNASeq, Software
Author: Joseph Bergenstraahle [aut, cre]
Maintainer: Joseph Bergenstraahle <joseph.bergenstrahle@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SpatialCPie
git_branch: devel
git_last_commit: 8f8ad85
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SpatialCPie_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SpatialCPie_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SpatialCPie_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SpatialCPie_1.23.0.tgz
vignettes: vignettes/SpatialCPie/inst/doc/SpatialCPie.html
vignetteTitles: SpatialCPie
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SpatialCPie/inst/doc/SpatialCPie.R
dependencyCount: 121
Package: spatialDE
Version: 1.13.0
Depends: R (>= 4.3)
Imports: reticulate, basilisk (>= 1.9.10), checkmate, stats,
SpatialExperiment, methods, SummarizedExperiment, Matrix,
ggplot2, ggrepel, scales, gridExtra
Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: 22aeb212f74362552a65b4967bc100b5
NeedsCompilation: no
Title: R wrapper for SpatialDE
Description: SpatialDE is a method to find spatially variable genes
(SVG) from spatial transcriptomics data. This package provides
wrappers to use the Python SpatialDE library in R, using
reticulate and basilisk.
biocViews: Software, Transcriptomics
Author: Davide Corso [aut] (ORCID:
<https://orcid.org/0000-0001-8845-3693>), Milan Malfait [aut]
(ORCID: <https://orcid.org/0000-0001-9144-3701>), Lambda Moses
[aut] (ORCID: <https://orcid.org/0000-0002-7092-9427>),
Gabriele Sales [cre]
Maintainer: Gabriele Sales <gabriele.sales@unipd.it>
URL: https://github.com/sales-lab/spatialDE,
https://bioconductor.org/packages/spatialDE/
VignetteBuilder: knitr
BugReports: https://github.com/sales-lab/spatialDE/issues
git_url: https://git.bioconductor.org/packages/spatialDE
git_branch: devel
git_last_commit: 03b4e0e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/spatialDE_1.13.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/spatialDE_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/spatialDE_1.13.0.tgz
vignettes: vignettes/spatialDE/inst/doc/spatialDE.html
vignetteTitles: Introduction to spatialDE
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/spatialDE/inst/doc/spatialDE.R
dependencyCount: 100
Package: SpatialDecon
Version: 1.17.0
Depends: R (>= 4.0.0)
Imports: grDevices, stats, utils, graphics, SeuratObject, Biobase,
GeomxTools, repmis, methods, Matrix, logNormReg (>= 0.4)
Suggests: testthat, knitr, rmarkdown, qpdf, Seurat
License: MIT + file LICENSE
Archs: x64
MD5sum: ed17ef6dc24428671de58bb56b7facad
NeedsCompilation: no
Title: Deconvolution of mixed cells from spatial and/or bulk gene
expression data
Description: Using spatial or bulk gene expression data, estimates
abundance of mixed cell types within each observation. Based on
"Advances in mixed cell deconvolution enable quantification of
cell types in spatial transcriptomic data", Danaher (2022).
Designed for use with the NanoString GeoMx platform, but
applicable to any gene expression data.
biocViews: ImmunoOncology, FeatureExtraction, GeneExpression,
Transcriptomics, Spatial
Author: Maddy Griswold [cre, aut], Patrick Danaher [aut]
Maintainer: Maddy Griswold <mgriswold@nanostring.com>
VignetteBuilder: knitr
BugReports: https://github.com/Nanostring-Biostats/SpatialDecon/issues
git_url: https://git.bioconductor.org/packages/SpatialDecon
git_branch: devel
git_last_commit: 7d7d839
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SpatialDecon_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SpatialDecon_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SpatialDecon_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SpatialDecon_1.17.0.tgz
vignettes:
vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette_NSCLC.html,
vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette.html
vignetteTitles: Use of SpatialDecon in a large GeoMx dataset with
GeomxTools, Use of SpatialDecon in a small GeoMx dataet
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette_NSCLC.R,
vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette.R
suggestsMe: GeomxTools
dependencyCount: 137
Package: SpatialExperiment
Version: 1.17.0
Depends: methods, SingleCellExperiment
Imports: rjson, grDevices, magick, utils, S4Vectors,
SummarizedExperiment, BiocGenerics, BiocFileCache
Suggests: knitr, rmarkdown, testthat, BiocStyle, BumpyMatrix,
DropletUtils
License: GPL-3
Archs: x64
MD5sum: 0edfabf3d5dd25433d329b50efc8a82f
NeedsCompilation: no
Title: S4 Class for Spatially Resolved -omics Data
Description: Defines an S4 class for storing data from spatial -omics
experiments. The class extends SingleCellExperiment to support
storage and retrieval of additional information from spot-based
and molecule-based platforms, including spatial coordinates,
images, and image metadata. A specialized constructor function
is included for data from the 10x Genomics Visium platform.
biocViews: DataRepresentation, DataImport, Infrastructure,
ImmunoOncology, GeneExpression, Transcriptomics, SingleCell,
Spatial
Author: Dario Righelli [aut, cre], Davide Risso [aut], Helena L.
Crowell [aut], Lukas M. Weber [aut], Nicholas J. Eagles [ctb]
Maintainer: Dario Righelli <dario.righelli@gmail.com>
URL: https://github.com/drighelli/SpatialExperiment
VignetteBuilder: knitr
BugReports: https://github.com/drighelli/SpatialExperiment/issues
git_url: https://git.bioconductor.org/packages/SpatialExperiment
git_branch: devel
git_last_commit: 29a659f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SpatialExperiment_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SpatialExperiment_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SpatialExperiment_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SpatialExperiment_1.17.0.tgz
vignettes: vignettes/SpatialExperiment/inst/doc/SpatialExperiment.html
vignetteTitles: Introduction to the SpatialExperiment class
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SpatialExperiment/inst/doc/SpatialExperiment.R
dependsOnMe: alabaster.spatial, ExperimentSubset, imcRtools, SPIAT,
tidySpatialExperiment, visiumStitched, imcdatasets,
MerfishData, MouseGastrulationData, spatialLIBD, STexampleData,
TENxVisiumData, VectraPolarisData, WeberDivechaLCdata
importsMe: Banksy, BulkSignalR, CARDspa, CatsCradle, concordexR, CTSV,
cytomapper, DESpace, escheR, FLAMES, ggspavis, GSVA, hoodscanR,
lisaClust, MoleculeExperiment, nnSVG, poem, scider,
signifinder, smoothclust, sosta, SpaNorm, spaSim, spatialDE,
SpatialExperimentIO, spatialFDA, SpatialFeatureExperiment,
spatialSimGP, spicyR, spoon, SpotClean, SpotSweeper, standR,
Statial, stJoincount, SVP, tpSVG, VisiumIO, Voyager, XeniumIO,
xenLite, HCATonsilData, SingleCellMultiModal,
SubcellularSpatialData, TENxXeniumData, SpatialDDLS
suggestsMe: GeomxTools, ggsc, SPOTlight, muSpaData
dependencyCount: 71
Package: SpatialExperimentIO
Version: 0.99.8
Imports: DropletUtils, SpatialExperiment, SingleCellExperiment,
methods, data.table, arrow, purrr, S4Vectors
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle
License: Artistic-2.0
MD5sum: 89b40a2fca3dadd3822f83e68fd891e7
NeedsCompilation: no
Title: Read in Xenium, CosMx, MERSCOPE or STARmapPLUS data as
SpatialExperiment object
Description: Read in imaging-based spatial transcriptomics technology
data. Current available modules are for Xenium by 10X Genomics,
CosMx by Nanostring, MERSCOPE by Vizgen, or STARmapPLUS from
Broad Institute. You can choose to read the data in as a
SpatialExperiment or a SingleCellExperiment object.
biocViews: DataRepresentation, DataImport, Infrastructure,
Transcriptomics, SingleCell, Spatial, GeneExpression
Author: Yixing E. Dong [aut, cre] (ORCID:
<https://orcid.org/0009-0003-5115-5686>)
Maintainer: Yixing E. Dong <estelladong729@gmail.com>
URL: https://github.com/estellad/SpatialExperimentIO
VignetteBuilder: knitr
BugReports: https://github.com/estellad/SpatialExperimentIO/issues
git_url: https://git.bioconductor.org/packages/SpatialExperimentIO
git_branch: devel
git_last_commit: 6daa463
git_last_commit_date: 2025-02-12
Date/Publication: 2025-02-13
source.ver: src/contrib/SpatialExperimentIO_0.99.8.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SpatialExperimentIO_0.99.8.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SpatialExperimentIO_0.99.8.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SpatialExperimentIO_0.99.8.tgz
vignettes:
vignettes/SpatialExperimentIO/inst/doc/SpatialExperimentIO.html
vignetteTitles: SpatialExperimentIO Reader Package Overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SpatialExperimentIO/inst/doc/SpatialExperimentIO.R
suggestsMe: OSTA.data
dependencyCount: 104
Package: spatialFDA
Version: 0.99.12
Depends: R (>= 4.3.0)
Imports: dplyr, ggplot2, parallel, patchwork, purrr, refund,
SpatialExperiment, spatstat.explore, spatstat.geom,
SummarizedExperiment, tidyr, methods, stats, fda, graphics,
magrittr, ExperimentHub
Suggests: stringr, knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0)
License: GPL (>= 3) + file LICENSE
MD5sum: e73131bace45695d1a5201247c3446af
NeedsCompilation: no
Title: A Tool for Spatial Multi-sample Comparisons
Description: spatialFDA is a package to calculate spatial statistics
metrics. The package takes a SpatialExperiment object and
calculates spatial statistics metrics using the package
spatstat. Then it compares the resulting functions across
samples/conditions using functional additive models as
implemented in the package refund. Furthermore, it provides
exploratory visualisations using functional principal component
analysis, as well implemented in refund.
biocViews: Software, Spatial, Transcriptomics
Author: Martin Emons [aut, cre] (ORCID:
<https://orcid.org/0009-0000-5219-5311>), Samuel Gunz [aut]
(ORCID: <https://orcid.org/0000-0002-8909-0932>), Mark Robinson
[aut, fnd] (ORCID: <https://orcid.org/0000-0002-3048-5518>)
Maintainer: Martin Emons <martin.emons@uzh.ch>
URL: https://github.com/mjemons/spatialFDA
VignetteBuilder: knitr
BugReports: https://github.com/mjemons/spatialFDA/issues
git_url: https://git.bioconductor.org/packages/spatialFDA
git_branch: devel
git_last_commit: 19a8193
git_last_commit_date: 2025-02-19
Date/Publication: 2025-02-20
source.ver: src/contrib/spatialFDA_0.99.12.tar.gz
win.binary.ver: bin/windows/contrib/4.5/spatialFDA_0.99.12.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/spatialFDA_0.99.12.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/spatialFDA_0.99.12.tgz
vignettes: vignettes/spatialFDA/inst/doc/DiabetesIsletExample.html
vignetteTitles: Functional Data Analysis of Spatial Metrics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/spatialFDA/inst/doc/DiabetesIsletExample.R
dependencyCount: 142
Package: SpatialFeatureExperiment
Version: 1.9.9
Depends: R (>= 4.2.0)
Imports: Biobase, BiocGenerics (>= 0.51.2), BiocNeighbors,
BiocParallel, data.table, DropletUtils, EBImage, grDevices,
lifecycle, Matrix, methods, rjson, rlang, S4Vectors, sf,
sfheaders, SingleCellExperiment, SpatialExperiment, spatialreg,
spdep (>= 1.1-7), SummarizedExperiment, stats, terra, utils,
zeallot
Suggests: arrow, BiocStyle, dplyr, knitr, RBioFormats, rhdf5,
rmarkdown, scater, sfarrow, SFEData (>= 1.5.3), Seurat,
SeuratObject, sparseMatrixStats, testthat (>= 3.0.0), tidyr,
Voyager (>= 1.7.2), withr, xml2
License: Artistic-2.0
MD5sum: 0f9de03a4c4c2daf874a3a42ee8afb16
NeedsCompilation: no
Title: Integrating SpatialExperiment with Simple Features in sf
Description: A new S4 class integrating Simple Features with the R
package sf to bring geospatial data analysis methods based on
vector data to spatial transcriptomics. Also implements
management of spatial neighborhood graphs and geometric
operations. This pakage builds upon SpatialExperiment and
SingleCellExperiment, hence methods for these parent classes
can still be used.
biocViews: DataRepresentation, Transcriptomics, Spatial
Author: Lambda Moses [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7092-9427>), Alik Huseynov [aut]
(ORCID: <https://orcid.org/0000-0002-1438-4389>), Lior Pachter
[aut, ths] (ORCID: <https://orcid.org/0000-0002-9164-6231>)
Maintainer: Lambda Moses <dl3764@columbia.edu>
URL: https://github.com/pachterlab/SpatialFeatureExperiment
VignetteBuilder: knitr
BugReports:
https://github.com/pachterlab/SpatialFeatureExperiment/issues
git_url: https://git.bioconductor.org/packages/SpatialFeatureExperiment
git_branch: devel
git_last_commit: eff8cee
git_last_commit_date: 2025-02-11
Date/Publication: 2025-02-12
source.ver: src/contrib/SpatialFeatureExperiment_1.9.9.tar.gz
win.binary.ver:
bin/windows/contrib/4.5/SpatialFeatureExperiment_1.9.9.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SpatialFeatureExperiment_1.9.9.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SpatialFeatureExperiment_1.9.7.tgz
vignettes: vignettes/SpatialFeatureExperiment/inst/doc/SFE.html
vignetteTitles: Introduction to the SpatialFeatureExperiment class
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SpatialFeatureExperiment/inst/doc/SFE.R
dependsOnMe: alabaster.sfe, Voyager
importsMe: TENxXeniumData
suggestsMe: concordexR, jazzPanda, xenLite, SFEData
dependencyCount: 157
Package: spatialHeatmap
Version: 2.13.1
Depends: R (>= 3.5.0)
Imports: data.table, dplyr, edgeR, genefilter, ggplot2, grImport, grid,
gridExtra, igraph, methods, Matrix, rsvg, shiny, grDevices,
graphics, ggplotify, parallel, reshape2, stats,
SummarizedExperiment, SingleCellExperiment, shinydashboard,
S4Vectors, spsComps (>= 0.3.3.0), tibble, utils, xml2
Suggests: AnnotationDbi, av, BiocParallel, BiocFileCache, BiocGenerics,
BiocStyle, BiocSingular, Biobase, cachem, DESeq2, dendextend,
DT, dynamicTreeCut, flashClust, gplots, ggdendro, HDF5Array,
htmltools, htmlwidgets, kableExtra, knitr, limma, magick,
memoise, ExpressionAtlas, GEOquery, org.Hs.eg.db, org.Mm.eg.db,
org.At.tair.db, org.Dr.eg.db, org.Dm.eg.db, pROC, plotly,
rmarkdown, rols, rappdirs, RUnit, Rtsne, rhdf5, scater,
scuttle, scran, shinyWidgets, shinyjs, shinyBS, sortable,
Seurat, sparkline, spsUtil, uwot, UpSetR, visNetwork, WGCNA,
xlsx, yaml
License: Artistic-2.0
MD5sum: 92a54977edcdeffe903a60e7c2e13421
NeedsCompilation: no
Title: spatialHeatmap: Visualizing Spatial Assays in Anatomical Images
and Large-Scale Data Extensions
Description: The spatialHeatmap package offers the primary
functionality for visualizing cell-, tissue- and organ-specific
assay data in spatial anatomical images. Additionally, it
provides extended functionalities for large-scale data mining
routines and co-visualizing bulk and single-cell data. A
description of the project is available here:
https://spatialheatmap.org.
biocViews: Spatial, Visualization, Microarray, Sequencing,
GeneExpression, DataRepresentation, Network, Clustering,
GraphAndNetwork, CellBasedAssays, ATACSeq, DNASeq,
TissueMicroarray, SingleCell, CellBiology, GeneTarget
Author: Jianhai Zhang [aut, trl, cre], Le Zhang [aut], Jordan Hayes
[aut], Brendan Gongol [aut], Alexander Borowsky [aut], Julia
Bailey-Serres [aut], Thomas Girke [aut]
Maintainer: Jianhai Zhang <jzhan067@ucr.edu>
URL: https://spatialheatmap.org,
https://github.com/jianhaizhang/spatialHeatmap
VignetteBuilder: knitr
BugReports: https://github.com/jianhaizhang/spatialHeatmap/issues
git_url: https://git.bioconductor.org/packages/spatialHeatmap
git_branch: devel
git_last_commit: 0485a13
git_last_commit_date: 2024-11-24
Date/Publication: 2024-11-24
source.ver: src/contrib/spatialHeatmap_2.13.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/spatialHeatmap_2.13.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/spatialHeatmap_2.13.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/spatialHeatmap_2.13.1.tgz
vignettes: vignettes/spatialHeatmap/inst/doc/covisualize.html,
vignettes/spatialHeatmap/inst/doc/custom_SVGs.html,
vignettes/spatialHeatmap/inst/doc/spatialHeatmap.html
vignetteTitles: (B) Co-visualizing Bulk and Single-cell Assay Data, (C)
Creating Custom Annotated SVGs, (A) Visualizing Spatial Assays
in Anatomical Images and Large-Scale Data Extensions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/spatialHeatmap/inst/doc/covisualize.R,
vignettes/spatialHeatmap/inst/doc/custom_SVGs.R,
vignettes/spatialHeatmap/inst/doc/spatialHeatmap.R
dependencyCount: 123
Package: SpatialOmicsOverlay
Version: 1.7.0
Depends: R (>= 4.1.0)
Imports: S4Vectors, Biobase, base64enc, EBImage, ggplot2, XML,
scattermore, dplyr, pbapply, data.table, readxl, magick,
grDevices, stringr, plotrix, GeomxTools, BiocFileCache, stats,
utils, methods, ggtext, tools, RBioFormats
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), stringi, qpdf,
pheatmap, viridis, cowplot, vdiffr, sf
License: MIT
MD5sum: 8136667fe9a92778c0f5c87430610c80
NeedsCompilation: no
Title: Spatial Overlay for Omic Data from Nanostring GeoMx Data
Description: Tools for NanoString Technologies GeoMx Technology.
Package to easily graph on top of an OME-TIFF image. Plotting
annotations can range from tissue segment to gene expression.
biocViews: GeneExpression, Transcription, CellBasedAssays, DataImport,
Transcriptomics, Proteomics, ProprietaryPlatforms, RNASeq,
Spatial, DataRepresentation, Visualization
Author: Maddy Griswold [cre, aut], Megan Vandenberg [ctb], Stephanie
Zimmerman [ctb]
Maintainer: Maddy Griswold <mgriswold@nanostring.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SpatialOmicsOverlay
git_branch: devel
git_last_commit: 7476c4f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SpatialOmicsOverlay_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SpatialOmicsOverlay_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SpatialOmicsOverlay_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SpatialOmicsOverlay_1.7.0.tgz
vignettes:
vignettes/SpatialOmicsOverlay/inst/doc/SpatialOmicsOverlay.html
vignetteTitles: Introduction to SpatialOmicsOverlay
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SpatialOmicsOverlay/inst/doc/SpatialOmicsOverlay.R
dependencyCount: 162
Package: spatialSimGP
Version: 1.1.0
Depends: R (>= 4.4)
Imports: SpatialExperiment, MASS, SummarizedExperiment
Suggests: testthat (>= 3.0.0), STexampleData, ggplot2, knitr
License: MIT + file LICENSE
MD5sum: 86b2b027d9461f792ee9bc85613e08de
NeedsCompilation: no
Title: Simulate Spatial Transcriptomics Data with the Mean-variance
Relationship
Description: This packages simulates spatial transcriptomics data with
the mean- variance relationship using a Gaussian Process model
per gene.
biocViews: Spatial, Transcriptomics, GeneExpression
Author: Kinnary Shah [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7098-2116>), Boyi Guo [aut]
(ORCID: <https://orcid.org/0000-0003-2950-2349>), Stephanie C.
Hicks [aut] (ORCID: <https://orcid.org/0000-0002-7858-0231>)
Maintainer: Kinnary Shah <kinnaryshahh@gmail.com>
URL: https://github.com/kinnaryshah/spatialSimGP
VignetteBuilder: knitr
BugReports: https://github.com/kinnaryshah/spatialSimGP/issues
git_url: https://git.bioconductor.org/packages/spatialSimGP
git_branch: devel
git_last_commit: ea76aca
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/spatialSimGP_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/spatialSimGP_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/spatialSimGP_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/spatialSimGP_1.1.0.tgz
vignettes: vignettes/spatialSimGP/inst/doc/spatialSimGP.html
vignetteTitles: spatialSimGP Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/spatialSimGP/inst/doc/spatialSimGP.R
dependencyCount: 73
Package: spatzie
Version: 1.13.0
Depends: R (>= 4.3)
Imports: BiocGenerics, BSgenome, GenomeInfoDb, GenomicFeatures,
GenomicInteractions, GenomicRanges, ggplot2, IRanges,
MatrixGenerics, matrixStats, motifmatchr, S4Vectors, stats,
SummarizedExperiment, TFBSTools, utils
Suggests: BiocManager, Biostrings, knitr, pheatmap, rmarkdown,
testthat, TxDb.Hsapiens.UCSC.hg19.knownGene,
TxDb.Hsapiens.UCSC.hg38.knownGene,
TxDb.Mmusculus.UCSC.mm10.knownGene,
TxDb.Mmusculus.UCSC.mm9.knownGene
License: GPL-3
MD5sum: 1ece21fd1a4f1359369a885fb7f0a76d
NeedsCompilation: no
Title: Identification of enriched motif pairs from chromatin
interaction data
Description: Identifies motifs that are significantly co-enriched from
enhancer-promoter interaction data. While enhancer-promoter
annotation is commonly used to define groups of interaction
anchors, spatzie also supports co-enrichment analysis between
preprocessed interaction anchors. Supports BEDPE interaction
data derived from genome-wide assays such as HiC, ChIA-PET, and
HiChIP. Can also be used to look for differentially enriched
motif pairs between two interaction experiments.
biocViews: DNA3DStructure, GeneRegulation, PeakDetection, Epigenetics,
FunctionalGenomics, Classification, HiC, Transcription
Author: Jennifer Hammelman [aut, cre, cph] (ORCID:
<https://orcid.org/0000-0002-1008-2666>), Konstantin Krismer
[aut] (ORCID: <https://orcid.org/0000-0001-8994-3416>), David
Gifford [ths, cph] (ORCID:
<https://orcid.org/0000-0003-1709-4034>)
Maintainer: Jennifer Hammelman <jhammelm@mit.edu>
URL: https://spatzie.mit.edu
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/spatzie
git_branch: devel
git_last_commit: c97feec
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/spatzie_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/spatzie_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/spatzie_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/spatzie_1.13.0.tgz
vignettes: vignettes/spatzie/inst/doc/individual-steps.html,
vignettes/spatzie/inst/doc/single-call.html
vignetteTitles: YY1 ChIA-PET motif analysis (step-by-step), YY1
ChIA-PET motif analysis (single call)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 168
Package: speckle
Version: 1.7.0
Depends: R (>= 4.2.0)
Imports: limma, edgeR, SingleCellExperiment, Seurat, ggplot2, methods,
stats, grDevices, graphics
Suggests: BiocStyle, knitr, rmarkdown, statmod, CellBench, scater,
patchwork, jsonlite, vdiffr, testthat (>= 3.0.0)
License: GPL-3
MD5sum: 0655cbab431cb42f5f17dce95b818038
NeedsCompilation: no
Title: Statistical methods for analysing single cell RNA-seq data
Description: The speckle package contains functions for the analysis of
single cell RNA-seq data. The speckle package currently
contains functions to analyse differences in cell type
proportions. There are also functions to estimate the
parameters of the Beta distribution based on a given counts
matrix, and a function to normalise a counts matrix to the
median library size. There are plotting functions to visualise
cell type proportions and the mean-variance relationship in
cell type proportions and counts. As our research into
specialised analyses of single cell data continues we
anticipate that the package will be updated with new functions.
biocViews: SingleCell, RNASeq, Regression, GeneExpression
Author: Belinda Phipson [aut, cre]
Maintainer: Belinda Phipson <phipson.b@wehi.edu.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/speckle
git_branch: devel
git_last_commit: 204269e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/speckle_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/speckle_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/speckle_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/speckle_1.7.0.tgz
vignettes: vignettes/speckle/inst/doc/speckle.html
vignetteTitles: speckle: statistical methods for analysing single cell
RNA-seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/speckle/inst/doc/speckle.R
dependencyCount: 175
Package: specL
Version: 1.41.0
Depends: R (>= 4.1), DBI (>= 0.5), methods (>= 3.3), protViz (>= 0.7),
RSQLite (>= 1.1), seqinr (>= 3.3)
Suggests: BiocGenerics, BiocStyle (>= 2.2), knitr (>= 1.15), rmarkdown,
RUnit (>= 0.4)
License: GPL-3
MD5sum: db5737c6df505bebd0523c9c21cae373
NeedsCompilation: no
Title: specL - Prepare Peptide Spectrum Matches for Use in Targeted
Proteomics
Description: provides a functions for generating spectra libraries that
can be used for MRM SRM MS workflows in proteomics. The package
provides a BiblioSpec reader, a function which can add the
protein information using a FASTA formatted amino acid file,
and an export method for using the created library in the
Spectronaut software. The package is developed, tested and used
at the Functional Genomics Center Zurich <https://fgcz.ch>.
biocViews: MassSpectrometry, Proteomics
Author: Christian Panse [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1975-3064>), Jonas Grossmann [aut]
(ORCID: <https://orcid.org/0000-0002-6899-9020>), Christian
Trachsel [aut], Witold E. Wolski [ctb]
Maintainer: Christian Panse <cp@fgcz.ethz.ch>
URL: http://bioconductor.org/packages/specL/
VignetteBuilder: knitr
BugReports: https://github.com/fgcz/specL/issues
git_url: https://git.bioconductor.org/packages/specL
git_branch: devel
git_last_commit: bb4f519
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/specL_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/specL_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/specL_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/specL_1.41.0.tgz
vignettes: vignettes/specL/inst/doc/report.html,
vignettes/specL/inst/doc/specL.html
vignetteTitles: Automatic specL Workflow, Introduction to specL
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/specL/inst/doc/report.R,
vignettes/specL/inst/doc/specL.R
suggestsMe: msqc1, NestLink
dependencyCount: 33
Package: SpeCond
Version: 1.61.0
Depends: R (>= 2.10.0), mclust (>= 3.3.1), Biobase (>= 1.15.13),
fields, hwriter (>= 1.1), RColorBrewer, methods
License: LGPL (>=2)
MD5sum: c43f302920b9108aff478d626707dcdf
NeedsCompilation: no
Title: Condition specific detection from expression data
Description: This package performs a gene expression data analysis to
detect condition-specific genes. Such genes are significantly
up- or down-regulated in a small number of conditions. It does
so by fitting a mixture of normal distributions to the
expression values. Conditions can be environmental conditions,
different tissues, organs or any other sources that you wish to
compare in terms of gene expression.
biocViews: Microarray, DifferentialExpression, MultipleComparison,
Clustering, ReportWriting
Author: Florence Cavalli
Maintainer: Florence Cavalli <florence@ebi.ac.uk>
git_url: https://git.bioconductor.org/packages/SpeCond
git_branch: devel
git_last_commit: 84f04f8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SpeCond_1.61.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SpeCond_1.61.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SpeCond_1.61.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SpeCond_1.61.0.tgz
vignettes: vignettes/SpeCond/inst/doc/SpeCond.pdf
vignetteTitles: SpeCond
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SpeCond/inst/doc/SpeCond.R
dependencyCount: 18
Package: Spectra
Version: 1.17.9
Depends: R (>= 4.0.0), S4Vectors, BiocParallel
Imports: ProtGenerics (>= 1.39.2), methods, IRanges, MsCoreUtils (>=
1.7.5), graphics, grDevices, stats, tools, utils, fs,
BiocGenerics, MetaboCoreUtils
Suggests: testthat, knitr (>= 1.1.0), msdata (>= 0.19.3), roxygen2,
BiocStyle (>= 2.5.19), mzR (>= 2.19.6), rhdf5 (>= 2.32.0),
rmarkdown, vdiffr (>= 1.0.0), msentropy, patrick
License: Artistic-2.0
MD5sum: 6e0bfbd1bca8fa60e573c8115749a72f
NeedsCompilation: no
Title: Spectra Infrastructure for Mass Spectrometry Data
Description: The Spectra package defines an efficient infrastructure
for storing and handling mass spectrometry spectra and
functionality to subset, process, visualize and compare spectra
data. It provides different implementations (backends) to store
mass spectrometry data. These comprise backends tuned for fast
data access and processing and backends for very large data
sets ensuring a small memory footprint.
biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics
Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto
[aut] (ORCID: <https://orcid.org/0000-0002-1520-2268>),
Johannes Rainer [aut] (ORCID:
<https://orcid.org/0000-0002-6977-7147>), Sebastian Gibb [aut]
(ORCID: <https://orcid.org/0000-0001-7406-4443>), Philippine
Louail [aut] (ORCID: <https://orcid.org/0009-0007-5429-6846>),
Jan Stanstrup [ctb] (ORCID:
<https://orcid.org/0000-0003-0541-7369>), Nir Shahaf [ctb], Mar
Garcia-Aloy [ctb] (ORCID:
<https://orcid.org/0000-0002-1330-6610>)
Maintainer: RforMassSpectrometry Package Maintainer
<maintainer@rformassspectrometry.org>
URL: https://github.com/RforMassSpectrometry/Spectra
VignetteBuilder: knitr
BugReports: https://github.com/RforMassSpectrometry/Spectra/issues
git_url: https://git.bioconductor.org/packages/Spectra
git_branch: devel
git_last_commit: bac7a3d
git_last_commit_date: 2025-03-07
Date/Publication: 2025-03-07
source.ver: src/contrib/Spectra_1.17.9.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Spectra_1.17.9.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Spectra_1.17.9.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Spectra_1.17.9.tgz
vignettes: vignettes/Spectra/inst/doc/MsBackend.html,
vignettes/Spectra/inst/doc/Spectra-large-scale.html,
vignettes/Spectra/inst/doc/Spectra.html
vignetteTitles: Creating new `MsBackend` class, Large-scale data
handling and processing with Spectra, Description and usage of
Spectra object
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Spectra/inst/doc/MsBackend.R,
vignettes/Spectra/inst/doc/Spectra-large-scale.R,
vignettes/Spectra/inst/doc/Spectra.R
dependsOnMe: hdxmsqc, MetCirc, MsBackendMassbank,
MsBackendMetaboLights, MsBackendMgf, MsBackendMsp,
MsBackendRawFileReader, MsBackendSql
importsMe: CompoundDb, MetaboAnnotation, MsExperiment, MsQuality,
SpectraQL, xcms
suggestsMe: koinar, MetNet, MsDataHub, MSnbase, PSMatch, RaMS
dependencyCount: 29
Package: SpectralTAD
Version: 1.23.0
Depends: R (>= 3.6)
Imports: dplyr, PRIMME, cluster, Matrix, parallel, BiocParallel,
magrittr, HiCcompare, GenomicRanges, utils
Suggests: BiocCheck, BiocManager, BiocStyle, knitr, rmarkdown,
microbenchmark, testthat, covr
License: MIT + file LICENSE
MD5sum: 40445c71c62bea40a71269bf8958f96e
NeedsCompilation: no
Title: SpectralTAD: Hierarchical TAD detection using spectral
clustering
Description: SpectralTAD is an R package designed to identify
Topologically Associated Domains (TADs) from Hi-C contact
matrices. It uses a modified version of spectral clustering
that uses a sliding window to quickly detect TADs. The function
works on a range of different formats of contact matrices and
returns a bed file of TAD coordinates. The method does not
require users to adjust any parameters to work and gives them
control over the number of hierarchical levels to be returned.
biocViews: Software, HiC, Sequencing, FeatureExtraction, Clustering
Author: Mikhail Dozmorov [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0086-8358>), Kellen Cresswell
[aut], John Stansfield [aut]
Maintainer: Mikhail Dozmorov <mikhail.dozmorov@gmail.com>
URL: https://github.com/dozmorovlab/SpectralTAD
VignetteBuilder: knitr
BugReports: https://github.com/dozmorovlab/SpectralTAD/issues
git_url: https://git.bioconductor.org/packages/SpectralTAD
git_branch: devel
git_last_commit: 1f05a5d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SpectralTAD_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SpectralTAD_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SpectralTAD_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SpectralTAD_1.23.0.tgz
vignettes: vignettes/SpectralTAD/inst/doc/SpectralTAD.html
vignetteTitles: SpectralTAD
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SpectralTAD/inst/doc/SpectralTAD.R
suggestsMe: TADCompare
dependencyCount: 87
Package: SpectraQL
Version: 1.1.0
Depends: R (>= 4.4.0), ProtGenerics (>= 1.25.1)
Imports: Spectra (>= 1.5.6), MsCoreUtils, methods
Suggests: testthat, msdata (>= 0.19.3), roxygen2, rmarkdown, knitr,
S4Vectors, BiocStyle, mzR
License: Artistic-2.0
MD5sum: 9995c2fa51263a6be0569ce90cc4ef1d
NeedsCompilation: no
Title: MassQL support for Spectra
Description: The Mass Spec Query Language (MassQL) is a domain-specific
language enabling to express a query and retrieve mass
spectrometry (MS) data in a more natural and understandable way
for MS users. It is inspired by SQL and is by design
programming language agnostic. The SpectraQL package adds
support for the MassQL query language to R, in particular to MS
data represented by Spectra objects. Users can thus apply
MassQL expressions to analyze and retrieve specific data from
Spectra objects.
biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics
Author: Johannes Rainer [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6977-7147>), Andrea Vicini [aut],
Sebastian Gibb [ctb] (ORCID:
<https://orcid.org/0000-0001-7406-4443>)
Maintainer: Johannes Rainer <Johannes.Rainer@eurac.edu>
URL: https://github.com/RforMassSpectrometry/SpectraQL
VignetteBuilder: knitr
BugReports: https://github.com/RforMassSpectrometry/SpectraQL/issues
git_url: https://git.bioconductor.org/packages/SpectraQL
git_branch: devel
git_last_commit: 85b34ee
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SpectraQL_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SpectraQL_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SpectraQL_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SpectraQL_1.1.0.tgz
vignettes: vignettes/SpectraQL/inst/doc/SpectraQL.html
vignetteTitles: Mass Spec Query Language Support to the Spectra Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SpectraQL/inst/doc/SpectraQL.R
dependencyCount: 30
Package: SPEM
Version: 1.47.0
Depends: R (>= 2.15.1), Rsolnp, Biobase, methods
License: GPL-2
MD5sum: 74484d4559ef0e5875bb55f569c2ee69
NeedsCompilation: no
Title: S-system parameter estimation method
Description: This package can optimize the parameter in S-system models
given time series data
biocViews: Network, NetworkInference, Software
Author: Xinyi YANG Developer, Jennifer E. DENT Developer and Christine
NARDINI Supervisor
Maintainer: Xinyi YANG <yangxinyi@picb.ac.cn>
git_url: https://git.bioconductor.org/packages/SPEM
git_branch: devel
git_last_commit: d1af4f2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SPEM_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SPEM_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SPEM_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SPEM_1.47.0.tgz
vignettes: vignettes/SPEM/inst/doc/SPEM-package.pdf
vignetteTitles: Vignette for SPEM
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SPEM/inst/doc/SPEM-package.R
importsMe: TMixClust
dependencyCount: 10
Package: SPIA
Version: 2.59.2
Depends: R (>= 2.14.0), graphics, KEGGgraph
Imports: graphics
Suggests: graph, Rgraphviz, hgu133plus2.db
License: file LICENSE
License_restricts_use: yes
MD5sum: 3df038f7fb64ef9903596b0d1d66acdb
NeedsCompilation: no
Title: Signaling Pathway Impact Analysis (SPIA) using combined evidence
of pathway over-representation and unusual signaling
perturbations
Description: This package implements the Signaling Pathway Impact
Analysis (SPIA) which uses the information form a list of
differentially expressed genes and their log fold changes
together with signaling pathways topology, in order to identify
the pathways most relevant to the condition under the study.
biocViews: Microarray, GraphAndNetwork
Author: Adi Laurentiu Tarca <atarca@med.wayne.edu>, Purvesh Kathri
<purvesh@cs.wayne.edu> and Sorin Draghici <sorin@wayne.edu>
Maintainer: Adi Laurentiu Tarca <atarca@med.wayne.edu>
URL: http://bioinformatics.oxfordjournals.org/cgi/reprint/btn577v1
git_url: https://git.bioconductor.org/packages/SPIA
git_branch: devel
git_last_commit: 99a4b6f
git_last_commit_date: 2025-01-24
Date/Publication: 2025-01-26
source.ver: src/contrib/SPIA_2.59.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SPIA_2.59.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SPIA_2.59.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SPIA_2.59.2.tgz
vignettes: vignettes/SPIA/inst/doc/SPIA.pdf
vignetteTitles: SPIA
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SPIA/inst/doc/SPIA.R
importsMe: EnrichmentBrowser
suggestsMe: graphite, KEGGgraph
dependencyCount: 15
Package: SPIAT
Version: 1.9.1
Depends: R (>= 4.2.0), SpatialExperiment (>= 1.8.0)
Imports: apcluster (>= 1.4.7), ggplot2 (>= 3.2.1), gridExtra (>= 2.3),
gtools (>= 3.8.1), reshape2 (>= 1.4.3), dplyr (>= 0.8.3), RANN
(>= 2.6.1), pracma (>= 2.2.5), dbscan (>= 1.1-5), mmand (>=
1.5.4), tibble (>= 2.1.3), grDevices, stats, utils, vroom,
dittoSeq, spatstat.geom, methods, spatstat.explore, raster, sp,
SummarizedExperiment, rlang
Suggests: BiocStyle, plotly (>= 4.9.0), knitr, rmarkdown, pkgdown,
testthat, graphics, alphahull, Rtsne, umap, ComplexHeatmap,
elsa
License: Artistic-2.0 + file LICENSE
MD5sum: 0e526c8f99753ce29a4f7b14ec3ee46f
NeedsCompilation: no
Title: Spatial Image Analysis of Tissues
Description: SPIAT (**Sp**atial **I**mage **A**nalysis of **T**issues)
is an R package with a suite of data processing, quality
control, visualization and data analysis tools. SPIAT is
compatible with data generated from single-cell spatial
proteomics platforms (e.g. OPAL, CODEX, MIBI, cellprofiler).
SPIAT reads spatial data in the form of X and Y coordinates of
cells, marker intensities and cell phenotypes. SPIAT includes
six analysis modules that allow visualization, calculation of
cell colocalization, categorization of the immune
microenvironment relative to tumor areas, analysis of cellular
neighborhoods, and the quantification of spatial heterogeneity,
providing a comprehensive toolkit for spatial data analysis.
biocViews: BiomedicalInformatics, CellBiology, Spatial, Clustering,
DataImport, ImmunoOncology, QualityControl, SingleCell,
Software, Visualization
Author: Anna Trigos [aut] (ORCID:
<https://orcid.org/0000-0002-5915-2952>), Yuzhou Feng [aut,
cre] (ORCID: <https://orcid.org/0000-0002-2955-4053>), Tianpei
Yang [aut], Mabel Li [aut], John Zhu [aut], Volkan Ozcoban
[aut], Maria Doyle [aut]
Maintainer: Yuzhou Feng <yuzhou0610@gmail.com>
URL: https://trigosteam.github.io/SPIAT/
VignetteBuilder: knitr
BugReports: https://github.com/trigosteam/SPIAT/issues
git_url: https://git.bioconductor.org/packages/SPIAT
git_branch: devel
git_last_commit: 8052e4f
git_last_commit_date: 2025-03-16
Date/Publication: 2025-03-17
source.ver: src/contrib/SPIAT_1.9.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SPIAT_1.9.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SPIAT_1.9.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SPIAT_1.9.1.tgz
vignettes: vignettes/SPIAT/inst/doc/basic_analysis.html,
vignettes/SPIAT/inst/doc/cell-colocalisation.html,
vignettes/SPIAT/inst/doc/data_reading-formatting.html,
vignettes/SPIAT/inst/doc/neighborhood.html,
vignettes/SPIAT/inst/doc/quality-control_visualisation.html,
vignettes/SPIAT/inst/doc/spatial-heterogeneity.html,
vignettes/SPIAT/inst/doc/SPIAT.html,
vignettes/SPIAT/inst/doc/tissue-structure.html
vignetteTitles: Basic analyses with SPIAT, Quantifying cell
colocalisation with SPIAT, Reading in data and data formatting
in SPIAT, Identifying cellular neighborhood with SPIAT, Quality
control and visualisation with SPIAT, Spatial heterogeneity
with SPIAT, Overview of the SPIAT package, Characterising
tissue structure with SPIAT
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SPIAT/inst/doc/basic_analysis.R,
vignettes/SPIAT/inst/doc/cell-colocalisation.R,
vignettes/SPIAT/inst/doc/data_reading-formatting.R,
vignettes/SPIAT/inst/doc/neighborhood.R,
vignettes/SPIAT/inst/doc/quality-control_visualisation.R,
vignettes/SPIAT/inst/doc/spatial-heterogeneity.R,
vignettes/SPIAT/inst/doc/SPIAT.R,
vignettes/SPIAT/inst/doc/tissue-structure.R
dependencyCount: 119
Package: spicyR
Version: 1.19.4
Depends: R (>= 4.1)
Imports: ggplot2, concaveman, BiocParallel, spatstat.explore,
spatstat.geom, lmerTest, S4Vectors, methods, pheatmap, rlang,
grDevices, stats, data.table, dplyr, tidyr, scam,
SingleCellExperiment, SpatialExperiment, SummarizedExperiment,
ggforce, ClassifyR, tibble, magrittr, cli, survival, ggthemes,
ggh4x, coxme, ggnewscale, lifecycle, simpleSeg
Suggests: SpatialDatasets, BiocStyle, knitr, rmarkdown, pkgdown,
imcRtools, testthat (>= 3.0.0)
License: GPL (>=2)
MD5sum: d46004eed2699e222531bea3c7f4cbcc
NeedsCompilation: no
Title: Spatial analysis of in situ cytometry data
Description: The spicyR package provides a framework for performing
inference on changes in spatial relationships between pairs of
cell types for cell-resolution spatial omics technologies.
spicyR consists of three primary steps: (i) summarizing the
degree of spatial localization between pairs of cell types for
each image; (ii) modelling the variability in localization
summary statistics as a function of cell counts and (iii)
testing for changes in spatial localizations associated with a
response variable.
biocViews: SingleCell, CellBasedAssays, Spatial
Author: Nicolas Canete [aut], Ellis Patrick [aut, cre], Nicholas
Robertson [ctb], Alex Qin [ctb], Farhan Ameen [ctb], Shreya Rao
[ctb]
Maintainer: Ellis Patrick <ellis.patrick@sydney.edu.au>
URL: https://ellispatrick.github.io/spicyR/
https://github.com/SydneyBioX/spicyR,
https://sydneybiox.github.io/spicyR/
VignetteBuilder: knitr
BugReports: https://github.com/SydneyBioX/spicyR/issues
git_url: https://git.bioconductor.org/packages/spicyR
git_branch: devel
git_last_commit: 4d4faa1
git_last_commit_date: 2025-03-04
Date/Publication: 2025-03-05
source.ver: src/contrib/spicyR_1.19.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/spicyR_1.19.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/spicyR_1.19.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/spicyR_1.19.4.tgz
vignettes: vignettes/spicyR/inst/doc/spicyR.html
vignetteTitles: "Spatial Linear and Mixed-Effects Modelling with spicy"
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/spicyR/inst/doc/spicyR.R
importsMe: lisaClust
suggestsMe: Statial
dependencyCount: 234
Package: spikeLI
Version: 2.67.0
Imports: graphics, grDevices, stats, utils
License: GPL-2
MD5sum: 4bc6d193f1f4eef50dea6dc6864187c1
NeedsCompilation: no
Title: Affymetrix Spike-in Langmuir Isotherm Data Analysis Tool
Description: SpikeLI is a package that performs the analysis of the
Affymetrix spike-in data using the Langmuir Isotherm. The aim
of this package is to show the advantages of a
physical-chemistry based analysis of the Affymetrix microarray
data compared to the traditional methods. The spike-in (or
Latin square) data for the HGU95 and HGU133 chipsets have been
downloaded from the Affymetrix web site. The model used in the
spikeLI package is described in details in E. Carlon and T.
Heim, Physica A 362, 433 (2006).
biocViews: Microarray, QualityControl
Author: Delphine Baillon, Paul Leclercq <paulleclercq@hotmail.com>,
Sarah Ternisien, Thomas Heim, Enrico Carlon
<enrico.carlon@fys.kuleuven.be>
Maintainer: Enrico Carlon <enrico.carlon@fys.kuleuven.be>
git_url: https://git.bioconductor.org/packages/spikeLI
git_branch: devel
git_last_commit: 443a12f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/spikeLI_2.67.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/spikeLI_2.67.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/spikeLI_2.67.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/spikeLI_2.67.0.tgz
vignettes: vignettes/spikeLI/inst/doc/spikeLI.pdf
vignetteTitles: spikeLI
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 4
Package: spiky
Version: 1.13.0
Depends: Rsamtools, GenomicRanges, R (>= 3.6.0)
Imports: stats, scales, bamlss, methods, tools, IRanges, Biostrings,
GenomicAlignments, BlandAltmanLeh, GenomeInfoDb, BSgenome,
S4Vectors, graphics, ggplot2, utils
Suggests: covr, testthat, rmarkdown, markdown, knitr, devtools,
BSgenome.Mmusculus.UCSC.mm10.masked,
BSgenome.Hsapiens.UCSC.hg38.masked, BiocManager
License: GPL-2
MD5sum: 26eae640a73b29c3f401ecc03c1e995e
NeedsCompilation: no
Title: Spike-in calibration for cell-free MeDIP
Description: spiky implements methods and model generation for cfMeDIP
(cell-free methylated DNA immunoprecipitation) with spike-in
controls. CfMeDIP is an enrichment protocol which avoids
destructive conversion of scarce template, making it ideal as a
"liquid biopsy," but creating certain challenges in comparing
results across specimens, subjects, and experiments. The use of
synthetic spike-in standard oligos allows diagnostics performed
with cfMeDIP to quantitatively compare samples across subjects,
experiments, and time points in both relative and absolute
terms.
biocViews: DifferentialMethylation, DNAMethylation, Normalization,
Preprocessing, QualityControl, Sequencing
Author: Samantha Wilson [aut], Lauren Harmon [aut], Tim Triche [aut,
cre]
Maintainer: Tim Triche <trichelab@gmail.com>
URL: https://github.com/trichelab/spiky
VignetteBuilder: knitr
BugReports: https://github.com/trichelab/spiky/issues
git_url: https://git.bioconductor.org/packages/spiky
git_branch: devel
git_last_commit: 1c71f59
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/spiky_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/spiky_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/spiky_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/spiky_1.13.0.tgz
vignettes: vignettes/spiky/inst/doc/spiky_vignette.html
vignetteTitles: Spiky: Analysing cfMeDIP-seq data with spike-in
controls
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/spiky/inst/doc/spiky_vignette.R
dependencyCount: 94
Package: spillR
Version: 1.3.0
Depends: R (>= 4.3.0), SummarizedExperiment, CATALYST
Imports: dplyr, tibble, tidyselect, stats, ggplot2, tidyr,
spatstat.univar, S4Vectors, parallel
Suggests: knitr, rmarkdown, cowplot, testthat (>= 3.0.0), BiocStyle,
hexbin
License: LGPL-3
MD5sum: 4a72ce740460d8087aa7370037944c6b
NeedsCompilation: no
Title: Spillover Compensation in Mass Cytometry Data
Description: Channel interference in mass cytometry can cause spillover
and may result in miscounting of protein markers. We develop a
nonparametric finite mixture model and use the mixture
components to estimate the probability of spillover. We
implement our method using expectation-maximization to fit the
mixture model.
biocViews: FlowCytometry, ImmunoOncology, MassSpectrometry,
Preprocessing, SingleCell, Software, StatisticalMethod,
Visualization, Regression
Author: Marco Guazzini [aut, cre] (ORCID:
<https://orcid.org/0009-0007-8111-5772>), Alexander G. Reisach
[aut] (ORCID: <https://orcid.org/0009-0003-5057-6278>),
Sebastian Weichwald [aut] (ORCID:
<https://orcid.org/0000-0003-0169-7244>), Christof Seiler [aut]
(ORCID: <https://orcid.org/0000-0001-8802-3642>)
Maintainer: Marco Guazzini <m.guazzini@student.maastrichtuniversity.nl>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/spillR
git_branch: devel
git_last_commit: 7a34e97
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/spillR_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/spillR_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/spillR_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/spillR_1.3.0.tgz
vignettes: vignettes/spillR/inst/doc/spillR-vignette.html
vignetteTitles: spillR Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/spillR/inst/doc/spillR-vignette.R
dependencyCount: 184
Package: spkTools
Version: 1.63.0
Depends: R (>= 2.7.0), Biobase (>= 2.5.5)
Imports: Biobase (>= 2.5.5), graphics, grDevices, gtools, methods,
RColorBrewer, stats, utils
Suggests: xtable
License: GPL (>= 2)
MD5sum: 6432311dcd63686234b762091ede4ebd
NeedsCompilation: no
Title: Methods for Spike-in Arrays
Description: The package contains functions that can be used to compare
expression measures on different array platforms.
biocViews: Software, Technology, Microarray
Author: Matthew N McCall <mccallm@gmail.com>, Rafael A Irizarry
<rafa@jhu.edu>
Maintainer: Matthew N McCall <mccallm@gmail.com>
URL: http://bioconductor.org
git_url: https://git.bioconductor.org/packages/spkTools
git_branch: devel
git_last_commit: c8630a9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/spkTools_1.63.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/spkTools_1.63.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/spkTools_1.63.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/spkTools_1.63.0.tgz
vignettes: vignettes/spkTools/inst/doc/spkDoc.pdf
vignetteTitles: spkTools: Spike-in Data Analysis and Visualization
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/spkTools/inst/doc/spkDoc.R
dependencyCount: 10
Package: splatter
Version: 1.31.0
Depends: R (>= 4.0), SingleCellExperiment
Imports: BiocGenerics, BiocParallel, checkmate (>= 2.0.0), crayon,
edgeR, fitdistrplus, grDevices, locfit, matrixStats, methods,
rlang, S4Vectors, scuttle, stats, SummarizedExperiment, utils,
withr
Suggests: BASiCS (>= 1.7.10), BiocManager, BiocSingular, BiocStyle,
Biostrings, covr, cowplot, GenomeInfoDb, GenomicRanges, ggplot2
(>= 3.4.0), igraph, IRanges, knitr, limSolve, lme4, magick,
mfa, phenopath, preprocessCore, progress, pscl, rmarkdown,
scales, scater (>= 1.15.16), scDD, scran, SparseDC, spelling,
testthat, VariantAnnotation, zinbwave
License: GPL-3 + file LICENSE
MD5sum: ff50d593ad6c5a09ee541218221add79
NeedsCompilation: no
Title: Simple Simulation of Single-cell RNA Sequencing Data
Description: Splatter is a package for the simulation of single-cell
RNA sequencing count data. It provides a simple interface for
creating complex simulations that are reproducible and
well-documented. Parameters can be estimated from real data and
functions are provided for comparing real and simulated
datasets.
biocViews: SingleCell, RNASeq, Transcriptomics, GeneExpression,
Sequencing, Software, ImmunoOncology
Author: Luke Zappia [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7744-8565>, GitHub: lazappi),
Belinda Phipson [aut] (ORCID:
<https://orcid.org/0000-0002-1711-7454>, GitHub: bphipson),
Christina Azodi [ctb] (ORCID:
<https://orcid.org/0000-0002-6097-606X>, GitHub: azodichr),
Alicia Oshlack [aut] (ORCID:
<https://orcid.org/0000-0001-9788-5690>)
Maintainer: Luke Zappia <luke@lazappi.id.au>
URL: https://bioconductor.org/packages/splatter/,
https://github.com/Oshlack/splatter,
http://oshlacklab.com/splatter/
VignetteBuilder: knitr
BugReports: https://github.com/Oshlack/splatter/issues
git_url: https://git.bioconductor.org/packages/splatter
git_branch: devel
git_last_commit: 77f3611
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/splatter_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/splatter_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/splatter_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/splatter_1.31.0.tgz
vignettes: vignettes/splatter/inst/doc/splat_params.html,
vignettes/splatter/inst/doc/splatPop.html,
vignettes/splatter/inst/doc/splatter.html
vignetteTitles: Splat simulation parameters, splatPop simulation, An
introduction to the Splatter package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/splatter/inst/doc/splat_params.R,
vignettes/splatter/inst/doc/splatPop.R,
vignettes/splatter/inst/doc/splatter.R
importsMe: SCRIP
suggestsMe: ccImpute, mastR, NewWave, scone, scPCA, smartid, scellpam
dependencyCount: 63
Package: SpliceWiz
Version: 1.9.1
Depends: R (>= 3.5.0), NxtIRFdata
Imports: ompBAM, methods, stats, utils, tools, parallel, scales,
magrittr, Rcpp (>= 1.0.5), data.table, fst, ggplot2,
AnnotationHub, RSQLite, BiocFileCache, BiocGenerics,
BiocParallel, Biostrings, BSgenome, DelayedArray,
DelayedMatrixStats, genefilter, GenomeInfoDb, GenomicRanges,
HDF5Array, h5mread, htmltools, IRanges, patchwork, pheatmap,
progress, plotly, R.utils, rhdf5, rtracklayer,
SummarizedExperiment, S4Vectors, shiny, shinyFiles,
shinyWidgets, shinydashboard, stringi, rhandsontable, DT,
grDevices, heatmaply, matrixStats, RColorBrewer, rvest, httr
LinkingTo: ompBAM, Rcpp, RcppProgress
Suggests: knitr, rmarkdown, crayon, splines, testthat (>= 3.0.0),
DESeq2, limma, DoubleExpSeq, edgeR, DBI, GO.db, AnnotationDbi,
fgsea, Rsubread
License: MIT + file LICENSE
MD5sum: 87345ecd8066be398d88f5d5685fc9a9
NeedsCompilation: yes
Title: interactive analysis and visualization of alternative splicing
in R
Description: The analysis and visualization of alternative splicing
(AS) events from RNA sequencing data remains challenging.
SpliceWiz is a user-friendly and performance-optimized R
package for AS analysis, by processing alignment BAM files to
quantify read counts across splice junctions, IRFinder-based
intron retention quantitation, and supports novel splicing
event identification. We introduce a novel visualization for AS
using normalized coverage, thereby allowing visualization of
differential AS across conditions. SpliceWiz features a
shiny-based GUI facilitating interactive data exploration of
results including gene ontology enrichment. It is performance
optimized with multi-threaded processing of BAM files and a new
COV file format for fast recall of sequencing coverage.
Overall, SpliceWiz streamlines AS analysis, enabling reliable
identification of functionally relevant AS events for further
characterization.
biocViews: Software, Transcriptomics, RNASeq, AlternativeSplicing,
Coverage, DifferentialSplicing, DifferentialExpression, GUI,
Sequencing
Author: Alex Chit Hei Wong [aut, cre, cph], Ulf Schmitz [ctb], William
Ritchie [cph]
Maintainer: Alex Chit Hei Wong <alexchwong.github@gmail.com>
URL: https://github.com/alexchwong/SpliceWiz
SystemRequirements: C++11, GNU make
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/
git_url: https://git.bioconductor.org/packages/SpliceWiz
git_branch: devel
git_last_commit: 7cd4bea
git_last_commit_date: 2025-03-12
Date/Publication: 2025-03-13
source.ver: src/contrib/SpliceWiz_1.9.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SpliceWiz_1.9.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SpliceWiz_1.9.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SpliceWiz_1.9.1.tgz
vignettes: vignettes/SpliceWiz/inst/doc/SW_Cookbook.html,
vignettes/SpliceWiz/inst/doc/SW_QuickStart.html
vignetteTitles: SpliceWiz: the cookbook, SpliceWiz: Quick Start
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SpliceWiz/inst/doc/SW_Cookbook.R,
vignettes/SpliceWiz/inst/doc/SW_QuickStart.R
dependencyCount: 193
Package: SplicingFactory
Version: 1.15.0
Depends: R (>= 4.1)
Imports: SummarizedExperiment, methods, stats
Suggests: testthat, knitr, rmarkdown, ggplot2, tidyr
License: GPL-3 + file LICENSE
MD5sum: f09aa2b929faebe5de2e7897567e48c3
NeedsCompilation: no
Title: Splicing Diversity Analysis for Transcriptome Data
Description: The SplicingFactory R package uses transcript-level
expression values to analyze splicing diversity based on
various statistical measures, like Shannon entropy or the Gini
index. These measures can quantify transcript isoform diversity
within samples or between conditions. Additionally, the package
analyzes the isoform diversity data, looking for significant
changes between conditions.
biocViews: Transcriptomics, RNASeq, DifferentialSplicing,
AlternativeSplicing, TranscriptomeVariant
Author: Peter A. Szikora [aut], Tamas Por [aut], Endre Sebestyen [aut,
cre] (ORCID: <https://orcid.org/0000-0001-5470-2161>)
Maintainer: Endre Sebestyen <endre.sebestyen@gmail.com>
URL: https://github.com/esebesty/SplicingFactory
VignetteBuilder: knitr
BugReports: https://github.com/esebesty/SplicingFactory/issues
git_url: https://git.bioconductor.org/packages/SplicingFactory
git_branch: devel
git_last_commit: ac49427
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SplicingFactory_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SplicingFactory_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SplicingFactory_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SplicingFactory_1.15.0.tgz
vignettes: vignettes/SplicingFactory/inst/doc/SplicingFactory.html
vignetteTitles: SplicingFactory
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SplicingFactory/inst/doc/SplicingFactory.R
dependencyCount: 36
Package: SplicingGraphs
Version: 1.47.0
Depends: GenomicFeatures (>= 1.17.13), GenomicAlignments (>= 1.1.22),
Rgraphviz (>= 2.3.7)
Imports: methods, utils, graphics, igraph, BiocGenerics, S4Vectors (>=
0.17.5), BiocParallel, IRanges (>= 2.21.2), GenomeInfoDb,
GenomicRanges (>= 1.23.21), Rsamtools, graph
Suggests: igraph, Gviz, txdbmaker, TxDb.Hsapiens.UCSC.hg19.knownGene,
RNAseqData.HNRNPC.bam.chr14, RUnit
License: Artistic-2.0
MD5sum: 7993f0431a742a208da65d9f8e135810
NeedsCompilation: no
Title: Create, manipulate, visualize splicing graphs, and assign
RNA-seq reads to them
Description: This package allows the user to create, manipulate, and
visualize splicing graphs and their bubbles based on a gene
model for a given organism. Additionally it allows the user to
assign RNA-seq reads to the edges of a set of splicing graphs,
and to summarize them in different ways.
biocViews: Genetics, Annotation, DataRepresentation, Visualization,
Sequencing, RNASeq, GeneExpression, AlternativeSplicing,
Transcription, ImmunoOncology
Author: D. Bindreither, M. Carlson, M. Morgan, H. Pagès
Maintainer: H. Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/SplicingGraphs
BugReports: https://github.com/Bioconductor/SplicingGraphs/issues
git_url: https://git.bioconductor.org/packages/SplicingGraphs
git_branch: devel
git_last_commit: 2e358aa
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SplicingGraphs_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SplicingGraphs_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SplicingGraphs_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SplicingGraphs_1.47.0.tgz
vignettes: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.pdf
vignetteTitles: Splicing graphs and RNA-seq data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.R
dependencyCount: 81
Package: SplineDV
Version: 0.99.14
Depends: R (>= 3.5.0)
Imports: plotly, dplyr, scuttle, methods, Biobase, BiocGenerics,
S4Vectors, sparseMatrixStats, SingleCellExperiment,
SummarizedExperiment, Matrix (>= 1.6.4), utils
Suggests: knitr, DelayedMatrixStats, rmarkdown, BiocStyle, ggplot2,
ggpubr, MASS, scales, scRNAseq, testthat (>= 3.0.0)
License: GPL-2
MD5sum: 134831aabe4a2cc859c6e2753f34e396
NeedsCompilation: no
Title: Differential Variability (DV) analysis for single-cell RNA
sequencing data. (e.g. Identify Differentially Variable Genes
across two experimental conditions)
Description: A spline based scRNA-seq method for identifying
differentially variable (DV) genes across two experimental
conditions. Spline-DV constructs a 3D spline from 3 key gene
statistics: mean expression, coefficient of variance, and
dropout rate. This is done for both conditions. The 3D spline
provides the “expected†behavior of genes in each condition.
The distance of the observed mean, CV and dropout rate of each
gene from the expected 3D spline is used to measure
variability. As the final step, the spline-DV method compares
the variabilities of each condition to identify differentially
variable (DV) genes.
biocViews: Software, SingleCell, Sequencing, DifferentialExpression,
RNASeq, GeneExpression, Transcriptomics, FeatureExtraction
Author: Shreyan Gupta [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1904-9862>), James Cai [aut]
(ORCID: <https://orcid.org/0000-0002-8081-6725>)
Maintainer: Shreyan Gupta <xenon8778@tamu.edu>
URL: https://github.com/Xenon8778/SplineDV
VignetteBuilder: knitr
BugReports: https://github.com/Xenon8778/SplineDV/issues
git_url: https://git.bioconductor.org/packages/SplineDV
git_branch: devel
git_last_commit: c4a133a
git_last_commit_date: 2025-02-07
Date/Publication: 2025-02-09
source.ver: src/contrib/SplineDV_0.99.14.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SplineDV_0.99.14.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SplineDV_0.99.14.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SplineDV_0.99.14.tgz
vignettes: vignettes/SplineDV/inst/doc/SplineDV.html
vignetteTitles: Introduction to Spline-DV
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SplineDV/inst/doc/SplineDV.R
dependencyCount: 110
Package: splineTimeR
Version: 1.35.0
Depends: R (>= 3.3), Biobase, igraph, limma, GSEABase, gtools, splines,
GeneNet (>= 1.2.13), longitudinal (>= 1.1.12), FIs
Suggests: knitr
License: GPL-3
MD5sum: 0797269530ae8d64453b3716a5c67792
NeedsCompilation: no
Title: Time-course differential gene expression data analysis using
spline regression models followed by gene association network
reconstruction
Description: This package provides functions for differential gene
expression analysis of gene expression time-course data.
Natural cubic spline regression models are used. Identified
genes may further be used for pathway enrichment analysis
and/or the reconstruction of time dependent gene regulatory
association networks.
biocViews: GeneExpression, DifferentialExpression, TimeCourse,
Regression, GeneSetEnrichment, NetworkEnrichment,
NetworkInference, GraphAndNetwork
Author: Agata Michna
Maintainer: Herbert Braselmann <hbraselmann@online.de>, Martin
Selmansberger <martin.selmansberger@helmholtz-muenchen.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/splineTimeR
git_branch: devel
git_last_commit: 3853c2f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/splineTimeR_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/splineTimeR_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/splineTimeR_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/splineTimeR_1.35.0.tgz
vignettes: vignettes/splineTimeR/inst/doc/splineTimeR.pdf
vignetteTitles: splineTimeR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/splineTimeR/inst/doc/splineTimeR.R
dependencyCount: 64
Package: SPLINTER
Version: 1.33.0
Depends: R (>= 3.6.0), grDevices, stats
Imports: graphics, ggplot2, seqLogo, Biostrings, pwalign, biomaRt,
GenomicAlignments, GenomicRanges, GenomicFeatures, Gviz,
IRanges, S4Vectors, GenomeInfoDb, utils, plyr,stringr, methods,
BSgenome.Mmusculus.UCSC.mm9, googleVis
Suggests: txdbmaker, BiocStyle, knitr, rmarkdown
License: GPL-2
MD5sum: 1dad8c6c6b81d0e891d586132830e6d0
NeedsCompilation: no
Title: Splice Interpreter of Transcripts
Description: Provides tools to analyze alternative splicing sites,
interpret outcomes based on sequence information, select and
design primers for site validiation and give visual
representation of the event to guide downstream experiments.
biocViews: ImmunoOncology, GeneExpression, RNASeq, Visualization,
AlternativeSplicing
Author: Diana Low [aut, cre]
Maintainer: Diana Low <lowdiana@gmail.com>
URL: https://github.com/dianalow/SPLINTER/
VignetteBuilder: knitr
BugReports: https://github.com/dianalow/SPLINTER/issues
git_url: https://git.bioconductor.org/packages/SPLINTER
git_branch: devel
git_last_commit: 15c4210
git_last_commit_date: 2024-10-29
Date/Publication: 2025-01-23
source.ver: src/contrib/SPLINTER_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SPLINTER_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SPLINTER_1.33.0.tgz
vignettes: vignettes/SPLINTER/inst/doc/vignette.pdf
vignetteTitles: SPLINTER
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SPLINTER/inst/doc/vignette.R
dependencyCount: 161
Package: splots
Version: 1.73.0
Imports: grid, RColorBrewer
Suggests: BiocStyle, knitr, rmarkdown, assertthat, HD2013SGI, dplyr,
ggplot2
License: LGPL
Archs: x64
MD5sum: a0f3e1c40661fc9ad85398615a316024
NeedsCompilation: no
Title: Visualization of high-throughput assays in microtitre plate or
slide format
Description: This package is here to support legacy usages of it, but
it should not be used for new code development. It provides a
single function, plotScreen, for visualising data in microtitre
plate or slide format. As a better alternative for such
functionality, please consider the platetools package on CRAN
(https://cran.r-project.org/package=platetools and
https://github.com/Swarchal/platetools), or ggplot2
(geom_raster, facet_wrap) as exemplified in the vignette of
this package.
biocViews: Visualization, Sequencing, MicrotitrePlateAssay
Author: Wolfgang Huber, Oleg Sklyar
Maintainer: Wolfgang Huber <wolfgang.huber@embl.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/splots
git_branch: devel
git_last_commit: da028a9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/splots_1.73.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/splots_1.73.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/splots_1.73.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/splots_1.73.0.tgz
vignettes: vignettes/splots/inst/doc/splots.html
vignetteTitles: splots: visualization of data from assays in microtitre
plate or slide format
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/splots/inst/doc/splots.R
dependsOnMe: HD2013SGI
dependencyCount: 2
Package: SPONGE
Version: 1.29.0
Depends: R (>= 3.6)
Imports: Biobase, stats, ppcor, logging, foreach, doRNG, data.table,
MASS, expm, gRbase, glmnet, igraph, iterators, tidyverse,
caret, dplyr, biomaRt, randomForest, ggridges, cvms,
ComplexHeatmap, ggplot2, MetBrewer, rlang, tnet, ggpubr,
stringr, tidyr
Suggests: testthat, knitr, rmarkdown, visNetwork, ggrepel, gridExtra,
digest, doParallel, bigmemory, GSVA
License: GPL (>=3)
MD5sum: 322ce39486340b36cfd4ad0de1f3ef05
NeedsCompilation: no
Title: Sparse Partial Correlations On Gene Expression
Description: This package provides methods to efficiently detect
competitive endogeneous RNA interactions between two genes.
Such interactions are mediated by one or several miRNAs such
that both gene and miRNA expression data for a larger number of
samples is needed as input. The SPONGE package now also
includes spongEffects: ceRNA modules offer patient-specific
insights into the miRNA regulatory landscape.
biocViews: GeneExpression, Transcription, GeneRegulation,
NetworkInference, Transcriptomics, SystemsBiology, Regression,
RandomForest, MachineLearning
Author: Markus List [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0941-4168>), Markus Hoffmann [aut]
(ORCID: <https://orcid.org/0000-0002-1920-288X>), Lena Strasser
[aut] (ORCID: <https://orcid.org/0009-0007-7881-6818>)
Maintainer: Markus List <markus.list@tum.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SPONGE
git_branch: devel
git_last_commit: 9357494
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SPONGE_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SPONGE_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SPONGE_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SPONGE_1.29.0.tgz
vignettes: vignettes/SPONGE/inst/doc/SPONGE.html,
vignettes/SPONGE/inst/doc/spongEffects.html
vignetteTitles: SPONGE vignette, spongEffects vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SPONGE/inst/doc/SPONGE.R,
vignettes/SPONGE/inst/doc/spongEffects.R
importsMe: miRspongeR
suggestsMe: mirTarRnaSeq
dependencyCount: 229
Package: spoon
Version: 1.3.3
Depends: R (>= 4.4)
Imports: SpatialExperiment, BRISC, nnSVG, BiocParallel, Matrix,
methods, SummarizedExperiment, stats, utils, scuttle
Suggests: testthat, STexampleData, knitr, rmarkdown, BiocStyle
License: MIT + file LICENSE
MD5sum: 49b3c2675d8f3d29310fca73cde8b260
NeedsCompilation: no
Title: Address the Mean-variance Relationship in Spatial
Transcriptomics Data
Description: This package addresses the mean-variance relationship in
spatially resolved transcriptomics data. Precision weights are
generated for individual observations using Empirical Bayes
techniques. These weights are used to rescale the data and
covariates, which are then used as input in spatially variable
gene detection tools.
biocViews: Spatial, SingleCell, Transcriptomics, GeneExpression,
Preprocessing
Author: Kinnary Shah [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7098-2116>), Boyi Guo [aut]
(ORCID: <https://orcid.org/0000-0003-2950-2349>), Stephanie C.
Hicks [aut] (ORCID: <https://orcid.org/0000-0002-7858-0231>)
Maintainer: Kinnary Shah <kinnaryshahh@gmail.com>
URL: https://github.com/kinnaryshah/spoon
VignetteBuilder: knitr
BugReports: https://github.com/kinnaryshah/spoon/issues
git_url: https://git.bioconductor.org/packages/spoon
git_branch: devel
git_last_commit: ee4814b
git_last_commit_date: 2025-03-19
Date/Publication: 2025-03-20
source.ver: src/contrib/spoon_1.3.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/spoon_1.3.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/spoon_1.3.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/spoon_1.3.3.tgz
vignettes: vignettes/spoon/inst/doc/spoon.html
vignetteTitles: spoon Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/spoon/inst/doc/spoon.R
dependencyCount: 90
Package: SpotClean
Version: 1.9.0
Depends: R (>= 4.2.0),
Imports: stats, methods, utils, dplyr, S4Vectors, SummarizedExperiment,
SpatialExperiment, Matrix, rhdf5, ggplot2, grid, readbitmap,
rjson, tibble, viridis, grDevices, RColorBrewer, Seurat, rlang
Suggests: testthat (>= 2.1.0), knitr, BiocStyle, rmarkdown, R.utils,
spelling
License: GPL-3
MD5sum: 1f9245363f0f932debb070e44b80bd1d
NeedsCompilation: yes
Title: SpotClean adjusts for spot swapping in spatial transcriptomics
data
Description: SpotClean is a computational method to adjust for spot
swapping in spatial transcriptomics data. Recent spatial
transcriptomics experiments utilize slides containing thousands
of spots with spot-specific barcodes that bind mRNA. Ideally,
unique molecular identifiers at a spot measure spot-specific
expression, but this is often not the case due to bleed from
nearby spots, an artifact we refer to as spot swapping.
SpotClean is able to estimate the contamination rate in
observed data and decontaminate the spot swapping effect, thus
increase the sensitivity and precision of downstream analyses.
biocViews: DataImport, RNASeq, Sequencing, GeneExpression, Spatial,
SingleCell, Transcriptomics, Preprocessing
Author: Zijian Ni [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1181-8337>), Christina Kendziorski
[ctb]
Maintainer: Zijian Ni <zni25@wisc.edu>
URL: https://github.com/zijianni/SpotClean
VignetteBuilder: knitr
BugReports: https://github.com/zijianni/SpotClean/issues
git_url: https://git.bioconductor.org/packages/SpotClean
git_branch: devel
git_last_commit: 0e3a77e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SpotClean_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SpotClean_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SpotClean_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SpotClean_1.9.0.tgz
vignettes: vignettes/SpotClean/inst/doc/SpotClean.html
vignetteTitles: SpotClean
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SpotClean/inst/doc/SpotClean.R
dependencyCount: 191
Package: SPOTlight
Version: 1.11.0
Depends: R (>= 4.1)
Imports: ggplot2, NMF, Matrix, matrixStats, nnls, SingleCellExperiment,
sparseMatrixStats, stats
Suggests: BiocStyle, colorBlindness, DelayedArray, DropletUtils,
ExperimentHub, ggcorrplot, grDevices, grid, igraph, jpeg,
knitr, methods, png, rmarkdown, scater, scatterpie, scran,
SpatialExperiment, SummarizedExperiment, S4Vectors,
TabulaMurisSenisData, TENxVisiumData, testthat
License: GPL-3
MD5sum: 1fea9bbebe67f856ed0b9a2b18ad3096
NeedsCompilation: no
Title: `SPOTlight`: Spatial Transcriptomics Deconvolution
Description: `SPOTlight`provides a method to deconvolute spatial
transcriptomics spots using a seeded NMF approach along with
visualization tools to assess the results. Spatially resolved
gene expression profiles are key to understand tissue
organization and function. However, novel spatial
transcriptomics (ST) profiling techniques lack single-cell
resolution and require a combination with single-cell RNA
sequencing (scRNA-seq) information to deconvolute the spatially
indexed datasets. Leveraging the strengths of both data types,
we developed SPOTlight, a computational tool that enables the
integration of ST with scRNA-seq data to infer the location of
cell types and states within a complex tissue. SPOTlight is
centered around a seeded non-negative matrix factorization
(NMF) regression, initialized using cell-type marker genes and
non-negative least squares (NNLS) to subsequently deconvolute
ST capture locations (spots).
biocViews: SingleCell, Spatial, StatisticalMethod
Author: Marc Elosua-Bayes [aut, cre], Helena L. Crowell [aut]
Maintainer: Marc Elosua-Bayes <elosua.marc@gmail.com>
URL: https://github.com/MarcElosua/SPOTlight
VignetteBuilder: knitr
BugReports: https://github.com/MarcElosua/SPOTlight/issues
git_url: https://git.bioconductor.org/packages/SPOTlight
git_branch: devel
git_last_commit: eae1ff5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SPOTlight_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SPOTlight_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SPOTlight_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SPOTlight_1.11.0.tgz
vignettes: vignettes/SPOTlight/inst/doc/SPOTlight_kidney.html
vignetteTitles: "SPOTlight"
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SPOTlight/inst/doc/SPOTlight_kidney.R
dependencyCount: 82
Package: SpotSweeper
Version: 1.3.3
Depends: R (>= 4.4.0)
Imports: SpatialExperiment, SummarizedExperiment, BiocNeighbors,
SingleCellExperiment, stats, escheR, MASS, ggplot2, spatialEco,
grDevices, BiocParallel
Suggests: knitr, BiocStyle, rmarkdown, scuttle, STexampleData, ggpubr,
testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: 525a1206f715fefb03d84f53fdb791bf
NeedsCompilation: no
Title: Spatially-aware quality control for spatial transcriptomics
Description: Spatially-aware quality control (QC) software for both
spot-level and artifact-level QC in spot-based spatial
transcripomics, such as 10x Visium. These methods calculate
local (nearest-neighbors) mean and variance of standard QC
metrics (library size, unique genes, and mitochondrial
percentage) to identify outliers spot and large technical
artifacts.
biocViews: Software, Spatial, Transcriptomics, QualityControl,
GeneExpression,
Author: Michael Totty [aut, cre] (ORCID:
<https://orcid.org/0000-0002-9292-8556>), Stephanie Hicks [aut]
(ORCID: <https://orcid.org/0000-0002-7858-0231>), Boyi Guo
[aut] (ORCID: <https://orcid.org/0000-0003-2950-2349>)
Maintainer: Michael Totty <mictott@gmail.com>
URL: https://github.com/MicTott/SpotSweeper
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/tag/SpotSweeper
git_url: https://git.bioconductor.org/packages/SpotSweeper
git_branch: devel
git_last_commit: d39575f
git_last_commit_date: 2024-12-24
Date/Publication: 2024-12-24
source.ver: src/contrib/SpotSweeper_1.3.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SpotSweeper_1.3.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SpotSweeper_1.3.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SpotSweeper_1.3.3.tgz
vignettes: vignettes/SpotSweeper/inst/doc/getting_started.html
vignetteTitles: Getting Started with `SpotSweeper`
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SpotSweeper/inst/doc/getting_started.R
dependencyCount: 109
Package: spqn
Version: 1.19.0
Depends: R (>= 4.0), ggplot2, ggridges, SummarizedExperiment,
BiocGenerics
Imports: graphics, stats, utils, matrixStats
Suggests: BiocStyle, knitr, rmarkdown, tools, spqnData (>= 0.99.3),
RUnit
License: Artistic-2.0
MD5sum: 005fc423b98190db42eb306a909ecb3a
NeedsCompilation: no
Title: Spatial quantile normalization
Description: The spqn package implements spatial quantile normalization
(SpQN). This method was developed to remove a mean-correlation
relationship in correlation matrices built from gene expression
data. It can serve as pre-processing step prior to a
co-expression analysis.
biocViews: NetworkInference, GraphAndNetwork, Normalization
Author: Yi Wang [cre, aut], Kasper Daniel Hansen [aut]
Maintainer: Yi Wang <yiwangthu5@gmail.com>
URL: https://github.com/hansenlab/spqn
VignetteBuilder: knitr
BugReports: https://github.com/hansenlab/spqn/issues
git_url: https://git.bioconductor.org/packages/spqn
git_branch: devel
git_last_commit: ebac315
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/spqn_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/spqn_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/spqn_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/spqn_1.19.0.tgz
vignettes: vignettes/spqn/inst/doc/spqn.html
vignetteTitles: spqn User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/spqn/inst/doc/spqn.R
dependencyCount: 63
Package: SPsimSeq
Version: 1.17.0
Depends: R (>= 4.0)
Imports: stats, methods, SingleCellExperiment, fitdistrplus, graphics,
edgeR, Hmisc, WGCNA, limma, mvtnorm, phyloseq, utils
Suggests: knitr, rmarkdown, LSD, testthat, BiocStyle
License: GPL-2
MD5sum: 5733e3feb51f11eaba5e6246f699c0b5
NeedsCompilation: no
Title: Semi-parametric simulation tool for bulk and single-cell RNA
sequencing data
Description: SPsimSeq uses a specially designed exponential family for
density estimation to constructs the distribution of gene
expression levels from a given real RNA sequencing data
(single-cell or bulk), and subsequently simulates a new dataset
from the estimated marginal distributions using
Gaussian-copulas to retain the dependence between genes. It
allows simulation of multiple groups and batches with any
required sample size and library size.
biocViews: GeneExpression, RNASeq, SingleCell, Sequencing, DNASeq
Author: Alemu Takele Assefa [aut], Olivier Thas [ths], Joris Meys
[cre], Stijn Hawinkel [aut]
Maintainer: Joris Meys <Joris.Meys@ugent.be>
URL: https://github.com/CenterForStatistics-UGent/SPsimSeq
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SPsimSeq
git_branch: devel
git_last_commit: 36e9409
git_last_commit_date: 2024-10-29
Date/Publication: 2025-01-23
source.ver: src/contrib/SPsimSeq_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SPsimSeq_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SPsimSeq_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SPsimSeq_1.17.0.tgz
vignettes: vignettes/SPsimSeq/inst/doc/SPsimSeq.html
vignetteTitles: Manual for the SPsimSeq package: semi-parametric
simulation for bulk and single cell RNA-seq data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SPsimSeq/inst/doc/SPsimSeq.R
importsMe: SurfR
suggestsMe: benchdamic
dependencyCount: 144
Package: SQLDataFrame
Version: 1.21.0
Depends: DelayedArray, S4Vectors
Imports: stats, utils, methods, BiocGenerics, RSQLite, duckdb, DBI
Suggests: knitr, rmarkdown, BiocStyle, testthat
License: LGPL (>= 3); File LICENSE
MD5sum: ba914ce1dbf33b80bede3c4af05ca510
NeedsCompilation: no
Title: Representation of SQL tables in DataFrame metaphor
Description: Implements bindings for SQL tables that are compatible
with Bioconductor S4 data structures, namely the DataFrame and
DelayedArray. This allows SQL-derived data to be easily used
inside other Bioconductor objects (e.g., SummarizedExperiments)
while keeping everything on disk.
biocViews: DataRepresentation, Infrastructure, Software
Author: Qian Liu [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1456-5099>), Aaron Lun [aut],
Martin Morgan [aut]
Maintainer: Qian Liu <Qian.Liu@RoswellPark.org>
URL: https://github.com/Bioconductor/SQLDataFrame
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/SQLDataFrame/issues
git_url: https://git.bioconductor.org/packages/SQLDataFrame
git_branch: devel
git_last_commit: 909e7f6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SQLDataFrame_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SQLDataFrame_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SQLDataFrame_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SQLDataFrame_1.21.0.tgz
vignettes: vignettes/SQLDataFrame/inst/doc/SQLDataFrame_userguide.html
vignetteTitles: User guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SQLDataFrame/inst/doc/SQLDataFrame_userguide.R
dependencyCount: 39
Package: squallms
Version: 1.1.0
Depends: R (>= 3.5.0)
Imports: xcms, MSnbase, MsExperiment, RaMS, dplyr, tidyr, tibble,
ggplot2, shiny, plotly, data.table, caret, stats, graphics,
utils, keys
Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: cd04d48df6385166f9b5ea1312bc8586
NeedsCompilation: no
Title: Speedy quality assurance via lasso labeling for LC-MS data
Description: squallms is a Bioconductor R package that implements a
"semi-labeled" approach to untargeted mass spectrometry data.
It pulls in raw data from mass-spec files to calculate several
metrics that are then used to label MS features in bulk as high
or low quality. These metrics of peak quality are then passed
to a simple logistic model that produces a fully-labeled
dataset suitable for downstream analysis.
biocViews: MassSpectrometry, Metabolomics, Proteomics, Lipidomics,
ShinyApps, Classification, Clustering, FeatureExtraction,
PrincipalComponent, Regression, Preprocessing, QualityControl,
Visualization
Author: William Kumler [aut, cre, cph] (ORCID:
<https://orcid.org/0000-0002-5022-8009>)
Maintainer: William Kumler <wkumler@uw.edu>
URL: https://github.com/wkumler/squallms
VignetteBuilder: knitr
BugReports: https://github.com/wkumler/squallms/issues
git_url: https://git.bioconductor.org/packages/squallms
git_branch: devel
git_last_commit: c717511
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/squallms_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/squallms_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/squallms_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/squallms_1.1.0.tgz
vignettes: vignettes/squallms/inst/doc/intro_to_squallms.html
vignetteTitles: Introduction to squallms
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/squallms/inst/doc/intro_to_squallms.R
dependencyCount: 186
Package: sRACIPE
Version: 1.99.1
Depends: R (>= 3.6.0),SummarizedExperiment, methods, Rcpp
Imports: ggplot2, reshape2, MASS, RColorBrewer, gridExtra,visNetwork,
gplots, umap, htmlwidgets, S4Vectors, BiocGenerics, grDevices,
stats, utils, graphics, doFuture, doRNG, future, foreach
LinkingTo: Rcpp
Suggests: knitr, BiocStyle, rmarkdown, tinytest
License: MIT + file LICENSE
MD5sum: 0f96b7de4f50484d783054bcfebbb4a6
NeedsCompilation: yes
Title: Systems biology tool to simulate gene regulatory circuits
Description: sRACIPE implements a randomization-based method for gene
circuit modeling. It allows us to study the effect of both the
gene expression noise and the parametric variation on any gene
regulatory circuit (GRC) using only its topology, and simulates
an ensemble of models with random kinetic parameters at
multiple noise levels. Statistical analysis of the generated
gene expressions reveals the basin of attraction and stability
of various phenotypic states and their changes associated with
intrinsic and extrinsic noises. sRACIPE provides a holistic
picture to evaluate the effects of both the stochastic nature
of cellular processes and the parametric variation.
biocViews: ResearchField, SystemsBiology, MathematicalBiology,
GeneExpression, GeneRegulation, GeneTarget
Author: Mingyang Lu [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8158-0593>), Vivek Kohar [aut],
Aidan Tillman [aut], Daniel Ramirez [aut]
Maintainer: Mingyang Lu <m.lu@northeastern.edu>
URL: https://github.com/lusystemsbio/sRACIPE, https://geneex.jax.org/,
https://vivekkohar.github.io/sRACIPE/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/sRACIPE
git_branch: devel
git_last_commit: bce5d5d
git_last_commit_date: 2025-02-24
Date/Publication: 2025-02-25
source.ver: src/contrib/sRACIPE_1.99.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sRACIPE_1.99.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sRACIPE_1.99.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sRACIPE_1.99.1.tgz
vignettes: vignettes/sRACIPE/inst/doc/sRACIPE.html
vignetteTitles: A systems biology tool for gene regulatory circuit
simulation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/sRACIPE/inst/doc/sRACIPE.R
dependencyCount: 114
Package: SRAdb
Version: 1.69.2
Depends: RSQLite, graph, RCurl
Imports: R.utils
Suggests: Rgraphviz
License: Artistic-2.0
MD5sum: e867e6fd7ae81c6b519d04950e235072
NeedsCompilation: no
Title: A compilation of metadata from NCBI SRA and tools
Description: The Sequence Read Archive (SRA) is the largest public
repository of sequencing data from the next generation of
sequencing platforms including Roche 454 GS System, Illumina
Genome Analyzer, Applied Biosystems SOLiD System, Helicos
Heliscope, and others. However, finding data of interest can be
challenging using current tools. SRAdb is an attempt to make
access to the metadata associated with submission, study,
sample, experiment and run much more feasible. This is
accomplished by parsing all the NCBI SRA metadata into a SQLite
database that can be stored and queried locally. Fulltext
search in the package make querying metadata very flexible and
powerful. fastq and sra files can be downloaded for doing
alignment locally. Beside ftp protocol, the SRAdb has funcitons
supporting fastp protocol (ascp from Aspera Connect) for faster
downloading large data files over long distance. The SQLite
database is updated regularly as new data is added to SRA and
can be downloaded at will for the most up-to-date metadata.
biocViews: Infrastructure, Sequencing, DataImport
Author: Jack Zhu and Sean Davis
Maintainer: Jack Zhu <zhujack@mail.nih.gov>
BugReports: https://github.com/zhujack/SRAdb/issues/new
git_url: https://git.bioconductor.org/packages/SRAdb
git_branch: devel
git_last_commit: b59574d
git_last_commit_date: 2024-12-10
Date/Publication: 2024-12-10
source.ver: src/contrib/SRAdb_1.69.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SRAdb_1.69.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SRAdb_1.69.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SRAdb_1.69.2.tgz
vignettes: vignettes/SRAdb/inst/doc/SRAdb.pdf
vignetteTitles: Using SRAdb to Query the Sequence Read Archive
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SRAdb/inst/doc/SRAdb.R
suggestsMe: parathyroidSE
dependencyCount: 30
Package: srnadiff
Version: 1.27.3
Depends: R (>= 3.6)
Imports: Rcpp (>= 0.12.8), stats, methods, S4Vectors, GenomeInfoDb,
rtracklayer, SummarizedExperiment, IRanges, GenomicRanges,
DESeq2, edgeR, Rsamtools, GenomicFeatures, GenomicAlignments,
grDevices, Gviz, BiocParallel, BiocManager, BiocStyle
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, testthat, BiocManager, BiocStyle
License: GPL-3
MD5sum: 13f1d974c384cc83e5893e4a30c281cc
NeedsCompilation: yes
Title: Finding differentially expressed unannotated genomic regions
from RNA-seq data
Description: srnadiff is a package that finds differently expressed
regions from RNA-seq data at base-resolution level without
relying on existing annotation. To do so, the package
implements the identify-then-annotate methodology that builds
on the idea of combining two pipelines approachs differential
expressed regions detection and differential expression
quantification. It reads BAM files as input, and outputs a list
differentially regions, together with the adjusted p-values.
biocViews: ImmunoOncology, GeneExpression, Coverage, SmallRNA,
Epigenetics, StatisticalMethod, Preprocessing,
DifferentialExpression
Author: Zytnicki Matthias [aut, cre], Gonzalez Ignacio [aut]
Maintainer: Zytnicki Matthias <matthias.zytnicki@inra.fr>
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/srnadiff
git_branch: devel
git_last_commit: 54a2b0c
git_last_commit_date: 2025-01-10
Date/Publication: 2025-01-10
source.ver: src/contrib/srnadiff_1.27.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/srnadiff_1.27.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/srnadiff_1.27.3.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/srnadiff_1.27.3.tgz
vignettes: vignettes/srnadiff/inst/doc/srnadiff.html
vignetteTitles: The srnadiff package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/srnadiff/inst/doc/srnadiff.R
dependencyCount: 165
Package: sscu
Version: 2.37.0
Depends: R (>= 3.3)
Imports: Biostrings (>= 2.36.4), seqinr (>= 3.1-3), BiocGenerics (>=
0.16.1)
Suggests: knitr, rmarkdown
License: GPL (>= 2)
MD5sum: 4fe0d55cd906d8f493c7f388f6703789
NeedsCompilation: no
Title: Strength of Selected Codon Usage
Description: The package calculates the indexes for selective stength
in codon usage in bacteria species. (1) The package can
calculate the strength of selected codon usage bias (sscu, also
named as s_index) based on Paul Sharp's method. The method take
into account of background mutation rate, and focus only on
four pairs of codons with universal translational advantages in
all bacterial species. Thus the sscu index is comparable among
different species. (2) The package can detect the strength of
translational accuracy selection by Akashi's test. The test
tabulating all codons into four categories with the feature as
conserved/variable amino acids and optimal/non-optimal codons.
(3) Optimal codon lists (selected codons) can be calculated by
either op_highly function (by using the highly expressed genes
compared with all genes to identify optimal codons), or
op_corre_CodonW/op_corre_NCprime function (by correlative
method developed by Hershberg & Petrov). Users will have a list
of optimal codons for further analysis, such as input to the
Akashi's test. (4) The detailed codon usage information, such
as RSCU value, number of optimal codons in the highly/all gene
set, as well as the genomic gc3 value, can be calculate by the
optimal_codon_statistics and genomic_gc3 function. (5)
Furthermore, we added one test function low_frequency_op in the
package. The function try to find the low frequency optimal
codons, among all the optimal codons identified by the
op_highly function.
biocViews: Genetics, GeneExpression, WholeGenome
Author: Yu Sun
Maintainer: Yu Sun <sunyu1357@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/sscu
git_branch: devel
git_last_commit: febf8e0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/sscu_2.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sscu_2.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sscu_2.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sscu_2.37.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 36
Package: sSeq
Version: 1.45.0
Depends: R (>= 3.0), caTools, RColorBrewer
License: GPL (>= 3)
MD5sum: 218fcf7e70cd8329ac7d5696875d5c12
NeedsCompilation: no
Title: Shrinkage estimation of dispersion in Negative Binomial models
for RNA-seq experiments with small sample size
Description: The purpose of this package is to discover the genes that
are differentially expressed between two conditions in RNA-seq
experiments. Gene expression is measured in counts of
transcripts and modeled with the Negative Binomial (NB)
distribution using a shrinkage approach for dispersion
estimation. The method of moment (MM) estimates for dispersion
are shrunk towards an estimated target, which minimizes the
average squared difference between the shrinkage estimates and
the initial estimates. The exact per-gene probability under the
NB model is calculated, and used to test the hypothesis that
the expected expression of a gene in two conditions identically
follow a NB distribution.
biocViews: ImmunoOncology, RNASeq
Author: Danni Yu <dyu@purdue.edu>, Wolfgang Huber <whuber@embl.de> and
Olga Vitek <ovitek@purdue.edu>
Maintainer: Danni Yu <dyu@purdue.edu>
git_url: https://git.bioconductor.org/packages/sSeq
git_branch: devel
git_last_commit: d459b87
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/sSeq_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sSeq_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sSeq_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sSeq_1.45.0.tgz
vignettes: vignettes/sSeq/inst/doc/sSeq.pdf
vignetteTitles: sSeq
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sSeq/inst/doc/sSeq.R
importsMe: MLSeq
suggestsMe: NBLDA
dependencyCount: 3
Package: ssize
Version: 1.81.0
Depends: gdata, xtable
License: LGPL
MD5sum: a48a13552dee83c5724e4ff51396c091
NeedsCompilation: no
Title: Estimate Microarray Sample Size
Description: Functions for computing and displaying sample size
information for gene expression arrays.
biocViews: Microarray, DifferentialExpression
Author: Gregory R. Warnes, Peng Liu, and Fasheng Li
Maintainer: Gregory R. Warnes <greg@random-technologies-llc.com>
git_url: https://git.bioconductor.org/packages/ssize
git_branch: devel
git_last_commit: 40be585
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ssize_1.81.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ssize_1.81.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ssize_1.81.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ssize_1.81.0.tgz
vignettes: vignettes/ssize/inst/doc/ssize.pdf
vignetteTitles: Sample Size Estimation for Microarray Experiments Using
the \code{ssize} package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ssize/inst/doc/ssize.R
suggestsMe: maGUI
dependencyCount: 6
Package: sSNAPPY
Version: 1.11.0
Depends: R (>= 4.3.0), ggplot2
Imports: dplyr (>= 1.1), magrittr, rlang, stats, graphite, tibble,
ggraph, igraph, reshape2, org.Hs.eg.db, SummarizedExperiment,
edgeR, methods, ggforce, pheatmap, utils, stringr, gtools,
tidyr
Suggests: BiocManager, BiocStyle, colorspace, cowplot, DT, htmltools,
knitr, pander, patchwork, rmarkdown, spelling, testthat (>=
3.0.0), tidyverse
License: GPL-3
Archs: x64
MD5sum: 0bfe3c1b9b01530ee6727bd2af663e41
NeedsCompilation: no
Title: Single Sample directioNAl Pathway Perturbation analYsis
Description: A single sample pathway perturbation testing method for
RNA-seq data. The method propagates changes in gene expression
down gene-set topologies to compute single-sample directional
pathway perturbation scores that reflect potential direction of
change. Perturbation scores can be used to test significance of
pathway perturbation at both individual-sample and treatment
levels.
biocViews: Software, GeneExpression, GeneSetEnrichment, GeneSignaling
Author: Wenjun Liu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8185-3069>), Stephen Pederson
[aut] (ORCID: <https://orcid.org/0000-0001-8197-3303>)
Maintainer: Wenjun Liu <wenjun.liu@adelaide.edu.au>
URL: https://wenjun-liu.github.io/sSNAPPY/
SystemRequirements: C++11
VignetteBuilder: knitr
BugReports: https://github.com/Wenjun-Liu/sSNAPPY/issues
git_url: https://git.bioconductor.org/packages/sSNAPPY
git_branch: devel
git_last_commit: 8ca48a7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/sSNAPPY_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sSNAPPY_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sSNAPPY_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sSNAPPY_1.11.0.tgz
vignettes: vignettes/sSNAPPY/inst/doc/sSNAPPY.html
vignetteTitles: Single Sample Directional Pathway Perturbation Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sSNAPPY/inst/doc/sSNAPPY.R
dependencyCount: 108
Package: ssPATHS
Version: 1.21.0
Depends: R (>= 3.5.0), SummarizedExperiment
Imports: ROCR, dml, MESS
Suggests: ggplot2, testthat (>= 2.1.0)
License: MIT + file LICENSE
MD5sum: 5332d79c3cfffd45f2d4e2aa078d9c74
NeedsCompilation: no
Title: ssPATHS: Single Sample PATHway Score
Description: This package generates pathway scores from expression data
for single samples after training on a reference cohort. The
score is generated by taking the expression of a gene set
(pathway) from a reference cohort and performing linear
discriminant analysis to distinguish samples in the cohort that
have the pathway augmented and not. The separating hyperplane
is then used to score new samples.
biocViews: Software, GeneExpression, BiomedicalInformatics, RNASeq,
Pathways, Transcriptomics, DimensionReduction, Classification
Author: Natalie R. Davidson
Maintainer: Natalie R. Davidson <natalie.davidson@inf.ethz.ch>
git_url: https://git.bioconductor.org/packages/ssPATHS
git_branch: devel
git_last_commit: bd3f81e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ssPATHS_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ssPATHS_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ssPATHS_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ssPATHS_1.21.0.tgz
vignettes: vignettes/ssPATHS/inst/doc/ssPATHS.pdf
vignetteTitles: Using ssPATHS
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ssPATHS/inst/doc/ssPATHS.R
dependencyCount: 118
Package: ssrch
Version: 1.23.0
Depends: R (>= 3.6), methods
Imports: shiny, DT, utils
Suggests: knitr, testthat, rmarkdown, BiocStyle
License: Artistic-2.0
Archs: x64
MD5sum: 6313e44aa89edb9779716d9bf319741c
NeedsCompilation: no
Title: a simple search engine
Description: Demonstrate tokenization and a search gadget for
collections of CSV files.
biocViews: Infrastructure
Author: Vince Carey
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ssrch
git_branch: devel
git_last_commit: 2b3fcf5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ssrch_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ssrch_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ssrch_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ssrch_1.23.0.tgz
vignettes: vignettes/ssrch/inst/doc/ssrch.html
vignetteTitles: ssrch: small search engine
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ssrch/inst/doc/ssrch.R
dependencyCount: 47
Package: ssviz
Version: 1.41.0
Depends: R (>= 3.5.0), methods, Rsamtools, Biostrings, reshape,
ggplot2, RColorBrewer, stats
Suggests: knitr
License: GPL-2
MD5sum: f4ed45f748b1d29ef34d8d7c6198f183
NeedsCompilation: no
Title: A small RNA-seq visualizer and analysis toolkit
Description: Small RNA sequencing viewer
biocViews: ImmunoOncology,
Sequencing,RNASeq,Visualization,MultipleComparison,Genetics
Author: Diana Low
Maintainer: Diana Low <lowdiana@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ssviz
git_branch: devel
git_last_commit: 6c33152
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ssviz_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ssviz_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ssviz_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ssviz_1.41.0.tgz
vignettes: vignettes/ssviz/inst/doc/ssviz.pdf
vignetteTitles: ssviz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ssviz/inst/doc/ssviz.R
dependencyCount: 71
Package: StabMap
Version: 1.1.0
Depends: R (>= 4.4.0),
Imports: igraph, slam, BiocNeighbors, Matrix, MASS, abind,
SummarizedExperiment, methods, MatrixGenerics, BiocGenerics,
BiocSingular, BiocParallel
Suggests: scran, scater, knitr, UpSetR, gridExtra,
SingleCellMultiModal, BiocStyle, magrittr, testthat (>= 3.0.0),
purrr, sparseMatrixStats
License: GPL-2
MD5sum: 4552eadf29943312462045a15d149416
NeedsCompilation: no
Title: Stabilised mosaic single cell data integration using unshared
features
Description: StabMap performs single cell mosaic data integration by
first building a mosaic data topology, and for each reference
dataset, traverses the topology to project and predict data
onto a common embedding. Mosaic data should be provided in a
list format, with all relevant features included in the data
matrices within each list object. The output of stabMap is a
joint low-dimensional embedding taking into account all
available relevant features. Expression imputation can also be
performed using the StabMap embedding and any of the original
data matrices for given reference and query cell lists.
biocViews: SingleCell, DimensionReduction, Software
Author: Shila Ghazanfar [aut, cre, ctb], Aiden Jin [ctb], Nicholas
Robertson [ctb]
Maintainer: Shila Ghazanfar <shazanfar@gmail.com>
URL: https://sydneybiox.github.io/StabMap,
https://sydneybiox.github.io/StabMap/
VignetteBuilder: knitr
BugReports: https://github.com/sydneybiox/StabMap/issues
git_url: https://git.bioconductor.org/packages/StabMap
git_branch: devel
git_last_commit: 2005245
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/StabMap_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/StabMap_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/StabMap_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/StabMap_1.1.0.tgz
vignettes: vignettes/StabMap/inst/doc/stabMap_PBMC_Multiome.html
vignetteTitles: Mosaic single cell data integration
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/StabMap/inst/doc/stabMap_PBMC_Multiome.R
dependencyCount: 64
Package: stageR
Version: 1.29.0
Depends: R (>= 3.4), SummarizedExperiment
Imports: methods, stats
Suggests: knitr, rmarkdown, BiocStyle, methods, Biobase, edgeR, limma,
DEXSeq, testthat
License: GNU General Public License version 3
MD5sum: 5591ee266b9a5c904b6c1eb792df025e
NeedsCompilation: no
Title: stageR: stage-wise analysis of high throughput gene expression
data in R
Description: The stageR package allows automated stage-wise analysis of
high-throughput gene expression data. The method is published
in Genome Biology at
https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1277-0
biocViews: Software, StatisticalMethod
Author: Koen Van den Berge and Lieven Clement
Maintainer: Koen Van den Berge <koen.vdberge@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/stageR
git_branch: devel
git_last_commit: 2a767cf
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/stageR_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/stageR_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/stageR_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/stageR_1.29.0.tgz
vignettes: vignettes/stageR/inst/doc/stageRVignette.html
vignetteTitles: stageR: stage-wise analysis of high-throughput gene
expression data in R
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/stageR/inst/doc/stageRVignette.R
dependsOnMe: rnaseqDTU
suggestsMe: MethReg, muscat, satuRn
dependencyCount: 36
Package: standR
Version: 1.11.2
Depends: R (>= 4.1)
Imports: dplyr, SpatialExperiment (>= 1.5.2), SummarizedExperiment,
SingleCellExperiment, edgeR, rlang, readr, tibble, ggplot2,
tidyr, ruv, limma, patchwork, S4Vectors, Biobase, BiocGenerics,
grDevices, stats, methods, ggalluvial, mclustcomp, RUVSeq
Suggests: knitr, ExperimentHub, rmarkdown, scater, uwot, ggpubr,
ggrepel, cluster, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: 45157fd126aa3ed9bf8cba7bf1147a2e
NeedsCompilation: no
Title: Spatial transcriptome analyses of Nanostring's DSP data in R
Description: standR is an user-friendly R package providing functions
to assist conducting good-practice analysis of Nanostring's
GeoMX DSP data. All functions in the package are built based on
the SpatialExperiment object, allowing integration into various
spatial transcriptomics-related packages from Bioconductor.
standR allows data inspection, quality control, normalization,
batch correction and evaluation with informative
visualizations.
biocViews: Spatial, Transcriptomics, GeneExpression,
DifferentialExpression, QualityControl, Normalization,
ExperimentHubSoftware
Author: Ning Liu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-9487-9305>), Dharmesh D Bhuva
[aut] (ORCID: <https://orcid.org/0000-0002-6398-9157>), Ahmed
Mohamed [aut]
Maintainer: Ning Liu <ning.liu@adelaide.edu.au>
URL: https://github.com/DavisLaboratory/standR
VignetteBuilder: knitr
BugReports: https://github.com/DavisLaboratory/standR/issues
git_url: https://git.bioconductor.org/packages/standR
git_branch: devel
git_last_commit: bc1785e
git_last_commit_date: 2025-02-09
Date/Publication: 2025-02-09
source.ver: src/contrib/standR_1.11.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/standR_1.11.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/standR_1.11.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/standR_1.11.2.tgz
vignettes: vignettes/standR/inst/doc/Quick_start.html
vignetteTitles: standR_introduction
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/standR/inst/doc/Quick_start.R
dependencyCount: 150
Package: STATegRa
Version: 1.43.0
Depends: R (>= 2.10)
Imports: Biobase, gridExtra, ggplot2, methods, stats, grid, MASS,
calibrate, gplots, edgeR, limma, foreach, affy
Suggests: RUnit, BiocGenerics, knitr (>= 1.6), rmarkdown, BiocStyle (>=
1.3), roxygen2, doSNOW
License: GPL-2
MD5sum: 1fb375f51e40f44d5f3e28c6bc2a3796
NeedsCompilation: no
Title: Classes and methods for multi-omics data integration
Description: Classes and tools for multi-omics data integration.
biocViews: Software, StatisticalMethod, Clustering, DimensionReduction,
PrincipalComponent
Author: STATegra Consortia
Maintainer: David Gomez-Cabrero <david.gomezcabrero@ki.se>, Núria
Planell <nuria.planell.picola@navarra.es>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/STATegRa
git_branch: devel
git_last_commit: 7a5e8cc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/STATegRa_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/STATegRa_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/STATegRa_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/STATegRa_1.43.0.tgz
vignettes: vignettes/STATegRa/inst/doc/STATegRa.html
vignetteTitles: STATegRa User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/STATegRa/inst/doc/STATegRa.R
dependencyCount: 56
Package: Statial
Version: 1.9.0
Depends: R (>= 4.1.0)
Imports: BiocParallel, spatstat.geom, concaveman, data.table,
spatstat.explore, dplyr, tidyr, SingleCellExperiment, tibble,
stringr, tidyselect, ggplot2, methods, stats,
SummarizedExperiment, S4Vectors, plotly, purrr, ranger,
magrittr, limma, SpatialExperiment, cluster, treekoR
Suggests: BiocStyle, knitr, testthat (>= 3.0.0), ClassifyR, spicyR,
ggsurvfit, lisaClust, survival
License: GPL-3
MD5sum: 024fde4afe64267c6487f02d1b2acb9e
NeedsCompilation: no
Title: A package to identify changes in cell state relative to spatial
associations
Description: Statial is a suite of functions for identifying changes in
cell state. The functionality provided by Statial provides
robust quantification of cell type localisation which are
invariant to changes in tissue structure. In addition to this
Statial uncovers changes in marker expression associated with
varying levels of localisation. These features can be used to
explore how the structure and function of different cell types
may be altered by the agents they are surrounded with.
biocViews: SingleCell, Spatial, Classification
Author: Farhan Ameen [aut, cre], Sourish Iyengar [aut], Shila Ghazanfar
[aut], Ellis Patrick [aut]
Maintainer: Farhan Ameen <fame2827@uni.sydney.edu.au>
URL: https://sydneybiox.github.io/Statial
https://github.com/SydneyBioX/Statial/issues
VignetteBuilder: knitr
BugReports: https://github.com/SydneyBioX/Statial/issues
git_url: https://git.bioconductor.org/packages/Statial
git_branch: devel
git_last_commit: b4c031f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Statial_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Statial_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Statial_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Statial_1.9.0.tgz
vignettes: vignettes/Statial/inst/doc/Statial.html
vignetteTitles: "Introduction to Statial"
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Statial/inst/doc/Statial.R
dependencyCount: 226
Package: statTarget
Version: 1.37.0
Depends: R (>= 3.6.0)
Imports:
randomForest,plyr,pdist,ROC,utils,grDevices,graphics,rrcov,stats,
pls,impute
Suggests: testthat, BiocStyle, knitr, rmarkdown
License: LGPL (>= 3)
MD5sum: 492cfd2bb1aab20bce4fa603e3da667d
NeedsCompilation: no
Title: Statistical Analysis of Molecular Profiles
Description: A streamlined tool provides a graphical user interface for
quality control based signal drift correction (QC-RFSC),
integration of data from multi-batch MS-based experiments, and
the comprehensive statistical analysis in metabolomics and
proteomics.
biocViews: ImmunoOncology, Metabolomics, Proteomics, Machine Learning,
Lipidomics, MassSpectrometry, QualityControl, Normalization,
QC-RFSC, ComBat, DifferentialExpression, BatchEffect,
Visualization, MultipleComparison,Preprocessing, Software
Author: Hemi Luan
Maintainer: Hemi Luan <hemi.luan@gmail.com>
URL: https://stattarget.github.io
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/statTarget
git_branch: devel
git_last_commit: 581d4ef
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/statTarget_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/statTarget_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/statTarget_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/statTarget_1.37.0.tgz
vignettes: vignettes/statTarget/inst/doc/Combat.html,
vignettes/statTarget/inst/doc/pathway_analysis.html,
vignettes/statTarget/inst/doc/statTarget.html
vignetteTitles: QC_free approach with Combat method, statTarget2 for
pathway analysis, statTarget2 On using the Graphical User
Interface
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/statTarget/inst/doc/Combat.R,
vignettes/statTarget/inst/doc/pathway_analysis.R,
vignettes/statTarget/inst/doc/statTarget.R
dependencyCount: 26
Package: stepNorm
Version: 1.79.0
Depends: R (>= 1.8.0), marray, methods
Imports: marray, MASS, methods, stats
License: LGPL
MD5sum: 8f6014ef04cd41199bb209b46b9cf7da
NeedsCompilation: no
Title: Stepwise normalization functions for cDNA microarrays
Description: Stepwise normalization functions for cDNA microarray data.
biocViews: Microarray, TwoChannel, Preprocessing
Author: Yuanyuan Xiao <yxiao@itsa.ucsf.edu>, Yee Hwa (Jean) Yang
<jean@biostat.ucsf.edu>
Maintainer: Yuanyuan Xiao <yxiao@itsa.ucsf.edu>
URL: http://www.biostat.ucsf.edu/jean/
git_url: https://git.bioconductor.org/packages/stepNorm
git_branch: devel
git_last_commit: c5798e7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/stepNorm_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/stepNorm_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/stepNorm_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/stepNorm_1.79.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 9
Package: stJoincount
Version: 1.9.0
Depends: R (>= 4.2.0)
Imports: graphics, stats, dplyr, magrittr, sp, raster, spdep, ggplot2,
pheatmap, grDevices, Seurat, SpatialExperiment,
SummarizedExperiment
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: 066261eadf90643a0d80ee09f8569cec
NeedsCompilation: no
Title: stJoincount - Join count statistic for quantifying spatial
correlation between clusters
Description: stJoincount facilitates the application of join count
analysis to spatial transcriptomic data generated from the 10x
Genomics Visium platform. This tool first converts a labeled
spatial tissue map into a raster object, in which each spatial
feature is represented by a pixel coded by label assignment.
This process includes automatic calculation of optimal raster
resolution and extent for the sample. A neighbors list is then
created from the rasterized sample, in which adjacent and
diagonal neighbors for each pixel are identified. After adding
binary spatial weights to the neighbors list, a
multi-categorical join count analysis is performed to tabulate
"joins" between all possible combinations of label pairs. The
function returns the observed join counts, the expected count
under conditions of spatial randomness, and the variance
calculated under non-free sampling. The z-score is then
calculated as the difference between observed and expected
counts, divided by the square root of the variance.
biocViews: Transcriptomics, Clustering, Spatial, BiocViews, Software
Author: Jiarong Song [cre, aut] (ORCID:
<https://orcid.org/0000-0002-3673-4853>), Rania Bassiouni
[aut], David Craig [aut]
Maintainer: Jiarong Song <songjiar@usc.edu>
URL: https://github.com/Nina-Song/stJoincount
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/stJoincount
git_branch: devel
git_last_commit: 0a115cb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/stJoincount_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/stJoincount_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/stJoincount_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/stJoincount_1.9.0.tgz
vignettes: vignettes/stJoincount/inst/doc/stJoincount-vignette.html
vignetteTitles: Introduction to stJoincount
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/stJoincount/inst/doc/stJoincount-vignette.R
dependencyCount: 197
Package: strandCheckR
Version: 1.25.0
Imports: dplyr, magrittr, GenomeInfoDb, GenomicAlignments,
GenomicRanges, IRanges, Rsamtools, S4Vectors, grid,
BiocGenerics, ggplot2, reshape2, stats, gridExtra,
TxDb.Hsapiens.UCSC.hg38.knownGene, methods, stringr, rmarkdown
Suggests: BiocStyle, knitr, testthat
License: GPL (>= 2)
MD5sum: 2d8bd6ddf845cf0d91123d26ea4efcbe
NeedsCompilation: no
Title: Calculate strandness information of a bam file
Description: This package aims to quantify and remove putative double
strand DNA from a strand-specific RNA sample. There are also
options and methods to plot the positive/negative proportions
of all sliding windows, which allow users to have an idea of
how much the sample was contaminated and the appropriate
threshold to be used for filtering.
biocViews: RNASeq, Alignment, QualityControl, Coverage, ImmunoOncology
Author: Thu-Hien To [aut, cre], Steve Pederson [aut]
Maintainer: Thu-Hien To <tothuhien@gmail.com>
URL: https://github.com/UofABioinformaticsHub/strandCheckR
VignetteBuilder: knitr
BugReports:
https://github.com/UofABioinformaticsHub/strandCheckR/issues
git_url: https://git.bioconductor.org/packages/strandCheckR
git_branch: devel
git_last_commit: d22ff46
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/strandCheckR_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/strandCheckR_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/strandCheckR_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/strandCheckR_1.25.0.tgz
vignettes: vignettes/strandCheckR/inst/doc/strandCheckR.html
vignetteTitles: An Introduction To strandCheckR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/strandCheckR/inst/doc/strandCheckR.R
dependencyCount: 121
Package: Streamer
Version: 1.53.0
Imports: methods, graph, RBGL, parallel, BiocGenerics
Suggests: RUnit, Rsamtools (>= 1.5.53), GenomicAlignments, Rgraphviz
License: Artistic-2.0
MD5sum: f69f5838ce73bf5a807aca786cd8e00d
NeedsCompilation: yes
Title: Enabling stream processing of large files
Description: Large data files can be difficult to work with in R, where
data generally resides in memory. This package encourages a
style of programming where data is 'streamed' from disk into R
via a `producer' and through a series of `consumers' that,
typically reduce the original data to a manageable size. The
package provides useful Producer and Consumer stream components
for operations such as data input, sampling, indexing, and
transformation; see package?Streamer for details.
biocViews: Infrastructure, DataImport
Author: Martin Morgan, Nishant Gopalakrishnan
Maintainer: Martin Morgan <martin.morgan@roswellpark.org>
git_url: https://git.bioconductor.org/packages/Streamer
git_branch: devel
git_last_commit: 6658a05
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Streamer_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Streamer_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Streamer_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Streamer_1.53.0.tgz
vignettes: vignettes/Streamer/inst/doc/Streamer.pdf
vignetteTitles: Streamer: A simple example
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Streamer/inst/doc/Streamer.R
dependencyCount: 11
Package: STRINGdb
Version: 2.19.0
Depends: R (>= 2.14.0)
Imports: png, sqldf, plyr, igraph, httr, methods, RColorBrewer, gplots,
hash, plotrix
Suggests: RUnit, BiocGenerics
License: GPL-2
MD5sum: 11c2b3878eb64348d550a23722f2e285
NeedsCompilation: no
Title: STRINGdb - Protein-Protein Interaction Networks and Functional
Enrichment Analysis
Description: The STRINGdb package provides a R interface to the STRING
protein-protein interactions database (https://string-db.org).
biocViews: Network
Author: Andrea Franceschini <andrea.franceschini@isb-sib.ch>
Maintainer: Damian Szklarczyk <damian.szklarczyk@imls.uzh.ch>
git_url: https://git.bioconductor.org/packages/STRINGdb
git_branch: devel
git_last_commit: 95ff4e9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/STRINGdb_2.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/STRINGdb_2.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/STRINGdb_2.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/STRINGdb_2.19.0.tgz
vignettes: vignettes/STRINGdb/inst/doc/STRINGdb.pdf
vignetteTitles: STRINGdb Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/STRINGdb/inst/doc/STRINGdb.R
dependsOnMe: PPInfer
importsMe: GeDi, IMMAN, RITAN, TDbasedUFEadv, XINA, crosstalkr
suggestsMe: epiNEM, GeneNetworkBuilder, martini, netSmooth, PCAN,
protti
dependencyCount: 50
Package: struct
Version: 1.19.0
Depends: R (>= 4.0)
Imports: methods,ontologyIndex, datasets, graphics, stats, utils,
knitr, SummarizedExperiment, S4Vectors, rols
Suggests: testthat, rstudioapi, rmarkdown, covr, BiocStyle, openxlsx,
ggplot2, magick
License: GPL-3
MD5sum: 68c8cbf1009ecdcb9d22e6ae3739e170
NeedsCompilation: no
Title: Statistics in R Using Class-based Templates
Description: Defines and includes a set of class-based templates for
developing and implementing data processing and analysis
workflows, with a strong emphasis on statistics and machine
learning. The templates can be used and where needed extended
to 'wrap' tools and methods from other packages into a common
standardised structure to allow for effective and fast
integration. Model objects can be combined into sequences, and
sequences nested in iterators using overloaded operators to
simplify and improve readability of the code. Ontology lookup
has been integrated and implemented to provide standardised
definitions for methods, inputs and outputs wrapped using the
class-based templates.
biocViews: WorkflowStep
Author: Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut]
Maintainer: Gavin Rhys Lloyd <g.r.lloyd@bham.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/struct
git_branch: devel
git_last_commit: fd866cf
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/struct_1.19.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/struct_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/struct_1.19.0.tgz
vignettes:
vignettes/struct/inst/doc/struct_templates_and_helper_functions.html
vignetteTitles: Introduction to STRUCT - STatistics in R using
Class-based Templates
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/struct/inst/doc/struct_templates_and_helper_functions.R
dependsOnMe: MetMashR, structToolbox
importsMe: metabolomicsWorkbenchR
dependencyCount: 53
Package: Structstrings
Version: 1.23.1
Depends: R (>= 4.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9),
Biostrings (>= 2.57.2)
Imports: methods, BiocGenerics, XVector, stringr, stringi, crayon,
grDevices
LinkingTo: IRanges, S4Vectors
Suggests: testthat, knitr, rmarkdown, tRNAscanImport, BiocStyle
License: Artistic-2.0
MD5sum: 2f1158be14cda8eb0cbb8bf8d5af0bc7
NeedsCompilation: yes
Title: Implementation of the dot bracket annotations with Biostrings
Description: The Structstrings package implements the widely used dot
bracket annotation for storing base pairing information in
structured RNA. Structstrings uses the infrastructure provided
by the Biostrings package and derives the DotBracketString and
related classes from the BString class. From these, base pair
tables can be produced for in depth analysis. In addition, the
loop indices of the base pairs can be retrieved as well. For
better efficiency, information conversion is implemented in C,
inspired to a large extend by the ViennaRNA package.
biocViews: DataImport, DataRepresentation, Infrastructure, Sequencing,
Software, Alignment, SequenceMatching
Author: Felix G.M. Ernst [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5064-0928>)
Maintainer: Felix G.M. Ernst <felix.gm.ernst@outlook.com>
URL: https://github.com/FelixErnst/Structstrings
VignetteBuilder: knitr
BugReports: https://github.com/FelixErnst/Structstrings/issues
git_url: https://git.bioconductor.org/packages/Structstrings
git_branch: devel
git_last_commit: b17276e
git_last_commit_date: 2024-11-01
Date/Publication: 2024-11-03
source.ver: src/contrib/Structstrings_1.23.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Structstrings_1.23.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Structstrings_1.23.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Structstrings_1.23.1.tgz
vignettes: vignettes/Structstrings/inst/doc/Structstrings.html
vignetteTitles: Structstrings
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Structstrings/inst/doc/Structstrings.R
dependsOnMe: tRNA, tRNAdbImport
importsMe: tRNAscanImport
dependencyCount: 33
Package: structToolbox
Version: 1.19.2
Depends: R (>= 4.0), struct (>= 1.5.1)
Imports: ggplot2, ggthemes, grid, gridExtra, methods, scales, sp, stats
Suggests: agricolae, BiocFileCache, BiocStyle, car, covr, cowplot,
e1071, emmeans, ggdendro, knitr, magick, nlme, openxlsx, pls,
pmp, reshape2, ropls, rmarkdown, Rtsne, testthat, rappdirs
License: GPL-3
MD5sum: 4e4af92356638c140b03114630e97e7e
NeedsCompilation: no
Title: Data processing & analysis tools for Metabolomics and other
omics
Description: An extensive set of data (pre-)processing and analysis
methods and tools for metabolomics and other omics, with a
strong emphasis on statistics and machine learning. This
toolbox allows the user to build extensive and standardised
workflows for data analysis. The methods and tools have been
implemented using class-based templates provided by the struct
(Statistics in R Using Class-based Templates) package. The
toolbox includes pre-processing methods (e.g. signal drift and
batch correction, normalisation, missing value imputation and
scaling), univariate (e.g. ttest, various forms of ANOVA,
Kruskal–Wallis test and more) and multivariate statistical
methods (e.g. PCA and PLS, including cross-validation and
permutation testing) as well as machine learning methods (e.g.
Support Vector Machines). The STATistics Ontology (STATO) has
been integrated and implemented to provide standardised
definitions for the different methods, inputs and outputs.
biocViews: WorkflowStep, Metabolomics
Author: Gavin Rhys Lloyd [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7989-6695>), Ralf Johannes Maria
Weber [aut]
Maintainer: Gavin Rhys Lloyd <g.r.lloyd@bham.ac.uk>
URL: https://github.com/computational-metabolomics/structToolbox,
https://computational-metabolomics.github.io/structToolbox/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/structToolbox
git_branch: devel
git_last_commit: 4fab996
git_last_commit_date: 2025-02-19
Date/Publication: 2025-02-19
source.ver: src/contrib/structToolbox_1.19.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/structToolbox_1.19.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/structToolbox_1.19.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/structToolbox_1.19.2.tgz
vignettes:
vignettes/structToolbox/inst/doc/data_analysis_omics_using_the_structtoolbox.html
vignetteTitles: Data analysis of metabolomics and other omics datasets
using the structToolbox
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/structToolbox/inst/doc/data_analysis_omics_using_the_structtoolbox.R
suggestsMe: metabolomicsWorkbenchR, MetMashR
dependencyCount: 79
Package: StructuralVariantAnnotation
Version: 1.23.0
Depends: GenomicRanges, rtracklayer, VariantAnnotation, BiocGenerics, R
(>= 4.1.0)
Imports: assertthat, Biostrings, pwalign, stringr, dplyr, methods,
rlang, GenomicFeatures, IRanges, S4Vectors,
SummarizedExperiment, GenomeInfoDb,
Suggests: ggplot2, devtools, testthat (>= 2.1.0), roxygen2, rmarkdown,
tidyverse, knitr, ggbio, biovizBase,
TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19,
License: GPL-3 + file LICENSE
MD5sum: 2ec5f64ba307776cbcfe5acface03bb2
NeedsCompilation: no
Title: Variant annotations for structural variants
Description: StructuralVariantAnnotation provides a framework for
analysis of structural variants within the Bioconductor
ecosystem. This package contains contains useful helper
functions for dealing with structural variants in VCF format.
The packages contains functions for parsing VCFs from a number
of popular callers as well as functions for dealing with
breakpoints involving two separate genomic loci encoded as
GRanges objects.
biocViews: DataImport, Sequencing, Annotation, Genetics,
VariantAnnotation
Author: Daniel Cameron [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0951-7116>), Ruining Dong [aut]
(ORCID: <https://orcid.org/0000-0003-1433-0484>)
Maintainer: Daniel Cameron <daniel.l.cameron@gmail.com>
VignetteBuilder: knitr
git_url:
https://git.bioconductor.org/packages/StructuralVariantAnnotation
git_branch: devel
git_last_commit: 0faca97
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/StructuralVariantAnnotation_1.23.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.5/StructuralVariantAnnotation_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/StructuralVariantAnnotation_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/StructuralVariantAnnotation_1.23.0.tgz
vignettes:
vignettes/StructuralVariantAnnotation/inst/doc/vignettes.html
vignetteTitles: Structural Variant Annotation Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/StructuralVariantAnnotation/inst/doc/vignettes.R
dependsOnMe: svaNUMT, svaRetro
suggestsMe: shiny.gosling
dependencyCount: 91
Package: SubCellBarCode
Version: 1.23.0
Depends: R (>= 3.6)
Imports: Rtsne, scatterplot3d, caret, e1071, ggplot2, gridExtra,
networkD3, ggrepel, graphics, stats, org.Hs.eg.db,
AnnotationDbi
Suggests: knitr, rmarkdown, BiocStyle
License: GPL-2
MD5sum: 49ced08324241ad505888525cf2129bc
NeedsCompilation: no
Title: SubCellBarCode: Integrated workflow for robust mapping and
visualizing whole human spatial proteome
Description: Mass-Spectrometry based spatial proteomics have enabled
the proteome-wide mapping of protein subcellular localization
(Orre et al. 2019, Molecular Cell). SubCellBarCode R package
robustly classifies proteins into corresponding subcellular
localization.
biocViews: Proteomics, MassSpectrometry, Classification
Author: Taner Arslan
Maintainer: Taner Arslan <taner.arslan@ki.se>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/SubCellBarCode
git_branch: devel
git_last_commit: 260c964
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SubCellBarCode_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SubCellBarCode_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SubCellBarCode_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SubCellBarCode_1.23.0.tgz
vignettes: vignettes/SubCellBarCode/inst/doc/SubCellBarCode.html
vignetteTitles: SubCellBarCode R Markdown vignettes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SubCellBarCode/inst/doc/SubCellBarCode.R
dependencyCount: 138
Package: subSeq
Version: 1.37.0
Depends: R (>= 3.2)
Imports: data.table, dplyr, tidyr, ggplot2, magrittr, qvalue (>= 1.99),
digest, Biobase
Suggests: limma, edgeR, DESeq2, DEXSeq (>= 1.9.7), testthat, knitr
License: MIT + file LICENSE
Archs: x64
MD5sum: 3fb3cdd26cefae528a575f42f2ad0ffe
NeedsCompilation: no
Title: Subsampling of high-throughput sequencing count data
Description: Subsampling of high throughput sequencing count data for
use in experiment design and analysis.
biocViews: ImmunoOncology, Sequencing, Transcription, RNASeq,
GeneExpression, DifferentialExpression
Author: David Robinson, John D. Storey, with contributions from Andrew
J. Bass
Maintainer: Andrew J. Bass <ajbass@princeton.edu>, John D. Storey
<jstorey@princeton.edu>
URL: http://github.com/StoreyLab/subSeq
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/subSeq
git_branch: devel
git_last_commit: 694bd4d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/subSeq_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/subSeq_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/subSeq_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/subSeq_1.37.0.tgz
vignettes: vignettes/subSeq/inst/doc/subSeq.pdf
vignetteTitles: subSeq Example
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/subSeq/inst/doc/subSeq.R
dependencyCount: 52
Package: SUITOR
Version: 1.9.0
Depends: R (>= 4.2.0)
Imports: stats, utils, graphics, ggplot2, BiocParallel
Suggests: devtools, MutationalPatterns, RUnit, BiocManager,
BiocGenerics, BiocStyle, knitr, rmarkdown
License: GPL-2
MD5sum: 3d80aae3578383bec661c91dbd1647d6
NeedsCompilation: yes
Title: Selecting the number of mutational signatures through
cross-validation
Description: An unsupervised cross-validation method to select the
optimal number of mutational signatures. A data set of
mutational counts is split into training and validation
data.Signatures are estimated in the training data and then
used to predict the mutations in the validation data.
biocViews: Genetics, Software, SomaticMutation
Author: DongHyuk Lee [aut], Bin Zhu [aut], Bill Wheeler [cre]
Maintainer: Bill Wheeler <wheelerb@imsweb.com>
VignetteBuilder: knitr
BugReports: https://github.com/wheelerb/SUITOR/issues
git_url: https://git.bioconductor.org/packages/SUITOR
git_branch: devel
git_last_commit: 293c905
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SUITOR_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SUITOR_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SUITOR_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SUITOR_1.9.0.tgz
vignettes: vignettes/SUITOR/inst/doc/vignette.pdf
vignetteTitles: SUITOR: selecting the number of mutational signatures
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SUITOR/inst/doc/vignette.R
dependencyCount: 45
Package: SummarizedExperiment
Version: 1.37.0
Depends: R (>= 4.0.0), methods, MatrixGenerics (>= 1.1.3),
GenomicRanges (>= 1.55.2), Biobase
Imports: utils, stats, tools, Matrix, BiocGenerics (>= 0.51.3),
S4Vectors (>= 0.33.7), IRanges (>= 2.23.9), GenomeInfoDb (>=
1.13.1), S4Arrays (>= 1.1.1), DelayedArray (>= 0.31.12)
Suggests: jsonlite, rhdf5, HDF5Array (>= 1.7.5), annotate,
AnnotationDbi, GenomicFeatures, SparseArray,
SingleCellExperiment, TxDb.Hsapiens.UCSC.hg19.knownGene,
hgu95av2.db, airway (>= 1.15.1), BiocStyle, knitr, rmarkdown,
RUnit, testthat, digest
License: Artistic-2.0
Archs: x64
MD5sum: 70e1a3afd21fa145657e4414256762b7
NeedsCompilation: no
Title: A container (S4 class) for matrix-like assays
Description: The SummarizedExperiment container contains one or more
assays, each represented by a matrix-like object of numeric or
other mode. The rows typically represent genomic ranges of
interest and the columns represent samples.
biocViews: Genetics, Infrastructure, Sequencing, Annotation, Coverage,
GenomeAnnotation
Author: Martin Morgan [aut], Valerie Obenchain [aut], Jim Hester [aut],
Hervé Pagès [aut, cre]
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/SummarizedExperiment
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/SummarizedExperiment/issues
git_url: https://git.bioconductor.org/packages/SummarizedExperiment
git_branch: devel
git_last_commit: cbdaaaa
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SummarizedExperiment_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SummarizedExperiment_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SummarizedExperiment_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SummarizedExperiment_1.37.0.tgz
vignettes: vignettes/SummarizedExperiment/inst/doc/Extensions.html,
vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.html
vignetteTitles: 2. Extending the SummarizedExperiment class, 1.
SummarizedExperiment for Coordinating Experimental Assays,,
Samples,, and Regions of Interest
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SummarizedExperiment/inst/doc/Extensions.R,
vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.R
dependsOnMe: AffiXcan, alabaster.se, AllelicImbalance, atena, bambu,
betaHMM, BiocSklearn, BiSeq, bnbc, broadSeq, bsseq, CAGEfightR,
celaref, clusterExperiment, CoreGx, coseq, csaw, CSSQ,
DaMiRseq, deepSNV, DeMixT, DESeq2, DEXSeq, DiffBind, diffcoexp,
diffHic, dinoR, divergence, DMCFB, DMCHMM, EnrichmentBrowser,
epigenomix, evaluomeR, EventPointer, ExperimentSubset,
ExpressionAtlas, extraChIPs, FEAST, FRASER, GenomicAlignments,
GenomicFiles, GenomicSuperSignature, GRmetrics,
GSEABenchmarkeR, HelloRanges, hermes, HERON, HiCDOC, hipathia,
InteractionSet, IntEREst, iSEE, iSEEhex, iSEEhub, iSEEindex,
ISLET, isomiRs, ivygapSE, lefser, LimROTS, lipidr,
LoomExperiment, lute, Macarron, made4, MatrixQCvis, MBASED,
methodical, methrix, methylPipe, MetNet, MGnifyR, mia, miaViz,
MICSQTL, minfi, moanin, mpra, MultiAssayExperiment,
multistateQTL, NADfinder, NBAMSeq, NewWave, orthos, OUTRIDER,
padma, PDATK, phenomis, PhIPData, profileplyr, PRONE, qmtools,
qsvaR, QTLExperiment, recount, recount3, ReducedExperiment,
RegEnrich, REMP, ROCpAI, rqt, runibic, Scale4C, scAnnotatR,
scGPS, scone, screenCounter, scTreeViz, SDAMS, sechm, SeqGate,
SEtools, SGSeq, signatureSearch, SingleCellExperiment,
singleCellTK, SingleR, soGGi, spillR, spqn, ssPATHS, stageR,
survtype, TENxIO, tidyCoverage, tidySummarizedExperiment,
TissueEnrich, TREG, UMI4Cats, VanillaICE, VariantAnnotation,
VariantExperiment, velociraptor, weitrix, yamss, zinbwave,
airway, benchmarkfdrData2019, BioPlex, bodymapRat, celldex,
curatedAdipoChIP, curatedAdipoRNA, curatedMetagenomicData,
fission, GSVAdata, HDCytoData, HighlyReplicatedRNASeq,
HMP16SData, MetaGxOvarian, MetaGxPancreas, MethylSeqData,
MicrobiomeBenchmarkData, microbiomeDataSets, microRNAome,
MouseGastrulationData, MouseThymusAgeing, ObMiTi,
parathyroidSE, sampleClassifierData, scMultiome,
spatialDmelxsim, spqnData, timecoursedata, tuberculosis,
TumourMethData, DRomics, OncoSubtype, ordinalbayes
importsMe: ADAM, ADImpute, aggregateBioVar, airpart, alabaster.sfe,
ALDEx2, animalcules, anota2seq, APAlyzer, apeglm, APL,
appreci8R, ASICS, ASURAT, ATACseqTFEA, AUCell, autonomics,
awst, Banksy, barcodetrackR, BASiCS, BASiCStan, batchelor,
BatchQC, BayesSpace, bayNorm, BBCAnalyzer, beer, benchdamic,
BERT, bettr, BioGA, BioNERO, biosigner, biotmle, biovizBase,
biscuiteer, BiSeq, blacksheepr, BloodGen3Module, BulkSignalR,
BUMHMM, BUScorrect, BUSseq, CaDrA, CAGEr, CARDspa, CATALYST,
CatsCradle, cBioPortalData, ccfindR, ccImpute, CDI, celda,
CelliD, CellMixS, CellScore, CellTrails, censcyt, Cepo, CeTF,
CHETAH, ChIPpeakAnno, ChromSCape, chromVAR, CiteFuse,
CleanUpRNAseq, clustifyr, cmapR, CNVfilteR, CNVRanger, CoGAPS,
comapr, combi, concordexR, condiments, consensusDE, consICA,
CopyNumberPlots, corral, COTAN, countsimQC, CPSM, CTexploreR,
CTSV, CuratedAtlasQueryR, cydar, cypress, cytofQC, cytoKernel,
cytomapper, cytoviewer, DAMEfinder, dandelionR, debCAM,
debrowser, decompTumor2Sig, decontX, DEFormats, DEGreport,
DELocal, deltaCaptureC, demuxSNP, DEP, DEScan2, DESpace,
destiny, DEWSeq, diffcyt, DifferentialRegulation, diffUTR,
Dino, DiscoRhythm, distinct, dittoSeq, DMRcate, DominoEffect,
doppelgangR, doseR, dreamlet, DropletUtils, Dune, easyRNASeq,
eisaR, ELMER, epigraHMM, EpiMix, epimutacions, epiregulon,
epiregulon.extra, epistack, epivizrData, erma, escape, escheR,
EWCE, fcScan, FeatSeekR, findIPs, FindIT2, fishpond, FLAMES,
FuseSOM, GARS, gCrisprTools, gDNAx, gDRcore, gDRimport,
gDRutils, gemma.R, GeneTonic, genomicInstability, GEOquery,
GeoTcgaData, getDEE2, geyser, ggbio, ggsc, ggspavis, gINTomics,
Glimma, glmGamPoi, glmSparseNet, GRaNIE, GreyListChIP,
gscreend, GSVA, gwasurvivr, GWENA, HarmonizR, HiContacts,
HiCParser, hoodscanR, hummingbird, HybridExpress, iasva,
icetea, ideal, IFAA, IgGeneUsage, ILoReg, imcRtools, iNETgrate,
infercnv, INSPEcT, iSEEde, iSEEfier, iSEEpathways, iSEEtree,
iSEEu, IsoBayes, IsoformSwitchAnalyzeR, kmcut, LACE, lemur,
limpca, lineagespot, lionessR, lisaClust, maaslin3, MADSEQ,
MAI, mariner, marr, MAST, mastR, mbkmeans, MBQN, mCSEA, MEAL,
MEAT, MEB, MetaboAnnotation, MetaboDynamics,
metabolomicsWorkbenchR, MetaNeighbor, metaseqR2, MethReg,
MethylAid, methyLImp2, methylscaper, methylumi, miaSim,
MicrobiotaProcess, midasHLA, miloR, MinimumDistance, miRSM,
missMethyl, mist, MLInterfaces, MLSeq, mobileRNA, monaLisa,
MoonlightR, mosdef, motifbreakR, motifmatchr, MotifPeeker,
MPAC, MPRAnalyze, MsExperiment, MsFeatures, msgbsR, mspms,
MSPrep, msqrob2, MuData, MultiDataSet, MultiRNAflow,
multiWGCNA, mumosa, muscat, musicatk, MWASTools, NanoMethViz,
Nebulosa, NetActivity, netSmooth, nipalsMCIA, nnSVG,
NormalyzerDE, oligoClasses, omicRexposome, omicsPrint,
omicsViewer, omXplore, oncomix, ontoProc, ORFik, OVESEG,
PAIRADISE, pairedGSEA, pairkat, pcaExplorer, peco, PepSetTest,
pgxRpi, PharmacoGx, phenopath, PhosR, pipeComp, Pirat, PIUMA,
planttfhunter, plyxp, pmp, poem, PolySTest, POMA, POWSC,
proActiv, proDA, psichomics, PureCN, QFeatures, qsmooth,
quantiseqr, R453Plus1Toolbox, RadioGx, raer, RaggedExperiment,
RareVariantVis, RcisTarget, ReactomeGSA, receptLoss,
RegionalST, regionReport, regsplice, rgsepd, rifi,
rifiComparative, Rmmquant, RNAAgeCalc, RNAsense, roar, RolDE,
ropls, rScudo, RTCGAToolbox, RTN, saseR, satuRn, SBGNview, SC3,
SCArray, SCArray.sat, scater, scBFA, scCB2, scDblFinder, scDD,
scDDboost, scDesign3, scDiagnostics, scds, scHOT, scider,
scmap, scMerge, scMET, scmeth, scMultiSim, SCnorm, scoreInvHap,
scp, scPipe, scQTLtools, scran, scReClassify, scRepertoire,
scruff, scry, scTensor, scTGIF, scuttle, scviR, segmenter,
seqCAT, sesame, SGCP, sigFeature, signifinder, SigsPack, SimBu,
simPIC, simpleSeg, SingleCellAlleleExperiment, singscore,
slalom, slingshot, smartid, smoothclust, snapcount, SNPhood,
sosta, Spaniel, SpaNorm, spaSim, SpatialCPie, spatialDE,
SpatialExperiment, spatialFDA, SpatialFeatureExperiment,
spatialHeatmap, spatialSimGP, spatzie, SPIAT, spicyR, splatter,
SpliceWiz, SplicingFactory, SplineDV, spoon, SpotClean,
SpotSweeper, srnadiff, sSNAPPY, StabMap, standR, Statial,
stJoincount, struct, StructuralVariantAnnotation, supersigs,
SurfR, SVMDO, SVP, switchde, systemPipeR, systemPipeTools,
TBSignatureProfiler, TCGAbiolinks, TCGAutils, TCseq, TENET,
tenXplore, TFutils, tidybulk, tidySingleCellExperiment,
tidySpatialExperiment, TOAST, tomoda, ToxicoGx, tpSVG,
tradeSeq, TrajectoryUtils, transformGamPoi, transmogR,
treeclimbR, TreeSummarizedExperiment, Trendy, tricycle, TSCAN,
TTMap, TVTB, tximeta, UCell, VAExprs, VariantFiltering,
VDJdive, vidger, VisiumIO, visiumStitched, Voyager, wpm,
XAItest, xCell2, xcms, XeniumIO, xenLite, zellkonverter, zFPKM,
zitools, BloodCancerMultiOmics2017, brgedata, CLLmethylation,
COSMIC.67, curatedTCGAData, easierData, emtdata,
FieldEffectCrc, FlowSorted.Blood.EPIC,
FlowSorted.CordBloodCombined.450k, GSE13015, HCATonsilData,
HiBED, HMP2Data, homosapienDEE2CellScore, IHWpaper, LegATo,
MerfishData, MetaGxBreast, MetaScope, orthosData, scRNAseq,
SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData,
TCGAWorkflowData, TENET.ExperimentHub, TENxXeniumData,
ExpHunterSuite, fluentGenomics, SingscoreAMLMutations, autoGO,
DWLS, HeritSeq, imcExperiment, karyotapR, MetAlyzer, microbial,
multimedia, PlasmaMutationDetector, RCPA, RNAseqQC, SC.MEB,
SCIntRuler, scPipeline, SCRIP, scROSHI, SpatialDDLS, treediff,
VSOLassoBag
suggestsMe: alabaster.mae, AlpsNMR, ANCOMBC, AnnotationHub,
BindingSiteFinder, biobroom, BiocPkgTools, cageminer, CTdata,
dar, dcanr, dce, dearseq, decoupleR, DelayedArray, easier,
edgeR, EnMCB, epialleleR, epivizr, epivizrChart, esetVis,
fobitools, funOmics, gDR, GENIE3, GenomicRanges, globalSeq,
gsean, hca, HDF5Array, HPiP, Informeasure,
InteractiveComplexHeatmap, interactiveDisplay, knowYourCG,
MatrixGenerics, microSTASIS, MOFA2, MSnbase, pathwayPCA, philr,
podkat, PSMatch, RiboProfiling, Rvisdiff, S4Vectors,
scFeatureFilter, scrapper, semisup, sketchR, sparrow,
SPOTlight, svaNUMT, svaRetro, systemPipeShiny, tidytof,
updateObject, biotmleData, curatedAdipoArray, curatedTBData,
dorothea, DuoClustering2018, gDRtestData, GSE103322,
multiWGCNAdata, pRolocdata, RforProteomics, SBGNview.data,
tissueTreg, CAGEWorkflow, Canek, clustree, conos, CytoSimplex,
dyngen, file2meco, lfc, MiscMetabar, parafac4microbiome,
polyRAD, RaceID, rliger, seqgendiff, Seurat, Signac,
singleCellHaystack, speakeasyR, SuperCell, teal.slice, tidydr,
volcano3D
dependencyCount: 35
Package: Summix
Version: 2.13.0
Depends: R (>= 4.3)
Imports: dplyr, nloptr, magrittr, methods, tibble, tidyselect,
BEDASSLE, scales, visNetwork, randomcoloR
Suggests: rmarkdown, markdown, knitr, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: 364f91b3a0de91670b810ef5fe070a37
NeedsCompilation: no
Title: Summix2: A suite of methods to estimate, adjust, and leverage
substructure in genetic summary data
Description: This package contains the Summix2 method for estimating
and adjusting for substructure in genetic summary allele
frequency data. The function summix() estimates reference group
proportions using a mixture model. The adjAF() function
produces adjusted allele frequencies for an observed group with
reference group proportions matching a target individual or
sample. The summix_local() function estimates local ancestry
mixture proportions and performs selection scans in genetic
summary data.
biocViews: StatisticalMethod, WholeGenome, Genetics
Author: Audrey Hendricks [cre], Price Adelle [aut], Stoneman Haley
[aut]
Maintainer: Audrey Hendricks <audrey.hendricks@cuanschutz.edu>
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/Summix/issues
git_url: https://git.bioconductor.org/packages/Summix
git_branch: devel
git_last_commit: a864fc6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Summix_2.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Summix_2.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Summix_2.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Summix_2.13.0.tgz
vignettes: vignettes/Summix/inst/doc/Summix.html
vignetteTitles: Summix.html
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Summix/inst/doc/Summix.R
dependencyCount: 75
Package: supersigs
Version: 1.15.0
Depends: R (>= 4.1)
Imports: assertthat, caret, dplyr, tidyr, rsample, methods, rlang,
utils, Biostrings, stats, SummarizedExperiment
Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38,
knitr, rmarkdown, ggplot2, testthat, VariantAnnotation
License: GPL-3
MD5sum: 8336d966189af1ea71990076bd9f80f3
NeedsCompilation: no
Title: Supervised mutational signatures
Description: Generate SuperSigs (supervised mutational signatures) from
single nucleotide variants in the cancer genome. Functions
included in the package allow the user to learn supervised
mutational signatures from their data and apply them to new
data. The methodology is based on the one described in Afsari
(2021, ELife).
biocViews: FeatureExtraction, Classification, Regression, Sequencing,
WholeGenome, SomaticMutation
Author: Albert Kuo [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5155-0748>), Yifan Zhang [aut],
Bahman Afsari [aut], Cristian Tomasetti [aut]
Maintainer: Albert Kuo <albertkuo@jhu.edu>
URL: https://tomasettilab.github.io/supersigs/
VignetteBuilder: knitr
BugReports: https://github.com/TomasettiLab/supersigs/issues
git_url: https://git.bioconductor.org/packages/supersigs
git_branch: devel
git_last_commit: 4d9190b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/supersigs_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/supersigs_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/supersigs_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/supersigs_1.15.0.tgz
vignettes: vignettes/supersigs/inst/doc/supersigs.html
vignetteTitles: Using supersigs
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/supersigs/inst/doc/supersigs.R
dependencyCount: 116
Package: surfaltr
Version: 1.13.0
Depends: R (>= 4.0)
Imports: dplyr (>= 1.0.6), biomaRt (>= 2.46.0), protr (>= 1.6-2),
seqinr (>= 4.2-5), ggplot2 (>= 3.3.2), utils (>= 2.10.1),
stringr (>= 1.4.0), Biostrings (>= 2.58.0),readr (>= 1.4.0),
httr (>= 1.4.2), testthat(>= 3.0.0), xml2(>= 1.3.2), msa (>=
1.22.0), methods (>= 4.0.3)
Suggests: knitr, rmarkdown, devtools, kableExtra
License: MIT + file LICENSE
MD5sum: c1dfcad3085002ae70289d8326144ade
NeedsCompilation: no
Title: Rapid Comparison of Surface Protein Isoform Membrane Topologies
Through surfaltr
Description: Cell surface proteins form a major fraction of the
druggable proteome and can be used for tissue-specific delivery
of oligonucleotide/cell-based therapeutics. Alternatively
spliced surface protein isoforms have been shown to differ in
their subcellular localization and/or their transmembrane (TM)
topology. Surface proteins are hydrophobic and remain difficult
to study thereby necessitating the use of TM topology
prediction methods such as TMHMM and Phobius. However, there
exists a need for bioinformatic approaches to streamline batch
processing of isoforms for comparing and visualizing
topologies. To address this gap, we have developed an R
package, surfaltr. It pairs inputted isoforms, either known
alternatively spliced or novel, with their APPRIS annotated
principal counterparts, predicts their TM topologies using
TMHMM or Phobius, and generates a customizable graphical
output. Further, surfaltr facilitates the prioritization of
biologically diverse isoform pairs through the incorporation of
three different ranking metrics and through protein alignment
functions. Citations for programs mentioned here can be found
in the vignette.
biocViews: Software, Visualization, DataRepresentation,
SplicedAlignment, Alignment, MultipleSequenceAlignment,
MultipleComparison
Author: Pooja Gangras [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0638-3941>), Aditi Merchant [aut]
Maintainer: Pooja Gangras <gangras_pooja@lilly.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/surfaltr
git_branch: devel
git_last_commit: 98fe9a8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/surfaltr_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/surfaltr_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/surfaltr_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/surfaltr_1.13.0.tgz
vignettes: vignettes/surfaltr/inst/doc/surfaltr_vignette.html
vignetteTitles: surfaltr_vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/surfaltr/inst/doc/surfaltr_vignette.R
dependencyCount: 112
Package: SurfR
Version: 1.3.7
Depends: R (>= 4.4.0)
Imports: httr, BiocFileCache, SPsimSeq, DESeq2, edgeR, openxlsx,
stringr, rhdf5, ggplot2, ggrepel, stats, magrittr, assertr,
tidyr, dplyr, TCGAbiolinks, biomaRt, metaRNASeq, scales, venn,
gridExtra, SummarizedExperiment, knitr, rjson, grDevices,
graphics, curl, utils
Suggests: BiocStyle, testthat (>= 3.0.0)
License: GPL-3 + file LICENSE
MD5sum: 8f5e9aa30d9706630a186737cb50e84e
NeedsCompilation: no
Title: Surface Protein Prediction and Identification
Description: Identify Surface Protein coding genes from a list of
candidates. Systematically download data from GEO and TCGA or
use your own data. Perform DGE on bulk RNAseq data. Perform
Meta-analysis. Descriptive enrichment analysis and plots.
biocViews: Software, Sequencing, RNASeq, GeneExpression, Transcription,
DifferentialExpression, PrincipalComponent, GeneSetEnrichment,
Pathways, BatchEffect, FunctionalGenomics, Visualization,
DataImport, FunctionalPrediction, GenePrediction, GO
Author: Aurora Maurizio [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7194-4637>), Anna Sofia Tascini
[aut, ctb] (ORCID: <https://orcid.org/0000-0001-5731-5490>)
Maintainer: Aurora Maurizio <auroramaurizio1@gmail.com>
URL: https://github.com/auroramaurizio/SurfR
VignetteBuilder: knitr
BugReports: https://github.com/auroramaurizio/SurfR/issues
git_url: https://git.bioconductor.org/packages/SurfR
git_branch: devel
git_last_commit: 805778c
git_last_commit_date: 2025-03-14
Date/Publication: 2025-03-17
source.ver: src/contrib/SurfR_1.3.7.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SurfR_1.3.7.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SurfR_1.3.7.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SurfR_1.3.7.tgz
vignettes: vignettes/SurfR/inst/doc/Intro_to_SurfR.html
vignetteTitles: Introduction to SurfR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/SurfR/inst/doc/Intro_to_SurfR.R
dependencyCount: 187
Package: survClust
Version: 1.1.0
Depends: R (>= 3.5.0)
Imports: Rcpp, MultiAssayExperiment, pdist, survival
LinkingTo: Rcpp
Suggests: knitr, testthat (>= 3.0.0), gplots, htmltools, BiocParallel
License: MIT + file LICENSE
Archs: x64
MD5sum: 27e183704a22631e2bcb8a577abc7da4
NeedsCompilation: yes
Title: Identification Of Clinically Relevant Genomic Subtypes Using
Outcome Weighted Learning
Description: survClust is an outcome weighted integrative clustering
algorithm used to classify multi-omic samples on their
available time to event information. The resulting clusters are
cross-validated to avoid over overfitting and output
classification of samples that are molecularly distinct and
clinically meaningful. It takes in binary (mutation) as well as
continuous data (other omic types).
biocViews: Software, Clustering, Survival, Classification
Author: Arshi Arora [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4040-1787>)
Maintainer: Arshi Arora <arshiaurora@gmail.com>
URL: https://github.com/arorarshi/survClust
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/survClust
git_url: https://git.bioconductor.org/packages/survClust
git_branch: devel
git_last_commit: ddcd08b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/survClust_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/survClust_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/survClust_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/survClust_1.1.0.tgz
vignettes: vignettes/survClust/inst/doc/survClust_vignette.html
vignetteTitles: An introduction to survClust package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/survClust/inst/doc/survClust_vignette.R
dependencyCount: 61
Package: survcomp
Version: 1.57.0
Depends: survival, prodlim, R (>= 3.4)
Imports: ipred, SuppDists, KernSmooth, survivalROC, bootstrap, grid,
rmeta, stats, graphics
Suggests: Hmisc, clinfun, xtable, Biobase, BiocManager
License: Artistic-2.0
MD5sum: bf6a43615270ed945956518a6e8dd98e
NeedsCompilation: yes
Title: Performance Assessment and Comparison for Survival Analysis
Description: Assessment and Comparison for Performance of Risk
Prediction (Survival) Models.
biocViews: GeneExpression, DifferentialExpression, Visualization
Author: Benjamin Haibe-Kains [aut, cre], Markus Schroeder [aut],
Catharina Olsen [aut], Christos Sotiriou [aut], Gianluca
Bontempi [aut], John Quackenbush [aut], Samuel Branders [aut],
Zhaleh Safikhani [aut]
Maintainer: Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>
URL: http://www.pmgenomics.ca/bhklab/
git_url: https://git.bioconductor.org/packages/survcomp
git_branch: devel
git_last_commit: d938b84
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/survcomp_1.57.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/survcomp_1.57.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/survcomp_1.57.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/survcomp_1.57.0.tgz
vignettes: vignettes/survcomp/inst/doc/survcomp.pdf
vignetteTitles: SurvComp: a package for performance assessment and
comparison for survival analysis
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/survcomp/inst/doc/survcomp.R
dependsOnMe: genefu
importsMe: metaseqR2, PDATK, Coxmos, FLORAL, pencal, plsRcox, SIGN
suggestsMe: GSgalgoR, breastCancerMAINZ, breastCancerNKI,
breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP,
breastCancerVDX
dependencyCount: 39
Package: survtype
Version: 1.23.0
Depends: SummarizedExperiment, pheatmap, survival, survminer,
clustvarsel, stats, utils
Suggests: maftools, scales, knitr, rmarkdown
License: Artistic-2.0
MD5sum: 2f374128127c956a7bf018507f2a7578
NeedsCompilation: no
Title: Subtype Identification with Survival Data
Description: Subtypes are defined as groups of samples that have
distinct molecular and clinical features. Genomic data can be
analyzed for discovering patient subtypes, associated with
clinical data, especially for survival information. This
package is aimed to identify subtypes that are both clinically
relevant and biologically meaningful.
biocViews: Software, StatisticalMethod, GeneExpression, Survival,
Clustering, Sequencing, Coverage
Author: Dongmin Jung
Maintainer: Dongmin Jung <dmdmjung@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/survtype
git_branch: devel
git_last_commit: 150b74a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/survtype_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/survtype_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/survtype_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/survtype_1.23.0.tgz
vignettes: vignettes/survtype/inst/doc/survtype.html
vignetteTitles: survtype
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/survtype/inst/doc/survtype.R
dependencyCount: 140
Package: sva
Version: 3.55.0
Depends: R (>= 3.2), mgcv, genefilter, BiocParallel
Imports: matrixStats, stats, graphics, utils, limma, edgeR
Suggests: pamr, bladderbatch, BiocStyle, zebrafishRNASeq, testthat
License: Artistic-2.0
MD5sum: 52dfcf2063cecc3e914553cfe9faf5e2
NeedsCompilation: yes
Title: Surrogate Variable Analysis
Description: The sva package contains functions for removing batch
effects and other unwanted variation in high-throughput
experiment. Specifically, the sva package contains functions
for the identifying and building surrogate variables for
high-dimensional data sets. Surrogate variables are covariates
constructed directly from high-dimensional data (like gene
expression/RNA sequencing/methylation/brain imaging data) that
can be used in subsequent analyses to adjust for unknown,
unmodeled, or latent sources of noise. The sva package can be
used to remove artifacts in three ways: (1) identifying and
estimating surrogate variables for unknown sources of variation
in high-throughput experiments (Leek and Storey 2007 PLoS
Genetics,2008 PNAS), (2) directly removing known batch effects
using ComBat (Johnson et al. 2007 Biostatistics) and (3)
removing batch effects with known control probes (Leek 2014
biorXiv). Removing batch effects and using surrogate variables
in differential expression analysis have been shown to reduce
dependence, stabilize error rate estimates, and improve
reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008
PNAS or Leek et al. 2011 Nat. Reviews Genetics).
biocViews: ImmunoOncology, Microarray, StatisticalMethod,
Preprocessing, MultipleComparison, Sequencing, RNASeq,
BatchEffect, Normalization
Author: Jeffrey T. Leek <jtleek@gmail.com>, W. Evan Johnson
<wej@bu.edu>, Hilary S. Parker <hiparker@jhsph.edu>, Elana J.
Fertig <ejfertig@jhmi.edu>, Andrew E. Jaffe <ajaffe@jhsph.edu>,
Yuqing Zhang <zhangyuqing.pkusms@gmail.com>, John D. Storey
<jstorey@princeton.edu>, Leonardo Collado Torres
<lcolladotor@gmail.com>
Maintainer: Jeffrey T. Leek <jtleek@gmail.com>, John D. Storey
<jstorey@princeton.edu>, W. Evan Johnson <wej@bu.edu>
git_url: https://git.bioconductor.org/packages/sva
git_branch: devel
git_last_commit: 5dc7e05
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/sva_3.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/sva_3.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/sva_3.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/sva_3.55.0.tgz
vignettes: vignettes/sva/inst/doc/sva.pdf
vignetteTitles: sva tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/sva/inst/doc/sva.R
dependsOnMe: DeMixT, IsoformSwitchAnalyzeR, SCAN.UPC, rnaseqGene,
bapred, leapp, SmartSVA
importsMe: ASSIGN, ballgown, BatchQC, BERT, BioNERO, bnbc, bnem,
DaMiRseq, debrowser, DExMA, doppelgangR, edge, GEOexplorer,
HarmonizR, KnowSeq, MatrixQCvis, MBECS, MSPrep, omicRexposome,
PAA, pairedGSEA, POMA, PROPS, qsmooth, qsvaR, SEtools,
singleCellTK, trigger, DeSousa2013,
ExpressionNormalizationWorkflow, causalBatch, cinaR, dSVA,
oncoPredict, scITD, seqgendiff, TransProR
suggestsMe: compcodeR, Harman, iasva, randRotation, RnBeads, scp,
SomaticSignatures, TBSignatureProfiler, TCGAbiolinks, tidybulk,
curatedBladderData, curatedOvarianData, curatedTBData,
FieldEffectCrc, CAGEWorkflow, DGEobj.utils, DRomics,
SuperLearner
dependencyCount: 71
Package: svaNUMT
Version: 1.13.0
Depends: GenomicRanges, rtracklayer, VariantAnnotation,
StructuralVariantAnnotation, BiocGenerics, Biostrings, R (>=
4.0)
Imports: assertthat, stringr, dplyr, methods, rlang, GenomeInfoDb,
S4Vectors, GenomicFeatures, pwalign
Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene,
BSgenome.Hsapiens.UCSC.hg19, ggplot2, devtools, testthat (>=
2.1.0), roxygen2, knitr, readr, plyranges, circlize, IRanges,
SummarizedExperiment, rmarkdown
License: GPL-3 + file LICENSE
MD5sum: 09ef8d622b09389e76c8dc98cabef187
NeedsCompilation: no
Title: NUMT detection from structural variant calls
Description: svaNUMT contains functions for detecting NUMT events from
structural variant calls. It takes structural variant calls in
GRanges of breakend notation and identifies NUMTs by
nuclear-mitochondrial breakend junctions. The main function
reports candidate NUMTs if there is a pair of valid insertion
sites found on the nuclear genome within a certain distance
threshold. The candidate NUMTs are reported by events.
biocViews: DataImport, Sequencing, Annotation, Genetics,
VariantAnnotation
Author: Ruining Dong [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1433-0484>)
Maintainer: Ruining Dong <lnyidrn@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/PapenfussLab/svaNUMT/issues
git_url: https://git.bioconductor.org/packages/svaNUMT
git_branch: devel
git_last_commit: 1a144f0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/svaNUMT_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/svaNUMT_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/svaNUMT_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/svaNUMT_1.13.0.tgz
vignettes: vignettes/svaNUMT/inst/doc/svaNUMT.html
vignetteTitles: svaNUMT Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/svaNUMT/inst/doc/svaNUMT.R
dependencyCount: 92
Package: svaRetro
Version: 1.13.0
Depends: GenomicRanges, rtracklayer, BiocGenerics,
StructuralVariantAnnotation, R (>= 4.0)
Imports: VariantAnnotation, assertthat, Biostrings, stringr, dplyr,
methods, rlang, GenomicFeatures, GenomeInfoDb, S4Vectors, utils
Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, ggplot2, devtools,
testthat (>= 2.1.0), roxygen2, knitr, BiocStyle, plyranges,
circlize, tictoc, IRanges, stats, SummarizedExperiment,
rmarkdown
License: GPL-3 + file LICENSE
MD5sum: 69b47570ac048ed05ebcd6831606b280
NeedsCompilation: no
Title: Retrotransposed transcript detection from structural variants
Description: svaRetro contains functions for detecting retrotransposed
transcripts (RTs) from structural variant calls. It takes
structural variant calls in GRanges of breakend notation and
identifies RTs by exon-exon junctions and insertion sites. The
candidate RTs are reported by events and annotated with
information of the inserted transcripts.
biocViews: DataImport, Sequencing, Annotation, Genetics,
VariantAnnotation, Coverage, VariantDetection
Author: Ruining Dong [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1433-0484>)
Maintainer: Ruining Dong <lnyidrn@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/PapenfussLab/svaRetro/issues
git_url: https://git.bioconductor.org/packages/svaRetro
git_branch: devel
git_last_commit: 4aeb18c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/svaRetro_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/svaRetro_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/svaRetro_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/svaRetro_1.13.0.tgz
vignettes: vignettes/svaRetro/inst/doc/svaRetro.html
vignetteTitles: svaRetro Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/svaRetro/inst/doc/svaRetro.R
dependencyCount: 92
Package: SVMDO
Version: 1.7.0
Depends: R(>= 4.4), shiny (>= 1.7.4)
Imports: shinyFiles (>= 0.9.3), shinytitle (>= 0.1.0), golem (>=
0.3.5), nortest (>= 1.0-4), e1071 (>= 1.7-12), BSDA (>= 1.2.1),
data.table (>= 1.14.6), sjmisc (>= 2.8.9), klaR (>= 1.7-1),
caTools (>= 1.18.2), caret (>= 6.0-93), survival (>= 3.4-0), DT
(>= 0.33.0), DOSE (>= 3.24.2), AnnotationDbi (>= 1.60.0),
org.Hs.eg.db (>= 3.16.0), dplyr (>= 1.0.10),
SummarizedExperiment (>= 1.28.0), grDevices, graphics, stats,
utils
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.1.6)
License: GPL-3
Archs: x64
MD5sum: d16eba166422453f574113d644b3ac0e
NeedsCompilation: no
Title: Identification of Tumor-Discriminating mRNA Signatures via
Support Vector Machines Supported by Disease Ontology
Description: It is an easy-to-use GUI using disease information for
detecting tumor/normal sample discriminating gene sets from
differentially expressed genes. Our approach is based on an
iterative algorithm filtering genes with disease ontology
enrichment analysis and wilk and wilks lambda criterion
connected to SVM classification model construction. Along with
gene set extraction, SVMDO also provides individual prognostic
marker detection. The algorithm is designed for FPKM and RPKM
normalized RNA-Seq transcriptome datasets.
biocViews: GeneSetEnrichment, DifferentialExpression, GUI,
Classification, RNASeq, Transcriptomics, Survival
Author: Mustafa Erhan Ozer [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1572-8008>), Pemra Ozbek Sarica
[aut], Kazim Yalcin Arga [aut]
Maintainer: Mustafa Erhan Ozer <erhanozer19@marun.edu.tr>
VignetteBuilder: knitr
BugReports: https://github.com/robogeno/SVMDO/issues
git_url: https://git.bioconductor.org/packages/SVMDO
git_branch: devel
git_last_commit: 5dea70d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SVMDO_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SVMDO_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SVMDO_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SVMDO_1.7.0.tgz
vignettes: vignettes/SVMDO/inst/doc/SVMDO_guide.html
vignetteTitles: SVMDO-Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SVMDO/inst/doc/SVMDO_guide.R
dependencyCount: 200
Package: SVP
Version: 0.99.4
Depends: R (>= 4.1.0)
Imports: Rcpp, RcppParallel, methods, cli, dplyr, rlang, S4Vectors,
SummarizedExperiment, SingleCellExperiment, SpatialExperiment,
BiocGenerics, BiocParallel, fastmatch, pracma, stats, withr,
Matrix, DelayedMatrixStats, deldir, utils, BiocNeighbors,
ggplot2, ggstar, ggtree, ggfun
LinkingTo: Rcpp, RcppArmadillo (>= 14.0), RcppParallel, RcppEigen,
dqrng
Suggests: rmarkdown, prettydoc, broman, RSpectra, BiasedUrn, knitr, ks,
igraph, testthat (>= 3.0.0), scuttle, magrittr, DropletUtils,
tibble, tidyr, harmony, aplot, scales, ggsc, scatterpie, scran,
scater, STexampleData, ape
License: GPL-3
Archs: x64
MD5sum: fdd0d48bdb9bcacac50ca14326565df6
NeedsCompilation: yes
Title: Predicting cell states and their variability in single-cell or
spatial omics data
Description: SVP uses the distance between cells and cells, features
and features, cells and features in the space of MCA to build
nearest neighbor graph, then uses random walk with restart
algorithm to calculate the activity score of gene sets (such as
cell marker genes, kegg pathway, go ontology, gene modules,
transcription factor or miRNA target sets, reactome pathway,
...), which is then further weighted using the hypergeometric
test results from the original expression matrix. To detect the
spatially or single cell variable gene sets or (other features)
and the spatial colocalization between the features accurately,
SVP provides some global and local spatial autocorrelation
method to identify the spatial variable features. SVP is
developed based on SingleCellExperiment class, which can be
interoperable with the existing computing ecosystem.
biocViews: SingleCell, Software, Spatial, Transcriptomics, GeneTarget,
GeneExpression, GeneSetEnrichment, Transcription, GO, KEGG
Author: Shuangbin Xu [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3513-5362>), Guangchuang Yu [aut,
ctb] (ORCID: <https://orcid.org/0000-0002-6485-8781>)
Maintainer: Shuangbin Xu <xshuangbin@163.com>
URL: https://github.com/YuLab-SMU/SVP
SystemRequirements: GNU make
VignetteBuilder: knitr
BugReports: https://github.com/YuLab-SMU/SVP/issues
git_url: https://git.bioconductor.org/packages/SVP
git_branch: devel
git_last_commit: e2f2db7
git_last_commit_date: 2025-03-15
Date/Publication: 2025-03-17
source.ver: src/contrib/SVP_0.99.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SVP_0.99.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SVP_0.99.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SVP_0.99.4.tgz
vignettes: vignettes/SVP/inst/doc/SVP.html
vignetteTitles: SVP Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SVP/inst/doc/SVP.R
dependencyCount: 122
Package: SWATH2stats
Version: 1.37.0
Depends: R(>= 2.10.0)
Imports: data.table, reshape2, ggplot2, stats, grDevices, graphics,
utils, biomaRt, methods
Suggests: testthat, knitr, rmarkdown
Enhances: MSstats, PECA, aLFQ
License: GPL-3
MD5sum: 6f9af9fb8174d97756d25b4a2bcf2fee
NeedsCompilation: no
Title: Transform and Filter SWATH Data for Statistical Packages
Description: This package is intended to transform SWATH data from the
OpenSWATH software into a format readable by other statistics
packages while performing filtering, annotation and FDR
estimation.
biocViews: Proteomics, Annotation, ExperimentalDesign, Preprocessing,
MassSpectrometry, ImmunoOncology
Author: Peter Blattmann [aut, cre] Moritz Heusel [aut] Ruedi Aebersold
[aut]
Maintainer: Peter Blattmann <peter_blattmann@bluewin.ch>
URL: https://peterblattmann.github.io/SWATH2stats/
VignetteBuilder: knitr
BugReports: https://github.com/peterblattmann/SWATH2stats
git_url: https://git.bioconductor.org/packages/SWATH2stats
git_branch: devel
git_last_commit: 8dd5551
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SWATH2stats_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SWATH2stats_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SWATH2stats_1.37.0.tgz
vignettes:
vignettes/SWATH2stats/inst/doc/SWATH2stats_example_script.pdf,
vignettes/SWATH2stats/inst/doc/SWATH2stats_vignette.pdf
vignetteTitles: SWATH2stats example script, SWATH2stats package
Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SWATH2stats/inst/doc/SWATH2stats_example_script.R,
vignettes/SWATH2stats/inst/doc/SWATH2stats_vignette.R
dependencyCount: 89
Package: SwathXtend
Version: 2.29.0
Depends: e1071, openxlsx, VennDiagram, lattice
License: GPL-2
Archs: x64
MD5sum: 35f27c683fcb2e77d9de3991c55632d8
NeedsCompilation: no
Title: SWATH extended library generation and statistical data analysis
Description: Contains utility functions for integrating spectral
libraries for SWATH and statistical data analysis for SWATH
generated data.
biocViews: Software
Author: J WU and D Pascovici
Maintainer: Jemma Wu <jwu@proteome.org.au>
git_url: https://git.bioconductor.org/packages/SwathXtend
git_branch: devel
git_last_commit: 4c2c9ad
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SwathXtend_2.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SwathXtend_2.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SwathXtend_2.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SwathXtend_2.29.0.tgz
vignettes: vignettes/SwathXtend/inst/doc/SwathXtend_vignette.pdf
vignetteTitles: SwathXtend
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SwathXtend/inst/doc/SwathXtend_vignette.R
dependencyCount: 21
Package: swfdr
Version: 1.33.0
Depends: R (>= 3.4)
Imports: methods, splines, stats4, stats
Suggests: dplyr, ggplot2, BiocStyle, knitr, qvalue, reshape2,
rmarkdown, testthat
License: GPL (>= 3)
MD5sum: c5266935511d0c75ce95a89e57212bd9
NeedsCompilation: no
Title: Estimation of the science-wise false discovery rate and the
false discovery rate conditional on covariates
Description: This package allows users to estimate the science-wise
false discovery rate from Jager and Leek, "Empirical estimates
suggest most published medical research is true," 2013,
Biostatistics, using an EM approach due to the presence of
rounding and censoring. It also allows users to estimate the
false discovery rate conditional on covariates, using a
regression framework, as per Boca and Leek, "A direct approach
to estimating false discovery rates conditional on covariates,"
2018, PeerJ.
biocViews: MultipleComparison, StatisticalMethod, Software
Author: Jeffrey T. Leek, Leah Jager, Simina M. Boca, Tomasz Konopka
Maintainer: Simina M. Boca <smb310@georgetown.edu>, Jeffrey T. Leek
<jtleek@gmail.com>
URL: https://github.com/leekgroup/swfdr
VignetteBuilder: knitr
BugReports: https://github.com/leekgroup/swfdr/issues
git_url: https://git.bioconductor.org/packages/swfdr
git_branch: devel
git_last_commit: fa8b787
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/swfdr_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/swfdr_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/swfdr_1.33.0.tgz
vignettes: vignettes/swfdr/inst/doc/swfdrQ.pdf,
vignettes/swfdr/inst/doc/swfdrTutorial.pdf
vignetteTitles: Computing covariate-adjusted q-values, Tutorial for
swfdr package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/swfdr/inst/doc/swfdrQ.R,
vignettes/swfdr/inst/doc/swfdrTutorial.R
dependencyCount: 4
Package: switchBox
Version: 1.43.0
Depends: R (>= 2.13.1), pROC, gplots
License: GPL-2
MD5sum: 1d71b2ec1e5685f710f303ee2f317114
NeedsCompilation: yes
Title: Utilities to train and validate classifiers based on pair
switching using the K-Top-Scoring-Pair (KTSP) algorithm
Description: The package offer different classifiers based on
comparisons of pair of features (TSP), using various decision
rules (e.g., majority wins principle).
biocViews: Software, StatisticalMethod, Classification
Author: Bahman Afsari <bahman@jhu.edu>, Luigi Marchionni
<marchion@jhu.edu>, Wikum Dinalankara <wdinala1@jhmi.edu>
Maintainer: Bahman Afsari <bahman@jhu.edu>, Luigi Marchionni
<marchion@jhu.edu>, Wikum Dinalankara <wdinala1@jhmi.edu>
git_url: https://git.bioconductor.org/packages/switchBox
git_branch: devel
git_last_commit: 606e3de
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/switchBox_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/switchBox_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/switchBox_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/switchBox_1.43.0.tgz
vignettes: vignettes/switchBox/inst/doc/switchBox.pdf
vignetteTitles: Working with the switchBox package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/switchBox/inst/doc/switchBox.R
importsMe: PDATK
suggestsMe: multiclassPairs
dependencyCount: 11
Package: switchde
Version: 1.33.0
Depends: R (>= 3.4), SingleCellExperiment
Imports: SummarizedExperiment, dplyr, ggplot2, methods, stats
Suggests: knitr, rmarkdown, BiocStyle, testthat, numDeriv, tidyr
License: GPL (>= 2)
MD5sum: beca95c533f01b5ec0ffa015b76d046d
NeedsCompilation: no
Title: Switch-like differential expression across single-cell
trajectories
Description: Inference and detection of switch-like differential
expression across single-cell RNA-seq trajectories.
biocViews: ImmunoOncology, Software, Transcriptomics, GeneExpression,
RNASeq, Regression, DifferentialExpression, SingleCell
Author: Kieran Campbell [aut, cre]
Maintainer: Kieran Campbell <kieranrcampbell@gmail.com>
URL: https://github.com/kieranrcampbell/switchde
VignetteBuilder: knitr
BugReports: https://github.com/kieranrcampbell/switchde
git_url: https://git.bioconductor.org/packages/switchde
git_branch: devel
git_last_commit: bf3d5a3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/switchde_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/switchde_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/switchde_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/switchde_1.33.0.tgz
vignettes: vignettes/switchde/inst/doc/switchde_vignette.html
vignetteTitles: An overview of the switchde package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/switchde/inst/doc/switchde_vignette.R
dependencyCount: 65
Package: synapsis
Version: 1.13.0
Depends: R (>= 4.1)
Imports: EBImage, stats, utils, graphics
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), ggplot2, tidyverse,
BiocStyle
License: MIT + file LICENSE
Archs: x64
MD5sum: 7c84d0cdc93d34ca0daf1daea0018ca4
NeedsCompilation: no
Title: An R package to automate the analysis of double-strand break
repair during meiosis
Description: Synapsis is a Bioconductor software package for automated
(unbiased and reproducible) analysis of meiotic
immunofluorescence datasets. The primary functions of the
software can i) identify cells in meiotic prophase that are
labelled by a synaptonemal complex axis or central element
protein, ii) isolate individual synaptonemal complexes and
measure their physical length, iii) quantify foci and
co-localise them with synaptonemal complexes, iv) measure
interference between synaptonemal complex-associated foci. The
software has applications that extend to multiple species and
to the analysis of other proteins that label meiotic prophase
chromosomes. The software converts meiotic immunofluorescence
images into R data frames that are compatible with machine
learning methods. Given a set of microscopy images of meiotic
spread slides, synapsis crops images around individual single
cells, counts colocalising foci on strands on a per cell basis,
and measures the distance between foci on any given strand.
biocViews: Software, SingleCell
Author: Lucy McNeill [aut, cre, cph] (ORCID:
<https://orcid.org/0000-0003-1752-4882>), Wayne Crismani [rev,
ctb] (ORCID: <https://orcid.org/0000-0003-0143-8293>)
Maintainer: Lucy McNeill <luc.mcneill@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/synapsis
git_branch: devel
git_last_commit: 5262f7d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/synapsis_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/synapsis_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/synapsis_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/synapsis_1.13.0.tgz
vignettes: vignettes/synapsis/inst/doc/synapsis_tutorial.html
vignetteTitles: Using-synapsis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/synapsis/inst/doc/synapsis_tutorial.R
dependencyCount: 46
Package: synapter
Version: 2.31.0
Depends: R (>= 3.1.0), methods, MSnbase (>= 2.1.2)
Imports: RColorBrewer, lattice, qvalue, multtest, utils, tools,
Biobase, Biostrings, cleaver (>= 1.3.3), readr (>= 0.2),
rmarkdown (>= 1.0)
Suggests: synapterdata (>= 1.13.2), xtable, testthat (>= 0.8), BRAIN,
BiocStyle, knitr
License: GPL-2
MD5sum: d8f6450a2a1639c7a5cb8888f57853db
NeedsCompilation: no
Title: Label-free data analysis pipeline for optimal identification and
quantitation
Description: The synapter package provides functionality to reanalyse
label-free proteomics data acquired on a Synapt G2 mass
spectrometer. One or several runs, possibly processed with
additional ion mobility separation to increase identification
accuracy can be combined to other quantitation files to
maximise identification and quantitation accuracy.
biocViews: ImmunoOncology, MassSpectrometry, Proteomics, QualityControl
Author: Laurent Gatto, Nick J. Bond, Pavel V. Shliaha and Sebastian
Gibb.
Maintainer: Laurent Gatto <laurent.gatto@uclouvain.be> Sebastian Gibb
<mail@sebastiangibb.de>
URL: https://lgatto.github.io/synapter/
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/synapter
git_branch: devel
git_last_commit: 4502012
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/synapter_2.31.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/synapter_2.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/synapter_2.31.0.tgz
vignettes: vignettes/synapter/inst/doc/fragmentmatching.html,
vignettes/synapter/inst/doc/synapter.html,
vignettes/synapter/inst/doc/synapter2.html
vignetteTitles: Fragment matching using 'synapter', Combining HDMSe/MSe
data using 'synapter' to optimise identification and
quantitation, Synapter2 and synergise2
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/synapter/inst/doc/fragmentmatching.R,
vignettes/synapter/inst/doc/synapter.R,
vignettes/synapter/inst/doc/synapter2.R
dependsOnMe: synapterdata
dependencyCount: 150
Package: synergyfinder
Version: 3.15.0
Depends: R (>= 4.0.0)
Imports: drc (>= 3.0-1), reshape2 (>= 1.4.4), tidyverse (>= 1.3.0),
dplyr (>= 1.0.3), tidyr (>= 1.1.2), purrr (>= 0.3.4), furrr (>=
0.2.2), ggplot2 (>= 3.3.3), ggforce (>= 0.3.2), grid (>=
4.0.2), vegan (>= 2.5-7), gstat (>= 2.0-6), sp (>= 1.4-5),
methods (>= 4.0.2), SpatialExtremes (>= 2.0-9), ggrepel (>=
0.9.1), kriging (>= 1.1), plotly (>= 4.9.3), stringr (>=
1.4.0), future (>= 1.21.0), mice (>= 3.13.0), lattice (>=
0.20-41), nleqslv (>= 3.3.2), stats (>= 4.0.2), graphics (>=
4.0.2), grDevices (>= 4.0.2), magrittr (>= 2.0.1), pbapply (>=
1.4-3), metR (>= 0.9.1)
Suggests: knitr, rmarkdown
License: Mozilla Public License 2.0
MD5sum: ebaeba298e4b0b3bd114073d5ae5ee45
NeedsCompilation: no
Title: Calculate and Visualize Synergy Scores for Drug Combinations
Description: Efficient implementations for analyzing pre-clinical
multiple drug combination datasets. It provides efficient
implementations for 1.the popular synergy scoring models,
including HSA, Loewe, Bliss, and ZIP to quantify the degree of
drug combination synergy; 2. higher order drug combination data
analysis and synergy landscape visualization for unlimited
number of drugs in a combination; 3. statistical analysis of
drug combination synergy and sensitivity with confidence
intervals and p-values; 4. synergy barometer for harmonizing
multiple synergy scoring methods to provide a consensus metric
of synergy; 5. evaluation of synergy and sensitivity
simultaneously to provide an unbiased interpretation of the
clinical potential of the drug combinations. Based on this
package, we also provide a web application
(http://www.synergyfinder.org) for users who prefer graphical
user interface.
biocViews: Software, StatisticalMethod
Author: Shuyu Zheng [aut, cre], Jing Tang [aut]
Maintainer: Shuyu Zheng <shuyu.zheng@helsinki.fi>
URL: http://www.synergyfinder.org
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/synergyfinder
git_branch: devel
git_last_commit: b44cfae
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/synergyfinder_3.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/synergyfinder_3.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/synergyfinder_3.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/synergyfinder_3.15.0.tgz
vignettes:
vignettes/synergyfinder/inst/doc/User_tutorual_of_the_SynergyFinder_plus.html
vignetteTitles: User tutorial of the SynergyFinder Plus
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/synergyfinder/inst/doc/User_tutorual_of_the_SynergyFinder_plus.R
dependencyCount: 208
Package: SynExtend
Version: 1.19.8
Depends: R (>= 4.4.0), DECIPHER (>= 2.28.0)
Imports: methods, Biostrings, S4Vectors, IRanges, utils, stats,
parallel, graphics, grDevices, RSQLite, DBI
Suggests: BiocStyle, knitr, igraph, markdown, rmarkdown
License: GPL-3
MD5sum: 7640fd8e0f57adda509d1996860ad819
NeedsCompilation: yes
Title: Tools for Working With Synteny Objects
Description: Shared order between genomic sequences provide a great
deal of information. Synteny objects produced by the R package
DECIPHER provides quantitative information about that shared
order. SynExtend provides tools for extracting information from
Synteny objects.
biocViews: Genetics, Clustering, ComparativeGenomics, DataImport
Author: Nicholas Cooley [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6029-304X>), Aidan Lakshman [aut,
ctb] (ORCID: <https://orcid.org/0000-0002-9465-6785>), Adelle
Fernando [ctb], Erik Wright [aut]
Maintainer: Nicholas Cooley <npc19@pitt.edu>
URL: https://github.com/npcooley/SynExtend
VignetteBuilder: knitr
BugReports: https://github.com/npcooley/SynExtend/issues/new/
git_url: https://git.bioconductor.org/packages/SynExtend
git_branch: devel
git_last_commit: 91943dc
git_last_commit_date: 2025-03-10
Date/Publication: 2025-03-11
source.ver: src/contrib/SynExtend_1.19.8.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SynExtend_1.19.8.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SynExtend_1.19.8.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SynExtend_1.19.8.tgz
vignettes: vignettes/SynExtend/inst/doc/UsingSynExtend.html
vignetteTitles: UsingSynExtend
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SynExtend/inst/doc/UsingSynExtend.R
dependencyCount: 43
Package: synlet
Version: 2.7.0
Depends: R (>= 3.5.0)
Imports: data.table, ggplot2, grDevices, magrittr, methods, patchwork,
RankProd, RColorBrewer, stats, utils
Suggests: BiocStyle, knitr, testthat, rmarkdown
License: GPL-3
MD5sum: 7908cf6433f8448ef903f19ca385de49
NeedsCompilation: no
Title: Hits Selection for Synthetic Lethal RNAi Screen Data
Description: Select hits from synthetic lethal RNAi screen data. For
example, there are two identical celllines except one gene is
knocked-down in one cellline. The interest is to find genes
that lead to stronger lethal effect when they are knocked-down
further by siRNA. Quality control and various visualisation
tools are implemented. Four different algorithms could be used
to pick up the interesting hits. This package is designed based
on 384 wells plates, but may apply to other platforms with
proper configuration.
biocViews: ImmunoOncology, CellBasedAssays, QualityControl,
Preprocessing, Visualization, FeatureExtraction
Author: Chunxuan Shao [aut, cre]
Maintainer: Chunxuan Shao <chunxuan@outlook.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/synlet
git_branch: devel
git_last_commit: 007191f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/synlet_2.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/synlet_2.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/synlet_2.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/synlet_2.7.0.tgz
vignettes: vignettes/synlet/inst/doc/synlet-vignette.html
vignetteTitles: A working Demo for synlet
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/synlet/inst/doc/synlet-vignette.R
dependencyCount: 40
Package: SynMut
Version: 1.23.0
Imports: seqinr, methods, Biostrings, stringr, BiocGenerics
Suggests: BiocManager, knitr, rmarkdown, testthat, devtools, prettydoc,
glue
License: GPL-2
Archs: x64
MD5sum: 6028356dd37f25ad42f3869b42a928ba
NeedsCompilation: no
Title: SynMut: Designing Synonymously Mutated Sequences with Different
Genomic Signatures
Description: There are increasing demands on designing virus mutants
with specific dinucleotide or codon composition. This tool can
take both dinucleotide preference and/or codon usage bias into
account while designing mutants. It is a powerful tool for in
silico designs of DNA sequence mutants.
biocViews: SequenceMatching, ExperimentalDesign, Preprocessing
Author: Haogao Gu [aut, cre], Leo L.M. Poon [led]
Maintainer: Haogao Gu <hggu@connect.hku.hk>
URL: https://github.com/Koohoko/SynMut
VignetteBuilder: knitr
BugReports: https://github.com/Koohoko/SynMut/issues
git_url: https://git.bioconductor.org/packages/SynMut
git_branch: devel
git_last_commit: 460f815
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/SynMut_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/SynMut_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/SynMut_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SynMut_1.23.0.tgz
vignettes: vignettes/SynMut/inst/doc/SynMut.html
vignetteTitles: SynMut: Designing Synonymous Mutants for DNA Sequences
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/SynMut/inst/doc/SynMut.R
dependencyCount: 44
Package: syntenet
Version: 1.9.1
Depends: R (>= 4.2)
Imports: Rcpp (>= 1.0.8), BiocParallel, GenomicRanges, rlang,
Biostrings, rtracklayer, utils, methods, igraph, stats,
grDevices, RColorBrewer, pheatmap, ggplot2, ggnetwork,
intergraph
LinkingTo: Rcpp, testthat
Suggests: BiocStyle, ggtree, labdsv, covr, knitr, rmarkdown, testthat
(>= 3.0.0), xml2, networkD3
License: GPL-3
MD5sum: efb95d590d0b2f1f3fe50800a0e91830
NeedsCompilation: yes
Title: Inference And Analysis Of Synteny Networks
Description: syntenet can be used to infer synteny networks from
whole-genome protein sequences and analyze them. Anchor pairs
are detected with the MCScanX algorithm, which was ported to
this package with the Rcpp framework for R and C++ integration.
Anchor pairs from synteny analyses are treated as an undirected
unweighted graph (i.e., a synteny network), and users can
perform: i. network clustering; ii. phylogenomic profiling (by
identifying which species contain which clusters) and; iii.
microsynteny-based phylogeny reconstruction with maximum
likelihood.
biocViews: Software, NetworkInference, FunctionalGenomics,
ComparativeGenomics, Phylogenetics, SystemsBiology,
GraphAndNetwork, WholeGenome, Network
Author: FabrÃcio Almeida-Silva [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5314-2964>), Tao Zhao [aut]
(ORCID: <https://orcid.org/0000-0001-7302-6445>), Kristian K
Ullrich [aut] (ORCID: <https://orcid.org/0000-0003-4308-9626>),
Yves Van de Peer [aut] (ORCID:
<https://orcid.org/0000-0003-4327-3730>)
Maintainer: FabrÃcio Almeida-Silva <fabricio_almeidasilva@hotmail.com>
URL: https://github.com/almeidasilvaf/syntenet
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/syntenet
git_url: https://git.bioconductor.org/packages/syntenet
git_branch: devel
git_last_commit: 1d7e1a6
git_last_commit_date: 2024-12-18
Date/Publication: 2024-12-18
source.ver: src/contrib/syntenet_1.9.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/syntenet_1.9.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/syntenet_1.9.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/syntenet_1.9.1.tgz
vignettes: vignettes/syntenet/inst/doc/syntenet.html
vignetteTitles: Inference and Analysis of Synteny Networks
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/syntenet/inst/doc/syntenet.R
importsMe: doubletrouble
dependencyCount: 109
Package: systemPipeR
Version: 2.13.0
Depends: Rsamtools (>= 1.31.2), Biostrings, ShortRead (>= 1.37.1),
methods
Imports: GenomicRanges, SummarizedExperiment, ggplot2, yaml, stringr,
magrittr, S4Vectors, crayon, BiocGenerics, htmlwidgets
Suggests: BiocStyle, knitr, rmarkdown, systemPipeRdata,
GenomicAlignments, grid, dplyr, testthat, rjson, annotate,
AnnotationDbi, kableExtra, GO.db, GenomeInfoDb, DT,
rtracklayer, limma, edgeR, DESeq2, IRanges, batchtools,
GenomicFeatures, txdbmaker, VariantAnnotation (>= 1.25.11)
License: Artistic-2.0
MD5sum: ce37af5f8ac40f8b34dc5c0b0d6df1b7
NeedsCompilation: no
Title: systemPipeR: Workflow Environment for Data Analysis and Report
Generation
Description: systemPipeR is a multipurpose data analysis workflow
environment that unifies R with command-line tools. It enables
scientists to analyze many types of large- or small-scale data
on local or distributed computer systems with a high level of
reproducibility, scalability and portability. At its core is a
command-line interface (CLI) that adopts the Common Workflow
Language (CWL). This design allows users to choose for each
analysis step the optimal R or command-line software. It
supports both end-to-end and partial execution of workflows
with built-in restart functionalities. Efficient management of
complex analysis tasks is accomplished by a flexible workflow
control container class. Handling of large numbers of input
samples and experimental designs is facilitated by consistent
sample annotation mechanisms. As a multi-purpose workflow
toolkit, systemPipeR enables users to run existing workflows,
customize them or design entirely new ones while taking
advantage of widely adopted data structures within the
Bioconductor ecosystem. Another important core functionality is
the generation of reproducible scientific analysis and
technical reports. For result interpretation, systemPipeR
offers a wide range of plotting functionality, while an
associated Shiny App offers many useful functionalities for
interactive result exploration. The vignettes linked from this
page include (1) a general introduction, (2) a description of
technical details, and (3) a collection of workflow templates.
biocViews: Genetics, Infrastructure, DataImport, Sequencing, RNASeq,
RiboSeq, ChIPSeq, MethylSeq, SNP, GeneExpression, Coverage,
GeneSetEnrichment, Alignment, QualityControl, ImmunoOncology,
ReportWriting, WorkflowStep, WorkflowManagement
Author: Thomas Girke
Maintainer: Thomas Girke <thomas.girke@ucr.edu>
URL: https://systempipe.org/, https://github.com/tgirke/systemPipeR
SystemRequirements: systemPipeR can be used to run external
command-line software (e.g. short read aligners), but the
corresponding tool needs to be installed on a system.
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/systemPipeR
git_branch: devel
git_last_commit: 94357f2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/systemPipeR_2.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/systemPipeR_2.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/systemPipeR_2.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/systemPipeR_2.13.0.tgz
vignettes: vignettes/systemPipeR/inst/doc/systemPipeR_workflows.html,
vignettes/systemPipeR/inst/doc/systemPipeR.html
vignetteTitles: systemPipeR: Workflow Templates, Overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/systemPipeR/inst/doc/systemPipeR_workflows.R,
vignettes/systemPipeR/inst/doc/systemPipeR.R
importsMe: DiffBind
suggestsMe: systemPipeShiny, systemPipeTools, systemPipeRdata
dependencyCount: 109
Package: systemPipeShiny
Version: 1.17.0
Depends: R (>= 4.0.0), shiny (>= 1.6.0), spsUtil (>= 0.2.2), spsComps
(>= 0.3.3), drawer (>= 0.2)
Imports: DT, assertthat, bsplus, crayon, dplyr, ggplot2, htmltools,
glue, magrittr, methods, plotly, rlang, rstudioapi, shinyAce,
shinyFiles, shinyWidgets, shinydashboard, shinydashboardPlus
(>= 2.0.0), shinyjqui, shinyjs, shinytoastr, stringr, stats,
styler, tibble, utils, vroom (>= 1.3.1), yaml, R6, RSQLite,
openssl
Suggests: testthat, BiocStyle, knitr, rmarkdown, systemPipeR (>=
2.2.0), systemPipeRdata (>= 2.0.0), rhandsontable, zip, callr,
pushbar, fs, readr, R.utils, DESeq2, SummarizedExperiment,
glmpca, pheatmap, grid, ape, Rtsne, UpSetR, tidyr, esquisse (>=
1.1.0), cicerone
License: GPL (>= 3)
MD5sum: 1e23ad0dbe1f65de0e4a037ab3b17b06
NeedsCompilation: no
Title: systemPipeShiny: An Interactive Framework for Workflow
Management and Visualization
Description: systemPipeShiny (SPS) extends the widely used systemPipeR
(SPR) workflow environment with a versatile graphical user
interface provided by a Shiny App. This allows non-R users,
such as experimentalists, to run many systemPipeR’s workflow
designs, control, and visualization functionalities
interactively without requiring knowledge of R. Most
importantly, SPS has been designed as a general purpose
framework for interacting with other R packages in an intuitive
manner. Like most Shiny Apps, SPS can be used on both local
computers as well as centralized server-based deployments that
can be accessed remotely as a public web service for using
SPR’s functionalities with community and/or private data. The
framework can integrate many core packages from the
R/Bioconductor ecosystem. Examples of SPS’ current
functionalities include: (a) interactive creation of
experimental designs and metadata using an easy to use tabular
editor or file uploader; (b) visualization of workflow
topologies combined with auto-generation of R Markdown preview
for interactively designed workflows; (d) access to a wide
range of data processing routines; (e) and an extendable set of
visualization functionalities. Complex visual results can be
managed on a 'Canvas Workbench’ allowing users to organize and
to compare plots in an efficient manner combined with a session
snapshot feature to continue work at a later time. The present
suite of pre-configured visualization examples. The modular
design of SPR makes it easy to design custom functions without
any knowledge of Shiny, as well as extending the environment in
the future with contributions from the community.
biocViews: ShinyApps, Infrastructure, DataImport, Sequencing,
QualityControl, ReportWriting, ExperimentalDesign, Clustering
Author: Le Zhang [aut, cre], Daniela Cassol [aut], Ponmathi Ramasamy
[aut], Jianhai Zhang [aut], Gordon Mosher [aut], Thomas Girke
[aut]
Maintainer: Le Zhang <le.zhang001@email.ucr.edu>
URL: https://systempipe.org/sps,
https://github.com/systemPipeR/systemPipeShiny
VignetteBuilder: knitr
BugReports: https://github.com/systemPipeR/systemPipeShiny/issues
git_url: https://git.bioconductor.org/packages/systemPipeShiny
git_branch: devel
git_last_commit: 204a1c6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/systemPipeShiny_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/systemPipeShiny_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/systemPipeShiny_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/systemPipeShiny_1.17.0.tgz
vignettes: vignettes/systemPipeShiny/inst/doc/systemPipeShiny.html
vignetteTitles: systemPipeShiny
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/systemPipeShiny/inst/doc/systemPipeShiny.R
dependencyCount: 120
Package: systemPipeTools
Version: 1.15.0
Imports: DESeq2, GGally, Rtsne, SummarizedExperiment, ape, dplyr,
ggplot2, ggrepel, ggtree, glmpca, pheatmap, plotly, tibble,
magrittr, DT, stats
Suggests: systemPipeR, knitr, BiocStyle, rmarkdown, testthat (>=
3.0.0), BiocGenerics, Biostrings, methods
License: Artistic-2.0
Archs: x64
MD5sum: d9065495c04090382d6783f2e66d1fc6
NeedsCompilation: no
Title: Tools for data visualization
Description: systemPipeTools package extends the widely used
systemPipeR (SPR) workflow environment with an enhanced toolkit
for data visualization, including utilities to automate the
data visualizaton for analysis of differentially expressed
genes (DEGs). systemPipeTools provides data transformation and
data exploration functions via scatterplots, hierarchical
clustering heatMaps, principal component analysis,
multidimensional scaling, generalized principal components,
t-Distributed Stochastic Neighbor embedding (t-SNE), and MA and
volcano plots. All these utilities can be integrated with the
modular design of the systemPipeR environment that allows users
to easily substitute any of these features and/or custom with
alternatives.
biocViews: Infrastructure, DataImport, Sequencing, QualityControl,
ReportWriting, ExperimentalDesign, Clustering,
DifferentialExpression, MultidimensionalScaling,
PrincipalComponent
Author: Daniela Cassol [aut, cre], Ponmathi Ramasamy [aut], Le Zhang
[aut], Thomas Girke [aut]
Maintainer: Daniela Cassol <danicassol@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/systemPipeTools
git_branch: devel
git_last_commit: 05bda0e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/systemPipeTools_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/systemPipeTools_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/systemPipeTools_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/systemPipeTools_1.15.0.tgz
vignettes: vignettes/systemPipeTools/inst/doc/systemPipeTools.html
vignetteTitles: systemPipeTools: Data Visualizations
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/systemPipeTools/inst/doc/systemPipeTools.R
dependencyCount: 131
Package: tadar
Version: 1.5.1
Depends: GenomicRanges, ggplot2, R (>= 4.4.0)
Imports: BiocGenerics, GenomeInfoDb, Gviz, IRanges, lifecycle,
MatrixGenerics, methods, rlang, Rsamtools, S4Vectors, stats,
VariantAnnotation
Suggests: BiocStyle, covr, knitr, limma, rmarkdown, testthat (>=
3.0.0), tidyverse
License: GPL-3
MD5sum: 1200865554ff09913837d911eca63507
NeedsCompilation: no
Title: Transcriptome Analysis of Differential Allelic Representation
Description: This package provides functions to standardise the
analysis of Differential Allelic Representation (DAR). DAR
compromises the integrity of Differential Expression analysis
results as it can bias expression, influencing the
classification of genes (or transcripts) as being
differentially expressed. DAR analysis results in an
easy-to-interpret value between 0 and 1 for each genetic
feature of interest, where 0 represents identical allelic
representation and 1 represents complete diversity. This metric
can be used to identify features prone to false-positive calls
in Differential Expression analysis, and can be leveraged with
statistical methods to alleviate the impact of such artefacts
on RNA-seq data.
biocViews: Sequencing, RNASeq, SNP, GenomicVariation,
VariantAnnotation, DifferentialExpression
Author: Lachlan Baer [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5213-3401>), Stevie Pederson [aut]
(ORCID: <https://orcid.org/0000-0001-8197-3303>)
Maintainer: Lachlan Baer <baerlachlan@gmail.com>
URL: https://github.com/baerlachlan/tadar
VignetteBuilder: knitr
BugReports: https://github.com/baerlachlan/tadar/issues
git_url: https://git.bioconductor.org/packages/tadar
git_branch: devel
git_last_commit: b6360e4
git_last_commit_date: 2025-01-26
Date/Publication: 2025-01-27
source.ver: src/contrib/tadar_1.5.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tadar_1.5.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tadar_1.5.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tadar_1.5.1.tgz
vignettes: vignettes/tadar/inst/doc/dar.html
vignetteTitles: DAR analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tadar/inst/doc/dar.R
dependencyCount: 156
Package: TADCompare
Version: 1.17.0
Depends: R (>= 4.0)
Imports: dplyr, PRIMME, cluster, Matrix, magrittr, HiCcompare, ggplot2,
tidyr, ggpubr, RColorBrewer, reshape2, cowplot
Suggests: BiocStyle, knitr, rmarkdown, microbenchmark, testthat, covr,
pheatmap, SpectralTAD, magick, qpdf
License: MIT + file LICENSE
MD5sum: 32881617a9c8052ad6afc0f5aaa22646
NeedsCompilation: no
Title: TADCompare: Identification and characterization of differential
TADs
Description: TADCompare is an R package designed to identify and
characterize differential Topologically Associated Domains
(TADs) between multiple Hi-C contact matrices. It contains
functions for finding differential TADs between two datasets,
finding differential TADs over time and identifying consensus
TADs across multiple matrices. It takes all of the main types
of HiC input and returns simple, comprehensive, easy to analyze
results.
biocViews: Software, HiC, Sequencing, FeatureExtraction, Clustering
Author: Mikhail Dozmorov [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0086-8358>), Kellen Cresswell
[aut]
Maintainer: Mikhail Dozmorov <mikhail.dozmorov@gmail.com>
URL: https://github.com/dozmorovlab/TADCompare
VignetteBuilder: knitr
BugReports: https://github.com/dozmorovlab/TADCompare/issues
git_url: https://git.bioconductor.org/packages/TADCompare
git_branch: devel
git_last_commit: e550de3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TADCompare_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TADCompare_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TADCompare_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TADCompare_1.17.0.tgz
vignettes: vignettes/TADCompare/inst/doc/Input_Data.html,
vignettes/TADCompare/inst/doc/Ontology_Analysis.html,
vignettes/TADCompare/inst/doc/TADCompare.html
vignetteTitles: Input data formats, Gene Ontology Enrichment Analysis,
TAD comparison between two conditions
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/TADCompare/inst/doc/Input_Data.R,
vignettes/TADCompare/inst/doc/Ontology_Analysis.R,
vignettes/TADCompare/inst/doc/TADCompare.R
dependencyCount: 125
Package: tanggle
Version: 1.13.0
Depends: R (>= 4.1), ggplot2 (>= 2.2.0), ggtree
Imports: ape (>= 5.0), phangorn (>= 2.5), utils, methods
Suggests: tinytest, BiocStyle, ggimage, knitr, rmarkdown
License: Artistic-2.0
Archs: x64
MD5sum: 62a08f23ec68815dc692d6b5de693c27
NeedsCompilation: no
Title: Visualization of Phylogenetic Networks
Description: Offers functions for plotting split (or implicit) networks
(unrooted, undirected) and explicit networks (rooted, directed)
with reticulations extending. 'ggtree' and using functions from
'ape' and 'phangorn'. It extends the 'ggtree' package [@Yu2017]
to allow the visualization of phylogenetic networks using the
'ggplot2' syntax. It offers an alternative to the plot
functions already available in 'ape' Paradis and Schliep (2019)
<doi:10.1093/bioinformatics/bty633> and 'phangorn' Schliep
(2011) <doi:10.1093/bioinformatics/btq706>.
biocViews: Software, Visualization, Phylogenetics, Alignment,
Clustering, MultipleSequenceAlignment, DataImport
Author: Klaus Schliep [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2941-0161>), Marta Vidal-Garcia
[aut], Claudia Solis-Lemus [aut] (ORCID:
<https://orcid.org/0000-0002-9789-8915>), Leann Biancani [aut],
Eren Ada [aut], L. Francisco Henao Diaz [aut], Guangchuang Yu
[ctb]
Maintainer: Klaus Schliep <klaus.schliep@gmail.com>
URL: https://klausvigo.github.io/tanggle,
https://github.com/KlausVigo/tanggle
VignetteBuilder: knitr
BugReports: https://github.com/KlausVigo/tanggle/issues
git_url: https://git.bioconductor.org/packages/tanggle
git_branch: devel
git_last_commit: 2fc19e5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tanggle_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tanggle_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tanggle_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tanggle_1.13.0.tgz
vignettes: vignettes/tanggle/inst/doc/tanggle_vignette_espanol.html,
vignettes/tanggle/inst/doc/tanggle_vignette.html
vignetteTitles: ***tanggle***: Visualización de redes filogenéticas con
*ggplot2*, ***tanggle***: Visualization of phylogenetic
networks in a *ggplot2* framework
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tanggle/inst/doc/tanggle_vignette_espanol.R,
vignettes/tanggle/inst/doc/tanggle_vignette.R
dependencyCount: 64
Package: TAPseq
Version: 1.19.0
Depends: R (>= 4.0.0)
Imports: methods, GenomicAlignments, GenomicRanges, IRanges,
BiocGenerics, S4Vectors (>= 0.20.1), GenomeInfoDb, BSgenome,
GenomicFeatures, Biostrings, dplyr, tidyr, BiocParallel
Suggests: testthat, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown,
ggplot2, Seurat, glmnet, cowplot, Matrix, rtracklayer,
BiocStyle
License: MIT + file LICENSE
MD5sum: 17721615c70cf3cad40c4ee3d5f04c57
NeedsCompilation: no
Title: Targeted scRNA-seq primer design for TAP-seq
Description: Design primers for targeted single-cell RNA-seq used by
TAP-seq. Create sequence templates for target gene panels and
design gene-specific primers using Primer3. Potential
off-targets can be estimated with BLAST. Requires working
installations of Primer3 and BLASTn.
biocViews: SingleCell, Sequencing, Technology, CRISPR, PooledScreens
Author: Andreas R. Gschwind [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0769-6907>), Lars Velten [aut]
(ORCID: <https://orcid.org/0000-0002-1233-5874>), Lars M.
Steinmetz [aut]
Maintainer: Andreas R. Gschwind <andreas.gschwind@stanford.edu>
URL: https://github.com/argschwind/TAPseq
SystemRequirements: Primer3 (>= 2.5.0), BLAST+ (>=2.6.0)
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TAPseq
git_branch: devel
git_last_commit: e6e4d9c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TAPseq_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TAPseq_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TAPseq_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TAPseq_1.19.0.tgz
vignettes: vignettes/TAPseq/inst/doc/tapseq_primer_design.html,
vignettes/TAPseq/inst/doc/tapseq_target_genes.html
vignetteTitles: TAP-seq primer design workflow, Select target genes for
TAP-seq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/TAPseq/inst/doc/tapseq_primer_design.R,
vignettes/TAPseq/inst/doc/tapseq_target_genes.R
dependencyCount: 90
Package: target
Version: 1.21.0
Depends: R (>= 3.6)
Imports: BiocGenerics, GenomicRanges, IRanges, matrixStats, methods,
stats, graphics, shiny
Suggests: testthat (>= 2.1.0), knitr, rmarkdown, shinytest, shinyBS,
covr
License: GPL-3
MD5sum: a260fb3073751a5663e61588e55351df
NeedsCompilation: no
Title: Predict Combined Function of Transcription Factors
Description: Implement the BETA algorithm for infering direct target
genes from DNA-binding and perturbation expression data Wang et
al. (2013) <doi: 10.1038/nprot.2013.150>. Extend the algorithm
to predict the combined function of two DNA-binding elements
from comprable binding and expression data.
biocViews: Software, StatisticalMethod, Transcription
Author: Mahmoud Ahmed [aut, cre]
Maintainer: Mahmoud Ahmed <mahmoud.s.fahmy@students.kasralainy.edu.eg>
URL: https://github.com/MahShaaban/target
VignetteBuilder: knitr
BugReports: https://github.com/MahShaaban/target/issues
git_url: https://git.bioconductor.org/packages/target
git_branch: devel
git_last_commit: 9383e81
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/target_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/target_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/target_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/target_1.21.0.tgz
vignettes: vignettes/target/inst/doc/extend-target.html,
vignettes/target/inst/doc/target.html
vignetteTitles: Using target to predict combined binding, Using the
target package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/target/inst/doc/extend-target.R,
vignettes/target/inst/doc/target.R
dependencyCount: 52
Package: TargetDecoy
Version: 1.13.0
Depends: R (>= 4.1)
Imports: ggplot2, ggpubr, methods, miniUI, mzID, mzR, shiny, stats
Suggests: BiocStyle, knitr, msdata, sessioninfo, rmarkdown, gridExtra,
testthat (>= 3.0.0), covr
License: Artistic-2.0
MD5sum: c0bcb1232fd8687a40b267bb604a39f8
NeedsCompilation: no
Title: Diagnostic Plots to Evaluate the Target Decoy Approach
Description: A first step in the data analysis of Mass Spectrometry
(MS) based proteomics data is to identify peptides and
proteins. With this respect the huge number of experimental
mass spectra typically have to be assigned to theoretical
peptides derived from a sequence database. Search engines are
used for this purpose. These tools compare each of the observed
spectra to all candidate theoretical spectra derived from the
sequence data base and calculate a score for each comparison.
The observed spectrum is then assigned to the theoretical
peptide with the best score, which is also referred to as the
peptide to spectrum match (PSM). It is of course crucial for
the downstream analysis to evaluate the quality of these
matches. Therefore False Discovery Rate (FDR) control is used
to return a reliable list PSMs. The FDR, however, requires a
good characterisation of the score distribution of PSMs that
are matched to the wrong peptide (bad target hits). In
proteomics, the target decoy approach (TDA) is typically used
for this purpose. The TDA method matches the spectra to a
database of real (targets) and nonsense peptides (decoys). A
popular approach to generate these decoys is to reverse the
target database. Hence, all the PSMs that match to a decoy are
known to be bad hits and the distribution of their scores are
used to estimate the distribution of the bad scoring target
PSMs. A crucial assumption of the TDA is that the decoy PSM
hits have similar properties as bad target hits so that the
decoy PSM scores are a good simulation of the target PSM
scores. Users, however, typically do not evaluate these
assumptions. To this end we developed TargetDecoy to generate
diagnostic plots to evaluate the quality of the target decoy
method.
biocViews: MassSpectrometry, Proteomics, QualityControl, Software,
Visualization
Author: Elke Debrie [aut, cre], Lieven Clement [aut] (ORCID:
<https://orcid.org/0000-0002-9050-4370>), Milan Malfait [aut]
(ORCID: <https://orcid.org/0000-0001-9144-3701>)
Maintainer: Elke Debrie <elkedebrie@gmail.com>
URL: https://www.bioconductor.org/packages/TargetDecoy,
https://statomics.github.io/TargetDecoy/,
https://github.com/statOmics/TargetDecoy/
VignetteBuilder: knitr
BugReports: https://github.com/statOmics/TargetDecoy/issues
git_url: https://git.bioconductor.org/packages/TargetDecoy
git_branch: devel
git_last_commit: d7af3b2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TargetDecoy_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TargetDecoy_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TargetDecoy_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TargetDecoy_1.13.0.tgz
vignettes: vignettes/TargetDecoy/inst/doc/TargetDecoy.html
vignetteTitles: Introduction to TargetDecoy
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TargetDecoy/inst/doc/TargetDecoy.R
dependencyCount: 116
Package: TargetScore
Version: 1.45.0
Depends: pracma, Matrix
Suggests: TargetScoreData, gplots, Biobase, GEOquery
License: GPL-2
MD5sum: 4cfb953bb22304b943a0456d58eece7b
NeedsCompilation: no
Title: TargetScore: Infer microRNA targets using
microRNA-overexpression data and sequence information
Description: Infer the posterior distributions of microRNA targets by
probabilistically modelling the likelihood
microRNA-overexpression fold-changes and sequence-based scores.
Variaitonal Bayesian Gaussian mixture model (VB-GMM) is applied
to log fold-changes and sequence scores to obtain the
posteriors of latent variable being the miRNA targets. The
final targetScore is computed as the sigmoid-transformed
fold-change weighted by the averaged posteriors of target
components over all of the features.
biocViews: miRNA
Author: Yue Li
Maintainer: Yue Li <yueli@cs.toronto.edu>
URL: http://www.cs.utoronto.ca/~yueli/software.html
git_url: https://git.bioconductor.org/packages/TargetScore
git_branch: devel
git_last_commit: d419059
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TargetScore_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TargetScore_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TargetScore_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TargetScore_1.45.0.tgz
vignettes: vignettes/TargetScore/inst/doc/TargetScore.pdf
vignetteTitles: TargetScore: Infer microRNA targets using
microRNA-overexpression data and sequence information
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TargetScore/inst/doc/TargetScore.R
suggestsMe: TargetScoreData
dependencyCount: 9
Package: TargetSearch
Version: 2.9.1
Imports: graphics, grDevices, methods, ncdf4, stats, utils, assertthat
Suggests: TargetSearchData, BiocStyle, knitr, tinytest
License: GPL (>= 2)
MD5sum: 2777b71dd65d1e71480fd9cf04cc1b28
NeedsCompilation: yes
Title: A package for the analysis of GC-MS metabolite profiling data
Description: This packages provides a flexible, fast and accurate
method for targeted pre-processing of GC-MS data. The user
provides a (often very large) set of GC chromatograms and a
metabolite library of targets. The package will automatically
search those targets in the chromatograms resulting in a data
matrix that can be used for further data analysis.
biocViews: MassSpectrometry, Preprocessing, DecisionTree,
ImmunoOncology
Author: Alvaro Cuadros-Inostroza [aut, cre], Jan Lisec [aut], Henning
Redestig [aut], Matt Hannah [aut]
Maintainer: Alvaro Cuadros-Inostroza <acuadros+bioc@gmail.com>
URL: https://github.com/acinostroza/TargetSearch
VignetteBuilder: knitr
BugReports: https://github.com/acinostroza/TargetSearch/issues
git_url: https://git.bioconductor.org/packages/TargetSearch
git_branch: devel
git_last_commit: 5a422d0
git_last_commit_date: 2024-11-18
Date/Publication: 2024-11-18
source.ver: src/contrib/TargetSearch_2.9.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TargetSearch_2.9.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TargetSearch_2.9.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TargetSearch_2.9.1.tgz
vignettes: vignettes/TargetSearch/inst/doc/RICorrection.pdf,
vignettes/TargetSearch/inst/doc/TargetSearch.pdf
vignetteTitles: RI correction extra, The TargetSearch Package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TargetSearch/inst/doc/RetentionIndexCorrection.R,
vignettes/TargetSearch/inst/doc/RICorrection.R,
vignettes/TargetSearch/inst/doc/TargetSearch.R
dependencyCount: 8
Package: TBSignatureProfiler
Version: 1.19.0
Depends: R (>= 4.4.0)
Imports: ASSIGN (>= 1.23.1), BiocParallel, ComplexHeatmap, DESeq2, DT,
edgeR, gdata, ggplot2, glmnet, GSVA (>= 1.51.3), HGNChelper,
magrittr, methods, pROC, RColorBrewer, reshape2, ROCit,
S4Vectors, singscore, stats, SummarizedExperiment
Suggests: BiocStyle, caret, circlize, class, covr, dplyr, e1071,
impute, knitr, lintr, MASS, plyr, randomForest, rmarkdown,
shiny, spelling, sva, testthat
License: MIT + file LICENSE
MD5sum: aa0286e25487845533860a2c6d28d499
NeedsCompilation: no
Title: Profile RNA-Seq Data Using TB Pathway Signatures
Description: Gene signatures of TB progression, TB disease, and other
TB disease states have been validated and published previously.
This package aggregates known signatures and provides
computational tools to enlist their usage on other datasets.
The TBSignatureProfiler makes it easy to profile RNA-Seq data
using these signatures and includes common signature profiling
tools including ASSIGN, GSVA, and ssGSEA. Original models for
some gene signatures are also available. A shiny app provides
some functionality alongside for detailed command line
accessibility.
biocViews: GeneExpression, DifferentialExpression
Author: Aubrey R. Odom [aut, cre, dtm] (ORCID:
<https://orcid.org/0000-0001-7113-7598>), David Jenkins [aut,
org] (ORCID: <https://orcid.org/0000-0002-7451-4288>), Xutao
Wang [aut], Yue Zhao [ctb] (ORCID:
<https://orcid.org/0000-0001-5257-5103>), Christian Love [ctb],
W. Evan Johnson [aut]
Maintainer: Aubrey R. Odom <aodom@bu.edu>
URL: https://github.com/wejlab/TBSignatureProfiler
https://wejlab.github.io/TBSignatureProfiler-docs/
VignetteBuilder: knitr
BugReports: https://github.com/wejlab/TBSignatureProfiler/issues
git_url: https://git.bioconductor.org/packages/TBSignatureProfiler
git_branch: devel
git_last_commit: d7f3c8a
git_last_commit_date: 2024-12-18
Date/Publication: 2024-12-19
source.ver: src/contrib/TBSignatureProfiler_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TBSignatureProfiler_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TBSignatureProfiler_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TBSignatureProfiler_1.19.0.tgz
vignettes: vignettes/TBSignatureProfiler/inst/doc/tbspVignetteT.html
vignetteTitles: "Introduction to the TBSignatureProfiler"
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/TBSignatureProfiler/inst/doc/tbspVignetteT.R
suggestsMe: LegATo
dependencyCount: 184
Package: TCC
Version: 1.47.0
Depends: R (>= 3.0), methods, DESeq2, edgeR, ROC
Suggests: RUnit, BiocGenerics
License: GPL-2
MD5sum: e8552fe4193d34ba7cad72f92188e466
NeedsCompilation: no
Title: TCC: Differential expression analysis for tag count data with
robust normalization strategies
Description: This package provides a series of functions for performing
differential expression analysis from RNA-seq count data using
robust normalization strategy (called DEGES). The basic idea of
DEGES is that potential differentially expressed genes or
transcripts (DEGs) among compared samples should be removed
before data normalization to obtain a well-ranked gene list
where true DEGs are top-ranked and non-DEGs are bottom ranked.
This can be done by performing a multi-step normalization
strategy (called DEGES for DEG elimination strategy). A major
characteristic of TCC is to provide the robust normalization
methods for several kinds of count data (two-group with or
without replicates, multi-group/multi-factor, and so on) by
virtue of the use of combinations of functions in depended
packages.
biocViews: ImmunoOncology, Sequencing, DifferentialExpression, RNASeq
Author: Jianqiang Sun, Tomoaki Nishiyama, Kentaro Shimizu, and Koji
Kadota
Maintainer: Jianqiang Sun <sun@biunit.dev>, Tomoaki Nishiyama
<tomoakin@staff.kanazawa-u.ac.jp>
git_url: https://git.bioconductor.org/packages/TCC
git_branch: devel
git_last_commit: 8a4c02e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TCC_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TCC_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TCC_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TCC_1.47.0.tgz
vignettes: vignettes/TCC/inst/doc/TCC.pdf
vignetteTitles: TCC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TCC/inst/doc/TCC.R
suggestsMe: compcodeR
dependencyCount: 85
Package: TCGAbiolinks
Version: 2.35.3
Depends: R (>= 4.0)
Imports: downloader (>= 0.4), grDevices, biomaRt, dplyr, graphics,
tibble, GenomicRanges, XML (>= 3.98.0), data.table, jsonlite
(>= 1.0.0), plyr, knitr, methods, ggplot2, stringr (>= 1.0.0),
IRanges, rvest (>= 0.3.0), stats, utils, S4Vectors, R.utils,
SummarizedExperiment (>= 1.4.0), TCGAbiolinksGUI.data (>=
1.15.1), readr, tools, tidyr, purrr, xml2, httr (>= 1.2.1)
Suggests: jpeg, png, BiocStyle, rmarkdown, devtools, maftools,
parmigene, c3net, minet, Biobase, affy, testthat, sesame,
AnnotationHub, ExperimentHub, pathview, clusterProfiler,
Seurat, ComplexHeatmap, circlize, ConsensusClusterPlus, igraph,
supraHex, limma, edgeR, sva, EDASeq, survminer, genefilter,
gridExtra, survival, doParallel, parallel, ggrepel (>= 0.6.3),
scales, grid, DT
License: GPL (>= 3)
MD5sum: b82d195acc000247f5e5382774acfc95
NeedsCompilation: no
Title: TCGAbiolinks: An R/Bioconductor package for integrative analysis
with GDC data
Description: The aim of TCGAbiolinks is : i) facilitate the GDC
open-access data retrieval, ii) prepare the data using the
appropriate pre-processing strategies, iii) provide the means
to carry out different standard analyses and iv) to easily
reproduce earlier research results. In more detail, the package
provides multiple methods for analysis (e.g., differential
expression analysis, identifying differentially methylated
regions) and methods for visualization (e.g., survival plots,
volcano plots, starburst plots) in order to easily develop
complete analysis pipelines.
biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation,
GeneExpression, MethylationArray, DifferentialExpression,
Pathways, Network, Sequencing, Survival, Software
Author: Antonio Colaprico, Tiago Chedraoui Silva, Catharina Olsen,
Luciano Garofano, Davide Garolini, Claudia Cava, Thais Sabedot,
Tathiane Malta, Stefano M. Pagnotta, Isabella Castiglioni,
Michele Ceccarelli, Gianluca Bontempi, Houtan Noushmehr
Maintainer: Tiago Chedraoui Silva <tiagochst@gmail.com>, Antonio
Colaprico <axc1833@med.miami.edu>
URL: https://github.com/BioinformaticsFMRP/TCGAbiolinks
VignetteBuilder: knitr
BugReports: https://github.com/BioinformaticsFMRP/TCGAbiolinks/issues
git_url: https://git.bioconductor.org/packages/TCGAbiolinks
git_branch: devel
git_last_commit: 40752356
git_last_commit_date: 2025-02-17
Date/Publication: 2025-02-18
source.ver: src/contrib/TCGAbiolinks_2.35.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TCGAbiolinks_2.35.3.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TCGAbiolinks_2.35.3.tgz
vignettes: vignettes/TCGAbiolinks/inst/doc/analysis.html,
vignettes/TCGAbiolinks/inst/doc/casestudy.html,
vignettes/TCGAbiolinks/inst/doc/classifiers.html,
vignettes/TCGAbiolinks/inst/doc/clinical.html,
vignettes/TCGAbiolinks/inst/doc/download_prepare.html,
vignettes/TCGAbiolinks/inst/doc/extension.html,
vignettes/TCGAbiolinks/inst/doc/index.html,
vignettes/TCGAbiolinks/inst/doc/mutation.html,
vignettes/TCGAbiolinks/inst/doc/query.html,
vignettes/TCGAbiolinks/inst/doc/stemness_score.html,
vignettes/TCGAbiolinks/inst/doc/subtypes.html
vignetteTitles: 7. Analyzing and visualizing TCGA data, 8. Case
Studies, 10. Classifiers, "4. Clinical data", "3. Downloading
and preparing files for analysis", "10.
TCGAbiolinks_Extension", "1. Introduction", "5. Mutation data",
"2. Searching GDC database", 11. Stemness score, 6. Compilation
of TCGA molecular subtypes
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TCGAbiolinks/inst/doc/analysis.R,
vignettes/TCGAbiolinks/inst/doc/casestudy.R,
vignettes/TCGAbiolinks/inst/doc/classifiers.R,
vignettes/TCGAbiolinks/inst/doc/clinical.R,
vignettes/TCGAbiolinks/inst/doc/download_prepare.R,
vignettes/TCGAbiolinks/inst/doc/extension.R,
vignettes/TCGAbiolinks/inst/doc/index.R,
vignettes/TCGAbiolinks/inst/doc/mutation.R,
vignettes/TCGAbiolinks/inst/doc/query.R,
vignettes/TCGAbiolinks/inst/doc/stemness_score.R,
vignettes/TCGAbiolinks/inst/doc/subtypes.R
importsMe: ELMER, MoonlightR, SurfR, TENET, SingscoreAMLMutations,
CureAuxSP, oncoPredict
suggestsMe: GeoTcgaData, iNETgrate, musicatk
dependencyCount: 113
Package: TCGAutils
Version: 1.27.6
Depends: R (>= 4.2.0)
Imports: AnnotationDbi, BiocGenerics, BiocBaseUtils, GenomeInfoDb,
GenomicFeatures, GenomicRanges, GenomicDataCommons, IRanges,
methods, MultiAssayExperiment, RaggedExperiment (>= 1.5.7),
rvest, S4Vectors, stats, stringr, SummarizedExperiment, utils,
xml2
Suggests: AnnotationHub, BiocStyle, curatedTCGAData, ComplexHeatmap,
devtools, dplyr, httr,
IlluminaHumanMethylation450kanno.ilmn12.hg19, impute, knitr,
magrittr, mirbase.db, org.Hs.eg.db, RColorBrewer, readr,
rmarkdown, RTCGAToolbox (>= 2.17.4), rtracklayer, R.utils,
testthat, TxDb.Hsapiens.UCSC.hg18.knownGene,
TxDb.Hsapiens.UCSC.hg19.knownGene
License: Artistic-2.0
MD5sum: 271b9907a3c60aa51fb238e21030c991
NeedsCompilation: no
Title: TCGA utility functions for data management
Description: A suite of helper functions for checking and manipulating
TCGA data including data obtained from the curatedTCGAData
experiment package. These functions aim to simplify and make
working with TCGA data more manageable. Exported functions
include those that import data from flat files into
Bioconductor objects, convert row annotations, and identifier
translation via the GDC API.
biocViews: Software, WorkflowStep, Preprocessing, DataImport
Author: Marcel Ramos [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3242-0582>), Lucas Schiffer [aut],
Sean Davis [ctb], Levi Waldron [aut]
Maintainer: Marcel Ramos <marcel.ramos@sph.cuny.edu>
VignetteBuilder: knitr
BugReports: https://github.com/waldronlab/TCGAutils/issues
git_url: https://git.bioconductor.org/packages/TCGAutils
git_branch: devel
git_last_commit: 8e4301d
git_last_commit_date: 2024-12-16
Date/Publication: 2024-12-19
source.ver: src/contrib/TCGAutils_1.27.6.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TCGAutils_1.27.6.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TCGAutils_1.27.6.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TCGAutils_1.27.6.tgz
vignettes: vignettes/TCGAutils/inst/doc/TCGAutils.html
vignetteTitles: TCGAutils Essentials
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TCGAutils/inst/doc/TCGAutils.R
importsMe: cBioPortalData, glmSparseNet, RTCGAToolbox, terraTCGAdata
suggestsMe: CNVRanger, dce, curatedTCGAData
dependencyCount: 104
Package: TCseq
Version: 1.31.0
Depends: R (>= 3.4)
Imports: edgeR, BiocGenerics, reshape2, GenomicRanges, IRanges,
SummarizedExperiment, GenomicAlignments, Rsamtools, e1071,
cluster, ggplot2, grid, grDevices, stats, utils, methods,
locfit
Suggests: testthat
License: GPL (>= 2)
MD5sum: bc1f4a462a2bba10b05a9ed120145239
NeedsCompilation: no
Title: Time course sequencing data analysis
Description: Quantitative and differential analysis of epigenomic and
transcriptomic time course sequencing data, clustering analysis
and visualization of the temporal patterns of time course data.
biocViews: Epigenetics, TimeCourse, Sequencing, ChIPSeq, RNASeq,
DifferentialExpression, Clustering, Visualization
Author: Mengjun Wu <minervajunjun@gmail.com>, Lei Gu
<leigu@broadinstitute.org>
Maintainer: Mengjun Wu <minervajunjun@gmail.com>
git_url: https://git.bioconductor.org/packages/TCseq
git_branch: devel
git_last_commit: 2ff417d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TCseq_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TCseq_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TCseq_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TCseq_1.31.0.tgz
vignettes: vignettes/TCseq/inst/doc/TCseq.pdf
vignetteTitles: TCseq Vignette
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TCseq/inst/doc/TCseq.R
dependencyCount: 90
Package: TDbasedUFE
Version: 1.7.0
Imports: GenomicRanges, rTensor, readr, methods, MOFAdata, tximport,
tximportData, graphics, stats, utils, shiny
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0)
License: GPL-3
Archs: x64
MD5sum: 1e278fe8bc4250b37bede529534aafe0
NeedsCompilation: no
Title: Tensor Decomposition Based Unsupervised Feature Extraction
Description: This is a comprehensive package to perform Tensor
decomposition based unsupervised feature extraction. It can
perform unsupervised feature extraction. It uses tensor
decomposition. It is applicable to gene expression, DNA
methylation, and histone modification etc. It can perform
multiomics analysis. It is also potentially applicable to
single cell omics data sets.
biocViews: GeneExpression, FeatureExtraction, MethylationArray,
SingleCell
Author: Y-h. Taguchi [aut, cre] (ORCID:
<https://orcid.org/0000-0003-0867-8986>)
Maintainer: Y-h. Taguchi <tag@granular.com>
URL: https://github.com/tagtag/TDbasedUFE
VignetteBuilder: knitr
BugReports: https://github.com/tagtag/TDbasedUFE/issues
git_url: https://git.bioconductor.org/packages/TDbasedUFE
git_branch: devel
git_last_commit: 5b8c87d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TDbasedUFE_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TDbasedUFE_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TDbasedUFE_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TDbasedUFE_1.7.0.tgz
vignettes: vignettes/TDbasedUFE/inst/doc/QuickStart.html,
vignettes/TDbasedUFE/inst/doc/TDbasedUFE.html
vignetteTitles: QuickStart, TDbasedUFE
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TDbasedUFE/inst/doc/QuickStart.R,
vignettes/TDbasedUFE/inst/doc/TDbasedUFE.R
importsMe: TDbasedUFEadv
dependencyCount: 72
Package: TDbasedUFEadv
Version: 1.7.0
Imports: TDbasedUFE, Biobase, GenomicRanges, utils, rTensor, methods,
graphics, RTCGA, stats, enrichplot, DOSE, STRINGdb, enrichR,
hash, shiny
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), RTCGA.rnaseq,
RTCGA.clinical, BiocStyle, MOFAdata
License: GPL-3
MD5sum: c60456738e4ccc821ecefee0d4cc93ca
NeedsCompilation: no
Title: Advanced package of tensor decomposition based unsupervised
feature extraction
Description: This is an advanced version of TDbasedUFE, which is a
comprehensive package to perform Tensor decomposition based
unsupervised feature extraction. In contrast to TDbasedUFE
which can perform simple the feature selection and the
multiomics analyses, this package can perform more complicated
and advanced features, but they are not so popularly required.
Only users who require more specific features can make use of
its functionality.
biocViews: GeneExpression, FeatureExtraction, MethylationArray,
SingleCell, Software
Author: Y-h. Taguchi [aut, cre] (ORCID:
<https://orcid.org/0000-0003-0867-8986>)
Maintainer: Y-h. Taguchi <tag@granular.com>
URL: https://github.com/tagtag/TDbasedUFEadv
VignetteBuilder: knitr
BugReports: https://github.com/tagtag/TDbasedUFEadv/issues
git_url: https://git.bioconductor.org/packages/TDbasedUFEadv
git_branch: devel
git_last_commit: 8134ead
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TDbasedUFEadv_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TDbasedUFEadv_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TDbasedUFEadv_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TDbasedUFEadv_1.7.0.tgz
vignettes: vignettes/TDbasedUFEadv/inst/doc/Enrichment.html,
vignettes/TDbasedUFEadv/inst/doc/Explanation_of_TDbasedUFEadv.html,
vignettes/TDbasedUFEadv/inst/doc/How_to_use_TDbasedUFEadv.html
vignetteTitles: Enrichment, Explanation of TDbasedUFEadv, How to use
TDbasedUFEadv
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TDbasedUFEadv/inst/doc/Enrichment.R,
vignettes/TDbasedUFEadv/inst/doc/Explanation_of_TDbasedUFEadv.R,
vignettes/TDbasedUFEadv/inst/doc/How_to_use_TDbasedUFEadv.R
dependencyCount: 223
Package: TEKRABber
Version: 1.11.0
Depends: R (>= 4.3)
Imports: apeglm, biomaRt, dplyr, doParallel, DESeq2, foreach,
GenomeInfoDb, magrittr, Rcpp (>= 1.0.7), rtracklayer, SCBN,
stats, utils
LinkingTo: Rcpp
Suggests: BiocStyle, bslib, ggplot2, ggpubr, plotly, rmarkdown, shiny,
knitr, testthat (>= 3.0.0)
License: LGPL (>=3)
MD5sum: 0cfc59ea227d7c5752a1cdcc1b9fdfce
NeedsCompilation: yes
Title: An R package estimates the correlations of orthologs and
transposable elements between two species
Description: TEKRABber is made to provide a user-friendly pipeline for
comparing orthologs and transposable elements (TEs) between two
species. It considers the orthology confidence between two
species from BioMart to normalize expression counts and detect
differentially expressed orthologs/TEs. Then it provides one to
one correlation analysis for desired orthologs and TEs. There
is also an app function to have a first insight on the result.
Users can prepare orthologs/TEs RNA-seq expression data by
their own preference to run TEKRABber following the data
structure mentioned in the vignettes.
biocViews: DifferentialExpression, Normalization, Transcription,
GeneExpression
Author: Yao-Chung Chen [aut, cre] (ORCID:
<https://orcid.org/0000-0002-9927-9130>), Katja Nowick [aut]
(ORCID: <https://orcid.org/0000-0003-3993-4479>)
Maintainer: Yao-Chung Chen <yao-chung.chen@fu-berlin.de>
URL: https://github.com/ferygood/TEKRABber
VignetteBuilder: knitr
BugReports: https://github.com/ferygood/TEKRABber/issues
git_url: https://git.bioconductor.org/packages/TEKRABber
git_branch: devel
git_last_commit: 3e1203e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TEKRABber_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TEKRABber_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TEKRABber_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TEKRABber_1.11.0.tgz
vignettes: vignettes/TEKRABber/inst/doc/TEKRABber.html
vignetteTitles: TEKRABber
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TEKRABber/inst/doc/TEKRABber.R
dependencyCount: 131
Package: TENET
Version: 0.99.6
Depends: R (>= 4.5)
Imports: graphics, grDevices, stats, utils, tools, S4Vectors,
GenomicRanges, IRanges, parallel, pastecs, ggplot2, RCircos,
survival, BSgenome.Hsapiens.UCSC.hg38, seqLogo, Biostrings,
matlab, TCGAbiolinks, methods, R.utils, MultiAssayExperiment,
SummarizedExperiment, sesame, sesameData, AnnotationHub,
ExperimentHub, TENET.ExperimentHub, rtracklayer
Suggests: TENET.AnnotationHub, knitr, rmarkdown, BiocStyle, MotifDb
License: GPL-2
MD5sum: cd84122f595cbc1b1c0ef855a7c67c36
NeedsCompilation: no
Title: R package for TENET (Tracing regulatory Element Networks using
Epigenetic Traits) to identify key transcription factors
Description: TENET identifies key transcription factors (TFs) and
regulatory elements (REs) linked to a specific cell type by
finding significantly correlated differences in gene expression
and RE methylation between case and control input datasets, and
identifying the top genes by number of significant RE DNA
methylation site links. It also includes many additional tools
to aid in visualization and analysis of the results, including
plots displaying and comparing methylation and expression data
and RE DNA methylation site link counts, survival analysis, TF
motif searching in the vicinity of linked RE DNA methylation
sites, custom TAD and peak overlap analysis, and UCSC Genome
Browser track file generation. A utility function is also
provided to download methylation, expression, and patient
survival data from The Cancer Genome Atlas (TCGA) for use in
TENET or other analyses.
biocViews: Software, BiomedicalInformatics, CellBiology, Genetics,
Epigenetics, MultipleComparison, GeneExpression,
DifferentialExpression, DNAMethylation,
DifferentialMethylation, MethylationArray, Sequencing,
MethylSeq, RNASeq, FunctionalGenomics, GeneRegulation,
GeneTarget, HistoneModification, Transcription,
Transcriptomics, Survival, Visualization
Author: Rhie Lab at the University of Southern California [cre], Daniel
Mullen [aut] (ORCID: <https://orcid.org/0000-0002-7639-0549>),
Zexun Wu [aut] (ORCID:
<https://orcid.org/0000-0003-2566-1326>), Ethan Nelson-Moore
[aut] (ORCID: <https://orcid.org/0009-0001-6903-9232>), Suhn
Rhie [aut] (ORCID: <https://orcid.org/0000-0002-5522-5296>)
Maintainer: Rhie Lab at the University of Southern California
<rhielab@gmail.com>
URL: https://github.com/rhielab/TENET
VignetteBuilder: knitr
BugReports: https://github.com/rhielab/TENET/issues
git_url: https://git.bioconductor.org/packages/TENET
git_branch: devel
git_last_commit: 37841f1
git_last_commit_date: 2025-03-13
Date/Publication: 2025-03-17
source.ver: src/contrib/TENET_0.99.6.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TENET_0.99.6.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TENET_0.99.6.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TENET_0.99.6.tgz
vignettes: vignettes/TENET/inst/doc/TENET_vignette.html
vignetteTitles: Using TENET
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TENET/inst/doc/TENET_vignette.R
dependencyCount: 152
Package: TENxIO
Version: 1.9.3
Depends: R (>= 4.2.0), SingleCellExperiment, SummarizedExperiment
Imports: BiocBaseUtils, BiocGenerics, BiocIO, GenomeInfoDb,
GenomicRanges, HDF5Array, Matrix, MatrixGenerics, methods,
RCurl, readr, rhdf5, R.utils, S4Vectors, utils
Suggests: BiocStyle, DropletTestFiles, ExperimentHub, knitr,
RaggedExperiment (>= 1.29.5), rmarkdown, Rsamtools, tinytest
License: Artistic-2.0
MD5sum: 3ae09f0ee661a459ff4a366f63a86f4a
NeedsCompilation: no
Title: Import methods for 10X Genomics files
Description: Provides a structured S4 approach to importing data files
from the 10X pipelines. It mainly supports Single Cell Multiome
ATAC + Gene Expression data among other data types. The main
Bioconductor data representations used are SingleCellExperiment
and RaggedExperiment.
biocViews: Software, Infrastructure, DataImport, SingleCell
Author: Marcel Ramos [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3242-0582>)
Maintainer: Marcel Ramos <marcel.ramos@sph.cuny.edu>
URL: https://github.com/waldronlab/TENxIO
VignetteBuilder: knitr
BugReports: https://github.com/waldronlab/TENxIO/issues
git_url: https://git.bioconductor.org/packages/TENxIO
git_branch: devel
git_last_commit: c6c5a73
git_last_commit_date: 2024-11-21
Date/Publication: 2024-11-21
source.ver: src/contrib/TENxIO_1.9.3.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TENxIO_1.9.3.zip
vignettes: vignettes/TENxIO/inst/doc/TENxIO.html
vignetteTitles: TENxIO Quick Start Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TENxIO/inst/doc/TENxIO.R
dependsOnMe: VisiumIO, XeniumIO
importsMe: xenLite
dependencyCount: 72
Package: tenXplore
Version: 1.29.0
Depends: R (>= 4.0), shiny
Imports: methods, ontoProc (>= 0.99.7), SummarizedExperiment,
AnnotationDbi, matrixStats, org.Mm.eg.db, stats, utils,
BiocFileCache
Suggests: org.Hs.eg.db, testthat, knitr, rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: 4c105d6cc268c46a462d653dd79c2ec3
NeedsCompilation: no
Title: ontological exploration of scRNA-seq of 1.3 million mouse
neurons from 10x genomics
Description: Perform ontological exploration of scRNA-seq of 1.3
million mouse neurons from 10x genomics.
biocViews: ImmunoOncology, DimensionReduction, PrincipalComponent,
Transcriptomics, SingleCell
Author: Vince Carey
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/tenXplore
git_branch: devel
git_last_commit: 4caaa52
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tenXplore_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tenXplore_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tenXplore_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tenXplore_1.29.0.tgz
vignettes: vignettes/tenXplore/inst/doc/tenXplore.html
vignetteTitles: tenXplore: ontology for scRNA-seq,, applied to 10x 1.3
million neurons
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tenXplore/inst/doc/tenXplore.R
dependencyCount: 121
Package: TEQC
Version: 4.29.0
Depends: methods, BiocGenerics (>= 0.1.0), IRanges (>= 1.13.5),
Rsamtools, hwriter
Imports: Biobase (>= 2.15.1)
License: GPL (>= 2)
MD5sum: b9618179f716d73ee969a5b22e3bad6b
NeedsCompilation: no
Title: Quality control for target capture experiments
Description: Target capture experiments combine hybridization-based (in
solution or on microarrays) capture and enrichment of genomic
regions of interest (e.g. the exome) with high throughput
sequencing of the captured DNA fragments. This package provides
functionalities for assessing and visualizing the quality of
the target enrichment process, like specificity and sensitivity
of the capture, per-target read coverage and so on.
biocViews: QualityControl, Microarray, Sequencing, Genetics
Author: M. Hummel, S. Bonnin, E. Lowy, G. Roma
Maintainer: Sarah Bonnin <Sarah.Bonnin@crg.eu>
git_url: https://git.bioconductor.org/packages/TEQC
git_branch: devel
git_last_commit: 73056f5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TEQC_4.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TEQC_4.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TEQC_4.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TEQC_4.29.0.tgz
vignettes: vignettes/TEQC/inst/doc/TEQC.pdf
vignetteTitles: TEQC
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TEQC/inst/doc/TEQC.R
dependencyCount: 41
Package: terapadog
Version: 0.99.6
Imports: DESeq2, KEGGREST, stats, utils, dplyr, plotly, htmlwidgets,
biomaRt, methods
Suggests: apeglm, BiocStyle, knitr, rmarkdown, testthat
License: GPL-2
MD5sum: 0e00fafb3ca3b8f714e99172038320c9
NeedsCompilation: no
Title: Translational Efficiency Regulation Analysis using the PADOG
Method
Description: This package performs a Gene Set Analysis with the
approach adopted by PADOG on the genes that are reported as
translationally regulated (ie. exhibit a significant change in
TE) by the DeltaTE package. It can be used on its own to see
the impact of translation regulation on gene sets, but it is
also integrated as an additional analysis method within
ReactomeGSA, where results are further contextualised in terms
of pathways and directionality of the change.
biocViews: RiboSeq, Transcriptomics, GeneSetEnrichment, GeneRegulation,
Reactome, Software
Author: Gionmattia Carancini [cre, aut] (ORCID:
<https://orcid.org/0000-0001-7936-4883>)
Maintainer: Gionmattia Carancini <gionmattia@gmail.com>
URL: https://github.com/Gionmattia/terapadog
VignetteBuilder: knitr
BugReports: https://github.com/Gionmattia/terapadog/issues
git_url: https://git.bioconductor.org/packages/terapadog
git_branch: devel
git_last_commit: edf72ce
git_last_commit_date: 2025-03-05
Date/Publication: 2025-03-10
source.ver: src/contrib/terapadog_0.99.6.tar.gz
win.binary.ver: bin/windows/contrib/4.5/terapadog_0.99.6.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/terapadog_0.99.6.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/terapadog_0.99.6.tgz
vignettes: vignettes/terapadog/inst/doc/terapadog_vignette.html
vignetteTitles: terapadog: Translational Efficiency Regulation Analysis
& Pathway Analysis with Down-weighting of Overlapping Genes
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/terapadog/inst/doc/terapadog_vignette.R
dependencyCount: 127
Package: ternarynet
Version: 1.51.0
Depends: R (>= 4.0)
Imports: utils, igraph, methods, graphics, stats, BiocParallel
Suggests: testthat
Enhances: Rmpi, snow
License: GPL (>= 2)
Archs: x64
MD5sum: 60d2f56af87db6806e1c378e3094611c
NeedsCompilation: yes
Title: Ternary Network Estimation
Description: Gene-regulatory network (GRN) modeling seeks to infer
dependencies between genes and thereby provide insight into the
regulatory relationships that exist within a cell. This package
provides a computational Bayesian approach to GRN estimation
from perturbation experiments using a ternary network model, in
which gene expression is discretized into one of 3 states: up,
unchanged, or down). The ternarynet package includes a parallel
implementation of the replica exchange Monte Carlo algorithm
for fitting network models, using MPI.
biocViews: Software, CellBiology, GraphAndNetwork, Network, Bayesian
Author: Matthew N. McCall <mccallm@gmail.com>, Anthony Almudevar
<Anthony_Alumudevar@urmc.rochester.edu>, David Burton
<David_Burton@urmc.rochester.edu>, Harry Stern
<harry.stern@rochester.edu>
Maintainer: McCall N. Matthew <mccallm@gmail.com>
git_url: https://git.bioconductor.org/packages/ternarynet
git_branch: devel
git_last_commit: 251d20a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ternarynet_1.51.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ternarynet_1.51.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ternarynet_1.51.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ternarynet_1.51.0.tgz
vignettes: vignettes/ternarynet/inst/doc/ternarynet.pdf
vignetteTitles: ternarynet: A Computational Bayesian Approach to
Ternary Network Estimation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ternarynet/inst/doc/ternarynet.R
dependencyCount: 26
Package: terraTCGAdata
Version: 1.11.0
Depends: AnVILGCP, MultiAssayExperiment
Imports: AnVIL, BiocFileCache, dplyr, GenomicRanges, methods,
RaggedExperiment, readr, S4Vectors, stats, tidyr, TCGAutils,
utils
Suggests: AnVILBase, knitr, rmarkdown, BiocStyle, withr, testthat (>=
3.0.0)
License: Artistic-2.0
MD5sum: 5d4e8cb16d91dc7ac9acdb7fbd36038c
NeedsCompilation: no
Title: OpenAccess TCGA Data on Terra as MultiAssayExperiment
Description: Leverage the existing open access TCGA data on Terra with
well-established Bioconductor infrastructure. Make use of the
Terra data model without learning its complexities. With a few
functions, you can copy / download and generate a
MultiAssayExperiment from the TCGA example workspaces provided
by Terra.
biocViews: Software, Infrastructure, DataImport
Author: Marcel Ramos [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3242-0582>)
Maintainer: Marcel Ramos <marcel.ramos@sph.cuny.edu>
URL: https://github.com/waldronlab/terraTCGAdata
VignetteBuilder: knitr
BugReports: https://github.com/waldronlab/terraTCGAdata/issues
git_url: https://git.bioconductor.org/packages/terraTCGAdata
git_branch: devel
git_last_commit: 3e21167
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/terraTCGAdata_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/terraTCGAdata_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/terraTCGAdata_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/terraTCGAdata_1.11.0.tgz
vignettes: vignettes/terraTCGAdata/inst/doc/terraTCGAdata.html
vignetteTitles: Obtain Terra TCGA data as MultiAssayExperiment
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/terraTCGAdata/inst/doc/terraTCGAdata.R
dependencyCount: 140
Package: TFARM
Version: 1.29.0
Depends: R (>= 3.5.0)
Imports: arules, fields, GenomicRanges, graphics, stringr, methods,
stats, gplots
Suggests: BiocStyle, knitr, plyr
License: Artistic-2.0
MD5sum: 4dda7040a617252c3e9e6df3e62778fb
NeedsCompilation: no
Title: Transcription Factors Association Rules Miner
Description: It searches for relevant associations of transcription
factors with a transcription factor target, in specific genomic
regions. It also allows to evaluate the Importance Index
distribution of transcription factors (and combinations of
transcription factors) in association rules.
biocViews: BiologicalQuestion, Infrastructure, StatisticalMethod,
Transcription
Author: Liuba Nausicaa Martino, Alice Parodi, Gaia Ceddia, Piercesare
Secchi, Stefano Campaner, Marco Masseroli
Maintainer: Liuba Nausicaa Martino <liuban.martino@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TFARM
git_branch: devel
git_last_commit: da642eb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TFARM_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TFARM_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TFARM_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TFARM_1.29.0.tgz
vignettes: vignettes/TFARM/inst/doc/TFARM.pdf
vignetteTitles: Transcription Factor Association Rule Miner
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TFARM/inst/doc/TFARM.R
dependencyCount: 47
Package: TFBSTools
Version: 1.45.2
Depends: R (>= 3.2.2)
Imports: Biobase(>= 2.28), Biostrings(>= 2.36.4), pwalign,
BiocGenerics(>= 0.14.0), BiocParallel(>= 1.2.21), BSgenome(>=
1.36.3), caTools(>= 1.17.1), DirichletMultinomial(>= 1.10.0),
GenomeInfoDb(>= 1.6.1), GenomicRanges(>= 1.20.6), gtools(>=
3.5.0), grid, IRanges(>= 2.2.7), methods, DBI (>= 0.6),
RSQLite(>= 1.0.0), rtracklayer(>= 1.28.10), seqLogo(>= 1.34.0),
S4Vectors(>= 0.9.25), TFMPvalue(>= 0.0.5), XML(>= 3.98-1.3),
XVector(>= 0.8.0), parallel
Suggests: BiocStyle(>= 1.7.7), JASPAR2014(>= 1.4.0), knitr(>= 1.11),
testthat, JASPAR2016(>= 1.0.0), JASPAR2018(>= 1.0.0), rmarkdown
License: GPL-2
MD5sum: d41badd9fb3f02e80ce5388a723621ff
NeedsCompilation: yes
Title: Software Package for Transcription Factor Binding Site (TFBS)
Analysis
Description: TFBSTools is a package for the analysis and manipulation
of transcription factor binding sites. It includes matrices
conversion between Position Frequency Matirx (PFM), Position
Weight Matirx (PWM) and Information Content Matrix (ICM). It
can also scan putative TFBS from sequence/alignment, query
JASPAR database and provides a wrapper of de novo motif
discovery software.
biocViews: MotifAnnotation, GeneRegulation, MotifDiscovery,
Transcription, Alignment
Author: Ge Tan [aut, cre]
Maintainer: Ge Tan <ge_tan@live.com>
URL: https://github.com/ge11232002/TFBSTools
VignetteBuilder: knitr
BugReports: https://github.com/ge11232002/TFBSTools/issues
git_url: https://git.bioconductor.org/packages/TFBSTools
git_branch: devel
git_last_commit: ad16c7e
git_last_commit_date: 2025-03-12
Date/Publication: 2025-03-12
source.ver: src/contrib/TFBSTools_1.45.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TFBSTools_1.45.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TFBSTools_1.45.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TFBSTools_1.45.2.tgz
vignettes: vignettes/TFBSTools/inst/doc/TFBSTools.html
vignetteTitles: Transcription factor binding site (TFBS) analysis with
the "TFBSTools" package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TFBSTools/inst/doc/TFBSTools.R
importsMe: ATACseqTFEA, chromVAR, esATAC, MatrixRider, MethReg,
monaLisa, motifmatchr, motifStack, primirTSS, spatzie
suggestsMe: enhancerHomologSearch, GRaNIE, MAGAR, pageRank,
universalmotif, JASPAR2018, JASPAR2020, JASPAR2022,
CAGEWorkflow, Signac
dependencyCount: 81
Package: TFEA.ChIP
Version: 1.27.0
Depends: R (>= 3.5)
Imports: GenomicRanges, IRanges, biomaRt, GenomicFeatures, grDevices,
dplyr, stats, utils, R.utils, methods, org.Hs.eg.db
Suggests: knitr, rmarkdown, S4Vectors, plotly, scales, tidyr, ggplot2,
DESeq2, BiocGenerics, ggrepel, rcompanion,
TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit
License: Artistic-2.0
MD5sum: 43072548f15ea8e9cd47915f2cefb674
NeedsCompilation: no
Title: Analyze Transcription Factor Enrichment
Description: Package to analize transcription factor enrichment in a
gene set using data from ChIP-Seq experiments.
biocViews: Transcription, GeneRegulation, GeneSetEnrichment,
Transcriptomics, Sequencing, ChIPSeq, RNASeq, ImmunoOncology
Author: Laura Puente SantamarÃa, Luis del Peso
Maintainer: Laura Puente SantamarÃa <lpsantamaria@iib.uam.es>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TFEA.ChIP
git_branch: devel
git_last_commit: 34b5d7e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TFEA.ChIP_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TFEA.ChIP_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TFEA.ChIP_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TFEA.ChIP_1.27.0.tgz
vignettes: vignettes/TFEA.ChIP/inst/doc/TFEA.ChIP.html
vignetteTitles: TFEA.ChIP: a tool kit for transcription factor
enrichment
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TFEA.ChIP/inst/doc/TFEA.ChIP.R
dependencyCount: 104
Package: TFHAZ
Version: 1.29.0
Depends: R (>= 3.5.0)
Imports: GenomicRanges, S4Vectors, grDevices, graphics, stats, utils,
IRanges, methods, ORFik
Suggests: BiocStyle, knitr, rmarkdown
License: Artistic-2.0
MD5sum: fae831b95472009758d8f0df44768303
NeedsCompilation: no
Title: Transcription Factor High Accumulation Zones
Description: It finds trascription factor (TF) high accumulation DNA
zones, i.e., regions along the genome where there is a high
presence of different transcription factors. Starting from a
dataset containing the genomic positions of TF binding regions,
for each base of the selected chromosome the accumulation of
TFs is computed. Three different types of accumulation (TF,
region and base accumulation) are available, together with the
possibility of considering, in the single base accumulation
computing, the TFs present not only in that single base, but
also in its neighborhood, within a window of a given width. Two
different methods for the search of TF high accumulation DNA
zones, called "binding regions" and "overlaps", are available.
In addition, some functions are provided in order to analyze,
visualize and compare results obtained with different input
parameters.
biocViews: Software, BiologicalQuestion, Transcription, ChIPSeq,
Coverage
Author: Alberto Marchesi, Silvia Cascianelli, Marco Masseroli
Maintainer: Gaia Ceddia <gaia.ceddia@polimi.it>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TFHAZ
git_branch: devel
git_last_commit: b5ab06b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TFHAZ_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TFHAZ_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TFHAZ_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TFHAZ_1.29.0.tgz
vignettes: vignettes/TFHAZ/inst/doc/TFHAZ.html
vignetteTitles: TFHAZ
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TFHAZ/inst/doc/TFHAZ.R
dependencyCount: 139
Package: TFutils
Version: 1.27.1
Depends: R (>= 4.1.0)
Imports: methods, dplyr, magrittr, miniUI, shiny, Rsamtools, GSEABase,
rjson, BiocFileCache, DT, httr, readxl, AnnotationDbi,
org.Hs.eg.db, utils, GenomicFiles, SummarizedExperiment
Suggests: knitr, data.table, testthat, AnnotationFilter, Biobase,
GenomicFeatures, GenomicRanges, Gviz, IRanges, S4Vectors,
EnsDb.Hsapiens.v75, BiocParallel, BiocStyle, GO.db,
GenomeInfoDb, UpSetR, ggplot2, png, gwascat, MotifDb,
motifStack, RColorBrewer, rmarkdown
License: Artistic-2.0
Archs: x64
MD5sum: 76c534adf00082e781104d83907e42cf
NeedsCompilation: no
Title: TFutils
Description: This package helps users to work with TF metadata from
various sources. Significant catalogs of TFs and
classifications thereof are made available. Tools for working
with motif scans are also provided.
biocViews: Transcriptomics
Author: Vincent Carey [aut, cre], Shweta Gopaulakrishnan [aut]
Maintainer: Vincent Carey <stvjc@channing.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TFutils
git_branch: devel
git_last_commit: 6c53651
git_last_commit_date: 2024-11-12
Date/Publication: 2024-11-13
source.ver: src/contrib/TFutils_1.27.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TFutils_1.27.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TFutils_1.27.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TFutils_1.27.1.tgz
vignettes: vignettes/TFutils/inst/doc/fimo16.html,
vignettes/TFutils/inst/doc/TFutils.html
vignetteTitles: A note on fimo16, TFutils -- representing TFBS and TF
target sets
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TFutils/inst/doc/fimo16.R,
vignettes/TFutils/inst/doc/TFutils.R
dependencyCount: 133
Package: tidybulk
Version: 1.19.1
Depends: R (>= 4.4.0), ttservice (>= 0.3.6)
Imports: tibble, readr, dplyr (>= 1.1.0), magrittr, tidyr, stringi,
stringr, rlang, purrr, tidyselect, preprocessCore, stats,
parallel, utils, lifecycle, scales, SummarizedExperiment,
GenomicRanges, methods, S4Vectors, crayon, Matrix
Suggests: BiocStyle, testthat, vctrs, AnnotationDbi, BiocManager,
Rsubread, e1071, edgeR, limma, org.Hs.eg.db, org.Mm.eg.db, sva,
GGally, knitr, qpdf, covr, Seurat, KernSmooth, Rtsne, ggplot2,
widyr, clusterProfiler, msigdbr, DESeq2, broom, survival, boot,
betareg, tidyHeatmap, pasilla, ggrepel, devtools, functional,
survminer, tidySummarizedExperiment, markdown, uwot,
matrixStats, igraph, EGSEA, IRanges, here, glmmSeq, pbapply,
pbmcapply, lme4, glmmTMB, MASS, pkgconfig
License: GPL-3
MD5sum: 8d7947315871ca95c8cc8c1700f1c0f6
NeedsCompilation: no
Title: Brings transcriptomics to the tidyverse
Description: This is a collection of utility functions that allow to
perform exploration of and calculations to RNA sequencing data,
in a modular, pipe-friendly and tidy fashion.
biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression,
GeneExpression, Normalization, Clustering, QualityControl,
Sequencing, Transcription, Transcriptomics
Author: Stefano Mangiola [aut, cre], Maria Doyle [ctb]
Maintainer: Stefano Mangiola <mangiolastefano@gmail.com>
URL: https://github.com/stemangiola/tidybulk
VignetteBuilder: knitr
BugReports: https://github.com/stemangiola/tidybulk/issues
git_url: https://git.bioconductor.org/packages/tidybulk
git_branch: devel
git_last_commit: 587f005
git_last_commit_date: 2025-03-17
Date/Publication: 2025-03-18
source.ver: src/contrib/tidybulk_1.19.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tidybulk_1.19.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tidybulk_1.19.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tidybulk_1.19.1.tgz
vignettes: vignettes/tidybulk/inst/doc/introduction.html
vignetteTitles: Overview of the tidybulk package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tidybulk/inst/doc/introduction.R
importsMe: tidyomics
dependencyCount: 108
Package: tidyCoverage
Version: 1.3.0
Depends: R (>= 4.3.0), SummarizedExperiment
Imports: S4Vectors, IRanges, GenomicRanges, GenomeInfoDb, BiocParallel,
BiocIO, rtracklayer, methods, tidyr, ggplot2, dplyr, fansi,
pillar, rlang, scales, cli, purrr, vctrs, stats
Suggests: tidySummarizedExperiment, plyranges,
TxDb.Hsapiens.UCSC.hg19.knownGene, AnnotationHub,
GenomicFeatures, BiocStyle, hues, knitr, rmarkdown,
sessioninfo, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: ac1ea52fcc07e9744b25a63fcb83a8cb
NeedsCompilation: no
Title: Extract and aggregate genomic coverage over features of interest
Description: `tidyCoverage` framework enables tidy manipulation of
collections of genomic tracks and features using
`tidySummarizedExperiment` methods. It facilitates the
extraction, aggregation and visualization of genomic coverage
over individual or thousands of genomic loci, relying on
`CoverageExperiment` and `AggregatedCoverage` classes. This
accelerates the integration of genomic track data in genomic
analysis workflows.
biocViews: Software, Sequencing, Coverage,
Author: Jacques Serizay [aut, cre]
Maintainer: Jacques Serizay <jacquesserizay@gmail.com>
URL: https://github.com/js2264/tidyCoverage
VignetteBuilder: knitr
BugReports: https://github.com/js2264/tidyCoverage/issues
git_url: https://git.bioconductor.org/packages/tidyCoverage
git_branch: devel
git_last_commit: defcab5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tidyCoverage_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tidyCoverage_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tidyCoverage_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tidyCoverage_1.3.0.tgz
vignettes: vignettes/tidyCoverage/inst/doc/tidyCoverage.html
vignetteTitles: Introduction to tidyCoverage
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/tidyCoverage/inst/doc/tidyCoverage.R
dependencyCount: 90
Package: tidyFlowCore
Version: 1.1.0
Depends: R (>= 4.3)
Imports: Biobase, dplyr, flowCore, ggplot2, methods, purrr, rlang,
stats, stringr, tibble, tidyr
Suggests: BiocStyle, HDCytoData, knitr, RefManageR, rmarkdown,
sessioninfo, testthat (>= 3.0.0)
License: MIT + file LICENSE
Archs: x64
MD5sum: 97d2f37a89700f78b254def2269957ee
NeedsCompilation: no
Title: tidyFlowCore: Bringing flowCore to the tidyverse
Description: tidyFlowCore bridges the gap between flow cytometry
analysis using the flowCore Bioconductor package and the tidy
data principles advocated by the tidyverse. It provides a suite
of dplyr-, ggplot2-, and tidyr-like verbs specifically designed
for working with flowFrame and flowSet objects as if they were
tibbles; however, your data remain flowCore data structures
under this layer of abstraction. tidyFlowCore enables intuitive
and streamlined analysis workflows that can leverage both the
Bioconductor and tidyverse ecosystems for cytometry data.
biocViews: SingleCell, FlowCytometry, Infrastructure
Author: Timothy Keyes [cre] (ORCID:
<https://orcid.org/0000-0003-0423-9679>), Kara Davis [rth,
own], Garry Nolan [rth, own]
Maintainer: Timothy Keyes <tkeyes@stanford.edu>
URL: https://github.com/keyes-timothy/tidyFlowCore,
https://keyes-timothy.github.io/tidyFlowCore/
VignetteBuilder: knitr
BugReports: https://github.com/keyes-timothy/tidyFlowCore/issues
git_url: https://git.bioconductor.org/packages/tidyFlowCore
git_branch: devel
git_last_commit: a9a0990
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tidyFlowCore_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tidyFlowCore_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tidyFlowCore_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tidyFlowCore_1.1.0.tgz
vignettes: vignettes/tidyFlowCore/inst/doc/tidyFlowCore.html
vignetteTitles: tidyFlowCore
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/tidyFlowCore/inst/doc/tidyFlowCore.R
dependencyCount: 55
Package: tidyomics
Version: 1.3.0
Depends: R (>= 4.2)
Imports: tidySummarizedExperiment, tidySingleCellExperiment,
tidyseurat, tidybulk, plyranges, nullranges, purrr, rlang,
stringr, cli, utils
Suggests: tidyr, dplyr, tibble, ggplot2, mockr (>= 0.2.0), knitr (>=
1.41), rmarkdown (>= 2.20), testthat (>= 3.1.6)
License: MIT + file LICENSE
MD5sum: bda7056bf0a22a1c3b8c87832c4b89ce
NeedsCompilation: no
Title: Easily install and load the tidyomics ecosystem
Description: The tidyomics ecosystem is a set of packages for ’omic
data analysis that work together in harmony; they share common
data representations and API design, consistent with the
tidyverse ecosystem. The tidyomics package is designed to make
it easy to install and load core packages from the tidyomics
ecosystem with a single command.
biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression,
GeneExpression, Normalization, Clustering, QualityControl,
Sequencing, Transcription, Transcriptomics
Author: Stefano Mangiola [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7474-836X>), Michael Love [aut]
(ORCID: <https://orcid.org/0000-0001-8401-0545>), William
Hutchison [aut] (ORCID:
<https://orcid.org/0009-0001-6242-4269>)
Maintainer: Stefano Mangiola <mangiolastefano@gmail.com>
URL: https://github.com/tidyomics/tidyomics
VignetteBuilder: knitr
BugReports: https://github.com/tidyomics/tidyomics/issues
git_url: https://git.bioconductor.org/packages/tidyomics
git_branch: devel
git_last_commit: b8e3de3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tidyomics_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tidyomics_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tidyomics_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tidyomics_1.3.0.tgz
vignettes: vignettes/tidyomics/inst/doc/loading-tidyomics.html
vignetteTitles: Loading the tidyomics ecosystem
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/tidyomics/inst/doc/loading-tidyomics.R
dependencyCount: 206
Package: tidysbml
Version: 1.1.0
Depends: R (>= 4.4.0)
Imports: xml2, methods
Suggests: rmarkdown, knitr, BiocStyle, biomaRt, RCy3, testthat (>=
3.0.0)
License: CC BY 4.0
MD5sum: 17d4687204a874679c479fca1cb2cf98
NeedsCompilation: no
Title: Extract SBML's data into dataframes
Description: Starting from one SBML file, it extracts information from
each listOfCompartments, listOfSpecies and listOfReactions
element by saving them into data frames. Each table provides
one row for each entity (i.e. either compartment, species,
reaction or speciesReference) and one set of columns for the
attributes, one column for the content of the 'notes'
subelement and one set of columns for the content of the
'annotation' subelement.
biocViews: GraphAndNetwork, Network, Pathways, Software
Author: Veronica Paparozzi [aut, cre] (ORCID:
<https://orcid.org/0009-0000-2961-1559>), Christine Nardini
[aut] (ORCID: <https://orcid.org/0000-0001-7601-321X>)
Maintainer: Veronica Paparozzi <veronicapaparozzi1@gmail.com>
URL: https://github.com/veronicapaparozzi/tidysbml
VignetteBuilder: knitr
BugReports: https://github.com/veronicapaparozzi/tidysbml/issues
git_url: https://git.bioconductor.org/packages/tidysbml
git_branch: devel
git_last_commit: 1c63b02
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tidysbml_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tidysbml_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tidysbml_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tidysbml_1.1.0.tgz
vignettes: vignettes/tidysbml/inst/doc/tidysbml-introduction.html
vignetteTitles: Introduction to the tidysbml package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tidysbml/inst/doc/tidysbml-introduction.R
dependencyCount: 5
Package: tidySingleCellExperiment
Version: 1.17.0
Depends: R (>= 4.4.0), SingleCellExperiment
Imports: dplyr, tidyr, ttservice (>= 0.4.0), SummarizedExperiment,
tibble, ggplot2, magrittr, rlang, purrr, pkgconfig, lifecycle,
methods, utils, S4Vectors, tidyselect, ellipsis, vctrs, pillar,
stringr, cli, fansi, Matrix, stats
Suggests: BiocStyle, testthat, knitr, markdown, rmarkdown,
SingleCellSignalR, SingleR, scater, scran, tidyHeatmap, igraph,
GGally, uwot, celldex, dittoSeq, plotly
License: GPL-3
MD5sum: da509aa04643501977faf2512a7fdbf8
NeedsCompilation: no
Title: Brings SingleCellExperiment to the Tidyverse
Description: 'tidySingleCellExperiment' is an adapter that abstracts
the 'SingleCellExperiment' container in the form of a 'tibble'.
This allows *tidy* data manipulation, nesting, and plotting.
For example, a 'tidySingleCellExperiment' is directly
compatible with functions from 'tidyverse' packages `dplyr` and
`tidyr`, as well as plotting with `ggplot2` and `plotly`. In
addition, the package provides various utility functions
specific to single-cell omics data analysis (e.g., aggregation
of cell-level data to pseudobulks).
biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression,
SingleCell, GeneExpression, Normalization, Clustering,
QualityControl, Sequencing
Author: Stefano Mangiola [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7474-836X>)
Maintainer: Stefano Mangiola <mangiolastefano@gmail.com>
URL: https://github.com/stemangiola/tidySingleCellExperiment
VignetteBuilder: knitr
BugReports:
https://github.com/stemangiola/tidySingleCellExperiment/issues
git_url: https://git.bioconductor.org/packages/tidySingleCellExperiment
git_branch: devel
git_last_commit: 9469430
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tidySingleCellExperiment_1.17.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.5/tidySingleCellExperiment_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tidySingleCellExperiment_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tidySingleCellExperiment_1.17.0.tgz
vignettes:
vignettes/tidySingleCellExperiment/inst/doc/introduction.html
vignetteTitles: Overview of the tidySingleCellExperiment package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tidySingleCellExperiment/inst/doc/introduction.R
dependsOnMe: tidySpatialExperiment
importsMe: tidyomics
suggestsMe: CuratedAtlasQueryR, sccomp
dependencyCount: 99
Package: tidySpatialExperiment
Version: 1.3.0
Depends: R (>= 4.3.0), SpatialExperiment, tidySingleCellExperiment
Imports: ttservice, SummarizedExperiment, SingleCellExperiment,
BiocGenerics, Matrix, S4Vectors, methods, utils, pkgconfig,
tibble, dplyr, tidyr, ggplot2, plotly, rlang, purrr, stringr,
vctrs, tidyselect, pillar, cli, fansi, lifecycle, magick,
tidygate (>= 1.0.13), shiny
Suggests: BiocStyle, testthat, knitr, markdown, scater, igraph,
cowplot, DropletUtils, tidySummarizedExperiment
License: GPL (>= 3)
MD5sum: 18f9001d2c8abfb27a4e124b19af9e4b
NeedsCompilation: no
Title: SpatialExperiment with tidy principles
Description: tidySpatialExperiment provides a bridge between the
SpatialExperiment package and the tidyverse ecosystem. It
creates an invisible layer that allows you to interact with a
SpatialExperiment object as if it were a tibble; enabling the
use of functions from dplyr, tidyr, ggplot2 and plotly. But,
underneath, your data remains a SpatialExperiment object.
biocViews: Infrastructure, RNASeq, GeneExpression, Sequencing, Spatial,
Transcriptomics, SingleCell
Author: William Hutchison [aut, cre] (ORCID:
<https://orcid.org/0009-0001-6242-4269>), Stefano Mangiola
[aut]
Maintainer: William Hutchison <hutchison.w@wehi.edu.au>
URL: https://github.com/william-hutchison/tidySpatialExperiment,
https://william-hutchison.github.io/tidySpatialExperiment/
VignetteBuilder: knitr
BugReports:
https://github.com/william-hutchison/tidySpatialExperiment/issues
git_url: https://git.bioconductor.org/packages/tidySpatialExperiment
git_branch: devel
git_last_commit: 317a6d5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tidySpatialExperiment_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tidySpatialExperiment_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tidySpatialExperiment_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tidySpatialExperiment_1.3.0.tgz
vignettes: vignettes/tidySpatialExperiment/inst/doc/overview.html
vignetteTitles: Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tidySpatialExperiment/inst/doc/overview.R
dependencyCount: 120
Package: tidySummarizedExperiment
Version: 1.17.0
Depends: R (>= 4.3.0), SummarizedExperiment, ttservice (>= 0.4.0)
Imports: dplyr, tibble (>= 3.0.4), magrittr, tidyr, ggplot2, rlang,
purrr, lifecycle, methods, utils, S4Vectors, tidyselect,
ellipsis, vctrs, pillar, stringr, cli, fansi, stats, pkgconfig
Suggests: BiocStyle, testthat, knitr, markdown, rmarkdown, plotly
License: GPL-3
MD5sum: 377d1b39fb4be6bdacb9e0845ad189fc
NeedsCompilation: no
Title: Brings SummarizedExperiment to the Tidyverse
Description: The tidySummarizedExperiment package provides a set of
tools for creating and manipulating tidy data representations
of SummarizedExperiment objects. SummarizedExperiment is a
widely used data structure in bioinformatics for storing
high-throughput genomic data, such as gene expression or DNA
sequencing data. The tidySummarizedExperiment package
introduces a tidy framework for working with
SummarizedExperiment objects. It allows users to convert their
data into a tidy format, where each observation is a row and
each variable is a column. This tidy representation simplifies
data manipulation, integration with other tidyverse packages,
and enables seamless integration with the broader ecosystem of
tidy tools for data analysis.
biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression,
GeneExpression, Normalization, Clustering, QualityControl,
Sequencing, Transcription, Transcriptomics
Author: Stefano Mangiola [aut, cre]
Maintainer: Stefano Mangiola <mangiolastefano@gmail.com>
URL: https://github.com/stemangiola/tidySummarizedExperiment
VignetteBuilder: knitr
BugReports:
https://github.com/stemangiola/tidySummarizedExperiment/issues
git_url: https://git.bioconductor.org/packages/tidySummarizedExperiment
git_branch: devel
git_last_commit: 84d246b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tidySummarizedExperiment_1.17.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.5/tidySummarizedExperiment_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tidySummarizedExperiment_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tidySummarizedExperiment_1.17.0.tgz
vignettes:
vignettes/tidySummarizedExperiment/inst/doc/introduction.html
vignetteTitles: Overview of the tidySummarizedExperiment package
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tidySummarizedExperiment/inst/doc/introduction.R
importsMe: tidyomics
suggestsMe: nullranges, tidybulk, tidyCoverage, tidySpatialExperiment
dependencyCount: 98
Package: tidytof
Version: 1.1.0
Depends: R (>= 4.3)
Imports: doParallel, dplyr, flowCore, foreach, ggplot2, ggraph, glmnet,
methods, parallel, purrr, readr, recipes, rlang, stringr,
survival, tidygraph, tidyr, tidyselect, yardstick, Rcpp,
tibble, stats, utils, RcppHNSW
LinkingTo: Rcpp
Suggests: ConsensusClusterPlus, Biobase, broom, covr, diffcyt, emdist,
FlowSOM, forcats, ggrepel, HDCytoData, knitr, markdown,
philentropy, rmarkdown, Rtsne, statmod, SummarizedExperiment,
testthat (>= 3.0.0), lmerTest, lme4, ggridges, spelling,
scattermore, preprocessCore, SingleCellExperiment, Seurat,
SeuratObject, embed, rsample, BiocGenerics
License: MIT + file LICENSE
MD5sum: eb48ada25d0f81466a915917589b6c5f
NeedsCompilation: yes
Title: Analyze High-dimensional Cytometry Data Using Tidy Data
Principles
Description: This package implements an interactive, scientific
analysis pipeline for high-dimensional cytometry data built
using tidy data principles. It is specifically designed to play
well with both the tidyverse and Bioconductor software
ecosystems, with functionality for reading/writing data files,
data cleaning, preprocessing, clustering, visualization,
modeling, and other quality-of-life functions. tidytof
implements a "grammar" of high-dimensional cytometry data
analysis.
biocViews: SingleCell, FlowCytometry
Author: Timothy Keyes [cre] (ORCID:
<https://orcid.org/0000-0003-0423-9679>), Kara Davis [rth,
own], Garry Nolan [rth, own]
Maintainer: Timothy Keyes <tkeyes@stanford.edu>
URL: https://keyes-timothy.github.io/tidytof,
https://keyes-timothy.github.io/tidytof/
VignetteBuilder: knitr
BugReports: https://github.com/keyes-timothy/tidytof/issues
git_url: https://git.bioconductor.org/packages/tidytof
git_branch: devel
git_last_commit: 3ca5f4a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tidytof_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tidytof_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tidytof_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tidytof_1.1.0.tgz
vignettes: vignettes/tidytof/inst/doc/clustering.html,
vignettes/tidytof/inst/doc/contributing-to-tidytof.html,
vignettes/tidytof/inst/doc/differential-discovery-analysis.html,
vignettes/tidytof/inst/doc/dimensionality-reduction.html,
vignettes/tidytof/inst/doc/downsampling.html,
vignettes/tidytof/inst/doc/feature-extraction.html,
vignettes/tidytof/inst/doc/modeling.html,
vignettes/tidytof/inst/doc/preprocessing.html,
vignettes/tidytof/inst/doc/quality-control.html,
vignettes/tidytof/inst/doc/reading-and-writing-data.html,
vignettes/tidytof/inst/doc/tidytof.html
vignetteTitles: 07. Clustering and metaclustering, 11. How to
contribute code, 08. Differential discovery analysis, 06.
Dimensionality reduction, 05. Downsampling, 09. Feature
extraction, 10. Modeling, 04. Preprocessing, 03. Quality
control, 02. Reading and writing data, 01. Getting started with
tidytof
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/tidytof/inst/doc/clustering.R,
vignettes/tidytof/inst/doc/contributing-to-tidytof.R,
vignettes/tidytof/inst/doc/differential-discovery-analysis.R,
vignettes/tidytof/inst/doc/dimensionality-reduction.R,
vignettes/tidytof/inst/doc/downsampling.R,
vignettes/tidytof/inst/doc/feature-extraction.R,
vignettes/tidytof/inst/doc/modeling.R,
vignettes/tidytof/inst/doc/preprocessing.R,
vignettes/tidytof/inst/doc/quality-control.R,
vignettes/tidytof/inst/doc/reading-and-writing-data.R,
vignettes/tidytof/inst/doc/tidytof.R
dependencyCount: 118
Package: tigre
Version: 1.61.0
Depends: R (>= 2.11.0), BiocGenerics, Biobase
Imports: methods, AnnotationDbi, gplots, graphics, grDevices, stats,
utils, annotate, DBI, RSQLite
Suggests: drosgenome1.db, puma, lumi, BiocStyle, BiocManager
License: AGPL-3
MD5sum: e7a209a121b70f5b65652618b1da6799
NeedsCompilation: yes
Title: Transcription factor Inference through Gaussian process
Reconstruction of Expression
Description: The tigre package implements our methodology of Gaussian
process differential equation models for analysis of gene
expression time series from single input motif networks. The
package can be used for inferring unobserved transcription
factor (TF) protein concentrations from expression measurements
of known target genes, or for ranking candidate targets of a
TF.
biocViews: Microarray, TimeCourse, GeneExpression, Transcription,
GeneRegulation, NetworkInference, Bayesian
Author: Antti Honkela, Pei Gao, Jonatan Ropponen, Miika-Petteri
Matikainen, Magnus Rattray, Neil D. Lawrence
Maintainer: Antti Honkela <antti.honkela@helsinki.fi>
URL: https://github.com/ahonkela/tigre
BugReports: https://github.com/ahonkela/tigre/issues
git_url: https://git.bioconductor.org/packages/tigre
git_branch: devel
git_last_commit: 34c6d0d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tigre_1.61.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tigre_1.61.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tigre_1.61.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tigre_1.61.0.tgz
vignettes: vignettes/tigre/inst/doc/tigre.pdf
vignetteTitles: tigre User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tigre/inst/doc/tigre.R
dependencyCount: 53
Package: TileDBArray
Version: 1.17.0
Depends: SparseArray (>= 1.5.20), DelayedArray (>= 0.31.7)
Imports: methods, tiledb, S4Vectors
Suggests: knitr, Matrix, rmarkdown, BiocStyle, BiocParallel, testthat
License: MIT + file LICENSE
MD5sum: 5bda2d76099598555776f593f05525cc
NeedsCompilation: no
Title: Using TileDB as a DelayedArray Backend
Description: Implements a DelayedArray backend for reading and writing
dense or sparse arrays in the TileDB format. The resulting
TileDBArrays are compatible with all Bioconductor pipelines
that can accept DelayedArray instances.
biocViews: DataRepresentation, Infrastructure, Software
Author: Aaron Lun [aut, cre], Genentech, Inc. [cph]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://github.com/LTLA/TileDBArray
VignetteBuilder: knitr
BugReports: https://github.com/LTLA/TileDBArray
git_url: https://git.bioconductor.org/packages/TileDBArray
git_branch: devel
git_last_commit: 277ceda
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TileDBArray_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TileDBArray_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TileDBArray_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TileDBArray_1.17.0.tgz
vignettes: vignettes/TileDBArray/inst/doc/userguide.html
vignetteTitles: User guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/TileDBArray/inst/doc/userguide.R
importsMe: beachmat.tiledb
dependencyCount: 34
Package: tilingArray
Version: 1.85.0
Depends: R (>= 2.11.0), Biobase, methods, pixmap
Imports: strucchange, affy, vsn, genefilter, RColorBrewer, grid, stats4
License: Artistic-2.0
MD5sum: 2df281cf6701be75f4f898bb997a1266
NeedsCompilation: yes
Title: Transcript mapping with high-density oligonucleotide tiling
arrays
Description: The package provides functionality that can be useful for
the analysis of high-density tiling microarray data (such as
from Affymetrix genechips) for measuring transcript abundance
and architecture. The main functionalities of the package are:
1. the class 'segmentation' for representing partitionings of a
linear series of data; 2. the function 'segment' for fitting
piecewise constant models using a dynamic programming algorithm
that is both fast and exact; 3. the function 'confint' for
calculating confidence intervals using the strucchange package;
4. the function 'plotAlongChrom' for generating pretty plots;
5. the function 'normalizeByReference' for probe-sequence
dependent response adjustment from a (set of) reference
hybridizations.
biocViews: Microarray, OneChannel, Preprocessing, Visualization
Author: Wolfgang Huber, Zhenyu Xu, Joern Toedling with contributions
from Matt Ritchie
Maintainer: Zhenyu Xu <zxu@embl.de>
git_url: https://git.bioconductor.org/packages/tilingArray
git_branch: devel
git_last_commit: 10dd5d0
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tilingArray_1.85.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tilingArray_1.85.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tilingArray_1.85.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tilingArray_1.85.0.tgz
vignettes: vignettes/tilingArray/inst/doc/assessNorm.pdf,
vignettes/tilingArray/inst/doc/costMatrix.pdf,
vignettes/tilingArray/inst/doc/findsegments.pdf,
vignettes/tilingArray/inst/doc/plotAlongChrom.pdf,
vignettes/tilingArray/inst/doc/segmentation.pdf
vignetteTitles: Normalisation with the normalizeByReference function in
the tilingArray package, Supplement. Calculation of the cost
matrix, Introduction to using the segment function to fit a
piecewise constant curve, Introduction to the plotAlongChrom
function, Segmentation demo
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tilingArray/inst/doc/findsegments.R,
vignettes/tilingArray/inst/doc/plotAlongChrom.R
dependsOnMe: davidTiling
importsMe: ADaCGH2
dependencyCount: 86
Package: timecourse
Version: 1.79.0
Depends: R (>= 2.1.1), MASS, methods
Imports: Biobase, graphics, limma (>= 1.8.6), MASS, marray, methods,
stats
License: LGPL
MD5sum: 4328777e387a0105b8192ff81b65a2ff
NeedsCompilation: no
Title: Statistical Analysis for Developmental Microarray Time Course
Data
Description: Functions for data analysis and graphical displays for
developmental microarray time course data.
biocViews: Microarray, TimeCourse, DifferentialExpression
Author: Yu Chuan Tai
Maintainer: Yu Chuan Tai <yuchuan@stat.berkeley.edu>
URL: http://www.bioconductor.org
git_url: https://git.bioconductor.org/packages/timecourse
git_branch: devel
git_last_commit: e883bf8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/timecourse_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/timecourse_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/timecourse_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/timecourse_1.79.0.tgz
vignettes: vignettes/timecourse/inst/doc/timecourse.pdf
vignetteTitles: timecourse manual
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/timecourse/inst/doc/timecourse.R
dependencyCount: 12
Package: timeOmics
Version: 1.19.0
Depends: mixOmics, R (>= 4.0)
Imports: dplyr, tidyr, tibble, purrr, magrittr, ggplot2, stringr,
ggrepel, lmtest, plyr, checkmate
Suggests: BiocStyle, knitr, rmarkdown, testthat, snow, tidyverse,
igraph, gplots
License: GPL-3
MD5sum: 1c3c9cc11e7454ac5f1b0c6512c5d46d
NeedsCompilation: no
Title: Time-Course Multi-Omics data integration
Description: timeOmics is a generic data-driven framework to integrate
multi-Omics longitudinal data measured on the same biological
samples and select key temporal features with strong
associations within the same sample group. The main steps of
timeOmics are: 1. Plaform and time-specific normalization and
filtering steps; 2. Modelling each biological into one time
expression profile; 3. Clustering features with the same
expression profile over time; 4. Post-hoc validation step.
biocViews:
Clustering,FeatureExtraction,TimeCourse,DimensionReduction,Software,
Sequencing, Microarray, Metabolomics, Metagenomics, Proteomics,
Classification, Regression, ImmunoOncology, GenePrediction,
MultipleComparison
Author: Antoine Bodein [aut, cre], Olivier Chapleur [aut], Kim-Anh Le
Cao [aut], Arnaud Droit [aut]
Maintainer: Antoine Bodein <antoine.bodein.1@ulaval.ca>
VignetteBuilder: knitr
BugReports: https://github.com/abodein/timeOmics/issues
git_url: https://git.bioconductor.org/packages/timeOmics
git_branch: devel
git_last_commit: 6546b5f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/timeOmics_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/timeOmics_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/timeOmics_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/timeOmics_1.19.0.tgz
vignettes: vignettes/timeOmics/inst/doc/vignette.html
vignetteTitles: timeOmics
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/timeOmics/inst/doc/vignette.R
dependencyCount: 95
Package: timescape
Version: 1.31.0
Depends: R (>= 3.3)
Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), stringr (>=
1.0.0), dplyr (>= 0.4.3), gtools (>= 3.5.0)
Suggests: knitr, rmarkdown
License: GPL-3
Archs: x64
MD5sum: a76b5ed82ef58f46cf63e07f1be5b07d
NeedsCompilation: no
Title: Patient Clonal Timescapes
Description: TimeScape is an automated tool for navigating temporal
clonal evolution data. The key attributes of this
implementation involve the enumeration of clones, their
evolutionary relationships and their shifting dynamics over
time. TimeScape requires two inputs: (i) the clonal phylogeny
and (ii) the clonal prevalences. Optionally, TimeScape accepts
a data table of targeted mutations observed in each clone and
their allele prevalences over time. The output is the TimeScape
plot showing clonal prevalence vertically, time horizontally,
and the plot height optionally encoding tumour volume during
tumour-shrinking events. At each sampling time point (denoted
by a faint white line), the height of each clone accurately
reflects its proportionate prevalence. These prevalences form
the anchors for bezier curves that visually represent the
dynamic transitions between time points.
biocViews: Visualization, BiomedicalInformatics
Author: Maia Smith [aut, cre]
Maintainer: Maia Smith <maiaannesmith@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/timescape
git_branch: devel
git_last_commit: 9a6dbe7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/timescape_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/timescape_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/timescape_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/timescape_1.31.0.tgz
vignettes: vignettes/timescape/inst/doc/timescape_vignette.html
vignetteTitles: TimeScape vignette
hasREADME: TRUE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/timescape/inst/doc/timescape_vignette.R
dependencyCount: 47
Package: TIN
Version: 1.39.0
Depends: R (>= 2.12.0), data.table, impute, aroma.affymetrix
Imports: WGCNA, squash, stringr
Suggests: knitr, aroma.light, affxparser, RUnit, BiocGenerics
License: Artistic-2.0
Archs: x64
MD5sum: 30a83a5e9a677815534787511fc386af
NeedsCompilation: no
Title: Transcriptome instability analysis
Description: The TIN package implements a set of tools for
transcriptome instability analysis based on exon expression
profiles. Deviating exon usage is studied in the context of
splicing factors to analyse to what degree transcriptome
instability is correlated to splicing factor expression. In the
transcriptome instability correlation analysis, the data is
compared to both random permutations of alternative splicing
scores and expression of random gene sets.
biocViews: ExonArray, Microarray, GeneExpression, AlternativeSplicing,
Genetics, DifferentialSplicing
Author: Bjarne Johannessen, Anita Sveen and Rolf I. Skotheim
Maintainer: Bjarne Johannessen <bjajoh@rr-research.no>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TIN
git_branch: devel
git_last_commit: 6ae7ba4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TIN_1.39.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TIN_1.39.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TIN_1.39.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TIN_1.39.0.tgz
vignettes: vignettes/TIN/inst/doc/TIN.pdf
vignetteTitles: Introduction to the TIN package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TIN/inst/doc/TIN.R
dependencyCount: 134
Package: TissueEnrich
Version: 1.27.0
Depends: R (>= 3.5), ggplot2 (>= 2.2.1), SummarizedExperiment (>=
1.6.5), GSEABase (>= 1.38.2)
Imports: dplyr (>= 0.7.3), tidyr (>= 0.8.0), stats
Suggests: knitr, rmarkdown, testthat
License: MIT + file LICENSE
MD5sum: d427cdc0fb06d1e8ab300955a3304a78
NeedsCompilation: no
Title: Tissue-specific gene enrichment analysis
Description: The TissueEnrich package is used to calculate enrichment
of tissue-specific genes in a set of input genes. For example,
the user can input the most highly expressed genes from RNA-Seq
data, or gene co-expression modules to determine which
tissue-specific genes are enriched in those datasets.
Tissue-specific genes were defined by processing RNA-Seq data
from the Human Protein Atlas (HPA) (Uhlén et al. 2015), GTEx
(Ardlie et al. 2015), and mouse ENCODE (Shen et al. 2012) using
the algorithm from the HPA (Uhlén et al. 2015).The
hypergeometric test is being used to determine if the
tissue-specific genes are enriched among the input genes. Along
with tissue-specific gene enrichment, the TissueEnrich package
can also be used to define tissue-specific genes from
expression datasets provided by the user, which can then be
used to calculate tissue-specific gene enrichments.
biocViews: GeneSetEnrichment, GeneExpression, Sequencing
Author: Ashish Jain [aut, cre], Geetu Tuteja [aut]
Maintainer: Ashish Jain <jain.ashishjain1@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TissueEnrich
git_branch: devel
git_last_commit: ba84df9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TissueEnrich_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TissueEnrich_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TissueEnrich_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TissueEnrich_1.27.0.tgz
vignettes: vignettes/TissueEnrich/inst/doc/TissueEnrich.html
vignetteTitles: TissueEnrich
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/TissueEnrich/inst/doc/TissueEnrich.R
dependencyCount: 87
Package: TitanCNA
Version: 1.45.0
Depends: R (>= 3.5.1)
Imports: BiocGenerics (>= 0.31.6), IRanges (>= 2.6.1), GenomicRanges
(>= 1.24.3), VariantAnnotation (>= 1.18.7), foreach (>= 1.4.3),
GenomeInfoDb (>= 1.8.7), data.table (>= 1.10.4), dplyr (>=
0.5.0),
License: GPL-3
MD5sum: 1d8b0cee3df546fc4c7768202c9590b9
NeedsCompilation: yes
Title: Subclonal copy number and LOH prediction from whole genome
sequencing of tumours
Description: Hidden Markov model to segment and predict regions of
subclonal copy number alterations (CNA) and loss of
heterozygosity (LOH), and estimate cellular prevalence of
clonal clusters in tumour whole genome sequencing data.
biocViews: Sequencing, WholeGenome, DNASeq, ExomeSeq,
StatisticalMethod, CopyNumberVariation, HiddenMarkovModel,
Genetics, GenomicVariation, ImmunoOncology
Author: Gavin Ha
Maintainer: Gavin Ha <gha@fredhutch.org>
URL: https://github.com/gavinha/TitanCNA
git_url: https://git.bioconductor.org/packages/TitanCNA
git_branch: devel
git_last_commit: 19f37d4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TitanCNA_1.45.0.tar.gz
vignettes: vignettes/TitanCNA/inst/doc/TitanCNA.pdf
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TitanCNA/inst/doc/TitanCNA.R
dependencyCount: 90
Package: tkWidgets
Version: 1.85.0
Depends: R (>= 2.0.0), methods, widgetTools (>= 1.1.7), DynDoc (>=
1.3.0), tools
Suggests: Biobase, hgu95av2
License: Artistic-2.0
Archs: x64
MD5sum: f4ddf5399ad2f79e05610843c21b5c25
NeedsCompilation: no
Title: R based tk widgets
Description: Widgets to provide user interfaces. tcltk should have been
installed for the widgets to run.
biocViews: Infrastructure
Author: J. Zhang <jzhang@jimmy.harvard.edu>
Maintainer: J. Zhang <jzhang@jimmy.harvard.edu>
git_url: https://git.bioconductor.org/packages/tkWidgets
git_branch: devel
git_last_commit: 7a9c05a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tkWidgets_1.85.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tkWidgets_1.85.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tkWidgets_1.85.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tkWidgets_1.85.0.tgz
vignettes: vignettes/tkWidgets/inst/doc/importWizard.pdf,
vignettes/tkWidgets/inst/doc/tkWidgets.pdf
vignetteTitles: tkWidgets importWizard, tkWidgets contents
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tkWidgets/inst/doc/importWizard.R,
vignettes/tkWidgets/inst/doc/tkWidgets.R
importsMe: Mfuzz, OLINgui
suggestsMe: affy, annotate, Biobase, genefilter, marray
dependencyCount: 6
Package: tLOH
Version: 1.15.0
Depends: R (>= 4.2)
Imports: scales, stats, utils, ggplot2, data.table, purrr, dplyr,
VariantAnnotation, GenomicRanges, MatrixGenerics,
bestNormalize, depmixS4, naniar, stringr
Suggests: knitr, rmarkdown
License: MIT + file LICENSE
MD5sum: 504b8c4e1c4025fbd311f6090c9bb672
NeedsCompilation: no
Title: Assessment of evidence for LOH in spatial transcriptomics
pre-processed data using Bayes factor calculations
Description: tLOH, or transcriptomicsLOH, assesses evidence for loss of
heterozygosity (LOH) in pre-processed spatial transcriptomics
data. This tool requires spatial transcriptomics cluster and
allele count information at likely heterozygous
single-nucleotide polymorphism (SNP) positions in VCF format.
Bayes factors are calculated at each SNP to determine
likelihood of potential loss of heterozygosity event. Two
plotting functions are included to visualize allele fraction
and aggregated Bayes factor per chromosome. Data generated with
the 10X Genomics Visium Spatial Gene Expression platform must
be pre-processed to obtain an individual sample VCF with
columns for each cluster. Required fields are allele depth (AD)
with counts for reference/alternative alleles and read depth
(DP).
biocViews: CopyNumberVariation, Transcription, SNP, GeneExpression,
Transcriptomics
Author: Michelle Webb [cre, aut], David Craig [aut]
Maintainer: Michelle Webb <michelgw@usc.edu>
URL: https://github.com/USCDTG/tLOH
VignetteBuilder: knitr
BugReports: https://github.com/USCDTG/tLOH/issues
git_url: https://git.bioconductor.org/packages/tLOH
git_branch: devel
git_last_commit: fba6f99
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tLOH_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tLOH_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tLOH_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tLOH_1.15.0.tgz
vignettes: vignettes/tLOH/inst/doc/tLOH_vignette.html
vignetteTitles: tLOH
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/tLOH/inst/doc/tLOH_vignette.R
dependencyCount: 165
Package: TMixClust
Version: 1.29.0
Depends: R (>= 3.4)
Imports: gss, mvtnorm, stats, zoo, cluster, utils, BiocParallel,
flexclust, grDevices, graphics, Biobase, SPEM
Suggests: rmarkdown, knitr, BiocStyle, testthat
License: GPL (>=2)
MD5sum: 6c0a537b36627863903784fc6fbbd092
NeedsCompilation: no
Title: Time Series Clustering of Gene Expression with Gaussian
Mixed-Effects Models and Smoothing Splines
Description: Implementation of a clustering method for time series gene
expression data based on mixed-effects models with Gaussian
variables and non-parametric cubic splines estimation. The
method can robustly account for the high levels of noise
present in typical gene expression time series datasets.
biocViews: Software, StatisticalMethod, Clustering, TimeCourse,
GeneExpression
Author: Monica Golumbeanu <golumbeanu.monica@gmail.com>
Maintainer: Monica Golumbeanu <golumbeanu.monica@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TMixClust
git_branch: devel
git_last_commit: 67da9fc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TMixClust_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TMixClust_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TMixClust_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TMixClust_1.29.0.tgz
vignettes: vignettes/TMixClust/inst/doc/TMixClust.pdf
vignetteTitles: Clustering time series gene expression data with
TMixClust
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TMixClust/inst/doc/TMixClust.R
dependencyCount: 32
Package: TMSig
Version: 1.1.0
Depends: R (>= 4.4.0), limma
Imports: circlize, ComplexHeatmap, data.table, grDevices, grid,
GSEABase, Matrix, methods, stats, utils
Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0)
License: GPL (>= 3)
MD5sum: 9246ff776e87b50a42f49fc346729b68
NeedsCompilation: no
Title: Tools for Molecular Signatures
Description: The TMSig package contains tools to prepare, analyze, and
visualize named lists of sets, with an emphasis on molecular
signatures (such as gene or kinase sets). It includes fast,
memory efficient functions to construct sparse incidence and
similarity matrices and filter, cluster, invert, and decompose
sets. Additionally, bubble heatmaps can be created to visualize
the results of any differential or molecular signatures
analysis.
biocViews: Clustering, GeneSetEnrichment, GraphAndNetwork, Pathways,
Visualization
Author: Tyler Sagendorf [aut, cre] (ORCID:
<https://orcid.org/0000-0003-1552-4870>), Di Wu [ctb], Gordon
Smyth [ctb]
Maintainer: Tyler Sagendorf <tyler.sagendorf@pnnl.gov>
URL: https://github.com/EMSL-Computing/TMSig
VignetteBuilder: knitr
BugReports: https://github.com/EMSL-Computing/TMSig/issues
git_url: https://git.bioconductor.org/packages/TMSig
git_branch: devel
git_last_commit: fcdbeea
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TMSig_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TMSig_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TMSig_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TMSig_1.1.0.tgz
vignettes: vignettes/TMSig/inst/doc/TMSig.html
vignetteTitles: An Introduction to TMSig
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TMSig/inst/doc/TMSig.R
dependencyCount: 73
Package: TnT
Version: 1.29.0
Depends: R (>= 3.4), GenomicRanges
Imports: methods, stats, utils, grDevices, htmlwidgets, jsonlite,
data.table, Biobase, GenomeInfoDb, IRanges, S4Vectors, knitr
Suggests: GenomicFeatures, shiny, BiocManager, rmarkdown, testthat
License: AGPL-3
MD5sum: 9bc4e132220369fe862332485979a3ed
NeedsCompilation: no
Title: Interactive Visualization for Genomic Features
Description: A R interface to the TnT javascript library
(https://github.com/ tntvis) to provide interactive and
flexible visualization of track-based genomic data.
biocViews: Infrastructure, Visualization
Author: Jialin Ma [cre, aut], Miguel Pignatelli [aut], Toby Hocking
[aut]
Maintainer: Jialin Ma <marlin-@gmx.cn>
URL: https://github.com/Marlin-Na/TnT
VignetteBuilder: knitr
BugReports: https://github.com/Marlin-Na/TnT/issues
git_url: https://git.bioconductor.org/packages/TnT
git_branch: devel
git_last_commit: 3af21c4
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TnT_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TnT_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TnT_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TnT_1.29.0.tgz
vignettes: vignettes/TnT/inst/doc/introduction.html
vignetteTitles: Introduction to TnT
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TnT/inst/doc/introduction.R
dependencyCount: 50
Package: TOAST
Version: 1.21.0
Depends: R (>= 3.6), EpiDISH, limma, nnls, quadprog
Imports: stats, methods, SummarizedExperiment, corpcor, doParallel,
parallel, ggplot2, tidyr, GGally
Suggests: BiocStyle, knitr, rmarkdown, gplots, matrixStats, Matrix
License: GPL-2
MD5sum: a23c4fa1cee103bf6368d051da1df8d9
NeedsCompilation: no
Title: Tools for the analysis of heterogeneous tissues
Description: This package is devoted to analyzing high-throughput data
(e.g. gene expression microarray, DNA methylation microarray,
RNA-seq) from complex tissues. Current functionalities include
1. detect cell-type specific or cross-cell type differential
signals 2. tree-based differential analysis 3. improve variable
selection in reference-free deconvolution 4. partial
reference-free deconvolution with prior knowledge.
biocViews: DNAMethylation, GeneExpression, DifferentialExpression,
DifferentialMethylation, Microarray, GeneTarget, Epigenetics,
MethylationArray
Author: Ziyi Li and Weiwei Zhang and Luxiao Chen and Hao Wu
Maintainer: Ziyi Li <zli16@mdanderson.org>
VignetteBuilder: knitr
BugReports: https://github.com/ziyili20/TOAST/issues
git_url: https://git.bioconductor.org/packages/TOAST
git_branch: devel
git_last_commit: 3fa4c26
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TOAST_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TOAST_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TOAST_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TOAST_1.21.0.tgz
vignettes: vignettes/TOAST/inst/doc/TOAST.html
vignetteTitles: The TOAST User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TOAST/inst/doc/TOAST.R
importsMe: MICSQTL, RegionalST
dependencyCount: 93
Package: tomoda
Version: 1.17.0
Depends: R (>= 4.0.0)
Imports: methods, stats, grDevices, reshape2, Rtsne, umap,
RColorBrewer, ggplot2, ggrepel, SummarizedExperiment
Suggests: knitr, rmarkdown, BiocStyle, testthat
License: MIT + file LICENSE
MD5sum: 435346198d6cb76c0ab1c442dfb21b81
NeedsCompilation: no
Title: Tomo-seq data analysis
Description: This package provides many easy-to-use methods to analyze
and visualize tomo-seq data. The tomo-seq technique is based on
cryosectioning of tissue and performing RNA-seq on consecutive
sections. (Reference: Kruse F, Junker JP, van Oudenaarden A,
Bakkers J. Tomo-seq: A method to obtain genome-wide expression
data with spatial resolution. Methods Cell Biol.
2016;135:299-307. doi:10.1016/bs.mcb.2016.01.006) The main
purpose of the package is to find zones with similar
transcriptional profiles and spatially expressed genes in a
tomo-seq sample. Several visulization functions are available
to create easy-to-modify plots.
biocViews: GeneExpression, Sequencing, RNASeq, Transcriptomics,
Spatial, Clustering, Visualization
Author: Wendao Liu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5124-9338>)
Maintainer: Wendao Liu <liuwd15@tsinghua.org.cn>
URL: https://github.com/liuwd15/tomoda
VignetteBuilder: knitr
BugReports: https://github.com/liuwd15/tomoda/issues
git_url: https://git.bioconductor.org/packages/tomoda
git_branch: devel
git_last_commit: dcf5dcb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tomoda_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tomoda_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tomoda_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tomoda_1.17.0.tgz
vignettes: vignettes/tomoda/inst/doc/tomoda.html
vignetteTitles: tomoda
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/tomoda/inst/doc/tomoda.R
dependencyCount: 78
Package: tomoseqr
Version: 1.11.0
Depends: R (>= 4.2)
Imports: grDevices, graphics, animation, tibble, dplyr, stringr, purrr,
methods, shiny, BiocFileCache, readr, tools, plotly, ggplot2
Suggests: rmarkdown, knitr, BiocStyle, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: f51a85e39fa55ea1b1977be9cd395bf0
NeedsCompilation: no
Title: R Package for Analyzing Tomo-seq Data
Description: `tomoseqr` is an R package for analyzing Tomo-seq data.
Tomo-seq is a genome-wide RNA tomography method that combines
combining high-throughput RNA sequencing with cryosectioning
for spatially resolved transcriptomics. `tomoseqr` reconstructs
3D expression patterns from tomo-seq data and visualizes the
reconstructed 3D expression patterns.
biocViews: GeneExpression, Sequencing, RNASeq, Transcriptomics,
Spatial, Visualization, Software
Author: Ryosuke Matsuzawa [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2999-4734>)
Maintainer: Ryosuke Matsuzawa <kinakomochi.work@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/tomoseqr
git_branch: devel
git_last_commit: d515f3f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tomoseqr_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tomoseqr_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tomoseqr_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tomoseqr_1.11.0.tgz
vignettes: vignettes/tomoseqr/inst/doc/tomoseqr.html
vignetteTitles: tomoseqr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/tomoseqr/inst/doc/tomoseqr.R
dependencyCount: 102
Package: TOP
Version: 1.7.0
Depends: R (>= 3.5.0)
Imports: assertthat, caret, ClassifyR, directPA, doParallel, dplyr,
ggnewscale, ggplot2, ggraph, ggrepel, ggthemes, glmnet, Hmisc,
igraph, latex2exp, limma, magrittr, methods, plotly, pROC,
purrr, reshape2, stats, stringr, survival, tibble, tidygraph,
tidyr, statmod
Suggests: knitr, rmarkdown, BiocStyle, Biobase, curatedOvarianData,
ggbeeswarm, ggsci, survminer, tidyverse
License: GPL-3
MD5sum: 1612a2158529fb5e90b70e73d1bc83cc
NeedsCompilation: no
Title: TOP Constructs Transferable Model Across Gene Expression
Platforms
Description: TOP constructs a transferable model across gene expression
platforms for prospective experiments. Such a transferable
model can be trained to make predictions on independent
validation data with an accuracy that is similar to a
re-substituted model. The TOP procedure also has the
flexibility to be adapted to suit the most common clinical
response variables, including linear response, binomial and Cox
PH models.
biocViews: Software, Survival, GeneExpression
Author: Harry Robertson [aut, cre] (ORCID:
<https://orcid.org/0000-0001-9203-3894>), Nicholas Robertson
[aut]
Maintainer: Harry Robertson <harry.robertson@sydney.edu.au>
URL: https://github.com/Harry25R/TOP
VignetteBuilder: knitr
BugReports: https://github.com/Harry25R/TOP/issues
git_url: https://git.bioconductor.org/packages/TOP
git_branch: devel
git_last_commit: ade71fe
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TOP_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TOP_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TOP_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TOP_1.7.0.tgz
vignettes: vignettes/TOP/inst/doc/BuildingATOPModel.html
vignetteTitles: "Introduction to TOP"
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TOP/inst/doc/BuildingATOPModel.R
suggestsMe: ClassifyR
dependencyCount: 221
Package: topconfects
Version: 1.23.2
Depends: R (>= 3.6.0)
Imports: methods, utils, stats, assertthat, ggplot2, scales, grid,
grDevices
Suggests: limma, edgeR, statmod, DESeq2, ashr, NBPSeq, dplyr, testthat,
reshape2, tidyr, readr, org.At.tair.db, AnnotationDbi, knitr,
rmarkdown, BiocStyle
License: LGPL-2.1 | file LICENSE
MD5sum: 8c986a2624e8c0f82d8c42ce05238c7c
NeedsCompilation: no
Title: Top Confident Effect Sizes
Description: Rank results by confident effect sizes, while maintaining
False Discovery Rate and False Coverage-statement Rate control.
Topconfects is an alternative presentation of TREAT results
with improved usability, eliminating p-values and instead
providing confidence bounds. The main application is
differential gene expression analysis, providing genes ranked
in order of confident log2 fold change, but it can be applied
to any collection of effect sizes with associated standard
errors.
biocViews: GeneExpression, DifferentialExpression, Transcriptomics,
RNASeq, mRNAMicroarray, Regression, MultipleComparison
Author: Paul Harrison [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3980-268X>)
Maintainer: Paul Harrison <paul.harrison@monash.edu>
URL: https://github.com/pfh/topconfects
VignetteBuilder: knitr
BugReports: https://github.com/pfh/topconfects/issues
git_url: https://git.bioconductor.org/packages/topconfects
git_branch: devel
git_last_commit: ef5968f
git_last_commit_date: 2024-12-09
Date/Publication: 2024-12-11
source.ver: src/contrib/topconfects_1.23.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/topconfects_1.23.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/topconfects_1.23.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/topconfects_1.23.2.tgz
vignettes: vignettes/topconfects/inst/doc/an_overview.html,
vignettes/topconfects/inst/doc/fold_change.html
vignetteTitles: An overview of topconfects, Confident fold change
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/topconfects/inst/doc/an_overview.R,
vignettes/topconfects/inst/doc/fold_change.R
importsMe: GeoTcgaData, weitrix
dependencyCount: 37
Package: topdownr
Version: 1.29.0
Depends: R (>= 3.5), methods, BiocGenerics (>= 0.20.0), ProtGenerics
(>= 1.10.0), Biostrings (>= 2.42.1), S4Vectors (>= 0.12.2)
Imports: grDevices, stats, tools, utils, Biobase, Matrix (>= 1.4-2),
MSnbase (>= 2.3.10), PSMatch (>= 1.6.0), ggplot2 (>= 2.2.1),
mzR (>= 2.27.5)
Suggests: topdownrdata (>= 0.2), knitr, rmarkdown, ranger, testthat,
BiocStyle, xml2
License: GPL (>= 3)
MD5sum: 185c46092d0570febe79b77ae16050c7
NeedsCompilation: no
Title: Investigation of Fragmentation Conditions in Top-Down Proteomics
Description: The topdownr package allows automatic and systemic
investigation of fragment conditions. It creates Thermo
Orbitrap Fusion Lumos method files to test hundreds of
fragmentation conditions. Additionally it provides functions to
analyse and process the generated MS data and determine the
best conditions to maximise overall fragment coverage.
biocViews: ImmunoOncology, Infrastructure, Proteomics,
MassSpectrometry, Coverage
Author: Sebastian Gibb [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7406-4443>), Pavel Shliaha [aut]
(ORCID: <https://orcid.org/0000-0003-3092-0724>), Ole
Nørregaard Jensen [aut] (ORCID:
<https://orcid.org/0000-0003-1862-8528>)
Maintainer: Sebastian Gibb <mail@sebastiangibb.de>
URL: https://github.com/sgibb/topdownr/
VignetteBuilder: knitr
BugReports: https://github.com/sgibb/topdownr/issues/
git_url: https://git.bioconductor.org/packages/topdownr
git_branch: devel
git_last_commit: 9e53e8c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/topdownr_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/topdownr_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/topdownr_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/topdownr_1.29.0.tgz
vignettes: vignettes/topdownr/inst/doc/analysis.html,
vignettes/topdownr/inst/doc/data-generation.html
vignetteTitles: Fragmentation Analysis with topdownr, Data Generation
for topdownr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/topdownr/inst/doc/analysis.R,
vignettes/topdownr/inst/doc/data-generation.R
dependsOnMe: topdownrdata
dependencyCount: 137
Package: topGO
Version: 2.59.0
Depends: R (>= 2.10.0), methods, BiocGenerics (>= 0.13.6), graph (>=
1.14.0), Biobase (>= 2.0.0), GO.db (>= 2.3.0), AnnotationDbi
(>= 1.7.19), SparseM (>= 0.73)
Imports: lattice, matrixStats, DBI
Suggests: ALL, hgu95av2.db, hgu133a.db, genefilter, xtable, multtest,
Rgraphviz, globaltest
License: LGPL
MD5sum: fe71239bebd66d43c7edbb9dd72c6d46
NeedsCompilation: no
Title: Enrichment Analysis for Gene Ontology
Description: topGO package provides tools for testing GO terms while
accounting for the topology of the GO graph. Different test
statistics and different methods for eliminating local
similarities and dependencies between GO terms can be
implemented and applied.
biocViews: Microarray, Visualization
Author: Adrian Alexa, Jorg Rahnenfuhrer
Maintainer: Adrian Alexa <adrian.alexa@gmail.com>
git_url: https://git.bioconductor.org/packages/topGO
git_branch: devel
git_last_commit: 2711dac
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/topGO_2.59.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/topGO_2.59.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/topGO_2.59.0.tgz
vignettes: vignettes/topGO/inst/doc/topGO.pdf
vignetteTitles: topGO
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/topGO/inst/doc/topGO.R
dependsOnMe: BgeeDB, compEpiTools, EGSEA, ideal, moanin, tRanslatome,
maEndToEnd
importsMe: APL, cellity, consICA, GRaNIE, mosdef, OmaDB, pcaExplorer,
transcriptogramer, ViSEAGO, ExpHunterSuite
suggestsMe: fenr, FGNet, GeDi, geva, IntramiRExploreR, miRNAtap
dependencyCount: 51
Package: ToxicoGx
Version: 2.11.0
Depends: R (>= 4.1), CoreGx
Imports: SummarizedExperiment, BiocGenerics, S4Vectors, Biobase,
BiocParallel, ggplot2, tibble, dplyr, caTools, downloader,
magrittr, methods, reshape2, tidyr, data.table, assertthat,
scales, graphics, grDevices, parallel, stats, utils, limma,
jsonlite
Suggests: rmarkdown, testthat, BiocStyle, knitr, tinytex, devtools,
PharmacoGx, xtable, markdown
License: MIT + file LICENSE
MD5sum: 7001ac978542466c845a2383c7b787e0
NeedsCompilation: no
Title: Analysis of Large-Scale Toxico-Genomic Data
Description: Contains a set of functions to perform large-scale
analysis of toxicogenomic data, providing a standardized data
structure to hold information relevant to annotation,
visualization and statistical analysis of toxicogenomic data.
biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software
Author: Sisira Nair [aut], Esther Yoo [aut], Christopher Eeles [aut],
Amy Tang [aut], Nehme El-Hachem [aut], Petr Smirnov [aut],
Jermiah Joseph [aut], Benjamin Haibe-Kains [aut, cre]
Maintainer: Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ToxicoGx
git_branch: devel
git_last_commit: 15ee7e7
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ToxicoGx_2.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ToxicoGx_2.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ToxicoGx_2.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ToxicoGx_2.11.0.tgz
vignettes: vignettes/ToxicoGx/inst/doc/toxicoGxCaseStudies.html
vignetteTitles: ToxicoGx: An R Platform for Integrated Toxicogenomics
Data Analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/ToxicoGx/inst/doc/toxicoGxCaseStudies.R
dependencyCount: 144
Package: TPP
Version: 3.35.0
Depends: R (>= 3.4), Biobase, dplyr, magrittr, tidyr
Imports: biobroom, broom, data.table, doParallel, foreach,
futile.logger, ggplot2, grDevices, gridExtra, grid, knitr,
limma, MASS, mefa, nls2, openxlsx (>= 2.4.0), parallel, plyr,
purrr, RColorBrewer, RCurl, reshape2, rmarkdown, splines,
stats, stringr, tibble, utils, VennDiagram, VGAM
Suggests: BiocStyle, testthat
License: Artistic-2.0
MD5sum: 58c234aafa878dd05299ddbad3ecd683
NeedsCompilation: no
Title: Analyze thermal proteome profiling (TPP) experiments
Description: Analyze thermal proteome profiling (TPP) experiments with
varying temperatures (TR) or compound concentrations (CCR).
biocViews: ImmunoOncology, Proteomics, MassSpectrometry
Author: Dorothee Childs, Nils Kurzawa, Holger Franken, Carola Doce,
Mikhail Savitski and Wolfgang Huber
Maintainer: Dorothee Childs <d.childs@posteo.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TPP
git_branch: devel
git_last_commit: d3e2611
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TPP_3.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TPP_3.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TPP_3.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TPP_3.35.0.tgz
vignettes: vignettes/TPP/inst/doc/NPARC_analysis_of_TPP_TR_data.pdf,
vignettes/TPP/inst/doc/TPP_introduction_1D.pdf,
vignettes/TPP/inst/doc/TPP_introduction_2D.pdf
vignetteTitles: TPP_introduction_NPARC, TPP_introduction_1D,
TPP_introduction_2D
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TPP/inst/doc/NPARC_analysis_of_TPP_TR_data.R,
vignettes/TPP/inst/doc/TPP_introduction_1D.R,
vignettes/TPP/inst/doc/TPP_introduction_2D.R
suggestsMe: Rtpca
dependencyCount: 96
Package: TPP2D
Version: 1.23.0
Depends: R (>= 3.6.0), stats, utils, dplyr, methods
Imports: ggplot2, tidyr, foreach, doParallel, openxlsx, stringr, RCurl,
parallel, MASS, BiocParallel, limma
Suggests: knitr, testthat, rmarkdown, BiocStyle
License: GPL-3
MD5sum: 60a62e959ebeb14d5800b8a2a7e51eaf
NeedsCompilation: no
Title: Detection of ligand-protein interactions from 2D thermal
profiles (DLPTP)
Description: Detection of ligand-protein interactions from 2D thermal
profiles (DLPTP), Performs an FDR-controlled analysis of 2D-TPP
experiments by functional analysis of dose-response curves
across temperatures.
biocViews: Software, Proteomics, DataImport
Author: Nils Kurzawa [aut, cre], Holger Franken [aut], Simon Anders
[aut], Wolfgang Huber [aut], Mikhail M. Savitski [aut]
Maintainer: Nils Kurzawa <nilskurzawa@gmail.com>
URL: http://bioconductor.org/packages/TPP2D
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/
git_url: https://git.bioconductor.org/packages/TPP2D
git_branch: devel
git_last_commit: 9179fb5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TPP2D_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TPP2D_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TPP2D_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TPP2D_1.23.0.tgz
vignettes: vignettes/TPP2D/inst/doc/TPP2D.html
vignetteTitles: Introduction to TPP2D for 2D-TPP analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TPP2D/inst/doc/TPP2D.R
dependencyCount: 63
Package: tpSVG
Version: 1.3.0
Depends: mgcv, R (>= 4.4)
Imports: stats, BiocParallel, MatrixGenerics, methods,
SingleCellExperiment, SummarizedExperiment, SpatialExperiment
Suggests: BiocStyle, knitr, nnSVG, rmarkdown, scran, scuttle,
STexampleData, escheR, ggpubr, colorspace, BumpyMatrix,
sessioninfo, testthat (>= 3.0.0)
License: MIT + file LICENSE
MD5sum: 40b2c680facb6fa89ed78440215e8ffe
NeedsCompilation: no
Title: Thin plate models to detect spatially variable genes
Description: The goal of `tpSVG` is to detect and visualize spatial
variation in the gene expression for spatially resolved
transcriptomics data analysis. Specifically, `tpSVG` introduces
a family of count-based models, with generalizable parametric
assumptions such as Poisson distribution or negative binomial
distribution. In addition, comparing to currently available
count-based model for spatially resolved data analysis, the
`tpSVG` models improves computational time, and hence greatly
improves the applicability of count-based models in SRT data
analysis.
biocViews: Spatial, Transcriptomics, GeneExpression, Software,
StatisticalMethod, DimensionReduction, Regression,
Preprocessing
Author: Boyi Guo [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2950-2349>), Lukas M. Weber [ctb]
(ORCID: <https://orcid.org/0000-0002-3282-1730>), Stephanie C.
Hicks [aut] (ORCID: <https://orcid.org/0000-0002-7858-0231>)
Maintainer: Boyi Guo <boyi.guo.work@gmail.com>
URL: https://github.com/boyiguo1/tpSVG
VignetteBuilder: knitr
BugReports: https://github.com/boyiguo1/tpSVG/issues
git_url: https://git.bioconductor.org/packages/tpSVG
git_branch: devel
git_last_commit: 3b8b10d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tpSVG_1.3.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tpSVG_1.3.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tpSVG_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tpSVG_1.3.0.tgz
vignettes: vignettes/tpSVG/inst/doc/intro_to_tpSVG.html
vignetteTitles: intro_to_tpSVG
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/tpSVG/inst/doc/intro_to_tpSVG.R
dependencyCount: 84
Package: tracktables
Version: 1.41.0
Depends: R (>= 3.5.0)
Imports: IRanges, GenomicRanges, XVector, Rsamtools, XML, tractor.base,
stringr, RColorBrewer, methods
Suggests: knitr, BiocStyle
License: GPL (>= 3)
MD5sum: fa65b43c4730046988c281ac5924dd4e
NeedsCompilation: no
Title: Build IGV tracks and HTML reports
Description: Methods to create complex IGV genome browser sessions and
dynamic IGV reports in HTML pages.
biocViews: Sequencing, ReportWriting
Author: Tom Carroll, Sanjay Khadayate, Anne Pajon, Ziwei Liang
Maintainer: Tom Carroll <tc.infomatics@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/tracktables
git_branch: devel
git_last_commit: 9970f63
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tracktables_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tracktables_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tracktables_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tracktables_1.41.0.tgz
vignettes: vignettes/tracktables/inst/doc/tracktables.pdf
vignetteTitles: Creating IGV HTML reports with tracktables
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tracktables/inst/doc/tracktables.R
dependencyCount: 55
Package: trackViewer
Version: 1.43.7
Depends: R (>= 3.5.0), grDevices, methods, GenomicRanges, grid
Imports: GenomeInfoDb, GenomicAlignments, GenomicFeatures, Gviz,
Rsamtools, S4Vectors, rtracklayer, BiocGenerics, scales, tools,
IRanges, AnnotationDbi, grImport, htmlwidgets, InteractionSet,
utils, rhdf5, strawr, txdbmaker
Suggests: biomaRt, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit,
org.Hs.eg.db, BiocStyle, knitr, VariantAnnotation, httr,
htmltools, rmarkdown, motifStack
License: GPL (>= 2)
MD5sum: cd39d9608482f71a145ae075ed746e44
NeedsCompilation: no
Title: A R/Bioconductor package with web interface for drawing elegant
interactive tracks or lollipop plot to facilitate integrated
analysis of multi-omics data
Description: Visualize mapped reads along with annotation as track
layers for NGS dataset such as ChIP-seq, RNA-seq, miRNA-seq,
DNA-seq, SNPs and methylation data.
biocViews: Visualization
Author: Jianhong Ou [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8652-2488>), Julie Lihua Zhu [aut]
Maintainer: Jianhong Ou <jou@morgridge.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/trackViewer
git_branch: devel
git_last_commit: 70f2315
git_last_commit_date: 2025-03-27
Date/Publication: 2025-03-27
source.ver: src/contrib/trackViewer_1.43.7.tar.gz
win.binary.ver: bin/windows/contrib/4.5/trackViewer_1.43.7.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/trackViewer_1.43.7.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/trackViewer_1.43.7.tgz
vignettes: vignettes/trackViewer/inst/doc/changeTracksStyles.html,
vignettes/trackViewer/inst/doc/dandelionPlot.html,
vignettes/trackViewer/inst/doc/lollipopPlot.html,
vignettes/trackViewer/inst/doc/plotInteractionData.html,
vignettes/trackViewer/inst/doc/trackViewer.html
vignetteTitles: trackViewer Vignette: change the track styles,
trackViewer Vignette: dandelionPlot, trackViewer Vignette:
lollipopPlot, trackViewer Vignette: plot interaction data,
trackViewer Vignette: overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/trackViewer/inst/doc/changeTracksStyles.R,
vignettes/trackViewer/inst/doc/dandelionPlot.R,
vignettes/trackViewer/inst/doc/lollipopPlot.R,
vignettes/trackViewer/inst/doc/plotInteractionData.R,
vignettes/trackViewer/inst/doc/trackViewer.R
importsMe: geomeTriD, NADfinder
suggestsMe: ATACseqQC, ChIPpeakAnno
dependencyCount: 163
Package: tradeSeq
Version: 1.21.0
Depends: R (>= 3.6)
Imports: mgcv, edgeR, SingleCellExperiment, SummarizedExperiment,
slingshot, magrittr, RColorBrewer, BiocParallel, Biobase,
pbapply, igraph, ggplot2, princurve, methods, S4Vectors,
tibble, Matrix, TrajectoryUtils, viridis, matrixStats, MASS
Suggests: knitr, rmarkdown, testthat, covr, clusterExperiment,
DelayedMatrixStats
License: MIT + file LICENSE
MD5sum: e5074bfbbe60b343c39343ff6b3b9166
NeedsCompilation: no
Title: trajectory-based differential expression analysis for sequencing
data
Description: tradeSeq provides a flexible method for fitting regression
models that can be used to find genes that are differentially
expressed along one or multiple lineages in a trajectory. Based
on the fitted models, it uses a variety of tests suited to
answer different questions of interest, e.g. the discovery of
genes for which expression is associated with pseudotime, or
which are differentially expressed (in a specific region) along
the trajectory. It fits a negative binomial generalized
additive model (GAM) for each gene, and performs inference on
the parameters of the GAM.
biocViews: Clustering, Regression, TimeCourse, DifferentialExpression,
GeneExpression, RNASeq, Sequencing, Software, SingleCell,
Transcriptomics, MultipleComparison, Visualization
Author: Koen Van den Berge [aut], Hector Roux de Bezieux [aut, cre]
(ORCID: <https://orcid.org/0000-0002-1489-8339>), Kelly Street
[aut, ctb], Lieven Clement [aut, ctb], Sandrine Dudoit [ctb]
Maintainer: Hector Roux de Bezieux <hector.rouxdebezieux@berkeley.edu>
URL: https://statomics.github.io/tradeSeq/index.html
VignetteBuilder: knitr
BugReports: https://github.com/statOmics/tradeSeq/issues
git_url: https://git.bioconductor.org/packages/tradeSeq
git_branch: devel
git_last_commit: 0de9e8b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tradeSeq_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tradeSeq_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tradeSeq_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tradeSeq_1.21.0.tgz
vignettes: vignettes/tradeSeq/inst/doc/fitGAM.html,
vignettes/tradeSeq/inst/doc/Monocle.html,
vignettes/tradeSeq/inst/doc/multipleConditions.html,
vignettes/tradeSeq/inst/doc/tradeSeq.html
vignetteTitles: More details on working with fitGAM, Monocle +
tradeSeq, Differential expression across conditions, The
tradeSeq workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/tradeSeq/inst/doc/fitGAM.R,
vignettes/tradeSeq/inst/doc/Monocle.R,
vignettes/tradeSeq/inst/doc/tradeSeq.R
dependencyCount: 85
Package: TrajectoryGeometry
Version: 1.15.0
Depends: R (>= 4.1)
Imports: pracma, rgl, ggplot2, stats, methods
Suggests: dplyr, knitr, RColorBrewer, rmarkdown
License: MIT + file LICENSE
MD5sum: 6936b5d9f0ee35f642983270762073bb
NeedsCompilation: no
Title: This Package Discovers Directionality in Time and Pseudo-times
Series of Gene Expression Patterns
Description: Given a time series or pseudo-times series of gene
expression data, we might wish to know: Do the changes in gene
expression in these data exhibit directionality? Are there
turning points in this directionality. Do different subsets of
the data move in different directions? This package uses
spherical geometry to probe these sorts of questions. In
particular, if we are looking at (say) the first n dimensions
of the PCA of gene expression, directionality can be detected
as the clustering of points on the (n-1)-dimensional sphere.
biocViews: BiologicalQuestion, StatisticalMethod, GeneExpression,
SingleCell
Author: Michael Shapiro [aut, cre] (ORCID:
<https://orcid.org/0000-0002-2769-9320>)
Maintainer: Michael Shapiro <michael.shapiro@crick.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TrajectoryGeometry
git_branch: devel
git_last_commit: b33d9f8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TrajectoryGeometry_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TrajectoryGeometry_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TrajectoryGeometry_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TrajectoryGeometry_1.15.0.tgz
vignettes:
vignettes/TrajectoryGeometry/inst/doc/SingleCellTrajectoryAnalysis.html,
vignettes/TrajectoryGeometry/inst/doc/TrajectoryGeometry.html
vignetteTitles: SingleCellTrajectoryAnalysis, TrajectoryGeometry
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles:
vignettes/TrajectoryGeometry/inst/doc/SingleCellTrajectoryAnalysis.R,
vignettes/TrajectoryGeometry/inst/doc/TrajectoryGeometry.R
dependencyCount: 60
Package: TrajectoryUtils
Version: 1.15.0
Depends: SingleCellExperiment
Imports: methods, stats, Matrix, igraph, S4Vectors,
SummarizedExperiment
Suggests: BiocNeighbors, DelayedArray, DelayedMatrixStats,
BiocParallel, testthat, knitr, BiocStyle, rmarkdown
License: GPL-3
MD5sum: 861be6e6a334fd5679cfa6d96b428f2d
NeedsCompilation: no
Title: Single-Cell Trajectory Analysis Utilities
Description: Implements low-level utilities for single-cell trajectory
analysis, primarily intended for re-use inside higher-level
packages. Include a function to create a cluster-level minimum
spanning tree and data structures to hold pseudotime inference
results.
biocViews: GeneExpression, SingleCell
Author: Aaron Lun [aut, cre], Kelly Street [aut]
Maintainer: Aaron Lun <infinite.monkeys.with.keyboards@gmail.com>
URL: https://bioconductor.org/packages/TrajectoryUtils
VignetteBuilder: knitr
BugReports: https://github.com/LTLA/TrajectoryUtils/issues
git_url: https://git.bioconductor.org/packages/TrajectoryUtils
git_branch: devel
git_last_commit: 80a5e71
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TrajectoryUtils_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TrajectoryUtils_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TrajectoryUtils_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TrajectoryUtils_1.15.0.tgz
vignettes: vignettes/TrajectoryUtils/inst/doc/overview.html
vignetteTitles: Trajectory utilities
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TrajectoryUtils/inst/doc/overview.R
dependsOnMe: slingshot, TSCAN
importsMe: condiments, singleCellTK, tradeSeq
dependencyCount: 46
Package: transcriptogramer
Version: 1.29.0
Depends: R (>= 3.4), methods
Imports: biomaRt, data.table, doSNOW, foreach, ggplot2, graphics,
grDevices, igraph, limma, parallel, progress, RedeR, snow,
stats, tidyr, topGO
Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics
License: GPL (>= 2)
MD5sum: 4e46733f8c014de67ac385187ae98635
NeedsCompilation: no
Title: Transcriptional analysis based on transcriptograms
Description: R package for transcriptional analysis based on
transcriptograms, a method to analyze transcriptomes that
projects expression values on a set of ordered proteins,
arranged such that the probability that gene products
participate in the same metabolic pathway exponentially
decreases with the increase of the distance between two
proteins of the ordering. Transcriptograms are, hence, genome
wide gene expression profiles that provide a global view for
the cellular metabolism, while indicating gene sets whose
expressions are altered.
biocViews: Software, Network, Visualization, SystemsBiology,
GeneExpression, GeneSetEnrichment, GraphAndNetwork, Clustering,
DifferentialExpression, Microarray, RNASeq, Transcription,
ImmunoOncology
Author: Diego Morais [aut, cre], Rodrigo Dalmolin [aut]
Maintainer: Diego Morais <vinx@ufrn.edu.br>
URL: https://github.com/arthurvinx/transcriptogramer
SystemRequirements: Java Runtime Environment (>= 6)
VignetteBuilder: knitr
BugReports: https://github.com/arthurvinx/transcriptogramer/issues
git_url: https://git.bioconductor.org/packages/transcriptogramer
git_branch: devel
git_last_commit: c3aae60
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/transcriptogramer_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/transcriptogramer_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/transcriptogramer_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/transcriptogramer_1.29.0.tgz
vignettes: vignettes/transcriptogramer/inst/doc/transcriptogramer.html
vignetteTitles: The transcriptogramer user's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/transcriptogramer/inst/doc/transcriptogramer.R
dependencyCount: 101
Package: transcriptR
Version: 1.35.0
Depends: R (>= 3.5.0), methods
Imports: BiocGenerics, caret, chipseq, e1071, GenomicAlignments,
GenomicRanges, GenomicFeatures, GenomeInfoDb, ggplot2,
graphics, grDevices, IRanges (>= 2.11.15), pROC, reshape2,
Rsamtools, rtracklayer, S4Vectors, stats, utils
Suggests: BiocStyle, knitr, rmarkdown,
TxDb.Hsapiens.UCSC.hg19.knownGene, testthat
License: GPL-3
MD5sum: 9dab2c49e6a659cfa78b558909e3e0dc
NeedsCompilation: no
Title: An Integrative Tool for ChIP- And RNA-Seq Based Primary
Transcripts Detection and Quantification
Description: The differences in the RNA types being sequenced have an
impact on the resulting sequencing profiles. mRNA-seq data is
enriched with reads derived from exons, while GRO-, nucRNA- and
chrRNA-seq demonstrate a substantial broader coverage of both
exonic and intronic regions. The presence of intronic reads in
GRO-seq type of data makes it possible to use it to
computationally identify and quantify all de novo continuous
regions of transcription distributed across the genome. This
type of data, however, is more challenging to interpret and
less common practice compared to mRNA-seq. One of the
challenges for primary transcript detection concerns the
simultaneous transcription of closely spaced genes, which needs
to be properly divided into individually transcribed units. The
R package transcriptR combines RNA-seq data with ChIP-seq data
of histone modifications that mark active Transcription Start
Sites (TSSs), such as, H3K4me3 or H3K9/14Ac to overcome this
challenge. The advantage of this approach over the use of, for
example, gene annotations is that this approach is data driven
and therefore able to deal also with novel and case specific
events. Furthermore, the integration of ChIP- and RNA-seq data
allows the identification all known and novel active
transcription start sites within a given sample.
biocViews: ImmunoOncology, Transcription, Software, Sequencing, RNASeq,
Coverage
Author: Armen R. Karapetyan <armen.karapetyan87@gmail.com>
Maintainer: Armen R. Karapetyan <armen.karapetyan87@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/transcriptR
git_branch: devel
git_last_commit: bd28191
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/transcriptR_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/transcriptR_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/transcriptR_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/transcriptR_1.35.0.tgz
vignettes: vignettes/transcriptR/inst/doc/transcriptR.html
vignetteTitles: transcriptR: an integrative tool for ChIP- and RNA-seq
based primary transcripts detection and quantification
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/transcriptR/inst/doc/transcriptR.R
dependencyCount: 151
Package: transformGamPoi
Version: 1.13.0
Imports: glmGamPoi, DelayedArray, Matrix, MatrixGenerics,
SummarizedExperiment, HDF5Array, methods, utils, Rcpp
LinkingTo: Rcpp
Suggests: testthat, TENxPBMCData, scran, knitr, rmarkdown, BiocStyle
License: GPL-3
MD5sum: 2eed1e011c1d8e9cecdf828726c1dd1b
NeedsCompilation: yes
Title: Variance Stabilizing Transformation for Gamma-Poisson Models
Description: Variance-stabilizing transformations help with the
analysis of heteroskedastic data (i.e., data where the variance
is not constant, like count data). This package provide two
types of variance stabilizing transformations: (1) methods
based on the delta method (e.g., 'acosh', 'log(x+1)'), (2)
model residual based (Pearson and randomized quantile
residuals).
biocViews: SingleCell, Normalization, Preprocessing, Regression
Author: Constantin Ahlmann-Eltze [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3762-068X>)
Maintainer: Constantin Ahlmann-Eltze <artjom31415@googlemail.com>
URL: https://github.com/const-ae/transformGamPoi
VignetteBuilder: knitr
BugReports: https://github.com/const-ae/transformGamPoi/issues
git_url: https://git.bioconductor.org/packages/transformGamPoi
git_branch: devel
git_last_commit: c014302
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-05
source.ver: src/contrib/transformGamPoi_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/transformGamPoi_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/transformGamPoi_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/transformGamPoi_1.13.0.tgz
vignettes: vignettes/transformGamPoi/inst/doc/transformGamPoi.html
vignetteTitles: glmGamPoi Quickstart
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/transformGamPoi/inst/doc/transformGamPoi.R
dependencyCount: 55
Package: transite
Version: 1.25.0
Depends: R (>= 3.5)
Imports: BiocGenerics (>= 0.26.0), Biostrings (>= 2.48.0), dplyr (>=
0.7.6), GenomicRanges (>= 1.32.6), ggplot2 (>= 3.0.0),
grDevices, gridExtra (>= 2.3), methods, parallel, Rcpp (>=
1.0.4.8), scales (>= 1.0.0), stats, TFMPvalue (>= 0.0.8), utils
LinkingTo: Rcpp (>= 1.0.4.8)
Suggests: knitr (>= 1.20), rmarkdown (>= 1.10), roxygen2 (>= 6.1.0),
testthat (>= 2.1.0)
License: MIT + file LICENSE
MD5sum: deba913508a7157de83a43cae1f1bb74
NeedsCompilation: yes
Title: RNA-binding protein motif analysis
Description: transite is a computational method that allows
comprehensive analysis of the regulatory role of RNA-binding
proteins in various cellular processes by leveraging
preexisting gene expression data and current knowledge of
binding preferences of RNA-binding proteins.
biocViews: GeneExpression, Transcription, DifferentialExpression,
Microarray, mRNAMicroarray, Genetics, GeneSetEnrichment
Author: Konstantin Krismer [aut, cre, cph] (ORCID:
<https://orcid.org/0000-0001-8994-3416>), Anna Gattinger [aut]
(ORCID: <https://orcid.org/0000-0001-7094-9279>), Michael Yaffe
[ths, cph] (ORCID: <https://orcid.org/0000-0002-9547-3251>),
Ian Cannell [ths] (ORCID:
<https://orcid.org/0000-0001-5832-9210>)
Maintainer: Konstantin Krismer <krismer@mit.edu>
URL: https://transite.mit.edu
SystemRequirements: C++11
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/transite
git_branch: devel
git_last_commit: 9b520b8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/transite_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/transite_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/transite_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/transite_1.25.0.tgz
vignettes: vignettes/transite/inst/doc/spma.html
vignetteTitles: Spectrum Motif Analysis (SPMA)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/transite/inst/doc/spma.R
dependencyCount: 61
Package: tRanslatome
Version: 1.45.0
Depends: R (>= 2.15.0), methods, limma, anota, DESeq2, edgeR, RankProd,
topGO, org.Hs.eg.db, GOSemSim, Heatplus, gplots, plotrix,
Biobase
License: GPL-3
MD5sum: 39c055eef59270538c2d533c8d553e01
NeedsCompilation: no
Title: Comparison between multiple levels of gene expression
Description: Detection of differentially expressed genes (DEGs) from
the comparison of two biological conditions (treated vs.
untreated, diseased vs. normal, mutant vs. wild-type) among
different levels of gene expression (transcriptome
,translatome, proteome), using several statistical methods:
Rank Product, Translational Efficiency, t-test, Limma, ANOTA,
DESeq, edgeR. Possibility to plot the results with
scatterplots, histograms, MA plots, standard deviation (SD)
plots, coefficient of variation (CV) plots. Detection of
significantly enriched post-transcriptional regulatory factors
(RBPs, miRNAs, etc) and Gene Ontology terms in the lists of
DEGs previously identified for the two expression levels.
Comparison of GO terms enriched only in one of the levels or in
both. Calculation of the semantic similarity score between the
lists of enriched GO terms coming from the two expression
levels. Visual examination and comparison of the enriched terms
with heatmaps, radar plots and barplots.
biocViews: CellBiology, GeneRegulation, Regulation, GeneExpression,
DifferentialExpression, Microarray, HighThroughputSequencing,
QualityControl, GO, MultipleComparisons, Bioinformatics
Author: Toma Tebaldi, Erik Dassi, Galena Kostoska
Maintainer: Toma Tebaldi <toma.tebaldi@unitn.it>, Erik Dassi
<erik.dassi@unitn.it>
git_url: https://git.bioconductor.org/packages/tRanslatome
git_branch: devel
git_last_commit: 7c3a2dc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tRanslatome_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tRanslatome_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tRanslatome_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tRanslatome_1.45.0.tgz
vignettes: vignettes/tRanslatome/inst/doc/tRanslatome_package.pdf
vignetteTitles: tRanslatome
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tRanslatome/inst/doc/tRanslatome_package.R
dependencyCount: 122
Package: transmogR
Version: 1.3.8
Depends: R (>= 4.1.0), Biostrings, GenomicRanges
Imports: BSgenome, data.table, GenomeInfoDb, GenomicFeatures, ggplot2
(>= 3.5.0), IRanges, jsonlite, matrixStats, methods, parallel,
rlang, scales, stats, S4Vectors, SummarizedExperiment,
VariantAnnotation
Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg38, ComplexUpset, edgeR,
extraChIPs, InteractionSet, knitr, readr, rmarkdown,
rtracklayer, testthat (>= 3.0.0)
License: GPL-3
MD5sum: 437c35f1053586fb1f6653dc198eeac4
NeedsCompilation: yes
Title: Modify a set of reference sequences using a set of variants
Description: transmogR provides the tools needed to crate a new
reference genome or reference transcriptome, using a set of
variants. Variants can be any combination of SNPs, Insertions
and Deletions. The intended use-case is to enable creation of
variant-modified reference transcriptomes for incorporation
into transcriptomic pseudo-alignment workflows, such as salmon.
biocViews: Alignment, GenomicVariation, Sequencing,
TranscriptomeVariant, VariantAnnotation
Author: Stevie Pederson [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8197-3303>)
Maintainer: Stevie Pederson <stephen.pederson.au@gmail.com>
URL: https://github.com/smped/transmogR
VignetteBuilder: knitr
BugReports: https://github.com/smped/transmogR/issues
git_url: https://git.bioconductor.org/packages/transmogR
git_branch: devel
git_last_commit: 0b28689
git_last_commit_date: 2025-03-16
Date/Publication: 2025-03-17
source.ver: src/contrib/transmogR_1.3.8.tar.gz
win.binary.ver: bin/windows/contrib/4.5/transmogR_1.3.8.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/transmogR_1.3.8.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/transmogR_1.3.8.tgz
vignettes: vignettes/transmogR/inst/doc/creating_a_new_reference.html
vignetteTitles: Creating a Variant-Modified Reference
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/transmogR/inst/doc/creating_a_new_reference.R
dependencyCount: 100
Package: transomics2cytoscape
Version: 1.17.0
Imports: RCy3, KEGGREST, dplyr, purrr, tibble, pbapply
Suggests: testthat, roxygen2, knitr, BiocStyle, rmarkdown
License: Artistic-2.0
MD5sum: 37512f505cb8e6fea4c36e23ebce6871
NeedsCompilation: no
Title: A tool set for 3D Trans-Omic network visualization with
Cytoscape
Description: transomics2cytoscape generates a file for 3D transomics
visualization by providing input that specifies the IDs of
multiple KEGG pathway layers, their corresponding Z-axis
heights, and an input that represents the edges between the
pathway layers. The edges are used, for example, to describe
the relationships between kinase on a pathway and enzyme on
another pathway. This package automates creation of a
transomics network as shown in the figure in Yugi.2014
(https://doi.org/10.1016/j.celrep.2014.07.021) using Cytoscape
automation (https://doi.org/10.1186/s13059-019-1758-4).
biocViews: Network, Software, Pathways, DataImport, KEGG
Author: Kozo Nishida [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8501-7319>), Katsuyuki Yugi [aut]
(ORCID: <https://orcid.org/0000-0002-2046-4289>)
Maintainer: Kozo Nishida <kozo.nishida@gmail.com>
SystemRequirements: Cytoscape >= 3.10.0
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/transomics2cytoscape
git_branch: devel
git_last_commit: 85fc186
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/transomics2cytoscape_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/transomics2cytoscape_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/transomics2cytoscape_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/transomics2cytoscape_1.17.0.tgz
vignettes:
vignettes/transomics2cytoscape/inst/doc/transomics2cytoscape.html
vignetteTitles: transomics2cytoscape
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/transomics2cytoscape/inst/doc/transomics2cytoscape.R
dependencyCount: 69
Package: traseR
Version: 1.37.0
Depends: R (>= 3.5.0), GenomicRanges, IRanges,
BSgenome.Hsapiens.UCSC.hg19
Suggests: BiocStyle,RUnit, BiocGenerics
License: GPL
MD5sum: f2dafa6660699e3620018bce24e240e3
NeedsCompilation: no
Title: GWAS trait-associated SNP enrichment analyses in genomic
intervals
Description: traseR performs GWAS trait-associated SNP enrichment
analyses in genomic intervals using different hypothesis
testing approaches, also provides various functionalities to
explore and visualize the results.
biocViews: Genetics,Sequencing, Coverage, Alignment, QualityControl,
DataImport
Author: Li Chen, Zhaohui S.Qin
Maintainer: li chen<li.chen@emory.edu>
git_url: https://git.bioconductor.org/packages/traseR
git_branch: devel
git_last_commit: 93e91fe
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/traseR_1.37.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/traseR_1.37.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/traseR_1.37.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/traseR_1.37.0.tgz
vignettes: vignettes/traseR/inst/doc/traseR.pdf
vignetteTitles: Perform GWAS trait-associated SNP enrichment analyses
in genomic intervals
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/traseR/inst/doc/traseR.R
dependencyCount: 60
Package: TreeAndLeaf
Version: 1.19.0
Depends: R(>= 4.0)
Imports: RedeR(>= 1.40.4), igraph, ape
Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, stringr,
geneplast, ggtree, ggplot2, dplyr, dendextend, RColorBrewer
License: Artistic-2.0
Archs: x64
MD5sum: 8899e853745cc65569301cbc22407019
NeedsCompilation: no
Title: Displaying binary trees with focus on dendrogram leaves
Description: The TreeAndLeaf package combines unrooted and
force-directed graph algorithms in order to layout binary
trees, aiming to represent multiple layers of information onto
dendrogram leaves.
biocViews: Infrastructure, GraphAndNetwork, Software, Network,
Visualization, DataRepresentation
Author: Leonardo W. Kume, Luis E. A. Rizzardi, Milena A. Cardoso, Mauro
A. A. Castro
Maintainer: Milena A. Cardoso <milenandreuzo@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TreeAndLeaf
git_branch: devel
git_last_commit: cc7657b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TreeAndLeaf_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TreeAndLeaf_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TreeAndLeaf_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TreeAndLeaf_1.19.0.tgz
vignettes: vignettes/TreeAndLeaf/inst/doc/TreeAndLeaf.html
vignetteTitles: TreeAndLeaf: an graph layout to dendrograms.
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TreeAndLeaf/inst/doc/TreeAndLeaf.R
suggestsMe: RedeR
dependencyCount: 31
Package: treeclimbR
Version: 1.3.1
Depends: R (>= 4.4.0)
Imports: TreeSummarizedExperiment (>= 1.99.0), edgeR, methods,
SummarizedExperiment, S4Vectors, dirmult, dplyr, tibble, tidyr,
ape, diffcyt, ggnewscale, ggplot2 (>= 3.4.0), viridis, ggtree,
stats, utils, rlang
Suggests: knitr, rmarkdown, scales, testthat (>= 3.0.0), BiocStyle
License: Artistic-2.0
Archs: x64
MD5sum: a4e678c63beae56dffe6c178bc6558a5
NeedsCompilation: no
Title: An algorithm to find optimal signal levels in a tree
Description: The arrangement of hypotheses in a hierarchical structure
appears in many research fields and often indicates different
resolutions at which data can be viewed. This raises the
question of which resolution level the signal should best be
interpreted on. treeclimbR provides a flexible method to select
optimal resolution levels (potentially different levels in
different parts of the tree), rather than cutting the tree at
an arbitrary level. treeclimbR uses a tuning parameter to
generate candidate resolutions and from these selects the
optimal one.
biocViews: StatisticalMethod, CellBasedAssays
Author: Ruizhu Huang [aut] (ORCID:
<https://orcid.org/0000-0003-3285-1945>), Charlotte Soneson
[aut, cre] (ORCID: <https://orcid.org/0000-0003-3833-2169>)
Maintainer: Charlotte Soneson <charlottesoneson@gmail.com>
URL: https://github.com/csoneson/treeclimbR
VignetteBuilder: knitr
BugReports: https://github.com/csoneson/treeclimbR/issues
git_url: https://git.bioconductor.org/packages/treeclimbR
git_branch: devel
git_last_commit: e00df7c
git_last_commit_date: 2024-12-31
Date/Publication: 2024-12-31
source.ver: src/contrib/treeclimbR_1.3.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/treeclimbR_1.3.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/treeclimbR_1.3.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/treeclimbR_1.3.1.tgz
vignettes: vignettes/treeclimbR/inst/doc/treeclimbR.html
vignetteTitles: treeclimbR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/treeclimbR/inst/doc/treeclimbR.R
dependencyCount: 170
Package: treeio
Version: 1.31.0
Depends: R (>= 3.6.0)
Imports: ape, dplyr, jsonlite, magrittr, methods, rlang, stats, tibble,
tidytree (>= 0.4.5), utils, yulab.utils (>= 0.1.6)
Suggests: Biostrings, cli, ggplot2, ggtree, igraph, knitr, rmarkdown,
phangorn, prettydoc, purrr, testthat, tidyr, vroom, xml2, yaml
License: Artistic-2.0
MD5sum: ded39e57431d05609228dc47c2855950
NeedsCompilation: no
Title: Base Classes and Functions for Phylogenetic Tree Input and
Output
Description: 'treeio' is an R package to make it easier to import and
store phylogenetic tree with associated data; and to link
external data from different sources to phylogeny. It also
supports exporting phylogenetic tree with heterogeneous
associated data to a single tree file and can be served as a
platform for merging tree with associated data and converting
file formats.
biocViews: Software, Annotation, Clustering, DataImport,
DataRepresentation, Alignment, MultipleSequenceAlignment,
Phylogenetics
Author: Guangchuang Yu [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6485-8781>), Tommy Tsan-Yuk Lam
[ctb, ths], Shuangbin Xu [ctb] (ORCID:
<https://orcid.org/0000-0003-3513-5362>), Bradley Jones [ctb],
Casey Dunn [ctb], Tyler Bradley [ctb], Konstantinos Geles [ctb]
Maintainer: Guangchuang Yu <guangchuangyu@gmail.com>
URL: https://yulab-smu.top/contribution-tree-data/
VignetteBuilder: knitr
BugReports: https://github.com/YuLab-SMU/treeio/issues
git_url: https://git.bioconductor.org/packages/treeio
git_branch: devel
git_last_commit: 79e207c
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/treeio_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/treeio_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/treeio_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/treeio_1.31.0.tgz
vignettes: vignettes/treeio/inst/doc/treeio.html
vignetteTitles: treeio
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/treeio/inst/doc/treeio.R
importsMe: ggtree, lefser, MicrobiotaProcess, TreeSummarizedExperiment,
geneplast.data, BioVizSeq, EvoPhylo, RevGadgets,
shinyTempSignal
suggestsMe: ggtreeDendro, ggtreeExtra, rfaRm, FossilSim, idiogramFISH,
MetaNet, nosoi
dependencyCount: 39
Package: treekoR
Version: 1.15.0
Depends: R (>= 4.1)
Imports: stats, utils, tidyr, dplyr, data.table, ggiraph, ggplot2,
hopach, ape, ggtree, patchwork, SingleCellExperiment, diffcyt,
edgeR, lme4, multcomp
Suggests: knitr, rmarkdown, BiocStyle, CATALYST, testthat (>= 3.0.0)
License: GPL-3
Archs: x64
MD5sum: e39f993b54aedb38257c56c11043a474
NeedsCompilation: no
Title: Cytometry Cluster Hierarchy and Cellular-to-phenotype
Associations
Description: treekoR is a novel framework that aims to utilise the
hierarchical nature of single cell cytometry data to find
robust and interpretable associations between cell subsets and
patient clinical end points. These associations are aimed to
recapitulate the nested proportions prevalent in workflows
inovlving manual gating, which are often overlooked in
workflows using automatic clustering to identify cell
populations. We developed treekoR to: Derive a hierarchical
tree structure of cell clusters; quantify a cell types as a
proportion relative to all cells in a sample (%total), and, as
the proportion relative to a parent population (%parent);
perform significance testing using the calculated proportions;
and provide an interactive html visualisation to help highlight
key results.
biocViews: Clustering, DifferentialExpression, FlowCytometry,
ImmunoOncology, MassSpectrometry, SingleCell, Software,
StatisticalMethod, Visualization
Author: Adam Chan [aut, cre], Ellis Patrick [ctb]
Maintainer: Adam Chan <adam.s.chan@sydney.edu.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/treekoR
git_branch: devel
git_last_commit: f14c924
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/treekoR_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/treekoR_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/treekoR_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/treekoR_1.15.0.tgz
vignettes: vignettes/treekoR/inst/doc/vignette.html
vignetteTitles: vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/treekoR/inst/doc/vignette.R
importsMe: Statial
dependencyCount: 182
Package: TreeSummarizedExperiment
Version: 2.15.0
Depends: R(>= 3.6.0), SingleCellExperiment, S4Vectors (>= 0.23.18),
Biostrings
Imports: methods, BiocGenerics, utils, ape, rlang, dplyr,
SummarizedExperiment, BiocParallel, IRanges, treeio
Suggests: ggtree, ggplot2, BiocStyle, knitr, rmarkdown, testthat
License: GPL (>=2)
MD5sum: a2fffb1e338af26fbb0e9a0f1e46a6bb
NeedsCompilation: no
Title: TreeSummarizedExperiment: a S4 Class for Data with Tree
Structures
Description: TreeSummarizedExperiment has extended SingleCellExperiment
to include hierarchical information on the rows or columns of
the rectangular data.
biocViews: DataRepresentation, Infrastructure
Author: Ruizhu Huang [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3285-1945>), Felix G.M. Ernst
[ctb] (ORCID: <https://orcid.org/0000-0001-5064-0928>)
Maintainer: Ruizhu Huang <ruizhuRH@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TreeSummarizedExperiment
git_branch: devel
git_last_commit: 20c5a8f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TreeSummarizedExperiment_2.15.0.tar.gz
win.binary.ver:
bin/windows/contrib/4.5/TreeSummarizedExperiment_2.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TreeSummarizedExperiment_2.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TreeSummarizedExperiment_2.15.0.tgz
vignettes:
vignettes/TreeSummarizedExperiment/inst/doc/Introduction_to_treeSummarizedExperiment.html,
vignettes/TreeSummarizedExperiment/inst/doc/The_combination_of_multiple_TSEs.html
vignetteTitles: 1. Introduction to TreeSE, 2. Combine TSEs
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/TreeSummarizedExperiment/inst/doc/Introduction_to_treeSummarizedExperiment.R,
vignettes/TreeSummarizedExperiment/inst/doc/The_combination_of_multiple_TSEs.R
dependsOnMe: ExperimentSubset, HoloFoodR, MGnifyR, mia, miaSim, miaViz,
curatedMetagenomicData, MicrobiomeBenchmarkData,
microbiomeDataSets
importsMe: benchdamic, iSEEtree, maaslin3, microSTASIS, PLSDAbatch,
treeclimbR
suggestsMe: ANCOMBC, dar, philr, LegATo, file2meco, parafac4microbiome
dependencyCount: 75
Package: TREG
Version: 1.11.0
Depends: R (>= 4.2), SummarizedExperiment
Imports: Matrix, purrr, rafalib
Suggests: BiocFileCache, BiocStyle, dplyr, ggplot2, knitr, pheatmap,
sessioninfo, RefManageR, rmarkdown, testthat (>= 3.0.0),
tibble, tidyr, SingleCellExperiment
License: Artistic-2.0
Archs: x64
MD5sum: bf6d790a2f7175356f180b0126664d8b
NeedsCompilation: no
Title: Tools for finding Total RNA Expression Genes in single nucleus
RNA-seq data
Description: RNA abundance and cell size parameters could improve
RNA-seq deconvolution algorithms to more accurately estimate
cell type proportions given the different cell type
transcription activity levels. A Total RNA Expression Gene
(TREG) can facilitate estimating total RNA content using single
molecule fluorescent in situ hybridization (smFISH). We
developed a data-driven approach using a measure of expression
invariance to find candidate TREGs in postmortem human brain
single nucleus RNA-seq. This R package implements the method
for identifying candidate TREGs from snRNA-seq data.
biocViews: Software, SingleCell, RNASeq, GeneExpression,
Transcriptomics, Transcription, Sequencing
Author: Louise Huuki-Myers [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5148-3602>), Leonardo
Collado-Torres [ctb] (ORCID:
<https://orcid.org/0000-0003-2140-308X>)
Maintainer: Louise Huuki-Myers <lahuuki@gmail.com>
URL: https://github.com/LieberInstitute/TREG,
http://research.libd.org/TREG/
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/t/TREG
git_url: https://git.bioconductor.org/packages/TREG
git_branch: devel
git_last_commit: 2c1d897
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TREG_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TREG_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TREG_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TREG_1.11.0.tgz
vignettes:
vignettes/TREG/inst/doc/finding_Total_RNA_Expression_Genes.html
vignetteTitles: How to find Total RNA Expression Genes (TREGs)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TREG/inst/doc/finding_Total_RNA_Expression_Genes.R
dependencyCount: 46
Package: Trendy
Version: 1.29.0
Depends: R (>= 3.4)
Imports: stats, utils, graphics, grDevices, segmented, gplots,
parallel, magrittr, BiocParallel, DT, S4Vectors,
SummarizedExperiment, methods, shiny, shinyFiles
Suggests: BiocStyle, knitr, rmarkdown, devtools
License: GPL-3
MD5sum: 257d938733f1bfdb35775061a4fa0160
NeedsCompilation: no
Title: Breakpoint analysis of time-course expression data
Description: Trendy implements segmented (or breakpoint) regression
models to estimate breakpoints which represent changes in
expression for each feature/gene in high throughput data with
ordered conditions.
biocViews: TimeCourse, RNASeq, Regression, ImmunoOncology
Author: Rhonda Bacher and Ning Leng
Maintainer: Rhonda Bacher <rbacher@ufl.edu>
URL: https://github.com/rhondabacher/Trendy
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Trendy
git_branch: devel
git_last_commit: e2f06d3
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Trendy_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Trendy_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Trendy_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Trendy_1.29.0.tgz
vignettes: vignettes/Trendy/inst/doc/Trendy_vignette.pdf
vignetteTitles: Trendy Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Trendy/inst/doc/Trendy_vignette.R
dependencyCount: 98
Package: TRESS
Version: 1.13.0
Depends: R (>= 4.1.0), parallel, S4Vectors
Imports: utils, rtracklayer, Matrix, matrixStats, stats, methods,
graphics, GenomicRanges, GenomicFeatures, IRanges, Rsamtools,
AnnotationDbi
Suggests: knitr, rmarkdown,BiocStyle
License: GPL-3 + file LICENSE
MD5sum: 4aab0f8c710dd33d1f13c335cbe75b43
NeedsCompilation: no
Title: Toolbox for mRNA epigenetics sequencing analysis
Description: This package is devoted to analyzing MeRIP-seq data.
Current functionalities include 1. detect transcriptome wide
m6A methylation regions 2. detect transcriptome wide
differential m6A methylation regions.
biocViews: Epigenetics, RNASeq, PeakDetection, DifferentialMethylation
Author: Zhenxing Guo [aut, cre], Hao Wu [ctb]
Maintainer: Zhenxing Guo <guozhenxing@cuhk.edu.cn>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TRESS
git_branch: devel
git_last_commit: a40c256
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TRESS_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TRESS_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TRESS_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TRESS_1.13.0.tgz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
importsMe: magpie
dependencyCount: 77
Package: tricycle
Version: 1.15.0
Depends: R (>= 4.0), SingleCellExperiment
Imports: methods, circular, ggplot2, ggnewscale, AnnotationDbi, scater,
GenomicRanges, IRanges, S4Vectors, scattermore, dplyr,
RColorBrewer, grDevices, stats, SummarizedExperiment, utils
Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown, CircStats,
cowplot, htmltools, Seurat, org.Hs.eg.db, org.Mm.eg.db
License: GPL-3
MD5sum: 956ec285d4cf7351db8410fb0deefb0b
NeedsCompilation: no
Title: tricycle: Transferable Representation and Inference of cell
cycle
Description: The package contains functions to infer and visualize cell
cycle process using Single Cell RNASeq data. It exploits the
idea of transfer learning, projecting new data to the previous
learned biologically interpretable space. We provide a
pre-learned cell cycle space, which could be used to infer cell
cycle time of human and mouse single cell samples. In addition,
we also offer functions to visualize cell cycle time on
different embeddings and functions to build new reference.
biocViews: SingleCell, Software, Transcriptomics, RNASeq,
Transcription, BiologicalQuestion, DimensionReduction,
ImmunoOncology
Author: Shijie Zheng [aut, cre]
Maintainer: Shijie Zheng <shijieczheng@gmail.com>
URL: https://github.com/hansenlab/tricycle
VignetteBuilder: knitr
BugReports: https://github.com/hansenlab/tricycle/issues
git_url: https://git.bioconductor.org/packages/tricycle
git_branch: devel
git_last_commit: f801f57
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tricycle_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tricycle_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tricycle_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tricycle_1.15.0.tgz
vignettes: vignettes/tricycle/inst/doc/tricycle.html
vignetteTitles: tricycle: Transferable Representation and Inference of
Cell Cycle
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tricycle/inst/doc/tricycle.R
dependencyCount: 126
Package: TrIdent
Version: 0.99.4
Depends: R (>= 4.2.0)
Imports: graphics, utils, stats, dplyr, ggplot2, patchwork, stringr,
tidyr, roll
Suggests: BiocStyle, knitr, rmarkdown, kableExtra, testthat (>= 3.0.0)
License: GPL-2
MD5sum: 21d7fd3889dc6ab69f71f12dab406306
NeedsCompilation: no
Title: TrIdent - Transduction Identification
Description: The `TrIdent` R package automates the analysis of
transductomics data by detecting, classifying, and
characterizing read coverage patterns associated with potential
transduction events. Transductomics is a DNA sequencing-based
method for the detection and characterization of transduction
events in pure cultures and complex communities. Transductomics
relies on mapping sequencing reads from a viral-like particle
(VLP)-fraction of a sample to contigs assembled from the
metagenome (whole-community) of the same sample. Reads from
bacterial DNA carried by VLPs will map back to the bacterial
contigs of origin creating read coverage patterns indicative of
ongoing transduction.
biocViews: Coverage, Metagenomics, PatternLogic, Classification,
Sequencing
Author: Jessie Maier [aut, cre] (ORCID:
<https://orcid.org/0009-0001-8575-5386>), Jorden Rabasco [aut,
ctb] (ORCID: <https://orcid.org/0000-0002-6971-6678>), Craig
Gin [aut] (ORCID: <https://orcid.org/0000-0002-7447-663X>),
Benjamin Callahan [aut] (ORCID:
<https://orcid.org/0000-0002-8752-117X>), Manuel Kleiner [aut,
ths] (ORCID: <https://orcid.org/0000-0001-6904-0287>)
Maintainer: Jessie Maier <jlmaier@ncsu.edu>
URL: https://github.com/jlmaier12/TrIdent,
https://jlmaier12.github.io/TrIdent/
VignetteBuilder: knitr
BugReports: https://github.com/jlmaier12/TrIdent/issues
git_url: https://git.bioconductor.org/packages/TrIdent
git_branch: devel
git_last_commit: 2818f6b
git_last_commit_date: 2025-03-03
Date/Publication: 2025-03-03
source.ver: src/contrib/TrIdent_0.99.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TrIdent_0.99.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TrIdent_0.99.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TrIdent_0.99.4.tgz
vignettes: vignettes/TrIdent/inst/doc/TrIdent-vignette.html
vignetteTitles: TrIdent
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TrIdent/inst/doc/TrIdent-vignette.R
dependencyCount: 49
Package: trigger
Version: 1.53.0
Depends: R (>= 2.14.0), corpcor, qtl
Imports: qvalue, methods, graphics, sva
License: GPL-3
MD5sum: 81d8b5dcc872c3fc9ea73b3f22b8c546
NeedsCompilation: yes
Title: Transcriptional Regulatory Inference from Genetics of Gene
ExpRession
Description: This R package provides tools for the statistical analysis
of integrative genomic data that involve some combination of:
genotypes, high-dimensional intermediate traits (e.g., gene
expression, protein abundance), and higher-order traits
(phenotypes). The package includes functions to: (1) construct
global linkage maps between genetic markers and gene
expression; (2) analyze multiple-locus linkage (epistasis) for
gene expression; (3) quantify the proportion of genome-wide
variation explained by each locus and identify eQTL hotspots;
(4) estimate pair-wise causal gene regulatory probabilities and
construct gene regulatory networks; and (5) identify causal
genes for a quantitative trait of interest.
biocViews: GeneExpression, SNP, GeneticVariability, Microarray,
Genetics
Author: Lin S. Chen <lchen@health.bsd.uchicago.edu>, Dipen P.
Sangurdekar <dps@genomics.princeton.edu> and John D. Storey
<jstorey@princeton.edu>
Maintainer: John D. Storey <jstorey@princeton.edu>
git_url: https://git.bioconductor.org/packages/trigger
git_branch: devel
git_last_commit: 4da3165
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/trigger_1.53.0.tar.gz
vignettes: vignettes/trigger/inst/doc/trigger.pdf
vignetteTitles: Trigger Tutorial
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/trigger/inst/doc/trigger.R
dependencyCount: 97
Package: trio
Version: 3.45.2
Depends: R (>= 3.0.1)
Imports: grDevices, graphics, methods, stats, survival, utils,
siggenes, LogicReg (>= 1.6.1), data.table
Suggests: haplo.stats, mcbiopi, splines, logicFS (>= 1.28.1),
KernSmooth, VariantAnnotation
License: LGPL-2
MD5sum: 41b9c04e588e11ebafb6d76788b2bae9
NeedsCompilation: no
Title: Testing of SNPs and SNP Interactions in Case-Parent Trio Studies
Description: Testing SNPs and SNP interactions with a genotypic TDT.
This package furthermore contains functions for computing
pairwise values of LD measures and for identifying LD blocks,
as well as functions for setting up matched case pseudo-control
genotype data for case-parent trios in order to run trio logic
regression, for imputing missing genotypes in trios, for
simulating case-parent trios with disease risk dependent on SNP
interaction, and for power and sample size calculation in trio
data.
biocViews: SNP, GeneticVariability, Microarray, Genetics
Author: Holger Schwender, Qing Li, Philipp Berger, Christoph Neumann,
Margaret Taub, Ingo Ruczinski
Maintainer: Holger Schwender <holger.schw@gmx.de>
git_url: https://git.bioconductor.org/packages/trio
git_branch: devel
git_last_commit: e7425b1
git_last_commit_date: 2024-12-28
Date/Publication: 2024-12-29
source.ver: src/contrib/trio_3.45.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/trio_3.45.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/trio_3.45.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/trio_3.45.2.tgz
vignettes: vignettes/trio/inst/doc/trio.pdf
vignetteTitles: Trio Logic Regression and genotypic TDT
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/trio/inst/doc/trio.R
dependencyCount: 20
Package: triplex
Version: 1.47.0
Depends: R (>= 2.15.0), S4Vectors (>= 0.5.14), IRanges (>= 2.5.27),
XVector (>= 0.11.6), Biostrings (>= 2.39.10)
Imports: methods, grid, GenomicRanges
LinkingTo: S4Vectors, IRanges, XVector, Biostrings
Suggests: rgl (>= 0.93.932), BSgenome.Celegans.UCSC.ce10, rtracklayer
License: BSD_2_clause + file LICENSE
MD5sum: 1ffb2b76980d2326292913d628ab1f06
NeedsCompilation: yes
Title: Search and visualize intramolecular triplex-forming sequences in
DNA
Description: This package provides functions for identification and
visualization of potential intramolecular triplex patterns in
DNA sequence. The main functionality is to detect the positions
of subsequences capable of folding into an intramolecular
triplex (H-DNA) in a much larger sequence. The potential H-DNA
(triplexes) should be made of as many cannonical nucleotide
triplets as possible. The package includes visualization
showing the exact base-pairing in 1D, 2D or 3D.
biocViews: SequenceMatching, GeneRegulation
Author: Jiri Hon, Matej Lexa, Tomas Martinek and Kamil Rajdl with
contributions from Daniel Kopecek
Maintainer: Jiri Hon <jiri.hon@gmail.com>
URL: http://www.fi.muni.cz/~lexa/triplex/
git_url: https://git.bioconductor.org/packages/triplex
git_branch: devel
git_last_commit: cc51819
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/triplex_1.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/triplex_1.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/triplex_1.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/triplex_1.47.0.tgz
vignettes: vignettes/triplex/inst/doc/triplex.pdf
vignetteTitles: Triplex User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/triplex/inst/doc/triplex.R
dependencyCount: 27
Package: tripr
Version: 1.13.0
Depends: shiny (>= 1.6.0), shinyBS
Imports: shinyjs, shinyFiles, plyr, data.table, DT, stringr,
stringdist, plot3D, gridExtra, RColorBrewer, plotly, dplyr,
config (>= 0.3.1), golem (>= 0.3.1), methods, grDevices,
graphics, stats, utils, vegan
Suggests: BiocGenerics, shinycssloaders, tidyverse, BiocManager,
Biostrings, xtable, rlist, motifStack, knitr, rmarkdown,
testthat (>= 3.0.0), fs, BiocStyle, RefManageR, biocthis, pryr
Enhances: parallel
License: MIT + file LICENSE
MD5sum: f548589ab9bb93b86346363c236bce31
NeedsCompilation: no
Title: T-cell Receptor/Immunoglobulin Profiler (TRIP)
Description: TRIP is a software framework that provides analytics
services on antigen receptor (B cell receptor immunoglobulin,
BcR IG | T cell receptor, TR) gene sequence data. It is a web
application written in R Shiny. It takes as input the output
files of the IMGT/HighV-Quest tool. Users can select to analyze
the data from each of the input samples separately, or the
combined data files from all samples and visualize the results
accordingly.
biocViews: BatchEffect, MultipleComparison, GeneExpression,
ImmunoOncology, TargetedResequencing
Author: Maria Th. Kotouza [aut], Katerina Gemenetzi [aut], Chrysi
Galigalidou [aut], Elisavet Vlachonikola [aut], Nikolaos
Pechlivanis [cre], Andreas Agathangelidis [aut], Raphael
Sandaltzopoulos [aut], Pericles A. Mitkas [aut], Kostas
Stamatopoulos [aut], Anastasia Chatzidimitriou [aut], Fotis E.
Psomopoulos [aut], Iason Ofeidis [aut], Aspasia Orfanou [aut]
Maintainer: Nikolaos Pechlivanis <inab.bioinformatics@lists.certh.gr>
URL: https://github.com/BiodataAnalysisGroup/tripr
VignetteBuilder: knitr
BugReports: https://github.com/BiodataAnalysisGroup/tripr/issues
git_url: https://git.bioconductor.org/packages/tripr
git_branch: devel
git_last_commit: 138f3c9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tripr_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tripr_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tripr_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tripr_1.13.0.tgz
vignettes: vignettes/tripr/inst/doc/tripr_guide.html
vignetteTitles: tripr User Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/tripr/inst/doc/tripr_guide.R
dependencyCount: 103
Package: tRNA
Version: 1.25.0
Depends: R (>= 3.5), GenomicRanges, Structstrings
Imports: stringr, S4Vectors, methods, BiocGenerics, IRanges, XVector,
Biostrings, Modstrings, ggplot2, scales
Suggests: knitr, rmarkdown, testthat, BiocStyle, tRNAscanImport
License: GPL-3 + file LICENSE
MD5sum: f9018a13693262425de16f80242e4535
NeedsCompilation: no
Title: Analyzing tRNA sequences and structures
Description: The tRNA package allows tRNA sequences and structures to
be accessed and used for subsetting. In addition, it provides
visualization tools to compare feature parameters of multiple
tRNA sets and correlate them to additional data. The tRNA
package uses GRanges objects as inputs requiring only few
additional column data sets.
biocViews: Software, Visualization
Author: Felix GM Ernst [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5064-0928>)
Maintainer: Felix GM Ernst <felix.gm.ernst@outlook.com>
VignetteBuilder: knitr
BugReports: https://github.com/FelixErnst/tRNA/issues
git_url: https://git.bioconductor.org/packages/tRNA
git_branch: devel
git_last_commit: 41b4ba9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tRNA_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tRNA_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tRNA_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tRNA_1.25.0.tgz
vignettes: vignettes/tRNA/inst/doc/tRNA.html
vignetteTitles: tRNA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/tRNA/inst/doc/tRNA.R
dependsOnMe: tRNAdbImport, tRNAscanImport
dependencyCount: 59
Package: tRNAdbImport
Version: 1.25.0
Depends: R (>= 3.6), GenomicRanges, Modstrings, Structstrings, tRNA
Imports: Biostrings, stringr, httr2, xml2, S4Vectors, methods, IRanges,
utils
Suggests: BiocGenerics, knitr, rmarkdown, testthat, httptest,
BiocStyle, rtracklayer
License: GPL-3 + file LICENSE
MD5sum: 40097ce34d60a04af468d70d8af48d31
NeedsCompilation: no
Title: Importing from tRNAdb and mitotRNAdb as GRanges objects
Description: tRNAdbImport imports the entries of the tRNAdb and mtRNAdb
(http://trna.bioinf.uni-leipzig.de) as GRanges object.
biocViews: Software, Visualization, DataImport
Author: Felix G.M. Ernst [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5064-0928>)
Maintainer: Felix G.M. Ernst <felix.gm.ernst@outlook.com>
VignetteBuilder: knitr
BugReports: https://github.com/FelixErnst/tRNAdbImport/issues
git_url: https://git.bioconductor.org/packages/tRNAdbImport
git_branch: devel
git_last_commit: 6e54ea5
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tRNAdbImport_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tRNAdbImport_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tRNAdbImport_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tRNAdbImport_1.25.0.tgz
vignettes: vignettes/tRNAdbImport/inst/doc/tRNAdbImport.html
vignetteTitles: tRNAdbImport
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/tRNAdbImport/inst/doc/tRNAdbImport.R
importsMe: EpiTxDb
dependencyCount: 63
Package: tRNAscanImport
Version: 1.27.0
Depends: R (>= 3.5), GenomicRanges, tRNA
Imports: methods, stringr, BiocGenerics, Biostrings, Structstrings,
S4Vectors, IRanges, XVector, GenomeInfoDb, rtracklayer,
BSgenome, Rsamtools
Suggests: BiocStyle, knitr, rmarkdown, testthat, ggplot2,
BSgenome.Scerevisiae.UCSC.sacCer3
License: GPL-3 + file LICENSE
MD5sum: b73f42b07666e33324a70b30fb1b58ad
NeedsCompilation: no
Title: Importing a tRNAscan-SE result file as GRanges object
Description: The package imports the result of tRNAscan-SE as a GRanges
object.
biocViews: Software, DataImport, WorkflowStep, Preprocessing,
Visualization
Author: Felix G.M. Ernst [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5064-0928>)
Maintainer: Felix G.M. Ernst <felix.gm.ernst@outlook.com>
URL: https://github.com/FelixErnst/tRNAscanImport
VignetteBuilder: knitr
BugReports: https://github.com/FelixErnst/tRNAscanImport/issues
git_url: https://git.bioconductor.org/packages/tRNAscanImport
git_branch: devel
git_last_commit: 5910f73
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tRNAscanImport_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tRNAscanImport_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tRNAscanImport_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tRNAscanImport_1.27.0.tgz
vignettes: vignettes/tRNAscanImport/inst/doc/tRNAscanImport.html
vignetteTitles: tRNAscanImport
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/tRNAscanImport/inst/doc/tRNAscanImport.R
suggestsMe: Structstrings, tRNA
dependencyCount: 90
Package: TRONCO
Version: 2.39.2
Depends: R (>= 4.1.0),
Imports: bnlearn, Rgraphviz, gtools, parallel, foreach, doParallel,
iterators, RColorBrewer, circlize, igraph, grid, gridExtra,
xtable, gtable, scales, R.matlab, grDevices, graphics, stats,
utils, methods
Suggests: BiocGenerics, BiocStyle, testthat, knitr, rWikiPathways,
magick
License: GPL-3
MD5sum: 3956032e1b51d2868dce32bb357b57dc
NeedsCompilation: no
Title: TRONCO, an R package for TRanslational ONCOlogy
Description: The TRONCO (TRanslational ONCOlogy) R package collects
algorithms to infer progression models via the approach of
Suppes-Bayes Causal Network, both from an ensemble of tumors
(cross-sectional samples) and within an individual patient
(multi-region or single-cell samples). The package provides
parallel implementation of algorithms that process binary
matrices where each row represents a tumor sample and each
column a single-nucleotide or a structural variant driving the
progression; a 0/1 value models the absence/presence of that
alteration in the sample. The tool can import data from plain,
MAF or GISTIC format files, and can fetch it from the
cBioPortal for cancer genomics. Functions for data manipulation
and visualization are provided, as well as functions to
import/export such data to other bioinformatics tools for, e.g,
clustering or detection of mutually exclusive alterations.
Inferred models can be visualized and tested for their
confidence via bootstrap and cross-validation. TRONCO is used
for the implementation of the Pipeline for Cancer Inference
(PICNIC).
biocViews: BiomedicalInformatics, Bayesian, GraphAndNetwork,
SomaticMutation, NetworkInference, Network, Clustering,
DataImport, SingleCell, ImmunoOncology
Author: Marco Antoniotti [ctb], Giulio Caravagna [aut], Luca De Sano
[cre, aut] (ORCID: <https://orcid.org/0000-0002-9618-3774>),
Alex Graudenzi [aut], Giancarlo Mauri [ctb], Bud Mishra [ctb],
Daniele Ramazzotti [aut] (ORCID:
<https://orcid.org/0000-0002-6087-2666>)
Maintainer: Luca De Sano <luca.desano@gmail.com>
URL: https://sites.google.com/site/troncopackage/
VignetteBuilder: knitr
BugReports: https://github.com/BIMIB-DISCo/TRONCO
git_url: https://git.bioconductor.org/packages/TRONCO
git_branch: devel
git_last_commit: 3b6f104
git_last_commit_date: 2025-03-26
Date/Publication: 2025-03-27
source.ver: src/contrib/TRONCO_2.39.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TRONCO_2.39.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TRONCO_2.39.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TRONCO_2.39.2.tgz
vignettes: vignettes/TRONCO/inst/doc/f1_introduction.html,
vignettes/TRONCO/inst/doc/f2_loading_data.html,
vignettes/TRONCO/inst/doc/f3_data_visualization.html,
vignettes/TRONCO/inst/doc/f4_data_manipulation.html,
vignettes/TRONCO/inst/doc/f5_model_inference.html,
vignettes/TRONCO/inst/doc/f6_post_reconstruction.html,
vignettes/TRONCO/inst/doc/f7_import_export.html
vignetteTitles: f1_introduction.html, Loading data, Data visualization,
Data manipulation, Model inference, Post reconstruction,
Import/export from other tools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TRONCO/inst/doc/f1_introduction.R,
vignettes/TRONCO/inst/doc/f2_loading_data.R,
vignettes/TRONCO/inst/doc/f3_data_visualization.R,
vignettes/TRONCO/inst/doc/f4_data_manipulation.R,
vignettes/TRONCO/inst/doc/f5_model_inference.R,
vignettes/TRONCO/inst/doc/f6_post_reconstruction.R,
vignettes/TRONCO/inst/doc/f7_import_export.R
dependencyCount: 48
Package: TSAR
Version: 1.5.0
Depends: R (>= 4.3.0)
Imports: dplyr (>= 1.0.7), ggplot2 (>= 3.3.5), ggpubr (>= 0.4.0),
magrittr (>= 2.0.3), mgcv (>= 1.8.38), readxl (>= 1.4.0),
stringr (>= 1.4.0), tidyr (>= 1.1.4), utils (>= 4.3.1), shiny
(>= 1.7.4.1), plotly (>= 4.10.2), shinyjs (>= 2.1.0), jsonlite
(>= 1.8.7), rhandsontable (>= 0.3.8), openxlsx (>= 4.2.5.2),
shinyWidgets (>= 0.7.6), minpack.lm (>= 1.2.3)
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
License: AGPL-3
MD5sum: 0157e88a25bb052ffcb1dbbe0a174709
NeedsCompilation: no
Title: Thermal Shift Analysis in R
Description: This package automates analysis workflow for Thermal Shift
Analysis (TSA) data. Processing, analyzing, and visualizing
data through both shiny applications and command lines. Package
aims to simplify data analysis and offer front to end workflow,
from raw data to multiple trial analysis.
biocViews: Software, ShinyApps, Visualization, qPCR
Author: Xinlin Gao [aut, cre] (ORCID:
<https://orcid.org/0009-0002-2518-235X>), William M. McFadden
[aut, fnd] (ORCID: <https://orcid.org/0000-0001-6911-2172>),
Stefan G. Sarafianos [fnd, aut, ths] (ORCID:
<https://orcid.org/0000-0002-5840-154X>)
Maintainer: Xinlin Gao <candygao2015@outlook.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TSAR
git_branch: devel
git_last_commit: c609932
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TSAR_1.5.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TSAR_1.5.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TSAR_1.5.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TSAR_1.5.0.tgz
vignettes: vignettes/TSAR/inst/doc/FAQ_assistance.html,
vignettes/TSAR/inst/doc/TSAR_Package_Structure.html,
vignettes/TSAR/inst/doc/TSAR_Workflow_by_Command.html,
vignettes/TSAR/inst/doc/TSAR_Workflow_by_Shiny.html
vignetteTitles: Frequently Asked Questions, TSAR Package Structure,
TSAR Workflow by Command, TSAR Workflow by Shiny
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TSAR/inst/doc/FAQ_assistance.R,
vignettes/TSAR/inst/doc/TSAR_Package_Structure.R,
vignettes/TSAR/inst/doc/TSAR_Workflow_by_Command.R,
vignettes/TSAR/inst/doc/TSAR_Workflow_by_Shiny.R
dependencyCount: 131
Package: TSCAN
Version: 1.45.0
Depends: R (>= 4.4.0), SingleCellExperiment, TrajectoryUtils
Imports: ggplot2, shiny, plyr, grid, fastICA, igraph, combinat, mgcv,
mclust, gplots, methods, stats, Matrix, SummarizedExperiment,
SparseArray (>= 1.5.23), DelayedArray (>= 0.31.9), S4Vectors
Suggests: knitr, testthat, scuttle, scran, metapod, BiocParallel,
BiocNeighbors, batchelor
License: GPL(>=2)
MD5sum: 69e623ecb162c79af3f6db86f06c06e6
NeedsCompilation: no
Title: Tools for Single-Cell Analysis
Description: Provides methods to perform trajectory analysis based on a
minimum spanning tree constructed from cluster centroids.
Computes pseudotemporal cell orderings by mapping cells in each
cluster (or new cells) to the closest edge in the tree. Uses
linear modelling to identify differentially expressed genes
along each path through the tree. Several plotting and
interactive visualization functions are also implemented.
biocViews: GeneExpression, Visualization, GUI
Author: Zhicheng Ji [aut, cre], Hongkai Ji [aut], Aaron Lun [ctb]
Maintainer: Zhicheng Ji <zji4@jhu.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/TSCAN
git_branch: devel
git_last_commit: a82164a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TSCAN_1.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TSCAN_1.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TSCAN_1.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TSCAN_1.45.0.tgz
vignettes: vignettes/TSCAN/inst/doc/TSCAN.pdf
vignetteTitles: TSCAN: Tools for Single-Cell ANalysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TSCAN/inst/doc/TSCAN.R
dependsOnMe: OSCA.multisample
importsMe: FEAST, singleCellTK, DIscBIO
suggestsMe: condiments
dependencyCount: 95
Package: ttgsea
Version: 1.15.0
Depends: keras
Imports: tm, text2vec, tokenizers, textstem, stopwords, data.table,
purrr, DiagrammeR, stats
Suggests: fgsea, knitr, testthat, reticulate, rmarkdown
License: Artistic-2.0
Archs: x64
MD5sum: 285055aa72511e4102029ff531df9b33
NeedsCompilation: no
Title: Tokenizing Text of Gene Set Enrichment Analysis
Description: Functional enrichment analysis methods such as gene set
enrichment analysis (GSEA) have been widely used for analyzing
gene expression data. GSEA is a powerful method to infer
results of gene expression data at a level of gene sets by
calculating enrichment scores for predefined sets of genes.
GSEA depends on the availability and accuracy of gene sets.
There are overlaps between terms of gene sets or categories
because multiple terms may exist for a single biological
process, and it can thus lead to redundancy within enriched
terms. In other words, the sets of related terms are
overlapping. Using deep learning, this pakage is aimed to
predict enrichment scores for unique tokens or words from text
in names of gene sets to resolve this overlapping set issue.
Furthermore, we can coin a new term by combining tokens and
find its enrichment score by predicting such a combined tokens.
biocViews: Software, GeneExpression, GeneSetEnrichment
Author: Dongmin Jung [cre, aut] (ORCID:
<https://orcid.org/0000-0001-7499-8422>)
Maintainer: Dongmin Jung <dmdmjung@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ttgsea
git_branch: devel
git_last_commit: 603fe2d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ttgsea_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ttgsea_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ttgsea_1.15.0.tgz
vignettes: vignettes/ttgsea/inst/doc/ttgsea.html
vignetteTitles: ttgsea
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ttgsea/inst/doc/ttgsea.R
importsMe: DeepPINCS, GenProSeq
dependencyCount: 125
Package: TTMap
Version: 1.29.0
Depends: rgl, colorRamps
Imports: grDevices,graphics,stats,utils, methods, SummarizedExperiment,
Biobase
Suggests: BiocStyle, airway
License: GPL-2
MD5sum: 22daaf962871e1d7b01f88eb66c398ce
NeedsCompilation: no
Title: Two-Tier Mapper: a clustering tool based on topological data
analysis
Description: TTMap is a clustering method that groups together samples
with the same deviation in comparison to a control group. It is
specially useful when the data is small. It is parameter free.
biocViews: Software, Microarray, DifferentialExpression,
MultipleComparison, Clustering, Classification
Author: Rachel Jeitziner
Maintainer: Rachel Jeitziner <rachel.jeitziner@epfl.ch>
git_url: https://git.bioconductor.org/packages/TTMap
git_branch: devel
git_last_commit: f7d844b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TTMap_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TTMap_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TTMap_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TTMap_1.29.0.tgz
vignettes: vignettes/TTMap/inst/doc/TTMap.pdf
vignetteTitles: Manual for the TTMap library
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TTMap/inst/doc/TTMap.R
dependencyCount: 63
Package: TurboNorm
Version: 1.55.0
Depends: R (>= 2.12.0), convert, limma (>= 1.7.0), marray
Imports: stats, grDevices, affy, lattice
Suggests: BiocStyle, affydata, hgu95av2cdf
License: LGPL
MD5sum: 065d87e0c94d41b691e44e10d5290e03
NeedsCompilation: yes
Title: A fast scatterplot smoother suitable for microarray
normalization
Description: A fast scatterplot smoother based on B-splines with
second-order difference penalty. Functions for microarray
normalization of single-colour data i.e. Affymetrix/Illumina
and two-colour data supplied as marray MarrayRaw-objects or
limma RGList-objects are available.
biocViews: Microarray, OneChannel, TwoChannel, Preprocessing,
DNAMethylation, CpGIsland, MethylationArray, Normalization
Author: Maarten van Iterson and Chantal van Leeuwen
Maintainer: Maarten van Iterson <mviterson@gmail.com>
URL: http://www.humgen.nl/MicroarrayAnalysisGroup.html
git_url: https://git.bioconductor.org/packages/TurboNorm
git_branch: devel
git_last_commit: 0c41566
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TurboNorm_1.55.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/TurboNorm_1.55.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TurboNorm_1.55.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TurboNorm_1.55.0.tgz
vignettes: vignettes/TurboNorm/inst/doc/turbonorm.pdf
vignetteTitles: TurboNorm Overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TurboNorm/inst/doc/turbonorm.R
dependencyCount: 18
Package: TVTB
Version: 1.33.0
Depends: R (>= 3.4), methods, utils, stats
Imports: AnnotationFilter, BiocGenerics (>= 0.25.1), BiocParallel,
Biostrings, ensembldb, GenomeInfoDb, GenomicRanges, GGally,
ggplot2, Gviz, limma, IRanges (>= 2.21.6), reshape2, Rsamtools,
S4Vectors (>= 0.25.14), SummarizedExperiment, VariantAnnotation
(>= 1.19.9)
Suggests: EnsDb.Hsapiens.v75 (>= 0.99.7), shiny (>= 0.13.2.9005), DT
(>= 0.1.67), rtracklayer, BiocStyle (>= 2.5.19), knitr (>=
1.12), rmarkdown, testthat, covr, pander
License: Artistic-2.0
MD5sum: 567ae04c945bc0d7d6599c24750e5a24
NeedsCompilation: no
Title: TVTB: The VCF Tool Box
Description: The package provides S4 classes and methods to filter,
summarise and visualise genetic variation data stored in VCF
files. In particular, the package extends the FilterRules class
(S4Vectors package) to define news classes of filter rules
applicable to the various slots of VCF objects. Functionalities
are integrated and demonstrated in a Shiny web-application, the
Shiny Variant Explorer (tSVE).
biocViews: Software, Genetics, GeneticVariability, GenomicVariation,
DataRepresentation, GUI, Genetics, DNASeq, WholeGenome,
Visualization, MultipleComparison, DataImport,
VariantAnnotation, Sequencing, Coverage, Alignment,
SequenceMatching
Author: Kevin Rue-Albrecht [aut, cre]
Maintainer: Kevin Rue-Albrecht <kevinrue67@gmail.com>
URL: https://github.com/kevinrue/TVTB
VignetteBuilder: knitr
BugReports: https://github.com/kevinrue/TVTB/issues
git_url: https://git.bioconductor.org/packages/TVTB
git_branch: devel
git_last_commit: 9b5085b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/TVTB_1.33.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TVTB_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TVTB_1.33.0.tgz
vignettes: vignettes/TVTB/inst/doc/Introduction.html,
vignettes/TVTB/inst/doc/tSVE.html,
vignettes/TVTB/inst/doc/VcfFilterRules.html
vignetteTitles: Introduction to TVTB, The Shiny Variant Explorer, VCF
filter rules
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/TVTB/inst/doc/Introduction.R,
vignettes/TVTB/inst/doc/tSVE.R,
vignettes/TVTB/inst/doc/VcfFilterRules.R
dependencyCount: 164
Package: tweeDEseq
Version: 1.53.0
Depends: R (>= 4.3.0)
Imports: Rcpp (>= 1.0.10), MASS, limma, edgeR, parallel, cqn,
grDevices, graphics, stats, utils
LinkingTo: Rcpp
Suggests: tweeDEseqCountData, xtable
License: GPL (>= 2)
MD5sum: 1021653b4898869fac7e47200e22c466
NeedsCompilation: yes
Title: RNA-seq data analysis using the Poisson-Tweedie family of
distributions
Description: Differential expression analysis of RNA-seq using the
Poisson-Tweedie (PT) family of distributions. PT distributions
are described by a mean, a dispersion and a shape parameter and
include Poisson and NB distributions, among others, as
particular cases. An important feature of this family is that,
while the Negative Binomial (NB) distribution only allows a
quadratic mean-variance relationship, the PT distributions
generalizes this relationship to any orde.
biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression,
Sequencing, RNASeq, DNASeq
Author: Dolors Pelegri-Siso [aut, cre] (ORCID:
<https://orcid.org/0000-0002-5993-3003>), Juan R. Gonzalez
[aut] (ORCID: <https://orcid.org/0000-0003-3267-2146>), Mikel
Esnaola [aut], Robert Castelo [aut]
Maintainer: Dolors Pelegri-Siso <dolors.pelegri@isglobal.org>
URL: https://github.com/isglobal-brge/tweeDEseq/
BugReports: https://github.com/isglobal-brge/tweeDEseq/issues
git_url: https://git.bioconductor.org/packages/tweeDEseq
git_branch: devel
git_last_commit: 3a20c3b
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tweeDEseq_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tweeDEseq_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tweeDEseq_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tweeDEseq_1.53.0.tgz
vignettes: vignettes/tweeDEseq/inst/doc/tweeDEseq.pdf
vignetteTitles: tweeDEseq: analysis of RNA-seq data using the
Poisson-Tweedie family of distributions
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tweeDEseq/inst/doc/tweeDEseq.R
importsMe: ptmixed
dependencyCount: 23
Package: twilight
Version: 1.83.9
Depends: R (>= 2.10)
Imports: Biobase, graphics, grDevices, splines, stats
Suggests: golubEsets (>= 1.4.2), vsn (>= 1.7.2)
License: GPL (>= 2)
MD5sum: 42a8399470e51f5ff8c034a33094ba0c
NeedsCompilation: yes
Title: Estimation of local false discovery rate
Description: In a typical microarray setting with gene expression data
observed under two conditions, the local false discovery rate
describes the probability that a gene is not differentially
expressed between the two conditions given its corrresponding
observed score or p-value level. The resulting curve of
p-values versus local false discovery rate offers an insight
into the twilight zone between clear differential and clear
non-differential gene expression. Package 'twilight' contains
two main functions: Function twilight.pval performs a
two-condition test on differences in means for a given input
matrix or expression set and computes permutation based
p-values. Function twilight performs a stochastic downhill
search to estimate local false discovery rates and effect size
distributions. The package further provides means to filter for
permutations that describe the null distribution correctly.
Using filtered permutations, the influence of hidden
confounders could be diminished.
biocViews: Microarray, DifferentialExpression, MultipleComparison
Author: Stefanie Senger [cre, aut] (ORCID:
<https://orcid.org/0000-0003-4144-1040>)
Maintainer: Stefanie Senger <stefanie.scheid@gmx.de>
URL: http://compdiag.molgen.mpg.de/software/twilight.shtml
git_url: https://git.bioconductor.org/packages/twilight
git_branch: devel
git_last_commit: 24163c6
git_last_commit_date: 2025-02-15
Date/Publication: 2025-02-16
source.ver: src/contrib/twilight_1.83.9.tar.gz
win.binary.ver: bin/windows/contrib/4.5/twilight_1.83.9.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/twilight_1.83.9.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/twilight_1.83.9.tgz
vignettes: vignettes/twilight/inst/doc/tr_2004_01.pdf
vignetteTitles: Estimation of Local False Discovery Rates
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/twilight/inst/doc/tr_2004_01.R
dependsOnMe: OrderedList
dependencyCount: 9
Package: twoddpcr
Version: 1.31.0
Depends: R (>= 3.4)
Imports: class, ggplot2, hexbin, methods, scales, shiny, stats, utils,
RColorBrewer, S4Vectors
Suggests: devtools, knitr, reshape2, rmarkdown, testthat, BiocStyle
License: GPL-3
MD5sum: 0e58a5c41d8f3c6db83a3b5260c39f48
NeedsCompilation: no
Title: Classify 2-d Droplet Digital PCR (ddPCR) data and quantify the
number of starting molecules
Description: The twoddpcr package takes Droplet Digital PCR (ddPCR)
droplet amplitude data from Bio-Rad's QuantaSoft and can
classify the droplets. A summary of the positive/negative
droplet counts can be generated, which can then be used to
estimate the number of molecules using the Poisson
distribution. This is the first open source package that
facilitates the automatic classification of general two channel
ddPCR data. Previous work includes 'definetherain' (Jones et
al., 2014) and 'ddpcRquant' (Trypsteen et al., 2015) which both
handle one channel ddPCR experiments only. The 'ddpcr' package
available on CRAN (Attali et al., 2016) supports automatic
gating of a specific class of two channel ddPCR experiments
only.
biocViews: ddPCR, Software, Classification
Author: Anthony Chiu [aut, cre]
Maintainer: Anthony Chiu <anthony@achiu.me>
URL: http://github.com/CRUKMI-ComputationalBiology/twoddpcr/
VignetteBuilder: knitr
BugReports:
http://github.com/CRUKMI-ComputationalBiology/twoddpcr/issues/
git_url: https://git.bioconductor.org/packages/twoddpcr
git_branch: devel
git_last_commit: c2e2c95
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/twoddpcr_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/twoddpcr_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/twoddpcr_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/twoddpcr_1.31.0.tgz
vignettes: vignettes/twoddpcr/inst/doc/twoddpcr.html
vignetteTitles: twoddpcr: A package for Droplet Digital PCR analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/twoddpcr/inst/doc/twoddpcr.R
dependencyCount: 65
Package: txcutr
Version: 1.13.0
Depends: R (>= 4.1.0)
Imports: AnnotationDbi, GenomicFeatures, txdbmaker, IRanges,
GenomicRanges, BiocGenerics, Biostrings, S4Vectors,
rtracklayer, BiocParallel, stats, methods, utils
Suggests: RefManageR, BiocStyle, knitr, sessioninfo, rmarkdown,
testthat (>= 3.0.0), TxDb.Scerevisiae.UCSC.sacCer3.sgdGene,
BSgenome.Scerevisiae.UCSC.sacCer3
License: GPL-3
MD5sum: ded857485f2031fd3f4d4852163161f3
NeedsCompilation: no
Title: Transcriptome CUTteR
Description: Various mRNA sequencing library preparation methods
generate sequencing reads specifically from the transcript
ends. Analyses that focus on quantification of isoform usage
from such data can be aided by using truncated versions of
transcriptome annotations, both at the alignment or
pseudo-alignment stage, as well as in downstream analysis. This
package implements some convenience methods for readily
generating such truncated annotations and their corresponding
sequences.
biocViews: Alignment, Annotation, RNASeq, Sequencing, Transcriptomics
Author: Mervin Fansler [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4108-4218>)
Maintainer: Mervin Fansler <fanslerm@mskcc.org>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/txcutr
git_branch: devel
git_last_commit: 68a3c9e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/txcutr_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/txcutr_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/txcutr_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/txcutr_1.13.0.tgz
vignettes: vignettes/txcutr/inst/doc/intro.html
vignetteTitles: Introduction to txcutr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/txcutr/inst/doc/intro.R
dependencyCount: 101
Package: txdbmaker
Version: 1.3.1
Depends: BiocGenerics, S4Vectors, GenomeInfoDb (>= 1.39.9),
GenomicRanges, GenomicFeatures
Imports: methods, utils, stats, tools, httr, rjson, DBI, RSQLite (>=
2.0), IRanges, UCSC.utils, AnnotationDbi, Biobase, BiocIO,
rtracklayer, biomaRt (>= 2.59.1)
Suggests: RMariaDB, mirbase.db, ensembldb, RUnit, BiocStyle, knitr
License: Artistic-2.0
MD5sum: 223bba60d510b0f57d6a1c8fb761bb18
NeedsCompilation: no
Title: Tools for making TxDb objects from genomic annotations
Description: A set of tools for making TxDb objects from genomic
annotations from various sources (e.g. UCSC, Ensembl, and GFF
files). These tools allow the user to download the genomic
locations of transcripts, exons, and CDS, for a given assembly,
and to import them in a TxDb object. TxDb objects are
implemented in the GenomicFeatures package, together with
flexible methods for extracting the desired features in
convenient formats.
biocViews: Infrastructure, DataImport, Annotation, GenomeAnnotation,
GenomeAssembly, Genetics, Sequencing
Author: H. Pagès [aut, cre], M. Carlson [aut], P. Aboyoun [aut], S.
Falcon [aut], M. Morgan [aut], R. Castelo [ctb], M. Lawrence
[ctb], J. MacDonald [ctb], M. Ramos [ctb], S. Saini [ctb], L.
Shepherd [ctb]
Maintainer: H. Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/txdbmaker
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/txdbmaker/issues
git_url: https://git.bioconductor.org/packages/txdbmaker
git_branch: devel
git_last_commit: 05d278f
git_last_commit_date: 2024-11-21
Date/Publication: 2024-11-22
source.ver: src/contrib/txdbmaker_1.3.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/txdbmaker_1.3.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/txdbmaker_1.3.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/txdbmaker_1.3.1.tgz
vignettes: vignettes/txdbmaker/inst/doc/txdbmaker.html
vignetteTitles: Making TxDb Objects
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/txdbmaker/inst/doc/txdbmaker.R
dependsOnMe: mygene
importsMe: ASpli, BgeeCall, crisprDesign, crisprViz, customProDB,
DegNorm, ELViS, EpiTxDb, FLAMES, GenomicPlot, IntEREst,
metaseqR2, ORFik, OrganismDbi, OUTRIDER, proActiv, proBAMr,
QuasR, RCAS, recoup, Rhisat2, RiboDiPA, ribosomeProfilingQC,
RNAmodR, scanMiRApp, scruff, sitadela, trackViewer, txcutr,
tximeta, GenomicState, geneLenDataBase
suggestsMe: AnnotationHub, BindingSiteFinder, bumphunter, BUSpaRse,
DEXSeq, doubletrouble, eisaR, GenomicFeatures, GenomicRanges,
raer, recount, SplicingGraphs, SPLINTER, systemPipeR
dependencyCount: 100
Package: tximeta
Version: 1.25.2
Imports: SummarizedExperiment, tximport, jsonlite, S4Vectors, IRanges,
GenomicRanges, AnnotationDbi, GenomicFeatures, txdbmaker,
ensembldb, BiocFileCache, AnnotationHub, Biostrings, tibble,
GenomeInfoDb, tools, utils, methods, Matrix
Suggests: knitr, rmarkdown, testthat, tximportData, org.Dm.eg.db,
DESeq2, fishpond, edgeR, limma, devtools
License: GPL-2
MD5sum: 85f63f57b0d1526e56c920afb0e4564e
NeedsCompilation: no
Title: Transcript Quantification Import with Automatic Metadata
Description: Transcript quantification import from Salmon and other
quantifiers with automatic attachment of transcript ranges and
release information, and other associated metadata. De novo
transcriptomes can be linked to the appropriate sources with
linkedTxomes and shared for computational reproducibility.
biocViews: Annotation, GenomeAnnotation, DataImport, Preprocessing,
RNASeq, SingleCell, Transcriptomics, Transcription,
GeneExpression, FunctionalGenomics, ReproducibleResearch,
ReportWriting, ImmunoOncology
Author: Michael Love [aut, cre], Charlotte Soneson [aut, ctb], Peter
Hickey [aut, ctb], Rob Patro [aut, ctb], NIH NHGRI [fnd], CZI
[fnd]
Maintainer: Michael Love <michaelisaiahlove@gmail.com>
URL: https://github.com/thelovelab/tximeta
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/tximeta
git_branch: devel
git_last_commit: 04d48aa
git_last_commit_date: 2025-01-17
Date/Publication: 2025-01-17
source.ver: src/contrib/tximeta_1.25.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tximeta_1.25.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tximeta_1.25.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tximeta_1.25.2.tgz
vignettes: vignettes/tximeta/inst/doc/tximeta.html
vignetteTitles: Transcript quantification import with automatic
metadata
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tximeta/inst/doc/tximeta.R
dependsOnMe: rnaseqGene
importsMe: IsoformSwitchAnalyzeR
suggestsMe: DESeq2, fishpond, fluentGenomics
dependencyCount: 109
Package: tximport
Version: 1.35.0
Imports: utils, stats, methods
Suggests: knitr, rmarkdown, testthat, tximportData,
TxDb.Hsapiens.UCSC.hg19.knownGene, readr (>= 0.2.2), arrow,
limma, edgeR, DESeq2 (>= 1.11.6), rhdf5, jsonlite, matrixStats,
Matrix, eds
License: LGPL (>=2)
MD5sum: 85f907c97443d37fbee475de8467a384
NeedsCompilation: no
Title: Import and summarize transcript-level estimates for transcript-
and gene-level analysis
Description: Imports transcript-level abundance, estimated counts and
transcript lengths, and summarizes into matrices for use with
downstream gene-level analysis packages. Average transcript
length, weighted by sample-specific transcript abundance
estimates, is provided as a matrix which can be used as an
offset for different expression of gene-level counts.
biocViews: DataImport, Preprocessing, RNASeq, Transcriptomics,
Transcription, GeneExpression, ImmunoOncology
Author: Michael Love [cre,aut], Charlotte Soneson [aut], Mark Robinson
[aut], Rob Patro [ctb], Andrew Parker Morgan [ctb], Ryan C.
Thompson [ctb], Matt Shirley [ctb], Avi Srivastava [ctb]
Maintainer: Michael Love <michaelisaiahlove@gmail.com>
URL: https://github.com/thelovelab/tximport
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/tximport
git_branch: devel
git_last_commit: 6275b97
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/tximport_1.35.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/tximport_1.35.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/tximport_1.35.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/tximport_1.35.0.tgz
vignettes: vignettes/tximport/inst/doc/tximport.html
vignetteTitles: Importing transcript abundance datasets with tximport
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/tximport/inst/doc/tximport.R
importsMe: alevinQC, BgeeCall, CleanUpRNAseq, DifferentialRegulation,
EventPointer, IsoformSwitchAnalyzeR, singleCellTK, TDbasedUFE,
tximeta, ExpHunterSuite, cpam
suggestsMe: BANDITS, DESeq2, variancePartition
dependencyCount: 3
Package: UCell
Version: 2.11.1
Depends: R(>= 4.3.0)
Imports: methods, data.table(>= 1.13.6), Matrix, stats, BiocParallel,
BiocNeighbors, SingleCellExperiment, SummarizedExperiment
Suggests: scater, scRNAseq, reshape2, patchwork, ggplot2, BiocStyle,
Seurat(>= 5.0.0), SeuratObject(>= 5.0.0), knitr, rmarkdown
License: GPL-3 + file LICENSE
Archs: x64
MD5sum: 00919e6937bbc94a355145cd55a79592
NeedsCompilation: no
Title: Rank-based signature enrichment analysis for single-cell data
Description: UCell is a package for evaluating gene signatures in
single-cell datasets. UCell signature scores, based on the
Mann-Whitney U statistic, are robust to dataset size and
heterogeneity, and their calculation demands less computing
time and memory than other available methods, enabling the
processing of large datasets in a few minutes even on machines
with limited computing power. UCell can be applied to any
single-cell data matrix, and includes functions to directly
interact with SingleCellExperiment and Seurat objects.
biocViews: SingleCell, GeneSetEnrichment, Transcriptomics,
GeneExpression, CellBasedAssays
Author: Massimo Andreatta [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8036-2647>), Santiago Carmona
[aut] (ORCID: <https://orcid.org/0000-0002-2495-0671>)
Maintainer: Massimo Andreatta <massimo.andreatta@unil.ch>
URL: https://github.com/carmonalab/UCell
VignetteBuilder: knitr
BugReports: https://github.com/carmonalab/UCell/issues
git_url: https://git.bioconductor.org/packages/UCell
git_branch: devel
git_last_commit: 10bccc9
git_last_commit_date: 2024-10-31
Date/Publication: 2024-11-01
source.ver: src/contrib/UCell_2.11.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/UCell_2.11.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/UCell_2.11.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/UCell_2.11.1.tgz
vignettes: vignettes/UCell/inst/doc/UCell_sce.html,
vignettes/UCell/inst/doc/UCell_Seurat.html,
vignettes/UCell/inst/doc/UCell_vignette_basic.html
vignetteTitles: 2. Using UCell with SingleCellExperiment, 3. Using
UCell with Seurat, 1. Gene signature scoring with UCell
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/UCell/inst/doc/UCell_sce.R,
vignettes/UCell/inst/doc/UCell_Seurat.R,
vignettes/UCell/inst/doc/UCell_vignette_basic.R
importsMe: escape, scGate
suggestsMe: SCpubr
dependencyCount: 51
Package: UCSC.utils
Version: 1.3.1
Imports: methods, stats, httr, jsonlite, S4Vectors
Suggests: DBI, RMariaDB, GenomeInfoDb, testthat, knitr, rmarkdown,
BiocStyle
License: Artistic-2.0
Archs: x64
MD5sum: 0d45e44348b54156372f94eb1dc4a4a5
NeedsCompilation: no
Title: Low-level utilities to retrieve data from the UCSC Genome
Browser
Description: A set of low-level utilities to retrieve data from the
UCSC Genome Browser. Most functions in the package access the
data via the UCSC REST API but some of them query the UCSC
MySQL server directly. Note that the primary purpose of the
package is to support higher-level functionalities implemented
in downstream packages like GenomeInfoDb or txdbmaker.
biocViews: Infrastructure, GenomeAssembly, Annotation,
GenomeAnnotation, DataImport
Author: Hervé Pagès [aut, cre]
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/UCSC.utils
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/UCSC.utils/issues
git_url: https://git.bioconductor.org/packages/UCSC.utils
git_branch: devel
git_last_commit: 6e85249
git_last_commit_date: 2025-01-15
Date/Publication: 2025-01-15
source.ver: src/contrib/UCSC.utils_1.3.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/UCSC.utils_1.3.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/UCSC.utils_1.3.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/UCSC.utils_1.3.1.tgz
vignettes: vignettes/UCSC.utils/inst/doc/UCSC.utils.html
vignetteTitles: The UCSC.utils package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/UCSC.utils/inst/doc/UCSC.utils.R
importsMe: GenomeInfoDb, txdbmaker
dependencyCount: 17
Package: Ularcirc
Version: 1.25.0
Depends: R (>= 3.4.0)
Imports: AnnotationHub, AnnotationDbi, BiocGenerics, Biostrings,
BSgenome, data.table (>= 1.9.4), DT, GenomicFeatures,
GenomeInfoDb, GenomeInfoDbData, GenomicAlignments,
GenomicRanges, ggplot2, ggrepel, gsubfn, mirbase.db, moments,
Organism.dplyr, plotgardener, R.utils, S4Vectors, shiny,
shinydashboard, shinyFiles, shinyjs, yaml
Suggests: BSgenome.Hsapiens.UCSC.hg38, BiocStyle, httpuv, knitr,
org.Hs.eg.db, rmarkdown, TxDb.Hsapiens.UCSC.hg38.knownGene
License: file LICENSE
MD5sum: a21cc2804ddf67d21f644ef8b054b030
NeedsCompilation: no
Title: Shiny app for canonical and back splicing analysis (i.e.
circular and mRNA analysis)
Description: Ularcirc reads in STAR aligned splice junction files and
provides visualisation and analysis tools for splicing
analysis. Users can assess backsplice junctions and forward
canonical junctions.
biocViews: DataRepresentation,Visualization, Genetics, Sequencing,
Annotation, Coverage, AlternativeSplicing, DifferentialSplicing
Author: David Humphreys [aut, cre]
Maintainer: David Humphreys <d.humphreys@victorchang.edu.au>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Ularcirc
git_branch: devel
git_last_commit: a6da876
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Ularcirc_1.25.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Ularcirc_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Ularcirc_1.25.0.tgz
vignettes: vignettes/Ularcirc/inst/doc/Ularcirc.html
vignetteTitles: Ularcirc
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/Ularcirc/inst/doc/Ularcirc.R
dependencyCount: 160
Package: UMI4Cats
Version: 1.17.0
Depends: R (>= 4.0.0), SummarizedExperiment
Imports: magick, cowplot, scales, GenomicRanges, ShortRead, zoo,
ggplot2, reshape2, regioneR, IRanges, S4Vectors, magrittr,
dplyr, BSgenome, Biostrings, DESeq2, R.utils, Rsamtools,
stringr, Rbowtie2, methods, GenomeInfoDb, GenomicAlignments,
RColorBrewer, utils, grDevices, stats, org.Hs.eg.db, annotate,
TxDb.Hsapiens.UCSC.hg19.knownGene, rlang, GenomicFeatures,
BiocFileCache, rappdirs, fda, BiocGenerics
Suggests: knitr, rmarkdown, BiocStyle, BSgenome.Hsapiens.UCSC.hg19,
tidyr, testthat
License: Artistic-2.0
MD5sum: 363f3d7ea72feba68401a1806672140f
NeedsCompilation: no
Title: UMI4Cats: Processing, analysis and visualization of UMI-4C
chromatin contact data
Description: UMI-4C is a technique that allows characterization of 3D
chromatin interactions with a bait of interest, taking
advantage of a sonication step to produce unique molecular
identifiers (UMIs) that help remove duplication bias, thus
allowing a better differential comparsion of chromatin
interactions between conditions. This package allows processing
of UMI-4C data, starting from FastQ files provided by the
sequencing facility. It provides two statistical methods for
detecting differential contacts and includes a visualization
function to plot integrated information from a UMI-4C assay.
biocViews: QualityControl, Preprocessing, Alignment, Normalization,
Visualization, Sequencing, Coverage
Author: Mireia Ramos-Rodriguez [aut, cre] (ORCID:
<https://orcid.org/0000-0001-8083-2445>), Marc Subirana-Granes
[aut], Lorenzo Pasquali [aut]
Maintainer: Mireia Ramos-Rodriguez <mireiarr9@gmail.com>
URL: https://github.com/Pasquali-lab/UMI4Cats
VignetteBuilder: knitr
BugReports: https://github.com/Pasquali-lab/UMI4Cats/issues
git_url: https://git.bioconductor.org/packages/UMI4Cats
git_branch: devel
git_last_commit: 7016307
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/UMI4Cats_1.17.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/UMI4Cats_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/UMI4Cats_1.17.0.tgz
vignettes: vignettes/UMI4Cats/inst/doc/UMI4Cats.html
vignetteTitles: Analyzing UMI-4C data with UMI4Cats
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/UMI4Cats/inst/doc/UMI4Cats.R
dependencyCount: 150
Package: uncoverappLib
Version: 1.17.0
Imports: markdown, shiny, shinyjs, shinyBS,
shinyWidgets,shinycssloaders, DT, Gviz, Homo.sapiens, openxlsx,
condformat, stringr, org.Hs.eg.db,
TxDb.Hsapiens.UCSC.hg38.knownGene, BiocFileCache,rappdirs,
TxDb.Hsapiens.UCSC.hg19.knownGene, rlist, utils,S4Vectors,
EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v86, OrganismDbi, processx,
Rsamtools, GenomicRanges
Suggests: BiocStyle, knitr, testthat, rmarkdown, dplyr
License: MIT + file LICENSE
Archs: x64
MD5sum: bbf6da69aab77d8b40a14546c19c01ff
NeedsCompilation: no
Title: Interactive graphical application for clinical assessment of
sequence coverage at the base-pair level
Description: a Shiny application containing a suite of graphical and
statistical tools to support clinical assessment of low
coverage regions.It displays three web pages each providing a
different analysis module: Coverage analysis, calculate AF by
allele frequency app and binomial distribution. uncoverAPP
provides a statisticl summary of coverage given target file or
genes name.
biocViews: Software, Visualization, Annotation, Coverage
Author: Emanuela Iovino [cre, aut], Tommaso Pippucci [aut]
Maintainer: Emanuela Iovino <emanuela.iovino@unibo.it>
URL: https://github.com/Manuelaio/uncoverappLib
VignetteBuilder: knitr
BugReports: https://github.com/Manuelaio/uncoverappLib/issues
git_url: https://git.bioconductor.org/packages/uncoverappLib
git_branch: devel
git_last_commit: cfd7ff6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/uncoverappLib_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/uncoverappLib_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/uncoverappLib_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/uncoverappLib_1.17.0.tgz
vignettes: vignettes/uncoverappLib/inst/doc/uncoverappLib.html
vignetteTitles: uncoverappLib
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/uncoverappLib/inst/doc/uncoverappLib.R
dependencyCount: 189
Package: UNDO
Version: 1.49.0
Depends: R (>= 2.15.2), methods, BiocGenerics, Biobase
Imports: MASS, boot, nnls, stats, utils
License: GPL-2
MD5sum: 51def95b5d9fb9dd21c5f24b3602a3d4
NeedsCompilation: no
Title: Unsupervised Deconvolution of Tumor-Stromal Mixed Expressions
Description: UNDO is an R package for unsupervised deconvolution of
tumor and stromal mixed expression data. It detects marker
genes and deconvolutes the mixing expression data without any
prior knowledge.
biocViews: Software
Author: Niya Wang <wangny@vt.edu>
Maintainer: Niya Wang <wangny@vt.edu>
git_url: https://git.bioconductor.org/packages/UNDO
git_branch: devel
git_last_commit: 0f29ad6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/UNDO_1.49.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/UNDO_1.49.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/UNDO_1.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/UNDO_1.49.0.tgz
vignettes: vignettes/UNDO/inst/doc/UNDO-vignette.pdf
vignetteTitles: UNDO Usage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/UNDO/inst/doc/UNDO-vignette.R
dependencyCount: 11
Package: unifiedWMWqPCR
Version: 1.43.0
Depends: methods
Imports: BiocGenerics, limma, stats, graphics
License: GPL (>=2)
MD5sum: fd5fd733afba1cba93dc4704e3b27bcd
NeedsCompilation: no
Title: Unified Wilcoxon-Mann Whitney Test for testing differential
expression in qPCR data
Description: This packages implements the unified Wilcoxon-Mann-Whitney
Test for qPCR data. This modified test allows for testing
differential expression in qPCR data.
biocViews: DifferentialExpression, GeneExpression,
MicrotitrePlateAssay, MultipleComparison, QualityControl,
Software, Visualization, qPCR
Author: Jan R. De Neve & Joris Meys
Maintainer: Joris Meys <Joris.Meys@UGent.be>
git_url: https://git.bioconductor.org/packages/unifiedWMWqPCR
git_branch: devel
git_last_commit: f2c0c05
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/unifiedWMWqPCR_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/unifiedWMWqPCR_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/unifiedWMWqPCR_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/unifiedWMWqPCR_1.43.0.tgz
vignettes: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.pdf
vignetteTitles: Using unifiedWMWqPCR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.R
dependencyCount: 9
Package: UniProt.ws
Version: 2.47.6
Imports: AnnotationDbi, AnVILBase, BiocFileCache, BiocBaseUtils,
BiocGenerics, httr2, jsonlite, methods, progress, rjsoncons,
utils
Suggests: BiocStyle, knitr, rmarkdown, tinytest
License: Artistic-2.0
MD5sum: 3bb9aade01227c4802ee1587635bdc83
NeedsCompilation: no
Title: R Interface to UniProt Web Services
Description: The Universal Protein Resource (UniProt) is a
comprehensive resource for protein sequence and annotation
data. This package provides a collection of functions for
retrieving, processing, and re-packaging UniProt web services.
The package makes use of UniProt's modernized REST API and
allows mapping of identifiers accross different databases.
biocViews: Annotation, Infrastructure, GO, KEGG, BioCarta
Author: Marc Carlson [aut], Csaba Ortutay [ctb], Marcel Ramos [aut,
cre] (ORCID: <https://orcid.org/0000-0002-3242-0582>)
Maintainer: Marcel Ramos <marcel.ramos@sph.cuny.edu>
URL: https://github.com/Bioconductor/UniProt.ws
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/UniProt.ws/issues
git_url: https://git.bioconductor.org/packages/UniProt.ws
git_branch: devel
git_last_commit: 1c38b61
git_last_commit_date: 2025-01-01
Date/Publication: 2025-01-02
source.ver: src/contrib/UniProt.ws_2.47.6.tar.gz
win.binary.ver: bin/windows/contrib/4.5/UniProt.ws_2.47.6.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/UniProt.ws_2.47.6.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/UniProt.ws_2.47.6.tgz
vignettes: vignettes/UniProt.ws/inst/doc/UniProt.ws.html
vignetteTitles: UniProt.ws: A package for retrieving data from the
UniProt web service
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/UniProt.ws/inst/doc/UniProt.ws.R
importsMe: dagLogo, drugTargetInteractions, ginmappeR, immunogenViewer
suggestsMe: autonomics, cleaver, qPLEXanalyzer
dependencyCount: 68
Package: Uniquorn
Version: 2.27.0
Depends: R (>= 3.5)
Imports: stringr, R.utils, WriteXLS, stats, doParallel, foreach,
GenomicRanges, IRanges, VariantAnnotation, data.table
Suggests: testthat, knitr, rmarkdown, BiocGenerics
License: Artistic-2.0
MD5sum: fc77181b6bbeb6753d3486b5803d4a68
NeedsCompilation: no
Title: Identification of cancer cell lines based on their weighted
mutational/ variational fingerprint
Description: 'Uniquorn' enables users to identify cancer cell lines.
Cancer cell line misidentification and cross-contamination
reprents a significant challenge for cancer researchers. The
identification is vital and in the frame of this package based
on the locations/ loci of somatic and germline mutations/
variations. The input format is vcf/ vcf.gz and the files have
to contain a single cancer cell line sample (i.e. a single
member/genotype/gt column in the vcf file).
biocViews: ImmunoOncology, StatisticalMethod, WholeGenome, ExomeSeq
Author: Raik Otto
Maintainer: 'Raik Otto' <raik.otto@hu-berlin.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/Uniquorn
git_branch: devel
git_last_commit: a1c9b41
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Uniquorn_2.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Uniquorn_2.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Uniquorn_2.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Uniquorn_2.27.0.tgz
vignettes: vignettes/Uniquorn/inst/doc/Uniquorn.html
vignetteTitles: Uniquorn vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 90
Package: universalmotif
Version: 1.25.1
Depends: R (>= 3.5.0)
Imports: methods, stats, utils, MASS, ggplot2, yaml, IRanges, Rcpp,
Biostrings, BiocGenerics, S4Vectors, rlang, grid,
MatrixGenerics
LinkingTo: Rcpp, RcppThread
Suggests: spelling, knitr, bookdown, TFBSTools, rmarkdown, MotifDb,
testthat, BiocParallel, seqLogo, motifStack, dplyr, ape,
ggtree, processx, ggseqlogo, cowplot, GenomicRanges, ggbio
Enhances: PWMEnrich, rGADEM
License: GPL-3
MD5sum: e91fcc1ca23a5da6e3711417d7e1799d
NeedsCompilation: yes
Title: Import, Modify, and Export Motifs with R
Description: Allows for importing most common motif types into R for
use by functions provided by other Bioconductor motif-related
packages. Motifs can be exported into most major motif formats
from various classes as defined by other Bioconductor packages.
A suite of motif and sequence manipulation and analysis
functions are included, including enrichment, comparison,
P-value calculation, shuffling, trimming, higher-order motifs,
and others.
biocViews: MotifAnnotation, MotifDiscovery, DataImport, GeneRegulation
Author: Benjamin Jean-Marie Tremblay [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7441-2951>), Spencer Nystrom [ctb]
(ORCID: <https://orcid.org/0000-0003-1000-1579>)
Maintainer: Benjamin Jean-Marie Tremblay
<benjamin.tremblay@uwaterloo.ca>
URL: https://bioconductor.org/packages/universalmotif/
VignetteBuilder: knitr
BugReports: https://github.com/bjmt/universalmotif/issues
git_url: https://git.bioconductor.org/packages/universalmotif
git_branch: devel
git_last_commit: 8fc0c9e
git_last_commit_date: 2024-11-11
Date/Publication: 2024-11-11
source.ver: src/contrib/universalmotif_1.25.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/universalmotif_1.25.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/universalmotif_1.25.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/universalmotif_1.25.1.tgz
vignettes: vignettes/universalmotif/inst/doc/Introduction.pdf,
vignettes/universalmotif/inst/doc/IntroductionToSequenceMotifs.pdf,
vignettes/universalmotif/inst/doc/MotifComparisonAndPvalues.pdf,
vignettes/universalmotif/inst/doc/MotifManipulation.pdf,
vignettes/universalmotif/inst/doc/SequenceSearches.pdf
vignetteTitles: Introduction to "universalmotif", Introduction to
sequence motifs, Motif comparisons and P-values, Motif import,,
export,, and manipulation, Sequence manipulation and scanning
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/universalmotif/inst/doc/Introduction.R,
vignettes/universalmotif/inst/doc/IntroductionToSequenceMotifs.R,
vignettes/universalmotif/inst/doc/MotifComparisonAndPvalues.R,
vignettes/universalmotif/inst/doc/MotifManipulation.R,
vignettes/universalmotif/inst/doc/SequenceSearches.R
importsMe: ChIPpeakAnno, circRNAprofiler, memes, MotifPeeker
dependencyCount: 59
Package: updateObject
Version: 1.11.0
Depends: R (>= 4.2.0), methods, BiocGenerics (>= 0.51.1), S4Vectors
Imports: utils, digest
Suggests: GenomicRanges, SummarizedExperiment, InteractionSet,
SingleCellExperiment, MultiAssayExperiment, BiSeq, testthat,
knitr, rmarkdown, BiocStyle
License: Artistic-2.0
MD5sum: 6312bf740fd11a9b1637621d273556c6
NeedsCompilation: no
Title: Find/fix old serialized S4 instances
Description: A set of tools built around updateObject() to work with
old serialized S4 instances. The package is primarily useful to
package maintainers who want to update the serialized S4
instances included in their package. This is still
work-in-progress.
biocViews: Infrastructure, DataRepresentation
Author: Hervé Pagès [aut, cre]
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/updateObject
SystemRequirements: git
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/updateObject/issues
git_url: https://git.bioconductor.org/packages/updateObject
git_branch: devel
git_last_commit: 5a18c6d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/updateObject_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/updateObject_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/updateObject_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/updateObject_1.11.0.tgz
vignettes: vignettes/updateObject/inst/doc/updateObject.html
vignetteTitles: A quick introduction to the updateObject package
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/updateObject/inst/doc/updateObject.R
dependencyCount: 9
Package: UPDhmm
Version: 1.3.0
Depends: R (>= 4.3.0)
Imports: HMM, utils, VariantAnnotation, GenomicRanges, S4Vectors,
IRanges, stats
Suggests: knitr, testthat (>= 2.1.0), BiocStyle, rmarkdown, markdown,
karyoploteR, regioneR, dplyr
License: MIT + file LICENSE
MD5sum: 9db56f22f707cda6ebc9ba5bec3950f6
NeedsCompilation: no
Title: Detecting Uniparental Disomy through NGS trio data
Description: Uniparental disomy (UPD) is a genetic condition where an
individual inherits both copies of a chromosome or part of it
from one parent, rather than one copy from each parent. This
package contains a HMM for detecting UPDs through HTS (High
Throughput Sequencing) data from trio assays. By analyzing the
genotypes in the trio, the model infers a hidden state (normal,
father isodisomy, mother isodisomy, father heterodisomy and
mother heterodisomy).
biocViews: Software, HiddenMarkovModel, Genetics
Author: Marta Sevilla [aut, cre] (ORCID:
<https://orcid.org/0009-0005-0179-920X>), Carlos Ruiz-Arenas
[aut] (ORCID: <https://orcid.org/0000-0002-6014-3498>)
Maintainer: Marta Sevilla <marta.sevilla@upf.edu>
URL: https://github.com/martasevilla/UPDhmm
VignetteBuilder: knitr
BugReports: https://github.com/martasevilla/UPDhmm/issues
git_url: https://git.bioconductor.org/packages/UPDhmm
git_branch: devel
git_last_commit: 025c593
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/UPDhmm_1.3.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/UPDhmm_1.3.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/UPDhmm_1.3.0.tgz
vignettes: vignettes/UPDhmm/inst/doc/UPDhmm.html
vignetteTitles: Detection of UPDs in HTS data
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/UPDhmm/inst/doc/UPDhmm.R
dependencyCount: 80
Package: uSORT
Version: 1.33.0
Depends: R (>= 3.3.0), tcltk
Imports: igraph, Matrix, RANN, RSpectra, VGAM, gplots, parallel, plyr,
methods, cluster, Biobase, fpc, BiocGenerics, monocle,
grDevices, graphics, stats, utils
Suggests: knitr, RUnit, testthat, ggplot2
License: Artistic-2.0
MD5sum: e6b73fdd0201a28b925b12332c02d174
NeedsCompilation: no
Title: uSORT: A self-refining ordering pipeline for gene selection
Description: This package is designed to uncover the intrinsic cell
progression path from single-cell RNA-seq data. It incorporates
data pre-processing, preliminary PCA gene selection,
preliminary cell ordering, feature selection, refined cell
ordering, and post-analysis interpretation and visualization.
biocViews: ImmunoOncology, RNASeq, GUI, CellBiology, DNASeq
Author: Mai Chan Lau, Hao Chen, Jinmiao Chen
Maintainer: Hao Chen <chen_hao@immunol.a-star.edu.sg>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/uSORT
git_branch: devel
git_last_commit: 9c366f2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/uSORT_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/uSORT_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/uSORT_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/uSORT_1.33.0.tgz
vignettes: vignettes/uSORT/inst/doc/uSORT_quick_start.html
vignetteTitles: Quick Start
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/uSORT/inst/doc/uSORT_quick_start.R
dependencyCount: 96
Package: VAExprs
Version: 1.13.0
Depends: keras, mclust
Imports: SingleCellExperiment, SummarizedExperiment, tensorflow,
scater, CatEncoders, DeepPINCS, purrr, DiagrammeR, stats
Suggests: SC3, knitr, testthat, reticulate, rmarkdown
License: Artistic-2.0
MD5sum: cc4fe1e1499b1399710a207769e0e756
NeedsCompilation: no
Title: Generating Samples of Gene Expression Data with Variational
Autoencoders
Description: A fundamental problem in biomedical research is the low
number of observations, mostly due to a lack of available
biosamples, prohibitive costs, or ethical reasons. By
augmenting a few real observations with artificially generated
samples, their analysis could lead to more robust and higher
reproducible. One possible solution to the problem is the use
of generative models, which are statistical models of data that
attempt to capture the entire probability distribution from the
observations. Using the variational autoencoder (VAE), a
well-known deep generative model, this package is aimed to
generate samples with gene expression data, especially for
single-cell RNA-seq data. Furthermore, the VAE can use
conditioning to produce specific cell types or subpopulations.
The conditional VAE (CVAE) allows us to create targeted samples
rather than completely random ones.
biocViews: Software, GeneExpression, SingleCell
Author: Dongmin Jung [cre, aut] (ORCID:
<https://orcid.org/0000-0001-7499-8422>)
Maintainer: Dongmin Jung <dmdmjung@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/VAExprs
git_branch: devel
git_last_commit: ed47049
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/VAExprs_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/VAExprs_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/VAExprs_1.13.0.tgz
vignettes: vignettes/VAExprs/inst/doc/VAExprs.html
vignetteTitles: VAExprs
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/VAExprs/inst/doc/VAExprs.R
suggestsMe: GenProSeq
dependencyCount: 210
Package: VanillaICE
Version: 1.69.0
Depends: R (>= 3.5.0), BiocGenerics (>= 0.13.6), GenomicRanges (>=
1.27.6), SummarizedExperiment (>= 1.5.3)
Imports: MatrixGenerics, Biobase, S4Vectors (>= 0.23.18), IRanges (>=
1.14.0), oligoClasses (>= 1.31.1), foreach, matrixStats,
data.table, grid, lattice, methods, GenomeInfoDb (>= 1.11.4),
crlmm, tools, stats, utils, BSgenome.Hsapiens.UCSC.hg18
Suggests: RUnit, human610quadv1bCrlmm
Enhances: doMC, doMPI, doSNOW, doParallel, doRedis
License: LGPL-2
MD5sum: 584557cfbe222533f7377edfbe9cae2d
NeedsCompilation: yes
Title: A Hidden Markov Model for high throughput genotyping arrays
Description: Hidden Markov Models for characterizing chromosomal
alteration in high throughput SNP arrays.
biocViews: CopyNumberVariation
Author: Robert Scharpf [aut, cre]
Maintainer: Robert Scharpf <rscharpf@jhu.edu>
git_url: https://git.bioconductor.org/packages/VanillaICE
git_branch: devel
git_last_commit: 6af9e70
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/VanillaICE_1.69.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/VanillaICE_1.69.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/VanillaICE_1.69.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/VanillaICE_1.69.0.tgz
vignettes: vignettes/VanillaICE/inst/doc/crlmmDownstream.pdf,
vignettes/VanillaICE/inst/doc/VanillaICE.pdf
vignetteTitles: crlmmDownstream, VanillaICE Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/VanillaICE/inst/doc/crlmmDownstream.R,
vignettes/VanillaICE/inst/doc/VanillaICE.R
dependsOnMe: MinimumDistance
suggestsMe: oligoClasses
dependencyCount: 95
Package: VarCon
Version: 1.15.0
Depends: Biostrings, BSgenome, GenomicRanges, R (>= 4.1)
Imports: methods, stats, IRanges, shiny, shinycssloaders, shinyFiles,
ggplot2
Suggests: testthat, knitr, rmarkdown
License: GPL-3
MD5sum: 8c82e6389f456bafaf40468f4d511800
NeedsCompilation: no
Title: VarCon: an R package for retrieving neighboring nucleotides of
an SNV
Description: VarCon is an R package which converts the positional
information from the annotation of an single nucleotide
variation (SNV) (either referring to the coding sequence or the
reference genomic sequence). It retrieves the genomic reference
sequence around the position of the single nucleotide
variation. To asses, whether the SNV could potentially
influence binding of splicing regulatory proteins VarCon
calcualtes the HEXplorer score as an estimation. Besides,
VarCon additionally reports splice site strengths of splice
sites within the retrieved genomic sequence and any changes due
to the SNV.
biocViews: FunctionalGenomics, AlternativeSplicing
Author: Johannes Ptok [aut, cre]
Maintainer: Johannes Ptok <Johannes.Ptok@posteo.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/VarCon
git_branch: devel
git_last_commit: b4f3a14
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/VarCon_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/VarCon_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/VarCon_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/VarCon_1.15.0.tgz
vignettes: vignettes/VarCon/inst/doc/VarCon.html
vignetteTitles: Analysing SNVs with VarCon
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/VarCon/inst/doc/VarCon.R
dependencyCount: 107
Package: variancePartition
Version: 1.37.2
Depends: R (>= 4.3.0), ggplot2, limma (>= 3.62.1), BiocParallel
Imports: MASS, pbkrtest (>= 0.4-4), lmerTest, Matrix (>= 1.4.0),
iterators, gplots, corpcor, matrixStats, RhpcBLASctl, reshape2,
remaCor (>= 0.0.15), fANCOVA, aod, scales, Rdpack, rlang, lme4
(>= 1.1.33), grDevices, graphics, Biobase, methods, utils,
stats
Suggests: BiocStyle, knitr, pander, rmarkdown, edgeR, dendextend,
tximport, tximportData, ballgown, DESeq2, RUnit, cowplot,
Rfast, zenith, statmod, BiocGenerics, r2glmm, readr
License: GPL-2
MD5sum: 7ed90a5b3ee5c1b67c5eb7357fb42831
NeedsCompilation: no
Title: Quantify and interpret drivers of variation in multilevel gene
expression experiments
Description: Quantify and interpret multiple sources of biological and
technical variation in gene expression experiments. Uses a
linear mixed model to quantify variation in gene expression
attributable to individual, tissue, time point, or technical
variables. Includes dream differential expression analysis for
repeated measures.
biocViews: RNASeq, GeneExpression, GeneSetEnrichment,
DifferentialExpression, BatchEffect, QualityControl,
Regression, Epigenetics, FunctionalGenomics, Transcriptomics,
Normalization, Preprocessing, Microarray, ImmunoOncology,
Software
Author: Gabriel Hoffman [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0957-0224>)
Maintainer: Gabriel E. Hoffman <gabriel.hoffman@mssm.edu>
URL: http://bioconductor.org/packages/variancePartition,
https://DiseaseNeuroGenomics.github.io/variancePartition
VignetteBuilder: knitr
BugReports:
https://github.com/DiseaseNeuroGenomics/variancePartition/issues
git_url: https://git.bioconductor.org/packages/variancePartition
git_branch: devel
git_last_commit: ccb5903
git_last_commit_date: 2025-01-07
Date/Publication: 2025-01-08
source.ver: src/contrib/variancePartition_1.37.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/variancePartition_1.37.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/variancePartition_1.37.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/variancePartition_1.37.2.tgz
vignettes:
vignettes/variancePartition/inst/doc/additional_visualization.html,
vignettes/variancePartition/inst/doc/dream.html,
vignettes/variancePartition/inst/doc/errors.html,
vignettes/variancePartition/inst/doc/FAQ.html,
vignettes/variancePartition/inst/doc/mvtests.html,
vignettes/variancePartition/inst/doc/rnd_effects.html,
vignettes/variancePartition/inst/doc/variancePartition.html
vignetteTitles: 2) Additional visualizations, 4) dream: differential
expression testing with repeated measures designs, 5) Error
handling, 6) Frequently asked questions, 7) Multivariate tests,
3) Theory and practice of random effects and REML, 1) Variance
partitioning analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles:
vignettes/variancePartition/inst/doc/additional_visualization.R,
vignettes/variancePartition/inst/doc/dream.R,
vignettes/variancePartition/inst/doc/errors.R,
vignettes/variancePartition/inst/doc/FAQ.R,
vignettes/variancePartition/inst/doc/mvtests.R,
vignettes/variancePartition/inst/doc/rnd_effects.R,
vignettes/variancePartition/inst/doc/variancePartition.R
dependsOnMe: dreamlet
importsMe: muscat, zenith
suggestsMe: GRaNIE
dependencyCount: 95
Package: VariantAnnotation
Version: 1.53.1
Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0),
MatrixGenerics, GenomeInfoDb (>= 1.15.2), GenomicRanges (>=
1.41.5), SummarizedExperiment (>= 1.19.5), Rsamtools (>=
2.19.1)
Imports: utils, DBI, Biobase, S4Vectors (>= 0.27.12), IRanges (>=
2.23.9), XVector (>= 0.29.2), Biostrings (>= 2.57.2),
AnnotationDbi (>= 1.27.9), rtracklayer (>= 1.39.7), BSgenome
(>= 1.47.3), GenomicFeatures (>= 1.31.3)
LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rhtslib (>= 2.99.0)
Suggests: RUnit, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19,
TxDb.Hsapiens.UCSC.hg19.knownGene,
SNPlocs.Hsapiens.dbSNP144.GRCh37, SIFT.Hsapiens.dbSNP132,
SIFT.Hsapiens.dbSNP137, PolyPhen.Hsapiens.dbSNP131, snpStats,
ggplot2, BiocStyle, knitr, magick, jsonlite, httr, rjsoncons
License: Artistic-2.0
MD5sum: d578c3c623b4ee43822d4850ddca4cc4
NeedsCompilation: yes
Title: Annotation of Genetic Variants
Description: Annotate variants, compute amino acid coding changes,
predict coding outcomes.
biocViews: DataImport, Sequencing, SNP, Annotation, Genetics,
VariantAnnotation
Author: Valerie Oberchain [aut], Martin Morgan [aut], Michael Lawrence
[aut], Stephanie Gogarten [ctb], Bioconductor Package
Maintainer [cre]
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
SystemRequirements: GNU make
VignetteBuilder: knitr
Video:
https://www.youtube.com/watch?v=Ro0lHQ_J--I&list=UUqaMSQd_h-2EDGsU6WDiX0Q
git_url: https://git.bioconductor.org/packages/VariantAnnotation
git_branch: devel
git_last_commit: 6f599d0
git_last_commit_date: 2025-01-07
Date/Publication: 2025-01-08
source.ver: src/contrib/VariantAnnotation_1.53.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/VariantAnnotation_1.53.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/VariantAnnotation_1.53.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/VariantAnnotation_1.53.1.tgz
vignettes: vignettes/VariantAnnotation/inst/doc/ensemblVEP.html,
vignettes/VariantAnnotation/inst/doc/filterVcf.html,
vignettes/VariantAnnotation/inst/doc/VariantAnnotation.html
vignetteTitles: ensemblVEP: using the REST API with Bioconductor, 2.
Using filterVcf to Select Variants from VCF Files, 1.
Introduction to VariantAnnotation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/VariantAnnotation/inst/doc/ensemblVEP.R,
vignettes/VariantAnnotation/inst/doc/filterVcf.R,
vignettes/VariantAnnotation/inst/doc/VariantAnnotation.R
dependsOnMe: alabaster.vcf, CNVrd2, deepSNV, demuxSNP, HelloRanges,
myvariant, PureCN, R453Plus1Toolbox, RareVariantVis, seqCAT,
signeR, SomaticSignatures, StructuralVariantAnnotation,
svaNUMT, VariantFiltering, VariantTools,
PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP132,
SIFT.Hsapiens.dbSNP137, VariantToolsData, sequencing, variants,
PlasmaMutationDetector
importsMe: AllelicImbalance, APAlyzer, appreci8R, BadRegionFinder,
BBCAnalyzer, biovizBase, biscuiteer, cardelino, CNVfilteR,
CopyNumberPlots, crisprDesign, customProDB, DAMEfinder,
decompTumor2Sig, DominoEffect, fcScan, GA4GHclient,
GenomicFiles, GenVisR, ggbio, gmapR, gwascat, gwasurvivr,
icetea, igvR, karyoploteR, katdetectr, lineagespot, MADSEQ,
motifbreakR, MungeSumstats, musicatk, MutationalPatterns,
ProteoDisco, RAIDS, scoreInvHap, SigsPack, SNPhood, svaRetro,
tadar, TitanCNA, tLOH, transmogR, TVTB, Uniquorn, UPDhmm,
VCFArray, YAPSA, ZygosityPredictor, COSMIC.67, gpcp
suggestsMe: alabaster.files, AnnotationHub, BiocParallel, cellbaseR,
CrispRVariants, epialleleR, GenomicDataCommons, GenomicRanges,
GenomicScores, GWASTools, igvShiny, ldblock, omicsPrint,
podkat, RVS, SeqArray, shiny.gosling, splatter, supersigs,
systemPipeR, trackViewer, trio, vtpnet, AshkenazimSonChr21,
GeuvadisTranscriptExpr, ldsep, polyRAD, SNPassoc, updog
dependencyCount: 78
Package: VariantExperiment
Version: 1.21.0
Depends: R (>= 3.6.0), S4Vectors (>= 0.21.24), SummarizedExperiment (>=
1.13.0), GenomicRanges,
Imports: GDSArray (>= 1.11.1), DelayedDataFrame (>= 1.6.0), tools,
utils, stats, methods, gdsfmt, SNPRelate, SeqArray,
DelayedArray, Biostrings, IRanges
Suggests: testthat, knitr, rmarkdown, markdown, BiocStyle
License: GPL-3
MD5sum: c2afba06a82dfd830eda984ddca97509
NeedsCompilation: no
Title: A RangedSummarizedExperiment Container for VCF/GDS Data with GDS
Backend
Description: VariantExperiment is a Bioconductor package for saving
data in VCF/GDS format into RangedSummarizedExperiment object.
The high-throughput genetic/genomic data are saved in GDSArray
objects. The annotation data for features/samples are saved in
DelayedDataFrame format with mono-dimensional GDSArray in each
column. The on-disk representation of both assay data and
annotation data achieves on-disk reading and processing and
saves memory space significantly. The interface of
RangedSummarizedExperiment data format enables easy and common
manipulations for high-throughput genetic/genomic data with
common SummarizedExperiment metaphor in R and Bioconductor.
biocViews: Infrastructure, DataRepresentation, Sequencing, Annotation,
GenomeAnnotation, GenotypingArray
Author: Qian Liu [aut, cre], Hervé Pagès [aut], Martin Morgan [aut]
Maintainer: Qian Liu <Qian.Liu@roswellpark.org>
URL: https://github.com/Bioconductor/VariantExperiment
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/VariantExperiment/issues
git_url: https://git.bioconductor.org/packages/VariantExperiment
git_branch: devel
git_last_commit: f3af19f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/VariantExperiment_1.21.0.tar.gz
vignettes:
vignettes/VariantExperiment/inst/doc/VariantExperiment-class.html
vignetteTitles: VariantExperiment-class
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/VariantExperiment/inst/doc/VariantExperiment-class.R
dependencyCount: 44
Package: VariantFiltering
Version: 1.43.0
Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.25.1),
VariantAnnotation (>= 1.13.29)
Imports: utils, stats, Biobase, S4Vectors (>= 0.9.25), IRanges (>=
2.3.23), RBGL, graph, AnnotationDbi, BiocParallel, Biostrings
(>= 2.33.11), GenomeInfoDb (>= 1.3.6), GenomicRanges (>=
1.19.13), SummarizedExperiment, GenomicFeatures, Rsamtools (>=
1.17.8), BSgenome, GenomicScores (>= 1.0.0), Gviz, shiny,
shinythemes, shinyjs, DT, shinyTree
LinkingTo: S4Vectors, IRanges, XVector, Biostrings
Suggests: RUnit, BiocStyle, org.Hs.eg.db,
BSgenome.Hsapiens.1000genomes.hs37d5,
TxDb.Hsapiens.UCSC.hg19.knownGene,
SNPlocs.Hsapiens.dbSNP144.GRCh37,
MafDb.1Kgenomes.phase1.hs37d5, phastCons100way.UCSC.hg19,
PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137
License: Artistic-2.0
Archs: x64
MD5sum: 04b204854a60b51e6d8ba22b2ae113d4
NeedsCompilation: yes
Title: Filtering of coding and non-coding genetic variants
Description: Filter genetic variants using different criteria such as
inheritance model, amino acid change consequence, minor allele
frequencies across human populations, splice site strength,
conservation, etc.
biocViews: Genetics, Homo_sapiens, Annotation, SNP, Sequencing,
HighThroughputSequencing
Author: Robert Castelo [aut, cre], Dei Martinez Elurbe [ctb], Pau
Puigdevall [ctb], Joan Fernandez [ctb]
Maintainer: Robert Castelo <robert.castelo@upf.edu>
URL: https://github.com/rcastelo/VariantFiltering
BugReports: https://github.com/rcastelo/VariantFiltering/issues
git_url: https://git.bioconductor.org/packages/VariantFiltering
git_branch: devel
git_last_commit: 779427d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/VariantFiltering_1.43.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/VariantFiltering_1.43.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/VariantFiltering_1.43.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/VariantFiltering_1.43.0.tgz
vignettes:
vignettes/VariantFiltering/inst/doc/usingVariantFiltering.pdf
vignetteTitles: VariantFiltering: filter coding and non-coding genetic
variants
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/VariantFiltering/inst/doc/usingVariantFiltering.R
dependencyCount: 179
Package: VariantTools
Version: 1.49.0
Depends: R (>= 3.5.0), S4Vectors (>= 0.17.33), IRanges (>= 2.13.12),
GenomicRanges (>= 1.31.8), VariantAnnotation (>= 1.11.16),
methods
Imports: Rsamtools (>= 1.31.2), BiocGenerics, Biostrings, parallel,
GenomicFeatures (>= 1.31.3), Matrix, rtracklayer (>= 1.39.7),
BiocParallel, GenomeInfoDb, BSgenome, Biobase
Suggests: RUnit, LungCancerLines (>= 0.0.6), RBGL, graph, gmapR (>=
1.21.3), TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db
License: Artistic-2.0
MD5sum: 04fc8c9a302c888dc3e55a4d7f376ba2
NeedsCompilation: no
Title: Tools for Exploratory Analysis of Variant Calls
Description: Explore, diagnose, and compare variant calls using
filters.
biocViews: Genetics, GeneticVariability, Sequencing
Author: Michael Lawrence, Jeremiah Degenhardt, Robert Gentleman
Maintainer: Michael Lawrence <michafla@gene.com>
git_url: https://git.bioconductor.org/packages/VariantTools
git_branch: devel
git_last_commit: e6dbe11
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/VariantTools_1.49.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/VariantTools_1.49.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/VariantTools_1.49.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/VariantTools_1.49.0.tgz
vignettes: vignettes/VariantTools/inst/doc/tutorial.pdf,
vignettes/VariantTools/inst/doc/VariantTools.pdf
vignetteTitles: tutorial.pdf, Introduction to VariantTools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/VariantTools/inst/doc/VariantTools.R
suggestsMe: VariantToolsData
dependencyCount: 79
Package: VaSP
Version: 1.19.0
Depends: R (>= 4.0), ballgown
Imports: IRanges, GenomicRanges, S4Vectors, parallel, matrixStats,
GenomicAlignments, GenomeInfoDb, Rsamtools, cluster, stats,
graphics, methods
Suggests: knitr, rmarkdown
License: GPL (>= 2.0)
MD5sum: 076a3408200d9eb08ebe6bd2ad090678
NeedsCompilation: no
Title: Quantification and Visualization of Variations of Splicing in
Population
Description: Discovery of genome-wide variable alternative splicing
events from short-read RNA-seq data and visualizations of gene
splicing information for publication-quality multi-panel
figures in a population. (Warning: The visualizing function is
removed due to the dependent package Sushi deprecated. If you
want to use it, please change back to an older version.)
biocViews: RNASeq, AlternativeSplicing, DifferentialSplicing,
StatisticalMethod, Visualization, Preprocessing, Clustering,
DifferentialExpression, KEGG, ImmunoOncology
Author: Huihui Yu [aut, cre] (ORCID:
<https://orcid.org/0000-0003-2725-1937>), Qian Du [aut] (ORCID:
<https://orcid.org/0000-0003-3864-8745>), Chi Zhang [aut]
(ORCID: <https://orcid.org/0000-0002-1827-8137>)
Maintainer: Huihui Yu <yuhuihui2011@foxmail.com>
URL: https://github.com/yuhuihui2011/VaSP
VignetteBuilder: knitr
BugReports: https://github.com/yuhuihui2011/VaSP/issues
git_url: https://git.bioconductor.org/packages/VaSP
git_branch: devel
git_last_commit: f2e104d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/VaSP_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/VaSP_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/VaSP_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/VaSP_1.19.0.tgz
vignettes: vignettes/VaSP/inst/doc/VaSP.html
vignetteTitles: user guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/VaSP/inst/doc/VaSP.R
dependencyCount: 91
Package: vbmp
Version: 1.75.0
Depends: R (>= 2.10)
Suggests: Biobase (>= 2.5.5), statmod
License: GPL (>= 2)
MD5sum: ffe0330eb972e06eb3bdcabd794ccfa0
NeedsCompilation: no
Title: Variational Bayesian Multinomial Probit Regression
Description: Variational Bayesian Multinomial Probit Regression with
Gaussian Process Priors. It estimates class membership
posterior probability employing variational and sparse
approximation to the full posterior. This software also
incorporates feature weighting by means of Automatic Relevance
Determination.
biocViews: Classification
Author: Nicola Lama <nicola.lama@unina2.it>, Mark Girolami
<girolami@dcs.gla.ac.uk>
Maintainer: Nicola Lama <nicola.lama@unina2.it>
URL:
http://bioinformatics.oxfordjournals.org/cgi/content/short/btm535v1
git_url: https://git.bioconductor.org/packages/vbmp
git_branch: devel
git_last_commit: 8c41b83
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/vbmp_1.75.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/vbmp_1.75.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/vbmp_1.75.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/vbmp_1.75.0.tgz
vignettes: vignettes/vbmp/inst/doc/vbmp.pdf
vignetteTitles: vbmp Tutorial
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/vbmp/inst/doc/vbmp.R
dependencyCount: 0
Package: VCFArray
Version: 1.23.0
Depends: R (>= 3.6), methods, BiocGenerics, DelayedArray (>= 0.7.28)
Imports: tools, GenomicRanges, VariantAnnotation (>= 1.29.3),
GenomicFiles (>= 1.17.3), S4Vectors (>= 0.19.19), Rsamtools
Suggests: SeqArray, BiocStyle, BiocManager, testthat, knitr, rmarkdown
License: GPL-3
Archs: x64
MD5sum: 02fd6a1339ba27cde9b5835870f372e9
NeedsCompilation: no
Title: Representing on-disk / remote VCF files as array-like objects
Description: VCFArray extends the DelayedArray to represent VCF data
entries as array-like objects with on-disk / remote VCF file as
backend. Data entries from VCF files, including info fields,
FORMAT fields, and the fixed columns (REF, ALT, QUAL, FILTER)
could be converted into VCFArray instances with different
dimensions.
biocViews: Infrastructure, DataRepresentation, Sequencing,
VariantAnnotation
Author: Qian Liu [aut, cre], Martin Morgan [aut]
Maintainer: Qian Liu <qliu7@buffalo.edu>
URL: https://github.com/Liubuntu/VCFArray
VignetteBuilder: knitr
BugReports: https://github.com/Liubuntu/VCFArray/issues
git_url: https://git.bioconductor.org/packages/VCFArray
git_branch: devel
git_last_commit: 2926f6d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/VCFArray_1.23.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/VCFArray_1.23.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/VCFArray_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/VCFArray_1.23.0.tgz
vignettes: vignettes/VCFArray/inst/doc/VCFArray.html
vignetteTitles: VCFArray: DelayedArray objects with on-disk/remote VCF
backend
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/VCFArray/inst/doc/VCFArray.R
dependencyCount: 80
Package: VDJdive
Version: 1.9.0
Depends: R (>= 4.2)
Imports: BiocParallel, cowplot, ggplot2, gridExtra, IRanges, Matrix,
methods, RColorBrewer, Rcpp, S4Vectors, SingleCellExperiment,
stats, SummarizedExperiment, utils
LinkingTo: Rcpp
Suggests: breakaway, covr, knitr, rmarkdown, testthat, BiocStyle
License: Artistic-2.0
MD5sum: 401eda6efe28d95a363d7c15d6eccc77
NeedsCompilation: yes
Title: Analysis Tools for 10X V(D)J Data
Description: This package provides functions for handling and analyzing
immune receptor repertoire data, such as produced by the
CellRanger V(D)J pipeline. This includes reading the data into
R, merging it with paired single-cell data, quantifying
clonotype abundances, calculating diversity metrics, and
producing common plots. It implements the E-M Algorithm for
clonotype assignment, along with other methods, which makes use
of ambiguous cells for improved quantification.
biocViews: Software, ImmunoOncology, SingleCell, Annotation, RNASeq,
TargetedResequencing
Author: Kelly Street [aut, cre] (ORCID:
<https://orcid.org/0000-0001-6379-5013>), Mercedeh Movassagh
[aut] (ORCID: <https://orcid.org/0000-0001-7690-0230>), Jill
Lundell [aut] (ORCID: <https://orcid.org/0000-0002-6048-4700>),
Jared Brown [ctb], Linglin Huang [ctb], Mingzhi Ye [ctb]
Maintainer: Kelly Street <street.kelly@gmail.com>
URL: https://github.com/kstreet13/VDJdive
VignetteBuilder: knitr
BugReports: https://github.com/kstreet13/VDJdive/issues
git_url: https://git.bioconductor.org/packages/VDJdive
git_branch: devel
git_last_commit: 9a1d595
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/VDJdive_1.9.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/VDJdive_1.9.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/VDJdive_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/VDJdive_1.9.0.tgz
vignettes: vignettes/VDJdive/inst/doc/workflow.html
vignetteTitles: VDJdive Workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/VDJdive/inst/doc/workflow.R
dependencyCount: 76
Package: VegaMC
Version: 3.45.0
Depends: R (>= 2.10.0), biomaRt, Biobase
Imports: methods
License: GPL-2
MD5sum: 8c24e7a935bb9c5e41a602053efcded8
NeedsCompilation: yes
Title: VegaMC: A Package Implementing a Variational Piecewise Smooth
Model for Identification of Driver Chromosomal Imbalances in
Cancer
Description: This package enables the detection of driver chromosomal
imbalances including loss of heterozygosity (LOH) from array
comparative genomic hybridization (aCGH) data. VegaMC performs
a joint segmentation of a dataset and uses a statistical
framework to distinguish between driver and passenger mutation.
VegaMC has been implemented so that it can be immediately
integrated with the output produced by PennCNV tool. In
addition, VegaMC produces in output two web pages that allows a
rapid navigation between both the detected regions and the
altered genes. In the web page that summarizes the altered
genes, the link to the respective Ensembl gene web page is
reported.
biocViews: aCGH, CopyNumberVariation
Author: S. Morganella and M. Ceccarelli
Maintainer: Sandro Morganella <morganellaalx@gmail.com>
git_url: https://git.bioconductor.org/packages/VegaMC
git_branch: devel
git_last_commit: 77ecd7a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/VegaMC_3.45.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/VegaMC_3.45.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/VegaMC_3.45.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/VegaMC_3.45.0.tgz
vignettes: vignettes/VegaMC/inst/doc/VegaMC.pdf
vignetteTitles: VegaMC
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/VegaMC/inst/doc/VegaMC.R
dependencyCount: 68
Package: velociraptor
Version: 1.17.0
Depends: SummarizedExperiment
Imports: methods, stats, Matrix, BiocGenerics, reticulate, S4Vectors,
DelayedArray, basilisk, zellkonverter, scuttle,
SingleCellExperiment, BiocParallel, BiocSingular
Suggests: BiocStyle, testthat, knitr, rmarkdown, pkgdown, scran,
scater, scRNAseq, Rtsne, graphics, grDevices, ggplot2, cowplot,
GGally, patchwork, metR
License: MIT + file LICENSE
Archs: x64
MD5sum: b283ee3a3111445827b14b4d801cdf54
NeedsCompilation: no
Title: Toolkit for Single-Cell Velocity
Description: This package provides Bioconductor-friendly wrappers for
RNA velocity calculations in single-cell RNA-seq data. We use
the basilisk package to manage Conda environments, and the
zellkonverter package to convert data structures between
SingleCellExperiment (R) and AnnData (Python). The information
produced by the velocity methods is stored in the various
components of the SingleCellExperiment class.
biocViews: SingleCell, GeneExpression, Sequencing, Coverage
Author: Kevin Rue-Albrecht [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3899-3872>), Aaron Lun [aut]
(ORCID: <https://orcid.org/0000-0002-3564-4813>), Charlotte
Soneson [aut] (ORCID: <https://orcid.org/0000-0003-3833-2169>),
Michael Stadler [aut] (ORCID:
<https://orcid.org/0000-0002-2269-4934>)
Maintainer: Kevin Rue-Albrecht <kevinrue67@gmail.com>
URL: https://github.com/kevinrue/velociraptor
VignetteBuilder: knitr
BugReports: https://github.com/kevinrue/velociraptor/issues
git_url: https://git.bioconductor.org/packages/velociraptor
git_branch: devel
git_last_commit: d1c9824
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/velociraptor_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/velociraptor_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/velociraptor_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/velociraptor_1.17.0.tgz
vignettes: vignettes/velociraptor/inst/doc/velociraptor.html
vignetteTitles: User's guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/velociraptor/inst/doc/velociraptor.R
dependencyCount: 69
Package: veloviz
Version: 1.13.0
Depends: R (>= 4.1)
Imports: Rcpp, Matrix, igraph, mgcv, RSpectra, grDevices, graphics,
stats
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, testthat
License: GPL-3
Archs: x64
MD5sum: 31260a7b7152c5b26da7f9500e08efe2
NeedsCompilation: yes
Title: VeloViz: RNA-velocity informed 2D embeddings for visualizing
cell state trajectories
Description: VeloViz uses each cell’s current observed and predicted
future transcriptional states inferred from RNA velocity
analysis to build a nearest neighbor graph between cells in the
population. Edges are then pruned based on a cosine correlation
threshold and/or a distance threshold and the resulting graph
is visualized using a force-directed graph layout algorithm.
VeloViz can help ensure that relationships between cell states
are reflected in the 2D embedding, allowing for more reliable
representation of underlying cellular trajectories.
biocViews: Transcriptomics, Visualization, GeneExpression, Sequencing,
RNASeq, DimensionReduction
Author: Lyla Atta [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6113-0082>), Jean Fan [aut]
(ORCID: <https://orcid.org/0000-0002-0212-5451>), Arpan Sahoo
[aut] (ORCID: <https://orcid.org/0000-0002-0325-2073>)
Maintainer: Lyla Atta <lylaatta@jhmi.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/veloviz
git_branch: devel
git_last_commit: 53cb575
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/veloviz_1.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/veloviz_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/veloviz_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/veloviz_1.13.0.tgz
vignettes: vignettes/veloviz/inst/doc/vignette.html
vignetteTitles: Visualizing cell cycle trajectory in MERFISH data using
VeloViz
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/veloviz/inst/doc/vignette.R
dependencyCount: 23
Package: VennDetail
Version: 1.23.0
Imports: utils, grDevices, stats, methods, dplyr, purrr, tibble,
magrittr, ggplot2, UpSetR, VennDiagram, grid, futile.logger
Suggests: knitr, rmarkdown, testthat, markdown
License: GPL-2
MD5sum: 742a0b6661552f1d118929c590b1bfad
NeedsCompilation: no
Title: A package for visualization and extract details
Description: A set of functions to generate high-resolution
Venn,Vennpie plot,extract and combine details of these subsets
with user datasets in data frame is available.
biocViews: DataRepresentation,GraphAndNetwork
Author: Kai Guo, Brett McGregor
Maintainer: Kai Guo <guokai8@gmail.com>
URL: https://github.com/guokai8/VennDetail
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/VennDetail
git_branch: devel
git_last_commit: cecf8f1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/VennDetail_1.23.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/VennDetail_1.23.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/VennDetail_1.23.0.tgz
vignettes: vignettes/VennDetail/inst/doc/VennDetail.html
vignetteTitles: VennDetail
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/VennDetail/inst/doc/VennDetail.R
dependencyCount: 48
Package: VERSO
Version: 1.17.1
Depends: R (>= 4.1.0)
Imports: utils, data.tree, ape, parallel, Rfast, stats
Suggests: BiocGenerics, BiocStyle, testthat, knitr
License: file LICENSE
MD5sum: 07fc99202b6f590ba9032142f7d8409b
NeedsCompilation: no
Title: Viral Evolution ReconStructiOn (VERSO)
Description: Mutations that rapidly accumulate in viral genomes during
a pandemic can be used to track the evolution of the virus and,
accordingly, unravel the viral infection network. To this
extent, sequencing samples of the virus can be employed to
estimate models from genomic epidemiology and may serve, for
instance, to estimate the proportion of undetected infected
people by uncovering cryptic transmissions, as well as to
predict likely trends in the number of infected, hospitalized,
dead and recovered people. VERSO is an algorithmic framework
that processes variants profiles from viral samples to produce
phylogenetic models of viral evolution. The approach solves a
Boolean Matrix Factorization problem with phylogenetic
constraints, by maximizing a log-likelihood function. VERSO
includes two separate and subsequent steps; in this package we
provide an R implementation of VERSO STEP 1.
biocViews: BiomedicalInformatics, Sequencing, SomaticMutation
Author: Daniele Ramazzotti [aut] (ORCID:
<https://orcid.org/0000-0002-6087-2666>), Fabrizio Angaroni
[aut], Davide Maspero [cre, aut], Alex Graudenzi [aut], Luca De
Sano [aut] (ORCID: <https://orcid.org/0000-0002-9618-3774>)
Maintainer: Davide Maspero <d.maspero@campus.unimib.it>
URL: https://github.com/BIMIB-DISCo/VERSO
VignetteBuilder: knitr
BugReports: https://github.com/BIMIB-DISCo/VERSO
git_url: https://git.bioconductor.org/packages/VERSO
git_branch: devel
git_last_commit: 61607dc
git_last_commit_date: 2025-03-15
Date/Publication: 2025-03-17
source.ver: src/contrib/VERSO_1.17.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/VERSO_1.17.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/VERSO_1.17.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/VERSO_1.17.1.tgz
vignettes: vignettes/VERSO/inst/doc/v1_introduction.html,
vignettes/VERSO/inst/doc/v2_running_VERSO.html
vignetteTitles: Introduction, Running VERSO
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/VERSO/inst/doc/v1_introduction.R,
vignettes/VERSO/inst/doc/v2_running_VERSO.R
dependencyCount: 20
Package: vidger
Version: 1.27.0
Depends: R (>= 3.5)
Imports: Biobase, DESeq2, edgeR, GGally, ggplot2, ggrepel, knitr,
RColorBrewer, rmarkdown, scales, stats, SummarizedExperiment,
tidyr, utils
Suggests: BiocStyle, testthat
License: GPL-3 | file LICENSE
MD5sum: f9c2831a0bd8ba1511c7cb6677457e55
NeedsCompilation: no
Title: Create rapid visualizations of RNAseq data in R
Description: The aim of vidger is to rapidly generate information-rich
visualizations for the interpretation of differential gene
expression results from three widely-used tools: Cuffdiff,
DESeq2, and edgeR.
biocViews: ImmunoOncology, Visualization, RNASeq,
DifferentialExpression, GeneExpression, ImmunoOncology
Author: Brandon Monier [aut, cre], Adam McDermaid [aut], Jing Zhao
[aut], Qin Ma [aut, fnd]
Maintainer: Brandon Monier <brandon.monier@gmail.com>
URL: https://github.com/btmonier/vidger,
https://bioconductor.org/packages/release/bioc/html/vidger.html
VignetteBuilder: knitr
BugReports: https://github.com/btmonier/vidger/issues
git_url: https://git.bioconductor.org/packages/vidger
git_branch: devel
git_last_commit: 030cd02
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/vidger_1.27.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/vidger_1.27.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/vidger_1.27.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/vidger_1.27.0.tgz
vignettes: vignettes/vidger/inst/doc/vidger.html
vignetteTitles: Visualizing RNA-seq data with ViDGER
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/vidger/inst/doc/vidger.R
dependencyCount: 113
Package: viper
Version: 1.41.0
Depends: R (>= 2.14.0), Biobase, methods
Imports: mixtools, stats, parallel, e1071, KernSmooth
Suggests: bcellViper
License: file LICENSE
MD5sum: f1b5ae441ebec13d64676e7a8ab978a6
NeedsCompilation: no
Title: Virtual Inference of Protein-activity by Enriched Regulon
analysis
Description: Inference of protein activity from gene expression data,
including the VIPER and msVIPER algorithms
biocViews: SystemsBiology, NetworkEnrichment, GeneExpression,
FunctionalPrediction, GeneRegulation
Author: Mariano J Alvarez <reef103@gmail.com>
Maintainer: Mariano J Alvarez <reef103@gmail.com>
git_url: https://git.bioconductor.org/packages/viper
git_branch: devel
git_last_commit: 6d8ed69
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/viper_1.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/viper_1.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/viper_1.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/viper_1.41.0.tgz
vignettes: vignettes/viper/inst/doc/viper.pdf
vignetteTitles: Using VIPER
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/viper/inst/doc/viper.R
dependsOnMe: vulcan, aracne.networks
importsMe: diggit, RTN, diggitdata
suggestsMe: decoupleR, easier, MethReg, MOMA, dorothea, vulcandata
dependencyCount: 89
Package: ViSEAGO
Version: 1.21.0
Depends: R (>= 3.6)
Imports: data.table, AnnotationDbi, AnnotationForge, biomaRt,
dendextend, DiagrammeR, DT, dynamicTreeCut, fgsea, GOSemSim,
ggplot2, GO.db, grDevices, heatmaply, htmlwidgets, igraph,
methods, plotly, processx, topGO, RColorBrewer, R.utils,
scales, stats, UpSetR, utils
Suggests: htmltools, org.Mm.eg.db, limma, Rgraphviz, BiocStyle, knitr,
rmarkdown, corrplot, remotes, BiocManager
License: GPL-3 bioconductor.org
MD5sum: 26174710a2e1fef209177bdd484f8e55
NeedsCompilation: no
Title: ViSEAGO: a Bioconductor package for clustering biological
functions using Gene Ontology and semantic similarity
Description: The main objective of ViSEAGO package is to carry out a
data mining of biological functions and establish links between
genes involved in the study. We developed ViSEAGO in R to
facilitate functional Gene Ontology (GO) analysis of complex
experimental design with multiple comparisons of interest. It
allows to study large-scale datasets together and visualize GO
profiles to capture biological knowledge. The acronym stands
for three major concepts of the analysis: Visualization,
Semantic similarity and Enrichment Analysis of Gene Ontology.
It provides access to the last current GO annotations, which
are retrieved from one of NCBI EntrezGene, Ensembl or Uniprot
databases for several species. Using available R packages and
novel developments, ViSEAGO extends classical functional GO
analysis to focus on functional coherence by aggregating
closely related biological themes while studying multiple
datasets at once. It provides both a synthetic and detailed
view using interactive functionalities respecting the GO graph
structure and ensuring functional coherence supplied by
semantic similarity. ViSEAGO has been successfully applied on
several datasets from different species with a variety of
biological questions. Results can be easily shared between
bioinformaticians and biologists, enhancing reporting
capabilities while maintaining reproducibility.
biocViews: Software, Annotation, GO, GeneSetEnrichment,
MultipleComparison, Clustering, Visualization
Author: Aurelien Brionne [aut, cre], Amelie Juanchich [aut], Christelle
hennequet-antier [aut]
Maintainer: Aurelien Brionne <aurelien.brionne@inrae.fr>
URL:
https://www.bioconductor.org/packages/release/bioc/html/ViSEAGO.html,
https://forgemia.inra.fr/UMR-BOA/ViSEAGO
VignetteBuilder: knitr
BugReports: https://forgemia.inra.fr/UMR-BOA/ViSEAGO/issues
git_url: https://git.bioconductor.org/packages/ViSEAGO
git_branch: devel
git_last_commit: 7633da9
git_last_commit_date: 2024-12-02
Date/Publication: 2024-12-02
source.ver: src/contrib/ViSEAGO_1.21.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ViSEAGO_1.21.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ViSEAGO_1.21.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ViSEAGO_1.21.0.tgz
vignettes: vignettes/ViSEAGO/inst/doc/fgsea_alternative.html,
vignettes/ViSEAGO/inst/doc/mouse_bioconductor.html,
vignettes/ViSEAGO/inst/doc/SS_choice.html,
vignettes/ViSEAGO/inst/doc/ViSEAGO.html
vignetteTitles: 3: fgsea_alternative, 2: mouse_bionconductor, 4:
SS_choice, 1: ViSEAGO
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ViSEAGO/inst/doc/fgsea_alternative.R,
vignettes/ViSEAGO/inst/doc/mouse_bioconductor.R,
vignettes/ViSEAGO/inst/doc/SS_choice.R,
vignettes/ViSEAGO/inst/doc/ViSEAGO.R
dependencyCount: 169
Package: VisiumIO
Version: 1.3.5
Depends: R (>= 4.4.0), TENxIO
Imports: BiocBaseUtils, BiocGenerics, BiocIO (>= 1.15.1), jsonlite,
methods, S4Vectors, SingleCellExperiment, SpatialExperiment,
SummarizedExperiment
Suggests: arrow, BiocStyle, data.table, knitr, readr, rmarkdown,
tinytest
License: Artistic-2.0
Archs: x64
MD5sum: 6a3ca62b65f7a65f54b22263215e1d10
NeedsCompilation: no
Title: Import Visium data from the 10X Space Ranger pipeline
Description: The package allows users to readily import spatial data
obtained from either the 10X website or from the Space Ranger
pipeline. Supported formats include tar.gz, h5, and mtx files.
Multiple files can be imported at once with *List type of
functions. The package represents data mainly as
SpatialExperiment objects.
biocViews: Software, Infrastructure, DataImport, SingleCell, Spatial
Author: Marcel Ramos [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3242-0582>), Dario Righelli [aut,
ctb], Helena Crowell [aut, ctb]
Maintainer: Marcel Ramos <marcel.ramos@sph.cuny.edu>
URL: https://github.com/waldronlab/VisiumIO
VignetteBuilder: knitr
BugReports: https://github.com/waldronlab/VisiumIO/issues
git_url: https://git.bioconductor.org/packages/VisiumIO
git_branch: devel
git_last_commit: 5970e95
git_last_commit_date: 2025-02-11
Date/Publication: 2025-02-11
source.ver: src/contrib/VisiumIO_1.3.5.tar.gz
win.binary.ver: bin/windows/contrib/4.5/VisiumIO_1.3.5.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/VisiumIO_1.3.5.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/VisiumIO_1.3.5.tgz
vignettes: vignettes/VisiumIO/inst/doc/VisiumIO.html
vignetteTitles: VisiumIO Quick Start Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/VisiumIO/inst/doc/VisiumIO.R
importsMe: XeniumIO
suggestsMe: OSTA.data
dependencyCount: 92
Package: visiumStitched
Version: 0.99.15
Depends: R (>= 4.4), SpatialExperiment
Imports: BiocBaseUtils, BiocGenerics, dplyr, DropletUtils, grDevices,
imager, Matrix, methods, pkgcond, readr, rjson, S4Vectors,
SingleCellExperiment, spatialLIBD (>= 1.17.8), stringr,
SummarizedExperiment, tibble, tidyr, xml2
Suggests: BiocFileCache, BiocStyle, ggplot2, knitr, RefManageR,
rmarkdown, sessioninfo, Seurat, testthat (>= 3.0.0)
License: Artistic-2.0
Archs: x64
MD5sum: 6e7bd087b0a233e0b85755b2057b5568
NeedsCompilation: no
Title: Enable downstream analysis of Visium capture areas stitched
together with Fiji
Description: This package provides helper functions for working with
multiple Visium capture areas that overlap each other. This
package was developed along with the companion example use case
data available from
https://github.com/LieberInstitute/visiumStitched_brain.
visiumStitched prepares SpaceRanger (10x Genomics) output files
so you can stitch the images from groups of capture areas
together with Fiji. Then visiumStitched builds a
SpatialExperiment object with the stitched data and makes an
artificial hexogonal grid enabling the seamless use of spatial
clustering methods that rely on such grid to identify
neighboring spots, such as PRECAST and BayesSpace. The
SpatialExperiment objects created by visiumStitched are
compatible with spatialLIBD, which can be used to build
interactive websites for stitched SpatialExperiment objects.
visiumStitched also enables casting SpatialExperiment objects
as Seurat objects.
biocViews: Software, Spatial, Transcriptomics, Transcription,
GeneExpression, Visualization, DataImport
Author: Nicholas J. Eagles [aut, cre] (ORCID:
<https://orcid.org/0000-0002-9808-5254>), Leonardo
Collado-Torres [ctb] (ORCID:
<https://orcid.org/0000-0003-2140-308X>)
Maintainer: Nicholas J. Eagles <nickeagles77@gmail.com>
URL: https://github.com/LieberInstitute/visiumStitched
VignetteBuilder: knitr
BugReports: https://support.bioconductor.org/tag/visiumStitched
git_url: https://git.bioconductor.org/packages/visiumStitched
git_branch: devel
git_last_commit: 4c54019
git_last_commit_date: 2024-12-13
Date/Publication: 2024-12-18
source.ver: src/contrib/visiumStitched_0.99.15.tar.gz
win.binary.ver: bin/windows/contrib/4.5/visiumStitched_0.99.15.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/visiumStitched_0.99.15.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/visiumStitched_0.99.15.tgz
vignettes: vignettes/visiumStitched/inst/doc/misc.html,
vignettes/visiumStitched/inst/doc/visiumStitched.html
vignetteTitles: Miscellaneous notes, Introduction to visiumStitched
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/visiumStitched/inst/doc/misc.R,
vignettes/visiumStitched/inst/doc/visiumStitched.R
dependencyCount: 231
Package: vissE
Version: 1.15.0
Depends: R (>= 4.1)
Imports: igraph, methods, plyr, ggplot2, scico, RColorBrewer, tm,
ggwordcloud, GSEABase, reshape2, grDevices, ggforce, msigdb,
ggrepel, textstem, tidygraph, stats, scales, ggraph
Suggests: testthat, org.Hs.eg.db, org.Mm.eg.db, patchwork, singscore,
knitr, rmarkdown, prettydoc, BiocStyle, pkgdown, covr
License: GPL-3
MD5sum: 6c495062d411be4d6541cb8089fb309c
NeedsCompilation: no
Title: Visualising Set Enrichment Analysis Results
Description: This package enables the interpretation and analysis of
results from a gene set enrichment analysis using network-based
and text-mining approaches. Most enrichment analyses result in
large lists of significant gene sets that are difficult to
interpret. Tools in this package help build a similarity-based
network of significant gene sets from a gene set enrichment
analysis that can then be investigated for their biological
function using text-mining approaches.
biocViews: Software, GeneExpression, GeneSetEnrichment,
NetworkEnrichment, Network
Author: Dharmesh D. Bhuva [aut, cre] (ORCID:
<https://orcid.org/0000-0002-6398-9157>), Ahmed Mohamed [ctb]
Maintainer: Dharmesh D. Bhuva <bhuva.d@wehi.edu.au>
URL: https://davislaboratory.github.io/vissE
VignetteBuilder: knitr
BugReports: https://github.com/DavisLaboratory/vissE/issues
git_url: https://git.bioconductor.org/packages/vissE
git_branch: devel
git_last_commit: 090cdb9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/vissE_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/vissE_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/vissE_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/vissE_1.15.0.tgz
vignettes: vignettes/vissE/inst/doc/vissE.html
vignetteTitles: vissE
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/vissE/inst/doc/vissE.R
suggestsMe: msigdb
dependencyCount: 143
Package: Voyager
Version: 1.9.2
Depends: R (>= 4.2.0), SpatialFeatureExperiment (>= 1.7.3)
Imports: BiocParallel, bluster, DelayedArray, ggnewscale, ggplot2 (>=
3.4.0), grDevices, grid, lifecycle, Matrix, MatrixGenerics,
memuse, methods, patchwork, rlang, RSpectra, S4Vectors, scales,
scico, sf, SingleCellExperiment, SpatialExperiment, spdep,
stats, SummarizedExperiment, terra, utils, zeallot
Suggests: arrow, automap, BiocSingular, BiocStyle, cowplot, data.table,
DelayedMatrixStats, EBImage, ExperimentHub, ggh4x, gstat,
hexbin, knitr, matrixStats, pheatmap, RBioFormats, rhdf5,
rmarkdown, scater, scattermore, scran, sfarrow, SFEData,
testthat (>= 3.0.0), vdiffr, xml2
License: Artistic-2.0
Archs: x64
MD5sum: e6f0f77c13dbbfa9158914e69d59122d
NeedsCompilation: no
Title: From geospatial to spatial omics
Description: SpatialFeatureExperiment (SFE) is a new S4 class for
working with spatial single-cell genomics data. The voyager
package implements basic exploratory spatial data analysis
(ESDA) methods for SFE. Univariate methods include univariate
global spatial ESDA methods such as Moran's I, permutation
testing for Moran's I, and correlograms. Bivariate methods
include Lee's L and cross variogram. Multivariate methods
include MULTISPATI PCA and multivariate local Geary's C
recently developed by Anselin. The Voyager package also
implements plotting functions to plot SFE data and ESDA
results.
biocViews: GeneExpression, Spatial, Transcriptomics, Visualization
Author: Lambda Moses [aut, cre] (ORCID:
<https://orcid.org/0000-0002-7092-9427>), Alik Huseynov [aut]
(ORCID: <https://orcid.org/0000-0002-1438-4389>), Kayla Jackson
[aut] (ORCID: <https://orcid.org/0000-0001-6483-0108>), Laura
Luebbert [aut] (ORCID:
<https://orcid.org/0000-0003-1379-2927>), Lior Pachter [aut,
rev] (ORCID: <https://orcid.org/0000-0002-9164-6231>)
Maintainer: Lambda Moses <dl3764@columbia.edu>
URL: https://github.com/pachterlab/voyager
VignetteBuilder: knitr
BugReports: https://github.com/pachterlab/voyager/issues
git_url: https://git.bioconductor.org/packages/Voyager
git_branch: devel
git_last_commit: ca5600f
git_last_commit_date: 2024-12-23
Date/Publication: 2024-12-23
source.ver: src/contrib/Voyager_1.9.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Voyager_1.9.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Voyager_1.9.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Voyager_1.9.2.tgz
vignettes: vignettes/Voyager/inst/doc/overview.html
vignetteTitles: Functionality overview
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Voyager/inst/doc/overview.R
suggestsMe: alabaster.sfe, SpatialFeatureExperiment
dependencyCount: 178
Package: VplotR
Version: 1.17.0
Depends: R (>= 4.0), GenomicRanges, IRanges, ggplot2
Imports: cowplot, magrittr, GenomeInfoDb, GenomicAlignments,
RColorBrewer, zoo, Rsamtools, S4Vectors, parallel, reshape2,
methods, graphics, stats
Suggests: GenomicFeatures, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene,
testthat, covr, knitr, rmarkdown, pkgdown
License: GPL (>= 3)
MD5sum: a118085335952c14511d6a032c3eaf2e
NeedsCompilation: no
Title: Set of tools to make V-plots and compute footprint profiles
Description: The pattern of digestion and protection from DNA nucleases
such as DNAse I, micrococcal nuclease, and Tn5 transposase can
be used to infer the location of associated proteins. This
package contains useful functions to analyze patterns of
paired-end sequencing fragment density. VplotR facilitates the
generation of V-plots and footprint profiles over single or
aggregated genomic loci of interest.
biocViews: NucleosomePositioning, Coverage, Sequencing,
BiologicalQuestion, ATACSeq, Alignment
Author: Jacques Serizay [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4295-0624>)
Maintainer: Jacques Serizay <jacquesserizay@gmail.com>
URL: https://github.com/js2264/VplotR
VignetteBuilder: knitr
BugReports: https://github.com/js2264/VplotR/issues
git_url: https://git.bioconductor.org/packages/VplotR
git_branch: devel
git_last_commit: 388e664
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/VplotR_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/VplotR_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/VplotR_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/VplotR_1.17.0.tgz
vignettes: vignettes/VplotR/inst/doc/VplotR.html
vignetteTitles: VplotR
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/VplotR/inst/doc/VplotR.R
dependencyCount: 84
Package: vsclust
Version: 1.9.10
Depends: R (>= 4.2.0)
Imports: matrixStats, limma, parallel, shiny, qvalue, grDevices, stats,
MultiAssayExperiment, graphics
LinkingTo: Rcpp
Suggests: knitr, yaml, testthat (>= 3.0.0), rmarkdown, BiocStyle,
clusterProfiler, httr
License: GPL-2
MD5sum: 5e7d3fbfb84224949f9e746f5295e650
NeedsCompilation: yes
Title: Feature-based variance-sensitive quantitative clustering
Description: Feature-based variance-sensitive clustering of omics data.
Optimizes cluster assignment by taking into account individual
feature variance. Includes several modules for statistical
testing, clustering and enrichment analysis.
biocViews: Clustering, Annotation, PrincipalComponent,
DifferentialExpression, Visualization, Proteomics, Metabolomics
Author: Veit Schwammle [aut, cre]
Maintainer: Veit Schwammle <veits@bmb.sdu.dk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/vsclust
git_branch: devel
git_last_commit: 0bcc7ba
git_last_commit_date: 2025-01-15
Date/Publication: 2025-01-15
source.ver: src/contrib/vsclust_1.9.10.tar.gz
win.binary.ver: bin/windows/contrib/4.5/vsclust_1.9.10.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/vsclust_1.9.10.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/vsclust_1.9.10.tgz
vignettes:
vignettes/vsclust/inst/doc/Integrate_With_Bioconductor_Objects.html,
vignettes/vsclust/inst/doc/Run_VSClust_Workflow.html
vignetteTitles: VSClust on Bioconductor object, VSClust workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles:
vignettes/vsclust/inst/doc/Integrate_With_Bioconductor_Objects.R,
vignettes/vsclust/inst/doc/Run_VSClust_Workflow.R
dependencyCount: 97
Package: vsn
Version: 3.75.0
Depends: R (>= 4.0.0), methods, Biobase
Imports: affy, limma, lattice, ggplot2
Suggests: affydata, hgu95av2cdf, BiocStyle, knitr, rmarkdown, dplyr,
testthat
License: Artistic-2.0
MD5sum: e41ee936701ec65753a4efdfb182c124
NeedsCompilation: yes
Title: Variance stabilization and calibration for microarray data
Description: The package implements a method for normalising microarray
intensities from single- and multiple-color arrays. It can also
be used for data from other technologies, as long as they have
similar format. The method uses a robust variant of the
maximum-likelihood estimator for an additive-multiplicative
error model and affine calibration. The model incorporates data
calibration step (a.k.a. normalization), a model for the
dependence of the variance on the mean intensity and a variance
stabilizing data transformation. Differences between
transformed intensities are analogous to "normalized
log-ratios". However, in contrast to the latter, their variance
is independent of the mean, and they are usually more sensitive
and specific in detecting differential transcription.
biocViews: Microarray, OneChannel, TwoChannel, Preprocessing
Author: Wolfgang Huber, with contributions from Anja von Heydebreck.
Many comments and suggestions by users are acknowledged, among
them Dennis Kostka, David Kreil, Hans-Ulrich Klein, Robert
Gentleman, Deepayan Sarkar and Gordon Smyth
Maintainer: Wolfgang Huber <wolfgang.huber@embl.org>
URL: http://www.r-project.org, http://www.ebi.ac.uk/huber
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/vsn
git_branch: devel
git_last_commit: 79d34f6
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/vsn_3.75.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/vsn_3.75.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/vsn_3.75.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/vsn_3.75.0.tgz
vignettes: vignettes/vsn/inst/doc/C-likelihoodcomputations.pdf,
vignettes/vsn/inst/doc/D-convergence.pdf,
vignettes/vsn/inst/doc/A-vsn.html
vignetteTitles: Likelihood Calculations for vsn, Verifying and
assessing the performance with simulated data, Introduction to
vsn (HTML version)
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/vsn/inst/doc/A-vsn.R,
vignettes/vsn/inst/doc/C-likelihoodcomputations.R
dependsOnMe: webbioc, rnaseqGene
importsMe: arrayQualityMetrics, autonomics, bnem, DEP, Doscheda,
MatrixQCvis, metaseqR2, MSnbase, NormalyzerDE, PRONE, pvca,
tilingArray, ExpressionNormalizationWorkflow, lfproQC
suggestsMe: adSplit, beadarray, DAPAR, DESeq2, ggbio, GlobalAncova,
globaltest, limma, lumi, MsCoreUtils, PAA, QFeatures, qmtools,
ribosomeProfilingQC, scp, twilight, estrogen, wrMisc
dependencyCount: 44
Package: vtpnet
Version: 0.47.0
Depends: R (>= 3.0.0), graph, GenomicRanges, gwascat, doParallel,
foreach
Suggests: MotifDb, VariantAnnotation, Rgraphviz
License: Artistic-2.0
MD5sum: 21fac43230cc46ddd7fb57c3b22c426d
NeedsCompilation: no
Title: variant-transcription factor-phenotype networks
Description: variant-transcription factor-phenotype networks, inspired
by Maurano et al., Science (2012), PMID 22955828
biocViews: Network
Author: VJ Carey <stvjc@channing.harvard.edu>
Maintainer: VJ Carey <stvjc@channing.harvard.edu>
git_url: https://git.bioconductor.org/packages/vtpnet
git_branch: devel
git_last_commit: 822bd57
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/vtpnet_0.47.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/vtpnet_0.47.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/vtpnet_0.47.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/vtpnet_0.47.0.tgz
vignettes: vignettes/vtpnet/inst/doc/vtpnet.pdf
vignetteTitles: vtpnet: variant-transcription factor-network tools
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/vtpnet/inst/doc/vtpnet.R
dependencyCount: 115
Package: vulcan
Version: 1.29.0
Depends: R (>= 4.0), ChIPpeakAnno,TxDb.Hsapiens.UCSC.hg19.knownGene,
zoo, GenomicRanges, S4Vectors, viper, DiffBind, locfit
Imports: wordcloud, csaw, gplots, stats, utils, caTools, graphics,
DESeq2, Biobase
Suggests: vulcandata
License: LGPL-3
MD5sum: c90bd2f5735f443085999fd5203a1334
NeedsCompilation: no
Title: VirtUaL ChIP-Seq data Analysis using Networks
Description: Vulcan (VirtUaL ChIP-Seq Analysis through Networks) is a
package that interrogates gene regulatory networks to infer
cofactors significantly enriched in a differential binding
signature coming from ChIP-Seq data. In order to do so, our
package combines strategies from different BioConductor
packages: DESeq for data normalization, ChIPpeakAnno and
DiffBind for annotation and definition of ChIP-Seq genomic
peaks, csaw to define optimal peak width and viper for applying
a regulatory network over a differential binding signature.
biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, ChIPSeq
Author: Federico M. Giorgi, Andrew N. Holding, Florian Markowetz
Maintainer: Federico M. Giorgi <federico.giorgi@gmail.com>
git_url: https://git.bioconductor.org/packages/vulcan
git_branch: devel
git_last_commit: 45481f2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/vulcan_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/vulcan_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/vulcan_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/vulcan_1.29.0.tgz
vignettes: vignettes/vulcan/inst/doc/vulcan.pdf
vignetteTitles: Vulcan: VirtUaL ChIP-Seq Analysis through Networks
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/vulcan/inst/doc/vulcan.R
dependencyCount: 200
Package: waddR
Version: 1.21.0
Depends: R (>= 3.6.0)
Imports: Rcpp (>= 1.0.1), arm (>= 1.10-1), eva, BiocFileCache (>=
2.6.0), BiocParallel, SingleCellExperiment, parallel, methods,
stats
LinkingTo: Rcpp, RcppArmadillo,
Suggests: knitr, devtools, testthat, roxygen2, rprojroot, rmarkdown,
scater
License: MIT + file LICENSE
MD5sum: 52d98d860ddf48368adc02cb0fd32511
NeedsCompilation: yes
Title: Statistical tests for detecting differential distributions based
on the 2-Wasserstein distance
Description: The package offers statistical tests based on the
2-Wasserstein distance for detecting and characterizing
differences between two distributions given in the form of
samples. Functions for calculating the 2-Wasserstein distance
and testing for differential distributions are provided, as
well as a specifically tailored test for differential
expression in single-cell RNA sequencing data.
biocViews: Software, StatisticalMethod, SingleCell,
DifferentialExpression
Author: Roman Schefzik [aut], Julian Flesch [cre]
Maintainer: Julian Flesch <julianflesch@gmail.com>
URL: https://github.com/goncalves-lab/waddR.git
VignetteBuilder: knitr
BugReports: https://github.com/goncalves-lab/waddR/issues
git_url: https://git.bioconductor.org/packages/waddR
git_branch: devel
git_last_commit: fc50998
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/waddR_1.21.0.tar.gz
vignettes: vignettes/waddR/inst/doc/waddR.html,
vignettes/waddR/inst/doc/wasserstein_metric.html,
vignettes/waddR/inst/doc/wasserstein_singlecell.html,
vignettes/waddR/inst/doc/wasserstein_test.html
vignetteTitles: waddR, wasserstein_metric, wasserstein_singlecell,
wasserstein_test
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/waddR/inst/doc/waddR.R,
vignettes/waddR/inst/doc/wasserstein_metric.R,
vignettes/waddR/inst/doc/wasserstein_singlecell.R,
vignettes/waddR/inst/doc/wasserstein_test.R
dependencyCount: 106
Package: wateRmelon
Version: 2.13.0
Depends: R (>= 3.5.0), Biobase, limma, methods, matrixStats, methylumi,
lumi, ROC, IlluminaHumanMethylation450kanno.ilmn12.hg19,
illuminaio
Imports: Biobase
Suggests: RPMM, IlluminaHumanMethylationEPICanno.ilm10b2.hg19,
BiocStyle, knitr, rmarkdown,
IlluminaHumanMethylationEPICmanifest, irlba,
FlowSorted.Blood.EPIC, FlowSorted.Blood.450k, preprocessCore
Enhances: minfi
License: GPL-3
MD5sum: 89dcfa6a01468c7ad4c1a68f6cfc62b8
NeedsCompilation: no
Title: Illumina DNA methylation array normalization and metrics
Description: 15 flavours of betas and three performance metrics, with
methods for objects produced by methylumi and minfi packages.
biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing,
QualityControl
Author: Leo C Schalkwyk [cre, aut], Tyler J Gorrie-Stone [aut], Ruth
Pidsley [aut], Chloe CY Wong [aut], Nizar Touleimat [ctb],
Matthieu Defrance [ctb], Andrew Teschendorff [ctb], Jovana
Maksimovic [ctb], Louis Y El Khoury [ctb], Yucheng Wang [ctb],
Alexandria Andrayas [ctb]
Maintainer: Leo C Schalkwyk <lschal@essex.ac.uk>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/wateRmelon
git_branch: devel
git_last_commit: 980e6cd
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/wateRmelon_2.13.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/wateRmelon_2.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/wateRmelon_2.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/wateRmelon_2.13.0.tgz
vignettes: vignettes/wateRmelon/inst/doc/wateRmelon.html
vignetteTitles: wateRmelon User's Guide
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/wateRmelon/inst/doc/wateRmelon.R
dependsOnMe: bigmelon, skewr
importsMe: ChAMP, MEAT
suggestsMe: RnBeads
dependencyCount: 172
Package: wavClusteR
Version: 2.41.0
Depends: R (>= 3.2), GenomicRanges (>= 1.31.8), Rsamtools
Imports: methods, BiocGenerics, S4Vectors (>= 0.17.25), IRanges (>=
2.13.12), Biostrings (>= 2.47.6), foreach, GenomicFeatures (>=
1.31.3), ggplot2, Hmisc, mclust, rtracklayer (>= 1.39.7),
seqinr, stringr
Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19
Enhances: doMC
License: GPL-2
MD5sum: 37cf9c0ee07c28e91d2e0dbd3eb686b5
NeedsCompilation: no
Title: Sensitive and highly resolved identification of RNA-protein
interaction sites in PAR-CLIP data
Description: The package provides an integrated pipeline for the
analysis of PAR-CLIP data. PAR-CLIP-induced transitions are
first discriminated from sequencing errors, SNPs and additional
non-experimental sources by a non- parametric mixture model.
The protein binding sites (clusters) are then resolved at high
resolution and cluster statistics are estimated using a
rigorous Bayesian framework. Post-processing of the results,
data export for UCSC genome browser visualization and motif
search analysis are provided. In addition, the package allows
to integrate RNA-Seq data to estimate the False Discovery Rate
of cluster detection. Key functions support parallel multicore
computing. Note: while wavClusteR was designed for PAR-CLIP
data analysis, it can be applied to the analysis of other NGS
data obtained from experimental procedures that induce
nucleotide substitutions (e.g. BisSeq).
biocViews: ImmunoOncology, Sequencing, Technology, RIPSeq, RNASeq,
Bayesian
Author: Federico Comoglio and Cem Sievers
Maintainer: Federico Comoglio <federico.comoglio@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/wavClusteR
git_branch: devel
git_last_commit: bd37a40
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/wavClusteR_2.41.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/wavClusteR_2.41.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/wavClusteR_2.41.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/wavClusteR_2.41.0.tgz
vignettes: vignettes/wavClusteR/inst/doc/wavCluster_vignette.html
vignetteTitles: wavClusteR: a workflow for PAR-CLIP data analysis
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/wavClusteR/inst/doc/wavCluster_vignette.R
dependencyCount: 138
Package: weaver
Version: 1.73.0
Depends: R (>= 2.5.0), digest, tools, utils, codetools
Suggests: codetools
License: GPL-2
MD5sum: 5bf6b6760e01d30cc35a8e63f11d8c3f
NeedsCompilation: no
Title: Tools and extensions for processing Sweave documents
Description: This package provides enhancements on the Sweave()
function in the base package. In particular a facility for
caching code chunk results is included.
biocViews: Infrastructure
Author: Seth Falcon
Maintainer: Seth Falcon <seth@userprimary.net>
git_url: https://git.bioconductor.org/packages/weaver
git_branch: devel
git_last_commit: 8e20e96
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/weaver_1.73.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/weaver_1.73.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/weaver_1.73.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/weaver_1.73.0.tgz
vignettes: vignettes/weaver/inst/doc/weaver_howTo.pdf
vignetteTitles: Using weaver to process Sweave documents
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/weaver/inst/doc/weaver_howTo.R
dependencyCount: 4
Package: webbioc
Version: 1.79.0
Depends: R (>= 1.8.0), Biobase, affy, multtest, annaffy, vsn, gcrma,
qvalue
Imports: multtest, qvalue, stats, utils, BiocManager
License: GPL (>= 2)
MD5sum: bd445d134d315b150a41a3eaacd523b4
NeedsCompilation: no
Title: Bioconductor Web Interface
Description: An integrated web interface for doing microarray analysis
using several of the Bioconductor packages. It is intended to
be deployed as a centralized bioinformatics resource for use by
many users. (Currently only Affymetrix oligonucleotide analysis
is supported.)
biocViews: Infrastructure, Microarray, OneChannel,
DifferentialExpression
Author: Colin A. Smith <colin@colinsmith.org>
Maintainer: Colin A. Smith <colin@colinsmith.org>
URL: http://www.bioconductor.org/
SystemRequirements: Unix, Perl (>= 5.6.0), Netpbm
git_url: https://git.bioconductor.org/packages/webbioc
git_branch: devel
git_last_commit: ab9af2a
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/webbioc_1.79.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/webbioc_1.79.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/webbioc_1.79.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/webbioc_1.79.0.tgz
vignettes: vignettes/webbioc/inst/doc/demoscript.pdf,
vignettes/webbioc/inst/doc/webbioc.pdf
vignetteTitles: webbioc Demo Script, webbioc Overview
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependencyCount: 86
Package: weitrix
Version: 1.19.0
Depends: R (>= 3.6), SummarizedExperiment
Imports: methods, utils, stats, grDevices, assertthat, S4Vectors,
DelayedArray, DelayedMatrixStats, BiocParallel, BiocGenerics,
limma, topconfects, dplyr, purrr, ggplot2, rlang, scales,
reshape2, splines, Ckmeans.1d.dp, glm2, RhpcBLASctl
Suggests: knitr, rmarkdown, BiocStyle, tidyverse, airway, edgeR,
EnsDb.Hsapiens.v86, org.Sc.sgd.db, AnnotationDbi,
ComplexHeatmap, patchwork, testthat (>= 2.1.0)
License: LGPL-2.1 | file LICENSE
Archs: x64
MD5sum: da901d913905515b178002a1b165bbd1
NeedsCompilation: no
Title: Tools for matrices with precision weights, test and explore
weighted or sparse data
Description: Data type and tools for working with matrices having
precision weights and missing data. This package provides a
common representation and tools that can be used with many
types of high-throughput data. The meaning of the weights is
compatible with usage in the base R function "lm" and the
package "limma". Calibrate weights to account for known
predictors of precision. Find rows with excess variability.
Perform differential testing and find rows with the largest
confident differences. Find PCA-like components of variation
even with many missing values, rotated so that individual
components may be meaningfully interpreted. DelayedArray
matrices and BiocParallel are supported.
biocViews: Software, DataRepresentation, DimensionReduction,
GeneExpression, Transcriptomics, RNASeq, SingleCell, Regression
Author: Paul Harrison [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3980-268X>)
Maintainer: Paul Harrison <paul.harrison@monash.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/weitrix
git_branch: devel
git_last_commit: 9762181
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/weitrix_1.19.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/weitrix_1.19.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/weitrix_1.19.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/weitrix_1.19.0.tgz
vignettes: vignettes/weitrix/inst/doc/V1_overview.html,
vignettes/weitrix/inst/doc/V2_tail_length.html,
vignettes/weitrix/inst/doc/V3_shift.html,
vignettes/weitrix/inst/doc/V4_airway.html,
vignettes/weitrix/inst/doc/V5_slam_seq.html
vignetteTitles: 1. Concepts and practical details, 2. poly(A) tail
length example, 3. Alternative polyadenylation, 4. RNA-Seq
expression example, 5. Proportions data example with SLAM-Seq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/weitrix/inst/doc/V2_tail_length.R,
vignettes/weitrix/inst/doc/V3_shift.R,
vignettes/weitrix/inst/doc/V4_airway.R,
vignettes/weitrix/inst/doc/V5_slam_seq.R
dependencyCount: 91
Package: widgetTools
Version: 1.85.0
Depends: R (>= 2.4.0), methods, utils, tcltk
Suggests: Biobase
License: LGPL
Archs: x64
MD5sum: 185bae757044573d842fd9c009e83acb
NeedsCompilation: no
Title: Creates an interactive tcltk widget
Description: This packages contains tools to support the construction
of tcltk widgets
biocViews: Infrastructure
Author: Jianhua Zhang
Maintainer: Jianhua Zhang <jzhang@jimmy.harvard.edu>
git_url: https://git.bioconductor.org/packages/widgetTools
git_branch: devel
git_last_commit: b5b1198
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/widgetTools_1.85.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/widgetTools_1.85.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/widgetTools_1.85.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/widgetTools_1.85.0.tgz
vignettes: vignettes/widgetTools/inst/doc/widgetTools.pdf
vignetteTitles: widgetTools Introduction
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/widgetTools/inst/doc/widgetTools.R
dependsOnMe: tkWidgets
importsMe: OLINgui, SeqFeatR
suggestsMe: affy
dependencyCount: 3
Package: wiggleplotr
Version: 1.31.0
Depends: R (>= 3.6)
Imports: dplyr, ggplot2 (>= 2.2.0), GenomicRanges, rtracklayer,
cowplot, assertthat, purrr, S4Vectors, IRanges, GenomeInfoDb
Suggests: knitr, rmarkdown, biomaRt, GenomicFeatures, testthat,
ensembldb, EnsDb.Hsapiens.v86, org.Hs.eg.db,
TxDb.Hsapiens.UCSC.hg38.knownGene, AnnotationDbi,
AnnotationFilter
License: Apache License 2.0
MD5sum: a2474fb9a4fa965fd693102ada4be3a5
NeedsCompilation: no
Title: Make read coverage plots from BigWig files
Description: Tools to visualise read coverage from sequencing
experiments together with genomic annotations (genes,
transcripts, peaks). Introns of long transcripts can be
rescaled to a fixed length for better visualisation of exonic
read coverage.
biocViews: ImmunoOncology, Coverage, RNASeq, ChIPSeq, Sequencing,
Visualization, GeneExpression, Transcription,
AlternativeSplicing
Author: Kaur Alasoo [aut, cre]
Maintainer: Kaur Alasoo <kaur.alasoo@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/wiggleplotr
git_branch: devel
git_last_commit: aad27cc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/wiggleplotr_1.31.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/wiggleplotr_1.31.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/wiggleplotr_1.31.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/wiggleplotr_1.31.0.tgz
vignettes: vignettes/wiggleplotr/inst/doc/wiggleplotr.html
vignetteTitles: Introduction to wiggleplotr
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/wiggleplotr/inst/doc/wiggleplotr.R
importsMe: chevreulPlot, chevreulShiny, factR
suggestsMe: MARVEL
dependencyCount: 89
Package: wpm
Version: 1.17.0
Depends: R (>= 4.1.0)
Imports: utils, methods, cli, Biobase, SummarizedExperiment, config,
golem, shiny, DT, ggplot2, dplyr, rlang, stringr,
shinydashboard, shinyWidgets, shinycustomloader, RColorBrewer,
logging
Suggests: MSnbase, testthat, BiocStyle, knitr, rmarkdown
License: Artistic-2.0
MD5sum: b83709c23aa91ff32e9d2d39e5bc4c24
NeedsCompilation: no
Title: Well Plate Maker
Description: The Well-Plate Maker (WPM) is a shiny application deployed
as an R package. Functions for a command-line/script use are
also available. The WPM allows users to generate well plate
maps to carry out their experiments while improving the
handling of batch effects. In particular, it helps controlling
the "plate effect" thanks to its ability to randomize samples
over multiple well plates. The algorithm for placing the
samples is inspired by the backtracking algorithm: the samples
are placed at random while respecting specific spatial
constraints.
biocViews: GUI, Proteomics, MassSpectrometry, BatchEffect,
ExperimentalDesign
Author: Helene Borges [aut, cre], Thomas Burger [aut]
Maintainer: Helene Borges <borges.helene.sophie@gmail.com>
URL: https://github.com/HelBor/wpm,
https://bioconductor.org/packages/release/bioc/html/wpm.html
VignetteBuilder: knitr
BugReports: https://github.com/HelBor/wpm/issues
git_url: https://git.bioconductor.org/packages/wpm
git_branch: devel
git_last_commit: a741648
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/wpm_1.17.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/wpm_1.17.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/wpm_1.17.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/wpm_1.17.0.tgz
vignettes: vignettes/wpm/inst/doc/wpm_vignette.html
vignetteTitles: How to use Well Plate Maker
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/wpm/inst/doc/wpm_vignette.R
dependencyCount: 106
Package: wppi
Version: 1.15.0
Depends: R(>= 4.1)
Imports: dplyr, igraph, logger, methods, magrittr, Matrix, OmnipathR(>=
2.99.8), progress, purrr, rlang, RCurl, stats, tibble, tidyr
Suggests: knitr, testthat, rmarkdown
License: MIT + file LICENSE
MD5sum: 52ac0c572df18ce8ac0f4af77786c6c7
NeedsCompilation: no
Title: Weighting protein-protein interactions
Description: Protein-protein interaction data is essential for omics
data analysis and modeling. Database knowledge is general, not
specific for cell type, physiological condition or any other
context determining which connections are functional and
contribute to the signaling. Functional annotations such as
Gene Ontology and Human Phenotype Ontology might help to
evaluate the relevance of interactions. This package predicts
functional relevance of protein-protein interactions based on
functional annotations such as Human Protein Ontology and Gene
Ontology, and prioritizes genes based on network topology,
functional scores and a path search algorithm.
biocViews: GraphAndNetwork, Network, Pathways, Software, GeneSignaling,
GeneTarget, SystemsBiology, Transcriptomics, Annotation
Author: Ana Galhoz [cre, aut] (ORCID:
<https://orcid.org/0000-0001-7402-5292>), Denes Turei [aut]
(ORCID: <https://orcid.org/0000-0002-7249-9379>), Michael P.
Menden [aut] (ORCID: <https://orcid.org/0000-0003-0267-5792>),
Albert Krewinkel [ctb, cph] (pagebreak Lua filter)
Maintainer: Ana Galhoz <ana.galhoz@helmholtz-muenchen.de>
URL: https://github.com/AnaGalhoz37/wppi
VignetteBuilder: knitr
BugReports: https://github.com/AnaGalhoz37/wppi/issues
git_url: https://git.bioconductor.org/packages/wppi
git_branch: devel
git_last_commit: 321e17d
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/wppi_1.15.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/wppi_1.15.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/wppi_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/wppi_1.15.0.tgz
vignettes: vignettes/wppi/inst/doc/wppi_workflow.html
vignetteTitles: WPPI workflow
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/wppi/inst/doc/wppi_workflow.R
dependencyCount: 92
Package: Wrench
Version: 1.25.0
Depends: R (>= 3.5.0)
Imports: limma, matrixStats, locfit, stats, graphics
Suggests: knitr, rmarkdown, metagenomeSeq, DESeq2, edgeR
License: Artistic-2.0
MD5sum: 9892c984775e31706960cfdfe7ac9067
NeedsCompilation: no
Title: Wrench normalization for sparse count data
Description: Wrench is a package for normalization sparse genomic count
data, like that arising from 16s metagenomic surveys.
biocViews: Normalization, Sequencing, Software
Author: Senthil Kumar Muthiah [aut], Hector Corrada Bravo [aut, cre]
Maintainer: Hector Corrada Bravo <hcorrada@gmail.com>
URL: https://github.com/HCBravoLab/Wrench
VignetteBuilder: knitr
BugReports: https://github.com/HCBravoLab/Wrench/issues
git_url: https://git.bioconductor.org/packages/Wrench
git_branch: devel
git_last_commit: 4f4b817
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/Wrench_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/Wrench_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Wrench_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Wrench_1.25.0.tgz
vignettes: vignettes/Wrench/inst/doc/vignette.html
vignetteTitles: Wrench
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Wrench/inst/doc/vignette.R
importsMe: metagenomeSeq
dependencyCount: 11
Package: XAItest
Version: 0.99.25
Depends: R (>= 3.5.0)
Imports: limma, randomForest, kernelshap, caret, lime, DT, methods,
SummarizedExperiment, ggplot2
Suggests: knitr, ggforce, shapr (>= 1.0.1), airway, xgboost,
BiocGenerics, RUnit, S4Vectors
License: MIT + file LICENSE
MD5sum: a2428c56af325eeb1f078fa6114a5e85
NeedsCompilation: no
Title: XAItest: Enhancing Feature Discovery with eXplainable AI
Description: XAItest is an R Package that identifies features using
eXplainable AI (XAI) methods such as SHAP or LIME. This package
allows users to compare these methods with traditional
statistical tests like t-tests, empirical Bayes, and Fisher's
test. Additionally, it includes a system that enables the
comparison of feature importance with p-values by incorporating
calibrated simulated data.
biocViews: Software, StatisticalMethod, FeatureExtraction,
Classification, Regression
Author: Ghislain FIEVET [aut, cre] (ORCID:
<https://orcid.org/0000-0002-0337-7327>), Sébastien HERGALANT
[aut] (ORCID: <https://orcid.org/0000-0001-8456-7992>)
Maintainer: Ghislain FIEVET <ghislain.fievet@gmail.com>
URL: https://github.com/GhislainFievet/XAItest
VignetteBuilder: knitr
BugReports: https://github.com/GhislainFievet/XAItest/issues
git_url: https://git.bioconductor.org/packages/XAItest
git_branch: devel
git_last_commit: 8d95c17
git_last_commit_date: 2025-03-23
Date/Publication: 2025-03-23
source.ver: src/contrib/XAItest_0.99.25.tar.gz
win.binary.ver: bin/windows/contrib/4.5/XAItest_0.99.25.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/XAItest_0.99.25.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/XAItest_0.99.25.tgz
vignettes: vignettes/XAItest/inst/doc/customFunction.html,
vignettes/XAItest/inst/doc/XAItest.html
vignetteTitles: 01_XAItest, 01_XAItest
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/XAItest/inst/doc/customFunction.R,
vignettes/XAItest/inst/doc/XAItest.R
dependencyCount: 143
Package: xCell2
Version: 0.99.110
Depends: R (>= 4.0.0)
Imports: SummarizedExperiment, SingleCellExperiment, Rfast, singscore,
AnnotationHub, ontologyIndex, tibble, dplyr, BiocParallel,
Matrix, minpack.lm, pracma, methods, readr, magrittr, progress,
quadprog
Suggests: testthat, knitr, rmarkdown, ggplot2, randomForest, tidyr,
EnhancedVolcano, BiocStyle
License: GPL (>= 3)
MD5sum: de151a96e7d4364866e69a7b1f67d65e
NeedsCompilation: no
Title: A Tool for Generic Cell Type Enrichment Analysis
Description: xCell2 provides methods for cell type enrichment analysis
using cell type signatures. It includes three main functions -
1. xCell2Train for training custom references objects from bulk
or single-cell RNA-seq datasets. 2. xCell2Analysis for
conducting the cell type enrichment analysis using the custom
reference. 3. xCell2GetLineage for identifying dependencies
between different cell types using ontology.
biocViews: GeneExpression, Transcriptomics, Microarray, RNASeq,
SingleCell, DifferentialExpression, ImmunoOncology,
GeneSetEnrichment
Author: Almog Angel [aut, cre] (ORCID:
<https://orcid.org/0009-0001-3297-6935>), Dvir Aran [aut]
(ORCID: <https://orcid.org/0000-0001-6334-5039>)
Maintainer: Almog Angel <almog.angel@campus.technion.ac.il>
URL: https://github.com/AlmogAngel/xCell2
VignetteBuilder: knitr
BugReports: https://github.com/AlmogAngel/xCell2/issues
git_url: https://git.bioconductor.org/packages/xCell2
git_branch: devel
git_last_commit: 4633459
git_last_commit_date: 2025-03-19
Date/Publication: 2025-03-19
source.ver: src/contrib/xCell2_0.99.110.tar.gz
win.binary.ver: bin/windows/contrib/4.5/xCell2_0.99.110.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/xCell2_0.99.110.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/xCell2_0.99.110.tgz
vignettes: vignettes/xCell2/inst/doc/xCell2-vignette.html
vignetteTitles: Introduction to xCell2
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/xCell2/inst/doc/xCell2-vignette.R
dependencyCount: 151
Package: xcms
Version: 4.5.4
Depends: R (>= 4.1.0), BiocParallel (>= 1.8.0)
Imports: MSnbase (>= 2.33.3), mzR (>= 2.25.3), methods, Biobase,
BiocGenerics, ProtGenerics (>= 1.37.1), lattice,
MassSpecWavelet (>= 1.66.0), S4Vectors, IRanges,
SummarizedExperiment, MsCoreUtils (>= 1.15.5), MsFeatures,
MsExperiment (>= 1.5.4), Spectra (>= 1.15.7), progress,
RColorBrewer, MetaboCoreUtils (>= 1.11.2)
Suggests: BiocStyle, caTools, knitr (>= 1.1.0), faahKO, msdata (>=
0.25.1), ncdf4, testthat (>= 3.1.9), pander, rmarkdown,
MALDIquant, pheatmap, RANN, multtest, MsBackendMgf, signal,
mgcv
Enhances: Rgraphviz, rgl
License: GPL (>= 2) + file LICENSE
MD5sum: 7dff5230f100485e1b974946167dc123
NeedsCompilation: yes
Title: LC-MS and GC-MS Data Analysis
Description: Framework for processing and visualization of
chromatographically separated and single-spectra mass spectral
data. Imports from AIA/ANDI NetCDF, mzXML, mzData and mzML
files. Preprocesses data for high-throughput, untargeted
analyte profiling.
biocViews: ImmunoOncology, MassSpectrometry, Metabolomics
Author: Colin A. Smith [aut], Ralf Tautenhahn [aut], Steffen Neumann
[aut, cre] (ORCID: <https://orcid.org/0000-0002-7899-7192>),
Paul Benton [aut], Christopher Conley [aut], Johannes Rainer
[aut] (ORCID: <https://orcid.org/0000-0002-6977-7147>), Michael
Witting [ctb], William Kumler [aut] (ORCID:
<https://orcid.org/0000-0002-5022-8009>), Philippine Louail
[aut] (ORCID: <https://orcid.org/0009-0007-5429-6846>), Pablo
Vangeenderhuysen [ctb] (ORCID:
<https://orcid.org/0000-0002-5492-6904>), Carl Brunius [ctb]
(ORCID: <https://orcid.org/0000-0003-3957-870X>)
Maintainer: Steffen Neumann <sneumann@ipb-halle.de>
URL: https://github.com/sneumann/xcms
VignetteBuilder: knitr
BugReports: https://github.com/sneumann/xcms/issues/new
git_url: https://git.bioconductor.org/packages/xcms
git_branch: devel
git_last_commit: b4e230e9
git_last_commit_date: 2025-03-14
Date/Publication: 2025-03-17
source.ver: src/contrib/xcms_4.5.4.tar.gz
win.binary.ver: bin/windows/contrib/4.5/xcms_4.5.4.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/xcms_4.5.4.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/xcms_4.5.4.tgz
vignettes: vignettes/xcms/inst/doc/LC-MS-feature-grouping.html,
vignettes/xcms/inst/doc/xcms-direct-injection.html,
vignettes/xcms/inst/doc/xcms-lcms-ms.html,
vignettes/xcms/inst/doc/xcms.html
vignetteTitles: LC-MS feature grouping, Grouping FTICR-MS data with
xcms, LC-MS/MS data analysis with xcms, LC-MS data
preprocessing and analysis with xcms
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/xcms/inst/doc/LC-MS-feature-grouping.R,
vignettes/xcms/inst/doc/xcms-direct-injection.R,
vignettes/xcms/inst/doc/xcms-lcms-ms.R,
vignettes/xcms/inst/doc/xcms.R
dependsOnMe: CAMERA, flagme, IPO, LOBSTAHS, metaMS, ncGTW, PtH2O2lipids
importsMe: CAMERA, cliqueMS, cosmiq, squallms, faahKO
suggestsMe: CluMSID, msPurity, RMassBank, msdata, mtbls2,
RforProteomics, CorrectOverloadedPeaks, isatabr, LCMSQA,
MetabolomicsBasics, RAMClustR
dependencyCount: 145
Package: xcore
Version: 1.11.0
Depends: R (>= 4.2)
Imports: DelayedArray (>= 0.18.0), edgeR (>= 3.34.1), foreach (>=
1.5.1), GenomicRanges (>= 1.44.0), glmnet (>= 4.1.2), IRanges
(>= 2.26.0), iterators (>= 1.0.13), magrittr (>= 2.0.1), Matrix
(>= 1.3.4), methods (>= 4.1.1), MultiAssayExperiment (>=
1.18.0), stats, S4Vectors (>= 0.30.0), utils
Suggests: AnnotationHub (>= 3.0.2), BiocGenerics (>= 0.38.0),
BiocParallel (>= 1.28), BiocStyle (>= 2.20.2), data.table (>=
1.14.0), devtools (>= 2.4.2), doParallel (>= 1.0.16),
ExperimentHub (>= 2.2.0), knitr (>= 1.37), pheatmap (>=
1.0.12), proxy (>= 0.4.26), ridge (>= 3.0), rmarkdown (>=
2.11), rtracklayer (>= 1.52.0), testthat (>= 3.0.0), usethis
(>= 2.0.1), xcoredata
License: GPL-2
MD5sum: 42f4ea7eefa4180de8cd5f758e3b99a3
NeedsCompilation: no
Title: xcore expression regulators inference
Description: xcore is an R package for transcription factor activity
modeling based on known molecular signatures and user's gene
expression data. Accompanying xcoredata package provides a
collection of molecular signatures, constructed from publicly
available ChiP-seq experiments. xcore use ridge regression to
model changes in expression as a linear combination of
molecular signatures and find their unknown activities.
Obtained, estimates can be further tested for significance to
select molecular signatures with the highest predicted effect
on the observed expression changes.
biocViews: GeneExpression, GeneRegulation, Epigenetics, Regression,
Sequencing
Author: Maciej Migdał [aut, cre] (ORCID:
<https://orcid.org/0000-0002-8021-7263>), Bogumił Kaczkowski
[aut] (ORCID: <https://orcid.org/0000-0001-6554-5608>)
Maintainer: Maciej Migdał <mcjmigdal@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/xcore
git_branch: devel
git_last_commit: 1a80b69
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/xcore_1.11.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/xcore_1.11.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/xcore_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/xcore_1.11.0.tgz
vignettes: vignettes/xcore/inst/doc/xcore_vignette.html
vignetteTitles: xcore vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/xcore/inst/doc/xcore_vignette.R
suggestsMe: xcoredata
dependencyCount: 70
Package: XDE
Version: 2.53.0
Depends: R (>= 2.10.0), Biobase (>= 2.5.5)
Imports: BiocGenerics, genefilter, graphics, grDevices, gtools,
methods, stats, utils, mvtnorm, RColorBrewer, GeneMeta,
siggenes
Suggests: MASS, RUnit
Enhances: coda
License: LGPL-2
MD5sum: 02c7270b21333e86c4f6e02d5d2405da
NeedsCompilation: yes
Title: XDE: a Bayesian hierarchical model for cross-study analysis of
differential gene expression
Description: Multi-level model for cross-study detection of
differential gene expression.
biocViews: Microarray, DifferentialExpression
Author: R.B. Scharpf, G. Parmigiani, A.B. Nobel, and H. Tjelmeland
Maintainer: Robert Scharpf <rscharpf@jhsph.edu>
git_url: https://git.bioconductor.org/packages/XDE
git_branch: devel
git_last_commit: ef93912
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/XDE_2.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/XDE_2.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/XDE_2.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/XDE_2.53.0.tgz
vignettes: vignettes/XDE/inst/doc/XDE.pdf,
vignettes/XDE/inst/doc/XdeParameterClass.pdf
vignetteTitles: XDE Vignette, XdeParameterClass Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/XDE/inst/doc/XDE.R,
vignettes/XDE/inst/doc/XdeParameterClass.R
dependencyCount: 64
Package: XeniumIO
Version: 0.99.8
Depends: TENxIO
Imports: BiocBaseUtils, BiocGenerics, BiocIO, jsonlite, methods,
S4Vectors, SingleCellExperiment, SpatialExperiment,
SummarizedExperiment, VisiumIO
Suggests: arrow, BiocFileCache, BiocStyle, knitr, rmarkdown, tinytest
License: Artistic-2.0
MD5sum: d028b42cf0b3de010e21daba02294696
NeedsCompilation: no
Title: Import and represent Xenium data from the 10X Xenium Analyzer
Description: The package allows users to readily import spatial data
obtained from the 10X Xenium Analyzer pipeline. Supported
formats include 'parquet', 'h5', and 'mtx' files. The package
mainly represents data as SpatialExperiment objects.
biocViews: Software, Infrastructure, DataImport, SingleCell, Spatial
Author: Marcel Ramos [aut, cre] (ORCID:
<https://orcid.org/0000-0002-3242-0582>), Dario Righelli [ctb],
Estella Dong [ctb]
Maintainer: Marcel Ramos <marcel.ramos@sph.cuny.edu>
URL: https://github.com/waldronlab/XeniumIO
VignetteBuilder: knitr
BugReports: https://github.com/waldronlab/XeniumIO/issues
git_url: https://git.bioconductor.org/packages/XeniumIO
git_branch: devel
git_last_commit: 5e97a63
git_last_commit_date: 2025-02-11
Date/Publication: 2025-02-19
source.ver: src/contrib/XeniumIO_0.99.8.tar.gz
win.binary.ver: bin/windows/contrib/4.5/XeniumIO_0.99.8.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/XeniumIO_0.99.8.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/XeniumIO_0.99.8.tgz
vignettes: vignettes/XeniumIO/inst/doc/XeniumIO.html
vignetteTitles: VisiumIO Quick Start Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/XeniumIO/inst/doc/XeniumIO.R
dependencyCount: 93
Package: xenLite
Version: 1.1.0
Depends: R (>= 4.1)
Imports: SpatialExperiment, BiocFileCache, Matrix, S4Vectors,
SummarizedExperiment, methods, utils, EBImage, shiny,
HDF5Array, arrow, ggplot2, SingleCellExperiment, TENxIO, dplyr,
graphics, stats
Suggests: knitr, testthat, BiocStyle, yesno, terra,
SpatialFeatureExperiment, SFEData, tiff
License: Artistic-2.0
MD5sum: 3b8fcc41591ce447ac4f935fdb80011e
NeedsCompilation: no
Title: Simple classes and methods for managing Xenium datasets
Description: Define a relatively light class for managing Xenium data
using Bioconductor. Address use of parquet for coordinates,
SpatialExperiment for assay and sample data. Address
serialization and use of cloud storage.
biocViews: Infrastructure
Author: Vincent Carey [aut, cre] (ORCID:
<https://orcid.org/0000-0003-4046-0063>)
Maintainer: Vincent Carey <stvjc@channing.harvard.edu>
URL: https://github.com/vjcitn/xenLite
VignetteBuilder: knitr
BugReports: https://github.com/vjcitn/xenLite/issues
git_url: https://git.bioconductor.org/packages/xenLite
git_branch: devel
git_last_commit: 921c057
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/xenLite_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/xenLite_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/xenLite_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/xenLite_1.1.0.tgz
vignettes: vignettes/xenLite/inst/doc/xenLite.html
vignetteTitles: xenLite: exploration of a class for Xenium
demonstration data
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/xenLite/inst/doc/xenLite.R
dependencyCount: 138
Package: Xeva
Version: 1.23.2
Depends: R (>= 3.6)
Imports: methods, stats, utils, BBmisc, Biobase, grDevices, ggplot2,
scales, ComplexHeatmap, parallel, doParallel, Rmisc, grid,
nlme, PharmacoGx, downloader
Suggests: BiocStyle, knitr, rmarkdown
License: GPL-3
MD5sum: c808af93ef91d0185d99bd114c3cd34c
NeedsCompilation: no
Title: Analysis of patient-derived xenograft (PDX) data
Description: The Xeva package provides efficient and powerful functions
for patient-drived xenograft (PDX) based pharmacogenomic data
analysis. This package contains a set of functions to perform
analysis of patient-derived xenograft data. This package was
developed by the BHKLab, for further information please see our
documentation.
biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics,
Software, Classification
Author: Arvind Mer [aut], Benjamin Haibe-Kains [aut, cre]
Maintainer: Benjamin Haibe-Kains <benjamin.haibe.kains@utoronto.ca>
VignetteBuilder: knitr
BugReports: https://github.com/bhklab/Xeva/issues
git_url: https://git.bioconductor.org/packages/Xeva
git_branch: devel
git_last_commit: 7418d16
git_last_commit_date: 2025-03-20
Date/Publication: 2025-03-20
source.ver: src/contrib/Xeva_1.23.2.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/Xeva_1.23.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/Xeva_1.23.2.tgz
vignettes: vignettes/Xeva/inst/doc/Xeva.pdf
vignetteTitles: The Xeva User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/Xeva/inst/doc/Xeva.R
dependencyCount: 168
Package: XINA
Version: 1.25.0
Depends: R (>= 3.5)
Imports: mclust, plyr, alluvial, ggplot2, igraph, gridExtra, tools,
grDevices, graphics, utils, STRINGdb
Suggests: knitr, rmarkdown
License: GPL-3
MD5sum: db61f27579625ccde5d74a153ba1909a
NeedsCompilation: no
Title: Multiplexes Isobaric Mass Tagged-based Kinetics Data for Network
Analysis
Description: The aim of XINA is to determine which proteins exhibit
similar patterns within and across experimental conditions,
since proteins with co-abundance patterns may have common
molecular functions. XINA imports multiple datasets, tags
dataset in silico, and combines the data for subsequent
subgrouping into multiple clusters. The result is a single
output depicting the variation across all conditions. XINA, not
only extracts coabundance profiles within and across
experiments, but also incorporates protein-protein interaction
databases and integrative resources such as KEGG to infer
interactors and molecular functions, respectively, and produces
intuitive graphical outputs.
biocViews: SystemsBiology, Proteomics, RNASeq, Network
Author: Lang Ho Lee <lhlee@bwh.harvard.edu> and Sasha A. Singh
<sasingh@bwh.harvard.edu>
Maintainer: Lang Ho Lee <lhlee@bwh.harvard.edu> and Sasha A. Singh
<sasingh@bwh.harvard.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/XINA
git_branch: devel
git_last_commit: 19afdec
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/XINA_1.25.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/XINA_1.25.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/XINA_1.25.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/XINA_1.25.0.tgz
vignettes: vignettes/XINA/inst/doc/xina_user_code.html
vignetteTitles: xina_user_code
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/XINA/inst/doc/xina_user_code.R
dependencyCount: 72
Package: xmapbridge
Version: 1.65.0
Depends: R (>= 2.0), methods
Suggests: RUnit, RColorBrewer
License: LGPL-3
MD5sum: f95f1854702b67c9b83e38281d795562
NeedsCompilation: no
Title: Export plotting files to the xmapBridge for visualisation in
X:Map
Description: xmapBridge can plot graphs in the X:Map genome browser.
This package exports plotting files in a suitable format.
biocViews: Annotation, ReportWriting, Visualization
Author: Tim Yates <Tim.Yates@cruk.manchester.ac.uk> and Crispin J
Miller <Crispin.Miller@cruk.manchester.ac.uk>
Maintainer: Chris Wirth <Christopher.Wirth@cruk.manchester.ac.uk>
URL: http://xmap.picr.man.ac.uk, http://www.bioconductor.org
git_url: https://git.bioconductor.org/packages/xmapbridge
git_branch: devel
git_last_commit: 13dcfde
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/xmapbridge_1.65.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/xmapbridge_1.65.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/xmapbridge_1.65.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/xmapbridge_1.65.0.tgz
vignettes: vignettes/xmapbridge/inst/doc/xmapbridge.pdf
vignetteTitles: xmapbridge primer
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/xmapbridge/inst/doc/xmapbridge.R
dependencyCount: 1
Package: XNAString
Version: 1.15.0
Depends: R (>= 4.1)
Imports: utils, Biostrings, pwalign, BSgenome, data.table,
GenomicRanges, IRanges, methods, Rcpp, stringi, S4Vectors,
future.apply, stringr, formattable, stats
LinkingTo: Rcpp
Suggests: BiocStyle, knitr, rmarkdown, markdown, testthat,
BSgenome.Hsapiens.UCSC.hg38, pander
License: GPL-2
MD5sum: 4dab584da0b70d4e85e5ca03dfbd3df0
NeedsCompilation: yes
Title: Efficient Manipulation of Modified Oligonucleotide Sequences
Description: The XNAString package allows for description of base
sequences and associated chemical modifications in a single
object. XNAString is able to capture single stranded, as well
as double stranded molecules. Chemical modifications are
represented as independent strings associated with different
features of the molecules (base sequence, sugar sequence,
backbone sequence, modifications) and can be read or written to
a HELM notation. It also enables secondary structure prediction
using RNAfold from ViennaRNA. XNAString is designed to be
efficient representation of nucleic-acid based therapeutics,
therefore it stores information about target sequences and
provides interface for matching and alignment functions from
Biostrings and pwalign packages.
biocViews: SequenceMatching, Alignment, Sequencing, Genetics
Author: Anna Górska [aut], Marianna Plucinska [aut, cre], Lykke
Pedersen [aut], Lukasz Kielpinski [aut], Disa Tehler [aut],
Peter H. Hagedorn [aut]
Maintainer: Marianna Plucinska <marianna.plucinska@roche.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/XNAString
git_branch: devel
git_last_commit: 6cd7edb
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/XNAString_1.15.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/XNAString_1.15.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/XNAString_1.15.0.tgz
vignettes: vignettes/XNAString/inst/doc/XNAString_vignette.html
vignetteTitles: XNAString classes and functionalities
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/XNAString/inst/doc/XNAString_vignette.R
dependencyCount: 95
Package: XVector
Version: 0.47.2
Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>=
0.27.12), IRanges (>= 2.23.9)
Imports: methods, utils, tools, BiocGenerics, S4Vectors, IRanges
LinkingTo: S4Vectors, IRanges
Suggests: Biostrings, drosophila2probe, RUnit
License: Artistic-2.0
MD5sum: 18510ddd9ac89a6bb31b260e76a1a7d5
NeedsCompilation: yes
Title: Foundation of external vector representation and manipulation in
Bioconductor
Description: Provides memory efficient S4 classes for storing sequences
"externally" (e.g. behind an R external pointer, or on disk).
biocViews: Infrastructure, DataRepresentation
Author: Hervé Pagès and Patrick Aboyoun
Maintainer: Hervé Pagès <hpages.on.github@gmail.com>
URL: https://bioconductor.org/packages/XVector
BugReports: https://github.com/Bioconductor/XVector/issues
git_url: https://git.bioconductor.org/packages/XVector
git_branch: devel
git_last_commit: 9f44218
git_last_commit_date: 2025-01-07
Date/Publication: 2025-01-08
source.ver: src/contrib/XVector_0.47.2.tar.gz
win.binary.ver: bin/windows/contrib/4.5/XVector_0.47.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/XVector_0.47.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/XVector_0.47.2.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
dependsOnMe: Biostrings, triplex
importsMe: BSgenome, ChIPsim, CNEr, compEpiTools, crisprScore, dada2,
DECIPHER, gcrma, GenomAutomorphism, GenomicFeatures,
GenomicRanges, Gviz, HiLDA, IONiseR, IsoformSwitchAnalyzeR,
kebabs, MatrixRider, Modstrings, monaLisa, ProteoDisco,
R453Plus1Toolbox, ribosomeProfilingQC, Rsamtools, rtracklayer,
SparseArray, Structstrings, TFBSTools, tracktables, tRNA,
tRNAscanImport, VariantAnnotation, simMP
suggestsMe: IRanges, musicatk
linksToMe: Biostrings, CNEr, DECIPHER, kebabs, MatrixRider, pwalign,
Rsamtools, rtracklayer, ShortRead, SparseArray, triplex,
VariantAnnotation, VariantFiltering
dependencyCount: 10
Package: yamss
Version: 1.33.0
Depends: R (>= 4.3.0), methods, BiocGenerics (>= 0.15.3),
SummarizedExperiment
Imports: IRanges, stats, S4Vectors, EBImage, Matrix, mzR, data.table,
grDevices, limma
Suggests: BiocStyle, knitr, rmarkdown, digest, mtbls2, testthat
License: Artistic-2.0
Archs: x64
MD5sum: d625f838264b524250e24eff8a3f2855
NeedsCompilation: no
Title: Tools for high-throughput metabolomics
Description: Tools to analyze and visualize high-throughput
metabolomics data aquired using chromatography-mass
spectrometry. These tools preprocess data in a way that enables
reliable and powerful differential analysis. At the core of
these methods is a peak detection phase that pools information
across all samples simultaneously. This is in contrast to other
methods that detect peaks in a sample-by-sample basis.
biocViews: MassSpectrometry, Metabolomics, PeakDetection, Software
Author: Leslie Myint [cre, aut] (ORCID:
<https://orcid.org/0000-0003-2478-0331>), Kasper Daniel Hansen
[aut]
Maintainer: Leslie Myint <leslie.myint@gmail.com>
URL: https://github.com/hansenlab/yamss
VignetteBuilder: knitr
BugReports: https://github.com/hansenlab/yamss/issues
git_url: https://git.bioconductor.org/packages/yamss
git_branch: devel
git_last_commit: 2ec1eaf
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/yamss_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/yamss_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/yamss_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/yamss_1.33.0.tgz
vignettes: vignettes/yamss/inst/doc/yamss.html
vignetteTitles: yamss User's Guide
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/yamss/inst/doc/yamss.R
dependencyCount: 76
Package: YAPSA
Version: 1.33.0
Depends: R (>= 4.0.0), GenomicRanges, ggplot2, grid
Imports: limSolve, SomaticSignatures, VariantAnnotation, GenomeInfoDb,
reshape2, gridExtra, corrplot, dendextend, GetoptLong,
circlize, gtrellis, doParallel, parallel, PMCMRplus,
ggbeeswarm, ComplexHeatmap, KEGGREST, grDevices, Biostrings,
BSgenome.Hsapiens.UCSC.hg19, magrittr, pracma, dplyr, utils
Suggests: testthat, BiocStyle, knitr, rmarkdown
License: GPL-3
MD5sum: 1dcfc83f3b2ec8974ad4fda596ee6c95
NeedsCompilation: no
Title: Yet Another Package for Signature Analysis
Description: This package provides functions and routines for
supervised analyses of mutational signatures (i.e., the
signatures have to be known, cf. L. Alexandrov et al., Nature
2013 and L. Alexandrov et al., Bioaxiv 2018). In particular,
the family of functions LCD (LCD = linear combination
decomposition) can use optimal signature-specific cutoffs which
takes care of different detectability of the different
signatures. Moreover, the package provides different sets of
mutational signatures, including the COSMIC and PCAWG SNV
signatures and the PCAWG Indel signatures; the latter infering
that with YAPSA, the concept of supervised analysis of
mutational signatures is extended to Indel signatures. YAPSA
also provides confidence intervals as computed by profile
likelihoods and can perform signature analysis on a stratified
mutational catalogue (SMC = stratify mutational catalogue) in
order to analyze enrichment and depletion patterns for the
signatures in different strata.
biocViews: Sequencing, DNASeq, SomaticMutation, Visualization,
Clustering, GenomicVariation, StatisticalMethod,
BiologicalQuestion
Author: Daniel Huebschmann [aut], Lea Jopp-Saile [aut], Carolin
Andresen [aut], Zuguang Gu [aut, cre], Matthias Schlesner [aut]
Maintainer: Zuguang Gu <z.gu@dkfz.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/YAPSA
git_branch: devel
git_last_commit: d41177e
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/YAPSA_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/YAPSA_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/YAPSA_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/YAPSA_1.33.0.tgz
vignettes: vignettes/YAPSA/inst/doc/vignette_confidenceIntervals.html,
vignettes/YAPSA/inst/doc/vignette_exomes.html,
vignettes/YAPSA/inst/doc/vignette_signature_specific_cutoffs.html,
vignettes/YAPSA/inst/doc/vignette_stratifiedAnalysis.html,
vignettes/YAPSA/inst/doc/vignettes_Indel.html,
vignettes/YAPSA/inst/doc/YAPSA.html
vignetteTitles: 3. Confidence Intervals, 6. Usage of YAPSA for WES
data, 2. Signature-specific cutoffs, 4. Stratified Analysis of
Mutational Signatures, 5. Indel signature analysis, 1. Usage of
YAPSA
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/YAPSA/inst/doc/vignette_confidenceIntervals.R,
vignettes/YAPSA/inst/doc/vignette_exomes.R,
vignettes/YAPSA/inst/doc/vignette_signature_specific_cutoffs.R,
vignettes/YAPSA/inst/doc/vignette_stratifiedAnalysis.R,
vignettes/YAPSA/inst/doc/vignettes_Indel.R,
vignettes/YAPSA/inst/doc/YAPSA.R
dependencyCount: 198
Package: yarn
Version: 1.33.0
Depends: Biobase
Imports: biomaRt, downloader, edgeR, gplots, graphics, limma,
matrixStats, preprocessCore, readr, RColorBrewer, stats,
quantro
Suggests: knitr, rmarkdown, testthat (>= 0.8)
License: Artistic-2.0
Archs: x64
MD5sum: 8c744e8a6446a582b88fcca664a621e4
NeedsCompilation: no
Title: YARN: Robust Multi-Condition RNA-Seq Preprocessing and
Normalization
Description: Expedite large RNA-Seq analyses using a combination of
previously developed tools. YARN is meant to make it easier for
the user in performing basic mis-annotation quality control,
filtering, and condition-aware normalization. YARN leverages
many Bioconductor tools and statistical techniques to account
for the large heterogeneity and sparsity found in very large
RNA-seq experiments.
biocViews: Software, QualityControl, GeneExpression, Sequencing,
Preprocessing, Normalization, Annotation, Visualization,
Clustering
Author: Joseph N Paulson [aut, cre], Cho-Yi Chen [aut], Camila
Lopes-Ramos [aut], Marieke Kuijjer [aut], John Platig [aut],
Abhijeet Sonawane [aut], Maud Fagny [aut], Kimberly Glass
[aut], John Quackenbush [aut]
Maintainer: Joseph N Paulson <paulson.joseph@gene.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/yarn
git_branch: devel
git_last_commit: 7e48450
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-30
source.ver: src/contrib/yarn_1.33.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/yarn_1.33.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/yarn_1.33.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/yarn_1.33.0.tgz
vignettes: vignettes/yarn/inst/doc/yarn.pdf
vignetteTitles: YARN: Robust Multi-Tissue RNA-Seq Preprocessing and
Normalization
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/yarn/inst/doc/yarn.R
dependencyCount: 167
Package: zellkonverter
Version: 1.17.1
Imports: basilisk, cli, DelayedArray, Matrix, methods, reticulate,
S4Vectors, SingleCellExperiment (>= 1.11.6),
SummarizedExperiment, utils
Suggests: anndata, BiocFileCache, BiocStyle, covr, HDF5Array, knitr,
pkgload, rhdf5 (>= 2.45.1), rmarkdown, scRNAseq, spelling,
testthat, withr
License: MIT + file LICENSE
MD5sum: 48ff567062f4f2b0dc2a40d79835ff0c
NeedsCompilation: no
Title: Conversion Between scRNA-seq Objects
Description: Provides methods to convert between Python AnnData objects
and SingleCellExperiment objects. These are primarily intended
for use by downstream Bioconductor packages that wrap Python
methods for single-cell data analysis. It also includes
functions to read and write H5AD files used for saving AnnData
objects to disk.
biocViews: SingleCell, DataImport, DataRepresentation
Author: Luke Zappia [aut, cre] (ORCID:
<https://orcid.org/0000-0001-7744-8565>), Aaron Lun [aut]
(ORCID: <https://orcid.org/0000-0002-3564-4813>), Jack Kamm
[ctb] (ORCID: <https://orcid.org/0000-0003-2412-756X>),
Robrecht Cannoodt [ctb] (ORCID:
<https://orcid.org/0000-0003-3641-729X>, github: rcannood),
Gabriel Hoffman [ctb] (ORCID:
<https://orcid.org/0000-0002-0957-0224>, github:
GabrielHoffman)
Maintainer: Luke Zappia <luke@lazappi.id.au>
URL: https://github.com/theislab/zellkonverter
VignetteBuilder: knitr
BugReports: https://github.com/theislab/zellkonverter/issues
git_url: https://git.bioconductor.org/packages/zellkonverter
git_branch: devel
git_last_commit: 6ca465c
git_last_commit_date: 2025-03-09
Date/Publication: 2025-03-09
source.ver: src/contrib/zellkonverter_1.17.1.tar.gz
win.binary.ver: bin/windows/contrib/4.5/zellkonverter_1.17.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/zellkonverter_1.17.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/zellkonverter_1.17.1.tgz
vignettes: vignettes/zellkonverter/inst/doc/zellkonverter.html
vignetteTitles: Converting to/from AnnData to SingleCellExperiments
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/zellkonverter/inst/doc/zellkonverter.R
dependsOnMe: scATAC.Explorer, OSCA.intro
importsMe: BgeeDB, singleCellTK, velociraptor
suggestsMe: cellxgenedp, CuratedAtlasQueryR, GloScope, HDF5Array,
HCATonsilData
dependencyCount: 52
Package: zenith
Version: 1.9.0
Depends: R (>= 4.2.0), limma, methods
Imports: variancePartition (>= 1.26.0), EnrichmentBrowser (>= 2.22.0),
GSEABase (>= 1.54.0), msigdbr (>= 7.5.1), Rfast, ggplot2,
tidyr, reshape2, progress, utils, Rdpack, stats
Suggests: BiocStyle, BiocGenerics, knitr, pander, rmarkdown,
tweeDEseqCountData, edgeR, kableExtra, RUnit
License: Artistic-2.0
MD5sum: 085d76a00fd480d14321bde5836b9ee7
NeedsCompilation: no
Title: Gene set analysis following differential expression using linear
(mixed) modeling with dream
Description: Zenith performs gene set analysis on the result of
differential expression using linear (mixed) modeling with
dream by considering the correlation between gene expression
traits. This package implements the camera method from the
limma package proposed by Wu and Smyth (2012). Zenith is a
simple extension of camera to be compatible with linear mixed
models implemented in variancePartition::dream().
biocViews: RNASeq, GeneExpression, GeneSetEnrichment,
DifferentialExpression, BatchEffect, QualityControl,
Regression, Epigenetics, FunctionalGenomics, Transcriptomics,
Normalization, Preprocessing, Microarray, ImmunoOncology,
Software
Author: Gabriel Hoffman [aut, cre]
Maintainer: Gabriel Hoffman <gabriel.hoffman@mssm.edu>
URL: https://DiseaseNeuroGenomics.github.io/zenith
VignetteBuilder: knitr
BugReports: https://github.com/DiseaseNeuroGenomics/zenith/issues
git_url: https://git.bioconductor.org/packages/zenith
git_branch: devel
git_last_commit: 5422050
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-12
source.ver: src/contrib/zenith_1.9.0.tar.gz
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/zenith_1.9.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/zenith_1.9.0.tgz
vignettes: vignettes/zenith/inst/doc/loading_genesets.html,
vignettes/zenith/inst/doc/zenith.html
vignetteTitles: Example usage of zenith on GEUVAIDIS RNA-seq, Example
usage of zenith on RNA-seq
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/zenith/inst/doc/loading_genesets.R,
vignettes/zenith/inst/doc/zenith.R
importsMe: dreamlet
suggestsMe: variancePartition
dependencyCount: 164
Package: zFPKM
Version: 1.29.0
Depends: R (>= 3.4.0)
Imports: checkmate, dplyr, ggplot2, tidyr, SummarizedExperiment
Suggests: knitr, limma, edgeR, GEOquery, stringr, printr, rmarkdown
License: GPL-3 | file LICENSE
MD5sum: 7e254537dcea3e436ae0038536107b6f
NeedsCompilation: no
Title: A suite of functions to facilitate zFPKM transformations
Description: Perform the zFPKM transform on RNA-seq FPKM data. This
algorithm is based on the publication by Hart et al., 2013
(Pubmed ID 24215113). Reference recommends using zFPKM > -3 to
select expressed genes. Validated with encode open/closed
chromosome data. Works well for gene level data using FPKM or
TPM. Does not appear to calibrate well for transcript level
data.
biocViews: ImmunoOncology, RNASeq, FeatureExtraction, Software,
GeneExpression
Author: Ron Ammar [aut, cre], John Thompson [aut]
Maintainer: Ron Ammar <ron.ammar@bms.com>
URL: https://github.com/ronammar/zFPKM/
VignetteBuilder: knitr
BugReports: https://github.com/ronammar/zFPKM/issues
git_url: https://git.bioconductor.org/packages/zFPKM
git_branch: devel
git_last_commit: 463b7d1
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/zFPKM_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/zFPKM_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/zFPKM_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/zFPKM_1.29.0.tgz
vignettes: vignettes/zFPKM/inst/doc/zFPKM.html
vignetteTitles: Introduction to zFPKM Transformation
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/zFPKM/inst/doc/zFPKM.R
suggestsMe: DGEobj.utils
dependencyCount: 71
Package: zinbwave
Version: 1.29.0
Depends: R (>= 3.4), methods, SummarizedExperiment,
SingleCellExperiment
Imports: BiocParallel, softImpute, stats, genefilter, edgeR, Matrix
Suggests: knitr, rmarkdown, testthat, matrixStats, magrittr, scRNAseq,
ggplot2, biomaRt, BiocStyle, Rtsne, DESeq2, sparseMatrixStats
License: Artistic-2.0
MD5sum: eb7f67aaeb71cd4efa6e0b22f10ecfd4
NeedsCompilation: no
Title: Zero-Inflated Negative Binomial Model for RNA-Seq Data
Description: Implements a general and flexible zero-inflated negative
binomial model that can be used to provide a low-dimensional
representations of single-cell RNA-seq data. The model accounts
for zero inflation (dropouts), over-dispersion, and the count
nature of the data. The model also accounts for the difference
in library sizes and optionally for batch effects and/or other
covariates, avoiding the need for pre-normalize the data.
biocViews: ImmunoOncology, DimensionReduction, GeneExpression, RNASeq,
Software, Transcriptomics, Sequencing, SingleCell
Author: Davide Risso [aut, cre, cph], Svetlana Gribkova [aut], Fanny
Perraudeau [aut], Jean-Philippe Vert [aut], Clara Bagatin [aut]
Maintainer: Davide Risso <risso.davide@gmail.com>
VignetteBuilder: knitr
BugReports: https://github.com/drisso/zinbwave/issues
git_url: https://git.bioconductor.org/packages/zinbwave
git_branch: devel
git_last_commit: 4cf7edc
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-05
source.ver: src/contrib/zinbwave_1.29.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/zinbwave_1.29.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/zinbwave_1.29.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/zinbwave_1.29.0.tgz
vignettes: vignettes/zinbwave/inst/doc/intro.html
vignetteTitles: zinbwave Vignette
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/zinbwave/inst/doc/intro.R
importsMe: benchdamic, clusterExperiment, scBFA, singleCellTK,
SpatialDDLS
suggestsMe: MAST, splatter
dependencyCount: 77
Package: zitools
Version: 1.1.0
Depends: R (>= 4.4.0), methods
Imports: phyloseq, pscl, ggplot2, MatrixGenerics, SummarizedExperiment,
stats, VGAM, matrixStats, tidyr, tibble, dplyr, DESeq2,
reshape2, RColorBrewer, magrittr, BiocGenerics, graphics, utils
Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0), tidyverse,
microbiome
License: BSD_3_clause + file LICENSE
MD5sum: 6d0fcd8b7bee3c03bca81a36ce8f6fc7
NeedsCompilation: no
Title: Analysis of zero-inflated count data
Description: zitools allows for zero inflated count data analysis by
either using down-weighting of excess zeros or by replacing an
appropriate proportion of excess zeros with NA. Through
overloading frequently used statistical functions (such as
mean, median, standard deviation), plotting functions (such as
boxplots or heatmap) or differential abundance tests, it allows
a wide range of downstream analyses for zero-inflated data in a
less biased manner. This becomes applicable in the context of
microbiome analyses, where the data is often overdispersed and
zero-inflated, therefore making data analysis extremly
challenging.
biocViews: Software, StatisticalMethod, Microbiome
Author: Carlotta Meyring [aut, cre] (ORCID:
<https://orcid.org/0009-0000-6201-7615>)
Maintainer: Carlotta Meyring <carlotta.meyring@uniklinik-freiburg.de>
URL: https://github.com/kreutz-lab/zitools
VignetteBuilder: knitr
BugReports: https://github.com/kreutz-lab/zitools/issues
git_url: https://git.bioconductor.org/packages/zitools
git_branch: devel
git_last_commit: 9887f7d
git_last_commit_date: 2024-10-29
Date/Publication: 2025-01-23
source.ver: src/contrib/zitools_1.1.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/zitools_1.1.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/zitools_1.1.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/zitools_1.1.0.tgz
vignettes: vignettes/zitools/inst/doc/zitools_tutorial.pdf
vignetteTitles: An Introduction to zitools
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/zitools/inst/doc/zitools_tutorial.R
dependencyCount: 107
Package: zlibbioc
Version: 1.53.0
Suggests: BiocStyle, knitr
License: Artistic-2.0 + file LICENSE
Archs: x64
MD5sum: e7e2281813c4523f308049af00d80414
NeedsCompilation: yes
Title: An R packaged zlib-1.2.5
Description: This package uses the source code of zlib-1.2.5 to create
libraries for systems that do not have these available via
other means (most Linux and Mac users should have system-level
access to zlib, and no direct need for this package). See the
vignette for instructions on use.
biocViews: Infrastructure
Author: Martin Morgan
Maintainer: Bioconductor Package Maintainer
<maintainer@bioconductor.org>
URL: https://bioconductor.org/packages/zlibbioc
VignetteBuilder: knitr
BugReports: https://github.com/Bioconductor/zlibbioc/issues
PackageStatus: Deprecated
git_url: https://git.bioconductor.org/packages/zlibbioc
git_branch: devel
git_last_commit: cd57560
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/zlibbioc_1.53.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/zlibbioc_1.53.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/zlibbioc_1.53.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/zlibbioc_1.53.0.tgz
vignettes: vignettes/zlibbioc/inst/doc/UsingZlibbioc.html
vignetteTitles: Using zlibbioc C libraries
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: TRUE
Rfiles: vignettes/zlibbioc/inst/doc/UsingZlibbioc.R
importsMe: bamsignals, ChemmineOB, GrafGen, MADSEQ, qckitfastq,
snpStats, jackalope
suggestsMe: metacoder
linksToMe: bamsignals, ChemmineOB, maftools, Rfastp, seqTools,
jackalope
dependencyCount: 0
Package: ZygosityPredictor
Version: 1.7.0
Depends: R (>= 4.3.0)
Imports: GenomicAlignments, GenomicRanges, Rsamtools, IRanges,
VariantAnnotation, DelayedArray, dplyr, stringr, purrr, tibble,
methods, knitr, igraph, readr, stats, magrittr, rlang
Suggests: rmarkdown, testthat, BiocStyle
License: GPL-2
MD5sum: 9500368a1acb148363195eda0ad1c55b
NeedsCompilation: no
Title: Package for prediction of zygosity for variants/genes in NGS
data
Description: The ZygosityPredictor allows to predict how many copies of
a gene are affected by small variants. In addition to the basic
calculations of the affected copy number of a variant, the
Zygosity-Predictor can integrate the influence of several
variants on a gene and ultimately make a statement if and how
many wild-type copies of the gene are left. This information
proves to be of particular use in the context of translational
medicine. For example, in cancer genomes, the
Zygosity-Predictor can address whether unmutated copies of
tumor-suppressor genes are present. Beyond this, it is possible
to make this statement for all genes of an organism. The
Zygosity-Predictor was primarily developed to handle SNVs and
INDELs (later addressed as small-variants) of somatic and
germline origin. In order not to overlook severe effects
outside of the small-variant context, it has been extended with
the assessment of large scale deletions, which cause losses of
whole genes or parts of them.
biocViews: BiomedicalInformatics, FunctionalPrediction,
SomaticMutation, GenePrediction
Author: Marco Rheinnecker [aut, cre] (ORCID:
<https://orcid.org/0009-0009-7181-3977>), Marc Ruebsam [aut],
Daniel Huebschmann [aut], Martina Froehlich [aut], Barbara
Hutter [aut]
Maintainer: Marco Rheinnecker <marco.rheinnecker@dkfz-heidelberg.de>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/ZygosityPredictor
git_branch: devel
git_last_commit: 1c2d5b8
git_last_commit_date: 2024-10-29
Date/Publication: 2024-10-29
source.ver: src/contrib/ZygosityPredictor_1.7.0.tar.gz
win.binary.ver: bin/windows/contrib/4.5/ZygosityPredictor_1.7.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/ZygosityPredictor_1.7.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/ZygosityPredictor_1.7.0.tgz
vignettes: vignettes/ZygosityPredictor/inst/doc/Usage.html
vignetteTitles: Usage
hasREADME: FALSE
hasNEWS: TRUE
hasINSTALL: FALSE
hasLICENSE: FALSE
Rfiles: vignettes/ZygosityPredictor/inst/doc/Usage.R
dependencyCount: 102
Package: bgx
Version: 1.73.0
Depends: R (>= 2.0.1), Biobase, affy (>= 1.5.0), gcrma (>= 2.4.1)
Imports: Rcpp (>= 0.11.0)
LinkingTo: Rcpp
Suggests: affydata, hgu95av2cdf
License: GPL-2
NeedsCompilation: yes
Title: Bayesian Gene eXpression
Description: Bayesian integrated analysis of Affymetrix GeneChips
biocViews: Microarray, DifferentialExpression
Author: Ernest Turro, Graeme Ambler, Anne-Mette K Hein
Maintainer: Ernest Turro <et341@cam.ac.uk>
git_url: https://git.bioconductor.org/packages/bgx
git_branch: devel
git_last_commit: 6430db5
git_last_commit_date: 2024-10-29
Date/Publication: 2025-03-25
win.binary.ver: bin/windows/contrib/4.5/bgx_1.73.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/bgx_1.73.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/bgx_1.73.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Package: CyTOFpower
Version: 1.13.1
Depends: R (>= 4.1)
Imports: CytoGLMM, diffcyt, DT, dplyr, ggplot2, magrittr, methods,
rlang, stats, shiny, shinyFeedback, shinyjs, shinyMatrix,
SummarizedExperiment, tibble, tidyr
Suggests: testthat (>= 3.0.0), BiocStyle, knitr
License: LGPL-3
NeedsCompilation: no
Title: Power analysis for CyTOF experiments
Description: This package is a tool to predict the power of CyTOF
experiments in the context of differential state analyses. The
package provides a shiny app with two options to predict the
power of an experiment: i. generation of in-sicilico CyTOF
data, using users input ii. browsing in a grid of parameters
for which the power was already precomputed.
biocViews: FlowCytometry, SingleCell, CellBiology, StatisticalMethod,
Software
Author: Anne-Maud Ferreira [cre, aut] (ORCID:
<https://orcid.org/0000-0002-4749-746X>), Catherine Blish
[aut], Susan Holmes [aut]
Maintainer: Anne-Maud Ferreira <anne-maud.ferreira@stanford.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/CyTOFpower
git_branch: devel
git_last_commit: c85b4db
git_last_commit_date: 2025-03-15
Date/Publication: 2025-03-25
win.binary.ver: bin/windows/contrib/4.5/CyTOFpower_1.13.1.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/CyTOFpower_1.13.1.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/CyTOFpower_1.13.1.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Package: GraphPAC
Version: 1.49.1
Depends: R(>= 2.15),iPAC, igraph, TSP, RMallow
Suggests: RUnit, BiocGenerics
License: GPL-2
NeedsCompilation: no
Title: Identification of Mutational Clusters in Proteins via a Graph
Theoretical Approach.
Description: Identifies mutational clusters of amino acids in a protein
while utilizing the proteins tertiary structure via a graph
theoretical model.
biocViews: Clustering, Proteomics
Author: Gregory Ryslik, Hongyu Zhao
Maintainer: Gregory Ryslik <gregory.ryslik@yale.edu>
PackageStatus: Deprecated
git_url: https://git.bioconductor.org/packages/GraphPAC
git_branch: devel
git_last_commit: aa38396
git_last_commit_date: 2025-03-14
Date/Publication: 2025-03-25
win.binary.ver: bin/windows/contrib/4.5/GraphPAC_1.49.1.zip
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/GraphPAC_1.49.1.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Package: iPAC
Version: 1.51.1
Depends: R(>= 2.15), scatterplot3d, Biostrings, pwalign, multtest
Imports: grDevices, graphics, stats, gdata
License: GPL-2
NeedsCompilation: no
Title: Identification of Protein Amino acid Clustering
Description: iPAC is a novel tool to identify somatic amino acid
mutation clustering within proteins while taking into account
protein structure.
biocViews: Clustering, Proteomics
Author: Gregory Ryslik, Hongyu Zhao
Maintainer: Gregory Ryslik <gregory.ryslik@yale.edu>
PackageStatus: Deprecated
git_url: https://git.bioconductor.org/packages/iPAC
git_branch: devel
git_last_commit: 3fea84c
git_last_commit_date: 2025-03-14
Date/Publication: 2025-03-25
win.binary.ver: bin/windows/contrib/4.5/iPAC_1.51.1.zip
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/iPAC_1.51.1.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Package: QuartPAC
Version: 1.39.1
Depends: iPAC, GraphPAC, SpacePAC, data.table
Imports: Biostrings, pwalign
Suggests: RUnit, BiocGenerics, rgl
License: GPL-2
NeedsCompilation: no
Title: Identification of mutational clusters in protein quaternary
structures
Description: Identifies clustering of somatic mutations in proteins
over the entire quaternary structure.
biocViews: Clustering, Proteomics, SomaticMutation
Author: Gregory Ryslik, Yuwei Cheng, Hongyu Zhao
Maintainer: Gregory Ryslik <gregory.ryslik@yale.edu>
PackageStatus: Deprecated
git_url: https://git.bioconductor.org/packages/QuartPAC
git_branch: devel
git_last_commit: b43ecdd
git_last_commit_date: 2025-03-14
Date/Publication: 2025-03-25
win.binary.ver: bin/windows/contrib/4.5/QuartPAC_1.39.1.zip
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Package: SpacePAC
Version: 1.45.1
Depends: R(>= 2.15),iPAC
Suggests: RUnit, BiocGenerics, rgl
License: GPL-2
NeedsCompilation: no
Title: Identification of Mutational Clusters in 3D Protein Space via
Simulation.
Description: Identifies clustering of somatic mutations in proteins via
a simulation approach while considering the protein's tertiary
structure.
biocViews: Clustering, Proteomics
Author: Gregory Ryslik, Hongyu Zhao
Maintainer: Gregory Ryslik <gregory.ryslik@yale.edu>
PackageStatus: Deprecated
git_url: https://git.bioconductor.org/packages/SpacePAC
git_branch: devel
git_last_commit: ce945b2
git_last_commit_date: 2025-03-14
Date/Publication: 2025-03-25
win.binary.ver: bin/windows/contrib/4.5/SpacePAC_1.45.1.zip
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/SpacePAC_1.45.1.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Package: TransView
Version: 1.51.2
Depends: methods, GenomicRanges
Imports: BiocGenerics, S4Vectors (>= 0.9.25), IRanges, gplots
LinkingTo: Rhtslib (>= 1.99.1)
Suggests: RUnit, pasillaBamSubset, BiocManager
License: GPL-3
Archs: x64
NeedsCompilation: yes
Title: Read density map construction and accession. Visualization of
ChIPSeq and RNASeq data sets
Description: This package provides efficient tools to generate, access
and display read densities of sequencing based data sets such
as from RNA-Seq and ChIP-Seq.
biocViews: ImmunoOncology, DNAMethylation, GeneExpression,
Transcription, Microarray, Sequencing, Sequencing, ChIPSeq,
RNASeq, MethylSeq, DataImport, Visualization, Clustering,
MultipleComparison
Author: Julius Muller
Maintainer: Julius Muller <ju-mu@alumni.ethz.ch>
URL: http://bioconductor.org/packages/release/bioc/html/TransView.html
SystemRequirements: GNU make
git_url: https://git.bioconductor.org/packages/TransView
git_branch: devel
git_last_commit: 7fb647b
git_last_commit_date: 2025-01-29
Date/Publication: 2025-03-25
win.binary.ver: bin/windows/contrib/4.5/TransView_1.51.2.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TransView_1.51.2.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/TransView_1.51.2.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Package: traviz
Version: 1.13.0
Depends: R (>= 4.0)
Imports: ggplot2, viridis, mgcv, SingleCellExperiment, slingshot,
princurve, Biobase, methods, RColorBrewer,
SummarizedExperiment, grDevices, graphics, rgl
Suggests: scater, dplyr, testthat (>= 3.0.0), covr, S4Vectors,
rmarkdown, knitr
License: MIT + file LICENSE
NeedsCompilation: no
Title: Trajectory functions for visualization and interpretation.
Description: traviz provides a suite of functions to plot trajectory
related objects from Bioconductor packages. It allows plotting
trajectories in reduced dimension, as well as averge gene
expression smoothers as a function of pseudotime. Asides from
general utility functions, traviz also allows plotting
trajectories estimated by Slingshot, as well as smoothers
estimated by tradeSeq. Furthermore, it allows for visualization
of Slingshot trajectories using ggplot2.
biocViews: GeneExpression, RNASeq, Sequencing, Software, SingleCell,
Transcriptomics, Visualization
Author: Hector Roux de Bezieux [aut, ctb], Kelly Street [aut, ctb],
Koen Van den Berge [aut, cre]
Maintainer: Koen Van den Berge <koen.vdberge@gmail.com>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/traviz
git_branch: devel
git_last_commit: 76fcdb4
git_last_commit_date: 2024-10-29
Date/Publication: 2025-03-25
win.binary.ver: bin/windows/contrib/4.5/traviz_1.13.0.zip
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/traviz_1.13.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/traviz_1.13.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Package: DeProViR
Version: 1.3.0
Depends: keras
Imports: caret, data.table, dplyr, fmsb, ggplot2, grDevices, pROC,
PRROC, readr, stats, BiocFileCache, utils
Suggests: rmarkdown, tensorflow, BiocStyle, RUnit, knitr, BiocGenerics
License: MIT+ file LICENSE
MD5sum: d94fb11ecf623eb7129e08bf38c4eb98
NeedsCompilation: no
Title: A Deep-Learning Framework Based on Pre-trained Sequence
Embeddings for Predicting Host-Viral Protein-Protein
Interactions
Description: Emerging infectious diseases, exemplified by the zoonotic
COVID-19 pandemic caused by SARS-CoV-2, are grave global
threats. Understanding protein-protein interactions (PPIs)
between host and viral proteins is essential for therapeutic
targets and insights into pathogen replication and immune
evasion. While experimental methods like yeast two-hybrid
screening and mass spectrometry provide valuable insights, they
are hindered by experimental noise and costs, yielding
incomplete interaction maps. Computational models, notably
DeProViR, predict PPIs from amino acid sequences, incorporating
semantic information with GloVe embeddings. DeProViR employs a
Siamese neural network, integrating convolutional and Bi-LSTM
networks to enhance accuracy. It overcomes the limitations of
feature engineering, offering an efficient means to predict
host-virus interactions, which holds promise for antiviral
therapies and advancing our understanding of infectious
diseases.
biocViews: Proteomics, SystemsBiology, NetworkInference, NeuralNetwork,
Network
Author: Matineh Rahmatbakhsh [aut, trl, cre]
Maintainer: Matineh Rahmatbakhsh <matinerb.94@gmail.com>
URL: https://github.com/mrbakhsh/DeProViR
VignetteBuilder: knitr
BugReports: https://github.com/mrbakhsh/DeProViR/issues
git_url: https://git.bioconductor.org/packages/DeProViR
git_branch: devel
git_last_commit: 612f1d9
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-22
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/DeProViR_1.3.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Package: netZooR
Version: 1.11.0
Depends: R (>= 4.2.0), igraph, reticulate, pandaR, yarn, matrixcalc
Imports: RCy3, viridisLite, STRINGdb, Biobase, GOstats, AnnotationDbi,
matrixStats, GO.db, org.Hs.eg.db, Matrix, gplots, nnet,
data.table, vegan, stats, utils, reshape, reshape2, penalized,
parallel, doParallel, foreach, ggplot2, ggdendro, grid, MASS,
assertthat, tidyr, methods, dplyr, graphics
Suggests: testthat (>= 2.1.0), knitr, rmarkdown, pkgdown
License: GPL-3
MD5sum: ccc0842d642691bb77ecd9bb0aab8aa4
NeedsCompilation: no
Title: Unified methods for the inference and analysis of gene
regulatory networks
Description: netZooR unifies the implementations of several Network Zoo
methods (netzoo, netzoo.github.io) into a single package by
creating interfaces between network inference and network
analysis methods. Currently, the package has 3 methods for
network inference including PANDA and its optimized
implementation OTTER (network reconstruction using mutliple
lines of biological evidence), LIONESS (single-sample network
inference), and EGRET (genotype-specific networks). Network
analysis methods include CONDOR (community detection), ALPACA
(differential community detection), CRANE (significance
estimation of differential modules), MONSTER (estimation of
network transition states). In addition, YARN allows to process
gene expresssion data for tissue-specific analyses and SAMBAR
infers missing mutation data based on pathway information.
biocViews: NetworkInference, Network, GeneRegulation, GeneExpression,
Transcription, Microarray, GraphAndNetwork
Author: Marouen Ben Guebila [aut, cre] (ORCID:
<https://orcid.org/0000-0001-5934-966X>), Tian Wang [aut]
(ORCID: <https://orcid.org/0000-0002-2767-3243>), John Platig
[aut], Marieke Kuijjer [aut] (ORCID:
<https://orcid.org/0000-0001-6280-3130>), Megha Padi [aut]
(ORCID: <https://orcid.org/0000-0002-3446-4562>), Rebekka
Burkholz [aut], Des Weighill [aut] (ORCID:
<https://orcid.org/0000-0003-4979-5871>), Kate Shutta [aut]
(ORCID: <https://orcid.org/0000-0003-0402-3771>)
Maintainer: Marouen Ben Guebila <marouen.b.guebila@gmail.com>
URL: https://github.com/netZoo/netZooR, https://netzoo.github.io/
VignetteBuilder: knitr
BugReports: https://github.com/netZoo/netZooR/issues
git_url: https://git.bioconductor.org/packages/netZooR
git_branch: devel
git_last_commit: fcf288f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-22
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/netZooR_1.11.0.tgz
mac.binary.big-sur-arm64.ver:
bin/macosx/big-sur-arm64/contrib/4.5/netZooR_1.11.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Package: NeuCA
Version: 1.13.0
Depends: R(>= 3.5.0), keras, limma, e1071, SingleCellExperiment,
kableExtra
Suggests: BiocStyle, knitr, rmarkdown, networkD3
License: GPL-2
MD5sum: 0f1f65796b0048ff5895c8f0c4ec73ca
NeedsCompilation: no
Title: NEUral network-based single-Cell Annotation tool
Description: NeuCA is is a neural-network based method for scRNA-seq
data annotation. It can automatically adjust its classification
strategy depending on cell type correlations, to accurately
annotate cell. NeuCA can automatically utilize the structure
information of the cell types through a hierarchical tree to
improve the annotation accuracy. It is especially helpful when
the data contain closely correlated cell types.
biocViews: SingleCell, Software, Classification, NeuralNetwork, RNASeq,
Transcriptomics, DataRepresentation, Transcription, Sequencing,
Preprocessing, GeneExpression, DataImport
Author: Ziyi Li [aut], Hao Feng [aut, cre]
Maintainer: Hao Feng <hxf155@case.edu>
VignetteBuilder: knitr
git_url: https://git.bioconductor.org/packages/NeuCA
git_branch: devel
git_last_commit: a59dea2
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-22
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/NeuCA_1.13.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Package: TypeInfo
Version: 1.73.0
Depends: methods
Suggests: Biobase
License: BSD_2_clause
MD5sum: 9fcbb46572ed4468b586af04f54216ce
NeedsCompilation: no
Title: Optional Type Specification Prototype
Description: A prototype for a mechanism for specifying the types of
parameters and the return value for an R function. This is
meta-information that can be used to generate stubs for servers
and various interfaces to these functions. Additionally, the
arguments in a call to a typed function can be validated using
the type specifications. We allow types to be specified as
either i) by class name using either inheritance - is(x,
className), or strict instance of - class(x) %in% className, or
ii) a dynamic test given as an R expression which is evaluated
at run-time. More precise information and interesting tests can
be done via ii), but it is harder to use this information as
meta-data as it requires more effort to interpret it and it is
of course run-time information. It is typically more
meaningful.
biocViews: Infrastructure
Author: Duncan Temple Lang Robert Gentleman (<rgentlem@fhcrc.org>)
Maintainer: Duncan Temple Lang <duncan@wald.ucdavis.edu>
git_url: https://git.bioconductor.org/packages/TypeInfo
git_branch: devel
git_last_commit: 507b15f
git_last_commit_date: 2024-10-29
Date/Publication: 2024-11-22
mac.binary.big-sur-x86_64.ver:
bin/macosx/big-sur-x86_64/contrib/4.5/TypeInfo_1.73.0.tgz
hasREADME: FALSE
hasNEWS: FALSE
hasINSTALL: FALSE
hasLICENSE: FALSE
Package: lapmix
Version: 1.73.1
Depends: R (>= 2.6.0),stats
Imports: Biobase, graphics, grDevices, methods, stats, tools, utils
License: GPL (>= 2)
Title: Laplace Mixture Model in Microarray Experiments
Description: Laplace mixture modelling of microarray experiments. A
hierarchical Bayesian approach is used, and the hyperparameters
are estimated using empirical Bayes. The main purpose is to
identify differentially expressed genes.
biocViews: Microarray, OneChannel, DifferentialExpression
Author: Yann Ruffieux, contributions from Debjani Bhowmick, Anthony C.
Davison, and Darlene R. Goldstein
Maintainer: Yann Ruffieux <yann.ruffieux@epfl.ch>
URL: http://www.r-project.org, http://www.bioconductor.org,
http://stat.epfl.ch
PackageStatus: Deprecated
Package: staRank
Version: 1.49.1
Depends: methods, cellHTS2, R (>= 2.10)
License: GPL
Title: Stability Ranking
Description: Detecting all relevant variables from a data set is
challenging, especially when only few samples are available and
data is noisy. Stability ranking provides improved variable
rankings of increased robustness using resampling or
subsampling.
biocViews: ImmunoOncology, MultipleComparison, CellBiology,
CellBasedAssays, MicrotitrePlateAssay
Author: Juliane Siebourg, Niko Beerenwinkel
Maintainer: Juliane Siebourg <juliane.siebourg@bsse.ethz.ch>
PackageStatus: Deprecated
Package: HTSeqGenie
Version: 4.37.1
Depends: R (>= 3.0.0), gmapR (>= 1.8.0), ShortRead (>= 1.19.13),
VariantAnnotation (>= 1.8.3)
Imports: BiocGenerics (>= 0.2.0), S4Vectors (>= 0.9.25), IRanges (>=
1.21.39), GenomicRanges (>= 1.23.21), Rsamtools (>= 1.8.5),
Biostrings (>= 2.24.1), pwalign, chipseq (>= 1.6.1), hwriter
(>= 1.3.0), Cairo (>= 1.5.5), GenomicFeatures (>= 1.9.31),
BiocParallel, parallel, tools, rtracklayer (>= 1.17.19),
GenomicAlignments, VariantTools (>= 1.7.7), GenomeInfoDb,
SummarizedExperiment, methods
Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, LungCancerLines,
org.Hs.eg.db, RUnit
License: Artistic-2.0
Title: A NGS analysis pipeline.
Description: Libraries to perform NGS analysis.
Author: Gregoire Pau, Jens Reeder
Maintainer: Jens Reeder <reeder.jens@gene.com>
PackageStatus: Deprecated
Package: supraHex
Version: 1.45.1
Depends: R (>= 3.6), hexbin
Imports: ape, MASS, grDevices, graphics, stats, readr, tibble, tidyr,
dplyr, stringr, purrr, magrittr, igraph, methods
License: GPL-2
Title: supraHex: a supra-hexagonal map for analysing tabular omics data
Description: A supra-hexagonal map is a giant hexagon on a
2-dimensional grid seamlessly consisting of smaller hexagons.
It is supposed to train, analyse and visualise a
high-dimensional omics input data. The supraHex is able to
carry out gene clustering/meta-clustering and sample
correlation, plus intuitive visualisations to facilitate
exploratory analysis. More importantly, it allows for
overlaying additional data onto the trained map to explore
relations between input and additional data. So with supraHex,
it is also possible to carry out multilayer omics data
comparisons. Newly added utilities are advanced heatmap
visualisation and tree-based analysis of sample relationships.
Uniquely to this package, users can ultrafastly understand any
tabular omics data, both scientifically and artistically,
especially in a sample-specific fashion but without loss of
information on large genes.
biocViews: Software, Clustering, Visualization, GeneExpression
Author: Hai Fang and Julian Gough
Maintainer: Hai Fang <hfang@well.ox.ac.uk>
URL: http://suprahex.r-forge.r-project.org
PackageStatus: Deprecated
Package: nondetects
Version: 2.37.1
Depends: R (>= 3.2), Biobase (>= 2.22.0)
Imports: limma, mvtnorm, utils, methods, arm, HTqPCR (>= 1.16.0)
Suggests: knitr, rmarkdown, BiocStyle (>= 1.0.0), RUnit, BiocGenerics
(>= 0.8.0)
License: GPL-3
Title: Non-detects in qPCR data
Description: Methods to model and impute non-detects in the results of
qPCR experiments.
biocViews: Software, AssayDomain, GeneExpression, Technology, qPCR,
WorkflowStep, Preprocessing
Author: Matthew N. McCall <mccallm@gmail.com>, Valeriia Sherina
<valery.sherina@gmail.com>
Maintainer: Valeriia Sherina <valery.sherina@gmail.com>
VignetteBuilder: knitr
PackageStatus: Deprecated
Package: paxtoolsr
Version: 1.41.1
Depends: R (>= 3.2), rJava (>= 0.9-8), methods, XML
Imports: utils, httr, igraph, plyr, rjson, R.utils, jsonlite, readr,
rappdirs
Suggests: testthat, knitr, BiocStyle, formatR, rmarkdown, RColorBrewer,
foreach, doSNOW, parallel, org.Hs.eg.db, clusterProfiler
License: LGPL-3
Title: Access Pathways from Multiple Databases Through BioPAX and
Pathway Commons
Description: The package provides a set of R functions for interacting
with BioPAX OWL files using Paxtools and the querying Pathway
Commons (PC) molecular interaction database. Pathway Commons is
a project by the Memorial Sloan-Kettering Cancer Center
(MSKCC), Dana-Farber Cancer Institute (DFCI), and the
University of Toronto. Pathway Commons databases include: BIND,
BioGRID, CORUM, CTD, DIP, DrugBank, HPRD, HumanCyc, IntAct,
KEGG, MirTarBase, Panther, PhosphoSitePlus, Reactome, RECON,
TRANSFAC.
biocViews: GeneSetEnrichment, GraphAndNetwork, Pathways, Software,
SystemsBiology, NetworkEnrichment, Network, Reactome, KEGG
Author: Augustin Luna [aut, cre]
Maintainer: Augustin Luna <lunaa@cbio.mskcc.org>
URL: https://github.com/BioPAX/paxtoolsr
SystemRequirements: Java (>= 1.6)
VignetteBuilder: knitr
PackageStatus: Deprecated
Package: gespeR
Version: 1.39.1
Depends: methods, graphics, ggplot2, R(>= 2.10)
Imports: Matrix, glmnet, cellHTS2, Biobase, biomaRt, doParallel,
parallel, foreach, reshape2, dplyr
Suggests: knitr
License: GPL-3
Title: Gene-Specific Phenotype EstimatoR
Description: Estimates gene-specific phenotypes from off-target
confounded RNAi screens. The phenotype of each siRNA is modeled
based on on-targeted and off-targeted genes, using a
regularized linear regression model.
biocViews: ImmunoOncology, CellBasedAssays, Preprocessing, GeneTarget,
Regression, Visualization
Author: Fabian Schmich
Maintainer: Fabian Schmich <fabian.schmich@bsse.ethz.ch>
URL: http://www.cbg.ethz.ch/software/gespeR
VignetteBuilder: knitr
PackageStatus: Deprecated
Package: crossmeta
Version: 1.33.1
Depends: R (>= 4.0)
Imports: affy (>= 1.52.0), affxparser (>= 1.46.0), AnnotationDbi (>=
1.36.2), Biobase (>= 2.34.0), BiocGenerics (>= 0.20.0),
BiocManager (>= 1.30.4), DT (>= 0.2), DBI (>= 1.0.0),
data.table (>= 1.10.4), edgeR, fdrtool (>= 1.2.15), GEOquery
(>= 2.40.0), limma (>= 3.30.13), matrixStats (>= 0.51.0),
metaMA (>= 3.1.2), miniUI (>= 0.1.1), methods, oligo (>=
1.38.0), reader(>= 1.0.6), RCurl (>= 1.95.4.11), RSQLite (>=
2.1.1), stringr (>= 1.2.0), sva (>= 3.22.0), shiny (>= 1.0.0),
shinyjs (>= 2.0.0), shinyBS (>= 0.61), shinyWidgets (>= 0.5.3),
shinypanel (>= 0.1.0), tibble, XML (>= 3.98.1.17), readxl (>=
1.3.1)
Suggests: knitr, rmarkdown, lydata, org.Hs.eg.db, testthat
License: MIT + file LICENSE
Title: Cross Platform Meta-Analysis of Microarray Data
Description: Implements cross-platform and cross-species meta-analyses
of Affymentrix, Illumina, and Agilent microarray data. This
package automates common tasks such as downloading,
normalizing, and annotating raw GEO data. The user then selects
control and treatment samples in order to perform differential
expression analyses for all comparisons. After analysing each
contrast seperately, the user can select tissue sources for
each contrast and specify any tissue sources that should be
grouped for the subsequent meta-analyses.
biocViews: GeneExpression, Transcription, DifferentialExpression,
Microarray, TissueMicroarray, OneChannel, Annotation,
BatchEffect, Preprocessing, GUI
Author: Alex Pickering
Maintainer: Alex Pickering <alexvpickering@gmail.com>
URL: https://github.com/alexvpickering/crossmeta
SystemRequirements: libxml2: libxml2-dev (deb), libxml2-devel (rpm)
libcurl: libcurl4-openssl-dev (deb), libcurl-devel (rpm)
openssl: libssl-dev (deb), openssl-devel (rpm), libssl_dev
(csw), openssl@1.1 (brew)
VignetteBuilder: knitr
BugReports: https://github.com/alexvpickering/crossmeta/issues
PackageStatus: Deprecated
Package: chromstaR
Version: 1.33.1
Depends: R (>= 3.3), GenomicRanges, ggplot2, chromstaRData
Imports: methods, utils, grDevices, graphics, stats, foreach,
doParallel, BiocGenerics (>= 0.31.6), S4Vectors, GenomeInfoDb,
IRanges, reshape2, Rsamtools, GenomicAlignments, bamsignals,
mvtnorm
Suggests: knitr, BiocStyle, testthat, biomaRt
License: Artistic-2.0
Title: Combinatorial and Differential Chromatin State Analysis for
ChIP-Seq Data
Description: This package implements functions for combinatorial and
differential analysis of ChIP-seq data. It includes uni- and
multivariate peak-calling, export to genome browser viewable
files, and functions for enrichment analyses.
biocViews: ImmunoOncology, Software, DifferentialPeakCalling,
HiddenMarkovModel, ChIPSeq, HistoneModification,
MultipleComparison, Sequencing, PeakDetection, ATACSeq
Author: Aaron Taudt, Maria Colome Tatche, Matthias Heinig, Minh Anh
Nguyen
Maintainer: Aaron Taudt <aaron.taudt@gmail.com>
URL: https://github.com/ataudt/chromstaR
VignetteBuilder: knitr
BugReports: https://github.com/ataudt/chromstaR/issues
PackageStatus: Deprecated
Package: MAGeCKFlute
Version: 2.11.1
Depends: R (>= 4.1)
Imports: Biobase, gridExtra, ggplot2, ggrepel, grDevices, grid,
reshape2, stats, utils, DOSE, clusterProfiler, pathview,
enrichplot, msigdbr, depmap
Suggests: biomaRt, BiocStyle, dendextend, graphics, knitr, pheatmap,
png, scales, sva, BiocManager
License: GPL (>=3)
NeedsCompilation: no
Title: Integrative Analysis Pipeline for Pooled CRISPR Functional
Genetic Screens
Description: CRISPR (clustered regularly interspaced short palindrome
repeats) coupled with nuclease Cas9 (CRISPR/Cas9) screens
represent a promising technology to systematically evaluate
gene functions. Data analysis for CRISPR/Cas9 screens is a
critical process that includes identifying screen hits and
exploring biological functions for these hits in downstream
analysis. We have previously developed two algorithms, MAGeCK
and MAGeCK-VISPR, to analyze CRISPR/Cas9 screen data in various
scenarios. These two algorithms allow users to perform quality
control, read count generation and normalization, and calculate
beta score to evaluate gene selection performance. In
downstream analysis, the biological functional analysis is
required for understanding biological functions of these
identified genes with different screening purposes. Here, We
developed MAGeCKFlute for supporting downstream analysis.
MAGeCKFlute provides several strategies to remove potential
biases within sgRNA-level read counts and gene-level beta
scores. The downstream analysis with the package includes
identifying essential, non-essential, and target-associated
genes, and performing biological functional category analysis,
pathway enrichment analysis and protein complex enrichment
analysis of these genes. The package also visualizes genes in
multiple ways to benefit users exploring screening data.
Collectively, MAGeCKFlute enables accurate identification of
essential, non-essential, and targeted genes, as well as their
related biological functions. This vignette explains the use of
the package and demonstrates typical workflows.
biocViews: FunctionalGenomics, CRISPR, PooledScreens, QualityControl,
Normalization, GeneSetEnrichment, Pathways, Visualization,
GeneTarget, KEGG
Author: Binbin Wang, Wubing Zhang, Feizhen Wu, Wei Li & X. Shirley Liu
Maintainer: Wubing Zhang <Watson5bZhang@gmail.com>
VignetteBuilder: knitr
PackageStatus: Deprecated
Package: RGMQL
Version: 1.27.1
Depends: R(>= 3.4.2), RGMQLlib
Imports: httr, rJava, GenomicRanges, rtracklayer, data.table, utils,
plyr, xml2, methods, S4Vectors, dplyr, stats, glue,
BiocGenerics
Suggests: BiocStyle, knitr, rmarkdown
License: Artistic-2.0
Title: GenoMetric Query Language for R/Bioconductor
Description: This package brings the GenoMetric Query Language (GMQL)
functionalities into the R environment. GMQL is a high-level,
declarative language to manage heterogeneous genomic datasets
for biomedical purposes, using simple queries to process
genomic regions and their metadata and properties. GMQL adopts
algorithms efficiently designed for big data using
cloud-computing technologies (like Apache Hadoop and Spark)
allowing GMQL to run on modern infrastructures, in order to
achieve scalability and high performance. It allows to create,
manipulate and extract genomic data from different data sources
both locally and remotely. Our RGMQL functions allow complex
queries and processing leveraging on the R idiomatic paradigm.
The RGMQL package also provides a rich set of ancillary classes
that allow sophisticated input/output management and sorting,
such as: ASC, DESC, BAG, MIN, MAX, SUM, AVG, MEDIAN, STD, Q1,
Q2, Q3 (and many others). Note that many RGMQL functions are
not directly executed in R environment, but are deferred until
real execution is issued.
biocViews: Software, Infrastructure, DataImport, Network,
ImmunoOncology, SingleCell
Author: Simone Pallotta [aut, cre], Marco Masseroli [aut]
Maintainer: Simone Pallotta <simonepallotta@hotmail.com>
URL: http://www.bioinformatics.deib.polimi.it/genomic_computing/GMQL/
VignetteBuilder: knitr
PackageStatus: Deprecated
Package: BEARscc
Version: 1.27.1
Imports: ggplot2, SingleCellExperiment, data.table, stats, utils,
graphics, compiler
Suggests: testthat, cowplot, knitr, rmarkdown, BiocStyle, NMF
License: GPL-3
Title: BEARscc (Bayesian ERCC Assesstment of Robustness of Single Cell
Clusters)
Description: BEARscc is a noise estimation and injection tool that is
designed to assess putative single-cell RNA-seq clusters in the
context of experimental noise estimated by ERCC spike-in
controls.
biocViews: ImmunoOncology, SingleCell, Clustering, Transcriptomics
Author: David T. Severson <david_severson@hms.harvard.edu>
Maintainer: Benjamin Schuster-Boeckler
<benjamin.schuster-boeckler@ludwig.ox.ac.uk>
VignetteBuilder: knitr
PackageStatus: Deprecated
Package: DIAlignR
Version: 2.15.1
Depends: methods, stats, R (>= 4.0)
Imports: zoo (>= 1.8-3), data.table, magrittr, dplyr, tidyr, rlang, mzR
(>= 2.18), signal, bit64, reticulate, ggplot2, RSQLite, DBI,
ape, phangorn, pracma, RMSNumpress, Rcpp
LinkingTo: Rcpp, RcppEigen
Suggests: knitr, akima, lattice, scales, gridExtra, latticeExtra,
rmarkdown, BiocStyle, BiocParallel, testthat (>= 2.1.0)
License: GPL-3
Title: Dynamic Programming Based Alignment of MS2 Chromatograms
Description: To obtain unbiased proteome coverage from a biological
sample, mass-spectrometer is operated in Data Independent
Acquisition (DIA) mode. Alignment of these DIA runs establishes
consistency and less missing values in complete data-matrix.
This package implements dynamic programming with affine gap
penalty based approach for pair-wise alignment of analytes. A
hybrid approach of global alignment (through MS2 features) and
local alignment (with MS2 chromatograms) is implemented in this
tool.
biocViews: MassSpectrometry, Metabolomics, Proteomics, Alignment,
Software
Author: Shubham Gupta [aut, cre] (ORCID:
<https://orcid.org/0000-0003-3500-8152>), Hannes Rost [aut]
(ORCID: <https://orcid.org/0000-0003-0990-7488>), Justin Sing
[aut]
Maintainer: Shubham Gupta <shubham.1637@gmail.com>
SystemRequirements: C++14
VignetteBuilder: knitr
BugReports: https://github.com/shubham1637/DIAlignR/issues
PackageStatus: Deprecated
Package: netDx
Version: 1.19.1
Depends: R (>= 3.6)
Imports: ROCR,pracma,ggplot2,glmnet,igraph,reshape2,
parallel,stats,utils,MultiAssayExperiment,graphics,grDevices,
methods,BiocFileCache,GenomicRanges,
bigmemory,doParallel,foreach,
combinat,rappdirs,GenomeInfoDb,S4Vectors,
IRanges,RColorBrewer,Rtsne,httr,plotrix
Suggests: curatedTCGAData, rmarkdown, testthat, knitr, BiocStyle, RCy3,
clusterExperiment, netSmooth, scater
License: MIT + file LICENSE
Title: Network-based patient classifier
Description: netDx is a general-purpose algorithm to build a patient
classifier from heterogenous patient data. The method converts
data into patient similarity networks at the level of features.
Feature selection identifies features of predictive value to
each class. Methods are provided for versatile predictor design
and performance evaluation using standard measures. netDx
natively groups molecular data into pathway-level features and
connects with Cytoscape for network visualization of pathway
themes. For method details see: Pai et al. (2019). netDx:
interpretable patient classification using integrated patient
similarity networks. Molecular Systems Biology. 15, e8497
biocViews: Classification, BiomedicalInformatics, Network,
SystemsBiology
Author: Shraddha Pai [aut, cre] (ORCID:
<https://orcid.org/0000-0002-1048-581X>), Philipp Weber [aut],
Ahmad Shah [aut], Luca Giudice [aut], Shirley Hui [aut], Anne
Nøhr [ctb], Indy Ng [ctb], Ruth Isserlin [aut], Hussam Kaka
[aut], Gary Bader [aut]
Maintainer: Shraddha Pai <shraddha.pai@utoronto.ca>
URL: http://netdx.org
VignetteBuilder: knitr
PackageStatus: Deprecated
Package: ReactomeContentService4R
Version: 1.15.1
Imports: httr, jsonlite, utils, magick (>= 2.5.1), data.table,
doParallel, foreach, parallel
Suggests: pdftools, testthat, knitr, rmarkdown
License: Apache License (>= 2.0) | file LICENSE
Title: Interface for the Reactome Content Service
Description: Reactome is a free, open-source, open access, curated and
peer-reviewed knowledgebase of bio-molecular pathways. This
package is to interact with the Reactome Content Service API.
Pre-built functions would allow users to retrieve data and
images that consist of proteins, pathways, and other molecules
related to a specific gene or entity in Reactome.
biocViews: DataImport, Pathways, Reactome
Author: Chi-Lam Poon [aut, cre] (ORCID:
<https://orcid.org/0000-0001-6298-7099>), Reactome [cph]
Maintainer: Chi-Lam Poon <clpoon807@gmail.com>
URL: https://github.com/reactome/ReactomeContentService4R
VignetteBuilder: knitr
BugReports: https://github.com/reactome/ReactomeContentService4R/issues
PackageStatus: Deprecated
Package: ReactomeGraph4R
Version: 1.15.0
Depends: R (>= 4.1)
Imports: neo4r, utils, getPass, jsonlite, purrr, magrittr, data.table,
rlang, ReactomeContentService4R, doParallel, parallel, foreach
Suggests: knitr, rmarkdown, testthat, stringr, networkD3, visNetwork,
wesanderson
License: Apache License (>= 2)
Title: Interface for the Reactome Graph Database
Description: Pathways, reactions, and biological entities in Reactome
knowledge are systematically represented as an ordered network.
Instances are represented as nodes and relationships between
instances as edges; they are all stored in the Reactome Graph
Database. This package serves as an interface to query the
interconnected data from a local Neo4j database, with the aim
of minimizing the usage of Neo4j Cypher queries.
biocViews: DataImport, Pathways, Reactome, Network, GraphAndNetwork
Author: Chi-Lam Poon [aut, cre] (ORCID:
<https://orcid.org/0000-0001-6298-7099>), Reactome [cph]
Maintainer: Chi-Lam Poon <clpoon807@gmail.com>
URL: https://github.com/reactome/ReactomeGraph4R
VignetteBuilder: knitr
BugReports: https://github.com/reactome/ReactomeGraph4R/issues
Package: CBEA
Version: 1.7.1
Depends: R (>= 4.2.0)
Imports: BiocParallel, BiocSet, dplyr, lmom, fitdistrplus, magrittr,
methods, mixtools, Rcpp (>= 1.0.7), stats,
SummarizedExperiment, tibble, TreeSummarizedExperiment, tidyr,
glue, generics, rlang, goftest
LinkingTo: Rcpp
Suggests: phyloseq, BiocStyle, covr, knitr, RefManageR, rmarkdown,
sessioninfo, testthat (>= 3.0.0), tidyverse, roxygen2, mia,
purrr
License: MIT + file LICENSE
Title: Competitive Balances for Taxonomic Enrichment Analysis in R
Description: This package implements CBEA, a method to perform
set-based analysis for microbiome relative abundance data. This
approach constructs a competitive balance between taxa within
the set and remainder taxa per sample. More details can be
found in the Nguyen et al. 2021+ manuscript. Additionally, this
package adds support functions to help users perform taxa-set
enrichment analyses using existing gene set analysis methods.
In the future we hope to also provide curated knowledge driven
taxa sets.
biocViews: Software, Microbiome, Metagenomics, GeneSetEnrichment,
DataImport
Author: Quang Nguyen [aut, cre] (ORCID:
<https://orcid.org/0000-0002-2072-3279>)
Maintainer: Quang Nguyen <quangpmnguyen@gmail.com>
URL: https://github.com/qpmnguyen/CBEA,
https://qpmnguyen.github.io/CBEA/
VignetteBuilder: knitr
BugReports: https://github.com/qpmnguyen/CBEA//issues
PackageStatus: Deprecated
Package: pareg
Version: 1.11.1
Depends: R (>= 4.2), tensorflow (>= 2.2.0), tfprobability (>= 0.10.0)
Imports: stats, tidyr, purrr, future, doFuture, foreach, doRNG, tibble,
glue, tidygraph, igraph, proxy, dplyr, magrittr, ggplot2,
ggraph, rlang, progress, Matrix, keras, nloptr, ggrepel,
methods, DOSE, stringr, reticulate, logger, hms, devtools,
basilisk
Suggests: knitr, rmarkdown, testthat (>= 2.1.0), BiocStyle, formatR,
plotROC, PRROC, mgsa, topGO, msigdbr, betareg, fgsea,
ComplexHeatmap, GGally, ggsignif, circlize, enrichplot,
ggnewscale, tidyverse, cowplot, ggfittext, simplifyEnrichment,
GSEABenchmarkeR, BiocParallel, ggupset, latex2exp,
org.Hs.eg.db, GO.db
License: GPL-3
Title: Pathway enrichment using a regularized regression approach
Description: Compute pathway enrichment scores while accounting for
term-term relations. This package uses a regularized multiple
linear regression to regress differential expression p-values
obtained from multi-condition experiments on a pathway
membership matrix. By doing so, it is able to incorporate
additional biological knowledge into the enrichment analysis
and to estimate pathway enrichment scores more robustly.
biocViews: Software, StatisticalMethod, GraphAndNetwork, Regression,
GeneExpression, DifferentialExpression, NetworkEnrichment,
Network
Author: Kim Philipp Jablonski [aut, cre] (ORCID:
<https://orcid.org/0000-0002-4166-4343>)
Maintainer: Kim Philipp Jablonski <kim.philipp.jablonski@gmail.com>
URL: https://github.com/cbg-ethz/pareg
VignetteBuilder: knitr
BugReports: https://github.com/cbg-ethz/pareg/issues
PackageStatus: Deprecated
Package: STdeconvolve
Version: 1.11.1
Depends: R (>= 4.1)
Imports: topicmodels, BiocParallel, Matrix, methods, mgcv, ggplot2,
scatterpie, viridis, slam, stats, clue, liger, reshape2,
graphics, grDevices, utils
Suggests: knitr, BiocStyle, rmarkdown, testthat, rcmdcheck, gplots,
gridExtra, hash, dplyr, parallel
License: GPL-3
Title: Reference-free Cell-Type Deconvolution of Multi-Cellular
Spatially Resolved Transcriptomics Data
Description: STdeconvolve as an unsupervised, reference-free approach
to infer latent cell-type proportions and transcriptional
profiles within multi-cellular spatially-resolved pixels from
spatial transcriptomics (ST) datasets. STdeconvolve builds on
latent Dirichlet allocation (LDA), a generative statistical
model commonly used in natural language processing for
discovering latent topics in collections of documents. In the
context of natural language processing, given a count matrix of
words in documents, LDA infers the distribution of words for
each topic and the distribution of topics in each document. In
the context of ST data, given a count matrix of gene expression
in multi-cellular ST pixels, STdeconvolve applies LDA to infer
the putative transcriptional profile for each cell-type and the
proportional representation of each cell-type in each
multi-cellular ST pixel.
biocViews: Transcriptomics, Visualization, RNASeq, Bayesian, Spatial,
Software, GeneExpression
Author: Brendan Miller [aut, cre] (ORCID:
<https://orcid.org/0000-0002-9559-4045>), Jean Fan [aut]
(ORCID: <https://orcid.org/0000-0002-0212-5451>)
Maintainer: Brendan Miller <bmill3r@gmail.com>
URL: https://jef.works/STdeconvolve/
VignetteBuilder: knitr
BugReports: https://github.com/JEFworks-Lab/STdeconvolve/issues
PackageStatus: Deprecated
Package: PanViz
Version: 1.9.1
Depends: R (>= 4.2.0)
Imports: tidyr, stringr, dplyr, tibble, magrittr, futile.logger, utils,
easycsv, rentrez, igraph, RColorBrewer, data.table, colorspace,
grDevices, rlang, methods
Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown, networkD3,
License: Artistic-2.0
Title: Integrating Multi-Omic Network Data With Summay-Level GWAS Data
Description: This pacakge integrates data from the Kyoto Encyclopedia
of Genes and Genomes (KEGG) with summary-level genome-wide
association (GWAS) data, such as that provided by the GWAS
Catalog or GWAS Central databases, or a user's own study or
dataset, in order to produce biological networks, termed IMONs
(Integrated Multi-Omic Networks). IMONs can be used to analyse
trait-specific polymorphic data within the context of
biochemical and metabolic reaction networks, providing greater
biological interpretability for GWAS data.
biocViews: GenomeWideAssociation, Reactome, Metabolomics, SNP,
GraphAndNetwork, Network, KEGG
Author: Luca Anholt [cre, aut]
Maintainer: Luca Anholt <la1317@ic.ac.uk>
URL: https://github.com/LucaAnholt/PanViz
VignetteBuilder: knitr
BugReports: https://github.com/LucaAnholt/PanViz/issues
PackageStatus: Deprecated
Package: coMET
Version: 1.39.1
Depends: R (>= 4.1.0), grid, utils, biomaRt, Gviz, psych
Imports: hash,grDevices, gridExtra, rtracklayer, IRanges, S4Vectors,
GenomicRanges, stats, corrplot
Suggests: BiocStyle, knitr, RUnit, BiocGenerics, showtext
License: GPL (>= 2)
NeedsCompilation: no
Title: coMET: visualisation of regional epigenome-wide association scan
(EWAS) results and DNA co-methylation patterns
Description: Visualisation of EWAS results in a genomic region. In
addition to phenotype-association P-values, coMET also
generates plots of co-methylation patterns and provides a
series of annotation tracks. It can be used to other omic-wide
association scans as lon:g as the data can be translated to
genomic level and for any species.
biocViews: Software, DifferentialMethylation, Visualization,
Sequencing, Genetics, FunctionalGenomics, Microarray,
MethylationArray, MethylSeq, ChIPSeq, DNASeq, RiboSeq, RNASeq,
ExomeSeq, DNAMethylation, GenomeWideAssociation,
MotifAnnotation
Author: Tiphaine C. Martin [aut,cre], Thomas Hardiman [aut], Idil Yet
[aut], Pei-Chien Tsai [aut], Jordana T. Bell [aut]
Maintainer: Tiphaine Martin <tiphaine.martin@mssm.edu>
URL: http://epigen.kcl.ac.uk/comet
VignetteBuilder: knitr
PackageStatus: Deprecated
Package: HTqPCR
Version: 1.61.1
Depends: Biobase, RColorBrewer, limma
Imports: affy, Biobase, gplots, graphics, grDevices, limma, methods,
RColorBrewer, stats, stats4, utils
Suggests: statmod
License: Artistic-2.0
Title: Automated analysis of high-throughput qPCR data
Description: Analysis of Ct values from high throughput quantitative
real-time PCR (qPCR) assays across multiple conditions or
replicates. The input data can be from spatially-defined
formats such ABI TaqMan Low Density Arrays or OpenArray;
LightCycler from Roche Applied Science; the CFX plates from
Bio-Rad Laboratories; conventional 96- or 384-well plates; or
microfluidic devices such as the Dynamic Arrays from Fluidigm
Corporation. HTqPCR handles data loading, quality assessment,
normalization, visualization and parametric or non-parametric
testing for statistical significance in Ct values between
features (e.g. genes, microRNAs).
biocViews: MicrotitrePlateAssay, DifferentialExpression,
GeneExpression, DataImport, QualityControl, Preprocessing,
Visualization, MultipleComparison, qPCR
Author: Heidi Dvinge, Paul Bertone
Maintainer: Matthew N. McCall <mccallm@gmail.com>
URL: http://www.ebi.ac.uk/bertone/software
PackageStatus: Deprecated