Package: a4 Version: 1.53.0 Depends: a4Base, a4Preproc, a4Classif, a4Core, a4Reporting Suggests: MLP, nlcv, ALL, Cairo, Rgraphviz, GOstats, hgu95av2.db License: GPL-3 MD5sum: fce80e1923c86992ec099f763500c220 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 git_url: https://git.bioconductor.org/packages/a4 git_branch: devel git_last_commit: 181f314 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/a4_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/a4_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/a4_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/a4_1.53.0.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: 1.53.0 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: efb7897f9b5f8f80d3796536447f4ec2 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 git_url: https://git.bioconductor.org/packages/a4Base git_branch: devel git_last_commit: 7a868d3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/a4Base_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/a4Base_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/a4Base_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/a4Base_1.53.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 suggestsMe: epimutacions dependencyCount: 78 Package: a4Classif Version: 1.53.0 Depends: a4Core, a4Preproc Imports: methods, Biobase, ROCR, pamr, glmnet, varSelRF, utils, graphics, stats Suggests: ALL, hgu95av2.db, knitr, rmarkdown License: GPL-3 MD5sum: 9a1827e914a91969659a7efd1bc04d0e 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Classif git_branch: devel git_last_commit: 7d62634 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/a4Classif_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/a4Classif_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/a4Classif_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/a4Classif_1.53.0.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: 32 Package: a4Core Version: 1.53.0 Imports: Biobase, glmnet, methods, stats Suggests: knitr, rmarkdown License: GPL-3 MD5sum: dd892071bcc88b745de5839a8e48cda6 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Core git_branch: devel git_last_commit: 97ffb35 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/a4Core_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/a4Core_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/a4Core_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/a4Core_1.53.0.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: 19 Package: a4Preproc Version: 1.53.0 Imports: BiocGenerics, Biobase Suggests: ALL, hgu95av2.db, knitr, rmarkdown License: GPL-3 MD5sum: 0b1baef89e6fc262335db61f0f5bac61 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Preproc git_branch: devel git_last_commit: 15dfe4e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/a4Preproc_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/a4Preproc_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/a4Preproc_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/a4Preproc_1.53.0.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: 6 Package: a4Reporting Version: 1.53.0 Imports: methods, xtable Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 6ad09551096d277ea397c683c91f80a1 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Reporting git_branch: devel git_last_commit: fa74cff git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/a4Reporting_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/a4Reporting_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/a4Reporting_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/a4Reporting_1.53.0.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: 1.73.0 Imports: Biobase, graphics, grDevices, methods, multtest, stats, tcltk, utils Suggests: limma, LPE License: GPL MD5sum: d66500da89f0950fa478e71cfff97f34 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 git_url: https://git.bioconductor.org/packages/ABarray git_branch: devel git_last_commit: 4759ae1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ABarray_1.73.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ABarray_1.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ABarray_1.73.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ABarray_1.73.0.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: 16 Package: abseqR Version: 1.23.0 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: f573afd7d78437a1589bee6f44aa2183 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 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: c25bd74 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/abseqR_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/abseqR_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/abseqR_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/abseqR_1.23.0.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: 1.59.0 Depends: R (>= 2.10), methods Imports: locfit, limma Suggests: edgeR License: GPL (>= 3) MD5sum: 28d4176a86f0627a317aefd40b44be2a 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 git_url: https://git.bioconductor.org/packages/ABSSeq git_branch: devel git_last_commit: dc09894 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ABSSeq_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ABSSeq_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ABSSeq_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ABSSeq_1.59.0.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: 1.35.0 Depends: R(>= 3.3), boot(>= 1.3) Imports: stats, graphics Suggests: BiocGenerics, RUnit License: GPL-3 MD5sum: fa5ea8ba1b5720ec7185e9b6c212441a 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 git_url: https://git.bioconductor.org/packages/acde git_branch: devel git_last_commit: b6f5b76 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/acde_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/acde_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/acde_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/acde_1.35.0.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: 1.23.0 Depends: R (>= 3.4) Imports: Biobase, QDNAseq, ggplot2, grid, stats, utils, methods, grDevices, GenomicRanges Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: ae8217005f7a8fd3e472febcb99ab017 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 URL: https://github.com/tgac-vumc/ACE VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ACE git_branch: devel git_last_commit: 123601a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ACE_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ACE_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ACE_1.23.0.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: 1.83.0 Depends: R (>= 2.10), cluster, survival, multtest Imports: Biobase, grDevices, graphics, methods, stats, splines, utils License: GPL-2 Archs: x64 MD5sum: 81a5b00b4a153a4f60b1cc88dfa451df 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 , Peter Dimitrov Maintainer: Peter Dimitrov git_url: https://git.bioconductor.org/packages/aCGH git_branch: devel git_last_commit: e912226 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/aCGH_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/aCGH_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/aCGH_1.83.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/aCGH_1.83.0.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: 16 Package: ACME Version: 2.61.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), methods, BiocGenerics Imports: graphics, stats License: GPL (>= 2) Archs: x64 MD5sum: f97a7a6a300bcadd58534d0d33536bd5 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 Maintainer: Sean Davis URL: http://watson.nci.nih.gov/~sdavis git_url: https://git.bioconductor.org/packages/ACME git_branch: devel git_last_commit: 9f15087 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ACME_2.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ACME_2.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ACME_2.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ACME_2.61.0.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: 6 Package: ADaCGH2 Version: 2.45.0 Depends: R (>= 3.2.0), parallel, ff, GLAD Imports: bit, DNAcopy, tilingArray, waveslim, cluster, aCGH Suggests: CGHregions, Cairo, limma Enhances: Rmpi License: GPL (>= 3) Archs: x64 MD5sum: 6ad2048f05dec5e13160e63fbc4d7216 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 and Oscar M. Rueda . Wavelet-based aCGH smoothing code from Li Hsu and Douglas Grove . Imagemap code from Barry Rowlingson . HaarSeg code from Erez Ben-Yaacov; downloaded from . Code from ffbase by Edwin de Jonge , Jan Wijffels, Jan van der Laan. Maintainer: Ramon Diaz-Uriarte URL: https://github.com/rdiaz02/adacgh2 git_url: https://git.bioconductor.org/packages/ADaCGH2 git_branch: devel git_last_commit: 3db9be4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ADaCGH2_2.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ADaCGH2_2.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ADaCGH2_2.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ADaCGH2_2.45.0.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: 98 Package: ADAM Version: 1.21.0 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: aa28bb0f90f96243e065c4e98141d804 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 SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ADAM git_branch: devel git_last_commit: 2809620 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ADAM_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ADAM_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ADAM_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ADAM_1.21.0.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: 94 Package: ADAMgui Version: 1.21.0 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: 4726ea8e5bf443b4ad56f2720bff2599 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 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: 665a4c8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ADAMgui_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ADAMgui_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ADAMgui_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ADAMgui_1.21.0.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: 160 Package: adductomicsR Version: 1.21.0 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: 2173915b675cdc149da26842905f60ad 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 Maintainer: Josie Hayes VignetteBuilder: knitr BugReports: https://github.com/JosieLHayes/adductomicsR/issues git_url: https://git.bioconductor.org/packages/adductomicsR git_branch: devel git_last_commit: d8a5960 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/adductomicsR_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/adductomicsR_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/adductomicsR_1.21.0.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: 137 Package: ADImpute Version: 1.15.0 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: 05106577d8085144df2cd94f49103316 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] () Maintainer: Ana Carolina Leote VignetteBuilder: knitr BugReports: https://github.com/anacarolinaleote/ADImpute/issues git_url: https://git.bioconductor.org/packages/ADImpute git_branch: devel git_last_commit: 5d42b66 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ADImpute_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ADImpute_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ADImpute_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ADImpute_1.15.0.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: 1.75.0 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: 46dec1bb45390725b6c4b8eff8239945 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 URL: http://compdiag.molgen.mpg.de/software/adSplit.shtml git_url: https://git.bioconductor.org/packages/adSplit git_branch: devel git_last_commit: 6b17e85 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/adSplit_1.75.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/adSplit_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/adSplit_1.75.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/adSplit_1.75.0.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: 1.3.0 Imports: gtools, S4Vectors, methods, DelayedArray Suggests: knitr, RUnit, BiocGenerics, TENxPBMCData, CHETAH, stringr, LoomExperiment License: MIT + file LICENSE MD5sum: bb992b080c78badeb2ab727180bb2aef NeedsCompilation: no Title: adverSCarial, generate and analyze the vulnerability of scRNA-seq classifiers 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] (), Sébastien HERGALANT [aut] () Maintainer: Ghislain FIEVET VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/adverSCarial git_branch: devel git_last_commit: df12473 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/adverSCarial_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/adverSCarial_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/adverSCarial_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/adverSCarial_1.3.0.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_adaptClassifier, 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: 1.23.0 Depends: R (>= 3.6), SummarizedExperiment Imports: MultiAssayExperiment, BiocParallel, crayon Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 60ace70c73cd454392ea3ef79acc80a1 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AffiXcan git_branch: devel git_last_commit: ac77a70 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AffiXcan_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/AffiXcan_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AffiXcan_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AffiXcan_1.23.0.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: 67 Package: affxparser Version: 1.77.0 Depends: R (>= 2.14.0) Suggests: R.oo (>= 1.22.0), R.utils (>= 2.7.0), AffymetrixDataTestFiles License: LGPL (>= 2) Archs: x64 MD5sum: d1a3e237a2eae85021c285aedece6646 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 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: 10de0fd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/affxparser_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/affxparser_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/affxparser_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/affxparser_1.77.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder importsMe: affyILM, cn.farms, crossmeta, EventPointer, GeneRegionScan, ITALICS, oligo suggestsMe: TIN, aroma.affymetrix, aroma.apd dependencyCount: 0 Package: affy Version: 1.83.0 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, zlibbioc LinkingTo: preprocessCore Suggests: tkWidgets (>= 1.19.0), affydata, widgetTools, hgu95av2cdf License: LGPL (>= 2.0) Archs: x64 MD5sum: 8392dcb314754301527d0344897d8a5a 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 , Laurent Gautier , Benjamin Milo Bolstad , and Crispin Miller with contributions from Magnus Astrand , Leslie M. Cope , Robert Gentleman, Jeff Gentry, Conrad Halling , Wolfgang Huber, James MacDonald , Benjamin I. P. Rubinstein, Christopher Workman , John Zhang Maintainer: Robert D. Shear 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: 44ce0b6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/affy_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/affy_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/affy_1.83.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/affy_1.83.0.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, bgx, Cormotif, DrugVsDisease, ExiMiR, frmaTools, gcrma, maskBAD, panp, prebs, qpcrNorm, RPA, SCAN.UPC, webbioc, affydata, ALLMLL, AmpAffyExample, bronchialIL13, CLL, curatedBladderData, ecoliLeucine, Hiiragi2013, MAQCsubset, mvoutData, PREDAsampledata, SpikeIn, SpikeInSubset, XhybCasneuf, RobLoxBioC importsMe: affycoretools, affyILM, affylmGUI, arrayQualityMetrics, bnem, CAFE, ChIPXpress, Cormotif, crossmeta, Doscheda, ffpe, frma, gcrma, GEOsubmission, Harshlight, iCheck, lumi, makecdfenv, mimager, MSnbase, PECA, plier, puma, pvac, Rnits, STATegRa, tilingArray, TurboNorm, vsn, rat2302frmavecs, DeSousa2013, signatureSearchData, bapred, seeker 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: 1.81.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.3.3) Suggests: splines, affycompData License: GPL (>= 2) MD5sum: 33a392a48b03637833d3f76506f77726 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 and Zhijin Wu with contributions from Simon Cawley Maintainer: Robert D. Shear 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: 52d7768 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/affycomp_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/affycomp_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/affycomp_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/affycomp_1.81.0.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: 6 Package: affyContam Version: 1.63.0 Depends: R (>= 2.7.0), tools, methods, utils, Biobase, affy, affydata Suggests: hgu95av2cdf License: Artistic-2.0 MD5sum: ca0d6366a8b39ebe9272a2c41a9e1c2e 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 git_url: https://git.bioconductor.org/packages/affyContam git_branch: devel git_last_commit: 076c866 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/affyContam_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/affyContam_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/affyContam_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/affyContam_1.63.0.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: 1.77.0 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: f24735716ab8a987c4eea518c231725b 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/affycoretools git_branch: devel git_last_commit: ee5d3df git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/affycoretools_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/affycoretools_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/affycoretools_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/affycoretools_1.77.0.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: 207 Package: affyILM Version: 1.57.0 Depends: R (>= 2.10.0), methods, gcrma Imports: affxparser (>= 1.16.0), affy, graphics, Biobase Suggests: AffymetrixDataTestFiles, hgfocusprobe License: GPL-3 MD5sum: 04839ecafd53ad3dca301ec9bfade419 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 git_url: https://git.bioconductor.org/packages/affyILM git_branch: devel git_last_commit: f9636fa git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/affyILM_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/affyILM_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/affyILM_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/affyILM_1.57.0.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: 1.75.0 Depends: R (>= 2.6.0) Imports: zlibbioc, methods License: LGPL (>= 2) Archs: x64 MD5sum: 33db6721398dbf46c917205798fd1c9a 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 Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/affyio git_url: https://git.bioconductor.org/packages/affyio git_branch: devel git_last_commit: a03ad7f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/affyio_1.75.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/affyio_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/affyio_1.75.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/affyio_1.75.0.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: 2 Package: affylmGUI Version: 1.79.0 Imports: grDevices, graphics, stats, utils, tcltk, tkrplot, limma, affy, affyio, affyPLM, gcrma, BiocGenerics, AnnotationDbi, BiocManager, R2HTML, xtable License: GPL (>=2) MD5sum: daaf269f46736f5faf3b0abca459ebd1 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 URL: http://bioinf.wehi.edu.au/affylmGUI/ git_url: https://git.bioconductor.org/packages/affylmGUI git_branch: devel git_last_commit: 9413ab5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/affylmGUI_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/affylmGUI_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/affylmGUI_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/affylmGUI_1.79.0.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: 1.81.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), affy (>= 1.11.0), Biobase (>= 2.17.8), gcrma, stats, preprocessCore (>= 1.5.1) Imports: zlibbioc, graphics, grDevices, methods LinkingTo: preprocessCore Suggests: affydata, MASS, hgu95av2cdf License: GPL (>= 2) Archs: x64 MD5sum: a54bbc7dec8b17420aae4dd10bbede34 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 Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/affyPLM git_url: https://git.bioconductor.org/packages/affyPLM git_branch: devel git_last_commit: 02f6bd3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/affyPLM_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/affyPLM_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/affyPLM_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/affyPLM_1.81.0.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: 1.51.0 Depends: R (>= 2.9.0), methods, affy Suggests: AmpAffyExample, hgu133acdf License: GPL-2 MD5sum: 56c515a514fec0affda3c6d4cf02b111 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 git_url: https://git.bioconductor.org/packages/AffyRNADegradation git_branch: devel git_last_commit: 69bae53 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AffyRNADegradation_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/AffyRNADegradation_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AffyRNADegradation_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AffyRNADegradation_1.51.0.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: 1.53.0 Depends: R (>= 2.10), Biobase, GSEABase Imports: stats License: GPL Version 2 or later MD5sum: 0ac32be79094fdc88f58f0dafb3964de 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 ; Cuilan Lani Gao Maintainer: Cuilan lani Gao git_url: https://git.bioconductor.org/packages/AGDEX git_branch: devel git_last_commit: ba8bb76 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AGDEX_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/AGDEX_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AGDEX_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AGDEX_1.53.0.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: 1.15.0 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: 209ffa593a88e703d9a8b7ff172083e7 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] (), Andrew Thurman [aut], Michael Chimenti [ctb], Alejandro Pezzulo [ctb] Maintainer: Jason Ratcliff 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: 9e7eb22 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/aggregateBioVar_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/aggregateBioVar_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/aggregateBioVar_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/aggregateBioVar_1.15.0.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: 3.37.0 Depends: R (>= 2.14.0) License: GPL-3 MD5sum: c01446f52a2fa153300e2eac61d5d0dd NeedsCompilation: no Title: Agilent expression array processing package Description: More about what it does (maybe more than one line) Author: Benny Chain Maintainer: Benny Chain git_url: https://git.bioconductor.org/packages/agilp git_branch: devel git_last_commit: f148b7a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/agilp_3.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/agilp_3.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/agilp_3.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/agilp_3.37.0.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: 2.55.0 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: a8c8cc19cf7f1b2649f54cfb2a70bdba 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 Maintainer: Pedro Lopez-Romero git_url: https://git.bioconductor.org/packages/AgiMicroRna git_branch: devel git_last_commit: 15a2ac5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AgiMicroRna_2.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/AgiMicroRna_2.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AgiMicroRna_2.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AgiMicroRna_2.55.0.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: 208 Package: AHMassBank Version: 1.5.0 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: 6eee6e9942cb2e58337c9ddae1f8130d 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] () Maintainer: Johannes Rainer 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: ba41227 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AHMassBank_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AHMassBank_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AHMassBank_1.5.0.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: 124 Package: AIMS Version: 1.37.0 Depends: R (>= 2.10), e1071, Biobase Suggests: breastCancerVDX, hgu133a.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: b395b3cd815f141fce2c1c7a0357b883 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 URL: http://www.bci.mcgill.ca/AIMS git_url: https://git.bioconductor.org/packages/AIMS git_branch: devel git_last_commit: 66d7250 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AIMS_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/AIMS_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AIMS_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AIMS_1.37.0.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: 11 Package: airpart Version: 1.13.0 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: 898f18cc126a52338578244f33c4fb71 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] (), Michael Love [aut, ctb] () Maintainer: Wancen Mu 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: 76baee5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/airpart_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/airpart_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/airpart_1.13.0.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: 144 Package: alabaster Version: 1.5.0 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: 731a5f87f37e87ef42d1e1af18a2af7f 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster git_branch: devel git_last_commit: 51f54c9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/alabaster_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/alabaster_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/alabaster_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/alabaster_1.5.0.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: 119 Package: alabaster.base Version: 1.5.1 Imports: alabaster.schemas, methods, utils, S4Vectors, rhdf5 (>= 2.47.6), jsonlite, jsonvalidate, Rcpp LinkingTo: Rcpp, Rhdf5lib Suggests: BiocStyle, rmarkdown, knitr, testthat, digest, Matrix License: MIT + file LICENSE Archs: x64 MD5sum: 850860e1717716472b63d7b1943bec9a 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 SystemRequirements: C++17, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.base git_branch: devel git_last_commit: 04ff2d9 git_last_commit_date: 2024-05-03 Date/Publication: 2024-05-03 source.ver: src/contrib/alabaster.base_1.5.1.tar.gz win.binary.ver: bin/windows/contrib/4.4/alabaster.base_1.5.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/alabaster.base_1.5.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/alabaster.base_1.5.1.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.spatial, alabaster.string, alabaster.vcf importsMe: celldex, scRNAseq dependencyCount: 16 Package: alabaster.bumpy Version: 1.5.0 Depends: BumpyMatrix, alabaster.base Imports: methods, rhdf5, Matrix, BiocGenerics, S4Vectors, IRanges Suggests: BiocStyle, rmarkdown, knitr, testthat, jsonlite License: MIT + file LICENSE MD5sum: acdf4cbe0c0be5ed01bc836e09a82eb4 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.bumpy git_branch: devel git_last_commit: 109be1e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/alabaster.bumpy_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/alabaster.bumpy_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/alabaster.bumpy_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/alabaster.bumpy_1.5.0.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: 23 Package: alabaster.files Version: 1.3.0 Depends: alabaster.base, Imports: methods, S4Vectors, BiocGenerics, Rsamtools Suggests: BiocStyle, rmarkdown, knitr, testthat, VariantAnnotation, rtracklayer, Biostrings License: MIT + file LICENSE MD5sum: 6f7801758ed1aa1b063cb9888cac273e 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.files git_branch: devel git_last_commit: ba2759d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/alabaster.files_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/alabaster.files_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/alabaster.files_1.3.0.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: 47 Package: alabaster.mae Version: 1.5.0 Depends: MultiAssayExperiment, alabaster.base Imports: methods, alabaster.se, S4Vectors, jsonlite, rhdf5 Suggests: testthat, knitr, SummarizedExperiment, BiocParallel, BiocStyle, rmarkdown License: MIT + file LICENSE MD5sum: b48b306929a78d88fa1df1db1f79fb1e 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.mae git_branch: devel git_last_commit: 3ad081d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/alabaster.mae_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/alabaster.mae_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/alabaster.mae_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/alabaster.mae_1.5.0.tgz vignettes: vignettes/alabaster.mae/inst/doc/userguide.html vignetteTitles: Saving and loading MultiAssayExperiments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.mae/inst/doc/userguide.R importsMe: alabaster dependencyCount: 70 Package: alabaster.matrix Version: 1.5.0 Depends: alabaster.base Imports: methods, BiocGenerics, S4Vectors, DelayedArray (>= 0.27.2), S4Arrays, SparseArray, rhdf5 (>= 2.47.1), HDF5Array, Matrix, Rcpp LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, chihaya, BiocSingular, ResidualMatrix License: MIT + file LICENSE Archs: x64 MD5sum: bcaabb181d642eefa0ce645fe84106c1 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.matrix git_branch: devel git_last_commit: a42f07a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/alabaster.matrix_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/alabaster.matrix_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/alabaster.matrix_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/alabaster.matrix_1.3.13.tgz vignettes: vignettes/alabaster.matrix/inst/doc/userguide.html vignetteTitles: Saving and loading arrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.matrix/inst/doc/userguide.R importsMe: alabaster, alabaster.se, celldex, scRNAseq dependencyCount: 33 Package: alabaster.ranges Version: 1.5.0 Depends: GenomicRanges, alabaster.base Imports: methods, S4Vectors, BiocGenerics, IRanges, GenomeInfoDb, rhdf5 Suggests: testthat, knitr, BiocStyle, jsonlite License: MIT + file LICENSE MD5sum: 8ff507c54b95e86d886d0825c2fccf6f 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.ranges git_branch: devel git_last_commit: bd57e4d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/alabaster.ranges_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/alabaster.ranges_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/alabaster.ranges_1.5.0.tgz vignettes: vignettes/alabaster.ranges/inst/doc/userguide.html vignetteTitles: Saving and loading genomic ranges hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.ranges/inst/doc/userguide.R importsMe: alabaster, alabaster.se dependencyCount: 31 Package: alabaster.sce Version: 1.5.0 Depends: SingleCellExperiment, alabaster.base Imports: methods, alabaster.se, jsonlite Suggests: knitr, testthat, BiocStyle, rmarkdown License: MIT + file LICENSE MD5sum: 8ada644250af0a9e8ad069cc7d2bf2ba 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.sce git_branch: devel git_last_commit: 2a8853b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/alabaster.sce_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/alabaster.sce_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/alabaster.sce_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/alabaster.sce_1.5.0.tgz vignettes: vignettes/alabaster.sce/inst/doc/userguide.html vignetteTitles: Saving and loading SingleCellExperiments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.sce/inst/doc/userguide.R importsMe: alabaster, alabaster.spatial, scRNAseq dependencyCount: 49 Package: alabaster.schemas Version: 1.5.0 Suggests: knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: da2879f955bff9459534e01027e85975 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.schemas git_branch: devel git_last_commit: 1bbcd30 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/alabaster.schemas_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/alabaster.schemas_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/alabaster.schemas_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/alabaster.schemas_1.5.0.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: 1.5.0 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: 50cf3581423466cee9625b992fb2a4ab 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.se git_branch: devel git_last_commit: f48c749 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/alabaster.se_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/alabaster.se_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/alabaster.se_1.5.0.tgz vignettes: vignettes/alabaster.se/inst/doc/userguide.html vignetteTitles: Saving and loading SummarizedExperiments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.se/inst/doc/userguide.R importsMe: alabaster, alabaster.mae, alabaster.sce, alabaster.vcf, celldex dependencyCount: 47 Package: alabaster.spatial Version: 1.5.0 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: fdc183e67de4e8a22ba54138a0a4dfdb 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.spatial git_branch: devel git_last_commit: 8a83ef3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/alabaster.spatial_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/alabaster.spatial_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/alabaster.spatial_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/alabaster.spatial_1.5.0.tgz vignettes: vignettes/alabaster.spatial/inst/doc/userguide.html vignetteTitles: Saving spatial experiments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alabaster.spatial/inst/doc/userguide.R importsMe: alabaster dependencyCount: 85 Package: alabaster.string Version: 1.5.0 Depends: Biostrings, alabaster.base Imports: utils, methods, S4Vectors Suggests: BiocStyle, rmarkdown, knitr, testthat License: MIT + file LICENSE MD5sum: 112200fd3d0d8717cb2869f5bb9fb856 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.string git_branch: devel git_last_commit: a4cf6fd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/alabaster.string_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/alabaster.string_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/alabaster.string_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/alabaster.string_1.5.0.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: 33 Package: alabaster.vcf Version: 1.5.0 Depends: alabaster.base, VariantAnnotation Imports: methods, S4Vectors, alabaster.se, alabaster.string, Rsamtools Suggests: knitr, rmarkdown, BiocStyle, testthat License: MIT + file LICENSE MD5sum: dc88062ebcd24ffc2b67ea7c16a3843e 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alabaster.vcf git_branch: devel git_last_commit: d7d3a10 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/alabaster.vcf_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/alabaster.vcf_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/alabaster.vcf_1.5.0.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: 92 Package: ALDEx2 Version: 1.37.0 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: 48abcc293124795ad87dfe65a160f21c 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 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: e0f7c08 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ALDEx2_1.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ALDEx2_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ALDEx2_1.37.0.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, microbiomeMarker, aIc, ggpicrust2 suggestsMe: dar, pctax dependencyCount: 68 Package: alevinQC Version: 1.21.0 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: 3dce3aa23cc650b2439736a402e03b22 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] (), Avi Srivastava [aut], Rob Patro [aut], Dongze He [aut] Maintainer: Charlotte Soneson 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: c45cc56 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/alevinQC_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/alevinQC_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/alevinQC_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/alevinQC_1.21.0.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: 102 Package: AllelicImbalance Version: 1.43.0 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: f3f04f30ce6412587fe28225875ee42c 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 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: 5cc7bec git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AllelicImbalance_1.43.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AllelicImbalance_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AllelicImbalance_1.43.0.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: 163 Package: AlphaBeta Version: 1.19.0 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: 90a52b76122541e71cedf89d753c3b78 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AlphaBeta git_branch: devel git_last_commit: 1aaed3e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AlphaBeta_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/AlphaBeta_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AlphaBeta_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AlphaBeta_1.19.0.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: 108 Package: AlphaMissenseR Version: 1.1.1 Depends: dplyr Imports: rjsoncons (>= 1.0.1), DBI, duckdb (>= 0.9.1), rlang, curl, BiocFileCache, spdl, memoise, BiocBaseUtils, utils, stats, methods, whisker Suggests: BiocManager, BiocGenerics, GenomicRanges, GenomeInfoDb, AnnotationHub, ensembldb, httr, tidyr, r3dmol, bio3d, shiny, colorspace, knitr, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 12ad283bec893902a8c5a1c52702d0c4 NeedsCompilation: no Title: Accessing AlphaMissense Data Resources in R Description: The AlphaMissense publication outlines how a variant of AlphaFold / DeepMind was used to predict missense variant pathogenicity. Supporting data on Zenodo 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] (), Tram Nguyen [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 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: e5c692c git_last_commit_date: 2024-05-05 Date/Publication: 2024-05-06 source.ver: src/contrib/AlphaMissenseR_1.1.1.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AlphaMissenseR_1.1.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AlphaMissenseR_1.1.1.tgz vignettes: vignettes/AlphaMissenseR/inst/doc/a_introduction.html, vignettes/AlphaMissenseR/inst/doc/b_visualization.html, vignettes/AlphaMissenseR/inst/doc/c_issues.html vignetteTitles: Accessing AlphaMissense Resources in R, Visualization, Issues & Solutions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AlphaMissenseR/inst/doc/a_introduction.R, vignettes/AlphaMissenseR/inst/doc/b_visualization.R, vignettes/AlphaMissenseR/inst/doc/c_issues.R dependencyCount: 53 Package: AlpsNMR Version: 4.7.0 Depends: R (>= 4.2), future (>= 1.10.0) 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 Suggests: BiocStyle, ChemoSpec, cowplot, curl, DT (>= 0.5), GGally (>= 1.4.0), ggrepel (>= 0.8.0), gridExtra, knitr, plotly (>= 4.7.1), progressr, SummarizedExperiment, S4Vectors, testthat (>= 2.0.0), writexl (>= 1.0), zip (>= 2.0.4) License: MIT + file LICENSE MD5sum: ebf818d535e2a543abd29ff20d728eab 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] (), Francisco Madrid Gambin [aut] (), Luis Fernandez [aut] (), Laura López Sánchez [ctb], Héctor Gracia Cabrera [aut], Santiago Marco Colás [aut] (), Nestlé Institute of Health Sciences [cph], Institute for Bioengineering of Catalonia [cph] Maintainer: Sergio Oller Moreno 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: 1bcba03 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AlpsNMR_4.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/AlpsNMR_4.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AlpsNMR_4.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AlpsNMR_4.7.0.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: 132 Package: altcdfenvs Version: 2.67.0 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: a8befd89156e69d4612e2c3dd9054d72 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 Maintainer: Laurent Gautier git_url: https://git.bioconductor.org/packages/altcdfenvs git_branch: devel git_last_commit: f8437dc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/altcdfenvs_2.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/altcdfenvs_2.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/altcdfenvs_2.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/altcdfenvs_2.67.0.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: 1.21.0 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: fa21161c93804053b3c75bb7fdd4a563 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AMARETTO git_branch: devel git_last_commit: c18b40e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AMARETTO_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/AMARETTO_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AMARETTO_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AMARETTO_1.21.0.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: 1.31.0 Depends: R (>= 3.3.0) Imports: stats Suggests: BiocStyle, qgraph, knitr, rmarkdown License: GPL (>= 2) Archs: x64 MD5sum: 7aadd96a5525e6201a1c96b11f8f54eb 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 SystemRequirements: gsl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AMOUNTAIN git_branch: devel git_last_commit: 19739fb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AMOUNTAIN_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/AMOUNTAIN_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AMOUNTAIN_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AMOUNTAIN_1.31.0.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: 1.27.0 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 MD5sum: d60c165e2606763e66b66a681f8237f7 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 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: 3451266 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/amplican_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/amplican_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/amplican_1.27.0.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: 128 Package: Anaquin Version: 2.29.0 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: 70a090d85878a17788700fb6089308f6 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 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: c65f43c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Anaquin_2.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Anaquin_2.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Anaquin_2.29.0.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: 2.7.0 Depends: R (>= 4.3.0) Imports: mia (>= 1.6.0), stats, CVXR, DescTools, Hmisc, MASS, Matrix, Rdpack, S4Vectors, SingleCellExperiment, SummarizedExperiment, TreeSummarizedExperiment, doParallel, doRNG, energy, foreach, gtools, lme4, lmerTest, multcomp, nloptr, parallel, utils Suggests: dplyr, knitr, rmarkdown, testthat, DT, tidyr, tidyverse, microbiome, magrittr License: Artistic-2.0 MD5sum: 66f73269470ab077a8a9010e2b608da2 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] () Maintainer: Huang Lin 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: 607faaa git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ANCOMBC_2.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ANCOMBC_2.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ANCOMBC_2.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ANCOMBC_2.7.0.tgz vignettes: vignettes/ANCOMBC/inst/doc/ANCOM.html, vignettes/ANCOMBC/inst/doc/ANCOMBC.html, vignettes/ANCOMBC/inst/doc/ANCOMBC2.html, vignettes/ANCOMBC/inst/doc/SECOM.html vignetteTitles: ANCOM Tutorial, ANCOM-BC Tutorial, ANCOM-BC2 Tutorial, 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/SECOM.R importsMe: benchdamic, microbiomeMarker suggestsMe: dar, MiscMetabar dependencyCount: 225 Package: AneuFinder Version: 1.33.0 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: e37428fa4a9972a1b41abf67d7223fa3 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 URL: https://github.com/ataudt/aneufinder.git VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AneuFinder git_branch: devel git_last_commit: 0fb90c6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AneuFinder_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AneuFinder_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AneuFinder_1.33.0.tgz 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: 93 Package: ANF Version: 1.27.0 Imports: igraph, Biobase, survival, MASS, stats, RColorBrewer Suggests: ExperimentHub, SNFtool, knitr, rmarkdown, testthat License: GPL-3 MD5sum: abff05a86eadd131327c8ab2aa05b8e9 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ANF git_branch: devel git_last_commit: b6b3562 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ANF_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ANF_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ANF_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ANF_1.27.0.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: 23 Package: animalcules Version: 1.21.0 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: b79cd5ed5e0653496b8b48a4dc9feae5 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] (), Anthony Federico [aut] (), W. Evan Johnson [aut] () Maintainer: Jessica McClintock 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: be043ae git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/animalcules_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/animalcules_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/animalcules_1.21.0.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 dependencyCount: 194 Package: annaffy Version: 1.77.0 Depends: R (>= 2.5.0), methods, Biobase, BiocManager, GO.db Imports: AnnotationDbi (>= 0.1.15), DBI Suggests: hgu95av2.db, multtest, tcltk License: LGPL MD5sum: a0da11e5826dd95b671b9db44e09fc26 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 Maintainer: Colin A. Smith git_url: https://git.bioconductor.org/packages/annaffy git_branch: devel git_last_commit: ea1ff2a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/annaffy_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/annaffy_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/annaffy_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/annaffy_1.77.0.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: 1.47.0 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: 409ebe79608de043630ae0d33e91108f 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 Maintainer: Chris Wirth URL: https://github.com/cruk-mi/annmap git_url: https://git.bioconductor.org/packages/annmap git_branch: devel git_last_commit: e2cea97 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/annmap_1.47.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/annmap_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/annmap_1.47.0.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: 1.83.0 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: b0356907086857f486c744a3da7ab9dd NeedsCompilation: no Title: Annotation for microarrays Description: Using R enviroments for annotation. biocViews: Annotation, Pathways, GO Author: R. Gentleman Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/annotate git_branch: devel git_last_commit: db1b4fd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/annotate_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/annotate_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/annotate_1.83.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/annotate_1.83.0.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 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AnnotationForge, graph, EnsDb.Hsapiens.v75, BiocStyle, knitr License: Artistic-2.0 MD5sum: d905d0a0577f665abf10777b4ef2b6c7 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 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: f92007d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AnnotationDbi_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/AnnotationDbi_1.67.0.zip mac.binary.big-sur-x86_64.ver: 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(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, 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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, 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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, crisprDesign, CrispRVariants, crossmeta, cTRAP, debrowser, derfinder, DominoEffect, DOSE, doubletrouble, EasyCellType, EDASeq, EnrichmentBrowser, ensembldb, EpiMix, epimutacions, erma, esATAC, FRASER, GA4GHshiny, gage, gDNAx, genefilter, geneplotter, GeneTonic, geneXtendeR, GenomicInteractionNodes, GenVisR, ggbio, ggkegg, gINTomics, GlobalAncova, globaltest, GmicR, GOfuncR, GOpro, GOSemSim, goSTAG, GOstats, goTools, graphite, GSEABase, GSEABenchmarkeR, Gviz, gwascat, IMAS, interactiveDisplay, isomiRs, 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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, 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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, jetset, Mega2R, MetaIntegrator, MOCHA, netgsa, pathfindR, prioGene, ProFAST, RCPA, RobLoxBioC, seeker, WGCNA suggestsMe: APAlyzer, ASURAT, autonomics, bambu, BiocGenerics, BiocOncoTK, CellTrails, cicero, cola, csaw, DAPAR, DEGreport, edgeR, eisaR, enrichplot, esetVis, FELLA, FGNet, fgsea, fishpond, GA4GHclient, gatom, gCrisprTools, GeDi, GeneRegionScan, GenomicPlot, GenomicRanges, gsean, hpar, iNETgrate, iSEEu, limma, MutationalPatterns, NetActivity, oligo, ontoProc, OUTRIDER, pathlinkR, piano, Pigengene, plotgardener, pRoloc, ProteoDisco, quantiseqr, R3CPET, simona, SingleCellAlleleExperiment, sparrow, spatialHeatmap, SpliceWiz, SummarizedExperiment, systemPipeR, tidybulk, topconfects, weitrix, wiggleplotr, BioPlex, BloodCancerMultiOmics2017, curatedAdipoChIP, RforProteomics, bulkAnalyseR, CALANGO, conos, easylabel, goat, MARVEL, pagoda2, Platypus, rliger, scITD, SCpubr dependencyCount: 44 Package: AnnotationFilter Version: 1.29.0 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: 77052c72fe101e9218f109a51289c81d 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 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: ee5978e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AnnotationFilter_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AnnotationFilter_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AnnotationFilter_1.29.0.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, drugTargetInteractions, ggbio, QFeatures, RAIDS, RITAN, scanMiRApp, TVTB, GenomicDistributionsData, locuszoomr, RNAseqQC suggestsMe: GenomicDistributions, GenomicFeatures, TFutils, wiggleplotr dependencyCount: 24 Package: AnnotationForge Version: 1.47.0 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, markdown, BiocStyle, knitr, BiocManager, BiocFileCache, RUnit License: Artistic-2.0 MD5sum: c9c143f8e5800719654246ae8c92870e 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 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: 0591b04 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AnnotationForge_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/AnnotationForge_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AnnotationForge_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AnnotationForge_1.47.0.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: 3.13.0 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: d03b3b752280614fe01a010e2f1f34e0 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 VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnnotationHub/issues git_url: https://git.bioconductor.org/packages/AnnotationHub git_branch: devel git_last_commit: cfa9c04 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AnnotationHub_3.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AnnotationHub_3.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AnnotationHub_3.13.0.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, annotation, sequencing, OSCA.basic, OSCA.workflows, SingleRBook importsMe: annotatr, atena, BiocHubsShiny, circRNAprofiler, coMethDMR, cTRAP, customCMPdb, DMRcate, dmrseq, EpiMix, epimutacions, epiregulon, gDNAx, GenomicScores, GRaNIE, GSEABenchmarkeR, gwascat, iSEEhub, MACSr, meshes, MetaboAnnotation, MethReg, Moonlight2R, MSnID, OGRE, ontoProc, orthos, partCNV, pwOmics, regutools, REMP, scanMiRApp, scAnnotatR, scmeth, scTensor, singleCellTK, SpliceWiz, tximeta, Ularcirc, AHLRBaseDbs, AHMeSHDbs, AHPathbankDbs, AHPubMedDbs, AHWikipathwaysDbs, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, EPICv2manifest, grasp2db, HPO.db, metaboliteIDmapping, MPO.db, synaptome.db, 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, RLHub, scpdata, scRNAseq, SFEData, SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData, TENxBrainData, TENxBUSData, TENxPBMCData, tuberculosis, TCGAWorkflow, RNAseqQC, SeedMatchR suggestsMe: AHMassBank, AlphaMissenseR, autonomics, BiocOncoTK, Chicago, ChIPpeakAnno, CINdex, clusterProfiler, CNVRanger, COCOA, crisprViz, DNAshapeR, dupRadar, ELMER, ensembldb, epiNEM, EpiTxDb, epivizrChart, epivizrData, factR, GenomicRanges, Glimma, GOSemSim, HiCool, LRBaseDbi, lute, maser, methodical, 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, GeneSelectR, locuszoomr dependencyCount: 65 Package: AnnotationHubData Version: 1.35.0 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: a6d05ba2133ae7b9fbb6fa4e092f996b 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnnotationHubData git_branch: devel git_last_commit: 056e918 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AnnotationHubData_1.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AnnotationHubData_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AnnotationHubData_1.35.0.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, homosapienDEE2CellScore, smokingMouse dependencyCount: 123 Package: annotationTools Version: 1.79.0 Imports: Biobase, stats Suggests: BiocStyle License: GPL MD5sum: b1c77f8f4a9234e9982c5b9f57cbb3eb 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 Maintainer: Alexandre Kuhn git_url: https://git.bioconductor.org/packages/annotationTools git_branch: devel git_last_commit: bacb534 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/annotationTools_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/annotationTools_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/annotationTools_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/annotationTools_1.79.0.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 dependencyCount: 6 Package: annotatr Version: 1.31.0 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: 1a77f00aa205ff45aabded412d4b78f5 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 VignetteBuilder: knitr BugReports: https://www.github.com/rcavalcante/annotatr/issues git_url: https://git.bioconductor.org/packages/annotatr git_branch: devel git_last_commit: a675e1f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/annotatr_1.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/annotatr_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/annotatr_1.31.0.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, scmeth, SOMNiBUS, ExpHunterSuite suggestsMe: borealis, methodical, ramr dependencyCount: 123 Package: anota Version: 1.53.0 Depends: qvalue Imports: multtest, qvalue License: GPL-3 MD5sum: e41608830efb1e18377ca278858e36eb 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 , Nahum Sonenberg , Robert Nadon Maintainer: Ola Larsson git_url: https://git.bioconductor.org/packages/anota git_branch: devel git_last_commit: 4da27e3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/anota_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/anota_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/anota_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/anota_1.53.0.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: 47 Package: anota2seq Version: 1.27.0 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: 3262cee8ed7d1efa76db44812e937e6b 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 , Julie Lorent , Ola Larsson Maintainer: Christian Oertlin , Ola Larsson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/anota2seq git_branch: devel git_last_commit: 62048e9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/anota2seq_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/anota2seq_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/anota2seq_1.27.0.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: 1.45.0 Depends: R (>= 3.0), matrixStats (>= 0.50.0), methods (>= 2.14), locfit (>= 1.5) Suggests: antiProfilesData, RColorBrewer License: Artistic-2.0 MD5sum: 4fc53783350cc0eb92927122d6955f8a 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 URL: https://github.com/HCBravoLab/antiProfiles git_url: https://git.bioconductor.org/packages/antiProfiles git_branch: devel git_last_commit: 93c7199 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/antiProfiles_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/antiProfiles_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/antiProfiles_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/antiProfiles_1.45.0.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: 1.17.0 Depends: R (>= 3.6), dplyr Imports: stats, utils, methods, futile.logger, jsonlite, httr, rapiclient (>= 0.1.3), yaml, tibble, tidyselect, tidyr, rlang, shiny, DT, miniUI, htmltools, BiocManager, BiocBaseUtils Suggests: parallel, knitr, rmarkdown, testthat, withr, readr, BiocStyle, devtools License: Artistic-2.0 MD5sum: 070b62077092ab873d5e28a4c086db51 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] (), Martin Morgan [aut] (), 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnVIL git_branch: devel git_last_commit: 0901854 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AnVIL_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/AnVIL_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AnVIL_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AnVIL_1.17.1.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, terraTCGAdata importsMe: AnVILPublish, AnVILWorkflow dependencyCount: 74 Package: AnVILBilling Version: 1.15.0 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: 7e043297dc97c2a65c5857b2415a6f31 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 VignetteBuilder: knitr BugReports: https://github.com/vjcitn/AnVILBilling/issues git_url: https://git.bioconductor.org/packages/AnVILBilling git_branch: devel git_last_commit: 169e05a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AnVILBilling_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/AnVILBilling_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AnVILBilling_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AnVILBilling_1.15.0.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: AnVILPublish Version: 1.15.0 Imports: AnVIL, BiocBaseUtils, BiocManager, httr, jsonlite, rmarkdown, yaml, readr, whisker, tools, utils, stats Suggests: knitr, BiocStyle, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 4ce6d77bef017b131f4217bb895c08c6 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] (), Martin Morgan [aut] (), Kayla Interdonato [aut], Vincent Carey [ctb] () Maintainer: Marcel Ramos VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnVILPublish git_branch: devel git_last_commit: 06da841 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AnVILPublish_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/AnVILPublish_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AnVILPublish_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AnVILPublish_1.15.0.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: 85 Package: AnVILWorkflow Version: 1.5.0 Depends: R (>= 4.2.0), Imports: utils, AnVIL, httr, methods, jsonlite, dplyr, tibble Suggests: knitr, BiocStyle License: Artistic-2.0 MD5sum: 176234b35fcaa61680d7cb2ba91b2abf 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] (), Kai Gravel-Pucillo [aut] Maintainer: Sehyun Oh VignetteBuilder: knitr BugReports: https://github.com/shbrief/AnVILWorkflow/issues git_url: https://git.bioconductor.org/packages/AnVILWorkflow git_branch: devel git_last_commit: b17b629 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AnVILWorkflow_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/AnVILWorkflow_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AnVILWorkflow_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AnVILWorkflow_1.5.0.tgz vignettes: vignettes/AnVILWorkflow/inst/doc/salmon.html vignetteTitles: Quickstart: Example 1. RNAseq analysis using salmon hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILWorkflow/inst/doc/salmon.R dependencyCount: 75 Package: APAlyzer Version: 1.19.0 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: ac51243e8e2c0c899a5006e90b062564 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] (), Bin Tian [aut], Wei-Chun Chen [aut] Maintainer: Ruijia Wang 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: efcd08d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/APAlyzer_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/APAlyzer_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/APAlyzer_1.19.0.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: 123 Package: apComplex Version: 2.71.0 Depends: R (>= 2.10), graph, RBGL Imports: Rgraphviz, stats, org.Sc.sgd.db License: LGPL MD5sum: 68e1d47412b6a4e6dd6b41cec215ddcd 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 Maintainer: Denise Scholtens git_url: https://git.bioconductor.org/packages/apComplex git_branch: devel git_last_commit: 5131f88 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/apComplex_2.71.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/apComplex_2.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/apComplex_2.71.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/apComplex_2.71.0.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: 1.27.0 Imports: emdbook, SummarizedExperiment, GenomicRanges, methods, stats, utils, Rcpp LinkingTo: Rcpp, RcppEigen, RcppNumerical Suggests: DESeq2, airway, knitr, rmarkdown, testthat License: GPL-2 MD5sum: 67f2dcaf4f21c0e9fc25d04d72210c4f 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 VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/apeglm git_branch: devel git_last_commit: 4ddfb38 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/apeglm_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/apeglm_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/apeglm_1.27.0.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, Rmmquant, TEKRABber suggestsMe: bambu, dar, DESeq2, fishpond, NanoporeRNASeq, RNAseqQC dependencyCount: 47 Package: APL Version: 1.9.0 Depends: R (>= 4.2) Imports: reticulate, ggrepel, ggplot2, viridisLite, plotly, Seurat, SingleCellExperiment, magrittr, SummarizedExperiment, topGO, methods, stats, utils, org.Hs.eg.db, org.Mm.eg.db, rlang, Matrix Suggests: BiocStyle, knitr, rmarkdown, scRNAseq, scater, scran, testthat License: GPL (>= 3) MD5sum: fb51e7a37186ee84751a0ae487511856 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 URL: https://vingronlab.github.io/APL/ SystemRequirements: python, pytorch, numpy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/APL git_branch: devel git_last_commit: e020161 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/APL_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/APL_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/APL_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/APL_1.9.0.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: 186 Package: appreci8R Version: 1.23.0 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: 3386bbf71e14ce805f19793774793a99 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 git_url: https://git.bioconductor.org/packages/appreci8R git_branch: devel git_last_commit: d9c0f80 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/appreci8R_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/appreci8R_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/appreci8R_1.23.0.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: 3.35.0 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: 801bacd64d630adb51d98e897008836c 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 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: 12ae795 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/aroma.light_3.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/aroma.light_3.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/aroma.light_3.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/aroma.light_3.35.0.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: 1.65.0 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: 27eecd98a1a88ececd61b629f973d77f 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 git_url: https://git.bioconductor.org/packages/ArrayExpress git_branch: devel git_last_commit: a6cc96b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ArrayExpress_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ArrayExpress_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ArrayExpress_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ArrayExpress_1.65.0.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: Hiiragi2013, bapred, seeker dependencyCount: 66 Package: arrayMvout Version: 1.63.0 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: 248de970aa7d4ff05d05d72f6e4a27ea 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 git_url: https://git.bioconductor.org/packages/arrayMvout git_branch: devel git_last_commit: 99522c8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/arrayMvout_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/arrayMvout_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/arrayMvout_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/arrayMvout_1.63.0.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: 166 Package: arrayQuality Version: 1.83.0 Depends: R (>= 2.2.0) Imports: graphics, grDevices, grid, gridBase, hexbin, limma, marray, methods, RColorBrewer, stats, utils Suggests: mclust, MEEBOdata, HEEBOdata License: LGPL MD5sum: 86a9c9d4db430353eb7b9e7e96b79b8d 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 Maintainer: Agnes Paquet URL: http://arrays.ucsf.edu/ git_url: https://git.bioconductor.org/packages/arrayQuality git_branch: devel git_last_commit: 1ca2acf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/arrayQuality_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/arrayQuality_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/arrayQuality_1.83.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/arrayQuality_1.83.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 13 Package: arrayQualityMetrics Version: 3.61.0 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: f36149ed2613be8fcfb6ccbfb828d620 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 VignetteBuilder: knitr BugReports: https://github.com/grimbough/arrayQualityMetrics/issues git_url: https://git.bioconductor.org/packages/arrayQualityMetrics git_branch: devel git_last_commit: 00bb3ba git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/arrayQualityMetrics_3.61.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/arrayQualityMetrics_3.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/arrayQualityMetrics_3.61.0.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: 1.45.0 Depends: R (>= 2.15.1), ARRmData License: Artistic-2.0 MD5sum: db86141ad65848539fdfce14c9d267a4 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 git_url: https://git.bioconductor.org/packages/ARRmNormalization git_branch: devel git_last_commit: 686c1e5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ARRmNormalization_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ARRmNormalization_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ARRmNormalization_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ARRmNormalization_1.45.0.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: 1.23.0 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: 2274dba1ad62e62d9baaf22001310c9a 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] (), Alexandre Rosa Campos [aut, ctb] (), John Von Dollen [aut], Nevan Krogan [aut] (), Danielle Swaney [aut, ctb] () Maintainer: David Jimenez-Morales 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: 1c02541 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/artMS_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/artMS_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/artMS_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/artMS_1.23.0.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: 159 Package: ASAFE Version: 1.31.0 Depends: R (>= 3.2) Suggests: knitr, testthat License: Artistic-2.0 MD5sum: c19aabe464d4260738e2fc4974598b2c 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 Maintainer: Qian Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASAFE git_branch: devel git_last_commit: 9fcf2c1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ASAFE_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ASAFE_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ASAFE_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ASAFE_1.31.0.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: 1.49.0 Depends: R (>= 2.8.0), methods Imports: graphics, methods, utils License: GPL (>= 3) Archs: x64 MD5sum: 20062cacc45e2fdaae35feafc73ad188 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 and Tingting Li . Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/ASEB git_branch: devel git_last_commit: cdcd402 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ASEB_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ASEB_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ASEB_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ASEB_1.49.0.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: 1.39.0 Imports: Matrix, MASS Suggests: BiocStyle License: GPL-3 MD5sum: bf2936a12112c312ff63f215b5a4891d 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 git_url: https://git.bioconductor.org/packages/ASGSCA git_branch: devel git_last_commit: 06f2276 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ASGSCA_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ASGSCA_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ASGSCA_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ASGSCA_1.39.0.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: 2.21.0 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: e04f83c2e8fe79191d565874840634be 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) . biocViews: Software, DataImport, Cheminformatics, Metabolomics Author: Gaëlle Lefort [aut, cre], Rémi Servien [aut], Patrick Tardivel [aut], Nathalie Vialaneix [aut] Maintainer: Gaëlle Lefort VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASICS git_branch: devel git_last_commit: 665f73b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ASICS_2.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ASICS_2.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ASICS_2.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ASICS_2.21.0.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 dependencyCount: 131 Package: ASpli Version: 2.15.0 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: 6e3c9aa8effb7657f786555653374562 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 git_url: https://git.bioconductor.org/packages/ASpli git_branch: devel git_last_commit: c7e1da6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ASpli_2.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ASpli_2.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ASpli_2.15.0.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: 174 Package: AssessORF Version: 1.23.0 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: 05d4db299a77bc64a5c1404013111270 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AssessORF git_branch: devel git_last_commit: 4dd2049 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AssessORF_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AssessORF_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AssessORF_1.23.0.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: 2.23.0 Depends: R (>= 3.5.0), stats, graphics Imports: MASS, msm, rmeta Suggests: RUnit, BiocGenerics, knitr License: GPL-2 + file LICENSE MD5sum: 3ea1027a873a7bd2cecc79e298bb0d49 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASSET git_branch: devel git_last_commit: 85827ba git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ASSET_2.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ASSET_2.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ASSET_2.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ASSET_2.23.0.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: 15 Package: ASSIGN Version: 1.41.0 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: 54aa95ae13d91277ed36795d1708aedd 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 , W. Evan Johnson , David Jenkins , Mumtehena Rahman 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: 39bb90e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ASSIGN_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ASSIGN_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ASSIGN_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ASSIGN_1.41.0.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: 100 Package: ASURAT Version: 1.9.0 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: 4b3937d335853f6e8cd59bef76648eb1 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] (), Johannes Nicolaus Wibisana [ctb] Maintainer: Keita Iida VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASURAT git_branch: devel git_last_commit: 8cc0b56 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ASURAT_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ASURAT_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ASURAT_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ASURAT_1.9.0.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: ATACCoGAPS Version: 1.7.0 Depends: R (>= 4.2.0), CoGAPS (>= 3.5.13) Imports: gtools, GenomicRanges, projectR, TFBSTools, GeneOverlap, msigdbr, tidyverse, gplots, motifmatchr, chromVAR, GenomicFeatures, IRanges, fgsea, rGREAT, JASPAR2016, Homo.sapiens, Mus.musculus, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, stringr, dplyr Suggests: knitr, viridis License: Artistic-2.0 MD5sum: a256618c71f26f264cf678b440e17e45 NeedsCompilation: no Title: Analysis Tools for scATACseq Data with CoGAPS Description: Provides tools for running the CoGAPS algorithm (Fertig et al, 2010) on single-cell ATAC sequencing data and analysis of the results. Can be used to perform analyses at the level of genes, motifs, TFs, or pathways. Additionally provides tools for transfer learning and data integration with single-cell RNA sequencing data. biocViews: Software, ResearchField, Epigenetics, SingleCell, Transcription, Bayesian, Clustering, DimensionReduction Author: Rossin Erbe [aut, cre] () Maintainer: Rossin Erbe VignetteBuilder: knitr BugReports: https://github.com/FertigLab/ATACCoGAPS/issues git_url: https://git.bioconductor.org/packages/ATACCoGAPS git_branch: devel git_last_commit: 1dfb959 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ATACCoGAPS_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ATACCoGAPS_1.7.0.tgz vignettes: vignettes/ATACCoGAPS/inst/doc/ATACCoGAPS.html vignetteTitles: ATACCoGAPS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ATACCoGAPS/inst/doc/ATACCoGAPS.R dependencyCount: 258 Package: ATACseqQC Version: 1.29.0 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: ec887a0e530979a61f0848ae70b8e5d1 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ATACseqQC git_branch: devel git_last_commit: 96f0204 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ATACseqQC_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ATACseqQC_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ATACseqQC_1.29.0.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: 189 Package: ATACseqTFEA Version: 1.7.0 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: da0dd143414304b1a7ff3456cf86a78b 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] () Maintainer: Jianhong Ou 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: f88ce03 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ATACseqTFEA_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ATACseqTFEA_1.7.0.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: 133 Package: atena Version: 1.11.0 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: fcc617b1524c057631e4bb8aa12ec1a1 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 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: 404c873 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/atena_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/atena_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/atena_1.11.0.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: 100 Package: atSNP Version: 1.21.0 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: 3d7ddbb785b6662b1037ae0f6ffa93eb 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 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: 777233a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/atSNP_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/atSNP_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/atSNP_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/atSNP_1.21.0.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: 168 Package: attract Version: 1.57.0 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: 2c92541e28411410e7b0c3ba6626368b 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 git_url: https://git.bioconductor.org/packages/attract git_branch: devel git_last_commit: afaeb10 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/attract_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/attract_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/attract_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/attract_1.57.0.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: 1.27.0 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: f9023369f060f132444272fe66cdc48f 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 URL: http://scenic.aertslab.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AUCell git_branch: devel git_last_commit: 36e631b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AUCell_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/AUCell_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AUCell_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AUCell_1.27.0.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, SCpubr dependencyCount: 121 Package: autonomics Version: 1.13.0 Depends: R (>= 4.0) Imports: abind, BiocFileCache, BiocGenerics, bit64, 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, tidyr, tidyselect, tools, utils, vsn Suggests: affy, AnnotationDbi, AnnotationHub, apcluster, BiocManager, BiocStyle, Biostrings, diagram, DBI, ensembldb, fpc, GenomicDataCommons, GenomicRanges, GEOquery, hgu95av2.db, ICSNP, jsonlite, knitr, lme4, lmerTest, MASS, 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, survival, survminer, testthat, UniProt.ws, writexl, XML License: GPL-3 MD5sum: 2c214a22c1c9bc51e5ddac9c67dd1345 NeedsCompilation: no Title: Unified statistal 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). 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 [aut], Shahina Hayat [aut], Laure Cougnaud [ctb], Witold Szymanski [ctb], Vanessa Beutgen [ctb], Willem Ligtenberg [sad], Hinrich Goehlmann [sad], Karsten Suhre [sad], Johannes Graumann [aut, sad, rth] Maintainer: Aditya Bhagwat URL: https://github.com/bhagwataditya/autonomics VignetteBuilder: knitr BugReports: https://github.com/bhagwataditya/autonomics git_url: https://git.bioconductor.org/packages/autonomics git_branch: devel git_last_commit: e68bda0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/autonomics_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/autonomics_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/autonomics_1.13.0.tgz vignettes: vignettes/autonomics/inst/doc/autonomics_detailed_manual.html, vignettes/autonomics/inst/doc/autonomics_platformaware_analysis.html, vignettes/autonomics/inst/doc/autonomics_unified_modeling.html vignetteTitles: autonomics_for_proteomics, autonomics_platformaware_analysis, autonomics_unified_modeling hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/autonomics/inst/doc/autonomics_detailed_manual.R, vignettes/autonomics/inst/doc/autonomics_platformaware_analysis.R, vignettes/autonomics/inst/doc/autonomics_unified_modeling.R dependencyCount: 114 Package: AWFisher Version: 1.19.0 Depends: R (>= 3.6) Imports: edgeR, limma, stats Suggests: knitr, tightClust License: GPL-3 Archs: x64 MD5sum: d796892dda456548ce55eb7ad9d6edbd 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 VignetteBuilder: knitr BugReports: https://github.com/Caleb-Huo/AWFisher/issues git_url: https://git.bioconductor.org/packages/AWFisher git_branch: devel git_last_commit: 65d7905 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/AWFisher_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/AWFisher_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/AWFisher_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/AWFisher_1.19.0.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: 12 Package: awst Version: 1.13.0 Imports: stats, methods, SummarizedExperiment Suggests: airway, ggplot2, testthat, EDASeq, knitr, BiocStyle, RefManageR, sessioninfo, rmarkdown License: MIT + file LICENSE MD5sum: 9d918bca032ba6070d68d13497883d55 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] (), Stefano Pagnotta [aut, cph] () Maintainer: Davide Risso 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: 398c0ee git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/awst_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/awst_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/awst_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/awst_1.13.0.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: 1.31.0 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: 6f9b7162801b4abc3177d8a5bb3ec8e2 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BaalChIP git_branch: devel git_last_commit: bac1109 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BaalChIP_1.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BaalChIP_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BaalChIP_1.31.0.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: 122 Package: bacon Version: 1.33.0 Depends: R (>= 3.3), methods, stats, ggplot2, graphics, BiocParallel, ellipse Suggests: BiocStyle, knitr, rmarkdown, testthat, roxygen2 License: GPL (>= 2) Archs: x64 MD5sum: b97ac7dca8e219bc2afde2c8d559e7e3 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bacon git_branch: devel git_last_commit: b34506f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/bacon_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/bacon_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/bacon_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/bacon_1.33.0.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: 1.43.0 Suggests: pasilla (>= 0.2.10) License: GPL-2 Archs: x64 MD5sum: 5d84462fe6c2cc74ede88f48be82af97 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 git_url: https://git.bioconductor.org/packages/BADER git_branch: devel git_last_commit: 9117e5e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BADER_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BADER_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BADER_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BADER_1.43.0.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: 1.33.0 Imports: VariantAnnotation, Rsamtools, biomaRt, GenomicRanges, S4Vectors, utils, stats, grDevices, graphics Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 52cba56397feeba961462d0af4a9f63d 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 git_url: https://git.bioconductor.org/packages/BadRegionFinder git_branch: devel git_last_commit: cdb24d0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BadRegionFinder_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BadRegionFinder_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BadRegionFinder_1.33.0.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: 103 Package: BAGS Version: 2.45.0 Depends: R (>= 2.10), breastCancerVDX, Biobase License: Artistic-2.0 Archs: x64 MD5sum: c6a6ea0d3a3e595c3f567f4c44968b0e 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 git_url: https://git.bioconductor.org/packages/BAGS git_branch: devel git_last_commit: 8a22096 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BAGS_2.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BAGS_2.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BAGS_2.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BAGS_2.45.0.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: 7 Package: ballgown Version: 2.37.0 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: 0dd4dde9e4d76052137b495140d5c751 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 VignetteBuilder: knitr BugReports: https://github.com/alyssafrazee/ballgown/issues git_url: https://git.bioconductor.org/packages/ballgown git_branch: devel git_last_commit: 9ecefe0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ballgown_2.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ballgown_2.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ballgown_2.37.0.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: polyester, variancePartition dependencyCount: 90 Package: bambu Version: 3.7.0 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 MD5sum: 9324f2ce74c65d7e5183b054cef03d6d 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 URL: https://github.com/GoekeLab/bambu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bambu git_branch: devel git_last_commit: 65fb8df git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/bambu_3.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/bambu_3.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/bambu_3.7.0.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: 95 Package: bamsignals Version: 1.37.0 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 MD5sum: a3a10d1a71a2bf9040ebd37a88697400 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 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: 92298a4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/bamsignals_1.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/bamsignals_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/bamsignals_1.37.0.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, chromstaR, DNAfusion, epigraHMM, karyoploteR, normr, segmenter, hoardeR dependencyCount: 25 Package: BANDITS Version: 1.21.0 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: 5147121bbf6972bad3d7297d2636525b 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 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: f75f35f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BANDITS_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BANDITS_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BANDITS_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BANDITS_1.21.0.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: 1.9.0 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 LinkingTo: Rcpp, RcppArmadillo, BH Suggests: coda (>= 0.19-4), testthat, interp, fields, pheatmap, viridis, rmarkdown, spelling License: Artistic-2.0 Archs: x64 MD5sum: b66e4dd46ab0f22312c764778efba094 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] (), Lisa Breckels [aut] () Maintainer: Oliver M. Crook 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: 8d29722 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/bandle_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/bandle_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/bandle_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/bandle_1.9.0.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: 239 Package: Banksy Version: 1.1.0 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: 591ff3918f996bd58cb036e7437bd25b 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] () Maintainer: Joseph Lee 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: 9d75169 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Banksy_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Banksy_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Banksy_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Banksy_1.1.0.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: 111 Package: banocc Version: 1.29.0 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: 4116bbbb2638c048024977ea93d87920 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 , Curtis Huttenhower VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/banocc git_branch: devel git_last_commit: 3a8575f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/banocc_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/banocc_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/banocc_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/banocc_1.29.0.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: 67 Package: barcodetrackR Version: 1.13.0 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: a44a29a6d07f94772a24831577184a35 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 URL: https://github.com/dunbarlabNIH/barcodetrackR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/barcodetrackR git_branch: devel git_last_commit: 6250e93 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/barcodetrackR_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/barcodetrackR_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/barcodetrackR_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/barcodetrackR_1.13.0.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: 103 Package: basecallQC Version: 1.29.0 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: eec16fc88895162506d619ee5135a551 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 SystemRequirements: bcl2Fastq (versions >= 2.1.7) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/basecallQC git_branch: devel git_last_commit: db96253 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/basecallQC_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/basecallQC_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/basecallQC_1.29.0.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: 126 Package: BaseSpaceR Version: 1.49.0 Depends: R (>= 2.15.0), RCurl, RJSONIO Imports: methods Suggests: RUnit, IRanges, Rsamtools License: Apache License 2.0 MD5sum: 9d98d4f59e1783791e52b561c127b62f 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 git_url: https://git.bioconductor.org/packages/BaseSpaceR git_branch: devel git_last_commit: 96bafdd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BaseSpaceR_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BaseSpaceR_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BaseSpaceR_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BaseSpaceR_1.49.0.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: 1.41.0 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: 29e7df0d2b5b7190df3aef6b49a969f7 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 git_url: https://git.bioconductor.org/packages/Basic4Cseq git_branch: devel git_last_commit: 6d5b128 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Basic4Cseq_1.41.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Basic4Cseq_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Basic4Cseq_1.41.0.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: 2.17.0 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 MD5sum: b9e4f34e2c59a77ee8960f3525ea0588 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] (), Nils Eling [aut], Alan O'Callaghan [aut], Sylvia Richardson [ctb], John Marioni [ctb] Maintainer: Catalina Vallejos 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: 61172a4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BASiCS_2.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BASiCS_2.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BASiCS_2.17.0.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: 144 Package: BASiCStan Version: 1.7.0 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 MD5sum: 2e3013bd1b444af59bfd63b4493189c7 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 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: ea995e9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BASiCStan_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BASiCStan_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BASiCStan_1.7.0.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: 1.33.0 Depends: GenomicRanges,GenomicAlignments Imports: S4Vectors,methods,IRanges,GenomeInfoDb,stats Suggests: knitr License: LGPL-3 MD5sum: 7ae5ae977ac0649be2b5ded7ebf3fff8 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BasicSTARRseq git_branch: devel git_last_commit: a482687 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BasicSTARRseq_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BasicSTARRseq_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BasicSTARRseq_1.33.0.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: 1.17.0 Depends: reticulate Imports: utils, methods, parallel, dir.expiry, basilisk.utils (>= 1.15.1) Suggests: knitr, rmarkdown, BiocStyle, testthat, callr License: GPL-3 MD5sum: d57d035d132bf1d253ebaac5226ef231 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 VignetteBuilder: knitr BugReports: https://github.com/LTLA/basilisk/issues git_url: https://git.bioconductor.org/packages/basilisk git_branch: devel git_last_commit: 1539ded git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/basilisk_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/basilisk_1.17.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/basilisk_1.17.0.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, FLAMES, HiCool, MACSr, MOFA2, orthos, pareg, Rcwl, recountmethylation, ReUseData, scPipe, SimBu, sketchR, snifter, spatialDE, velociraptor, zellkonverter suggestsMe: basilisk.utils, CuratedAtlasQueryR dependencyCount: 23 Package: basilisk.utils Version: 1.17.0 Imports: utils, methods, tools, dir.expiry Suggests: reticulate, knitr, rmarkdown, BiocStyle, testthat, basilisk License: GPL-3 MD5sum: 407f3d02444733475bd0133aab1e0d08 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/basilisk.utils git_branch: devel git_last_commit: e331a58 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/basilisk.utils_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/basilisk.utils_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/basilisk.utils_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/basilisk.utils_1.17.0.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 dependencyCount: 5 Package: batchelor Version: 1.21.0 Depends: SingleCellExperiment Imports: SummarizedExperiment, S4Vectors, BiocGenerics, Rcpp, stats, methods, utils, igraph, BiocNeighbors, BiocSingular, Matrix, DelayedArray, DelayedMatrixStats, BiocParallel, scuttle, ResidualMatrix, ScaledMatrix, beachmat LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, scran, scater, bluster, scRNAseq License: GPL-3 MD5sum: 6548245d82fd0463899063103deeecd9 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 SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/batchelor git_branch: devel git_last_commit: 8b03562 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/batchelor_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/batchelor_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/batchelor_1.21.0.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: ChromSCape, mumosa, scMerge, singleCellTK suggestsMe: TSCAN, Canek, RaceID dependencyCount: 67 Package: BatchQC Version: 2.1.0 Depends: R (>= 4.3.0) Imports: data.table, DESeq2, dplyr, EBSeq, ggdendro, ggnewscale, ggplot2, limma, matrixStats, pheatmap, RColorBrewer, reader, reshape2, scran, shiny, stats, SummarizedExperiment, sva, tibble, tidyr, tidyverse, utils Suggests: BiocManager, BiocStyle, bladderbatch, dendextend, devtools, knitr, lintr, plotly, rmarkdown, shinythemes, spelling, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 69a4a6ea3e22438da258ee07cbe9c376 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 McClintock [aut, cre] (), W. Evan Johnson [aut] (), 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 McClintock 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: 9ca1d98 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BatchQC_2.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BatchQC_2.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BatchQC_2.1.0.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: 209 Package: BayesKnockdown Version: 1.31.0 Depends: R (>= 3.3) Imports: stats, Biobase License: GPL-3 MD5sum: 6a1031d499bdc04bc13ebedf8012085e 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 git_url: https://git.bioconductor.org/packages/BayesKnockdown git_branch: devel git_last_commit: e8f90eb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BayesKnockdown_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BayesKnockdown_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BayesKnockdown_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BayesKnockdown_1.31.0.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: 6 Package: BayesSpace Version: 1.15.0 Depends: R (>= 4.0.0), SingleCellExperiment Imports: Rcpp (>= 1.0.4.6), stats, purrr, scater, scran, SummarizedExperiment, coda, rhdf5, S4Vectors, Matrix, assertthat, mclust, RCurl, DirichletReg, xgboost, utils, ggplot2, scales, BiocFileCache, BiocSingular LinkingTo: Rcpp, RcppArmadillo, RcppDist, RcppProgress Suggests: testthat, knitr, rmarkdown, igraph, spatialLIBD, dplyr, viridis, patchwork, RColorBrewer, Seurat License: MIT + file LICENSE MD5sum: 9a68245a6249126ce2981333ec1d0efb 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], Matt Stone [aut, cre], Xing Ren [ctb], Raphael Gottardo [ctb] Maintainer: Matt Stone URL: edward130603.github.io/BayesSpace SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/edward130603/BayesSpace/issues git_url: https://git.bioconductor.org/packages/BayesSpace git_branch: devel git_last_commit: 251a88c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BayesSpace_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BayesSpace_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BayesSpace_1.15.0.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: 1.23.0 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: 1a33413ddad582e76e1b80cae1231889 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], Franois Bertaux [aut], Philipp Thomas [aut], Claire Stefanelli [aut], Malika Saint [aut], Samuel Marguerat [aut], Vahid Shahrezaei [aut] Maintainer: Wenhao Tang 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: c178c52 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/bayNorm_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/bayNorm_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/bayNorm_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/bayNorm_1.23.0.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: 60 Package: baySeq Version: 2.39.0 Depends: R (>= 2.3.0), methods Imports: edgeR, GenomicRanges, abind, parallel, graphics, stats, utils Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: 305d4b3987e64d5527e047e8fd5718c3 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] () Maintainer: Samuel Granjeaud 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: c69daa1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/baySeq_2.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/baySeq_2.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/baySeq_2.39.0.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: 33 Package: BBCAnalyzer Version: 1.35.0 Imports: SummarizedExperiment, VariantAnnotation, Rsamtools, grDevices, GenomicRanges, IRanges, Biostrings Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 9c8e6ff341d38022d02851ee900a501f 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 git_url: https://git.bioconductor.org/packages/BBCAnalyzer git_branch: devel git_last_commit: 876004b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BBCAnalyzer_1.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BBCAnalyzer_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BBCAnalyzer_1.35.0.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: 1.67.0 Depends: methods Imports: Biostrings Suggests: seqLogo License: GPL-2 Archs: x64 MD5sum: 770a96fd3d4ef79f3cbaa7563de4e5d2 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 Maintainer: Adam Ameur git_url: https://git.bioconductor.org/packages/BCRANK git_branch: devel git_last_commit: 29ea395 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BCRANK_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BCRANK_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BCRANK_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BCRANK_1.67.0.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: 1.27.0 Depends: R (>= 3.4.0) Imports: Rcpp (>= 0.12.12), Matrix, Biostrings LinkingTo: Rcpp, Matrix Suggests: knitr License: GPL (>= 2) Archs: x64 MD5sum: 2293a125611985a2b85cf207092f479a 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 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: b384e6c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/bcSeq_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/bcSeq_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/bcSeq_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/bcSeq_1.27.0.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: 2.21.0 Imports: methods, DelayedArray (>= 0.27.2), SparseArray, BiocGenerics, Matrix, Rcpp LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, rcmdcheck, BiocParallel, HDF5Array License: GPL-3 Archs: x64 MD5sum: 8365d0fd4e408e05f135e22089c2500d 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 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: 15a173a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/beachmat_2.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/beachmat_2.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/beachmat_2.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/beachmat_2.19.4.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, BiocSingular, DropletUtils, epiregulon, mumosa, scater, scran, scuttle, SingleR suggestsMe: bsseq, glmGamPoi, mbkmeans, PCAtools, scCB2 linksToMe: beachmat.hdf5, BiocSingular, bsseq, dreamlet, DropletUtils, epiregulon, glmGamPoi, mbkmeans, PCAtools, scran, scuttle, SingleR dependencyCount: 23 Package: beachmat.hdf5 Version: 1.3.0 Imports: methods, beachmat, HDF5Array, DelayedArray, Rcpp LinkingTo: Rcpp, beachmat, Rhdf5lib Suggests: testthat, BiocStyle, knitr, rmarkdown, rhdf5, Matrix License: GPL-3 Archs: x64 MD5sum: a201cc63fe3324d23e4481e8dcd9dd52 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 SystemRequirements: C++17, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/beachmat.hdf5 git_branch: devel git_last_commit: 26aa382 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/beachmat.hdf5_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/beachmat.hdf5_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/beachmat.hdf5_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/beachmat.hdf5_1.3.0.tgz vignettes: vignettes/beachmat.hdf5/inst/doc/userguide.html vignetteTitles: User guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beachmat.hdf5/inst/doc/userguide.R suggestsMe: sketchR dependencyCount: 28 Package: beadarray Version: 2.55.0 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 MD5sum: 478209c9e2db67dd1dad5769c78a8b3c 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/beadarray git_branch: devel git_last_commit: d65889a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/beadarray_2.55.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/beadarray_2.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/beadarray_2.55.0.tgz vignettes: vignettes/beadarray/inst/doc/beadarray.pdf, vignettes/beadarray/inst/doc/beadlevel.pdf, vignettes/beadarray/inst/doc/beadsummary.pdf, vignettes/beadarray/inst/doc/ImageProcessing.pdf vignetteTitles: beadarray.pdf, beadlevel.pdf, beadsummary.pdf, ImageProcessing.pdf 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: 1.57.0 Imports: stats, utils Suggests: BiocStyle, knitr License: GPL-2 Archs: x64 MD5sum: 84b8441167a5450d5fa5780ae9c9864d 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BeadDataPackR git_branch: devel git_last_commit: 45ca1e8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BeadDataPackR_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BeadDataPackR_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BeadDataPackR_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BeadDataPackR_1.57.0.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: BEARscc Version: 1.25.0 Depends: R (>= 3.5.0) Imports: ggplot2, SingleCellExperiment, data.table, stats, utils, graphics, compiler Suggests: testthat, cowplot, knitr, rmarkdown, BiocStyle, NMF License: GPL-3 MD5sum: bd788e0a033ff72413127ee2fd4dd0dd NeedsCompilation: no 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 Maintainer: Benjamin Schuster-Boeckler VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BEARscc git_branch: devel git_last_commit: ca08839 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BEARscc_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BEARscc_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BEARscc_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BEARscc_1.25.0.tgz vignettes: vignettes/BEARscc/inst/doc/BEARscc.pdf vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BEARscc/inst/doc/BEARscc.R dependencyCount: 65 Package: BEAT Version: 1.43.0 Depends: R (>= 2.13.0) Imports: GenomicRanges, ShortRead, Biostrings, BSgenome License: LGPL (>= 3.0) MD5sum: 6d339444393b57d5c680ff9b0f2ea265 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 Maintainer: Kemal Akman git_url: https://git.bioconductor.org/packages/BEAT git_branch: devel git_last_commit: aba9eb8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BEAT_1.43.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BEAT_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BEAT_1.43.0.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: 2.21.0 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: 34a6d83636927b89123e5885553cd418 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] (), Markus Merl [aut], Ruslan Akulenko [aut] Maintainer: Livia Rasp 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: 47a0129 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BEclear_2.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BEclear_2.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BEclear_2.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BEclear_2.21.0.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: beer Version: 1.9.0 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: 0e596d9af832da924d76a1d824ae95db 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] (), Rob Scharpf [aut], Ingo Ruczinski [aut] Maintainer: Athena Chen 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: a5ffae4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/beer_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/beer_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/beer_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/beer_1.9.0.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: 87 Package: benchdamic Version: 1.11.0 Depends: R (>= 4.3.0) Imports: stats, stats4, utils, methods, phyloseq, TreeSummarizedExperiment, BiocParallel, zinbwave, edgeR, DESeq2, limma, ALDEx2, corncob, SummarizedExperiment, MAST, Seurat, ANCOMBC, 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: 6678f3bea5f50e0fc23f0943d01b380f 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] (), Chiara Romualdi [aut] (), Davide Risso [aut] (), Nicola Vitulo [aut] () Maintainer: Matteo Calgaro VignetteBuilder: knitr BugReports: https://github.com/mcalgaro93/benchdamic/issues git_url: https://git.bioconductor.org/packages/benchdamic git_branch: devel git_last_commit: be905b6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/benchdamic_1.11.0.tar.gz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/benchdamic_1.11.0.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: 387 Package: BERT Version: 1.1.0 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: 0949149155c610b39d7d0d588cebca1d NeedsCompilation: no Title: Hierarchical Batch-Effect Adjustment with Trees 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 Author: Yannis Schumann [aut, cre] (), Simon Schlumbohm [aut] () Maintainer: Yannis Schumann 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: 17da2e9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BERT_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BERT_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BERT_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BERT_1.1.0.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: 103 Package: betaHMM Version: 1.1.0 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: d3d34e7114e2e5bd18458fac8787583c 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] (), Romina Silva [aut], Antoinette Sabrina Perry [aut], Ronald William Watson [aut], Isobel Claire Gorley [aut] (), Thomas Brendan Murphy [aut] (), Florence Jaffrezic [aut], Andrea Rau [aut] () Maintainer: Koyel Majumdar VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/betaHMM git_branch: devel git_last_commit: 047a0a3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/betaHMM_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/betaHMM_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/betaHMM_1.1.0.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: 79 Package: bettr Version: 1.1.0 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: f6eb1103268d68977287d64b8d559105 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] (), Charlotte Soneson [aut, cre] () Maintainer: Charlotte Soneson 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: 87ddc5b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/bettr_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/bettr_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/bettr_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/bettr_1.1.0.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: 1.5.0 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: ba9797ee95709a9684b746f413e50cf1 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] (), Shuangshuang Xu [aut], Marco Ferreira [aut] () Maintainer: Jacob Williams VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BG2 git_branch: devel git_last_commit: a1d19df git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BG2_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BG2_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BG2_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BG2_1.5.0.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: BgeeDB Version: 2.31.0 Depends: R (>= 3.6.0), topGO, tidyr Imports: R.utils, data.table, curl, RCurl, digest, methods, stats, utils, dplyr, RSQLite, graph, Biobase Suggests: knitr, BiocStyle, testthat, rmarkdown, markdown License: GPL-3 + file LICENSE MD5sum: 490c7993e634a7d4b8a15e9e5303c9f7 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 Roux , Andrea Komljenovic , Frederic Bastian 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: b559d69 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BgeeDB_2.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BgeeDB_2.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BgeeDB_2.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BgeeDB_2.31.0.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: psygenet2r, RITAN suggestsMe: RITAN dependencyCount: 72 Package: bgx Version: 1.71.0 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 Archs: x64 MD5sum: a4b096e773fb477041c5711e3261e4b2 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 git_url: https://git.bioconductor.org/packages/bgx git_branch: devel git_last_commit: 531f983 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/bgx_1.71.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/bgx_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/bgx_1.71.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/bgx_1.71.0.tgz vignettes: vignettes/bgx/inst/doc/bgx.pdf vignetteTitles: HowTo BGX hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bgx/inst/doc/bgx.R dependencyCount: 33 Package: BicARE Version: 1.63.0 Depends: R (>= 1.8.0), Biobase (>= 2.5.5), multtest, GSEABase, GO.db Imports: methods Suggests: hgu95av2 License: GPL-2 Archs: x64 MD5sum: df906cdb64c53068b04b4582da8966c0 NeedsCompilation: yes Title: Biclustering Analysis and Results Exploration Description: Biclustering Analysis and Results Exploration. biocViews: Microarray, Transcription, Clustering Author: Pierre Gestraud Maintainer: Pierre Gestraud URL: http://bioinfo.curie.fr git_url: https://git.bioconductor.org/packages/BicARE git_branch: devel git_last_commit: b8ff3f3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BicARE_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BicARE_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BicARE_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BicARE_1.63.0.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: 1.25.0 Depends: R (>= 3.5.0) Imports: stats, poibin, GenomicRanges Suggests: rmarkdown, testthat, knitr License: GPL-3 MD5sum: 76e37b406c1bd9bbd6fee75f395339d5 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiFET git_branch: devel git_last_commit: 0e5eca9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BiFET_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BiFET_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BiFET_1.25.0.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: 1.41.0 Depends: R (>= 2.14.0), rsbml, hyperdraw, LIM,stringr Imports: hypergraph, limSolve License: file LICENSE MD5sum: f2bfd45c96fc11242502f0aea5b8de60 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 , Hannes Hettling URL: http://www.bioconductor.org/ git_url: https://git.bioconductor.org/packages/BiGGR git_branch: devel git_last_commit: 7de164b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BiGGR_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BiGGR_1.41.0.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: 29 Package: bigmelon Version: 1.31.0 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: 84297fe3674e9ce9bbbcd1ff0ab8c702 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 git_url: https://git.bioconductor.org/packages/bigmelon git_branch: devel git_last_commit: bdfe364 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/bigmelon_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/bigmelon_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/bigmelon_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/bigmelon_1.31.0.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: 169 Package: BindingSiteFinder Version: 2.3.0 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 License: Artistic-2.0 MD5sum: dc68a53c215c1ea738decaa514e7023c 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] (), Kathi Zarnack [aut] () Maintainer: Mirko Brüggemann VignetteBuilder: knitr BugReports: https://github.com/ZarnackGroup/BindingSiteFinder/issues git_url: https://git.bioconductor.org/packages/BindingSiteFinder git_branch: devel git_last_commit: ec1be3f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BindingSiteFinder_2.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BindingSiteFinder_2.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BindingSiteFinder_2.3.0.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: 145 Package: bioassayR Version: 1.43.0 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, cellHTS2, knitr, knitcitations, knitrBootstrap, testthat, ggplot2, rmarkdown License: Artistic-2.0 MD5sum: d041db3e7b98f7a70647a1523558d6c0 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: Daniela Cassol 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: 76ecf25 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/bioassayR_1.43.0.tar.gz 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: 83 Package: Biobase Version: 2.65.0 Depends: R (>= 2.10), BiocGenerics (>= 0.27.1), utils Imports: methods Suggests: tools, tkWidgets, ALL, RUnit, golubEsets, BiocStyle, knitr, limma License: Artistic-2.0 Archs: x64 MD5sum: ecb12e69ec7d0c60fe33469879d23017 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 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: a613de8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Biobase_2.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Biobase_2.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Biobase_2.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Biobase_2.65.0.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, bgx, BicARE, bigmelon, BioMVCClass, BioQC, BLMA, borealis, CAMERA, cancerclass, casper, Category, categoryCompare, CCPROMISE, cellHTS2, CGHbase, CGHcall, CGHregions, clippda, clusterStab, CMA, cn.farms, codelink, convert, copa, covEB, covRNA, 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, 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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, Hiiragi2013, 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, TOmicsVis importsMe: a4Base, a4Classif, a4Core, a4Preproc, ABarray, ACE, aCGH, 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GENESIS, GenomicInteractions, GenomicScores, GenomicSuperSignature, GEOsubmission, gep2pep, gespeR, ggbio, girafe, GlobalAncova, globaltest, gmapR, GSRI, GSVA, Gviz, Harshlight, HEM, hermes, HTSFilter, infinityFlow, IsoformSwitchAnalyzeR, isomiRs, katdetectr, kissDE, lapmix, LiquidAssociation, LRBaseDbi, lute, MAGeCKFlute, makecdfenv, MAPFX, maSigPro, MAST, mastR, mBPCR, MeSHDbi, metaseqR2, MethylAid, methylCC, methylclock, methylumi, mfa, MiChip, microbiomeDASim, microbiomeMarker, minfi, MinimumDistance, MiPP, MIRA, miRSM, missMethyl, MLSeq, mogsa, Moonlight2R, MoonlightR, MSnID, MultiAssayExperiment, MultiRNAflow, multiscan, mzR, netZooR, NormalyzerDE, npGSEA, nucleR, oligoClasses, omicade4, omicsViewer, ontoProc, oposSOM, oppar, OrganismDbi, panp, phantasus, phantasusLite, PharmacoGx, phenomis, phyloseq, piano, plgem, plier, PLSDAbatch, podkat, prebs, PrInCE, progeny, pRoloc, PROMISE, PROPS, Prostar, protGear, psygenet2r, ptairMS, puma, PureCN, pvac, pvca, pwOmics, qcmetrics, QDNAseq, QFeatures, qpgraph, quantiseqr, quantro, QuasR, qusage, RadioGx, randPack, ReactomeGSA, RIVER, Rmagpie, RNAinteract, RNAseqCovarImpute, roastgsa, rols, ropls, ROTS, rqubic, rScudo, Rtpca, Rtreemix, RUVnormalize, scmap, scTGIF, SeqVarTools, shinyMethyl, ShortRead, SigsPack, sigsquared, singscore, sitadela, sketchR, SomaticSignatures, SpatialDecon, SpatialOmicsOverlay, spkTools, standR, STATegRa, subSeq, synapter, TDbasedUFEadv, TEQC, TFBSTools, timecourse, TMixClust, topdownr, ToxicoGx, tradeSeq, traviz, 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, RNAinteractMAPK, seqc, signatureSearchData, ExpHunterSuite, ExpressionNormalizationWorkflow, GeoMxWorkflows, AnnoProbe, bapred, BisqueRNA, CIARA, ClassComparison, ClassDiscovery, easyDifferentialGeneCoexpression, FMradio, geneExpressionFromGEO, IntegratedJM, maGUI, MetaIntegrator, nlcv, NMF, PerseusR, RCPA, RobLox, RobLoxBioC, RPPanalyzer, SCdeconR, ssizeRNA, TailRank suggestsMe: AUCell, BiocCheck, BiocGenerics, BiocOncoTK, 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, tkWidgets, TOP, TypeInfo, 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, D4TAlink.light, distrDoc, GenAlgo, ggpicrust2, hexbin, HTSCluster, isatabr, mi4p, MOCHA, Modeler, multiclassPairs, NACHO, ordinalbayes, Patterns, pkgmaker, rsconnect, seeker, Seurat, sigminer, SomaDataIO dependencyCount: 5 Package: biobroom Version: 1.37.0 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 MD5sum: 7f5ba7693e3a5facf96b00f7bca2596d 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 and Andrew J. Bass 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: 9ed01a1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/biobroom_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/biobroom_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/biobroom_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/biobroom_1.37.0.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: 32 Package: biobtreeR Version: 1.17.0 Imports: httr, httpuv, stringi,jsonlite,methods,utils Suggests: BiocStyle, knitr,testthat,rmarkdown,markdown License: MIT + file LICENSE MD5sum: 500970842db2aee427dfd0187539dccc 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 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: 816d501 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/biobtreeR_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/biobtreeR_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/biobtreeR_1.17.0.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: 1.33.0 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: 941bb205d9102984bfe024c7e6887761 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 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: a889558 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/bioCancer_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/bioCancer_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/bioCancer_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/bioCancer_1.33.0.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: 264 Package: BioCartaImage Version: 1.3.0 Depends: R (>= 4.3.0) Imports: magick, grid, stats, grDevices, utils Suggests: testthat, knitr, BiocStyle, ragg License: MIT + file LICENSE MD5sum: efceef0832f69f700892718842f8580e 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] () Maintainer: Zuguang Gu 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: 354beab git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BioCartaImage_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BioCartaImage_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BioCartaImage_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BioCartaImage_1.3.0.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: 1.7.0 Depends: R (>= 4.2.0) Imports: methods, utils Suggests: knitr, rmarkdown, BiocStyle, tinytest License: Artistic-2.0 MD5sum: a3add5d35c4d065cdcfb6ad01b5d4f82 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] (), Martin Morgan [ctb], Hervé Pagès [ctb] Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://www.github.com/Bioconductor/BiocBaseUtils/issues git_url: https://git.bioconductor.org/packages/BiocBaseUtils git_branch: devel git_last_commit: 3b3832f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BiocBaseUtils_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BiocBaseUtils_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BiocBaseUtils_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BiocBaseUtils_1.7.0.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, AnVILPublish, BiocCheck, BiocFHIR, DNAfusion, iSEEfier, MultiAssayExperiment, RaggedExperiment, TCGAutils, TENxIO, UniProt.ws, VisiumIO, SingleCellMultiModal dependencyCount: 2 Package: BiocBook Version: 1.3.0 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: bd7191f3ec91aeacafe5e78369b5eeb7 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 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: f781ac3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BiocBook_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BiocBook_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BiocBook_1.3.0.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: 1.41.0 Depends: R (>= 4.3.0) Imports: BiocBaseUtils, BiocFileCache, BiocManager, biocViews (>= 1.33.7), callr, codetools, graph, httr2, knitr, methods, rvest, stringdist, tools, utils Suggests: RUnit, BiocGenerics, Biobase, jsonlite, rmarkdown, downloader, devtools (>= 1.4.1), usethis, BiocStyle, GenomicRanges, gert License: Artistic-2.0 MD5sum: 845f2b700fe5f58b8906f182d06e4f3d 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] (), Leonardo Collado-Torres [ctb], Federico Marini [ctb] Maintainer: Marcel Ramos 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: 5f56c4b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BiocCheck_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BiocCheck_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BiocCheck_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BiocCheck_1.41.0.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 suggestsMe: GEOfastq, packFinder, preciseTAD, SpectralTAD, HMP16SData, HMP2Data, scpdata, MainExistingDatasets dependencyCount: 75 Package: BiocFHIR Version: 1.7.0 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: 944f521f413011014e84d06d8bc4010f 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] () Maintainer: Vincent Carey 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: b363fa4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BiocFHIR_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BiocFHIR_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BiocFHIR_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BiocFHIR_1.7.0.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: 2.13.0 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: 040b47550459f9dbb64c1848f9dfed05 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 VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocFileCache/issues git_url: https://git.bioconductor.org/packages/BiocFileCache git_branch: devel git_last_commit: 8458c9d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BiocFileCache_2.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BiocFileCache_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BiocFileCache_2.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BiocFileCache_2.13.0.tgz vignettes: vignettes/BiocFileCache/inst/doc/BiocFileCache.html vignetteTitles: BiocFileCache: Managing File Resources Across Sessions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE 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4c7283d9060353f076103fdd8767f979 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 Maintainer: Hervé Pagès 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: 7b9834d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BiocGenerics_0.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BiocGenerics_0.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BiocGenerics_0.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BiocGenerics_0.51.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ACME, affy, affyPLM, altcdfenvs, amplican, AnnotationDbi, 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slingshot, SNPRelate, SpacePAC, SparseSignatures, spatialHeatmap, specL, STATegRa, STRINGdb, SUITOR, systemPipeTools, TCC, TFEA.ChIP, TIN, transcriptogramer, traseR, TreeAndLeaf, tripr, tRNAdbImport, TRONCO, Uniquorn, variancePartition, VERSO, xcore, zenith, ENCODExplorerData, geneplast.data, ConnectivityMap, FieldEffectCrc, grndata, HarmanData, healthyControlsPresenceChecker, microRNAome, RegParallel, scMultiome, sesameData, xcoredata, adjclust, aroma.affymetrix, asteRisk, ggpicrust2, gkmSVM, MarZIC, MetaIntegrator, NutrienTrackeR, openSkies, pagoda2, Platypus, polyRAD, Rediscover, Seurat dependencyCount: 4 Package: biocGraph Version: 1.67.0 Depends: Rgraphviz, graph Imports: Rgraphviz, geneplotter, graph, BiocGenerics, methods Suggests: fibroEset, geneplotter, hgu95av2.db License: Artistic-2.0 MD5sum: d20b345671f20b2cfc2a76b4f6d98d26 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 , Robert Gentleman , Seth Falcon Florian Hahne Maintainer: Florian Hahne git_url: https://git.bioconductor.org/packages/biocGraph git_branch: devel git_last_commit: d2aa05a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/biocGraph_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/biocGraph_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/biocGraph_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/biocGraph_1.67.0.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: 1.5.0 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: e95885f33d2af1ec5ff6f2521e1d56eb 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] () Maintainer: Vincent Carey 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: 1fda8c6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BiocHail_1.5.0.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: 1.5.0 Depends: R (>= 4.3.0), shiny Imports: AnnotationHub, ExperimentHub, DT, htmlwidgets, S4Vectors, shinyAce, shinyjs, shinythemes, shinytoastr, utils Suggests: BiocManager, BiocStyle, knitr, rmarkdown, sessioninfo, shinytest2 License: Artistic-2.0 MD5sum: 490e17106a0068177d104a4010a163af 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] (), Vincent Carey [ctb] Maintainer: Marcel Ramos 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: 6ea0f54 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BiocHubsShiny_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BiocHubsShiny_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BiocHubsShiny_1.5.0.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: 1.15.0 Depends: R (>= 4.3.0) Imports: BiocGenerics, S4Vectors, methods, tools Suggests: testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: e188fd9e18656eba24a46f38d382ad74 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] () Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocIO/issues git_url: https://git.bioconductor.org/packages/BiocIO git_branch: devel git_last_commit: 5e4bf19 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BiocIO_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BiocIO_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BiocIO_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BiocIO_1.15.0.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, extraChIPs, HiCExperiment, HiContacts, HiCool, rtracklayer, TENxIO, tidyCoverage, txdbmaker, VisiumIO dependencyCount: 8 Package: BiocNeighbors Version: 1.23.0 Imports: Rcpp, S4Vectors, BiocParallel, stats, methods, Matrix LinkingTo: Rcpp, RcppHNSW Suggests: testthat, BiocStyle, knitr, rmarkdown, FNN, RcppAnnoy, RcppHNSW License: GPL-3 Archs: x64 MD5sum: 8831a91097c9a70394d9c305d68fced2 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 SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocNeighbors git_branch: devel git_last_commit: e0cd963 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BiocNeighbors_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BiocNeighbors_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BiocNeighbors_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BiocNeighbors_1.23.0.tgz vignettes: vignettes/BiocNeighbors/inst/doc/approx.html, vignettes/BiocNeighbors/inst/doc/exact.html, vignettes/BiocNeighbors/inst/doc/range.html vignetteTitles: 2. Detecting approximate nearest neighbors, 1. Detecting exact nearest neighbors, 3. Detecting neighbors within range hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocNeighbors/inst/doc/approx.R, vignettes/BiocNeighbors/inst/doc/exact.R, vignettes/BiocNeighbors/inst/doc/range.R dependsOnMe: OSCA.workflows, SingleRBook importsMe: batchelor, bluster, CellMixS, cydar, GeDi, imcRtools, lemur, miloR, mumosa, scater, scDblFinder, scMerge, SpotSweeper, UCell suggestsMe: concordexR, TrajectoryUtils, TSCAN linksToMe: SingleR dependencyCount: 23 Package: BioCor Version: 1.29.0 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 MD5sum: 37ea21090cfb089d1a9d05b60cdd6bc7 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: Lluís Revilla Sancho [aut, cre] (), Pau Sancho-Bru [ths] (), Juan José Salvatella Lozano [ths] () Maintainer: Lluís Revilla Sancho 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: f08087e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BioCor_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BioCor_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BioCor_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BioCor_1.29.0.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: 1.39.0 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 Archs: x64 MD5sum: d9a045e151571e0dfb5d6cb0a2f21f67 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., ) Maintainer: Martin Morgan 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: aa329b3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BiocParallel_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BiocParallel_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BiocParallel_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BiocParallel_1.39.0.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, 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, bayNorm, beer, benchdamic, BERT, BiocNeighbors, BioCor, BiocSingular, BioNERO, 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HiContacts, HTSeqGenie, HTSFilter, HybridExpress, iasva, icetea, IMAS, imcRtools, IntEREst, IONiseR, IPO, IsoformSwitchAnalyzeR, katdetectr, KinSwingR, lisaClust, loci2path, LRcell, Macarron, magpie, magrene, mariner, mbkmeans, MCbiclust, MetaboAnnotation, MetaboCoreUtils, metabomxtr, metaseqR2, methodical, MethylAid, methylGSA, methyLImp2, methylInheritance, methylscaper, MetNet, mia, miaViz, microbiomeMarker, microSTASIS, MICSQTL, miloR, minfi, MIRit, mixOmics, MOGAMUN, MoleculeExperiment, monaLisa, motifbreakR, 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, pram, proActiv, profileplyr, ProteoDisco, PSMatch, qpgraph, qsea, QuasR, RadioGx, raer, Rcwl, RegEnrich, REMP, RiboCrypt, RJMCMCNucleosomes, RNAmodR, RNAseqCovarImpute, Rsamtools, RUVcorr, saseR, satuRn, scanMiR, scanMiRApp, SCArray, SCArray.sat, scater, scDblFinder, scDD, scDDboost, scde, scDesign3, SCFA, scFeatures, scHOT, scMerge, SCnorm, scone, scoreInvHap, scPCA, scran, scRecover, screenCounter, scruff, scShapes, scTHI, scuttle, seqArchR, seqArchRplus, sesame, SEtools, sigFeature, signatureSearch, SimBu, SingleCellAlleleExperiment, singleCellTK, SingleR, singscore, SNPhood, soGGi, sparrow, SpectralTAD, spicyR, splatter, SpliceWiz, SplicingGraphs, spoon, srnadiff, Statial, STdeconvolve, SUITOR, syntenet, TAPseq, TBSignatureProfiler, ternarynet, TFBSTools, tidyCoverage, TMixClust, ToxicoGx, TPP2D, tpSVG, tradeSeq, TreeSummarizedExperiment, Trendy, TVTB, txcutr, UCell, VariantFiltering, VariantTools, VDJdive, velociraptor, waddR, weitrix, zinbwave, CytoMethIC, IHWpaper, JohnsonKinaseData, ExpHunterSuite, seqpac, DCLEAR, DTSEA, DysPIA, enviGCMS, Holomics, LDM, minSNPs, oosse, scGate suggestsMe: alabaster.mae, beachmat, DelayedArray, DIAlignR, GenomicDataCommons, ggsc, glmGamPoi, GRaNIE, HDF5Array, ISAnalytics, MungeSumstats, netSmooth, omicsPrint, pareg, plyinteractions, PureCN, randRotation, RcisTarget, rebook, rhdf5, S4Arrays, scGPS, SeqArray, spatialHeatmap, TFutils, TileDBArray, TrajectoryUtils, TSCAN, universalmotif, xcore, MethylAidData, Single.mTEC.Transcriptomes, TENxBrainData, TENxPBMCData, CAGEWorkflow, clustermq, conos, Corbi, digitalDLSorteR, pagoda2, phase1RMD, RaMS, SpatialDDLS, survBootOutliers, wrTopDownFrag dependencyCount: 12 Package: BiocPkgTools Version: 1.23.0 Depends: htmlwidgets Imports: BiocFileCache, BiocManager, biocViews, tibble, magrittr, methods, rlang, stringr, stats, rvest, dplyr, xml2, readr, httr, htmltools, DT, tools, utils, igraph, jsonlite, gh, RBGL, graph, rorcid Suggests: BiocStyle, knitr, rmarkdown, testthat, tm, lubridate, networkD3, visNetwork, clipr, blastula, kableExtra, DiagrammeR, SummarizedExperiment License: MIT + file LICENSE MD5sum: 0c52aabcc5b3adf0f5d8636c76cd4930 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] (), Felix G.M. Ernst [ctb], Jennifer Wokaty [ctb], Charlotte Soneson [ctb], Martin Morgan [ctb], Vince Carey [ctb], Sean Davis [aut, cre] Maintainer: Sean Davis 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: cefc334 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BiocPkgTools_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BiocPkgTools_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BiocPkgTools_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BiocPkgTools_1.21.11.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: 109 Package: biocroxytest Version: 1.1.0 Depends: R (>= 4.4.0) Imports: cli, glue, roxygen2, stringr Suggests: BiocStyle, here, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL (>= 3) MD5sum: feab47bf3b07293302c2122934f2c0ad 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] () Maintainer: Francesc Catala-Moll 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: 5993813 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/biocroxytest_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/biocroxytest_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/biocroxytest_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/biocroxytest_1.1.0.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: 36 Package: BiocSet Version: 1.19.0 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: f51151717e0619ff64e63f07c83479fe 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], Lluís Revilla Sancho [ctb] Maintainer: Kayla Morrell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocSet git_branch: devel git_last_commit: 32ae204 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BiocSet_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BiocSet_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BiocSet_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BiocSet_1.19.0.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: CBEA, sparrow suggestsMe: dearseq dependencyCount: 62 Package: BiocSingular Version: 1.21.0 Imports: BiocGenerics, S4Vectors, Matrix, methods, utils, DelayedArray, BiocParallel, ScaledMatrix, irlba, rsvd, Rcpp, beachmat LinkingTo: Rcpp, beachmat Suggests: testthat, BiocStyle, knitr, rmarkdown, ResidualMatrix License: GPL-3 Archs: x64 MD5sum: 08aee923afd7b4c26bf31fb479b41eb8 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 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: 7985b20 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BiocSingular_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BiocSingular_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BiocSingular_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BiocSingular_1.21.0.tgz vignettes: vignettes/BiocSingular/inst/doc/decomposition.html, vignettes/BiocSingular/inst/doc/representations.html vignetteTitles: 1. SVD and PCA, 2. 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dependencyCount: 33 Package: biocthis Version: 1.15.0 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: e9adfa645adcecbed8df0c47cd5141d0 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] (), Marcel Ramos [ctb] () Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/biocthis VignetteBuilder: knitr BugReports: https://support.bioconductor.org/tag/biocthis git_url: https://git.bioconductor.org/packages/biocthis git_branch: devel git_last_commit: a9e2371 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/biocthis_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/biocthis_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/biocthis_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/biocthis_1.15.0.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: 3.20.0 Depends: R (>= 4.4.0) License: Artistic-2.0 MD5sum: 7f7bcb1f11436fbceba4210d370de382 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 git_url: https://git.bioconductor.org/packages/BiocVersion git_branch: devel git_last_commit: 43c716a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BiocVersion_3.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BiocVersion_3.20.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BiocVersion_3.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BiocVersion_3.20.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: AnnotationHub, pkgndep suggestsMe: BiocBookDemo, BiocManager dependencyCount: 0 Package: biocViews Version: 1.73.0 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: b9a983e8aaed8358d0d0e47b225613b0 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 , BJ Harshfield , S Falcon , Sonali Arora, Lori Shepherd Maintainer: Bioconductor Package Maintainer 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: acbc4c9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/biocViews_1.73.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/biocViews_1.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/biocViews_1.73.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/biocViews_1.73.0.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, monocle, sigFeature, RforProteomics suggestsMe: packFinder, plasmut, rworkflows dependencyCount: 16 Package: BiocWorkflowTools Version: 1.31.0 Depends: R (>= 3.4) Imports: BiocStyle, bookdown, git2r, httr, knitr, rmarkdown, rstudioapi, stringr, tools, utils, usethis License: MIT + file LICENSE MD5sum: 28fa5cd4c1c0b2fbe0aadd55e6b9fdd4 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 Oleś [aut] Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocWorkflowTools git_branch: devel git_last_commit: 8859bb8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BiocWorkflowTools_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BiocWorkflowTools_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BiocWorkflowTools_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BiocWorkflowTools_1.31.0.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: BiocMetaWorkflow, CAGEWorkflow, recountWorkflow, SingscoreAMLMutations dependencyCount: 61 Package: biodb Version: 1.13.0 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 Archs: x64 MD5sum: dfd56c026c68c218b4556d535379ec4f 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] (), Alexis Delabrière [ctb] () Maintainer: Pierrick Roger 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: e265a9a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/biodb_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/biodb_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/biodb_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/biodb_1.11.0.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, biodbExpasy, biodbHmdb, biodbKegg, biodbNcbi, biodbNci, biodbUniprot, phenomis dependencyCount: 76 Package: biodbChebi Version: 1.11.0 Depends: R (>= 4.1) Imports: R6, biodb (>= 1.1.5) Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, lgr License: AGPL-3 MD5sum: 7770d0f741db5a2bb70443c1a15a7760 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] () Maintainer: Pierrick Roger 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: dfe3f6c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/biodbChebi_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/biodbChebi_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/biodbChebi_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/biodbChebi_1.9.0.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: 77 Package: biodbExpasy Version: 1.9.0 Depends: R (>= 4.1) Imports: biodb (>= 1.3.1), R6, stringr, chk Suggests: roxygen2, BiocStyle, testthat (>= 2.0.0), devtools, knitr, rmarkdown, covr, lgr License: AGPL-3 MD5sum: 552eca0c93aed39d62737d3810b35530 NeedsCompilation: no Title: biodbExpasy, a library for connecting to Expasy ENZYME database. Description: The biodbExpasy library provides access to Expasy ENZYME 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 or comments. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] () Maintainer: Pierrick Roger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biodbExpasy git_branch: devel git_last_commit: 25fc0fc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/biodbExpasy_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/biodbExpasy_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/biodbExpasy_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/biodbExpasy_1.9.0.tgz vignettes: vignettes/biodbExpasy/inst/doc/biodbExpasy.html vignetteTitles: Introduction to the biodbExpasy package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbExpasy/inst/doc/biodbExpasy.R dependencyCount: 77 Package: biodbHmdb Version: 1.11.0 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 Archs: x64 MD5sum: 98962d9d0a40b8b4704325d07a5ef42d 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] () Maintainer: Pierrick Roger 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: c3d59a4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/biodbHmdb_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/biodbHmdb_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/biodbHmdb_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/biodbHmdb_1.11.0.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: 78 Package: biodbKegg Version: 1.11.0 Depends: R (>= 4.1) Imports: R6, biodb (>= 1.4.2), chk, lifecycle Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, igraph, magick, lgr License: AGPL-3 MD5sum: c0359f94d9cfe047f21f185a0b2267b0 NeedsCompilation: no Title: biodbKegg, a library for connecting to the KEGG Database Description: The biodbKegg library is an extension of the biodb framework package that provides access to the KEGG databases Compound, Enzyme, Genes, Module, Orthology and Reaction. It allows to retrieve entries by their accession numbers. Web services like "find", "list" and "findExactMass" are also available. Some functions for navigating along the pathways have also been implemented. biocViews: Software, Infrastructure, DataImport, Pathways, KEGG Author: Pierrick Roger [aut, cre] () Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodbKegg VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodbKegg/issues git_url: https://git.bioconductor.org/packages/biodbKegg git_branch: devel git_last_commit: 2d13b73 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/biodbKegg_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/biodbKegg_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/biodbKegg_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/biodbKegg_1.11.0.tgz vignettes: vignettes/biodbKegg/inst/doc/biodbKegg.html vignetteTitles: Introduction to the biodbKegg package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbKegg/inst/doc/biodbKegg.R dependencyCount: 77 Package: biodbNcbi Version: 1.9.0 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 MD5sum: 0f6bc835f03105ce6111d6e46816ee8c 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] () Maintainer: Pierrick Roger 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: 347e93f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/biodbNcbi_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/biodbNcbi_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/biodbNcbi_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/biodbNcbi_1.9.0.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: 77 Package: biodbNci Version: 1.9.0 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 Archs: x64 MD5sum: 5f143086411feeca874961ff26f67e24 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] () Maintainer: Pierrick Roger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biodbNci git_branch: devel git_last_commit: 12acfea git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/biodbNci_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/biodbNci_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/biodbNci_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/biodbNci_1.9.0.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: 77 Package: biodbUniprot Version: 1.11.0 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 MD5sum: 8a7e255ff0ba50ef87262ca7226d1feb 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] () Maintainer: Pierrick Roger 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: c58cfad git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/biodbUniprot_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/biodbUniprot_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/biodbUniprot_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/biodbUniprot_1.11.0.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: 77 Package: bioDist Version: 1.77.0 Depends: R (>= 2.0), methods, Biobase,KernSmooth Suggests: locfit License: Artistic-2.0 MD5sum: 99a3a0fc88b7821f4a182f2d98780e15 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 git_url: https://git.bioconductor.org/packages/bioDist git_branch: devel git_last_commit: 2166111 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/bioDist_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/bioDist_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/bioDist_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/bioDist_1.77.0.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: 7 Package: biomaRt Version: 2.61.0 Depends: methods Imports: utils, AnnotationDbi, progress, stringr, httr2, digest, BiocFileCache, rappdirs, xml2 Suggests: BiocStyle, knitr, mockery, rmarkdown, testthat, httptest2 License: Artistic-2.0 MD5sum: a7fd25a8b7fe7e7d9b26202cf0433879 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 (). 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] () Maintainer: Mike Smith URL: https://github.com/grimbough/biomaRt VignetteBuilder: knitr BugReports: https://github.com/grimbough/biomaRt/issues git_url: https://git.bioconductor.org/packages/biomaRt git_branch: devel git_last_commit: 5efb280 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/biomaRt_2.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/biomaRt_2.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/biomaRt_2.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/biomaRt_2.61.0.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, annotation importsMe: BadRegionFinder, branchpointer, BUSpaRse, ChIPpeakAnno, CHRONOS, dagLogo, DEXSeq, DMRcate, DominoEffect, easyRNASeq, EDASeq, ELMER, EpiMix, epimutacions, FRASER, GDCRNATools, GenVisR, gespeR, gINTomics, glmSparseNet, GOexpress, goSTAG, GRaNIE, Gviz, hermes, InterCellar, isobar, LACE, mCSEA, MEDIPS, MetaboSignal, metaseqR2, MGFR, MouseFM, OncoScore, oposSOM, ORFik, pcaExplorer, phenoTest, pRoloc, ProteoMM, psygenet2r, pwOmics, R453Plus1Toolbox, ramwas, recoup, scPipe, seq2pathway, SeqGSEA, sitadela, SPLINTER, surfaltr, SurfR, SWATH2stats, TCGAbiolinks, TEKRABber, TFEA.ChIP, transcriptogramer, txdbmaker, ViSEAGO, yarn, ExpHunterSuite, TCGAWorkflow, biomartr, BioVenn, convertid, DiNAMIC.Duo, GOxploreR, MantaID, ProFAST, scGOclust, seeker, snplist suggestsMe: AnnotationForge, bioassayR, celda, chromstaR, ClusterJudge, crisprDesign, cTRAP, DELocal, epistack, fedup, FELLA, GeDi, h5vc, MAGeCKFlute, martini, massiR, MethReg, MineICA, MiRaGE, MIRit, MutationalPatterns, netSmooth, oligo, OmnipathR, OrganismDbi, pathlinkR, piano, Pigengene, progeny, R3CPET, RnBeads, rTRM, scater, ShortRead, SIM, sincell, trackViewer, wiggleplotr, zinbwave, BioMartGOGeneSets, BloodCancerMultiOmics2017, leeBamViews, RegParallel, RforProteomics, BED, BioInsight, DGEobj, DGEobj.utils, grandR, kangar00, MoBPS, Patterns, Platypus, scDiffCom, SNPassoc, snplinkage dependencyCount: 68 Package: biomformat Version: 1.33.0 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: 1573f4979b6a017c3d56e6bb8bc1a031 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 and Joseph N Paulson Maintainer: Paul J. McMurdie 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: 7eee05a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/biomformat_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/biomformat_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/biomformat_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/biomformat_1.33.0.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, microbiomeMarker, phyloseq suggestsMe: animalcules, metagenomeSeq, MGnifyR, mia, MicrobiotaProcess, MetaScope, metacoder dependencyCount: 14 Package: BioMVCClass Version: 1.73.0 Depends: R (>= 2.1.0), methods, MVCClass, Biobase, graph, Rgraphviz License: LGPL MD5sum: 8c053e763d92d3c90c2115f587645901 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 git_url: https://git.bioconductor.org/packages/BioMVCClass git_branch: devel git_last_commit: e595997 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BioMVCClass_1.73.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BioMVCClass_1.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BioMVCClass_1.73.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BioMVCClass_1.73.0.tgz vignettes: vignettes/BioMVCClass/inst/doc/BioMVCClass.pdf vignetteTitles: BioMVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 12 Package: biomvRCNS Version: 1.45.0 Depends: IRanges, GenomicRanges, Gviz Imports: methods, mvtnorm Suggests: cluster, parallel, GenomicFeatures, dynamicTreeCut, Rsamtools, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) MD5sum: ef2129f83c97caeb81c776928b3862f9 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 git_url: https://git.bioconductor.org/packages/biomvRCNS git_branch: devel git_last_commit: 03672c7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/biomvRCNS_1.45.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/biomvRCNS_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/biomvRCNS_1.45.0.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: 158 Package: BioNAR Version: 1.7.0 Depends: R (>= 3.5.0), igraph (>= 2.0.1.1), poweRlaw, latex2exp, RSpectra, Rdpack Imports: stringr, viridis, clusterCons, 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 MD5sum: 0cdd691a98aa12f0c018bd18b4b4c3ff 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], T. Ian Simpson [ctb, fnd] Maintainer: Anatoly Sorokin VignetteBuilder: knitr BugReports: https://github.com/lptolik/BioNAR/issues/ git_url: https://git.bioconductor.org/packages/BioNAR git_branch: devel git_last_commit: 8a620ea git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BioNAR_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BioNAR_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BioNAR_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BioNAR_1.7.0.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: 143 Package: BioNERO Version: 1.13.0 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: 19abd6f3a4092749a6d57ace16d1ba07 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] (), Thiago Venancio [aut] () Maintainer: Fabricio Almeida-Silva 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: 27adc4d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BioNERO_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BioNERO_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BioNERO_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BioNERO_1.13.0.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: 165 Package: BioNet Version: 1.65.0 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: 6c5bb72849d29fef0efdb161aba13721 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 URL: http://bionet.bioapps.biozentrum.uni-wuerzburg.de/ git_url: https://git.bioconductor.org/packages/BioNet git_branch: devel git_last_commit: 63db8f8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BioNet_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BioNet_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BioNet_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BioNet_1.65.0.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 suggestsMe: mwcsr dependencyCount: 53 Package: BioQC Version: 1.33.0 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 Archs: x64 MD5sum: 202f271d84bbc93771c234e44fa3f269 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 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: 9aef270 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BioQC_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BioQC_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BioQC_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BioQC_1.33.0.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: 14 Package: biosigner Version: 1.33.0 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 MD5sum: f111a4aa9bc8736bc858cba6b1df1f9c 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] () Maintainer: Etienne A. Thevenot 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: 59069cf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/biosigner_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/biosigner_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/biosigner_1.33.0.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: 110 Package: Biostrings Version: 2.73.0 Depends: R (>= 4.0.0), 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 Enhances: Rmpi License: Artistic-2.0 Archs: x64 MD5sum: 3245170e5f1222d0cafb041ae456b44d 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), 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 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: 3a5c776 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Biostrings_2.73.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Biostrings_2.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Biostrings_2.73.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Biostrings_2.73.0.tgz vignettes: vignettes/Biostrings/inst/doc/Biostrings2Classes.pdf, vignettes/Biostrings/inst/doc/BiostringsQuickOverview.pdf, vignettes/Biostrings/inst/doc/MultipleAlignments.pdf, vignettes/Biostrings/inst/doc/PairwiseAlignments.pdf, vignettes/Biostrings/inst/doc/matchprobes.html vignetteTitles: A short presentation of the basic classes defined in Biostrings 2, Biostrings Quick Overview, Multiple Alignments, Pairwise Sequence Alignments, Handling probe sequence information 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, iPAC, kebabs, MethTargetedNGS, minfi, Modstrings, MotifDb, motifTestR, msa, muscle, oligo, ORFhunteR, periodicDNA, pqsfinder, pwalign, PWMEnrich, QSutils, R453Plus1Toolbox, 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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, STRMPS, SubVis importsMe: AllelicImbalance, AneuFinder, AnnotationHubData, appreci8R, AssessORF, ATACseqQC, BBCAnalyzer, BCRANK, bcSeq, BEAT, biovizBase, branchpointer, bsseq, BUMHMM, BUSpaRse, CellBarcode, ChIPpeakAnno, ChIPseqR, ChIPsim, chromVAR, circRNAprofiler, CircSeqAlignTk, cleanUpdTSeq, cliProfiler, CNEr, CNVfilteR, cogeqc, compEpiTools, consensusDE, coRdon, crisprBase, crisprBowtie, crisprDesign, crisprScore, crisprShiny, CrispRVariants, crisprViz, customProDB, dada2, dagLogo, DAMEfinder, decompTumor2Sig, diffHic, DNAshapeR, DominoEffect, doubletrouble, easyRNASeq, EDASeq, enhancerHomologSearch, ensembldb, ensemblVEP, EpiTxDb, esATAC, eudysbiome, EventPointer, factR, FastqCleaner, FLAMES, 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, HTSeqGenie, 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copyseparator, crispRdesignR, CSESA, cubar, deepredeff, dowser, EncDNA, ensembleTax, EpiSemble, GaMaBioMD, GB5mcPred, genBaRcode, geneHapR, hoardeR, ICAMS, iimi, immuneSIM, kibior, longreadvqs, metaCluster, MitoHEAR, MixviR, ogrdbstats, PACVr, Platypus, PredCRG, revert, SeedMatchR, seqmagick, simMP, SMITIDstruct, vhcub suggestsMe: alabaster.files, annotate, AnnotationForge, AnnotationHub, autonomics, bambu, BANDITS, BiocGenerics, CSAR, eisaR, GenomicFiles, GenomicRanges, GenomicTuples, GWASTools, HiContacts, HPiP, maftools, methrix, methylumi, MiRaGE, mitoClone2, nuCpos, plyinteractions, RNAmodR.AlkAnilineSeq, rpx, rSWeeP, 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, AhoCorasickTrie, bbl, bio3d, datelife, DDPNA, file2meco, FSAtools, gkmSVM, karyotapR, maGUI, MARVEL, MiscMetabar, msaR, NameNeedle, phangorn, polyRAD, protr, ptm, rDNAse, seqtrie, sigminer, Signac, tidysq linksToMe: DECIPHER, kebabs, MatrixRider, pwalign, Rsamtools, ShortRead, triplex, VariantAnnotation, VariantFiltering dependencyCount: 24 Package: biotmle Version: 1.29.0 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: 16ec894ec3d3bfe6de9dc50fe74932a5 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] (), Alan Hubbard [aut, ths] (), Mark van der Laan [aut, ths] (), Weixin Cai [ctb] (), Philippe Boileau [ctb] () Maintainer: Nima Hejazi 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: 86a3c1d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/biotmle_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/biotmle_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/biotmle_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/biotmle_1.29.0.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: 111 Package: biovizBase Version: 1.53.0 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: 8c8ce2fb1e8f2227bf15410c33bcc06a 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 git_url: https://git.bioconductor.org/packages/biovizBase git_branch: devel git_last_commit: a1b7afc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/biovizBase_1.53.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/biovizBase_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/biovizBase_1.53.0.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, derfinderPlot, FRASER, NanoStringNCTools, OUTRIDER, R3CPET, regionReport, shiny.gosling, StructuralVariantAnnotation, Signac dependencyCount: 135 Package: BiRewire Version: 3.37.0 Depends: igraph, slam, Rtsne, Matrix Suggests: RUnit, BiocGenerics License: GPL-3 Archs: x64 MD5sum: 23fb7dd9ea3da8a8caabfad33cfd0fc8 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 URL: http://www.ebi.ac.uk/~iorio/BiRewire git_url: https://git.bioconductor.org/packages/BiRewire git_branch: devel git_last_commit: 5f4185d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BiRewire_3.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BiRewire_3.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BiRewire_3.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BiRewire_3.37.0.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: 1.19.0 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 MD5sum: 7b7950fbd3a475ef93263f5a65720dcd 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 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: acd4f5d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/biscuiteer_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/biscuiteer_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/biscuiteer_1.19.0.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: 180 Package: BiSeq Version: 1.45.0 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: 44f49f42c77cebb9bac534fd93325c03 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 git_url: https://git.bioconductor.org/packages/BiSeq git_branch: devel git_last_commit: cd005cd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BiSeq_1.45.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BiSeq_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BiSeq_1.45.0.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 dependencyCount: 91 Package: blacksheepr Version: 1.19.0 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: 22f2ae6d4c93c3a9312e56986d5c126f 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 VignetteBuilder: knitr BugReports: https://github.com/ruggleslab/blacksheepr/issues git_url: https://git.bioconductor.org/packages/blacksheepr git_branch: devel git_last_commit: 2a4087a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/blacksheepr_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/blacksheepr_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/blacksheepr_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/blacksheepr_1.19.0.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: 133 Package: blima Version: 1.39.0 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: 3f1de38101141ed2caff8ee37492aba2 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 URL: https://bitbucket.org/kulvait/blima VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/blima git_branch: devel git_last_commit: 77bff79 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/blima_1.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/blima_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/blima_1.39.0.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: 1.29.0 Depends: ROntoTools, GSA, PADOG, limma, graph, stats, utils, parallel, Biobase, metafor, methods Suggests: RUnit, BiocGenerics License: GPL (>=2) MD5sum: 868fb9cbc5e3fb343325f911cbd87b20 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 , Hung Nguyen , and Sorin Draghici Maintainer: Van-Dung Pham git_url: https://git.bioconductor.org/packages/BLMA git_branch: devel git_last_commit: 98378fd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BLMA_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BLMA_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BLMA_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BLMA_1.29.0.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: 1.13.0 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: ee9149943de173b7dbf2bfe761451196 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] () Maintainer: Darawan Rinchai VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BloodGen3Module git_branch: devel git_last_commit: 0ea0f5f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BloodGen3Module_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BloodGen3Module_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BloodGen3Module_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BloodGen3Module_1.13.0.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: 131 Package: bluster Version: 1.15.0 Imports: stats, methods, utils, cluster, Matrix, Rcpp, igraph, S4Vectors, BiocParallel, BiocNeighbors LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle, dynamicTreeCut, scRNAseq, scuttle, scater, scran, pheatmap, viridis, mbkmeans, kohonen, apcluster, DirichletMultinomial, vegan, fastcluster License: GPL-3 Archs: x64 MD5sum: 5541e2aca541abe43c199753c636db12 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 SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bluster git_branch: devel git_last_commit: 63c5edb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/bluster_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/bluster_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/bluster_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/bluster_1.15.0.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: epiregulon, mia, scDblFinder, scran, Canek suggestsMe: batchelor, ChromSCape, concordexR, dittoSeq, mbkmeans, miaViz, MOSim, mumosa, SpatialDDLS dependencyCount: 33 Package: bnbc Version: 1.27.0 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 MD5sum: 6afdd4735bbd94f3bf598216d460113d 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 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: 89c087e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/bnbc_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/bnbc_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/bnbc_1.27.0.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: 1.13.0 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: 0979f3baca831f742bf477cc798b538e 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 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: 3ac5d3e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/bnem_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/bnem_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/bnem_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/bnem_1.13.0.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: 176 Package: BOBaFIT Version: 1.9.0 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) MD5sum: 45bc0afbef0f51c21f9a75eadfa5cf50 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 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: 0637d46 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BOBaFIT_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BOBaFIT_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BOBaFIT_1.9.0.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: 179 Package: borealis Version: 1.9.0 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: cacb1df6ce33045e8034b452e9b87400 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] () Maintainer: Garrett Jenkinson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/borealis git_branch: devel git_last_commit: 1c2c24b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/borealis_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/borealis_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/borealis_1.9.0.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: 117 Package: BPRMeth Version: 1.31.0 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: ae68c5d88a127072e7c551b8ef172e86 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BPRMeth git_branch: devel git_last_commit: b599e4f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BPRMeth_1.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BPRMeth_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BPRMeth_1.31.0.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: 1.51.0 Depends: R (>= 2.8.1), PolynomF, Biostrings, lattice License: GPL-2 MD5sum: ce47c9b2e9aaf847253168c35e2746a7 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 git_url: https://git.bioconductor.org/packages/BRAIN git_branch: devel git_last_commit: 2dec858 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BRAIN_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BRAIN_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BRAIN_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BRAIN_1.51.0.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: 1.31.0 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: a101767dcea2590e0a2ab1cbe84daf63 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/branchpointer git_branch: devel git_last_commit: 561611b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/branchpointer_1.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/branchpointer_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/branchpointer_1.31.0.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: 157 Package: breakpointR Version: 1.23.0 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: 2d7be7c3b1c81efc56c5cb60a5ab7afc 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 URL: https://github.com/daewoooo/BreakPointR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/breakpointR git_branch: devel git_last_commit: ddc1396 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/breakpointR_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/breakpointR_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/breakpointR_1.23.0.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: 1.19.0 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 Archs: x64 MD5sum: 18daed070b365eca1a7a3de1c0471aef 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] () Maintainer: Yi Zhou 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: 1d08ce5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/brendaDb_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/brendaDb_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/brendaDb_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/brendaDb_1.19.0.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: 1.1.0 Imports: GenomicRanges, methods, rlang, S4Vectors, utils Suggests: testthat (>= 3.0.0), IRanges, knitr, rmarkdown, BiocStyle, rtracklayer License: GPL-3 MD5sum: b6b0ebca07009fc18a58a74473d297e0 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] () Maintainer: Lucille Lopez-Delisle 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: 0657aab git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BREW3R.r_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BREW3R.r_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BREW3R.r_1.1.0.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: 2.15.0 Depends: R (>= 3.3.0), rJava Imports: curl Suggests: BiocStyle, knitr, rmarkdown, testthat License: AGPL-3 MD5sum: 6ae1c073d09675f515092d626f8ba748 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 , Egon Willighagen , Denise Slenter, Anwesha Bohler , Lars Eijssen , Tooba Abbassi-Daloii Maintainer: Egon Willighagen 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: cad74c2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BridgeDbR_2.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BridgeDbR_2.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BridgeDbR_2.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BridgeDbR_2.15.0.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: BrowserViz Version: 2.27.0 Depends: R (>= 3.5.0), jsonlite (>= 1.5), httpuv(>= 1.5.0) Imports: methods, BiocGenerics Suggests: RUnit, BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: bae3992aa8d6e37b3da8424d0838f094 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 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: 4c58f6b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BrowserViz_2.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BrowserViz_2.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BrowserViz_2.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BrowserViz_2.27.0.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: 14 Package: BSgenome Version: 1.73.0 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: dc44633ad269fb88fbec69e44ac659ec 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 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: e89b08b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BSgenome_1.73.0.tar.gz mac.binary.big-sur-x86_64.ver: 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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, 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, 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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.v3.1.2.GRCh38, 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, deconstructSigs, ICAMS, MOCHA, revert, simMP suggestsMe: Biostrings, biovizBase, ChIPpeakAnno, chipseq, DegCre, DiffBind, easyRNASeq, eisaR, factR, GeneRegionScan, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, maftools, metaseqR2, MiRaGE, plotgardener, ProteoDisco, PWMEnrich, QDNAseq, recoup, RiboCrypt, rtracklayer, sitadela, gkmSVM, MARVEL, sigminer, Signac dependencyCount: 58 Package: BSgenomeForge Version: 1.5.0 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: 473bbc38d770d582e012b4058ae870da NeedsCompilation: no Title: Forge BSgenome data packages 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 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: 46f8531 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BSgenomeForge_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BSgenomeForge_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BSgenomeForge_1.5.0.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: 1.41.0 Depends: R (>= 4.0), methods, BiocGenerics, GenomicRanges (>= 1.41.5), SummarizedExperiment (>= 1.19.5) Imports: IRanges (>= 2.23.9), GenomeInfoDb, scales, stats, 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 LinkingTo: Rcpp, beachmat Suggests: testthat, bsseqData, BiocStyle, rmarkdown, knitr, Matrix, doParallel, rtracklayer, BSgenome.Hsapiens.UCSC.hg38, beachmat (>= 1.5.2), batchtools License: Artistic-2.0 MD5sum: 6ceb4693364e0aa2d412012459078766 NeedsCompilation: yes Title: Analyze, manage and store bisulfite sequencing data Description: A collection of tools for analyzing and visualizing bisulfite sequencing data. biocViews: DNAMethylation Author: Kasper Daniel Hansen [aut, cre], Peter Hickey [aut] Maintainer: Kasper Daniel Hansen URL: https://github.com/kasperdanielhansen/bsseq VignetteBuilder: knitr BugReports: https://github.com/kasperdanielhansen/bsseq/issues git_url: https://git.bioconductor.org/packages/bsseq git_branch: devel git_last_commit: ebf8701 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/bsseq_1.41.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/bsseq_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/bsseq_1.41.0.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: 87 Package: BubbleTree Version: 2.35.0 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) MD5sum: 6792dc84f0b28056540e28a8d6bc11d6 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 , Michael Kuziora , Todd Creasy , Brandon Higgs Maintainer: Todd Creasy , Wei Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BubbleTree git_branch: devel git_last_commit: 80c0779 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BubbleTree_2.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BubbleTree_2.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BubbleTree_2.35.0.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: 151 Package: BufferedMatrix Version: 1.69.0 Depends: R (>= 2.6.0), methods License: LGPL (>= 2) Archs: x64 MD5sum: b96fcf4f1bcc56e35edee3d9faefeae9 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 Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/BufferedMatrix git_url: https://git.bioconductor.org/packages/BufferedMatrix git_branch: devel git_last_commit: d422a05 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BufferedMatrix_1.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BufferedMatrix_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BufferedMatrix_1.69.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BufferedMatrix_1.69.0.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: 1.69.0 Depends: R (>= 2.6.0), BufferedMatrix (>= 1.3.0), methods LinkingTo: BufferedMatrix Suggests: affyio, affy License: GPL (>= 2) Archs: x64 MD5sum: c65d43ea9ab3f078b5baf9a4a46f0668 NeedsCompilation: yes Title: Microarray Data related methods that utlize BufferedMatrix objects Description: Microarray analysis methods that use BufferedMatrix objects biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.bom/bmbolstad/BufferedMatrixMethods git_url: https://git.bioconductor.org/packages/BufferedMatrixMethods git_branch: devel git_last_commit: 1d30eac git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BufferedMatrixMethods_1.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BufferedMatrixMethods_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BufferedMatrixMethods_1.69.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BufferedMatrixMethods_1.69.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 2 Package: bugsigdbr Version: 1.11.0 Depends: R (>= 4.1) Imports: BiocFileCache, methods, vroom, utils Suggests: BiocStyle, knitr, ontologyIndex, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: fb085248fb1007e10185dc9f26468a19 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 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: 1d70990 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/bugsigdbr_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/bugsigdbr_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/bugsigdbr_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/bugsigdbr_1.11.0.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: BUMHMM Version: 1.29.0 Depends: R (>= 3.5.0) Imports: devtools, stringi, gtools, stats, utils, SummarizedExperiment, Biostrings, IRanges Suggests: testthat, knitr, BiocStyle License: GPL-3 MD5sum: a4d1582980e99a7a2ab7e0171c02ff92 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BUMHMM git_branch: devel git_last_commit: deddba9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BUMHMM_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BUMHMM_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BUMHMM_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BUMHMM_1.29.0.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: 125 Package: bumphunter Version: 1.47.0 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: fb313f30114ffabb979ccef2393d3b40 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 URL: https://github.com/rafalab/bumphunter git_url: https://git.bioconductor.org/packages/bumphunter git_branch: devel git_last_commit: be8ed8f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/bumphunter_1.47.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/bumphunter_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/bumphunter_1.47.0.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: 1.13.0 Imports: utils, methods, Matrix, S4Vectors, IRanges Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 4c1db651aaf24226e11c865f2455bfba 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 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: 1d9e561 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BumpyMatrix_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BumpyMatrix_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BumpyMatrix_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BumpyMatrix_1.13.0.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: 12 Package: BUS Version: 1.61.0 Depends: R (>= 2.3.0), minet Imports: stats, infotheo License: GPL-3 Archs: x64 MD5sum: 2e71a7d62ee080476c7ab9dedcfc2e45 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 git_url: https://git.bioconductor.org/packages/BUS git_branch: devel git_last_commit: 51ec0f7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BUS_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BUS_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BUS_1.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BUS_1.61.0.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: 1.23.0 Depends: R (>= 3.5.0) Imports: gplots, methods, grDevices, stats, SummarizedExperiment Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL (>= 2) Archs: x64 MD5sum: 819b0a75bdad010c84c48e5934aef32f 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 , Yingying Wei Maintainer: Xiangyu Luo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BUScorrect git_branch: devel git_last_commit: c3ba6b2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BUScorrect_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BUScorrect_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BUScorrect_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BUScorrect_1.23.0.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: 1.19.0 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, BSgenome.Hsapiens.UCSC.hg38, EnsDb.Hsapiens.v86 License: BSD_2_clause + file LICENSE MD5sum: 395384a2612f636362703d2d32937909 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] (), Lior Pachter [aut, ths] () Maintainer: Lambda Moses 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: 1bebbb0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BUSpaRse_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BUSpaRse_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BUSpaRse_1.17.2.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: 125 Package: BUSseq Version: 1.11.0 Depends: R (>= 3.6) Imports: SingleCellExperiment, SummarizedExperiment, S4Vectors, gplots, grDevices, methods, stats, utils Suggests: BiocStyle, knitr, BiocGenerics License: Artistic-2.0 Archs: x64 MD5sum: 6af95e4134ade1606f8748e52b47610e 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] (), Ga Ming Chan [aut], Yingying Wei [aut] () Maintainer: Fangda Song 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: 976e904 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/BUSseq_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/BUSseq_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BUSseq_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BUSseq_1.11.0.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: 1.3.0 Depends: R (>= 4.3.0) Imports: doParallel, ggplot2, gplots, graphics, grid, gtable, 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: 12284be0ffc08930d233c46311711de1 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] (), Katia Bulekova [aut] (), Vinay Kartha [aut], Stefano Monti [aut] () Maintainer: Reina Chau 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: f2aad15 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CaDrA_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CaDrA_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CaDrA_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CaDrA_1.3.0.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: 85 Package: CAEN Version: 1.13.0 Depends: R (>= 4.1) Imports: stats,PoiClaClu,SummarizedExperiment,methods Suggests: knitr,rmarkdown,BiocManager,SummarizedExperiment,BiocStyle License: GPL-2 MD5sum: 07d85fd10e25a28b66c72fa9f69f9d29 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: b7293e6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CAEN_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CAEN_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CAEN_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CAEN_1.13.0.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: 1.41.0 Depends: R (>= 2.10), biovizBase, GenomicRanges, IRanges, ggbio Imports: affy, ggplot2, annotate, grid, gridExtra, tcltk, Biobase Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: d0956001b5c3d1105a800de442f9a50b 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 git_url: https://git.bioconductor.org/packages/CAFE git_branch: devel git_last_commit: d4e0b91 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CAFE_1.41.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CAFE_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CAFE_1.41.0.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: 178 Package: CAGEfightR Version: 1.25.0 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: 17e64c76b3714513d5d64f99dbfdd214 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 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: 28828d6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CAGEfightR_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CAGEfightR_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CAGEfightR_1.25.0.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: 164 Package: cageminer Version: 1.11.0 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: 435904e6c425fe7b6ba5f0ff6eb57eb7 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: Fabrício Almeida-Silva [aut, cre] (), Thiago Venancio [aut] () Maintainer: Fabrício Almeida-Silva 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: 2307bbe git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cageminer_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cageminer_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cageminer_1.11.0.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: 217 Package: CAGEr Version: 2.11.0 Depends: methods, MultiAssayExperiment, R (>= 4.1.0) Imports: BiocGenerics, BiocParallel, BSgenome, CAGEfightR, data.table, DelayedArray, DelayedMatrixStats, formula.tools, GenomeInfoDb, GenomicAlignments, GenomicRanges (>= 1.37.16), ggplot2 (>= 2.2.0), gtools, IRanges (>= 2.18.0), KernSmooth, memoise, plyr, Rsamtools, reshape2, rtracklayer, S4Vectors (>= 0.27.5), som, stringdist, stringi, SummarizedExperiment, utils, vegan, VGAM Suggests: BSgenome.Drerio.UCSC.danRer7, DESeq2, FANTOM3and4CAGE, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: edc3532305f5949353fc3794c0a794e9 NeedsCompilation: no Title: Analysis of CAGE (Cap Analysis of Gene Expression) sequencing data for precise mapping of transcription start sites and promoterome mining Description: Preprocessing of CAGE sequencing data, identification and normalization of transcription start sites and downstream analysis of transcription start sites clusters (promoters). 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CAGEr git_branch: devel git_last_commit: dfb9c6f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CAGEr_2.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CAGEr_2.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CAGEr_2.11.0.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: 180 Package: calm Version: 1.19.0 Imports: mgcv, stats, graphics Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: d989db17b8d5afec26f52857338269b8 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 VignetteBuilder: knitr BugReports: https://github.com/k22liang/calm/issues git_url: https://git.bioconductor.org/packages/calm git_branch: devel git_last_commit: 9e28016 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/calm_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/calm_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/calm_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/calm_1.19.0.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: 1.61.0 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) Archs: x64 MD5sum: b325a798f7494862d7cf7d05371830d9 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 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: 499f547 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CAMERA_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CAMERA_1.61.0.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: RMassBank, mtbls2 dependencyCount: 159 Package: CaMutQC Version: 1.1.0 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 MD5sum: 096b84e46c5311d76667cf44c4da65cc 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] () Maintainer: Xin Wang 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: f2d131d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CaMutQC_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CaMutQC_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CaMutQC_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CaMutQC_1.1.0.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: 177 Package: canceR Version: 1.39.0 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: ed244035500919fd6a0527ab5f1d4d53 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 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: b8c2c91 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/canceR_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/canceR_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/canceR_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/canceR_1.39.0.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: 214 Package: cancerclass Version: 1.49.0 Depends: R (>= 2.14.0), Biobase, binom, methods, stats Suggests: cancerdata License: GPL 3 Archs: x64 MD5sum: 9086cdb4e4cd90aa5cc1dc4d41d764a3 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 git_url: https://git.bioconductor.org/packages/cancerclass git_branch: devel git_last_commit: e945e63 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cancerclass_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cancerclass_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cancerclass_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cancerclass_1.49.0.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: 7 Package: cardelino Version: 1.7.0 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 MD5sum: a40ad699b9758504dde3a45e9d816450 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 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: abf5cef git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cardelino_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cardelino_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cardelino_1.7.0.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: 3.7.0 Depends: R (>= 4.3), ProtGenerics, BiocGenerics, BiocParallel, S4Vectors (>= 0.27.3), methods, stats, stats4 Imports: CardinalIO, Biobase, EBImage, graphics, grDevices, irlba, Matrix, matter, nlme, parallel, utils Suggests: BiocStyle, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: 031ea66920616cc3aab4ca2e72e5e41b 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 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: 5a5d2f2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Cardinal_3.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Cardinal_3.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Cardinal_3.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Cardinal_3.7.0.tgz vignettes: vignettes/Cardinal/inst/doc/Cardinal3-guide.html, vignettes/Cardinal/inst/doc/Cardinal3-stats.html vignetteTitles: 1. Cardinal 2: User guide for mass spectrometry imaging analysis, 2. Cardinal 3: Statistical methods for mass spectrometry imaging hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cardinal/inst/doc/Cardinal3-guide.R, vignettes/Cardinal/inst/doc/Cardinal3-stats.R dependsOnMe: CardinalWorkflows dependencyCount: 67 Package: CardinalIO Version: 1.3.0 Depends: R (>= 4.3), matter, ontologyIndex Imports: methods, S4Vectors, stats, utils, tools Suggests: BiocStyle, testthat, knitr, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: ed22868bbe1b098c7615f4905a491e83 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 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: da14137 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CardinalIO_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CardinalIO_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CardinalIO_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CardinalIO_1.3.0.tgz vignettes: vignettes/CardinalIO/inst/doc/CardinalIO-guide.html vignetteTitles: Parsing and writing imzML files hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CardinalIO/inst/doc/CardinalIO-guide.R importsMe: Cardinal dependencyCount: 29 Package: CARNIVAL Version: 2.15.0 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 MD5sum: 55fa4d3e05cfde8f6f077667a1127827 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] (), Panuwat Trairatphisan [aut], Anika Liu [ctb], Alberto Valdeolivas [ctb], Nikolas Peschke [ctb], Aurelien Dugourd [ctb], Attila Gabor [cre], Olga Ivanova [aut] Maintainer: Attila Gabor 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: 8598a5e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CARNIVAL_2.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CARNIVAL_2.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CARNIVAL_2.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CARNIVAL_2.15.0.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: 2.39.0 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: 78ccb1d472874cd50b4a8e664470f3db 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 git_url: https://git.bioconductor.org/packages/casper git_branch: devel git_last_commit: ba31da4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/casper_2.39.0.tar.gz 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: 1.29.0 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) MD5sum: 2ae56ccb47b0179590ea7bbff97004c5 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] (), Vito R.T. Zanotelli [aut], Stéphane Chevrier [aut, dtc], Mark D. Robinson [aut, fnd], Bernd Bodenmiller [fnd] Maintainer: Helena L. Crowell 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: f078f96 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CATALYST_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CATALYST_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CATALYST_1.29.0.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: 192 Package: Category Version: 2.71.0 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: a9b102c714a10898f621d8e6c436a865 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 git_url: https://git.bioconductor.org/packages/Category git_branch: devel git_last_commit: 685e74f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Category_2.71.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Category_2.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Category_2.71.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Category_2.71.0.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, cellHTS2, GmicR, interactiveDisplay, meshr, miRLAB, phenoTest, scTensor suggestsMe: qpgraph, RnBeads, maGUI dependencyCount: 60 Package: categoryCompare Version: 1.49.0 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: 6b79f2c313f971f1ca9dc82791d8f330 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 Maintainer: Robert M. Flight 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: 0551947 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/categoryCompare_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/categoryCompare_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/categoryCompare_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/categoryCompare_1.49.0.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: 92 Package: CausalR Version: 1.37.0 Depends: R (>= 3.2.0) Imports: igraph Suggests: knitr, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: c46a6d579b10d2b0670224e7c2b19007 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 , Steven Barrett VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CausalR git_branch: devel git_last_commit: 860acc0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CausalR_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CausalR_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CausalR_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CausalR_1.37.0.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: 1.27.0 Depends: R (>= 4.1) Imports: BiocFileCache, RColorBrewer, cBioPortalData, genefilter, gplots, grDevices, stats, utils, openxlsx Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 913f8eac529adc1b61d46e65fef70f85 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cbaf git_branch: devel git_last_commit: 55f1bc1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cbaf_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cbaf_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cbaf_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cbaf_1.27.0.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: CBEA Version: 1.5.0 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 Archs: x64 MD5sum: 16960fa49afd9f1049b629ed12c3f012 NeedsCompilation: yes 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] () Maintainer: Quang Nguyen URL: https://github.com/qpmnguyen/CBEA, https://qpmnguyen.github.io/CBEA/ VignetteBuilder: knitr BugReports: https://github.com/qpmnguyen/CBEA//issues git_url: https://git.bioconductor.org/packages/CBEA git_branch: devel git_last_commit: 0234eb6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CBEA_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CBEA_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CBEA_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CBEA_1.5.0.tgz vignettes: vignettes/CBEA/inst/doc/basic_usage.html vignetteTitles: Basic Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CBEA/inst/doc/basic_usage.R dependencyCount: 133 Package: cBioPortalData Version: 2.17.0 Depends: R (>= 4.2.0), AnVIL (>= 1.7.1), MultiAssayExperiment Imports: 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, knitr, survival, survminer, rmarkdown, testthat License: AGPL-3 MD5sum: 3d0e28e0c43f3e5af8f6d34e09a8e775 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] (), Karim Mezhoud [ctb] Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/waldronlab/cBioPortalData/issues git_url: https://git.bioconductor.org/packages/cBioPortalData git_branch: devel git_last_commit: b171bcd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cBioPortalData_2.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cBioPortalData_2.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cBioPortalData_2.17.0.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 dependencyCount: 142 Package: CBNplot Version: 1.5.0 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, oaqc Suggests: knitr, arules, concaveman, ReactomePA, bnviewer, DESeq2, GEOquery, rmarkdown, withr, BiocStyle, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: a9cfd725dd58791bc008a6a487c15243 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 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: feb27bf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CBNplot_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CBNplot_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CBNplot_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CBNplot_1.5.0.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: 152 Package: cbpManager Version: 1.13.0 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: 3d9e811d34d7f821f74a5befac57304d 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] (), Federico Marini [aut] () Maintainer: Arsenij Ustjanzew 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: 7d190ff git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cbpManager_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cbpManager_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cbpManager_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cbpManager_1.13.0.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: 89 Package: ccfindR Version: 1.25.0 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) Archs: x64 MD5sum: 11988f86629f920a9c1a4752ed9c8ed0 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 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: c0ae7f1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ccfindR_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ccfindR_1.25.0.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: 1.7.0 Imports: Rcpp, matrixStats, stats, SIMLR, BiocParallel LinkingTo: Rcpp, RcppEigen Suggests: knitr, rmarkdown, BiocStyle, sessioninfo, scRNAseq, scater, SingleCellExperiment, mclust, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: b4d81cf5a18a7ee8c519af2baa81bc1c 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, PrincipalComponent, DimensionReduction, Clustering, RNASeq, Transcriptomics Author: Marcin Malec [cre, aut] () Maintainer: Marcin Malec VignetteBuilder: knitr BugReports: https://github.com/khazum/ccImpute/issues git_url: https://git.bioconductor.org/packages/ccImpute git_branch: devel git_last_commit: 86e6bfc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ccImpute_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ccImpute_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ccImpute_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ccImpute_1.7.0.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: 25 Package: ccmap Version: 1.31.0 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 MD5sum: 98cffd53907d91ed0ab313debae949b8 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ccmap git_branch: devel git_last_commit: ee4b549 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ccmap_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ccmap_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ccmap_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ccmap_1.31.0.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: 1.3.0 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: 884abc4e8b882194c5fa0848d4841b6e 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] (), Pilib Ó Broin [aut], Eva Szegezdi [aut] Maintainer: Sarah Ennis 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: 626b49b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CCPlotR_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CCPlotR_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CCPlotR_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CCPlotR_1.3.0.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: 97 Package: CCPROMISE Version: 1.31.0 Depends: R (>= 3.3.0), stats, methods, CCP, PROMISE, Biobase, GSEABase, utils License: GPL (>= 2) MD5sum: 4404046f073b61d7177e7f4b2dafc246 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 and Stanley.pounds Maintainer: Xueyuan Cao git_url: https://git.bioconductor.org/packages/CCPROMISE git_branch: devel git_last_commit: 2d88d64 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CCPROMISE_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CCPROMISE_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CCPROMISE_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CCPROMISE_1.31.0.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: 1.41.0 Imports: infotheo (>= 1.1) Suggests: knitr, BiocStyle, BiocGenerics, testthat, RUnit License: MIT + file LICENSE MD5sum: d68f03c681c994a41b26b15d9e7cbcac 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 ,Craig Bielski, George Weingart Maintainer: Emma Schwager ,Craig Bielski, George Weingart VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ccrepe git_branch: devel git_last_commit: 1d8316d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ccrepe_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ccrepe_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ccrepe_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ccrepe_1.41.0.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: 1.3.0 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: 841095b4fce2990dd22e2d4bad3a1cce 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] (), 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 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: 0130ee0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CDI_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CDI_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CDI_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CDI_1.3.0.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: 1.23.0 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: 9939bfff97628cd5866f7d19f08e0d4e 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/celaref git_branch: devel git_last_commit: c4533d6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/celaref_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/celaref_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/celaref_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/celaref_1.23.0.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: 84 Package: celda Version: 1.21.0 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 LinkingTo: Rcpp, RcppEigen Suggests: testthat, knitr, roxygen2, rmarkdown, biomaRt, covr, BiocManager, BiocStyle, TENxPBMCData, singleCellTK, M3DExampleData License: MIT + file LICENSE MD5sum: 938b890d54eb580dd05c5c75862b79ca 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 VignetteBuilder: knitr BugReports: https://github.com/campbio/celda/issues git_url: https://git.bioconductor.org/packages/celda git_branch: devel git_last_commit: 705c668 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/celda_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/celda_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/celda_1.21.0.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: 139 Package: CellBarcode Version: 1.11.0 Depends: R (>= 4.1.0) Imports: methods, stats, Rcpp (>= 1.0.5), data.table (>= 1.12.6), plyr, ggplot2, stringr, magrittr, ShortRead (>= 1.48.0), Biostrings (>= 2.58.0), egg, Ckmeans.1d.dp, utils, S4Vectors, seqinr, zlibbioc, Rsamtools LinkingTo: Rcpp, BH Suggests: BiocStyle, testthat (>= 3.0.0), knitr, rmarkdown License: Artistic-2.0 MD5sum: 6653a7b33057ea61c322fea167e5c8d4 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] (), Anne-Marie Lyne [aut], Leila Perie [aut] Maintainer: Wenjie Sun URL: https://wenjie1991.github.io/CellBarcode/ VignetteBuilder: knitr BugReports: https://github.com/wenjie1991/CellBarcode/issues git_url: https://git.bioconductor.org/packages/CellBarcode git_branch: devel git_last_commit: 85eb0aa git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CellBarcode_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CellBarcode_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CellBarcode_1.11.0.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: 1.29.0 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) MD5sum: 8f06423987b02bb160a023cf5f101ca4 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 URL: https://github.com/melsiddieg/cellbaseR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellbaseR git_branch: devel git_last_commit: 349c4d0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cellbaseR_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cellbaseR_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cellbaseR_1.29.0.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: 66 Package: CellBench Version: 1.21.0 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: 772ccbe367c81005681af97a9a3cfa0e 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 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: 5be76b3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CellBench_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CellBench_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CellBench_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CellBench_1.21.0.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: 82 Package: CelliD Version: 1.13.0 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 Archs: x64 MD5sum: 22211fbd229ced1bd2260fbe9bce0ea8 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CelliD git_branch: devel git_last_commit: de49f2d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CelliD_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CelliD_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CelliD_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CelliD_1.13.0.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: 201 Package: cellity Version: 1.33.0 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) MD5sum: 59dd387bf8799aedd35b036908f653e1 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellity git_branch: devel git_last_commit: 9cd7471 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cellity_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cellity_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cellity_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cellity_1.33.0.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: 1.31.0 Depends: S4Vectors, methods Imports: stats, utils Suggests: CellMapperData, Biobase, HumanAffyData, ALL, BiocStyle, ExperimentHub License: Artistic-2.0 MD5sum: 8c5cca3774128baa5e6653d125868bd6 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 git_url: https://git.bioconductor.org/packages/CellMapper git_branch: devel git_last_commit: 878ee10 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CellMapper_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CellMapper_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CellMapper_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CellMapper_1.31.0.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: 7 Package: cellmigRation Version: 1.13.0 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: 7dd75b7674bea007f9574764585d5b87 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 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: def9c17 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cellmigRation_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cellmigRation_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cellmigRation_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cellmigRation_1.13.0.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: 148 Package: CellMixS Version: 1.21.0 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: c544902f453b8ba63cf7ad12ce728f97 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 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: 29434d2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CellMixS_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CellMixS_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CellMixS_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CellMixS_1.21.0.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: 119 Package: CellNOptR Version: 1.51.0 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 Archs: x64 MD5sum: 8bc68c95194eeabc1f9caa9c7fb31f4d 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 SystemRequirements: Graphviz version >= 2.2 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellNOptR git_branch: devel git_last_commit: f49fdc0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CellNOptR_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CellNOptR_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CellNOptR_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CellNOptR_1.51.0.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, CNORfeeder, CNORfuzzy, CNORode importsMe: bnem suggestsMe: MEIGOR dependencyCount: 70 Package: cellscape Version: 1.29.0 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: fc846039b589941ea85e0c0878d3120b 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] (), Maia Smith [aut] Maintainer: Shixiang Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellscape git_branch: devel git_last_commit: 373fa9e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cellscape_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cellscape_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cellscape_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cellscape_1.29.0.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: 1.25.0 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: 99c644f1383fbb6096bdbda9cf946796 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellScore git_branch: devel git_last_commit: fc6e998 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CellScore_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CellScore_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CellScore_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CellScore_1.25.0.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: 1.23.0 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: b3225f4270c743640f98c77e22d8d4e3 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellTrails git_branch: devel git_last_commit: 1adbebd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CellTrails_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CellTrails_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CellTrails_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CellTrails_1.23.0.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: 1.9.0 Depends: dplyr Imports: httr, curl, utils, tools, shiny, DT, rjsoncons Suggests: zellkonverter, SingleCellExperiment, HDF5Array, tidyr, BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), mockery License: Artistic-2.0 MD5sum: 6993df58bea0cd4b2be882bb5fdaac6f 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] (), Kayla Interdonato [aut] Maintainer: Martin Morgan 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: 525c4aa git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cellxgenedp_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cellxgenedp_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cellxgenedp_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cellxgenedp_1.9.0.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: 1.29.0 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: ba757e56a9e4eb28b78313b93758959f 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CEMiTool git_branch: devel git_last_commit: 5bf6507 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CEMiTool_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CEMiTool_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CEMiTool_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CEMiTool_1.29.0.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: 196 Package: censcyt Version: 1.13.0 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: db06d3f5f697db1f336a4a84553cea77 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] () Maintainer: Reto Gerber 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: 42d6257 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/censcyt_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/censcyt_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/censcyt_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/censcyt_1.13.0.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: 190 Package: ceRNAnetsim Version: 1.17.0 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: 181e4420a5c57324c5913084650a0012 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] (), Alper Yilmaz [aut] () Maintainer: Selcen Ari Yuka 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: e399d6e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ceRNAnetsim_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ceRNAnetsim_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ceRNAnetsim_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ceRNAnetsim_1.17.0.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: 69 Package: CeTF Version: 1.17.0 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: b9e5969792635d02dbd82878e2dbc1f3 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], Patrícia 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 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: 7988071 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CeTF_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CeTF_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CeTF_1.17.0.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: 224 Package: CexoR Version: 1.43.0 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 MD5sum: f7000b4edb7c61560da4622e5a26e64b 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] () Maintainer: Pedro Madrigal git_url: https://git.bioconductor.org/packages/CexoR git_branch: devel git_last_commit: 16301bb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CexoR_1.43.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CexoR_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CexoR_1.43.0.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: 1.39.0 Depends: R (>= 2.10.0) License: LGPL MD5sum: de3c9b28979f989bbcaa9317df0b1905 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 git_url: https://git.bioconductor.org/packages/CFAssay git_branch: devel git_last_commit: b99bab8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CFAssay_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CFAssay_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CFAssay_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CFAssay_1.39.0.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: 1.3.0 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: baf885abbe55dda8bbe38d7e568ce519 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] () Maintainer: Pitithat Puranachot VignetteBuilder: knitr BugReports: https://github.com/Pitithat-pu/cfdnakit/issues git_url: https://git.bioconductor.org/packages/cfdnakit git_branch: devel git_last_commit: 02ed35d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cfdnakit_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cfdnakit_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cfdnakit_1.3.0.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: 94 Package: cfDNAPro Version: 1.11.0 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: bafeefdd69ff32a5bc982a023171d6b9 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 URL: https://github.com/hw538/cfDNAPro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cfDNAPro git_branch: devel git_last_commit: 2858d53 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cfDNAPro_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cfDNAPro_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cfDNAPro_1.11.0.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: 98 Package: cfTools Version: 1.5.0 Imports: Rcpp, utils, GenomicRanges, basilisk, R.utils, stats, cfToolsData LinkingTo: Rcpp, BH Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: file LICENSE MD5sum: c47b02eb1c726e534bff8c25beafa9e0 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] (), Mary Louisa Stackpole [aut] (), Shuo Li [aut] (), Xianghong Jasmine Zhou [aut] (), Wenyuan Li [aut] () Maintainer: Ran Hu 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: 4df84a5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cfTools_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cfTools_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cfTools_1.5.0.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: 85 Package: CGEN Version: 3.41.0 Depends: R (>= 4.0), survival, mvtnorm Imports: stats, graphics, utils, grDevices Suggests: cluster License: GPL-2 + file LICENSE Archs: x64 MD5sum: 59c343fdf2cc9b8dbabdf00435ea1c4b 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 git_url: https://git.bioconductor.org/packages/CGEN git_branch: devel git_last_commit: 4d02ba4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CGEN_3.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CGEN_3.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CGEN_3.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CGEN_3.41.0.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: 1.65.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), marray License: GPL MD5sum: e95345703b1c37103257cd07b33ac91e 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 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: 3192314 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CGHbase_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CGHbase_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CGHbase_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CGHbase_1.65.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, CGHnormaliter, CGHregions, GeneBreak importsMe: CGHnormaliter, QDNAseq dependencyCount: 10 Package: CGHcall Version: 2.67.0 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: d6be31841b1d8bc9be711c4384c2351b 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 git_url: https://git.bioconductor.org/packages/CGHcall git_branch: devel git_last_commit: 0d5df8a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CGHcall_2.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CGHcall_2.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CGHcall_2.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CGHcall_2.67.0.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: 15 Package: cghMCR Version: 1.63.0 Depends: methods, DNAcopy, CNTools, limma Imports: BiocGenerics (>= 0.1.6), stats4 License: LGPL MD5sum: 2826c95f848af0212fa7afaa8f239451 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 git_url: https://git.bioconductor.org/packages/cghMCR git_branch: devel git_last_commit: d7f3fbe git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cghMCR_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cghMCR_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cghMCR_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cghMCR_1.63.0.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: 1.59.0 Depends: CGHcall (>= 2.17.0), CGHbase (>= 1.15.0) Imports: Biobase, CGHbase, CGHcall, methods, stats, utils License: GPL (>= 3) MD5sum: 5d7d8dc96cdff19a246648efc2769b39 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 git_url: https://git.bioconductor.org/packages/CGHnormaliter git_branch: devel git_last_commit: 2497eb7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CGHnormaliter_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CGHnormaliter_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CGHnormaliter_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CGHnormaliter_1.59.0.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: 16 Package: CGHregions Version: 1.63.0 Depends: R (>= 2.0.0), methods, Biobase, CGHbase License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: 76e8f17fe9717ecd41cb850bcffa291a 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 git_url: https://git.bioconductor.org/packages/CGHregions git_branch: devel git_last_commit: 3799788 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CGHregions_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CGHregions_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CGHregions_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CGHregions_1.63.0.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: 11 Package: ChemmineOB Version: 1.43.0 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 Enhances: ChemmineR (>= 2.13.0) License: Artistic-2.0 Archs: x64 MD5sum: 13e942ff8761f7965b0f963990ba31e5 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 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: 33fd022 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ChemmineOB_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ChemmineOB_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ChemmineOB_1.43.0.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 dependencyCount: 8 Package: ChemmineR Version: 3.57.0 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 Enhances: ChemmineOB License: Artistic-2.0 Archs: x64 MD5sum: 8ba1f7596cee8feac405b79a1d4054df 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 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: 10274c2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ChemmineR_3.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ChemmineR_3.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ChemmineR_3.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ChemmineR_3.57.0.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, chemodiv, DrugSim2DR, DRviaSPCN suggestsMe: ChemmineOB, xnet dependencyCount: 75 Package: CHETAH Version: 1.21.0 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 MD5sum: 7b64a42d20e8d2d1872c12da0f948ad9 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 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: 4634009 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CHETAH_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CHETAH_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CHETAH_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CHETAH_1.21.0.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: 114 Package: Chicago Version: 1.33.0 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: c6c46864f0a2f53a338381f2db80f334 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Chicago git_branch: devel git_last_commit: c56ab0e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Chicago_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Chicago_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Chicago_1.33.0.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: 1.5.0 Depends: DelayedArray Imports: methods, Matrix, rhdf5, Rcpp, HDF5Array LinkingTo: Rcpp, Rhdf5lib Suggests: BiocGenerics, S4Vectors, BiocSingular, ResidualMatrix, BiocStyle, testthat, rmarkdown, knitr License: GPL-3 Archs: x64 MD5sum: 486d7cf966fefcfb891b5de96ba4a6cd 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 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: 8c31ab5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/chihaya_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/chihaya_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/chihaya_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/chihaya_1.5.0.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: 27 Package: chimeraviz Version: 1.31.0 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 MD5sum: dfcc7d6b50f486e0a83fa8797fde993e 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 Lågstad [aut, cre], Sen Zhao [ctb], Andreas M. Hoff [ctb], Bjarne Johannessen [ctb], Ole Christian Lingjærde [ctb], Rolf Skotheim [ctb] Maintainer: Stian Lågstad 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: e57f666 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/chimeraviz_1.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/chimeraviz_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/chimeraviz_1.31.0.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: 173 Package: ChIPanalyser Version: 1.27.0 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: 899d07c06f1c14a798a4775862336464 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChIPanalyser git_branch: devel git_last_commit: 6ba9b03 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ChIPanalyser_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ChIPanalyser_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ChIPanalyser_1.27.0.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: 1.35.0 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: 2809eb9fdcdb75746166cf46091b44b6 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 git_url: https://git.bioconductor.org/packages/ChIPComp git_branch: devel git_last_commit: 2f402aa git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ChIPComp_1.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ChIPComp_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ChIPComp_1.35.0.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: 2.29.0 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 MD5sum: 49684e8c0c21af78b74416cf669cf7c5 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chipenrich git_branch: devel git_last_commit: 32711e6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/chipenrich_2.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/chipenrich_2.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/chipenrich_2.29.0.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: 1.29.0 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: e4376be1f3a943a3a2054204656af3c2 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 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: 22bd18a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ChIPexoQual_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ChIPexoQual_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ChIPexoQual_1.29.0.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: 144 Package: ChIPpeakAnno Version: 3.39.0 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: 7531c6cfdce2c893fcb777da80f4dbfa 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 , Lihua Julie Zhu , Kai Hu , Junhui Li VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChIPpeakAnno git_branch: devel git_last_commit: d19cc74 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ChIPpeakAnno_3.39.0.tar.gz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ChIPpeakAnno_3.39.0.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: 132 Package: ChIPQC Version: 1.41.0 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: 0c677d1bb1222f3d46e391639f3e7813 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 , Rory Stark git_url: https://git.bioconductor.org/packages/ChIPQC git_branch: devel git_last_commit: 448ae73 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ChIPQC_1.41.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ChIPQC_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ChIPQC_1.41.0.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: 168 Package: ChIPseeker Version: 1.41.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi, BiocGenerics, boot, enrichplot, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, ggplot2, gplots, graphics, grDevices, gtools, methods, plotrix, dplyr, parallel, magrittr, rtracklayer, S4Vectors, stats, TxDb.Hsapiens.UCSC.hg19.knownGene, utils, aplot, yulab.utils, tibble Suggests: clusterProfiler, ggimage, ggplotify, ggupset, ggVennDiagram, ReactomePA, org.Hs.eg.db, knitr, rmarkdown, testthat, prettydoc License: Artistic-2.0 MD5sum: 38378e1458fc2214ad298958ec089626 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] (), Ming Li [ctb], Qianwen Wang [ctb], Yun Yan [ctb], Hervé Pagès [ctb], Michael Kluge [ctb], Thomas Schwarzl [ctb], Zhougeng Xu [ctb] Maintainer: Guangchuang Yu URL: https://onlinelibrary.wiley.com/share/author/GYJGUBYCTRMYJFN2JFZZ?target=10.1002/cpz1.585 VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ChIPseeker/issues git_url: https://git.bioconductor.org/packages/ChIPseeker git_branch: devel git_last_commit: 29ae8a1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ChIPseeker_1.41.0.tar.gz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ChIPseeker_1.41.0.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: esATAC, segmenter, seqArchRplus, TCGAWorkflow, cinaR suggestsMe: GRaNIE, curatedAdipoChIP dependencyCount: 153 Package: chipseq Version: 1.55.0 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: 7158cb70017713332a28348a937158f1 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chipseq git_branch: devel git_last_commit: 8bbec21 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/chipseq_1.55.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/chipseq_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/chipseq_1.55.0.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, HTSeqGenie, soGGi, transcriptR dependencyCount: 63 Package: ChIPseqR Version: 1.59.0 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) MD5sum: 853a86312c2c8d851bc5eb4afee650e5 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 git_url: https://git.bioconductor.org/packages/ChIPseqR git_branch: devel git_last_commit: 4c40787 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ChIPseqR_1.59.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ChIPseqR_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ChIPseqR_1.59.0.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: 1.59.0 Depends: Biostrings (>= 2.29.2) Imports: IRanges, XVector, Biostrings, ShortRead, graphics, methods, stats, utils Suggests: actuar, zoo License: GPL (>= 2) MD5sum: 59f1f07c81e98caa4d77feccef348e29 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 git_url: https://git.bioconductor.org/packages/ChIPsim git_branch: devel git_last_commit: 0f3731a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ChIPsim_1.59.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ChIPsim_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ChIPsim_1.59.0.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: 1.49.0 Depends: R (>= 2.10), ChIPXpressData Imports: Biobase, GEOquery, frma, affy, bigmemory, biganalytics Suggests: mouse4302frmavecs, mouse4302.db, mouse4302cdf, RUnit, BiocGenerics License: GPL(>=2) MD5sum: e56b96c301421fa53aa0fd5c7b7f5965 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 git_url: https://git.bioconductor.org/packages/ChIPXpress git_branch: devel git_last_commit: b27768f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ChIPXpress_1.49.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ChIPXpress_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ChIPXpress_1.49.0.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: 103 Package: chopsticks Version: 1.71.0 Imports: graphics, stats, utils, methods, survival Suggests: hexbin License: GPL-3 Archs: x64 MD5sum: afe3ac4d0dd5966c3fef605334fcfdee 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 Maintainer: Hin-Tak Leung URL: http://outmodedbonsai.sourceforge.net/ git_url: https://git.bioconductor.org/packages/chopsticks git_branch: devel git_last_commit: 37ef827 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/chopsticks_1.71.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/chopsticks_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/chopsticks_1.71.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/chopsticks_1.71.0.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: 2.35.0 Depends: R (>= 3.0.0) Imports: Rcpp (>= 0.11.1), GenomicRanges (>= 1.17.46) LinkingTo: Rcpp License: GPL-3 MD5sum: 76394c6fd2407737b7dfe1610f4105d4 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 URL: www.plantcytogenomics.org/chromDraw SystemRequirements: Rtools (>= 3.1) git_url: https://git.bioconductor.org/packages/chromDraw git_branch: devel git_last_commit: 1921708 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/chromDraw_2.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/chromDraw_2.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/chromDraw_2.35.0.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: 1.59.0 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 MD5sum: 495ae86f69c76b79eebc64410f623e84 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 git_url: https://git.bioconductor.org/packages/ChromHeatMap git_branch: devel git_last_commit: 798c610 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ChromHeatMap_1.59.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ChromHeatMap_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ChromHeatMap_1.59.0.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: 1.33.0 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: 19eabaea3845e0dd2044bcc2b3a92d09 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 git_url: https://git.bioconductor.org/packages/chromPlot git_branch: devel git_last_commit: 04a5b0a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/chromPlot_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/chromPlot_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/chromPlot_1.33.0.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: 72 Package: ChromSCape Version: 1.15.0 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: bded7008a7bf4082163601979d486089 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] (), Celine Vallot [aut] () Maintainer: Pacome Prompsy 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: f4aa771 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ChromSCape_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ChromSCape_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ChromSCape_1.15.0.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: chromstaR Version: 1.31.0 Depends: R (>= 3.5.0), 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 MD5sum: 697f088d1491444b45267da24425e371 NeedsCompilation: yes 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 URL: https://github.com/ataudt/chromstaR VignetteBuilder: knitr BugReports: https://github.com/ataudt/chromstaR/issues git_url: https://git.bioconductor.org/packages/chromstaR git_branch: devel git_last_commit: 3f120ed git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/chromstaR_1.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/chromstaR_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/chromstaR_1.31.0.tgz vignettes: vignettes/chromstaR/inst/doc/chromstaR.pdf vignetteTitles: The chromstaR user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromstaR/inst/doc/chromstaR.R dependencyCount: 88 Package: chromVAR Version: 1.27.0 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: cee37d0c774c8686d0f1ceb749b3fe67 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 SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chromVAR git_branch: devel git_last_commit: 641df9d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/chromVAR_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/chromVAR_1.27.0.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 importsMe: ATACCoGAPS suggestsMe: MOCHA, Signac dependencyCount: 162 Package: CHRONOS Version: 1.33.0 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 MD5sum: 746ea6a1ab423a3783e58a131e7bff9b 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 SystemRequirements: Java version >= 1.7, Pandoc VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CHRONOS git_branch: devel git_last_commit: 8526dba git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CHRONOS_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CHRONOS_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CHRONOS_1.33.0.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: 92 Package: cicero Version: 1.23.0 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: f80828ab4be855f5273dd2a7d858acc9 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cicero git_branch: devel git_last_commit: c8c4519 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cicero_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cicero_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cicero_1.23.0.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: 183 Package: CIMICE Version: 1.13.0 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: d6adf9f47d8fe2093474f14e464b2189 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, ) Maintainer: Nicolò Rossi 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: a2435a1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CIMICE_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CIMICE_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CIMICE_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CIMICE_1.13.0.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: 93 Package: CINdex Version: 1.33.0 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: 8577fbdc07fcb344b50da41c0c2fe3a9 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CINdex git_branch: devel git_last_commit: 6621e09 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CINdex_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CINdex_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CINdex_1.33.0.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: 51 Package: circRNAprofiler Version: 1.19.0 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: 9e71d36dd9b7be72fe95fd027c87dbab 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 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: ceaee11 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-03 source.ver: src/contrib/circRNAprofiler_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/circRNAprofiler_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/circRNAprofiler_1.19.0.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: 143 Package: CircSeqAlignTk Version: 1.7.0 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: 9ff132d670cfc21090e8899cb32306a4 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] (), Xi Fu [ctb], Wei Cao [ctb] Maintainer: Jianqiang Sun 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: c022694 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CircSeqAlignTk_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CircSeqAlignTk_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CircSeqAlignTk_1.7.0.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: 160 Package: cisPath Version: 1.45.0 Depends: R (>= 2.10.0) Imports: methods, utils License: GPL (>= 3) Archs: x64 MD5sum: 720d66ce74af08965b9ee2e0cbc80784 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 Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/cisPath git_branch: devel git_last_commit: 94a2229 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cisPath_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cisPath_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cisPath_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cisPath_1.45.0.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: 1.17.0 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 MD5sum: 1b17679a7cc945c4bd7866d63601ad26 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 VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/CiteFuse/issues git_url: https://git.bioconductor.org/packages/CiteFuse git_branch: devel git_last_commit: edba263 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CiteFuse_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CiteFuse_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CiteFuse_1.17.0.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: 165 Package: ClassifyR Version: 3.9.0 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 Suggests: limma, edgeR, car, Rmixmod, gridExtra (>= 2.0.0), cowplot, BiocStyle, pamr, PoiClaClu, parathyroidSE, knitr, htmltools, gtable, scales, e1071, rmarkdown, IRanges, robustbase, glmnet, class, randomForestSRC, MatrixModels, xgboost, data.tree, ggnewscale License: GPL-3 Archs: x64 MD5sum: e4c45ea25641b3ba8fbfde57ff426fbb 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 URL: https://sydneybiox.github.io/ClassifyR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClassifyR git_branch: devel git_last_commit: a34c1e3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ClassifyR_3.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ClassifyR_3.9.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ClassifyR_3.9.0.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, spicyWorkflow dependencyCount: 145 Package: cleanUpdTSeq Version: 1.43.0 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 MD5sum: 82bb29ceced77af06c1ff5c88b0c0c29 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 ; Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cleanUpdTSeq git_branch: devel git_last_commit: 189466a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cleanUpdTSeq_1.43.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cleanUpdTSeq_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cleanUpdTSeq_1.43.0.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: cleaver Version: 1.43.0 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: 34b97418621b6c30c77c2aea3d2a9eb9 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] () Maintainer: Sebastian Gibb 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: ddf3309 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cleaver_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cleaver_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cleaver_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cleaver_1.43.0.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: 1.5.0 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: 79fb8746440f350cf1e3acea2a586c80 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] () Maintainer: Sarah Sandmann 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: d3649dd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/clevRvis_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/clevRvis_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/clevRvis_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/clevRvis_1.5.0.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: 121 Package: clippda Version: 1.55.0 Depends: R (>= 2.13.1),limma, statmod, rgl, lattice, scatterplot3d, graphics, grDevices, stats, utils, Biobase, tools, methods License: GPL (>=2) MD5sum: dd3188c2375dbda428fbbe7b4141da4c 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 URL: http://www.cancerstudies.bham.ac.uk/crctu/CLIPPDA.shtml git_url: https://git.bioconductor.org/packages/clippda git_branch: devel git_last_commit: f043756 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/clippda_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/clippda_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/clippda_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/clippda_1.55.0.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: 42 Package: clipper Version: 1.45.0 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: 97c5c1086eb96891b48c969dd4056a4c 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 , Gabriele Sales , Chiara Romualdi Maintainer: Paolo Martini git_url: https://git.bioconductor.org/packages/clipper git_branch: devel git_last_commit: 65b0821 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/clipper_1.45.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/clipper_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/clipper_1.45.0.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: 1.11.0 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: 4bb704901a94ec1fd4c74f0faf6bf0ed 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] (), Kathi Zarnack [aut] () Maintainer: You Zhou 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: 08b502b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cliProfiler_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cliProfiler_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cliProfiler_1.11.0.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: 88 Package: Clomial Version: 1.41.0 Depends: R (>= 2.10), matrixStats Imports: methods, permute License: GPL (>= 2) MD5sum: 4641d4f2b0030a5c1a7b5bdf4e2cb576 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 git_url: https://git.bioconductor.org/packages/Clomial git_branch: devel git_last_commit: 2c4398c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Clomial_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Clomial_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Clomial_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Clomial_1.41.0.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: 1.53.0 Depends: R (>= 2.10) Imports: ROC, lattice Suggests: RUnit License: GPL-3 MD5sum: eaa47363af3d707ce9084d2867f6e326 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 git_url: https://git.bioconductor.org/packages/clst git_branch: devel git_last_commit: d898425 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/clst_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/clst_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/clst_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/clst_1.53.0.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: 1.53.0 Depends: R (>= 2.10), clst, rjson, ape Imports: lattice, RSQLite Suggests: RUnit License: GPL-3 MD5sum: 6e44a657f349089fee24cd7562241ea8 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 git_url: https://git.bioconductor.org/packages/clstutils git_branch: devel git_last_commit: 2ffb05f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/clstutils_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/clstutils_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/clstutils_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/clstutils_1.53.0.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: 1.21.0 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: 0d320c8cd781950e5ca243f7779460a9 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 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: c4712fb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CluMSID_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CluMSID_1.21.0.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: 166 Package: ClustAll Version: 1.1.0 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: e403517ca3db90d01fae3740e713d70d NeedsCompilation: no Title: ClustAll: Data driven strategy to find groups 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] (), Sara Palomino-Echeverria [aut] Maintainer: Asier Ortega-Legarreta VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClustAll git_branch: devel git_last_commit: 86f35b6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ClustAll_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ClustAll_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ClustAll_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ClustAll_1.1.0.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: 190 Package: clustComp Version: 1.33.0 Depends: R (>= 3.3) Imports: sm, stats, graphics, grDevices Suggests: Biobase, colonCA, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: f2206f912df6c5a53846e584df9dc5b4 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 git_url: https://git.bioconductor.org/packages/clustComp git_branch: devel git_last_commit: 2d5bf95 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/clustComp_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/clustComp_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/clustComp_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/clustComp_1.33.0.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: 2.25.0 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 Archs: x64 MD5sum: 9015914d4fe6d5e03d0a6bf174716fea 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 VignetteBuilder: knitr BugReports: https://github.com/epurdom/clusterExperiment/issues git_url: https://git.bioconductor.org/packages/clusterExperiment git_branch: devel git_last_commit: b9692d8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/clusterExperiment_2.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/clusterExperiment_2.25.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/clusterExperiment_2.25.0.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: netDx, slingshot, tradeSeq dependencyCount: 148 Package: ClusterFoldSimilarity Version: 1.1.0 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: 19e4745e36f25d9f53f20976b545c318 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] () Maintainer: Oscar Gonzalez-Velasco VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClusterFoldSimilarity git_branch: devel git_last_commit: e3fa215 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ClusterFoldSimilarity_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ClusterFoldSimilarity_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ClusterFoldSimilarity_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ClusterFoldSimilarity_1.1.0.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: 1.27.0 Depends: R (>= 3.6), stats, utils, graphics, infotheo, lattice, latticeExtra, httr, jsonlite Suggests: yeastExpData, knitr, rmarkdown, devtools, testthat, biomaRt License: Artistic-2.0 MD5sum: 3ce10a72c94d443d0ef8386b48c8583d 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClusterJudge git_branch: devel git_last_commit: 0114674 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ClusterJudge_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ClusterJudge_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ClusterJudge_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ClusterJudge_1.27.0.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: 4.13.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi, downloader, 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 (>= 0.0.7) Suggests: AnnotationHub, knitr, jsonlite, readr, rmarkdown, org.Hs.eg.db, prettydoc, BiocManager, testthat License: Artistic-2.0 MD5sum: 87b3721181f5bbb891652577918d530b 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] (), Li-Gen Wang [ctb], Erqiang Hu [ctb], Xiao Luo [ctb], Meijun Chen [ctb], Giovanni Dall'Olio [ctb], Wanqian Wei [ctb], Chun-Hui Gao [ctb] () Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ (docs), https://doi.org/10.1016/j.xinn.2021.100141 (paper) VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/clusterProfiler/issues git_url: https://git.bioconductor.org/packages/clusterProfiler git_branch: devel git_last_commit: 3dc99f8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/clusterProfiler_4.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/clusterProfiler_4.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/clusterProfiler_4.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/clusterProfiler_4.13.0.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, CaMutQC, CBNplot, CEMiTool, CeTF, debrowser, EasyCellType, epiregulon.extra, esATAC, famat, GDCRNATools, gINTomics, goSorensen, MAGeCKFlute, MetaPhOR, methylGSA, MicrobiomeProfiler, miRSM, Moonlight2R, MoonlightR, PanomiR, pathlinkR, Pigengene, seqArchRplus, signatureSearch, ExpHunterSuite, recountWorkflow, TCGAWorkflow, DRviaSPCN, Grouphmap, immcp, pathwayTMB, PMAPscore, RVA, TOmicsVis suggestsMe: ChIPseeker, cola, DAPAR, DOSE, enrichplot, EpiMix, GeDi, GeneTonic, GenomicSuperSignature, GeoTcgaData, ggkegg, GOSemSim, GRaNIE, GSEAmining, mastR, MesKit, ReactomePA, rrvgo, scFeatures, scGPS, simplifyEnrichment, TCGAbiolinks, tidybulk, vsclust, org.Mxanthus.db, aPEAR, GeneSelectR, grandR, MARVEL, OlinkAnalyze, ReporterScore, SCpubr dependencyCount: 128 Package: clusterSeq Version: 1.29.0 Depends: R (>= 3.0.0), methods, BiocParallel, baySeq, graphics, stats, utils Imports: BiocGenerics Suggests: BiocStyle License: GPL-3 MD5sum: 211009281cd56a36f04aa6b1175a7d1f 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] () Maintainer: Samuel Granjeaud 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: cfc065b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/clusterSeq_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/clusterSeq_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/clusterSeq_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/clusterSeq_1.29.0.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: 43 Package: ClusterSignificance Version: 1.33.0 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: 46f7d269c628f980c90ceffd2762d9cc 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 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: 03f7b4f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ClusterSignificance_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ClusterSignificance_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ClusterSignificance_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ClusterSignificance_1.33.0.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: 1.77.0 Depends: Biobase (>= 1.4.22), R (>= 1.9.0), methods Suggests: fibroEset, genefilter License: Artistic-2.0 MD5sum: a5f01bd818c0408735590694c5086cf2 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 git_url: https://git.bioconductor.org/packages/clusterStab git_branch: devel git_last_commit: c30003b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/clusterStab_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/clusterStab_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/clusterStab_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/clusterStab_1.77.0.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: 6 Package: clustifyr Version: 1.17.0 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: ec483d85619687603e88aa8b2508778e 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 [aut], Kent Riemondy [cre, 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: Kent Riemondy 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: f621538 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/clustifyr_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/clustifyr_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/clustifyr_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/clustifyr_1.17.0.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: 99 Package: ClustIRR Version: 1.3.0 Depends: R (>= 4.3.0) Imports: stringdist, methods, stats, utils, igraph, visNetwork, blaster, pwalign, grDevices, parallel Suggests: BiocStyle, knitr, testthat, ggplot2, patchwork, ggrepel License: GPL-3 + file LICENSE MD5sum: f8c96200e445eec0f22622d7c260eb97 NeedsCompilation: no Title: Clustering of immune receptor repertoires Description: ClustIRR is a quantitative method for clustering of immune receptor repertoires (IRRs). The algorithm identifies groups of T or B cell receptors (TCRs or BCRs) with possibly similar specificity directly from the sequences of their complementarity determining regions. ClustIRR uses graphs to visualize the specificity structures of IRRs. biocViews: Clustering, ImmunoOncology, SingleCell, Software, Classification Author: Simo Kitanovski [aut, cre] (), Kai Wollek [aut] () Maintainer: Simo Kitanovski URL: https://github.com/snaketron/ClustIRR VignetteBuilder: knitr BugReports: https://github.com/snaketron/ClustIRR/issues git_url: https://git.bioconductor.org/packages/ClustIRR git_branch: devel git_last_commit: bc22fed git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ClustIRR_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ClustIRR_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ClustIRR_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ClustIRR_1.3.0.tgz vignettes: vignettes/ClustIRR/inst/doc/User_manual.html vignetteTitles: Introduction to ClustIRR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ClustIRR/inst/doc/User_manual.R dependencyCount: 63 Package: CMA Version: 1.63.0 Depends: R (>= 2.10), methods, stats, Biobase Suggests: MASS, class, nnet, glmnet, e1071, randomForest, plsgenomics, gbm, mgcv, corpcor, limma, st, mvtnorm License: GPL (>= 2) MD5sum: 3201ada3200195e2357c446c457a0d7f 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 , Anne-Laure Boulesteix , Christoph Bernau . Maintainer: Roman Hornung git_url: https://git.bioconductor.org/packages/CMA git_branch: devel git_last_commit: 6991900 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CMA_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CMA_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CMA_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CMA_1.63.0.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: 6 Package: cmapR Version: 1.17.0 Depends: R (>= 4.0) Imports: methods, rhdf5, data.table, flowCore, SummarizedExperiment, matrixStats Suggests: knitr, testthat, BiocStyle, rmarkdown License: file LICENSE MD5sum: 9bb0ce7c293ec1a2f1f71a8c735acb4b 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] () Maintainer: Ted Natoli 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: f48edb8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cmapR_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cmapR_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cmapR_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cmapR_1.17.0.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: 1.53.0 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) Archs: x64 MD5sum: 5976810d2d04a80ccf59b6758c570178 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 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: c0d7cd9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cn.farms_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cn.farms_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cn.farms_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cn.farms_1.53.0.tgz 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: 1.51.0 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: 6a402e8f89c871bb120055cfb26c8d8d 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 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: ee78d2e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cn.mops_1.51.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cn.mops_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cn.mops_1.51.0.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: 1.51.0 Depends: R (>= 2.10.1), methods Imports: DNAcopy License: GPL-2 Archs: x64 MD5sum: 9979a3ad86406d77c9c3867c798e7e05 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 , Henry M. Wood , Arief Gusnanto Maintainer: Stefano Berri URL: http://www.r-project.org, git_url: https://git.bioconductor.org/packages/CNAnorm git_branch: devel git_last_commit: e3f8a1c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CNAnorm_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CNAnorm_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CNAnorm_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CNAnorm_1.51.0.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: 1.41.0 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: 9b9a56bfa3e6b5464683c359f6896b2e 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 Maintainer: Ge Tan 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: cd329da git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CNEr_1.41.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CNEr_1.41.0.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 importsMe: TFBSTools dependencyCount: 118 Package: CNORdt Version: 1.47.0 Depends: R (>= 1.8.0), CellNOptR (>= 0.99), abind License: GPL-2 Archs: x64 MD5sum: 271053c4c84c9a4c11d1cf873ab08c78 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 git_url: https://git.bioconductor.org/packages/CNORdt git_branch: devel git_last_commit: b218001 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CNORdt_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CNORdt_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CNORdt_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CNORdt_1.47.0.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: 72 Package: CNORfeeder Version: 1.45.0 Depends: R (>= 3.6.0), CellNOptR (>= 1.4.0), graph Suggests: minet, Rgraphviz, RUnit, BiocGenerics, igraph Enhances: MEIGOR License: GPL-3 MD5sum: c1a95e3303335df544082e9ed17076c7 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], Enio Gjerga [ctb], Attila Gabor [cre] Maintainer: Attila Gabor git_url: https://git.bioconductor.org/packages/CNORfeeder git_branch: devel git_last_commit: 49fc8d1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CNORfeeder_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CNORfeeder_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CNORfeeder_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CNORfeeder_1.45.0.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: 71 Package: CNORfuzzy Version: 1.47.0 Depends: R (>= 2.15.0), CellNOptR (>= 1.4.0), nloptr (>= 0.8.5) Suggests: xtable, Rgraphviz, RUnit, BiocGenerics License: GPL-2 Archs: x64 MD5sum: a2ed9ee478742ca0281d238b6be3c616 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 git_url: https://git.bioconductor.org/packages/CNORfuzzy git_branch: devel git_last_commit: ea2b2dc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CNORfuzzy_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CNORfuzzy_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CNORfuzzy_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CNORfuzzy_1.47.0.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: 86 Package: CNORode Version: 1.47.0 Depends: CellNOptR, genalg Suggests: knitr, rmarkdown Enhances: doParallel, foreach License: GPL-2 Archs: x64 MD5sum: d88a2a1bf466bc7937644c0db523d9ea 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNORode git_branch: devel git_last_commit: b6b1538 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CNORode_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CNORode_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CNORode_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CNORode_1.47.0.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: 72 Package: CNTools Version: 1.61.0 Depends: R (>= 2.10), methods, tools, stats, genefilter License: LGPL Archs: x64 MD5sum: b455a8f74c94194de4177b854b49d3bd 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 git_url: https://git.bioconductor.org/packages/CNTools git_branch: devel git_last_commit: 8b8b7ab git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CNTools_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CNTools_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CNTools_1.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CNTools_1.61.0.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: 1.19.0 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: 33e80ed56be44fc9bf8d36dbeb26fefa 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] (), Bernat Gel [aut] Maintainer: Jose Marcos Moreno-Cabrera 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: f665198 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CNVfilteR_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CNVfilteR_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CNVfilteR_1.19.0.tgz 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: 148 Package: cnvGSA Version: 1.49.0 Depends: brglm, doParallel, foreach, GenomicRanges, methods, splitstackshape Suggests: cnvGSAdata, org.Hs.eg.db License: LGPL MD5sum: 45e64e849cf8791a0117fde659e95440 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 , Robert Ziman ; packaged by Joseph Lugo Maintainer: Joseph Lugo git_url: https://git.bioconductor.org/packages/cnvGSA git_branch: devel git_last_commit: 82dcad7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cnvGSA_1.49.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cnvGSA_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cnvGSA_1.49.0.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: 1.13.0 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: 0b8b6f82ab03528f83182313473efc25 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNViz git_branch: devel git_last_commit: c757067 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CNViz_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CNViz_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CNViz_1.13.0.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: 1.9.0 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: 10ffe249038a8df767a6f9955d5b89dc 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] (), Pascal Belleau [aut] (), David A. Tuveson [aut] (), Alexander Krasnitz [aut] Maintainer: Astrid Deschênes 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: 436b355 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CNVMetrics_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CNVMetrics_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CNVMetrics_1.9.0.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: 1.37.0 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: b4125be8dc0aeb557d5b485475756ded 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNVPanelizer git_branch: devel git_last_commit: d027772 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CNVPanelizer_1.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CNVPanelizer_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CNVPanelizer_1.37.0.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: 118 Package: CNVRanger Version: 1.21.0 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: 1efed2108f01b1e799b6d0e709988238 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 VignetteBuilder: knitr BugReports: https://github.com/waldronlab/CNVRanger/issues git_url: https://git.bioconductor.org/packages/CNVRanger git_branch: devel git_last_commit: aa23032 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CNVRanger_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CNVRanger_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CNVRanger_1.21.0.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: 73 Package: CNVrd2 Version: 1.43.0 Depends: R (>= 3.0.0), methods, VariantAnnotation, parallel, rjags, ggplot2, gridExtra Imports: DNAcopy, IRanges, Rsamtools Suggests: knitr License: GPL-2 MD5sum: 3f324b914922d480d4e4bad0feacfa8c 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 URL: https://github.com/hoangtn/CNVrd2 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNVrd2 git_branch: devel git_last_commit: fb16ed9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CNVrd2_1.43.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CNVrd2_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CNVrd2_1.43.0.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: 1.77.0 Depends: R (>= 2.0), org.Hs.eg.db Imports: AnnotationDbi License: CPL MD5sum: 786c10ca028bc7ae6afdeed0fd66b4ca 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 git_url: https://git.bioconductor.org/packages/CoCiteStats git_branch: devel git_last_commit: 6cfab8b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CoCiteStats_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CoCiteStats_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CoCiteStats_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CoCiteStats_1.77.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 46 Package: COCOA Version: 2.19.0 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: 6735157701581d3d018663db706642aa 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 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: b96f068 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/COCOA_2.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/COCOA_2.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/COCOA_2.19.0.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: 126 Package: codelink Version: 1.73.0 Depends: R (>= 2.10), BiocGenerics (>= 0.3.2), methods, Biobase (>= 2.17.8), limma Imports: annotate Suggests: genefilter, parallel, knitr License: GPL-2 MD5sum: 536a1ad1e52dd34365c1b6830aebfd04 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 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: ebb68ab git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/codelink_1.73.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/codelink_1.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/codelink_1.73.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/codelink_1.73.0.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: 1.37.0 Depends: R (>= 3.2.3), Rsamtools, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19, IRanges, Biostrings, S4Vectors Suggests: WES.1KG.WUGSC License: GPL-2 MD5sum: e6658d8757e16c0bceb36cc3a0738d23 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 git_url: https://git.bioconductor.org/packages/CODEX git_branch: devel git_last_commit: bb24adc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CODEX_1.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CODEX_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CODEX_1.37.0.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: 3.25.0 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 Suggests: testthat, knitr, rmarkdown, BiocStyle, SeuratObject, BiocFileCache License: BSD_3_clause + file LICENSE Archs: x64 MD5sum: c17300899aef74fee90fe8fc5fcf7a21 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 , Thomas D. Sherman , Jeanette Johnson , Dmitrijs Lvovs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoGAPS git_branch: devel git_last_commit: 5e91cbe git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CoGAPS_3.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CoGAPS_3.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CoGAPS_3.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CoGAPS_3.25.0.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 dependsOnMe: ATACCoGAPS suggestsMe: projectR, SpaceMarkers dependencyCount: 91 Package: cogena Version: 1.39.0 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 MD5sum: e03b401a69932ae178ff02468f1084fa 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 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: a43d1af git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cogena_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cogena_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cogena_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cogena_1.39.0.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: 148 Package: cogeqc Version: 1.9.0 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: c97a0ddfd1a504f3591835775e7f6618 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: Fabrício Almeida-Silva [aut, cre] (), Yves Van de Peer [aut] () Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/cogeqc SystemRequirements: BUSCO (>= 5.1.3) VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/cogeqc git_url: https://git.bioconductor.org/packages/cogeqc git_branch: devel git_last_commit: 88dde65 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cogeqc_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cogeqc_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cogeqc_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cogeqc_1.9.0.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: 86 Package: Cogito Version: 1.11.0 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: 27ac3762b566c695cad3ffb243962285 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Cogito git_branch: devel git_last_commit: acf728d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Cogito_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Cogito_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Cogito_1.11.0.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: 1.49.0 Depends: R (>= 2.13.0) Imports: graphics, grDevices Suggests: limma License: GPL-2 MD5sum: f3df14621b0ef3de26edff26e5ecba3b 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 git_url: https://git.bioconductor.org/packages/coGPS git_branch: devel git_last_commit: 7fe07e0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/coGPS_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/coGPS_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/coGPS_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/coGPS_1.49.0.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: 2.11.0 Depends: R (>= 4.0.0) Imports: grDevices, graphics, grid, stats, utils, ComplexHeatmap (>= 2.5.4), matrixStats, 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 Archs: x64 MD5sum: 0f2651c8654e60ca8c5da6a126bf7c6c 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] () Maintainer: Zuguang Gu 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: 4d5e322 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cola_2.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cola_2.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cola_2.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cola_2.11.0.tgz vignettes: vignettes/cola/inst/doc/cola.html vignetteTitles: Use of cola hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: InteractiveComplexHeatmap, simplifyEnrichment dependencyCount: 65 Package: comapr Version: 1.9.0 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 MD5sum: 84e4969ddff65b6417fceb1dc52b007a 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] () Maintainer: Ruqian Lyu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/comapr git_branch: devel git_last_commit: 68eb288 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/comapr_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/comapr_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/comapr_1.9.0.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: 168 Package: combi Version: 1.17.0 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: 7e06cbd36d7907be7b81e4d8ee7067d7 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] () Maintainer: Stijn Hawinkel VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/combi/issues git_url: https://git.bioconductor.org/packages/combi git_branch: devel git_last_commit: 125ac63 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/combi_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/combi_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/combi_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/combi_1.17.0.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: coMET Version: 1.37.0 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) MD5sum: 1bc490fa69fa0bae7bbe1303b0cc4caf 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 URL: http://epigen.kcl.ac.uk/comet VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/coMET git_branch: devel git_last_commit: d78add3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/coMET_1.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/coMET_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/coMET_1.37.0.tgz vignettes: vignettes/coMET/inst/doc/coMET.pdf vignetteTitles: coMET users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/coMET/inst/doc/coMET.R Package: coMethDMR Version: 1.9.0 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: be4b774354b68787914f79226d9fd312 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 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: dcdc135 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/coMethDMR_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/coMethDMR_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/coMethDMR_1.9.0.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: 144 Package: COMPASS Version: 1.43.0 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 Archs: x64 MD5sum: 155eb7eff63c35b08ca6df24b3c80e8e 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 VignetteBuilder: knitr BugReports: https://github.com/RGLab/COMPASS/issues git_url: https://git.bioconductor.org/packages/COMPASS git_branch: devel git_last_commit: a127bb6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-03 source.ver: src/contrib/COMPASS_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/COMPASS_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/COMPASS_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/COMPASS_1.43.0.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: 1.41.0 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) MD5sum: 2cb2391676033f559512f3ca67fa6b29 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] (), Paul Bastide [aut] (), Mélina Gallopin [aut] () Maintainer: Charlotte Soneson 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: f54678b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/compcodeR_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/compcodeR_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/compcodeR_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/compcodeR_1.41.0.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: 106 Package: compEpiTools Version: 1.39.0 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: bbc366d74a2c48bdd7db0fd0ac864030 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/compEpiTools git_branch: devel git_last_commit: 8c7ce3c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/compEpiTools_1.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/compEpiTools_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/compEpiTools_1.39.0.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: 169 Package: ComplexHeatmap Version: 2.21.0 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: ec08b45bad3f691f86583400f8d35f1a 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] () Maintainer: Zuguang Gu 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: 1e8215b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ComplexHeatmap_2.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ComplexHeatmap_2.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ComplexHeatmap_2.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ComplexHeatmap_2.21.0.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, BiocOncoTK, BioNERO, blacksheepr, BloodGen3Module, CATALYST, CCPlotR, celda, CeTF, ClustAll, COCOA, cola, COTAN, CRISPRball, CTexploreR, cytoKernel, dar, DEGreport, DEP, diffcyt, diffUTR, dinoR, ELMER, epiregulon.extra, fCCAC, FLAMES, gCrisprTools, GeDi, GeneTonic, GenomicPlot, GenomicSuperSignature, gINTomics, gmoviz, GRaNIE, hermes, hoodscanR, HybridExpress, InterCellar, iSEE, MAPFX, MatrixQCvis, MesKit, microbiomeMarker, MOMA, monaLisa, Moonlight2R, MultiRNAflow, muscat, musicatk, MWASTools, nipalsMCIA, pathlinkR, PathoStat, PeacoQC, pipeComp, POMA, profileplyr, scRNAseqApp, segmenter, signifinder, simona, simplifyEnrichment, singleCellTK, sparrow, TBSignatureProfiler, Xeva, YAPSA, TCGAWorkflow, armada, autoGO, bulkAnalyseR, coda4microbiome, conos, GSSTDA, inferCSN, karyotapR, mineSweepR, missoNet, MitoHEAR, MKomics, ogrdbstats, pkgndep, rKOMICS, rliger, RNAseqQC, RVA, scITD, sigQC, tidyHeatmap, TOmicsVis, visxhclust, wilson suggestsMe: artMS, bambu, clustifyr, CNVRanger, demuxSNP, dittoSeq, EnrichmentBrowser, gtrellis, HilbertCurve, mastR, msImpute, pareg, plotgardener, projectR, QFeatures, raer, scDblFinder, SPIAT, TCGAbiolinks, TCGAutils, weitrix, curatedPCaData, NanoporeRNASeq, BeeBDC, CIARA, circlize, eclust, ggpicrust2, ggsector, grandR, IOHanalyzer, MOSS, multipanelfigure, scCustomize, SCpubr, singleCellHaystack, SpatialDDLS, spiralize dependencyCount: 28 Package: CompoundDb Version: 1.9.0 Depends: R (>= 4.1), methods, AnnotationFilter, S4Vectors Imports: BiocGenerics, ChemmineR, tibble, jsonlite, dplyr, DBI, dbplyr, RSQLite, Biobase, ProtGenerics (>= 1.35.3), xml2, IRanges, Spectra (>= 1.9.12), MsCoreUtils, MetaboCoreUtils, BiocParallel Suggests: knitr, rmarkdown, testthat, BiocStyle (>= 2.5.19), MsBackendMgf License: Artistic-2.0 MD5sum: fa24cb59fe393b986a2f89973aee13c6 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] (), Johannes Rainer [aut, cre] (), Josep M. Badia [ctb] (), Roger Gine [aut] (), Andrea Vicini [aut] () Maintainer: Johannes Rainer 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: 7c2ef96 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CompoundDb_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CompoundDb_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CompoundDb_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CompoundDb_1.9.0.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 dependencyCount: 120 Package: ComPrAn Version: 1.13.0 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: d998a57b2f4db4a9b480925fab4fa7e4 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] () Maintainer: Petra Palenikova VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ComPrAn git_branch: devel git_last_commit: 871e9f4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ComPrAn_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ComPrAn_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ComPrAn_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ComPrAn_1.13.0.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: 1.3.0 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: 494b8dbcbcfc104caa6f30f771c1f550 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] (), Ella Sampson [aut], Rhea Rodrigues [aut] (), Gyorgy Paragh [aut] () Maintainer: Sydney Grant 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: 29cb294 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/compSPOT_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/compSPOT_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/compSPOT_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/compSPOT_1.3.0.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: 121 Package: concordexR Version: 1.5.0 Depends: R (>= 4.2) Imports: BiocParallel, cli, DelayedArray, ggplot2, Matrix, methods, pheatmap, rlang, scales Suggests: BiocNeighbors, BiocStyle, bluster, covr, knitr, patchwork, rmarkdown, scater, TENxPBMCData, testthat (>= 3.0.0), vdiffr License: Artistic-2.0 MD5sum: f9589e18c6f70b4d8f20ce51177f930a NeedsCompilation: no Title: Calculate the concordex coefficient Description: Many analysis workflows include approximation of a nearest neighbors graph followed by clustering of the graph structure. The concordex coefficient estimates the concordance between the graph and clustering results. The package 'concordexR' is an R implementation of the original concordex Python-based command line tool. biocViews: SingleCell, Clustering, GraphAndNetwork Author: Kayla Jackson [aut, cre] (), A. Sina Booeshaghi [aut] (), Angel Galvez-Merchan [aut] (), Lambda Moses [aut] (), Laura Luebbert [ctb] (), Lior Pachter [aut, rev, ths] () Maintainer: Kayla Jackson 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: 281397f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/concordexR_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/concordexR_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/concordexR_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/concordexR_1.5.0.tgz vignettes: vignettes/concordexR/inst/doc/concordex-demo.html, vignettes/concordexR/inst/doc/overview.html vignetteTitles: concordex-demo, overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/concordexR/inst/doc/concordex-demo.R, vignettes/concordexR/inst/doc/overview.R dependencyCount: 60 Package: condiments Version: 1.13.0 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 License: MIT + file LICENSE MD5sum: 575fd222d20f7f6361a6fc755959dfeb 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] (), Koen Van den Berge [aut, ctb], Kelly Street [aut, ctb] Maintainer: Hector Roux de Bezieux 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: 38d7d61 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/condiments_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/condiments_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/condiments_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/condiments_1.13.0.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: 182 Package: CONFESS Version: 1.33.0 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: 66a5ee12ebca11644db43e30aa307929 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CONFESS git_branch: devel git_last_commit: a992203 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CONFESS_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CONFESS_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CONFESS_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CONFESS_1.33.0.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: 151 Package: consensus Version: 1.23.0 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: d70b66ae41b81e84121d295a489f0497 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/consensus git_branch: devel git_last_commit: 3f398e9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/consensus_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/consensus_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/consensus_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/consensus_1.23.0.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: 1.69.0 Imports: Biobase, ALL, graphics, stats, utils, cluster License: GPL version 2 MD5sum: e7360ce0d2a383061fb6542be9341e31 NeedsCompilation: no Title: ConsensusClusterPlus Description: algorithm for determining cluster count and membership by stability evidence in unsupervised analysis biocViews: Software, Clustering Author: Matt Wilkerson , Peter Waltman Maintainer: Matt Wilkerson git_url: https://git.bioconductor.org/packages/ConsensusClusterPlus git_branch: devel git_last_commit: 8bd7fdf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ConsensusClusterPlus_1.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ConsensusClusterPlus_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ConsensusClusterPlus_1.69.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ConsensusClusterPlus_1.69.0.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, ccml, iSubGen, longmixr, neatmaps, scRNAtools suggestsMe: TCGAbiolinks dependencyCount: 9 Package: consensusDE Version: 1.23.0 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: edc0b3235966dfb5533ffdb73fd72a2d 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/consensusDE git_branch: devel git_last_commit: 1479d01 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/consensusDE_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/consensusDE_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/consensusDE_1.23.0.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: 151 Package: consensusOV Version: 1.27.0 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: 1591b01ddf410765464de44489a9e552 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 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: 7b6f2d8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/consensusOV_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/consensusOV_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/consensusOV_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/consensusOV_1.27.0.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: 153 Package: consensusSeekeR Version: 1.33.0 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: d3cad2164aca943d28992fa78233bb4e 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] (), Fabien Claude Lamaze [ctb], Pascal Belleau [aut] (), Arnaud Droit [aut] Maintainer: Astrid Deschênes 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: b3d93cc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/consensusSeekeR_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/consensusSeekeR_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/consensusSeekeR_1.33.0.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 dependencyCount: 66 Package: consICA Version: 2.3.0 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: a6a37e87c37460fd9935ac8bff764a37 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] (), Tony Kaoma [aut] (), Maryna Chepeleva [aut] () Maintainer: Petr V. Nazarov VignetteBuilder: knitr BugReports: https://github.com/biomod-lih/consICA/issues git_url: https://git.bioconductor.org/packages/consICA git_branch: devel git_last_commit: 6227be0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/consICA_2.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/consICA_2.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/consICA_2.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/consICA_2.3.0.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: 100 Package: CONSTANd Version: 1.13.0 Depends: R (>= 4.1) Suggests: BiocStyle, knitr, rmarkdown, tidyr, ggplot2, gridExtra, magick, Cairo, limma License: file LICENSE MD5sum: 31f1fb5097d4771958dc01b81cb19cb7 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 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: 3906c17 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CONSTANd_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CONSTANd_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CONSTANd_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CONSTANd_1.13.0.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: 1.39.0 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: adcf3252faa835bbbe1bf0e81ce8e46b 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/conumee git_branch: devel git_last_commit: 07777b1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/conumee_1.39.0.tar.gz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/conumee_1.39.0.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: 145 Package: convert Version: 1.81.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.33), limma (>= 1.7.0), marray, utils, methods License: LGPL MD5sum: 31cd84da079cbe1366f9154bb894299c NeedsCompilation: no Title: Convert Microarray Data Objects Description: Define coerce methods for microarray data objects. biocViews: Infrastructure, Microarray, TwoChannel Author: Gordon Smyth , James Wettenhall , Yee Hwa (Jean Yang) , Martin Morgan Maintainer: Yee Hwa (Jean) Yang URL: http://bioinf.wehi.edu.au/limma/convert.html git_url: https://git.bioconductor.org/packages/convert git_branch: devel git_last_commit: 1edec05 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/convert_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/convert_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/convert_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/convert_1.81.0.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: 10 Package: copa Version: 1.73.0 Depends: Biobase, methods Suggests: colonCA License: Artistic-2.0 Archs: x64 MD5sum: 6bb42aeb06b57d8b2489d563c892c987 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 git_url: https://git.bioconductor.org/packages/copa git_branch: devel git_last_commit: 1297686 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/copa_1.73.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/copa_1.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/copa_1.73.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/copa_1.73.0.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: 6 Package: CopyNumberPlots Version: 1.21.0 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: 6b9ac11f234578a85c7eba772ee6137f 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 and Miriam Magallon Maintainer: Bernat Gel 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: f3b8d6f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CopyNumberPlots_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CopyNumberPlots_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CopyNumberPlots_1.21.0.tgz 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: 145 Package: coRdon Version: 1.23.0 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 MD5sum: 3689bf1643079154a5bfc256026fab01 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 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: 225be74 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/coRdon_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/coRdon_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/coRdon_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/coRdon_1.23.0.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: 62 Package: CoreGx Version: 2.9.0 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: 8bca38e2b4bdfe18a52ff06dfd5c562d 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) . Manem, V., Labie, M., Smirnov, P., Kofia, V., Freeman, M., Koritzinksy, M., Abazeed, M., Haibe-Kains, B., Bratman, S. (2018) . 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoreGx git_branch: devel git_last_commit: f467415 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CoreGx_2.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CoreGx_2.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CoreGx_2.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CoreGx_2.9.0.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: 140 Package: Cormotif Version: 1.51.0 Depends: R (>= 2.12.0), affy, limma Imports: affy, graphics, grDevices License: GPL-2 MD5sum: 00782850ad5653ba8e5d0a029afce74b 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 git_url: https://git.bioconductor.org/packages/Cormotif git_branch: devel git_last_commit: c4ec20c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Cormotif_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Cormotif_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Cormotif_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Cormotif_1.51.0.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: 1.15.0 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: 6d43cbe4e16152b054e24435d8fb3b34 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] (), Aedin Culhane [aut] () Maintainer: Lauren Hsu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/corral git_branch: devel git_last_commit: 9b176e9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/corral_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/corral_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/corral_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/corral_1.15.0.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: 86 Package: coseq Version: 1.29.0 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: 39b5f1a3842cb4a7b57e2c5151a04dc8 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] (), Cathy Maugis-Rabusseau [ctb], Antoine Godichon-Baggioni [ctb] Maintainer: Andrea Rau VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/coseq git_branch: devel git_last_commit: c3db3ad git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/coseq_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/coseq_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/coseq_1.29.0.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: cosmiq Version: 1.39.0 Depends: R (>= 3.6), Rcpp Imports: pracma, xcms, MassSpecWavelet, faahKO Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 Archs: x64 MD5sum: e32a5c9e61ed9d63edcdcb146dc0571a 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] (), Endre Laczko [ctb] Maintainer: David Fischer 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: dfffadb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cosmiq_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cosmiq_1.39.0.zip 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: COSNet Version: 1.39.0 Suggests: bionetdata, PerfMeas, RUnit, BiocGenerics License: GPL (>= 2) Archs: x64 MD5sum: b3416a589ccfb32a3211da8254ae3dcd 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 URL: https://github.com/m1frasca/COSNet_GitHub git_url: https://git.bioconductor.org/packages/COSNet git_branch: devel git_last_commit: 1373425 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/COSNet_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/COSNet_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/COSNet_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/COSNet_1.39.0.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: 2.3.6 Depends: R (>= 4.2) Imports: stats, plyr, dplyr, methods, grDevices, Matrix, ggplot2, ggrepel, ggthemes, graphics, parallel, parallelly, tibble, tidyr, BiocSingular, PCAtools, parallelDist, ComplexHeatmap, circlize, grid, scales, RColorBrewer, utils, rlang, Rfast, stringr, Seurat, umap, dendextend, zeallot, assertthat, withr Suggests: testthat (>= 3.0.0), proto, spelling, knitr, data.table, gsubfn, R.utils, tidyverse, rmarkdown, htmlwidgets, MASS, Rtsne, plotly, BiocStyle, cowplot, qpdf, GEOquery, sf License: GPL-3 MD5sum: 83426c590b87de13fa42e98f355fc932 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] (), Morandin Francesco [aut] (), Fantozzi Marco [aut] (), Pietrosanto Marco [aut] (), Puttini Daniel [aut] (), Priami Corrado [aut] (), Cremisi Federico [aut] (), Helmer-Citterich Manuela [aut] () Maintainer: Galfrè Silvia Giulia 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: e8671db git_last_commit_date: 2024-04-15 Date/Publication: 2024-04-29 source.ver: src/contrib/COTAN_2.3.6.tar.gz win.binary.ver: bin/windows/contrib/4.4/COTAN_2.3.6.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/COTAN_2.3.6.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/COTAN_2.3.6.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: 199 Package: countsimQC Version: 1.23.0 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: c867291d140dc88ff7065c4b0178777b 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] () Maintainer: Charlotte Soneson 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: 5d1081c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/countsimQC_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/countsimQC_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/countsimQC_1.23.0.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: 133 Package: covEB Version: 1.31.0 Depends: R (>= 3.3), mvtnorm, igraph, gsl, Biobase, stats, LaplacesDemon, Matrix Suggests: curatedBladderData License: GPL-3 MD5sum: bce77479b97ddde557ffe180c01eda43 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 git_url: https://git.bioconductor.org/packages/covEB git_branch: devel git_last_commit: de8cfa0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/covEB_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/covEB_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/covEB_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/covEB_1.31.0.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: 23 Package: CoverageView Version: 1.43.0 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: 56ec74c97aa498e89280e7951c7f8f43 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 git_url: https://git.bioconductor.org/packages/CoverageView git_branch: devel git_last_commit: 2a804ff git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CoverageView_1.43.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CoverageView_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CoverageView_1.43.0.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: 1.31.0 Depends: ade4, Biobase Imports: parallel, genefilter, grDevices, stats, graphics Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 0f17d7873498a8cc8c75a9a37b49e0fa 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 Maintainer: Lara Urban VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/covRNA git_branch: devel git_last_commit: c8ee5dd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/covRNA_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/covRNA_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/covRNA_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/covRNA_1.31.0.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: cpvSNP Version: 1.37.0 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: af1eb64352b88124e35065b8229eb093 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 git_url: https://git.bioconductor.org/packages/cpvSNP git_branch: devel git_last_commit: f0d2886 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cpvSNP_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cpvSNP_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cpvSNP_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cpvSNP_1.37.0.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: 1.51.0 Depends: R (>= 2.10.0), mclust, nor1mix, stats, preprocessCore, splines, quantreg Imports: splines Suggests: scales, edgeR License: Artistic-2.0 MD5sum: 773fc33a635fd87f4b689436d7a1a6c4 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 git_url: https://git.bioconductor.org/packages/cqn git_branch: devel git_last_commit: 46d54ee git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cqn_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cqn_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cqn_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cqn_1.51.0.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 suggestsMe: extraChIPs dependencyCount: 17 Package: CRImage Version: 1.53.0 Depends: EBImage, DNAcopy, aCGH Imports: MASS, e1071, foreach, sgeostat License: Artistic-2.0 MD5sum: 8ad9f624fe18ca293f4a2373fede6ca0 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 , Yinyin Yuan , Oscar Rueda , Florian Markowetz Maintainer: Henrik Failmezger , Yinyin Yuan git_url: https://git.bioconductor.org/packages/CRImage git_branch: devel git_last_commit: 8563bd5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CRImage_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CRImage_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CRImage_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CRImage_1.53.0.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: 62 Package: CRISPRball Version: 1.1.0 Depends: R (>= 4.4.0), shinyBS Imports: DT, shiny, grid, ComplexHeatmap, InteractiveComplexHeatmap, graphics, stats, ggplot2, plotly, shinyWidgets, shinycssloaders, shinyjqui, dittoSeq, matrixStats, colourpicker, shinyjs, MAGeCKFlute, circlize, PCAtools, utils, grDevices, htmlwidgets, methods Suggests: BiocStyle, msigdbr, depmap, pool, RSQLite, mygene, testthat (>= 3.0.0), knitr, rmarkdown License: MIT + file LICENSE MD5sum: f9f6c09ce1e351071adcc45f675eb33e 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] (), Jacob Steele [ctb] () Maintainer: Jared Andrews 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: 429fdab git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CRISPRball_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CRISPRball_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CRISPRball_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CRISPRball_1.1.0.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: 221 Package: crisprBase Version: 1.9.0 Depends: utils, methods, R (>= 4.1) Imports: BiocGenerics, Biostrings, GenomicRanges, graphics, IRanges, S4Vectors, stringr Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: a9ec690d63609483bd3e9a001f6c0a29 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 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: 1cc9791 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/crisprBase_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/crisprBase_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/crisprBase_1.9.0.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: 1.9.0 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: 44fb98f9a93cf557cc284da7eb15b4d7 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 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: c78e8a1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/crisprBowtie_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/crisprBowtie_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/crisprBowtie_1.9.0.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: 1.9.0 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: 84dfbef1fb757a06e676b209c259ee2d 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 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: d4684ac git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/crisprBwa_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/crisprBwa_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/crisprBwa_1.9.0.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: 1.7.0 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: e8392c06eaf021e9caf3194f933c87c0 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 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: 00a6fdc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/crisprDesign_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/crisprDesign_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/crisprDesign_1.7.0.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: 126 Package: crisprScore Version: 1.9.0 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: 711a3a467c6d5e817bfd60e2ef5dbb47 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 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: 5d52cdb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/crisprScore_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/crisprScore_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/crisprScore_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/crisprScore_1.9.0.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: 81 Package: CRISPRseek Version: 1.45.0 Depends: R (>= 3.5.0), BiocGenerics, Biostrings Imports: parallel, data.table, seqinr, S4Vectors (>= 0.9.25), IRanges, BSgenome, hash, methods,reticulate,rhdf5,XVector, DelayedArray, GenomeInfoDb, GenomicRanges, dplyr, keras, mltools 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, testthat License: GPL (>= 2) MD5sum: 6fa05e29a10e68f111043a2fa5bca76b NeedsCompilation: no Title: Design of target-specific guide RNAs in CRISPR-Cas9, genome-editing systems Description: The package includes functions to find potential guide RNAs for the CRISPR editing system including Base Editors and the Prime Editor for input target sequences, optionally filter guide RNAs without restriction enzyme cut site, or without paired guide RNAs, genome-wide search for off-targets, score, rank, fetch flank sequence and indicate whether the target and off-targets are located in exon region or not. Potential guide RNAs are annotated with total score of the top5 and topN off-targets, detailed topN mismatch sites, restriction enzyme cut sites, and paired guide RNAs. The package also output 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 and Michael Brodsky Maintainer: Lihua Julie Zhu git_url: https://git.bioconductor.org/packages/CRISPRseek git_branch: devel git_last_commit: d63723f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CRISPRseek_1.45.0.tar.gz vignettes: vignettes/CRISPRseek/inst/doc/CRISPRseek.pdf vignetteTitles: CRISPRseek Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CRISPRseek/inst/doc/CRISPRseek.R importsMe: GUIDEseq, multicrispr dependencyCount: 107 Package: crisprShiny Version: 1.1.0 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: 4a45bc0fc91b394a6f0cc71aa9a92acf 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 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: 90d8860 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/crisprShiny_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/crisprShiny_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/crisprShiny_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/crisprShiny_1.1.0.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: 194 Package: CrispRVariants Version: 1.33.0 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: c495878214da0b8fcb301536c845f861 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CrispRVariants git_branch: devel git_last_commit: e5a758e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CrispRVariants_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CrispRVariants_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CrispRVariants_1.33.0.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: 1.7.0 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: f6c230031dec995d230615b048f4f8a5 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 . biocViews: CRISPR, FunctionalGenomics, GeneTarget Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin 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: 2cac05e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/crisprVerse_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/crisprVerse_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/crisprVerse_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/crisprVerse_1.5.0.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: 181 Package: crisprViz Version: 1.7.0 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: 570a5876130b4ccea8cce9a1a30a7c5a 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 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: d9192ad git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/crisprViz_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/crisprViz_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/crisprViz_1.7.0.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: 180 Package: crlmm Version: 1.63.0 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 Archs: x64 MD5sum: 0a6634953ebad8a239621623110aab9d 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 , Robert Scharpf , Matt Ritchie git_url: https://git.bioconductor.org/packages/crlmm git_branch: devel git_last_commit: 7f9b0fd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/crlmm_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/crlmm_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/crlmm_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/crlmm_1.63.0.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: crossmeta Version: 1.31.0 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 MD5sum: a3b3e406025399f4d33529b51f5f0aab NeedsCompilation: no 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 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 git_url: https://git.bioconductor.org/packages/crossmeta git_branch: devel git_last_commit: ba40297 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/crossmeta_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/crossmeta_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/crossmeta_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/crossmeta_1.31.0.tgz vignettes: vignettes/crossmeta/inst/doc/crossmeta-vignette.html vignetteTitles: crossmeta vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crossmeta/inst/doc/crossmeta-vignette.R suggestsMe: ccmap dependencyCount: 157 Package: CSAR Version: 1.57.0 Depends: R (>= 2.15.0), S4Vectors, IRanges, GenomeInfoDb, GenomicRanges Imports: stats, utils Suggests: ShortRead, Biostrings License: Artistic-2.0 MD5sum: 2e56c68975939d75f0c8d6524ede5e9e 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 git_url: https://git.bioconductor.org/packages/CSAR git_branch: devel git_last_commit: d5d5da1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CSAR_1.57.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CSAR_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CSAR_1.57.0.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: 1.39.0 Depends: R (>= 3.5.0), GenomicRanges, SummarizedExperiment Imports: Rcpp, Matrix, BiocGenerics, Rsamtools, edgeR, limma, methods, S4Vectors, IRanges, GenomeInfoDb, stats, BiocParallel, metapod, utils LinkingTo: Rhtslib, zlibbioc, Rcpp Suggests: AnnotationDbi, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene, testthat, GenomicFeatures, GenomicAlignments, knitr, BiocStyle, rmarkdown, BiocManager License: GPL-3 MD5sum: 07f8732ee0fe364f3f5b006ec40d965a 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 SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/csaw git_branch: devel git_last_commit: 11222da git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/csaw_1.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/csaw_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/csaw_1.39.0.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: 1.11.0 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: ba524352aefdb643ab9086f7de32e73f 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] () Maintainer: Jakob Peder Pettersen 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: 1440727 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/csdR_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/csdR_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/csdR_1.11.0.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: 1.17.0 Depends: SummarizedExperiment, GenomicRanges, IRanges, S4Vectors, rtracklayer Imports: GenomicAlignments, GenomicFeatures, Rsamtools, ggplot2, grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown, markdown License: Artistic-2.0 MD5sum: 3318d7c358bac975d1233eb4911ee6c8 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CSSQ git_branch: devel git_last_commit: f7f6b2d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CSSQ_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CSSQ_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CSSQ_1.17.0.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: 1.79.0 Depends: amap License: GPL-2 MD5sum: 39fc12e3d800d3c8c84fd64111de4a56 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 , Laurent Gautier Maintainer: Antoine Lucas URL: http://antoinelucas.free.fr/ctc git_url: https://git.bioconductor.org/packages/ctc git_branch: devel git_last_commit: 5a4349b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ctc_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ctc_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ctc_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ctc_1.79.0.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: 1.5.0 Depends: R (>= 4.2) Imports: ExperimentHub, utils Suggests: testthat (>= 3.0.0), DT, BiocStyle, knitr, rmarkdown, SummarizedExperiment, SingleCellExperiment License: Artistic-2.0 MD5sum: 32ccf734d7452fb584338a21f4156875 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] (), Julie Devis [aut] (), Anna Diacofotaki [ctb], Charles De Smet [ths], Laurent Gatto [aut, ths, cre] () Maintainer: Laurent Gatto VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/CTdata/issues git_url: https://git.bioconductor.org/packages/CTdata git_branch: devel git_last_commit: a0144cb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CTdata_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CTdata_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CTdata_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CTdata_1.5.0.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: 67 Package: CTDquerier Version: 2.13.0 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: 58cf2e5deb6b4e6a2333f86c8d3dc205 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 VignetteBuilder: rmarkdown git_url: https://git.bioconductor.org/packages/CTDquerier git_branch: devel git_last_commit: 5486baa git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CTDquerier_2.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CTDquerier_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CTDquerier_2.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CTDquerier_2.13.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 72 Package: CTexploreR Version: 1.1.0 Depends: R (>= 4.3), CTdata 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) License: Artistic-2.0 MD5sum: ba463b5a821908e03f8eb1d834bbeb24 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] (), Julie Devis [aut] (), Anna Diacofotaki [ctb], Charles De Smet [ths], Laurent Gatto [aut, ths] () Maintainer: Axelle Loriot 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: da2dcf3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CTexploreR_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CTexploreR_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CTexploreR_1.1.0.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: 110 Package: cTRAP Version: 1.23.0 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: c8bf448dc564593ff7614b8b44ff62b2 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 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: 5a438f9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cTRAP_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cTRAP_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cTRAP_1.23.0.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: 161 Package: ctsGE Version: 1.31.0 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: e2f80353cca5d0cea51857d4477b3082 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 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: 6f0d3ea git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ctsGE_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ctsGE_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ctsGE_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ctsGE_1.31.0.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: 1.7.0 Depends: R (>= 4.2), Imports: stats, pscl, qvalue, BiocParallel, methods, knitr, SpatialExperiment, SummarizedExperiment Suggests: testthat, BiocStyle License: GPL-3 MD5sum: 372cfa24110e249f067ac2bf4e9bdba0 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 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: 1a6e09e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CTSV_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CTSV_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CTSV_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CTSV_1.7.0.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: 106 Package: cummeRbund Version: 2.47.0 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: 6e9a0b84dcaaa06d46bdc8b6f2c71845 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 git_url: https://git.bioconductor.org/packages/cummeRbund git_branch: devel git_last_commit: 2c5659e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cummeRbund_2.47.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cummeRbund_2.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cummeRbund_2.47.0.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: 160 Package: CuratedAtlasQueryR Version: 1.3.0 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: f42aaa21cbcb28d22446ab216ab77a22 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] (), Michael Milton [aut, rev] (), 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 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: 73d995f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CuratedAtlasQueryR_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CuratedAtlasQueryR_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CuratedAtlasQueryR_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CuratedAtlasQueryR_1.3.0.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: 180 Package: customCMPdb Version: 1.15.0 Depends: R (>= 4.0) Imports: AnnotationHub, RSQLite, XML, utils, ChemmineR, methods, stats, rappdirs, BiocFileCache Suggests: knitr, rmarkdown, testthat, BiocStyle License: Artistic-2.0 MD5sum: de78030dea5517183317786a3175cb84 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 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: e3059d0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/customCMPdb_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/customCMPdb_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/customCMPdb_1.15.0.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: 113 Package: customProDB Version: 1.45.0 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: 3f54fe3948e6799409a7c30ab6eaf546 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 Bo Wen git_url: https://git.bioconductor.org/packages/customProDB git_branch: devel git_last_commit: 1030221 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/customProDB_1.45.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/customProDB_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/customProDB_1.45.0.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: 107 Package: cyanoFilter Version: 1.13.0 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: 9dda0973389335375c1377392ee7f8dc 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 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: ba8c0db git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cyanoFilter_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cyanoFilter_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cyanoFilter_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cyanoFilter_1.13.0.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: 139 Package: cycle Version: 1.59.0 Depends: R (>= 2.10.0), Mfuzz Imports: Biobase, stats License: GPL-2 MD5sum: 687a5050fb2985e4f384e951f67704d1 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 Maintainer: Matthias Futschik URL: http://cycle.sysbiolab.eu git_url: https://git.bioconductor.org/packages/cycle git_branch: devel git_last_commit: 9074360 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cycle_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cycle_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cycle_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cycle_1.59.0.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: 17 Package: cydar Version: 1.29.0 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 Archs: x64 MD5sum: a3465e265942b302d6edeabbd35265e5 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 SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cydar git_branch: devel git_last_commit: bb6c3e1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cydar_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cydar_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cydar_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cydar_1.29.0.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: 1.1.0 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 MD5sum: c4f1b05d65ee633bd83f38087b25aeaa 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] (), Guanqun Meng [aut], Wen Tang [aut] Maintainer: Shilin Yu 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: f34ee0a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cypress_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cypress_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cypress_1.1.0.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: 156 Package: CytoDx Version: 1.25.0 Depends: R (>= 3.5) Imports: doParallel, dplyr, glmnet, rpart, rpart.plot, stats, flowCore,grDevices, graphics, utils Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 366e69abba4100f1e57ea26bfe818f1a 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 VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/CytoDx git_branch: devel git_last_commit: 35e0c5c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CytoDx_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CytoDx_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CytoDx_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CytoDx_1.25.0.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: CyTOFpower Version: 1.11.0 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 MD5sum: 335efaaf39f8bfe6ba634cc4746e3f6c 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] (), Catherine Blish [aut], Susan Holmes [aut] Maintainer: Anne-Maud Ferreira VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CyTOFpower git_branch: devel git_last_commit: 7888dec git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CyTOFpower_1.11.0.tar.gz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CyTOFpower_1.11.0.tgz vignettes: vignettes/CyTOFpower/inst/doc/CyTOFpower.html vignetteTitles: Power analysis for CyTOF experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CyTOFpower/inst/doc/CyTOFpower.R dependencyCount: 242 Package: cytofQC Version: 1.5.0 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: 77b09169b8c4fea730139e7aa0cab258 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] (), Kelly Street [aut] () Maintainer: Jill Lundell 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: dd1933b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cytofQC_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cytofQC_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cytofQC_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cytofQC_1.5.0.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: 251 Package: CytoGLMM Version: 1.13.0 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: e61f29527f436a44997f1aa4a96a8822 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] () Maintainer: Christof Seiler 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: 8559ef8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CytoGLMM_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CytoGLMM_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CytoGLMM_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CytoGLMM_1.13.0.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 importsMe: CyTOFpower dependencyCount: 183 Package: cytoKernel Version: 1.11.0 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 Archs: x64 MD5sum: 947b67671bf5396195c32a7df217958b 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 VignetteBuilder: knitr BugReports: https://github.com/Ghoshlab/cytoKernel/issues git_url: https://git.bioconductor.org/packages/cytoKernel git_branch: devel git_last_commit: 52a4864 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cytoKernel_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cytoKernel_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cytoKernel_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cytoKernel_1.11.0.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: 86 Package: cytolib Version: 2.17.0 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 Archs: x64 MD5sum: 8162cb1e297265edf01bf9997dd96863 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 SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cytolib git_branch: devel git_last_commit: 947bcbd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cytolib_2.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cytolib_2.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cytolib_2.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cytolib_2.17.0.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: 1.17.0 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) MD5sum: 9544f3bdcb13ecccc860761ac0c5118d 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] (), Nicolas Damond [aut] (), Tobias Hoch [ctb], Lasse Meyer [cre, ctb] () Maintainer: Lasse Meyer 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: 1992640 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cytomapper_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cytomapper_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cytomapper_1.15.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cytomapper_1.17.0.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 suggestsMe: spicyWorkflow dependencyCount: 145 Package: CytoMDS Version: 1.1.0 Depends: R (>= 4.3) 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 MD5sum: 9dca879db8f568d8ee2ec96597c8d061 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] (), Laurent Gatto [aut] (), Dan Lin [ctb] Maintainer: Philippe Hauchamps 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: 5acdc8f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CytoMDS_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CytoMDS_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CytoMDS_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CytoMDS_1.1.0.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: 215 Package: cytoMEM Version: 1.9.0 Depends: R (>= 4.2.0) Imports: gplots, tools, flowCore, grDevices, stats, utils, matrixStats, methods Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 61572418e468481c5d18be68bf2da8a4 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] (), Kirsten Diggins [aut] (), Jonathan Irish [aut, cre] () Maintainer: Jonathan Irish URL: https://github.com/cytolab/cytoMEM VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cytoMEM git_branch: devel git_last_commit: 0ce41d7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cytoMEM_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cytoMEM_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cytoMEM_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cytoMEM_1.9.0.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: 23 Package: CytoML Version: 2.17.0 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 Archs: x64 MD5sum: d266100499642759932ffa10803b6870 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 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: e3d2e9e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CytoML_2.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CytoML_2.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CytoML_2.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CytoML_2.17.0.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: 85 Package: CytoPipeline Version: 1.5.0 Depends: R (>= 4.3) 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 License: GPL-3 MD5sum: 60645a187bd441825e9370a50a7dba78 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] (), Laurent Gatto [aut] (), Dan Lin [ctb] Maintainer: Philippe Hauchamps 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: 378459b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CytoPipeline_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CytoPipeline_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CytoPipeline_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CytoPipeline_1.5.0.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: 136 Package: CytoPipelineGUI Version: 1.3.0 Depends: R (>= 4.3), CytoPipeline Imports: shiny, plotly, ggplot2, flowCore Suggests: testthat (>= 3.0.0), vdiffr, diffviewer, knitr, rmarkdown, BiocStyle, patchwork License: GPL-3 MD5sum: 0ccfa7178f31eed925c7b582fa3572ac 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] (), Laurent Gatto [aut] (), Dan Lin [ctb] Maintainer: Philippe Hauchamps 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: 8e614da git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/CytoPipelineGUI_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/CytoPipelineGUI_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/CytoPipelineGUI_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/CytoPipelineGUI_1.3.0.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 dependencyCount: 148 Package: cytoviewer Version: 1.5.0 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: ac29ff231d9cf90def12d1803f2c9f50 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] (), Nils Eling [aut] () Maintainer: Lasse Meyer 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: e71cc3c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/cytoviewer_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/cytoviewer_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/cytoviewer_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/cytoviewer_1.5.0.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: 1.33.0 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: 83798fb44769e992e46fd3d63c4b5cbc 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 , Paul McMurdie, Susan Holmes Maintainer: Benjamin Callahan 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: 05f276f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/dada2_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/dada2_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/dada2_1.33.0.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: 1.43.0 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: c15435cafe4c856798f767ef2f91c94f 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dagLogo git_branch: devel git_last_commit: cce62f3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-03 source.ver: src/contrib/dagLogo_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/dagLogo_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/dagLogo_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/dagLogo_1.43.0.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: 162 Package: daMA Version: 1.77.0 Imports: MASS, stats License: GPL (>= 2) MD5sum: d18376e84d8df2f1534a09d1a0b2ebd2 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 and Frank Bretz Maintainer: Jobst Landgrebe URL: http://www.microarrays.med.uni-goettingen.de git_url: https://git.bioconductor.org/packages/daMA git_branch: devel git_last_commit: 9eede9e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/daMA_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/daMA_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/daMA_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/daMA_1.77.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: DAMEfinder Version: 1.17.0 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: 8d9297f5f1f0392f4ead10eaf3335f5d 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] (), Dania Machlab [aut], Mark Robinson [aut] Maintainer: Stephany Orjuela VignetteBuilder: knitr BugReports: https://github.com/markrobinsonuzh/DAMEfinder/issues git_url: https://git.bioconductor.org/packages/DAMEfinder git_branch: devel git_last_commit: 0fb1441 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DAMEfinder_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DAMEfinder_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DAMEfinder_1.17.0.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: 2.17.0 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: 6ca57c4073a574f3028307ff3bee6c5a 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 , Luca Piacentini Maintainer: Mattia Chiesa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DaMiRseq git_branch: devel git_last_commit: 7961b11 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DaMiRseq_2.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DaMiRseq_2.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DaMiRseq_2.17.0.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: 260 Package: DAPAR Version: 1.37.0 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: ccc53459a16054f5caf05735a0652177 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 URL: http://www.prostar-proteomics.org/ VignetteBuilder: knitr BugReports: https://github.com/prostarproteomics/DAPAR/issues git_url: https://git.bioconductor.org/packages/DAPAR git_branch: devel git_last_commit: a0803ea git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DAPAR_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DAPAR_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DAPAR_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DAPAR_1.37.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: Prostar suggestsMe: DAPARdata, mi4p dependencyCount: 150 Package: dar Version: 1.1.0 Depends: R (>= 4.4.0) 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: ce1d47c59f8e059bb2ab228008e7c7fe 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] () Maintainer: Francesc Catala-Moll 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: b69c1d0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/dar_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/dar_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/dar_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/dar_1.1.0.tgz vignettes: vignettes/dar/inst/doc/article.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, 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/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: 215 Package: DART Version: 1.53.0 Depends: R (>= 2.10.0), igraph (>= 0.6.0) Suggests: breastCancerVDX, breastCancerMAINZ, Biobase License: GPL-2 MD5sum: 8bc4df7ca7c37ae9244bbc52080a8fdd 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 git_url: https://git.bioconductor.org/packages/DART git_branch: devel git_last_commit: ddf5e27 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DART_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DART_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DART_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DART_1.53.0.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: 1.21.0 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: 56cd069c4806554775ef1ca1091cc641 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] () Maintainer: Dharmesh D. Bhuva 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: d9e562e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/dcanr_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/dcanr_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/dcanr_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/dcanr_1.21.0.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: multiWGCNA, SingscoreAMLMutations dependencyCount: 35 Package: DCATS Version: 1.3.0 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: 2407bb3ea980a1adad018f298de5fb5b 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] (), Chuen Chau [aut], Yuanhua Huang [aut], Joshua W.K. Ho [aut] Maintainer: Xinyi Lin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DCATS git_branch: devel git_last_commit: 1e756e6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DCATS_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DCATS_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DCATS_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DCATS_1.3.0.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: dc8de51d70c6f78c18192c5fe3e0eb63 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] (), Martin Pirkl [aut] Maintainer: Kim Philipp Jablonski 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: 2024-05-01 source.ver: src/contrib/dce_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/dce_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/dce_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/dce_1.13.0.tgz 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: 1.33.0 Depends: R (>= 3.3), Matrix Imports: BiocParallel Suggests: knitr License: GPL-2 MD5sum: d4181306905702250e9e9df606ad9e9a 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dcGSA git_branch: devel git_last_commit: 9b76460 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/dcGSA_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/dcGSA_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/dcGSA_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/dcGSA_1.33.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 18 Package: ddCt Version: 1.61.0 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: a5afa2931efb771fd9e1c1ef9087b51a 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 git_url: https://git.bioconductor.org/packages/ddCt git_branch: devel git_last_commit: 1bfc606 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ddCt_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ddCt_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ddCt_1.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ddCt_1.61.0.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: 11 Package: ddPCRclust Version: 1.25.0 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: ae793f97cc5c436b3ca816434eb278cd 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 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: 24e04b8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ddPCRclust_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ddPCRclust_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ddPCRclust_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ddPCRclust_1.25.0.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 dependencyCount: 117 Package: dearseq Version: 1.17.0 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: 8f8b51268f234fb459b7044fbb756755 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] (), Marine Gauthier [aut], Mélanie Huchon [ctb] Maintainer: Boris P. Hejblum VignetteBuilder: knitr BugReports: https://github.com/borishejblum/dearseq/issues git_url: https://git.bioconductor.org/packages/dearseq git_branch: devel git_last_commit: 0791316 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/dearseq_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/dearseq_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/dearseq_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/dearseq_1.17.0.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: 1.23.0 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: 325d114ca24cbe6be91a9ec8a0e36c41 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 Maintainer: Lulu Chen 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: dc55f76 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/debCAM_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/debCAM_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/debCAM_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/debCAM_1.23.0.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: 118 Package: debrowser Version: 1.33.0 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: 5aad9cc8603c84dca600200fe397d2e4 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 , Onur Yukselen , Manuel Garber Maintainer: Alper Kucukural 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: ec057cc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/debrowser_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/debrowser_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/debrowser_1.33.0.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: 228 Package: DECIPHER Version: 3.1.0 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 Archs: x64 MD5sum: bff63faf7ab0e3770295d7467563fa96 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 git_url: https://git.bioconductor.org/packages/DECIPHER git_branch: devel git_last_commit: 52b4a76 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DECIPHER_3.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DECIPHER_3.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DECIPHER_3.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DECIPHER_3.1.0.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: 2.21.0 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: 55ac14f91dd33318a03742d20dca4184 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 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: 559c87f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/decompTumor2Sig_2.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/decompTumor2Sig_2.21.0.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: 1.47.0 Depends: R (>= 2.14.0), limSolve, pcaMethods, ggplot2, grid License: GPL-2 MD5sum: 6251ef8521a54429ed427f1d57280c6f 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 Joseph D. Szustakowski Maintainer: Ting Gong git_url: https://git.bioconductor.org/packages/DeconRNASeq git_branch: devel git_last_commit: eaa29a7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DeconRNASeq_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DeconRNASeq_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DeconRNASeq_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DeconRNASeq_1.47.0.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: 42 Package: decontam Version: 1.25.0 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: 4c5a64eb3412a86cfbf1ab5b1bc8e80c 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] () Maintainer: Benjamin Callahan 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: bec1570 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/decontam_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/decontam_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/decontam_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/decontam_1.25.0.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: 1.3.0 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: 9b161e777259cb80f731559d34823b9e 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, cre] (), Masanao Yajima [aut] (), Joshua Campbell [aut] () Maintainer: Yuan Yin SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/decontX git_branch: devel git_last_commit: 5d43c57 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/decontX_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/decontX_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/decontX_1.3.0.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: 238 Package: deconvR Version: 1.11.0 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: a03224b5b4e1d83c98bc3507512f4a6c 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] (), Veronika Ebenal [aut] (), Altuna Akalin [aut] () Maintainer: Irem B. Gündüz 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: 1de875b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/deconvR_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/deconvR_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/deconvR_1.11.0.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: 190 Package: DeepPINCS Version: 1.13.0 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: e448a1e22fa134833ac7464d59e8febb 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] () Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeepPINCS git_branch: devel git_last_commit: ff29da0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DeepPINCS_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DeepPINCS_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DeepPINCS_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DeepPINCS_1.13.0.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: 1.51.0 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: c6641bb1452fe51aebb3502ce8de8574 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 SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deepSNV git_branch: devel git_last_commit: 3d78da1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/deepSNV_1.51.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/deepSNV_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/deepSNV_1.51.0.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: DEFormats Version: 1.33.0 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 MD5sum: e514d801d48341722f1ffd0a3a4b8a33 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 Oleś Maintainer: Andrzej Oleś 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: 4813632 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DEFormats_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DEFormats_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DEFormats_1.33.0.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 dependencyCount: 82 Package: DegCre Version: 1.1.0 Depends: R (>= 4.4) Imports: GenomicRanges, InteractionSet, plotgardener, S4Vectors, stats, graphics, grDevices, BiocGenerics, GenomeInfoDb, IRanges, utils Suggests: BSgenome, BSgenome.Hsapiens.UCSC.hg38, org.Hs.eg.db, BiocStyle, magick, knitr, rmarkdown, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: cff515d66f4c8c3b106e54eecd60a889 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] () Maintainer: Brian S. Roberts 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: ee9a9ae git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DegCre_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DegCre_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DegCre_1.1.0.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: 102 Package: DegNorm Version: 1.15.0 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: 138d5e8d930c500ea89f7f4a5708b61f 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 VignetteBuilder: knitr BugReports: https://github.com/jipingw/DegNorm/issues git_url: https://git.bioconductor.org/packages/DegNorm git_branch: devel git_last_commit: 1305d2b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DegNorm_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DegNorm_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DegNorm_1.15.0.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: 163 Package: DEGraph Version: 1.57.0 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: 5b0976dda4ed8e11d4c073ec8daa501a 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 git_url: https://git.bioconductor.org/packages/DEGraph git_branch: devel git_last_commit: d53511c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DEGraph_1.57.0.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: 1.41.0 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: 1e503a8bef0edb1dee16bfa02894e6de NeedsCompilation: no Title: Report of DEG analysis Description: Creation of a HTML report 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 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: 8ac77b4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DEGreport_1.41.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DEGreport_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DEGreport_1.41.0.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: 121 Package: DEGseq Version: 1.59.0 Depends: R (>= 2.8.0), qvalue, methods Imports: graphics, grDevices, methods, stats, utils License: LGPL (>=2) Archs: x64 MD5sum: 9c9634ea145deaa2806e5e5c3f1aac41 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 , Xiaowo Wang and Xuegong Zhang . Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/DEGseq git_branch: devel git_last_commit: 15e7988 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DEGseq_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DEGseq_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DEGseq_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DEGseq_1.59.0.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: 0.31.0 Depends: R (>= 4.0.0), methods, stats4, Matrix, BiocGenerics (>= 0.43.4), MatrixGenerics (>= 1.1.3), S4Vectors (>= 0.27.2), IRanges (>= 2.17.3), S4Arrays (>= 1.3.5), SparseArray (>= 1.1.10) Imports: stats LinkingTo: S4Vectors Suggests: BiocParallel, HDF5Array (>= 1.17.12), genefilter, SummarizedExperiment, airway, lobstr, DelayedMatrixStats, knitr, rmarkdown, BiocStyle, RUnit License: Artistic-2.0 Archs: x64 MD5sum: 469b4fd39e9d7d3710731fa58f7b627d 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 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: f3fd5e7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DelayedArray_0.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DelayedArray_0.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DelayedArray_0.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DelayedArray_0.31.1.tgz vignettes: vignettes/DelayedArray/inst/doc/01-Working_with_large_arrays.pdf, vignettes/DelayedArray/inst/doc/03-DelayedArray_HDF5Array_update.pdf, vignettes/DelayedArray/inst/doc/02-Implementing_a_backend.html vignetteTitles: Working with large arrays in R, DelayedArray / HDF5Array update, Implementing A DelayedArray Backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedArray/inst/doc/01-Working_with_large_arrays.R, vignettes/DelayedArray/inst/doc/03-DelayedArray_HDF5Array_update.R dependsOnMe: chihaya, DelayedDataFrame, DelayedMatrixStats, DelayedRandomArray, GDSArray, HDF5Array, rhdf5client, SCArray, singleCellTK, SQLDataFrame, TileDBArray, VCFArray, restfulSEData importsMe: adverSCarial, alabaster.matrix, AUCell, batchelor, beachmat, beachmat.hdf5, BiocSingular, bsseq, CAGEr, celaref, celda, ChromSCape, clusterExperiment, concordexR, CRISPRseek, cytomapper, decontX, DelayedTensor, DEScan2, dreamlet, DropletUtils, ELMER, EWCE, 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, scry, scuttle, signatureSearch, SingleCellAlleleExperiment, SingleCellExperiment, SingleR, sketchR, SpliceWiz, SummarizedExperiment, transformGamPoi, TSCAN, VariantExperiment, velociraptor, weitrix, xcore, zellkonverter, ZygosityPredictor, celldex, imcdatasets, scMultiome, scRNAseq, ebvcube, scDiffCom suggestsMe: BiocGenerics, ChIPpeakAnno, gwascat, hermes, iSEE, lute, MAST, MatrixGenerics, ProteoDisco, S4Arrays, S4Vectors, satuRn, SparseArray, SPOTlight, TrajectoryUtils, digitalDLSorteR, Seurat, SeuratObject, SpatialDDLS dependencyCount: 21 Package: DelayedDataFrame Version: 1.21.0 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 MD5sum: fb420850f55e61efa1986353104036ee 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 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: 088256d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DelayedDataFrame_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DelayedDataFrame_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DelayedDataFrame_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DelayedDataFrame_1.21.0.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: 1.27.0 Depends: MatrixGenerics (>= 1.15.1), DelayedArray (>= 0.27.10) Imports: methods, sparseMatrixStats (>= 1.13.2), Matrix (>= 1.5-0), S4Vectors (>= 0.17.5), IRanges (>= 2.25.10) Suggests: testthat, knitr, rmarkdown, BiocStyle, microbenchmark, profmem, HDF5Array, matrixStats (>= 1.0.0) License: MIT + file LICENSE MD5sum: f31c142c1e7bc9faacb46266cd8d603d 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] (), Hervé Pagès [ctb], Aaron Lun [ctb] Maintainer: Peter Hickey 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: cee2cf6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DelayedMatrixStats_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DelayedMatrixStats_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DelayedMatrixStats_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DelayedMatrixStats_1.27.0.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, CAGEr, dmrseq, dreamlet, DropletUtils, FRASER, glmGamPoi, GSVA, lemur, methrix, methylSig, mia, minfi, mumosa, NetActivity, PCAtools, recountmethylation, SCArray, scFeatures, scMerge, scran, scuttle, singleCellTK, SingleR, sparrow, SpliceWiz, weitrix, celldex suggestsMe: DelayedArray, EWCE, HDF5Array, lute, MatrixGenerics, mbkmeans, ScaledMatrix, scPCA, slingshot, tradeSeq, TrajectoryUtils, digitalDLSorteR, SpatialDDLS dependencyCount: 24 Package: DelayedRandomArray Version: 1.13.0 Depends: DelayedArray (>= 0.27.2) Imports: methods, dqrng, Rcpp LinkingTo: dqrng, BH, Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown, Matrix License: GPL-3 Archs: x64 MD5sum: 2bb5364cbdc7d6cd884e4099dba1124f 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 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: 3032daf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DelayedRandomArray_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DelayedRandomArray_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DelayedRandomArray_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DelayedRandomArray_1.13.0.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: 1.11.0 Depends: R (>= 4.1.0) Imports: methods, utils, DelayedArray, HDF5Array, BiocSingular, rTensor, DelayedRandomArray, irlba, Matrix, einsum, Suggests: markdown, rmarkdown, BiocStyle, knitr, testthat, magrittr, dplyr, reticulate License: Artistic-2.0 MD5sum: 13a7da4adf2231e9030ee1d19e33d3c0 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 VignetteBuilder: knitr BugReports: https://github.com/rikenbit/DelayedTensor/issues git_url: https://git.bioconductor.org/packages/DelayedTensor git_branch: devel git_last_commit: 7fb64d7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DelayedTensor_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DelayedTensor_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DelayedTensor_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DelayedTensor_1.11.0.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: 49 Package: DELocal Version: 1.5.0 Imports: DESeq2, dplyr, reshape2, limma, SummarizedExperiment, ggplot2, matrixStats, stats Suggests: biomaRt, knitr, rmarkdown, stringr, BiocStyle License: MIT + file LICENSE MD5sum: cc217edd9d97553a25b592346bd7a838 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] () Maintainer: Rishi Das Roy 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: 9f8de40 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DELocal_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DELocal_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DELocal_1.5.0.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 dependencyCount: 85 Package: deltaCaptureC Version: 1.19.0 Depends: R (>= 3.6) Imports: IRanges, GenomicRanges, SummarizedExperiment, ggplot2, DESeq2, tictoc Suggests: knitr, rmarkdown License: MIT + file LICENSE MD5sum: 34d188224a610defbd710250be9ae956 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] () Maintainer: Michael Shapiro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deltaCaptureC git_branch: devel git_last_commit: ecefa7e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/deltaCaptureC_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/deltaCaptureC_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/deltaCaptureC_1.19.0.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: 1.45.0 Depends: R (>= 2.15.1), methods, ggplot2, changepoint, wavethresh, tseries, pvclust, fBasics, grid, reshape, scales Suggests: knitr License: GPL-2 MD5sum: 0480b9105120e0f03e562df13a7a994c 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deltaGseg git_branch: devel git_last_commit: 6653df9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/deltaGseg_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/deltaGseg_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/deltaGseg_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/deltaGseg_1.45.0.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: 1.35.0 Depends: R (>= 2.14.0), KernSmooth, methods License: file LICENSE MD5sum: 16ecb3e0ecb474ea1ced08dfbfd81a4b 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 , Yishai Shimoni Maintainer: Jung Hoon Woo , Mariano Alvarez git_url: https://git.bioconductor.org/packages/DeMAND git_branch: devel git_last_commit: d3391b4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DeMAND_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DeMAND_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DeMAND_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DeMAND_1.35.0.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: 1.21.0 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: 05a9b36fbc9cea16b4ddf1977028810e 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 , Shaolong Cao, Wenyi Wang Maintainer: Shuai Guo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeMixT git_branch: devel git_last_commit: 7df08b0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DeMixT_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DeMixT_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DeMixT_1.21.0.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: 147 Package: demuxmix Version: 1.7.0 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: e536fc5044a21ff757236eeaf2e38e4a 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] () Maintainer: Hans-Ulrich Klein 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: f35ec72 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/demuxmix_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/demuxmix_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/demuxmix_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/demuxmix_1.7.0.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: 1.3.0 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 MD5sum: b5751b20e8cbf8a66dda8d4369b285b3 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] (), Aedin Culhane [aut] () Maintainer: Michael Lynch 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: b9fcdaf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/demuxSNP_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/demuxSNP_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/demuxSNP_1.3.0.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: 113 Package: densvis Version: 1.15.0 Imports: Rcpp, basilisk, assertthat, reticulate, Rtsne, irlba LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, ggplot2, uwot, testthat License: MIT + file LICENSE Archs: x64 MD5sum: 765a70d670aa7959d4d3f3ff11f4931d 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) . 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 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: 92f8288 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/densvis_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/densvis_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/densvis_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/densvis_1.15.0.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: 1.27.0 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: c252cec4e69de69467310fca3538f5f5 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEP git_branch: devel git_last_commit: 7fcd1ce git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DEP_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DEP_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DEP_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DEP_1.27.0.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: 1.21.0 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: d60b40fc71d52ae63b2cc0661ff7936e 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] (), Axel Theorell [aut] Maintainer: Jakob Theorell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DepecheR git_branch: devel git_last_commit: 4fe8d4f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DepecheR_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DepecheR_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DepecheR_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DepecheR_1.21.0.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: 86 Package: DepInfeR Version: 1.9.0 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: 3de79a309c891bedf47b5d9332f0ade7 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] (), Alina Batzilla [aut] Maintainer: Junyan Lu 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: 7349c48 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DepInfeR_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DepInfeR_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DepInfeR_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DepInfeR_1.9.0.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: DeProViR Version: 1.1.0 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: 23a95b5bba8121fabbcef79703b4e610 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 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: 4c1b40b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DeProViR_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DeProViR_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DeProViR_1.1.0.tgz vignettes: vignettes/DeProViR/inst/doc/DeProViR_tutorial.html vignetteTitles: Introduction to DeProViR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DeProViR/inst/doc/DeProViR_tutorial.R dependencyCount: 133 Package: derfinder Version: 1.39.0 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: 38174b633891af95e11e6863d43628fb 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] (), Alyssa C. Frazee [ctb], Andrew E. Jaffe [aut] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres 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: f9cd986 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/derfinder_1.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/derfinder_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/derfinder_1.39.0.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, regionReport, GenomicState, recountWorkflow suggestsMe: megadepth dependencyCount: 144 Package: derfinderHelper Version: 1.39.0 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: 7d1f817ebe9b85651e07a35afb0ce357 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] (), Andrew E. Jaffe [aut] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres 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: ff9e848 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/derfinderHelper_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/derfinderHelper_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/derfinderHelper_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/derfinderHelper_1.39.0.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: 12 Package: derfinderPlot Version: 1.39.0 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: 5013d23b1c5601f55e32944dfeb7104e 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] (), Andrew E. Jaffe [aut] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres 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: 6406f0e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/derfinderPlot_1.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/derfinderPlot_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/derfinderPlot_1.39.0.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: 184 Package: DEScan2 Version: 1.25.0 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: c2539a6c08627961036929c426b8f976 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEScan2 git_branch: devel git_last_commit: 4184efe git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DEScan2_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DEScan2_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DEScan2_1.25.0.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: 135 Package: DESeq2 Version: 1.45.0 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, pasilla (>= 0.2.10), glmGamPoi, BiocManager License: LGPL (>= 3) MD5sum: 2329dac4cea272ee6cc0407e34d47d21 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 URL: https://github.com/thelovelab/DESeq2 VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/DESeq2 git_branch: devel git_last_commit: 182d456 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DESeq2_1.45.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DESeq2_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DESeq2_1.45.0.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, SeqGSEA, TCC, tRanslatome, rnaseqDTU, rnaseqGene, Anaconda, DRomics, ordinalbayes importsMe: Anaquin, animalcules, APAlyzer, BatchQC, benchdamic, CeTF, circRNAprofiler, consensusDE, coseq, countsimQC, cypress, DaMiRseq, debrowser, DEFormats, DEGreport, DELocal, deltaCaptureC, DEsubs, DiffBind, easier, EBSEA, ERSSA, GDCRNATools, GeneTonic, gg4way, Glimma, GRaNIE, hermes, HTSFilter, HybridExpress, icetea, INSPEcT, IntEREst, iSEEde, isomiRs, kissDE, magpie, microbiomeExplorer, microbiomeMarker, MIRit, MLSeq, mobileRNA, MultiRNAflow, muscat, NBAMSeq, NetActivity, ORFik, OUTRIDER, pairedGSEA, PathoStat, pcaExplorer, phantasus, POMA, proActiv, RegEnrich, regionReport, ReportingTools, RiboDiPA, Rmmquant, saseR, scBFA, scGPS, SEtools, singleCellTK, SNPhood, srnadiff, SurfR, systemPipeTools, TBSignatureProfiler, TEKRABber, UMI4Cats, vidger, vulcan, BloodCancerMultiOmics2017, FieldEffectCrc, homosapienDEE2CellScore, IHWpaper, ExpHunterSuite, recountWorkflow, autoGO, bulkAnalyseR, cinaR, ExpGenetic, ggpicrust2, HeritSeq, HTSSIP, limorhyde2, MetaLonDA, microbial, RCPA, RNAseqQC, SIPmg, sRNAGenetic, wilson suggestsMe: aggregateBioVar, apeglm, bambu, BindingSiteFinder, biobroom, BiocGenerics, BioCor, BiocSet, BioNERO, CAGEr, CBNplot, compcodeR, dar, dearseq, derfinder, dittoSeq, EDASeq, EnhancedVolcano, EnrichmentBrowser, EWCE, extraChIPs, fishpond, gage, GeDi, GenomicAlignments, GenomicRanges, GeoTcgaData, glmGamPoi, HiCDCPlus, IHW, InteractiveComplexHeatmap, methodical, OPWeight, pathlinkR, PCAtools, phyloseq, progeny, raer, 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, seqpac, bakR, cellpypes, conos, FateID, glmmSeq, grandR, lfc, metacoder, metaRNASeq, MiscMetabar, pctax, pmartR, RaceID, rliger, SCdeconR, seqgendiff, Seurat, SQMtools, volcano3D dependencyCount: 75 Package: DEsingle Version: 1.25.0 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 MD5sum: cfdfe341d746b6c1d59be7f7f3998713 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 Maintainer: Zhun Miao URL: https://miaozhun.github.io/DEsingle/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEsingle git_branch: devel git_last_commit: aa5d770 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DEsingle_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DEsingle_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DEsingle_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DEsingle_1.25.0.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: 1.5.0 Depends: R (>= 4.3.0) Imports: edgeR, limma, dplyr, stats, Matrix, SpatialExperiment, ggplot2, ggpubr, scales, SummarizedExperiment, S4Vectors, BiocGenerics, data.table, assertthat, cowplot, ggforce, ggnewscale, patchwork, BiocParallel, methods Suggests: knitr, rmarkdown, testthat, BiocStyle, ExperimentHub, concaveman, spatialLIBD, purrr, scuttle, utils License: GPL-3 MD5sum: 7aec2e66e41990aa65db13c0546aaf06 NeedsCompilation: no Title: DESpace: a framework to discover spatially variable genes Description: Intuitive framework for identifying spatially variable genes (SVGs) 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. The method is flexible and robust, and is faster than the most SV methods. Furthermore, to the best of our knowledge, it is the only SV approach that allows: - performing a SV test on each individual spatial cluster, hence identifying the key regions of the tissue affected by spatial variability; - jointly fitting multiple samples, targeting genes with consistent spatial patterns across replicates. biocViews: Spatial, SingleCell, RNASeq, Transcriptomics, GeneExpression, Sequencing, DifferentialExpression,StatisticalMethod, Visualization Author: Peiying Cai [aut, cre] (), Simone Tiberi [aut] () Maintainer: Peiying Cai URL: https://github.com/peicai/DESpace VignetteBuilder: knitr BugReports: https://github.com/peicai/DESpace/issues git_url: https://git.bioconductor.org/packages/DESpace git_branch: devel git_last_commit: d366399 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DESpace_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DESpace_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DESpace_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DESpace_1.5.0.tgz vignettes: vignettes/DESpace/inst/doc/DESpace.html vignetteTitles: A framework to discover spatially variable genes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DESpace/inst/doc/DESpace.R dependencyCount: 150 Package: destiny Version: 3.19.0 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: 2faae8f9288d2147382459033751c856 NeedsCompilation: yes Title: Creates diffusion maps Description: Create and plot diffusion maps. biocViews: CellBiology, CellBasedAssays, Clustering, Software, Visualization Author: Philipp Angerer [cre, aut] (), Laleh Haghverdi [ctb], Maren Büttner [ctb] (), Fabian Theis [ctb] (), Carsten Marr [ctb] (), Florian Büttner [ctb] () Maintainer: Philipp Angerer 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: 3aca834 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/destiny_3.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/destiny_3.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/destiny_3.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/destiny_3.19.0.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 suggestsMe: CelliD, CellTrails, monocle dependencyCount: 131 Package: DEsubs Version: 1.31.0 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: 447c2dd0a4df4823ef9ab0b830ac1a2a 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 , Panos Balomenos VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEsubs git_branch: devel git_last_commit: aec940f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DEsubs_1.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DEsubs_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DEsubs_1.31.0.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: 116 Package: DEWSeq Version: 1.19.0 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) MD5sum: 2ee5d4c402eb6a4dd109916126337458 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 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: 8bcd678 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DEWSeq_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DEWSeq_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DEWSeq_1.19.0.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: 1.13.0 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: fe75755b7336b373c4220877a430bf9d 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-García [aut, cre], Pedro Carmona-Sáez [aut] Maintainer: Juan Antonio Villatoro-García git_url: https://git.bioconductor.org/packages/DExMA git_branch: devel git_last_commit: 5d23315 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DExMA_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DExMA_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DExMA_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DExMA_1.13.0.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: 122 Package: DEXSeq Version: 1.51.0 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), parathyroidSE, BiocStyle, knitr, rmarkdown, testthat, pasillaBamSubset, GenomicAlignments, roxygen2, glmGamPoi License: GPL (>= 3) MD5sum: c44410367f50e4f394b5a95c068943db 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 and Alejandro Reyes Maintainer: Alejandro Reyes VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEXSeq git_branch: devel git_last_commit: c33f17a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DEXSeq_1.51.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DEXSeq_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DEXSeq_1.51.0.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: 118 Package: DFP Version: 1.63.0 Depends: methods, Biobase (>= 2.5.5) License: GPL-2 MD5sum: 0ad55ec2821308b416daa5db58e2b828 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 git_url: https://git.bioconductor.org/packages/DFP git_branch: devel git_last_commit: da888f7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DFP_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DFP_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DFP_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DFP_1.63.0.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: 6 Package: DIAlignR Version: 2.13.0 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 Archs: x64 MD5sum: aab8fbed7302c0dbaa8338c507abb3fb NeedsCompilation: yes 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] (), Hannes Rost [aut] (), Justin Sing [aut] Maintainer: Shubham Gupta SystemRequirements: C++14 VignetteBuilder: knitr BugReports: https://github.com/shubham1637/DIAlignR/issues git_url: https://git.bioconductor.org/packages/DIAlignR git_branch: devel git_last_commit: 782a3f0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DIAlignR_2.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DIAlignR_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DIAlignR_2.13.0.tgz vignettes: vignettes/DIAlignR/inst/doc/DIAlignR-vignette.html vignetteTitles: MS2 chromatograms based alignment of targeted mass-spectrometry runs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DIAlignR/inst/doc/DIAlignR-vignette.R dependencyCount: 80 Package: DiffBind Version: 3.15.0 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: 52e12b5bde1e4ddec06c577448256d33 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 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: 09998c6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DiffBind_3.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DiffBind_3.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DiffBind_3.15.0.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: 148 Package: diffcoexp Version: 1.25.0 Depends: R (>= 3.5), WGCNA, SummarizedExperiment Imports: stats, DiffCorr, psych, igraph, BiocGenerics Suggests: GEOquery, RUnit License: GPL (>2) MD5sum: 548898ff97bfa6e950d0b68a4d33a105 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 URL: https://github.com/hidelab/diffcoexp git_url: https://git.bioconductor.org/packages/diffcoexp git_branch: devel git_last_commit: 40ec682 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/diffcoexp_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/diffcoexp_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/diffcoexp_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/diffcoexp_1.25.0.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, easyDifferentialGeneCoexpression dependencyCount: 128 Package: diffcyt Version: 1.25.0 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 MD5sum: e6f4f9dcea2a4e3963b78dd2051df6e6 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] () Maintainer: Lukas M. Weber 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: c791207 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/diffcyt_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/diffcyt_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/diffcyt_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/diffcyt_1.25.0.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: CyTOFpower, treeclimbR, treekoR suggestsMe: CATALYST dependencyCount: 155 Package: DifferentialRegulation Version: 2.3.0 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 Archs: x64 MD5sum: 669a813450a52a5e7a93c15796fa73fe 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] (), Charlotte Soneson [aut] () Maintainer: Simone Tiberi 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: fb49478 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DifferentialRegulation_2.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DifferentialRegulation_2.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DifferentialRegulation_2.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DifferentialRegulation_2.3.0.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: 1.87.0 Imports: graphics, grDevices, minpack.lm (>= 1.0-4), stats, utils License: GPL MD5sum: 0b57fec93ed0018c14edc45790251d5d NeedsCompilation: no Title: Performs differential gene expression Analysis Description: Analyze microarray data biocViews: Microarray, DifferentialExpression Author: Choudary Jagarlamudi Maintainer: Choudary Jagarlamudi git_url: https://git.bioconductor.org/packages/diffGeneAnalysis git_branch: devel git_last_commit: 4e9b320 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/diffGeneAnalysis_1.87.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/diffGeneAnalysis_1.87.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/diffGeneAnalysis_1.87.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/diffGeneAnalysis_1.87.0.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: 1.37.0 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), zlibbioc, Rcpp Suggests: BSgenome.Ecoli.NCBI.20080805, Matrix, testthat License: GPL-3 MD5sum: 2cd4458a7aa901b05cac64463c936594 NeedsCompilation: yes Title: Differential Analyis 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 [aut, cre], Gordon Smyth [aut] Maintainer: Aaron Lun SystemRequirements: C++11, GNU make git_url: https://git.bioconductor.org/packages/diffHic git_branch: devel git_last_commit: 74f4871 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/diffHic_1.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/diffHic_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/diffHic_1.37.0.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: 2.29.0 Depends: R (>= 3.4), stats, cba Imports: grDevices, graphics, utils, tools Suggests: knitr, testthat, seqLogo, MotifDb License: GPL (>= 2) MD5sum: da259ce3ee67f05ddcd5a794a043e707 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 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: bff5c6a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DiffLogo_2.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DiffLogo_2.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DiffLogo_2.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DiffLogo_2.29.0.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: 1.25.0 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: 01cb8f979fc4030748834200187ad2d0 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 SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/diffuStats git_branch: devel git_last_commit: d3fbdfc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/diffuStats_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/diffuStats_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/diffuStats_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/diffuStats_1.25.0.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: 1.13.0 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: 7a19e49b5a7638d42cfa08be3c2f3405 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] (), Stefan Gerber [aut] Maintainer: Pierre-Luc Germain VignetteBuilder: knitr BugReports: https://github.com/ETHZ-INS/diffUTR git_url: https://git.bioconductor.org/packages/diffUTR git_branch: devel git_last_commit: 0ca783b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/diffUTR_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/diffUTR_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/diffUTR_1.13.0.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: 145 Package: diggit Version: 1.37.0 Depends: R (>= 3.0.2), Biobase, methods Imports: ks, viper(>= 1.3.1), parallel Suggests: diggitdata License: file LICENSE MD5sum: a934fb7e3d9d3792dedf563d1fe1bc87 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 Maintainer: Mariano J Alvarez git_url: https://git.bioconductor.org/packages/diggit git_branch: devel git_last_commit: a40b8a9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/diggit_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/diggit_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/diggit_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/diggit_1.37.0.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: 1.11.0 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: 25d851986caad8f30244c04171bed7a7 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] (), Christina Kendziorski [ctb] Maintainer: Jared Brown 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: c9c0971 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Dino_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Dino_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Dino_1.11.0.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: 192 Package: dinoR Version: 1.1.0 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: 64d5bf5d07915d113ebd75e1350bbae7 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] () Maintainer: Michaela Schwaiger 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: e69c162 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/dinoR_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/dinoR_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/dinoR_1.1.0.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: 91 Package: dir.expiry Version: 1.13.0 Imports: utils, filelock Suggests: rmarkdown, knitr, testthat, BiocStyle License: GPL-3 MD5sum: 406c5c7d6d5837528e5a8b1acbbe7b29 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dir.expiry git_branch: devel git_last_commit: 8710668 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/dir.expiry_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/dir.expiry_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/dir.expiry_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/dir.expiry_1.13.0.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, rebook dependencyCount: 2 Package: Director Version: 1.31.0 Depends: R (>= 4.0) Imports: htmltools, utils, grDevices License: GPL-3 + file LICENSE MD5sum: 1deaca14934163ad2962567a3e2ffd6a 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 URL: https://github.com/kzouchka/Director BugReports: https://github.com/kzouchka/Director/issues git_url: https://git.bioconductor.org/packages/Director git_branch: devel git_last_commit: 9d07019 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Director_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Director_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Director_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Director_1.31.0.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: 1.47.0 Depends: S4Vectors, IRanges Imports: stats4, methods, BiocGenerics Suggests: lattice, parallel, MASS, RColorBrewer, xtable License: LGPL-3 Archs: x64 MD5sum: 18984efc312ae8639fab9b25b24b870f 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 Maintainer: Martin Morgan SystemRequirements: gsl git_url: https://git.bioconductor.org/packages/DirichletMultinomial git_branch: devel git_last_commit: c72408c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DirichletMultinomial_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DirichletMultinomial_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DirichletMultinomial_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DirichletMultinomial_1.47.0.tgz vignettes: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.pdf vignetteTitles: An introduction to DirichletMultinomial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.R importsMe: mia, miaViz, TFBSTools suggestsMe: bluster, MicrobiotaProcess dependencyCount: 8 Package: discordant Version: 1.29.0 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 Archs: x64 MD5sum: 53123ebfecb7fd9a2c3f8865ee6030ba 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 URL: https://github.com/siskac/discordant VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/discordant git_branch: devel git_last_commit: 866e1ff git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/discordant_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/discordant_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/discordant_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/discordant_1.29.0.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: 1.21.0 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 MD5sum: 1bcc4c2f324d9fcd792e35964b5e16d9 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 Kriščiūnas [aut], Haohan Li [aut], Povilas Gibas [aut], Karolis Koncevičius [aut], Art Petronis [aut], Gabriel Oh [aut] Maintainer: Matthew Carlucci 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: 6ee1cd1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DiscoRhythm_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DiscoRhythm_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DiscoRhythm_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DiscoRhythm_1.21.0.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: 160 Package: distinct Version: 1.17.0 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: 47532b64157fa2810dbd9b0275d9d09f 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 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: 9bf9341 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/distinct_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/distinct_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/distinct_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/distinct_1.17.0.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: 117 Package: dittoSeq Version: 1.17.0 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: 5d95ff941ec4bea46946431a2aa192a6 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dittoSeq git_branch: devel git_last_commit: 3f77dfe git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/dittoSeq_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/dittoSeq_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/dittoSeq_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/dittoSeq_1.17.0.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, tidySpatialExperiment, magmaR, scCustomize dependencyCount: 73 Package: divergence Version: 1.21.0 Depends: R (>= 3.6), SummarizedExperiment Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 47f76e68f1585289e37b80dea59a5efc 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 , Luigi Marchionni , Qian Ke Maintainer: Wikum Dinalankara , Luigi Marchionni VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/divergence git_branch: devel git_last_commit: a8fbbe9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/divergence_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/divergence_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/divergence_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/divergence_1.21.0.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: 1.51.0 Depends: R (>= 2.8) Imports: cubature License: GPL MD5sum: 6b6f900b8ca562c955e2b95f1d0906f5 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 Maintainer: Jeffrey T. Leek git_url: https://git.bioconductor.org/packages/dks git_branch: devel git_last_commit: b0b4fe2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/dks_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/dks_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/dks_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/dks_1.51.0.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: 1.19.0 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: a01c6a6fc6b9d105449c62c0b61153cf 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] () Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/DMCFB/issues git_url: https://git.bioconductor.org/packages/DMCFB git_branch: devel git_last_commit: 32e9c21 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DMCFB_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DMCFB_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DMCFB_1.19.0.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: 113 Package: DMCHMM Version: 1.27.0 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: efc8fa583b8bcf050361ba67dd28c68c 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 VignetteBuilder: knitr BugReports: https://github.com/shokoohi/DMCHMM/issues git_url: https://git.bioconductor.org/packages/DMCHMM git_branch: devel git_last_commit: 0f60ef7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DMCHMM_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DMCHMM_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DMCHMM_1.27.0.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: 1.37.0 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: 8af865c7410d654242b8e05dd4f247a4 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 , Jonathan Michael Foonlan Tsang , Alessandro Pio Greco and Ryan Merritt Maintainer: Nicolae Radu Zabet VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DMRcaller git_branch: devel git_last_commit: 72bb239 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DMRcaller_1.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DMRcaller_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DMRcaller_1.37.0.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: 3.1.0 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, FlowSorted.Blood.EPIC, tissueTreg, DMRcatedata, EPICv2manifest License: file LICENSE MD5sum: 83ada2b636f28a972d5093660e3e0401 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DMRcate git_branch: devel git_last_commit: 5d83c68 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DMRcate_3.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DMRcate_3.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DMRcate_3.1.0.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 dependsOnMe: methylationArrayAnalysis suggestsMe: missMethyl dependencyCount: 218 Package: DMRScan Version: 1.27.0 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: 6ffcc95a0c31d2dc19840cc22850e625 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 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: c387d95 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DMRScan_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DMRScan_1.27.0.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: 1.25.0 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: a953be45c50e0d4b66e768b3b46e0b89 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] (), Rafael Irizarry [aut] (), Yuval Benjamini [aut], Sutirtha Chakraborty [aut] Maintainer: Keegan Korthauer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dmrseq git_branch: devel git_last_commit: 0ee8f66 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/dmrseq_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/dmrseq_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/dmrseq_1.25.0.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: 148 Package: DNAcopy Version: 1.79.0 License: GPL (>= 2) Archs: x64 MD5sum: 5065482989708d1605210cf8da5cba55 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 git_url: https://git.bioconductor.org/packages/DNAcopy git_branch: devel git_last_commit: 7cc695a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DNAcopy_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DNAcopy_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DNAcopy_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DNAcopy_1.79.0.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, 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: DNAfusion Version: 1.7.0 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 MD5sum: f8af3d73f0a8ca5a0a0472d7c8af08dd 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] (), Emma Roger Andersen [ctb, rev], Maiken Parm Ulhoi [dtc], Peter Meldgaard [dtc], Boe Sandahl Sorensen [rev, fnd] Maintainer: Christoffer Trier Maansson 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: ee3b533 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DNAfusion_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DNAfusion_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DNAfusion_1.7.0.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: 81 Package: DNAshapeR Version: 1.33.0 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: a137fb33b9750a1800a2f74346f2929a 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DNAshapeR git_branch: devel git_last_commit: b9cd43d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DNAshapeR_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DNAshapeR_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DNAshapeR_1.33.0.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: 1.25.0 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: ee34a7073f4ff936fa35b518f386a903 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 , Peter Blattmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DominoEffect git_branch: devel git_last_commit: 5aa8cf4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DominoEffect_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DominoEffect_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DominoEffect_1.25.0.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: 105 Package: doppelgangR Version: 1.33.0 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: 024d327d07cab74799710c9ad9eaaf0f 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 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: a2e666d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/doppelgangR_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/doppelgangR_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/doppelgangR_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/doppelgangR_1.33.0.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: 82 Package: Doscheda Version: 1.27.0 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: 1798acd04836c9ce3fff02182293d2e2 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Doscheda git_branch: devel git_last_commit: 34b085c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Doscheda_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Doscheda_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Doscheda_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Doscheda_1.27.0.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: 160 Package: DOSE Version: 3.31.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi, HDO.db, BiocParallel, fgsea, ggplot2, GOSemSim (>= 2.27.1), methods, qvalue, reshape2, stats, utils, yulab.utils Suggests: prettydoc, clusterProfiler, gson (>= 0.0.5), knitr, rmarkdown, org.Hs.eg.db, HPO.db, MPO.db, testthat License: Artistic-2.0 MD5sum: 9c70cba08c8b26412f32daf2a01ce6d3 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], Erqiang Hu [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/DOSE/issues git_url: https://git.bioconductor.org/packages/DOSE git_branch: devel git_last_commit: 7ce9fcb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DOSE_3.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DOSE_3.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DOSE_3.31.0.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, MAGeCKFlute, meshes, miRSM, Moonlight2R, MoonlightR, pareg, Pigengene, ReactomePA, RegEnrich, scTensor, signatureSearch, SVMDO, TDbasedUFEadv, ExpHunterSuite, GseaVis, immcp suggestsMe: cola, GOSemSim, GRaNIE, rrvgo, scGPS, simplifyEnrichment, aPEAR dependencyCount: 93 Package: doseR Version: 1.21.0 Depends: R (>= 3.6) Imports: edgeR, methods, stats, graphics, matrixStats, mclust, lme4, RUnit, SummarizedExperiment, digest, S4Vectors Suggests: BiocStyle, knitr, rmarkdown License: GPL MD5sum: 3fb707e0c5640f7ea64da68975fe0229 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/doseR git_branch: devel git_last_commit: 3341ce2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/doseR_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/doseR_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/doseR_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/doseR_1.21.0.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: 80 Package: doubletrouble Version: 1.5.0 Depends: R (>= 4.2.0) Imports: syntenet, GenomicRanges, Biostrings, mclust, MSA2dist (>= 1.1.5), ggplot2, rlang, stats, utils, AnnotationDbi, GenomicFeatures, BiocParallel Suggests: txdbmaker, testthat (>= 3.0.0), knitr, feature, patchwork, BiocStyle, rmarkdown, covr, sessioninfo License: GPL-3 MD5sum: 5e05504aaa1342362d3578403d2a77a5 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: Fabrício Almeida-Silva [aut, cre] (), Yves Van de Peer [aut] () Maintainer: Fabrício Almeida-Silva 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: d0f690b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/doubletrouble_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/doubletrouble_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/doubletrouble_1.5.0.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: 159 Package: drawProteins Version: 1.25.0 Depends: R (>= 4.0) Imports: ggplot2, httr, dplyr, readr, tidyr Suggests: covr, testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 26c313f6ff1058d740ad09ae55ac734f 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 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: 4122c26 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/drawProteins_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/drawProteins_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/drawProteins_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/drawProteins_1.25.0.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: 1.3.0 Depends: R (>= 4.3.0), variancePartition (>= 1.33.11), 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, 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, scater, scuttle License: Artistic-2.0 Archs: x64 MD5sum: 2f2abfaded94ab8df0047aa04ad5658a 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] () Maintainer: Gabriel Hoffman 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: cf98b09 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/dreamlet_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/dreamlet_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/dreamlet_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/dreamlet_1.3.0.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: 202 Package: DRIMSeq Version: 1.33.0 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: cb2c7b20cd48bc351c7d0ce6b29ee75f 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DRIMSeq git_branch: devel git_last_commit: 280b850 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DRIMSeq_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DRIMSeq_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DRIMSeq_1.33.0.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: 1.45.0 Depends: R (>= 2.10), methods License: GPL-3 MD5sum: 6be45b2af634f01c06f9cc8cf3142333 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 git_url: https://git.bioconductor.org/packages/DriverNet git_branch: devel git_last_commit: 6554729 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DriverNet_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DriverNet_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DriverNet_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DriverNet_1.45.0.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: 1.25.0 Depends: SingleCellExperiment Imports: utils, stats, methods, Matrix, Rcpp, BiocGenerics, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, BiocParallel, DelayedArray, 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: 5f7c0ee7df583050d6b4d80f3aac836e 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 SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DropletUtils git_branch: devel git_last_commit: 09b8cf7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DropletUtils_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DropletUtils_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DropletUtils_1.25.0.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 suggestsMe: alabaster.spatial, demuxmix, mumosa, Nebulosa, SingleCellAlleleExperiment, SpatialExperiment, SPOTlight, tidySpatialExperiment, DropletTestFiles, MerfishData, muscData, spatialLIBD, scCustomize, SoupX dependencyCount: 65 Package: drugTargetInteractions Version: 1.13.0 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: cb1ea954479ad30219dd872d747b5274 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 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: 6c1c6dc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-03 source.ver: src/contrib/drugTargetInteractions_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/drugTargetInteractions_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/drugTargetInteractions_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/drugTargetInteractions_1.13.0.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: 109 Package: DrugVsDisease Version: 2.47.0 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 MD5sum: c969f2348ac40cfc588625e2119f3365 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 git_url: https://git.bioconductor.org/packages/DrugVsDisease git_branch: devel git_last_commit: 25c2c7a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DrugVsDisease_2.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DrugVsDisease_2.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DrugVsDisease_2.47.0.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: 131 Package: DSS Version: 2.53.0 Depends: R (>= 3.5.0), methods, Biobase, BiocParallel, bsseq, parallel Imports: utils, graphics, stats, splines Suggests: BiocStyle, knitr, rmarkdown, edgeR License: GPL MD5sum: 4cd474b549d33d03e4aca9e1a20c171b 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 Feng Maintainer: Hao Wu , Hao Feng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DSS git_branch: devel git_last_commit: 22b05ef git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DSS_2.53.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DSS_2.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DSS_2.53.0.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: 89 Package: dStruct Version: 1.11.0 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: 3afc9f9703cdcd7053dbb41fb2c70224 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] (), Sharon Aviran [aut] () Maintainer: Krishna Choudhary 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: d23992a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/dStruct_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/dStruct_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/dStruct_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/dStruct_1.11.0.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: 48 Package: DTA Version: 2.51.0 Depends: R (>= 2.10), LSD Imports: scatterplot3d License: Artistic-2.0 MD5sum: 6d43223bb8c47eddbd1691de0fe1309a 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 git_url: https://git.bioconductor.org/packages/DTA git_branch: devel git_last_commit: e998bdf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DTA_2.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DTA_2.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DTA_2.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DTA_2.51.0.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: 1.17.0 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: 99a19681958e13d0b87a6fd54ae2eba8 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] (), Kelly Street [aut] Maintainer: Hector Roux de Bezieux VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Dune git_branch: devel git_last_commit: ff40be9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Dune_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Dune_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Dune_1.17.0.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: 98 Package: dupRadar Version: 1.35.0 Depends: R (>= 3.2.0) Imports: Rsubread (>= 1.14.1), KernSmooth Suggests: BiocStyle, knitr, rmarkdown, AnnotationHub License: GPL-3 MD5sum: 376fe8a89f6f075c7d79775f718138cb 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 , Holger Klein Maintainer: Sergi Sayols , Holger Klein 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: 59703ff git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/dupRadar_1.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/dupRadar_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/dupRadar_1.35.0.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: 1.65.0 Depends: R (>= 1.4.1), marray, Biobase Suggests: limma, convert, GEOquery, dyebiasexamples, methods License: GPL-3 MD5sum: 9f6b51942fe35a6f1bbe1291d7d3b0cf 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 URL: http://www.holstegelab.nl/publications/margaritis_lijnzaad git_url: https://git.bioconductor.org/packages/dyebias git_branch: devel git_last_commit: 499d7fd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/dyebias_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/dyebias_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/dyebias_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/dyebias_1.65.0.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: 10 Package: DynDoc Version: 1.83.0 Depends: methods, utils Imports: methods License: Artistic-2.0 MD5sum: 66c46ee06e06a1f1a0a0fbee33533351 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 git_url: https://git.bioconductor.org/packages/DynDoc git_branch: devel git_last_commit: b691c52 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/DynDoc_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/DynDoc_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/DynDoc_1.83.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/DynDoc_1.83.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: tkWidgets dependencyCount: 2 Package: EasyCellType Version: 1.7.0 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: 496000fbcdcca19a49d289269ff17217 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EasyCellType git_branch: devel git_last_commit: ecf1fde git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/EasyCellType_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/EasyCellType_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/EasyCellType_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/EasyCellType_1.7.0.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: 149 Package: easylift Version: 1.3.0 Depends: GenomicRanges, BiocFileCache Imports: rtracklayer, GenomeInfoDb, R.utils, tools, methods Suggests: testthat (>= 3.0.0), IRanges, knitr, BiocStyle, rmarkdown License: MIT + file LICENSE MD5sum: a56129b089e44223ba89ea278c3013f0 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] (), Hervé Pagès [aut, rev], Michael Love [aut, rev] () Maintainer: Abdullah Al Nahid 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: cdb26da git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/easylift_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/easylift_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/easylift_1.3.0.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: 92 Package: easyreporting Version: 1.17.0 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: 76c2b4ca015647009113f6cfa6694f59 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 VignetteBuilder: knitr BugReports: https://github.com/drighelli/easyreporting/issues git_url: https://git.bioconductor.org/packages/easyreporting git_branch: devel git_last_commit: ca567d6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/easyreporting_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/easyreporting_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/easyreporting_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/easyreporting_1.17.0.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: EBarrays Version: 2.69.0 Depends: R (>= 1.8.0), Biobase, lattice, methods Imports: Biobase, cluster, graphics, grDevices, lattice, methods, stats License: GPL (>= 2) Archs: x64 MD5sum: 26449a8465a755af9f6610631e95f69b 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 git_url: https://git.bioconductor.org/packages/EBarrays git_branch: devel git_last_commit: f31d8e0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/EBarrays_2.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/EBarrays_2.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/EBarrays_2.69.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/EBarrays_2.69.0.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: 10 Package: EBcoexpress Version: 1.49.0 Depends: EBarrays, mclust, minqa Suggests: graph, igraph, colorspace License: GPL (>= 2) Archs: x64 MD5sum: 528236ae08335cebf56f85eff4a80e00 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 git_url: https://git.bioconductor.org/packages/EBcoexpress git_branch: devel git_last_commit: 52db3d8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/EBcoexpress_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/EBcoexpress_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/EBcoexpress_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/EBcoexpress_1.49.0.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: 14 Package: EBImage Version: 4.47.0 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: 2d580a36ec87f53fea358b349d493aa0 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 Oleś, Gregoire Pau, Mike Smith, Oleg Sklyar, Wolfgang Huber, with contributions from Joseph Barry and Philip A. Marais Maintainer: Andrzej Oleś 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: 68bd71e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/EBImage_4.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/EBImage_4.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/EBImage_4.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/EBImage_4.47.0.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, GiNA, nucim importsMe: bnbc, Cardinal, cytoviewer, flowCHIC, heatmaps, imcRtools, MoleculeExperiment, RBioFormats, SpatialOmicsOverlay, synapsis, yamss, BioImageDbs, bioimagetools, GoogleImage2Array, LFApp, LOMAR, RockFab, SAFARI suggestsMe: HilbertVis, DmelSGI, spicyWorkflow, aroma.core, ExpImage, glow, ijtiff, juicr, lidR, metagear, pliman, rcaiman, SIPmg dependencyCount: 44 Package: EBSEA Version: 1.33.0 Depends: R (>= 4.0.0) Imports: DESeq2, graphics, stats, EmpiricalBrownsMethod Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 0d05ce3b22c89f5799130f5609c74e2b 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EBSEA git_branch: devel git_last_commit: 4ae5ff7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/EBSEA_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/EBSEA_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/EBSEA_1.33.0.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: 2.3.0 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: 1eea383e0f1cf077cc16454838f0eadc 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 SystemRequirements: c++11 git_url: https://git.bioconductor.org/packages/EBSeq git_branch: devel git_last_commit: 18d0eb3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/EBSeq_2.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/EBSeq_2.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/EBSeq_2.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/EBSeq_2.3.0.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, DEsubs, scDD suggestsMe: compcodeR dependencyCount: 50 Package: ecolitk Version: 1.77.0 Depends: R (>= 2.10) Imports: Biobase, graphics, methods Suggests: ecoliLeucine, ecolicdf, graph, multtest, affy License: GPL (>= 2) MD5sum: e31b8dd4cc52efdd37943b50338b2034 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 git_url: https://git.bioconductor.org/packages/ecolitk git_branch: devel git_last_commit: b2edc32 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ecolitk_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ecolitk_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ecolitk_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ecolitk_1.77.0.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: 6 Package: EDASeq Version: 2.39.0 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 MD5sum: 38ab95c8ed00b403c2df1ce9ac012534 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 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: 466878d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/EDASeq_2.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/EDASeq_2.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/EDASeq_2.39.0.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, TCGAbiolinks dependencyCount: 117 Package: edge Version: 2.37.0 Depends: R(>= 3.1.0), Biobase Imports: methods, splines, sva, qvalue(>= 1.99.0), MASS Suggests: testthat, knitr, ggplot2, reshape2 License: MIT + file LICENSE Archs: x64 MD5sum: 9f4a8a535c1ea9905de5bb8e3bf41788 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 , Andrew J. Bass 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: ab31833 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/edge_2.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/edge_2.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/edge_2.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/edge_2.37.0.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: 4.3.0 Depends: R (>= 3.6.0), limma (>= 3.41.5) Imports: methods, graphics, stats, utils, locfit, Rcpp LinkingTo: Rcpp Suggests: jsonlite, readr, rhdf5, splines, knitr, AnnotationDbi, Biobase, BiocStyle, SummarizedExperiment, org.Hs.eg.db, Matrix, SeuratObject License: GPL (>=2) Archs: x64 MD5sum: 744772576ea48f991fcc1215308d067f NeedsCompilation: yes Title: Empirical Analysis of Digital Gene Expression Data in R Description: Differential expression analysis of RNA-seq expression profiles with biological replication. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. As well as RNA-seq, it be applied to differential signal analysis of other types of genomic data that produce read counts, including ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE and CAGE. biocViews: GeneExpression, Transcription, AlternativeSplicing, Coverage, DifferentialExpression, DifferentialSplicing, DifferentialMethylation, GeneSetEnrichment, Pathways, Genetics, DNAMethylation, Bayesian, Clustering, ChIPSeq, Regression, TimeCourse, Sequencing, RNASeq, BatchEffect, SAGE, Normalization, QualityControl, MultipleComparison, BiomedicalInformatics, CellBiology, FunctionalGenomics, Epigenetics, Genetics, ImmunoOncology, SystemsBiology, Transcriptomics, SingleCell Author: Yunshun Chen, Aaron TL Lun, Davis J McCarthy, Lizhong Chen, Matthew E Ritchie, Belinda Phipson, Yifang Hu, Xiaobei Zhou, Mark D Robinson, Gordon K Smyth Maintainer: Yunshun Chen , Gordon Smyth , Aaron Lun , Mark Robinson 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: 5c1ded8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/edgeR_4.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/edgeR_4.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/edgeR_4.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/edgeR_4.3.0.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, censcyt, ChromSCape, circRNAprofiler, clusterExperiment, CNVRanger, compcodeR, consensusDE, coseq, countsimQC, crossmeta, csaw, cypress, DaMiRseq, 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, microbiomeMarker, MIRit, MLSeq, moanin, mobileRNA, MOSim, Motif2Site, msgbsR, msmsTests, multiHiCcompare, muscat, PathoStat, phantasus, PhIPData, ppcseq, PROPER, RCM, regsplice, Repitools, RNAseqCovarImpute, ROSeq, Rvisdiff, saseR, scCB2, scde, scone, scran, ScreenR, SEtools, SIMD, SingleCellSignalR, singscore, 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, cinaR, CoreMicrobiomeR, ggpicrust2, HTSCluster, MetaLonDA, microbial, myTAI, RCPA, RVA, scITD, SCRIP, scRNAtools, SPUTNIK, ssizeRNA, TSGS suggestsMe: ABSSeq, biobroom, ClassifyR, cqn, cydar, dcanr, dearseq, DEScan2, DiffBind, dittoSeq, DSS, easyreporting, EDASeq, gage, gCrisprTools, GenomicAlignments, GenomicRanges, GeoTcgaData, glmGamPoi, groHMM, GSAR, GSVA, iSEEpathways, iSEEu, lemur, missMethyl, MoonlightR, multiMiR, raer, regionReport, ribosomeProfilingQC, satuRn, scider, SeqGate, signifinder, SpliceWiz, stageR, subSeq, systemPipeR, TCGAbiolinks, tidybulk, topconfects, tximeta, tximport, variancePartition, weitrix, Wrench, zenith, zFPKM, leeBamViews, CAGEWorkflow, chipseqDB, DGEobj, DGEobj.utils, DiPALM, glmmSeq, MiscMetabar, pctax, pmartR, seqgendiff, SIBERG, volcano3D dependencyCount: 11 Package: EDIRquery Version: 1.5.0 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 MD5sum: f52fd7ee5cb04cf8c7902c949f91bf5d 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] () Maintainer: Laura D.T. Vo Ngoc VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EDIRquery git_branch: devel git_last_commit: e4f0003 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/EDIRquery_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/EDIRquery_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/EDIRquery_1.5.0.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: 1.7.0 Depends: Matrix Imports: Rcpp LinkingTo: Rcpp Suggests: knitr, tximportData, testthat (>= 3.0.0) License: GPL-2 Archs: x64 MD5sum: 545e17530d746e71edcc3bafa336a145 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 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: 4b935eb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/eds_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/eds_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/eds_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/eds_1.7.0.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: 1.33.0 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: 688ebf8de82d59816d92baf964f23da8 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 VignetteBuilder: rmarkdown git_url: https://git.bioconductor.org/packages/EGAD git_branch: devel git_last_commit: 78ebe6d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/EGAD_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/EGAD_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/EGAD_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/EGAD_1.33.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 64 Package: EGSEA Version: 1.33.0 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 MD5sum: 4370de39f83c7675d8dd988120ec4df7 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EGSEA git_branch: devel git_last_commit: 3a1df6a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/EGSEA_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/EGSEA_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/EGSEA_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/EGSEA_1.33.0.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: 199 Package: eiR Version: 1.45.0 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 License: Artistic-2.0 Archs: x64 MD5sum: 1a0b7e75056092c2b0b98c34ca4ab7ac 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 URL: https://github.com/girke-lab/eiR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/eiR git_branch: devel git_last_commit: 09203f6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/eiR_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/eiR_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/eiR_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/eiR_1.45.0.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: 80 Package: eisaR Version: 1.17.0 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: 6d08e86a5a234b5c6f29f53c1e26e5e4 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 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: 550297f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/eisaR_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/eisaR_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/eisaR_1.17.0.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: 41 Package: ELMER Version: 2.29.0 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 MD5sum: b522713f4d7288bd15963c0be8eb1cd1 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ELMER git_branch: devel git_last_commit: 80d0841 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ELMER_2.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ELMER_2.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ELMER_2.29.0.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 importsMe: TCGAWorkflow dependencyCount: 223 Package: EMDomics Version: 2.35.0 Depends: R (>= 3.2.1) Imports: emdist, BiocParallel, matrixStats, ggplot2, CDFt, preprocessCore Suggests: knitr License: MIT + file LICENSE MD5sum: 7de7410009db937bd1716a057f28f220 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 and Daniel Schmolze VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EMDomics git_branch: devel git_last_commit: 84cabcc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/EMDomics_2.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/EMDomics_2.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/EMDomics_2.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/EMDomics_2.35.0.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: 1.33.0 Depends: R (>= 3.2.0) Suggests: BiocStyle, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 909572ebf88e6d48888b2e0f42fc62c0 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 URL: https://github.com/IlyaLab/CombiningDependentPvaluesUsingEBM.git VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EmpiricalBrownsMethod git_branch: devel git_last_commit: 94bebe9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/EmpiricalBrownsMethod_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/EmpiricalBrownsMethod_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/EmpiricalBrownsMethod_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/EmpiricalBrownsMethod_1.33.0.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: 1.23.0 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: 730b133235ffe3a037487ad5f9e816a6 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 URL: https://github.com/kevinblighe/EnhancedVolcano VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EnhancedVolcano git_branch: devel git_last_commit: 872adba git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/EnhancedVolcano_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/EnhancedVolcano_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/EnhancedVolcano_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/EnhancedVolcano_1.23.0.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: ExpHunterSuite, TOmicsVis suggestsMe: rliger dependencyCount: 37 Package: enhancerHomologSearch Version: 1.11.0 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: f2b7330f7f40481ef7a49f492997ff51 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] (), Valentina Cigliola [dtc], Kenneth Poss [fnd] Maintainer: Jianhong Ou 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: 844376b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/enhancerHomologSearch_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/enhancerHomologSearch_1.11.0.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: 135 Package: EnMCB Version: 1.17.0 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: 745f153aded02e32f1c1252b12ab1eff 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 VignetteBuilder: knitr BugReports: https://github.com/whirlsyu/EnMCB/issues git_url: https://git.bioconductor.org/packages/EnMCB git_branch: devel git_last_commit: 89c6dda git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/EnMCB_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/EnMCB_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/EnMCB_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/EnMCB_1.17.0.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: 1.41.0 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 MD5sum: 483a0b04c25262e2e4a533bc3922cc83 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 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: 562d4ec git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ENmix_1.41.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ENmix_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ENmix_1.41.0.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: 159 Package: EnrichedHeatmap Version: 1.35.0 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: e09922f33d5604433614c9a19e204dd8 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] () Maintainer: Zuguang Gu URL: https://github.com/jokergoo/EnrichedHeatmap VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EnrichedHeatmap git_branch: devel git_last_commit: cb7981d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/EnrichedHeatmap_1.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/EnrichedHeatmap_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/EnrichedHeatmap_1.35.0.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: 2.35.1 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: 2db6a2855c436bf27467f11aa2fcac69 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 VignetteBuilder: knitr BugReports: https://github.com/lgeistlinger/EnrichmentBrowser/issues git_url: https://git.bioconductor.org/packages/EnrichmentBrowser git_branch: devel git_last_commit: 2f98d2d git_last_commit_date: 2024-05-05 Date/Publication: 2024-05-06 source.ver: src/contrib/EnrichmentBrowser_2.35.1.tar.gz win.binary.ver: bin/windows/contrib/4.4/EnrichmentBrowser_2.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/EnrichmentBrowser_2.35.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/EnrichmentBrowser_2.35.1.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: epiregulon.extra, GenomicSuperSignature, roastgsa dependencyCount: 96 Package: enrichplot Version: 1.25.0 Depends: R (>= 3.5.0) Imports: aplot (>= 0.2.1), DOSE (>= 3.16.0), ggfun (>= 0.1.3), ggnewscale, ggplot2, ggraph, graphics, grid, igraph, methods, plyr, purrr, RColorBrewer, reshape2, rlang, stats, utils, scatterpie, shadowtext, GOSemSim, magrittr, ggtree, yulab.utils (>= 0.0.8) Suggests: clusterProfiler, dplyr, europepmc, ggupset, knitr, rmarkdown, org.Hs.eg.db, prettydoc, tibble, tidyr, ggforce, AnnotationDbi, ggplotify, ggridges, grDevices, gridExtra, ggrepel (>= 0.9.0), ggstar, scales, ggtreeExtra, tidydr License: Artistic-2.0 MD5sum: ee69d8a25ae47cdf97f2e8bd53f8df75 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] (), Erqiang Hu [ctb] (), Chun-Hui Gao [ctb] () Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/enrichplot/issues git_url: https://git.bioconductor.org/packages/enrichplot git_branch: devel git_last_commit: e20c821 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/enrichplot_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/enrichplot_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/enrichplot_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/enrichplot_1.25.0.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, MAGeCKFlute, meshes, MicrobiomeProfiler, ReactomePA, TDbasedUFEadv, ExpHunterSuite, TOmicsVis suggestsMe: GeoTcgaData, mastR, methylGSA, pareg, ReporterScore, SCpubr dependencyCount: 125 Package: enrichViewNet Version: 1.3.0 Depends: R (>= 4.2.0) Imports: gprofiler2, strex, RCy3, jsonlite, stringr, enrichplot, DOSE, methods Suggests: BiocStyle, knitr, rmarkdown, ggplot2, testthat License: Artistic-2.0 MD5sum: 3df041db998c797cccc882b2879c8a36 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] (), Pascal Belleau [aut] (), Robert L. Faure [aut] (), Maria J. Fernandes [aut] (), Alexander Krasnitz [aut], David A. Tuveson [aut] () Maintainer: Astrid Deschênes 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: 9e646f5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/enrichViewNet_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/enrichViewNet_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/enrichViewNet_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/enrichViewNet_1.3.0.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: 165 Package: ensembldb Version: 2.29.0 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: 92c21ed1423c99fe447950830439ddab 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 with contributions from Tim Triche, Sebastian Gibb, Laurent Gatto Christian Weichenberger and Boyu Yu. Maintainer: Johannes Rainer 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: f9c0e8c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ensembldb_2.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ensembldb_2.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ensembldb_2.29.0.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, ChIPpeakAnno, consensusDE, diffUTR, epimutacions, epivizrData, ggbio, GRaNIE, Gviz, RAIDS, RITAN, scanMiRApp, scFeatures, signifinder, singleCellTK, TVTB, tximeta, GenomicDistributionsData, scRNAseq, locuszoomr, MOCHA, RNAseqQC suggestsMe: AlphaMissenseR, AnnotationHub, autonomics, CNVRanger, eisaR, EpiTxDb, fishpond, GenomicFeatures, ldblock, multicrispr, nullranges, satuRn, txdbmaker, wiggleplotr, celldex, GeneSelectR dependencyCount: 80 Package: epialleleR Version: 1.13.0 Depends: R (>= 4.1) Imports: stats, methods, utils, GenomicRanges, BiocGenerics, GenomeInfoDb, SummarizedExperiment, VariantAnnotation, data.table, Rcpp LinkingTo: Rcpp, BH, Rhtslib, zlibbioc Suggests: RUnit, knitr, rmarkdown, ggplot2, ggstance, gridExtra License: Artistic-2.0 MD5sum: 80077472ec32e20f9c19d2e8c8e966c0 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 methylation patterns and assess allele specificity of methylation. biocViews: DNAMethylation, Epigenetics, MethylSeq Author: Oleksii Nikolaienko [aut, cre] () Maintainer: Oleksii Nikolaienko 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: 356cd8e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/epialleleR_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/epialleleR_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/epialleleR_1.13.0.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: 81 Package: epidecodeR Version: 1.13.0 Depends: R (>= 3.1.0) Imports: EnvStats, ggplot2, rtracklayer, GenomicRanges, IRanges, rstatix, ggpubr, methods, stats, utils, dplyr Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 36b9cb22df77878c0d1b8e16e527dfe0 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 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: 32a3738 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/epidecodeR_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/epidecodeR_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/epidecodeR_1.13.0.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: 136 Package: EpiDISH Version: 2.21.0 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: 34d7d3b530c764f4bf50d572e92c8bad 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 whole blood, 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 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: 0b6c715 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/EpiDISH_2.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/EpiDISH_2.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/EpiDISH_2.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/EpiDISH_2.21.0.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: 1.45.0 Depends: R (>= 3.5.0), methods, Biobase, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment Imports: BiocGenerics, MCMCpack, Rsamtools, parallel, GenomeInfoDb, beadarray License: LGPL-3 MD5sum: 4c300409a856a62c013b1f597c0c8d57 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 git_url: https://git.bioconductor.org/packages/epigenomix git_branch: devel git_last_commit: 22e94a5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/epigenomix_1.45.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/epigenomix_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/epigenomix_1.45.0.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: 1.13.0 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: 397aec197a2ec37eab8759ff0df27d86 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 SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epigraHMM git_branch: devel git_last_commit: 52c707d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/epigraHMM_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/epigraHMM_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/epigraHMM_1.13.0.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: 149 Package: EpiMix Version: 1.7.1 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: ce06ddd561dfc614370ba7cc407af388 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 VignetteBuilder: knitr BugReports: https://github.com/gevaertlab/EpiMix/issues git_url: https://git.bioconductor.org/packages/EpiMix git_branch: devel git_last_commit: bb3a46f git_last_commit_date: 2024-05-01 Date/Publication: 2024-05-01 source.ver: src/contrib/EpiMix_1.7.1.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/EpiMix_1.7.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/EpiMix_1.7.1.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: 138 Package: epimutacions Version: 1.9.0 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 MD5sum: 28dc914f8255c5cac5acd8fcb464ef6a 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] (), Juan R. Gonzalez [aut] (), Carlos Ruiz-Arenas [aut] (), Carles Hernandez-Ferrer [aut] (), Leire Abarrategui [aut] () Maintainer: Dolors Pelegri-Siso 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: 2d99781 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/epimutacions_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/epimutacions_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/epimutacions_1.9.0.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: 220 Package: epiNEM Version: 1.29.0 Depends: R (>= 4.1) Imports: 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: 274b44489dab5d7e417fb32b83acdafc 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 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: cbadc69 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/epiNEM_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/epiNEM_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/epiNEM_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/epiNEM_1.29.0.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: 109 Package: epiregulon Version: 1.1.0 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 Suggests: knitr, rmarkdown, parallel, BiocStyle, testthat (>= 3.0.0), coin, scater License: MIT + file LICENSE MD5sum: 7154d12c93df592d1ab794ec12f8766d 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] (), Tomasz Włodarczyk [aut] (), Aaron Lun [aut], Shang-Yang Chen [aut] Maintainer: Xiaosai Yao 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: 8da3156 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/epiregulon_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/epiregulon_1.1.0.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: 210 Package: epiregulon.extra Version: 1.1.0 Depends: R (>= 4.4), SingleCellExperiment Imports: scran, ComplexHeatmap, Matrix, SummarizedExperiment, checkmate, circlize, clusterProfiler, ggplot2, ggraph, igraph, lifecycle, patchwork, reshape2, scales, scater, stats Suggests: epiregulon, knitr, rmarkdown, parallel, BiocStyle, testthat (>= 3.0.0), EnrichmentBrowser, msigdbr, dorothea, scMultiome, S4Vectors, scuttle, vdiffr, ggrastr, ggrepel License: MIT + file LICENSE MD5sum: 438063a5798cfbfba3b746ef743a5bb2 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] (), Tomasz Włodarczyk [aut] (), Timothy Keyes [aut], Shang-Yang Chen [aut] Maintainer: Xiaosai Yao 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: 9bb71fe git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/epiregulon.extra_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/epiregulon.extra_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/epiregulon.extra_1.1.0.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: 186 Package: epistack Version: 1.11.0 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 MD5sum: fdc9a2f0529544a79c94ca807db25abf 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 URL: https://github.com/GenEpi-GenPhySE/epistack VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epistack git_branch: devel git_last_commit: 04b02ca git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/epistack_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/epistack_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/epistack_1.11.0.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: 1.7.0 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 Archs: x64 MD5sum: 5d6bcd1bd40eecb1bb431780648cecba 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 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: 2374862 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/epistasisGA_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/epistasisGA_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/epistasisGA_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/epistasisGA_1.7.0.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: 116 Package: EpiTxDb Version: 1.17.0 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: 8ba9a8c63f3873bfee36ffbd8f5adf74 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] () Maintainer: Felix G.M. Ernst 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: 6310866 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/EpiTxDb_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/EpiTxDb_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/EpiTxDb_1.17.0.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: 122 Package: epivizr Version: 2.35.0 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 MD5sum: 2e8527748183f84b2f6b3a2485efb951 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 VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=099c4wUxozA git_url: https://git.bioconductor.org/packages/epivizr git_branch: devel git_last_commit: dcf23aa git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/epivizr_2.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/epivizr_2.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/epivizr_2.35.0.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: 124 Package: epivizrChart Version: 1.27.0 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: d694ae6233f6b156e742d66fcce65b9f 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epivizrChart git_branch: devel git_last_commit: a126ef0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/epivizrChart_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/epivizrChart_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/epivizrChart_1.27.0.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: 118 Package: epivizrData Version: 1.33.0 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: 45b374b1cb5e61eba679ebe74a9a702d 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 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: a2df6d5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/epivizrData_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/epivizrData_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/epivizrData_1.33.0.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: 115 Package: epivizrServer Version: 1.33.0 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: e2c53fedcebd6d75328c5a3b61c461c3 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 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: dec5ea4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/epivizrServer_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/epivizrServer_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/epivizrServer_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/epivizrServer_1.33.0.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: 1.33.0 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: 1a6819337f069a781d5f8d5870049c5c 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epivizrStandalone git_branch: devel git_last_commit: e65b17d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/epivizrStandalone_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/epivizrStandalone_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/epivizrStandalone_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/epivizrStandalone_1.33.0.tgz vignettes: vignettes/epivizrStandalone/inst/doc/EpivizrStandalone.html vignetteTitles: Introduction to epivizrStandalone hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: scTreeViz dependencyCount: 126 Package: erccdashboard Version: 1.39.0 Depends: R (>= 3.2), ggplot2 (>= 2.1.0), gridExtra (>= 2.0.0) Imports: edgeR, gplots, grid, gtools, limma, locfit, MASS, plyr, qvalue, reshape2, ROCR, scales, stringr License: GPL (>=2) MD5sum: 4af0abb64fd5648eb6976029ea85d2c0 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 URL: https://github.com/munrosa/erccdashboard, http://tinyurl.com/erccsrm BugReports: https://github.com/munrosa/erccdashboard/issues git_url: https://git.bioconductor.org/packages/erccdashboard git_branch: devel git_last_commit: 9dfd214 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/erccdashboard_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/erccdashboard_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/erccdashboard_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/erccdashboard_1.39.0.tgz vignettes: vignettes/erccdashboard/inst/doc/erccdashboard.pdf vignetteTitles: erccdashboard examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/erccdashboard/inst/doc/erccdashboard.R dependencyCount: 53 Package: erma Version: 1.21.0 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: fbad1c7454d8a5b116581174518720d0 NeedsCompilation: no Title: epigenomic road map adventures Description: Software and data to support epigenomic road map adventures. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/erma git_branch: devel git_last_commit: d7e2885 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/erma_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/erma_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/erma_1.21.0.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: 142 Package: ERSSA Version: 1.23.0 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: 6dcafe7447659082ef1ccc10553de3c0 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 URL: https://github.com/zshao1/ERSSA VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ERSSA git_branch: devel git_last_commit: 6435b60 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ERSSA_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ERSSA_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ERSSA_1.23.0.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: 1.27.0 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 Archs: x64 MD5sum: c2162d9f8135fbc5d3bdbf37b519748f 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 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: 150e526 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/esATAC_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/esATAC_1.27.0.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: 207 Package: escape Version: 2.1.0 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 Suggests: Seurat, hexbin, scran, knitr, rmarkdown, markdown, BiocStyle, RColorBrewer, rlang, spelling, testthat (>= 3.0.0), vdiffr License: MIT + file LICENSE MD5sum: 24795122e6860be631c36b83ac657840 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] Maintainer: Nick Borcherding VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/escape git_branch: devel git_last_commit: dbea780 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/escape_2.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/escape_2.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/escape_2.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/escape_2.1.0.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 dependencyCount: 173 Package: esetVis Version: 1.31.0 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: 7ea784d54f627b371f47d7011fc03c0a 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 Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/esetVis git_branch: devel git_last_commit: 5a16f3e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/esetVis_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/esetVis_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/esetVis_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/esetVis_1.31.0.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: 1.35.0 Depends: R (>= 3.1.0) Imports: plyr, Rsamtools, R.utils, Biostrings License: GPL-2 MD5sum: fd95b87e477ec516d2c4f957abd3cd72 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 git_url: https://git.bioconductor.org/packages/eudysbiome git_branch: devel git_last_commit: 002ac3d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/eudysbiome_1.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/eudysbiome_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/eudysbiome_1.35.0.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: 1.21.0 Depends: R (>= 3.6), SummarizedExperiment, MultiAssayExperiment, cluster (>= 2.0.9), fpc (>= 2.2-3), randomForest (>= 4.6.14), flexmix (>= 2.3.15) Imports: corrplot (>= 0.84), grDevices, graphics, reshape2, ggplot2, ggdendro, plotrix, stats, matrixStats, Rdpack, MASS, class, prabclus, mclust, kableExtra Suggests: BiocStyle, knitr, rmarkdown, magrittr License: GPL-3 MD5sum: 013ccae8fe894b88ff4133226434f547 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-Díaz [aut, cre], Manuel Franco [aut], Juana-María Vivo [aut], Manuel Quesada-Martínez [aut], Astrid Duque-Ramos [aut], Jesualdo Tomás Fernández-Breis [aut] Maintainer: José Antonio Bernabé-Díaz 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: 4b8b364 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/evaluomeR_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/evaluomeR_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/evaluomeR_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/evaluomeR_1.21.0.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: 118 Package: EventPointer Version: 3.13.0 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: b3b3be93a2a6fb0cb07b2599caa2b0be 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 VignetteBuilder: knitr BugReports: https://github.com/jpromeror/EventPointer/issues git_url: https://git.bioconductor.org/packages/EventPointer git_branch: devel git_last_commit: 6c3c36a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/EventPointer_3.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/EventPointer_3.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/EventPointer_3.13.0.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: 1.13.0 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: d36082983e36dac67e298025c876e781 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] (), Brian Schilder [aut] (), Nathan Skene [aut] () Maintainer: Alan Murphy 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: ce52346 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/EWCE_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/EWCE_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/EWCE_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/EWCE_1.13.0.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: 200 Package: ExCluster Version: 1.23.0 Depends: Rsubread, GenomicRanges, rtracklayer, matrixStats, IRanges Imports: stats, methods, grDevices, graphics, utils License: GPL-3 MD5sum: 853e19b7c81a87351d67621c613362b2 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 git_url: https://git.bioconductor.org/packages/ExCluster git_branch: devel git_last_commit: 220d883 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ExCluster_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ExCluster_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ExCluster_1.23.0.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: 2.47.0 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 MD5sum: 72d1ac926436f6114760d3bfc04a334d 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 , Alain Sewer , PMP SA Maintainer: Sylvain Gubian git_url: https://git.bioconductor.org/packages/ExiMiR git_branch: devel git_last_commit: b780f6b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ExiMiR_2.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ExiMiR_2.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ExiMiR_2.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ExiMiR_2.47.0.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: 2.13.0 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: 977b5cfaeb76e374ed48e3a81321b296 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 VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/ExperimentHub/issues git_url: https://git.bioconductor.org/packages/ExperimentHub git_branch: devel git_last_commit: f758190 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ExperimentHub_2.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ExperimentHub_2.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ExperimentHub_2.13.0.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, iSEEhub, LRcell, octad, SeqSQC, BeadSorted.Saliva.EPIC, benchmarkfdrData2019, biscuiteerData, bodymapRat, CellMapperData, clustifyrdatahub, CoSIAdata, crisprScoreData, curatedAdipoChIP, CytoMethIC, DMRcatedata, EpiMix.data, ewceData, FlowSorted.Blood.EPIC, FlowSorted.CordBloodCombined.450k, HDCytoData, HiContactsData, HighlyReplicatedRNASeq, HumanAffyData, mcsurvdata, MetaGxBreast, MetaGxOvarian, MetaGxPancreas, multiWGCNAdata, muscData, NanoporeRNASeq, NestLink, nullrangesData, ObMiTi, octad.db, restfulSEData, RNAmodR.Data, scMultiome, scpdata, sesameData, SimBenchData, SpatialDatasets, spatialDmelxsim, STexampleData, tartare, TENxVisiumData, TENxXeniumData, VectraPolarisData, WeberDivechaLCdata importsMe: BiocHubsShiny, BloodGen3Module, CBNplot, coMethDMR, CTdata, DMRcate, EpiMix, epimutacions, epiregulon, ExperimentHubData, GSEABenchmarkeR, hpar, m6Aboost, MACSr, MatrixQCvis, methodical, MethReg, methylclock, Moonlight2R, MsDataHub, orthos, PhyloProfile, signatureSearch, singleCellTK, adductData, BioImageDbs, celldex, cfToolsData, chipseqDBData, CLLmethylation, curatedMetagenomicData, curatedPCaData, curatedTBData, curatedTCGAData, depmap, DropletTestFiles, DuoClustering2018, easierData, emtdata, FieldEffectCrc, gDNAinRNAseqData, GenomicDistributionsData, HarmonizedTCGAData, HCAData, HCATonsilData, HMP16SData, HMP2Data, homosapienDEE2CellScore, imcdatasets, JohnsonKinaseData, LRcellTypeMarkers, marinerData, MerfishData, methylclockData, MethylSeqData, microbiomeDataSets, MouseAgingData, MouseGastrulationData, MouseThymusAgeing, msigdb, NxtIRFdata, orthosData, PhyloProfileData, preciseTADhub, raerdata, RLHub, scaeData, scRNAseq, SFEData, signatureSearchData, SingleCellMultiModal, SingleMoleculeFootprintingData, spatialLIBD, TabulaMurisData, TabulaMurisSenisData, TENxBrainData, TENxBUSData, TENxPBMCData, tuberculosis, TumourMethData, xcoredata suggestsMe: ANF, AnnotationHub, bambu, Banksy, celaref, CellMapper, DESpace, dreamlet, ELMER, genomicInstability, HDF5Array, lute, mariner, missMethyl, MsBackendRawFileReader, multiWGCNA, muscat, nullranges, quantiseqr, rawrr, recountmethylation, SingleMoleculeFootprinting, SPOTlight, standR, TCGAbiolinks, TENxIO, xcore, BioPlex, celarefData, curatedAdipoArray, epimutacionsData, GSE103322, GSE13015, GSE159526, GSE62944, muleaData, smokingMouse, SubcellularSpatialData, tissueTreg, TransOmicsData dependencyCount: 66 Package: ExperimentHubData Version: 1.31.0 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: 52591827356903c508bd78c2b3df9338 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExperimentHubData git_branch: devel git_last_commit: 9995a23 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ExperimentHubData_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ExperimentHubData_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ExperimentHubData_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ExperimentHubData_1.31.0.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, JohnsonKinaseData, marinerData, scMultiome, smokingMouse dependencyCount: 125 Package: ExperimentSubset Version: 1.15.0 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: cdd5bd3a70d89dc1dd7367f7ac025868 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] (), Muhammad Asif [aut, ths] (), Joshua D. Campbell [aut] () Maintainer: Irzam Sarfraz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExperimentSubset git_branch: devel git_last_commit: 7e854da git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ExperimentSubset_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ExperimentSubset_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ExperimentSubset_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ExperimentSubset_1.15.0.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: 92 Package: ExploreModelMatrix Version: 1.17.0 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: 399c249cc1cade124a33ad6c53807ca9 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] (), Federico Marini [aut] (), Michael Love [aut] (), Florian Geier [aut] (), Michael Stadler [aut] () Maintainer: Charlotte Soneson 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: 130d586 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ExploreModelMatrix_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ExploreModelMatrix_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ExploreModelMatrix_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ExploreModelMatrix_1.17.0.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: 1.33.0 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: 59ea1a66607ae68d602dc1c96ece450f 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] (), Pedro Madrigal [cre] () Maintainer: Pedro Madrigal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExpressionAtlas git_branch: devel git_last_commit: 630cf9b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ExpressionAtlas_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ExpressionAtlas_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ExpressionAtlas_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ExpressionAtlas_1.33.0.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: 1.9.1 Depends: BiocParallel, R (>= 4.2.0), GenomicRanges, ggplot2 (>= 3.5.0), ggside (>= 0.3.1), SummarizedExperiment, tibble Imports: BiocIO, broom, ComplexUpset, csaw, dplyr (>= 1.1.1), edgeR (>= 4.0), forcats, GenomeInfoDb, GenomicInteractions, ggforce, ggrepel, glue, grDevices, grid, InteractionSet, IRanges, matrixStats, methods, patchwork, RColorBrewer, rlang, Rsamtools, rtracklayer, S4Vectors, scales, stats, stringr, tidyr, tidyselect, utils, vctrs, VennDiagram Suggests: BiocStyle, covr, cqn, DESeq2, EnrichedHeatmap, Gviz, harmonicmeanp, here, knitr, limma, magrittr, plyranges, quantro, rmarkdown, testthat (>= 3.0.0), tidyverse License: GPL-3 MD5sum: d315069912d31b230bf9f6485dc861f6 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] () Maintainer: Stevie Pederson 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: 9a4222f git_last_commit_date: 2024-05-04 Date/Publication: 2024-05-06 source.ver: src/contrib/extraChIPs_1.9.1.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/extraChIPs_1.9.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/extraChIPs_1.9.1.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: 178 Package: fabia Version: 2.51.0 Depends: R (>= 3.6.0), Biobase Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) Archs: x64 MD5sum: 7334ce2daba2c0fda64dab94d05c2109 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 Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/fabia/fabia.html git_url: https://git.bioconductor.org/packages/fabia git_branch: devel git_last_commit: e6744fb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/fabia_2.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/fabia_2.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/fabia_2.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/fabia_2.51.0.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: 7 Package: factDesign Version: 1.81.0 Depends: Biobase (>= 2.5.5) Imports: stats Suggests: affy, genefilter, multtest License: LGPL MD5sum: 0819ceedf1fdc4c043d49f39e253f305 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 git_url: https://git.bioconductor.org/packages/factDesign git_branch: devel git_last_commit: 9ce6d81 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/factDesign_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/factDesign_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/factDesign_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/factDesign_1.81.0.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: 6 Package: factR Version: 1.7.0 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 MD5sum: 6b06a009489e38d9afde4ae22e630a2e 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 URL: https://fursham-h.github.io/factR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/factR git_branch: devel git_last_commit: db81afc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/factR_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/factR_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/factR_1.7.0.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: 117 Package: faers Version: 1.1.0 Depends: R (>= 4.4.0) 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: 63e1288e3b715dd0fcd3923bd9983f31 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] (), YuXuan Song [aut], Caipeng Qin [aut], JiaXing Lin [aut] Maintainer: Yun Peng VignetteBuilder: knitr BugReports: https://github.com/Yunuuuu/faers git_url: https://git.bioconductor.org/packages/faers git_branch: devel git_last_commit: a621c54 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/faers_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/faers_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/faers_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/faers_1.1.0.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: 1.33.0 Depends: methods, kinship2, igraph Imports: gap (>= 1.1-17), Matrix, BiocGenerics, utils, survey Suggests: BiocStyle, knitr, RUnit, rmarkdown License: MIT + file LICENSE MD5sum: e8d7a3a80a89577a1e821f4f0ab85980 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 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: fda6c07 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/FamAgg_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/FamAgg_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/FamAgg_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/FamAgg_1.33.0.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: 1.15.0 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: f13b6c2631601327f034282be2aa5e45 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] () Maintainer: Mathieu Charles 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: 043e161 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/famat_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/famat_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/famat_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/famat_1.15.0.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: 175 Package: fastLiquidAssociation Version: 1.41.0 Depends: methods, LiquidAssociation, parallel, doParallel, stats, Hmisc, utils Imports: WGCNA, impute, preprocessCore Suggests: GOstats, yeastCC, org.Sc.sgd.db License: GPL-2 MD5sum: ff45e458c070419036649c1c3ab49780 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 git_url: https://git.bioconductor.org/packages/fastLiquidAssociation git_branch: devel git_last_commit: 89f54e1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/fastLiquidAssociation_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/fastLiquidAssociation_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/fastLiquidAssociation_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/fastLiquidAssociation_1.41.0.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: 125 Package: FastqCleaner Version: 1.23.0 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: 3f43260db5c32a24921d01a2509d81e2 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FastqCleaner git_branch: devel git_last_commit: 5160d8c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/FastqCleaner_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/FastqCleaner_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/FastqCleaner_1.23.0.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: 1.9.0 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: 6c80e55296b9c3824e01c0991a68eaf3 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] () Maintainer: Anestis Gkanogiannis 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: c50e2ba git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/fastreeR_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/fastreeR_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/fastreeR_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/fastreeR_1.9.0.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: 1.51.0 Depends: R (>= 2.13), GenomicRanges, Biobase Imports: methods, graphics, grDevices, stats, BiocGenerics, S4Vectors, IRanges Suggests: DNAcopy, oligo, BiocStyle, knitr License: LGPL (>= 2.0) MD5sum: 5e1c34fb2a2f9c91aa4848e261d46da5 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 URL: http://www.bioinf.jku.at/software/fastseg/index.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fastseg git_branch: devel git_last_commit: e93a082 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/fastseg_1.51.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/fastseg_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/fastseg_1.51.0.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: 1.31.0 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: f5ce838b70a8af4a4521c09b6fb444f7 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] () Maintainer: Pedro Madrigal git_url: https://git.bioconductor.org/packages/fCCAC git_branch: devel git_last_commit: c9ac724 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/fCCAC_1.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/fCCAC_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/fCCAC_1.31.0.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: 1.35.0 Depends: R (>= 3.1),FNN, psych, gtools, zoo, rgl, grid, VennDiagram Suggests: knitr, rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: 3984d77c5568d53f04f8ec6342efa312 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fCI git_branch: devel git_last_commit: 323dd48 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/fCI_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/fCI_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/fCI_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/fCI_1.35.0.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: 49 Package: fcScan Version: 1.19.0 Imports: stats, plyr, VariantAnnotation, SummarizedExperiment, rtracklayer, GenomicRanges, methods, IRanges, foreach, doParallel, parallel Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 98183fd286e790eecb1ec538faf5e111 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 Abdullah El-Kurdi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fcScan git_branch: devel git_last_commit: 0005855 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/fcScan_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/fcScan_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/fcScan_1.19.0.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: 1.77.0 Imports: tcltk, graphics, grDevices, stats, utils License: GPL (>= 2) Archs: x64 MD5sum: 10cb2e555b56927ee97dc19dc273f1bd 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 git_url: https://git.bioconductor.org/packages/fdrame git_branch: devel git_last_commit: 9bdf7a8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/fdrame_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/fdrame_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/fdrame_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/fdrame_1.77.0.tgz vignettes: vignettes/fdrame/inst/doc/fdrame.pdf vignetteTitles: Annotation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 5 Package: FEAST Version: 1.13.0 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 Archs: x64 MD5sum: d5379f8457f347fa5fdc978d9d55105e 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 “features”), 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 VignetteBuilder: knitr BugReports: https://github.com/suke18/FEAST/issues git_url: https://git.bioconductor.org/packages/FEAST git_branch: devel git_last_commit: c3f7d2e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/FEAST_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/FEAST_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/FEAST_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/FEAST_1.13.0.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: 1.5.0 Imports: pheatmap, MASS, pracma, stats, SummarizedExperiment, methods Suggests: rmarkdown, knitr, BiocStyle, DmelSGI, testthat (>= 3.0.0) License: GPL-3 MD5sum: ba68cc01b8f50cd3fb3faac616d50d91 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] () Maintainer: Tuemay Capraz 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: 318833d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/FeatSeekR_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/FeatSeekR_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/FeatSeekR_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/FeatSeekR_1.5.0.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: 1.13.0 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 MD5sum: a111e3e89960f906db824390a6d5f997 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 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: 8a26479 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/fedup_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/fedup_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/fedup_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/fedup_1.13.0.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: 1.25.0 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: c61caabde15bc366c69c99cac57ac36a 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FELLA git_branch: devel git_last_commit: cc2086e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/FELLA_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/FELLA_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/FELLA_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/FELLA_1.25.0.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: 1.3.0 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: ea2591f3b6d4ba2db3a5f0093c3ad0d1 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] () Maintainer: Marek Gierlinski 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: 47b4c84 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/fenr_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/fenr_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/fenr_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/fenr_1.3.0.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: 1.49.0 Depends: R (>= 2.10.0), TTR, methods Imports: Biobase, BiocGenerics, affy, lumi, methylumi, sfsmisc Suggests: genefilter, ffpeExampleData License: GPL (>2) MD5sum: 412faf99195df45c3e4f9ba86953b78a 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 git_url: https://git.bioconductor.org/packages/ffpe git_branch: devel git_last_commit: 4e5607a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ffpe_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ffpe_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ffpe_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ffpe_1.49.0.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: 165 Package: fgga Version: 1.13.0 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: a1e3b719eed54f06b7e5f0af86c30426 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 URL: https://github.com/fspetale/fgga VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fgga git_branch: devel git_last_commit: 8b10ba7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/fgga_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/fgga_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/fgga_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/fgga_1.13.0.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: 3.39.0 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: e03797d0f0290ba7826f878381cde1f3 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 URL: http://www.cicancer.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FGNet git_branch: devel git_last_commit: b55312a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/FGNet_3.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/FGNet_3.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/FGNet_3.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/FGNet_3.39.0.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: 1.31.0 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 Archs: x64 MD5sum: 68ec6435d942f0d6ac3282224900dd5a 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], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://github.com/ctlab/fgsea/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/ctlab/fgsea/issues git_url: https://git.bioconductor.org/packages/fgsea git_branch: devel git_last_commit: 6d2787e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/fgsea_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/fgsea_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/fgsea_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/fgsea_1.31.0.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: ATACCoGAPS, BioNAR, CelliD, CEMiTool, clustifyr, CoGAPS, cTRAP, DOSE, EventPointer, fobitools, lipidr, mCSEA, MIRit, multiGSEA, NanoTube, nipalsMCIA, omicsViewer, pairedGSEA, phantasus, piano, projectR, RegEnrich, RegionalST, signatureSearch, ViSEAGO, cinaR, DTSEA, scITD suggestsMe: decoupleR, gatom, gCrisprTools, iSEEpathways, mdp, pareg, sparrow, SpliceWiz, ttgsea, GeneNMF, goat, grandR, Platypus, RCPA, rliger dependencyCount: 49 Package: FilterFFPE Version: 1.15.0 Imports: foreach, doParallel, GenomicRanges, IRanges, Rsamtools, parallel, S4Vectors Suggests: BiocStyle License: LGPL-3 MD5sum: 8021d0e216f62c1a989a03db6b70c1fb 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] () Maintainer: Lanying Wei git_url: https://git.bioconductor.org/packages/FilterFFPE git_branch: devel git_last_commit: c749f1b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/FilterFFPE_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/FilterFFPE_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/FilterFFPE_1.15.0.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: 1.1.0 Depends: graphics, R (>= 4.4.0) Imports: Biobase, BiocParallel, parallel, stats, SummarizedExperiment, survival, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: e952067108de4ff678a346092e70b6d6 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] (), Junyan Lu [aut] Maintainer: Shuo Wang 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: fda12ef git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/findIPs_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/findIPs_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/findIPs_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/findIPs_1.1.0.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: 1.11.0 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: 5307abe7eb371c11b5b52f06e0d5d91a 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] () Maintainer: Guandong Shang 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: f468499 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/FindIT2_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/FindIT2_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/FindIT2_1.11.0.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: 121 Package: FISHalyseR Version: 1.39.0 Depends: EBImage,abind Suggests: knitr License: Artistic-2.0 MD5sum: 3ca3829e645356a43d2bd6869ccf288f 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 , Andreas Heindl Maintainer: Karesh Arunakirinathan , Andreas Heindl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FISHalyseR git_branch: devel git_last_commit: f17941c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/FISHalyseR_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/FISHalyseR_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/FISHalyseR_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/FISHalyseR_1.39.0.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: 45 Package: fishpond Version: 2.11.0 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: 6a13e79c7eb035febffc162ea5388bd3 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 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: 868d7df git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/fishpond_2.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/fishpond_2.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/fishpond_2.11.0.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: 1.31.0 Imports: data.table, fdrtool, grDevices, graphics, Rcpp, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: GPL (>= 2) Archs: x64 MD5sum: 3a9230ff41e9127d717f3f4c958d5086 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FitHiC git_branch: devel git_last_commit: 232384e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/FitHiC_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/FitHiC_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/FitHiC_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/FitHiC_1.31.0.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: 1.61.0 Depends: gcspikelite, xcms, CAMERA Imports: gplots, graphics, MASS, methods, SparseM, stats, utils License: LGPL (>= 2) Archs: x64 MD5sum: e40b1bae79237688587a2e84fb8d61c7 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 , Riccardo Romoli Maintainer: Mark Robinson , Riccardo Romoli git_url: https://git.bioconductor.org/packages/flagme git_branch: devel git_last_commit: 05d260a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flagme_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flagme_1.61.0.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: 1.11.0 Depends: R (>= 4.1.0) Imports: basilisk, bambu, Biostrings, BiocGenerics, circlize, ComplexHeatmap, cowplot, dplyr, DropletUtils, GenomicRanges, GenomicFeatures, txdbmaker, GenomicAlignments, GenomeInfoDb, ggplot2, ggbio, grid, gridExtra, igraph, jsonlite, magrittr, Matrix, parallel, reticulate, Rsamtools, rtracklayer, RColorBrewer, SingleCellExperiment, SummarizedExperiment, scater, S4Vectors, scuttle, stats, scran, stringr, MultiAssayExperiment, tidyr, utils, withr, zlibbioc, future, methods, tibble, tidyselect, IRanges LinkingTo: Rcpp, Rhtslib, zlibbioc, testthat Suggests: txdbmaker, BiocStyle, GEOquery, knitr, rmarkdown, markdown, BiocFileCache, R.utils, ShortRead, uwot, testthat (>= 3.0.0), xml2 License: GPL (>= 3) MD5sum: f818d441bc4d66153e5ef9ee1b150add 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 URL: https://github.com/OliverVoogd/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: 7d0c263 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/FLAMES_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/FLAMES_1.11.0.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: 1.35.0 Depends: R (>= 3.6) Imports: ggplot2, flowCore, plyr, changepoint, knitr, reshape2, RColorBrewer, scales, methods, graphics, stats, utils, rmarkdown Suggests: testthat, shiny, BiocStyle License: GPL (>= 2) MD5sum: 5c9361646889f64c737b6f79131038bc 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, Hao Chen Maintainer: Gianni Monaco VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowAI git_branch: devel git_last_commit: 913ee42 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowAI_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowAI_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowAI_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowAI_1.35.0.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: 75 Package: flowBeads Version: 1.43.0 Depends: R (>= 2.15.0), methods, Biobase, rrcov, flowCore Imports: flowCore, rrcov, knitr, xtable Suggests: flowViz License: Artistic-2.0 MD5sum: dd07eaee3ebc9a2064c40249263fd35b 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 git_url: https://git.bioconductor.org/packages/flowBeads git_branch: devel git_last_commit: 032bc33 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowBeads_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowBeads_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowBeads_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowBeads_1.43.0.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: 31 Package: flowBin Version: 1.41.0 Depends: methods, flowCore, flowFP, R (>= 2.10) Imports: class, limma, snow, BiocGenerics Suggests: parallel License: Artistic-2.0 MD5sum: 7c410494786a206be0ec9db617a7210a 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 git_url: https://git.bioconductor.org/packages/flowBin git_branch: devel git_last_commit: 279ce73 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowBin_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowBin_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowBin_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowBin_1.41.0.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: 36 Package: flowcatchR Version: 1.39.0 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: c77b590c4b7ee778ac8e2abd74c290fe 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] () Maintainer: Federico Marini 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: 3ff81a5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowcatchR_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowcatchR_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowcatchR_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowcatchR_1.39.0.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: 1.39.0 Depends: R (>= 3.1.0) Imports: methods, flowCore, EBImage, vegan, hexbin, ggplot2, grid License: GPL-2 MD5sum: 514b8c79acd6c13403646c6987f1c551 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 , Christin Koch , Ingo Fetzer , Susann Müller Maintainer: Author: Joachim Schumann URL: http://www.ufz.de/index.php?en=16773 git_url: https://git.bioconductor.org/packages/flowCHIC git_branch: devel git_last_commit: 62cdab5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowCHIC_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowCHIC_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowCHIC_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowCHIC_1.39.0.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: 83 Package: flowClean Version: 1.43.0 Depends: R (>= 2.15.0), flowCore Imports: bit, changepoint, sfsmisc Suggests: flowViz, grid, gridExtra License: Artistic-2.0 MD5sum: 07270e5bcb1621b9864e719ab14910b4 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 git_url: https://git.bioconductor.org/packages/flowClean git_branch: devel git_last_commit: b8d2bc7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowClean_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowClean_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowClean_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowClean_1.43.0.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: 24 Package: flowClust Version: 3.43.0 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 Archs: x64 MD5sum: 6e715b8d6b2b948659906add111d34ba 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 , Greg Finak Maintainer: Greg Finak , Mike Jiang SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowClust git_branch: devel git_last_commit: a59c5fa git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowClust_3.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowClust_3.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowClust_3.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowClust_3.43.0.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 suggestsMe: BiocGenerics, flowTime, segmenTier dependencyCount: 19 Package: flowCore Version: 2.17.0 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 Archs: x64 MD5sum: e9b9ce9c634b29ff6413482823c9f3e8 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 SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowCore git_branch: devel git_last_commit: 55cfde0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowCore_2.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowCore_2.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowCore_2.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowCore_2.17.0.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, PeacoQC, scDataviz, scifer, Sconify suggestsMe: COMPASS, flowPeaks, flowPloidyData, hypergate, segmenTier dependencyCount: 16 Package: flowCut Version: 1.15.0 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 MD5sum: 1859379e7c44e96e233ee3da735431a1 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowCut git_branch: devel git_last_commit: 6584983 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowCut_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowCut_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowCut_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowCut_1.15.0.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: 108 Package: flowCyBar Version: 1.41.0 Depends: R (>= 3.0.0) Imports: gplots, vegan, methods License: GPL-2 MD5sum: e12e49b0975c46e93580336d292b62c1 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 , Christin Koch , Susanne Günther , Ingo Fetzer , Susann Müller Maintainer: Joachim Schumann URL: http://www.ufz.de/index.php?de=16773 git_url: https://git.bioconductor.org/packages/flowCyBar git_branch: devel git_last_commit: aed5528 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowCyBar_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowCyBar_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowCyBar_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowCyBar_1.41.0.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: 1.39.0 Imports: flowCore, graphics, flowViz (>= 1.42), car, polyclip, gplots, methods, stats, grDevices Suggests: knitr,rmarkdown License: Artistic-2.0 MD5sum: a0604f2ce6accd757ff34832d644dd84 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 SystemRequirements: xml2, GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowDensity git_branch: devel git_last_commit: c1497e8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowDensity_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowDensity_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowDensity_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowDensity_1.39.0.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowDensity/inst/doc/flowDensity.R importsMe: cyanoFilter, ddPCRclust, flowCut dependencyCount: 103 Package: flowFP Version: 1.63.0 Depends: R (>= 2.10), flowCore, flowViz Imports: Biobase, BiocGenerics (>= 0.1.6), graphics, grDevices, methods, stats, stats4 Suggests: RUnit License: Artistic-2.0 Archs: x64 MD5sum: 7cc23ae00983f2a953dd2dba2e43990c 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 , Wade Rogers Maintainer: Herb Holyst , Wade Rogers git_url: https://git.bioconductor.org/packages/flowFP git_branch: devel git_last_commit: 3a6c78e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowFP_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowFP_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowFP_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowFP_1.63.0.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: 31 Package: flowGate Version: 1.5.0 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: cc0248be3c2ccddeee59569a1098c641 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowGate git_branch: devel git_last_commit: 14452a1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowGate_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowGate_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowGate_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowGate_1.5.0.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: 94 Package: flowGraph Version: 1.13.0 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 MD5sum: 49a17ea280c54fa515ca201fded9d4e4 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 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: 4f830fb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowGraph_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowGraph_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowGraph_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowGraph_1.13.0.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: 1.41.0 Depends: R (>= 3.0.0), Rcpp (>= 0.11.0), methods, flowCore Imports: Biobase LinkingTo: Rcpp Suggests: healthyFlowData License: Artistic-2.0 Archs: x64 MD5sum: f97f50c81b0c5135e0efe1ceecbc5c10 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 git_url: https://git.bioconductor.org/packages/flowMatch git_branch: devel git_last_commit: b5bfc79 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowMatch_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowMatch_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowMatch_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowMatch_1.41.0.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: 17 Package: flowMeans Version: 1.65.0 Depends: R (>= 2.10.0) Imports: Biobase, graphics, grDevices, methods, rrcov, stats, feature, flowCore License: Artistic-2.0 MD5sum: da21108b51306c48e0298d45be5a37ee 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 git_url: https://git.bioconductor.org/packages/flowMeans git_branch: devel git_last_commit: 25ed60a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowMeans_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowMeans_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowMeans_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowMeans_1.65.0.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: 39 Package: flowMerge Version: 2.53.0 Depends: graph,feature,flowClust,Rgraphviz,foreach,snow Imports: rrcov,flowCore, graphics, methods, stats, utils Suggests: knitr, rmarkdown Enhances: doMC, multicore License: Artistic-2.0 MD5sum: daf0459d1ad33acb3231a566cc99326d 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 , Raphael Gottardo Maintainer: Greg Finak VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowMerge git_branch: devel git_last_commit: 2ee07e0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowMerge_2.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowMerge_2.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowMerge_2.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowMerge_2.53.0.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: 47 Package: flowPeaks Version: 1.51.0 Depends: R (>= 2.12.0) Suggests: flowCore License: Artistic-1.0 Archs: x64 MD5sum: 76584da9de7da5e7edbcd42ece21a542 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 Maintainer: Yongchao Ge SystemRequirements: gsl git_url: https://git.bioconductor.org/packages/flowPeaks git_branch: devel git_last_commit: c495955 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowPeaks_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowPeaks_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowPeaks_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowPeaks_1.51.0.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 dependencyCount: 0 Package: flowPloidy Version: 1.31.0 Imports: flowCore, car, caTools, knitr, rmarkdown, minpack.lm, shiny, methods, graphics, stats, utils Suggests: flowPloidyData, testthat License: GPL-3 MD5sum: 217b155cd4208d9f3df2a5a019d2b894 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 Maintainer: Tyler Smith 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: d060f80 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowPloidy_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowPloidy_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowPloidy_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowPloidy_1.31.0.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: 116 Package: flowPlots Version: 1.53.0 Depends: R (>= 2.13.0), methods Suggests: vcd License: Artistic-2.0 MD5sum: fae0f588f2f2d12fc5a57985a426a623 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 git_url: https://git.bioconductor.org/packages/flowPlots git_branch: devel git_last_commit: 7b54306 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowPlots_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowPlots_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowPlots_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowPlots_1.53.0.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: 2.13.0 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) Archs: x64 MD5sum: a6fa77e99ff721992aed05b0844b4199 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 URL: http://www.r-project.org, http://dambi.ugent.be git_url: https://git.bioconductor.org/packages/FlowSOM git_branch: devel git_last_commit: 95a62de git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/FlowSOM_2.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/FlowSOM_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/FlowSOM_2.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/FlowSOM_2.13.0.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: HDCytoData dependencyCount: 113 Package: flowSpecs Version: 1.19.0 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: 165d713271545097c5cba566c22cee70 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 VignetteBuilder: knitr BugReports: https://github.com/jtheorell/flowSpecs/issues git_url: https://git.bioconductor.org/packages/flowSpecs git_branch: devel git_last_commit: 78b2e0d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowSpecs_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowSpecs_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowSpecs_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowSpecs_1.19.0.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: 61 Package: flowStats Version: 4.17.0 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 MD5sum: 28cc91ef2bd182eb151e331750f7dd95 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 , Mike Jiang 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: 4aa4c36 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowStats_4.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowStats_4.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowStats_4.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowStats_4.17.0.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: 101 Package: flowTime Version: 1.29.0 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: 1ac9938ccf96e1b98589817a39932bc3 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowTime git_branch: devel git_last_commit: 4fd0e16 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowTime_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowTime_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowTime_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowTime_1.29.0.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: 1.57.0 Depends: R (>= 2.11.0), flowCore, flowViz,flowClust Imports: flowCore, methods, flowViz, stats, flowClust License: Artistic-2.0 MD5sum: 3dd32bbcf24abcb01b9f1eed73947a8f 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 , Juan Manuel-Perez , Raphael Gottardo Maintainer: Greg Finak git_url: https://git.bioconductor.org/packages/flowTrans git_branch: devel git_last_commit: 1e162f9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowTrans_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowTrans_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowTrans_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowTrans_1.57.0.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: 34 Package: flowViz Version: 1.69.0 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: 62c7bedd2bb14bf09c748f978eb41d4f 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowViz git_branch: devel git_last_commit: 7438f27 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowViz_1.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowViz_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowViz_1.69.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowViz_1.69.0.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 suggestsMe: flowBeads, flowClean, flowCore, flowTime, ggcyto dependencyCount: 30 Package: flowVS Version: 1.37.0 Depends: R (>= 3.2), methods, flowCore, flowViz, flowStats Suggests: knitr, vsn, License: Artistic-2.0 MD5sum: 2bf99067cff34354db81150abaee6261 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowVS git_branch: devel git_last_commit: bcc68aa git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowVS_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowVS_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowVS_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowVS_1.37.0.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: 102 Package: flowWorkspace Version: 4.17.0 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 Archs: x64 MD5sum: 7ce63fe06b17fac082d93c0f628c0376 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 , Mike Jiang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowWorkspace git_branch: devel git_last_commit: cdae1b1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/flowWorkspace_4.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/flowWorkspace_4.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/flowWorkspace_4.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/flowWorkspace_4.17.0.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, PeacoQC suggestsMe: CATALYST, COMPASS, flowClust, flowCore, FlowSOM linksToMe: CytoML dependencyCount: 66 Package: fmcsR Version: 1.47.0 Depends: R (>= 2.10.0), ChemmineR, methods Imports: RUnit, methods, ChemmineR, BiocGenerics, parallel Suggests: BiocStyle, knitr, knitcitations, knitrBootstrap,rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 6b1009c2dc783da9e307dce8742e56cb 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 URL: https://github.com/girke-lab/fmcsR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fmcsR git_branch: devel git_last_commit: b740e8a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/fmcsR_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/fmcsR_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/fmcsR_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/fmcsR_1.47.0.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: 78 Package: fmrs Version: 1.15.0 Depends: R (>= 4.3.0) Imports: methods, survival, stats Suggests: BiocGenerics, testthat, knitr, utils License: GPL-3 Archs: x64 MD5sum: 665230b8b673e8fd57e5dc5ccbf2cbb0 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] () Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/fmrs/issues git_url: https://git.bioconductor.org/packages/fmrs git_branch: devel git_last_commit: 1156121 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/fmrs_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/fmrs_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/fmrs_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/fmrs_1.15.0.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: 1.13.0 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 MD5sum: de8abffd5ebc54b9be9fd1ffcbfc79b5 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] (), Alex Sánchez-Pla [aut] () Maintainer: Pol Castellano-Escuder 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: d142106 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/fobitools_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/fobitools_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/fobitools_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/fobitools_1.13.0.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: 126 Package: FRASER Version: 2.1.0 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 MD5sum: 817bebea4f247841b5d9dc8977a2a1ac 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] (), Ines Scheller [aut] (), Karoline Lutz [ctb], Vicente Yepez [aut] (), Julien Gagneur [aut] () Maintainer: Christian Mertes 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: 14fe09e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/FRASER_2.1.0.tar.gz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/FRASER_2.1.0.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: 1.17.0 Imports: utils, MCMCpack, NHPoisson Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 7e5d3b632e77c13c3c8c5d993546ad5a 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/frenchFISH git_branch: devel git_last_commit: 5cd80d5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/frenchFISH_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/frenchFISH_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/frenchFISH_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/frenchFISH_1.17.0.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: 84 Package: FRGEpistasis Version: 1.41.0 Depends: R (>= 2.15), MASS, fda, methods, stats Imports: utils License: GPL-2 MD5sum: 7153364f46d6173a990511d2f56dc611 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 git_url: https://git.bioconductor.org/packages/FRGEpistasis git_branch: devel git_last_commit: b13eec6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/FRGEpistasis_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/FRGEpistasis_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/FRGEpistasis_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/FRGEpistasis_1.41.0.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: 1.57.0 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: 1ada49f76d10d2c92f1add5a91b4f92d 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 , Rafael A. Irizarry , with contributions from Terry Therneau Maintainer: Matthew N. McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/frma git_branch: devel git_last_commit: 74072e9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/frma_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/frma_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/frma_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/frma_1.57.0.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: 1.57.0 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: 1d7309bd34e0032f48545826599ecca6 NeedsCompilation: no Title: Frozen RMA Tools Description: Tools for advanced use of the frma package. biocViews: Software, Microarray, Preprocessing Author: Matthew N. McCall , Rafael A. Irizarry Maintainer: Matthew N. McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/frmaTools git_branch: devel git_last_commit: 2e9b1b6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/frmaTools_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/frmaTools_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/frmaTools_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/frmaTools_1.57.0.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: funtooNorm Version: 1.29.0 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: 91a0cebf321b31ff1cf878e226d46e98 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 ,Stepan Grinek , Maxime Turgeon , Kathleen Klein Maintainer: Kathleen Klein VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/funtooNorm git_branch: devel git_last_commit: a406fc9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/funtooNorm_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/funtooNorm_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/funtooNorm_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/funtooNorm_1.29.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 143 Package: FuseSOM Version: 1.7.0 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 Archs: x64 MD5sum: ce2825c8cf488b6354675d137c46db04 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 VignetteBuilder: knitr BugReports: https://github.com/ecool50/FuseSOM/issues git_url: https://git.bioconductor.org/packages/FuseSOM git_branch: devel git_last_commit: de6df1e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/FuseSOM_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/FuseSOM_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/FuseSOM_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/FuseSOM_1.7.0.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 suggestsMe: spicyWorkflow dependencyCount: 150 Package: GA4GHclient Version: 1.29.0 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) MD5sum: 7216da790f7cbabd4608ca1abfd61b3d 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 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: 9d635c1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GA4GHclient_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GA4GHclient_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GA4GHclient_1.29.0.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: 88 Package: GA4GHshiny Version: 1.27.0 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 MD5sum: 9a94be95226f04682594b234034faa69 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 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: caebd6d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GA4GHshiny_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GA4GHshiny_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GA4GHshiny_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GA4GHshiny_1.27.0.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: 124 Package: gaga Version: 2.51.0 Depends: R (>= 2.8.0), Biobase, coda, EBarrays, mgcv Enhances: parallel License: GPL (>= 2) Archs: x64 MD5sum: 7031f51787aa38d98aca6c3686d84cf1 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 . Maintainer: David Rossell git_url: https://git.bioconductor.org/packages/gaga git_branch: devel git_last_commit: 3397e45 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gaga_2.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/gaga_2.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gaga_2.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gaga_2.51.0.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: 16 Package: gage Version: 2.55.0 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: 42d4cc3f517c0a2478dc0446c9fb769c 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 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: a58b43c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gage_2.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/gage_2.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gage_2.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gage_2.55.0.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: 1.31.0 Depends: R (>= 3.3) Imports: glmnet, stats, utils, Matrix Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: dca12f54ba475f42e0d6db33d5b7a2d6 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GAprediction git_branch: devel git_last_commit: 34c5bca git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GAprediction_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GAprediction_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GAprediction_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GAprediction_1.31.0.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: 1.33.0 Suggests: knitr License: GPL-3 Archs: x64 MD5sum: 268d206e4e6acf7be2e1503da1b3ad98 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 Maintainer: Valentina Iotchkova VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/garfield git_branch: devel git_last_commit: 03b981b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/garfield_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/garfield_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/garfield_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/garfield_1.33.0.tgz vignettes: vignettes/garfield/inst/doc/vignette.pdf vignetteTitles: garfield Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 0 Package: GARS Version: 1.25.0 Depends: R (>= 3.5), ggplot2, cluster Imports: DaMiRseq, MLSeq, stats, methods, SummarizedExperiment Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: 79f2b3471482ec15d3530e7d23040d25 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 , Luca Piacentini Maintainer: Mattia Chiesa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GARS git_branch: devel git_last_commit: 15b7355 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GARS_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GARS_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GARS_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GARS_1.25.0.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: 266 Package: GateFinder Version: 1.25.0 Imports: splancs, mvoutlier, methods, stats, diptest, flowCore, flowFP Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: abbc5a5038c859bee566b6acee957a22 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 , Erin F. Simonds Maintainer: Nima Aghaeepour git_url: https://git.bioconductor.org/packages/GateFinder git_branch: devel git_last_commit: 897cf40 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GateFinder_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GateFinder_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GateFinder_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GateFinder_1.25.0.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: 39 Package: gatom Version: 1.3.0 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: 19438eba6ab7816f0ced34c3731dfe3b 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 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: 886bc80 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gatom_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/gatom_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gatom_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gatom_1.3.0.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: 129 Package: GBScleanR Version: 1.9.0 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: f30da27f67ec3cf196bb3d46a7436fca 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] () Maintainer: Tomoyuki Furuta 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: 863547d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GBScleanR_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GBScleanR_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GBScleanR_1.9.0.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: 1.29.0 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: b9f24eff2458bbd37464bdc912df594d 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 URL: https://github.com/tengmx/gcapc VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gcapc git_branch: devel git_last_commit: 0e8092a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gcapc_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gcapc_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gcapc_1.29.0.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: 2.5.0 Depends: R (>= 4.0) Imports: methods, lfa Suggests: knitr, ggplot2, testthat, BEDMatrix, genio License: GPL (>= 3) MD5sum: bf3b10a272870bec39013a0dc85ad5a3 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] (), John D. Storey [aut] () Maintainer: Alejandro Ochoa 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: 9e27e38 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gcatest_2.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/gcatest_2.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gcatest_2.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gcatest_2.5.0.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 dependencyCount: 13 Package: gCrisprTools Version: 2.11.0 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: 177b76243ef9bd79dc437e7bafc4228d 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gCrisprTools git_branch: devel git_last_commit: c0682ad git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gCrisprTools_2.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/gCrisprTools_2.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gCrisprTools_2.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gCrisprTools_2.11.0.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: 2.77.0 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 Archs: x64 MD5sum: 92715fec6b052d49c0ad558da073c2c5 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 Jeff Gentry Maintainer: Z. Wu git_url: https://git.bioconductor.org/packages/gcrma git_branch: devel git_last_commit: be4744c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gcrma_2.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/gcrma_2.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gcrma_2.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gcrma_2.77.0.tgz vignettes: vignettes/gcrma/inst/doc/gcrma2.0.pdf vignetteTitles: gcrma1.2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyILM, affyPLM, bgx, maskBAD, webbioc importsMe: affycoretools, affylmGUI suggestsMe: panp, aroma.affymetrix dependencyCount: 31 Package: GDCRNATools Version: 1.25.0 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: 5eafb91099eddf8917951eb81c2431f2 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 , Han Qu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GDCRNATools git_branch: devel git_last_commit: fd0461c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GDCRNATools_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GDCRNATools_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GDCRNATools_1.25.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 240 Package: gDNAx Version: 1.3.0 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: 147217cdd85cd0febc40f2e7cd27b420 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 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: 73a455d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gDNAx_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gDNAx_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gDNAx_1.3.0.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: 102 Package: gDR Version: 1.3.0 Depends: R (>= 4.2), gDRcore (>= 1.1.11), gDRimport (>= 1.1.4), gDRutils (>= 1.1.5) Suggests: BiocStyle, BumpyMatrix, futile.logger, gDRstyle (>= 1.1.3), gDRtestData (>= 1.1.7), kableExtra, knitr, markdown, purrr, rmarkdown, SummarizedExperiment, testthat, yaml License: Artistic-2.0 MD5sum: ff957986b2f168f21e07f0b8b06bfcfc 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] (), Arkadiusz Gladki [cre, aut] (), Marc Hafner [aut] (), Dariusz Scigocki [aut], Janina Smola [aut], Sergiu Mocanu [aut] Maintainer: Arkadiusz Gladki 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: c0fc1b5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gDR_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/gDR_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gDR_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gDR_1.3.0.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: 219 Package: gDRcore Version: 1.3.0 Depends: R (>= 4.2) Imports: BumpyMatrix, BiocParallel, checkmate, futile.logger, gDRutils (>= 1.1.3), MultiAssayExperiment, purrr, stringr, S4Vectors, SummarizedExperiment, data.table Suggests: BiocStyle, gDRstyle (>= 1.1.2), gDRimport (>= 1.1.4), gDRtestData (>= 1.1.6), IRanges, knitr, pkgbuild, qs, testthat, yaml License: Artistic-2.0 Archs: x64 MD5sum: 7696aa58ea40701870bfc5e7b4c5fac7 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] (), Arkadiusz Gladki [cre, aut] (), Marc Hafner [aut] (), Pawel Piatkowski [aut], Natalia Potocka [aut], Dariusz Scigocki [aut], Janina Smola [aut], Sergiu Mocanu [aut], Marcin Kamianowski [aut], Allison Vuong [aut] Maintainer: Arkadiusz Gladki 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: 54a378c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gDRcore_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/gDRcore_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gDRcore_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gDRcore_1.3.0.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: 126 Package: gDRimport Version: 1.3.0 Depends: R (>= 4.2) Imports: assertthat, BumpyMatrix, checkmate, CoreGx, PharmacoGx, data.table, futile.logger, gDRutils (>= 1.1.5), magrittr, methods, MultiAssayExperiment, readxl, rio, S4Vectors, stats, stringi, SummarizedExperiment, tibble, tools, utils, XML, yaml, openxlsx Suggests: BiocStyle, gDRtestData (>= 1.1.7), gDRstyle (>= 1.1.3), knitr, purrr, qs, testthat License: Artistic-2.0 MD5sum: c5e6ec9b24329266f8e92b0083ca3ffa 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] (), Bartosz Czech [aut] (), Marc Hafner [aut] (), Sergiu Mocanu [aut], Dariusz Scigocki [aut], Allison Vuong [aut], Luca Gerosa [aut] (), Janina Smola [aut] Maintainer: Arkadiusz Gladki 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: bcd41ec git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gDRimport_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/gDRimport_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gDRimport_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gDRimport_1.3.0.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: 217 Package: gDRstyle Version: 1.3.0 Depends: R (>= 4.2) Imports: BiocCheck, BiocManager, checkmate, desc, git2r, lintr (>= 3.0.0), rcmdcheck, remotes, yaml, rjson, pkgbuild, withr Suggests: BiocStyle, knitr, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: d8536dfdcf557fff2b085a5bdd56f291 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] (), Bartosz Czech [aut] () Maintainer: Arkadiusz Gladki 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: ce6a622 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gDRstyle_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/gDRstyle_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gDRstyle_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gDRstyle_1.3.0.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: 95 Package: gDRutils Version: 1.3.0 Depends: R (>= 4.2) Imports: BiocParallel, BumpyMatrix, checkmate, data.table, drc, jsonlite, jsonvalidate, methods, MultiAssayExperiment, S4Vectors, stats, stringr, SummarizedExperiment Suggests: BiocManager, BiocStyle, futile.logger, gDRstyle (>= 1.1.5), gDRtestData (>= 1.1.10), IRanges, knitr, lintr, purrr, qs, rcmdcheck, rmarkdown, testthat, tools, yaml License: Artistic-2.0 MD5sum: 832295e65ead1532e1ab2786529adca5 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] (), Arkadiusz Gladki [cre, aut] (), Aleksander Chlebowski [aut], Marc Hafner [aut] (), Pawel Piatkowski [aut], Dariusz Scigocki [aut], Janina Smola [aut], Sergiu Mocanu [aut], Allison Vuong [aut] Maintainer: Arkadiusz Gladki 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: 29cb67a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gDRutils_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/gDRutils_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gDRutils_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gDRutils_1.3.0.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: 125 Package: GDSArray Version: 1.25.0 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: 1c7860bdbdea14583fcd9a5466de828f 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, cre], Martin Morgan [aut], Hervé Pagès [aut], Xiuwen Zheng [aut] Maintainer: Qian Liu 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: 1ff7fbe git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GDSArray_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GDSArray_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GDSArray_1.25.0.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: 39 Package: gdsfmt Version: 1.41.0 Depends: R (>= 2.15.0), methods Suggests: parallel, digest, Matrix, crayon, RUnit, knitr, markdown, rmarkdown, BiocGenerics License: LGPL-3 Archs: x64 MD5sum: 5e2f3c40f71df60c2e58bfb3777cdb25 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] (), 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 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: d505de2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gdsfmt_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/gdsfmt_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gdsfmt_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gdsfmt_1.41.0.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: 1.1.0 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 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: db16a90b896cc84b16b1bf6b72d502e6 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] (), Federico Marini [aut] () Maintainer: Annekathrin Nedwed 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: 3edc9b7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GeDi_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GeDi_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GeDi_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GeDi_1.1.0.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: 202 Package: GEM Version: 1.31.0 Depends: R (>= 3.3) Imports: tcltk, ggplot2, methods, stats, grDevices, graphics, utils Suggests: knitr, RUnit, testthat, BiocGenerics, rmarkdown, markdown License: Artistic-2.0 MD5sum: 76572677f2b3ac01ff63306b4c1f0cae 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GEM git_branch: devel git_last_commit: d98eeaf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GEM_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GEM_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GEM_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GEM_1.31.0.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: 1.19.0 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: 4dbbdadb0025db9c4b5e6c8a8078d9ce 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 VignetteBuilder: knitr BugReports: https://github.com/sellerslab/gemini/issues git_url: https://git.bioconductor.org/packages/gemini git_branch: devel git_last_commit: bc43081 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gemini_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/gemini_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gemini_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gemini_1.19.0.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: 3.1.2 Imports: magrittr, glue, memoise, jsonlite, data.table, rlang, lubridate, utils, stringr, SummarizedExperiment, Biobase, tibble, tidyr, S4Vectors, httr, rappdirs, bit64, assertthat, digest 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: 126c16b934555bca376201c1244c87bf 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] (), Jordan Sicherman [aut] (), Ogan Mancarci [cre, aut] (), Guillaume Poirier-Morency [aut] () Maintainer: Ogan Mancarci 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: 6567fb7 git_last_commit_date: 2024-05-03 Date/Publication: 2024-05-06 source.ver: src/contrib/gemma.R_3.1.2.tar.gz win.binary.ver: bin/windows/contrib/4.4/gemma.R_3.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gemma.R_3.1.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gemma.R_3.1.2.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: 67 Package: genArise Version: 1.81.0 Depends: R (>= 1.7.1), locfit, tkrplot, methods Imports: graphics, grDevices, methods, stats, tcltk, utils, xtable License: file LICENSE License_restricts_use: yes MD5sum: 172cce0c98be8fd43dc17f8065508f68 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 ,\\ Gustavo Corral Guille , \\ Lina Riego Ruiz ,\\ Gerardo Coello Coutino Maintainer: IFC Development Team URL: http://www.ifc.unam.mx/genarise git_url: https://git.bioconductor.org/packages/genArise git_branch: devel git_last_commit: 2a8d3b6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/genArise_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/genArise_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/genArise_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/genArise_1.81.0.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: 1.31.0 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: 8e6c351e6e8f0a15642736811c1dfdd3 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geneAttribution git_branch: devel git_last_commit: e27bc55 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/geneAttribution_1.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/geneAttribution_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/geneAttribution_1.31.0.tgz vignettes: vignettes/geneAttribution/inst/doc/geneAttribution.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 78 Package: GeneBreak Version: 1.35.0 Depends: R(>= 3.2), QDNAseq, CGHcall, CGHbase, GenomicRanges Imports: graphics, methods License: GPL-2 MD5sum: 345ee3e21b12bb65668bd080b5b3479a 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 URL: https://github.com/stefvanlieshout/GeneBreak git_url: https://git.bioconductor.org/packages/GeneBreak git_branch: devel git_last_commit: 1b00bc9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GeneBreak_1.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GeneBreak_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GeneBreak_1.35.0.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: 1.29.0 Depends: R (>= 3.6.0) Imports: utils, methods, stats, Biobase, BiocGenerics Suggests: testthat License: GPL-2 MD5sum: ee8494d59b8bcfba3f51b6b8b0b99027 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] () Maintainer: R Kuiper 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: 6ccbc07 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/geneClassifiers_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/geneClassifiers_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/geneClassifiers_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/geneClassifiers_1.29.0.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: 6 Package: GeneExpressionSignature Version: 1.51.0 Depends: R (>= 4.0) Imports: Biobase, stats, methods Suggests: apcluster, GEOquery, knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: c95c6afdf045ad7826f17d3e4f3e4e4d 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 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: 4c92dca git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GeneExpressionSignature_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GeneExpressionSignature_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GeneExpressionSignature_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GeneExpressionSignature_1.51.0.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: 6 Package: genefilter Version: 1.87.0 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 Archs: x64 MD5sum: a8a60aaba1f61e4455e679a331e2b198 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genefilter git_branch: devel git_last_commit: 1bff583 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/genefilter_1.87.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/genefilter_1.87.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/genefilter_1.87.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/genefilter_1.87.0.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, howtogenefinder.knit 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: cellHTS2, CNTools, GeneMeta, sva, Hiiragi2013, maEndToEnd, rnaseqGene, lmQCM importsMe: a4Base, annmap, arrayQualityMetrics, Category, cbaf, ClassifyR, countsimQC, covRNA, DEXSeq, GSRI, metaseqR2, methylCC, methylumi, minfi, MLInterfaces, mogsa, NBAMSeq, pcaExplorer, PECA, phenoTest, protGear, spatialHeatmap, SpliceWiz, tilingArray, XDE, zinbwave, FlowSorted.Blood.EPIC, IHWpaper, RNAinteractMAPK, CoNI, dGAselID, INCATome, 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, curatedCRCData, curatedOvarianData, estrogen, ffpeExampleData, gageData, MAQCsubset, RforProteomics, rheumaticConditionWOLLBOLD, Single.mTEC.Transcriptomes, maGUI, oncoPredict, SuperLearner dependencyCount: 55 Package: genefu Version: 2.37.0 Depends: R (>= 4.1), survcomp, biomaRt, iC10, AIMS Imports: amap, impute, mclust, limma, graphics, stats, utils Suggests: GeneMeta, breastCancerVDX, breastCancerMAINZ, breastCancerTRANSBIG, breastCancerUPP, breastCancerUNT, breastCancerNKI, rmeta, Biobase, xtable, knitr, caret, survival, BiocStyle, magick, rmarkdown License: Artistic-2.0 MD5sum: 95a35934b290ee775958efe21c460f18 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], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains URL: http://www.pmgenomics.ca/bhklab/software/genefu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genefu git_branch: devel git_last_commit: 83765c0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/genefu_2.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/genefu_2.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/genefu_2.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/genefu_2.37.0.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: 113 Package: GeneGA Version: 1.55.0 Depends: seqinr, hash, methods License: GPL version 2 MD5sum: 4826a3c0f34690dc98fa38c4ef685439 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 URL: http://www.tbi.univie.ac.at/~ivo/RNA/ git_url: https://git.bioconductor.org/packages/GeneGA git_branch: devel git_last_commit: 425132f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GeneGA_1.55.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GeneGA_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GeneGA_1.55.0.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: 1.31.0 Depends: R (>= 4.0) Imports: snpStats, mvtnorm, Rsamtools, igraph, kernlab, FactoMineR, IRanges, GenomicRanges, data.table,grDevices, graphics,stats, utils, methods License: GPL (>= 2) MD5sum: 574476671b47dc5602e38fff19351dc6 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. . biocViews: GenomeWideAssociation, SNP, Genetics, GeneticVariability Author: Mathieu Emily [aut, cre], Nicolas Sounac [ctb], Florian Kroell [ctb], Magalie Houee-Bigot [aut] Maintainer: Mathieu Emily git_url: https://git.bioconductor.org/packages/GeneGeneInteR git_branch: devel git_last_commit: 442f1b3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GeneGeneInteR_1.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GeneGeneInteR_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GeneGeneInteR_1.31.0.tgz 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: 148 Package: GeneMeta Version: 1.77.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), genefilter Imports: methods, Biobase (>= 2.5.5) Suggests: RColorBrewer License: Artistic-2.0 MD5sum: a6de4bf6a8046fd0a303379a3d90d878 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 , R. Gentleman, M. Ruschhaupt Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/GeneMeta git_branch: devel git_last_commit: 0ba2b12 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GeneMeta_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GeneMeta_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GeneMeta_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GeneMeta_1.77.0.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: 1.47.0 Depends: R (>= 2.15.1), Rcpp (>= 0.9.13) Imports: plyr, graph, htmlwidgets, Rgraphviz, rjson, XML, methods, grDevices, stats, graphics LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, RBGL, knitr, simpIntLists, shiny, STRINGdb, BiocStyle, magick, rmarkdown, org.Hs.eg.db License: GPL (>= 2) Archs: x64 MD5sum: 8dfd9b40ad5ec4765bbf1dd49a649fac 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeneNetworkBuilder git_branch: devel git_last_commit: d35be9c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GeneNetworkBuilder_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GeneNetworkBuilder_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GeneNetworkBuilder_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GeneNetworkBuilder_1.47.0.tgz vignettes: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.html, vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkFromGenes.html, vignettes/GeneNetworkBuilder/inst/doc/with.BioGRID.STRING.html vignetteTitles: GeneNetworkBuilder Vignette, Generate Network from a list of gene, Working with BioGRID,, STRING hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.R, vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkFromGenes.R, vignettes/GeneNetworkBuilder/inst/doc/with.BioGRID.STRING.R dependencyCount: 42 Package: GeneOverlap Version: 1.41.0 Imports: stats, RColorBrewer, gplots, methods Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: c67dec4664cb078c24cd19c7a4ef0ab2 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 Maintainer: Antnio Miguel de Jesus Domingues, Max-Planck Institute for Cell Biology and Genetics URL: http://shenlab-sinai.github.io/shenlab-sinai/ git_url: https://git.bioconductor.org/packages/GeneOverlap git_branch: devel git_last_commit: 8f0d95c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GeneOverlap_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GeneOverlap_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GeneOverlap_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GeneOverlap_1.41.0.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 importsMe: ATACCoGAPS dependencyCount: 9 Package: geneplast Version: 1.31.0 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) MD5sum: e8299dc330ce2d17b429dce4fac641e4 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geneplast git_branch: devel git_last_commit: c41ec86 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/geneplast_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/geneplast_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/geneplast_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/geneplast_1.31.0.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: 1.83.0 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: f1ae19ad4a803413febdddd4087d8031 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geneplotter git_branch: devel git_last_commit: 7f7fe9b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/geneplotter_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/geneplotter_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/geneplotter_1.83.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/geneplotter_1.83.0.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, Hiiragi2013, maEndToEnd importsMe: biocGraph, DEXSeq, MethylSeekR, RNAinteract suggestsMe: biocGraph, Category, EnrichmentBrowser, GOstats, Single.mTEC.Transcriptomes dependencyCount: 51 Package: geneRecommender Version: 1.77.0 Depends: R (>= 1.8.0), Biobase (>= 1.4.22), methods Imports: Biobase, methods, stats License: GPL (>= 2) MD5sum: 19c064852e62f9aba5e6cd44c3d135b9 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 , with contributions from Art B. Owen and Terence P. Speed Maintainer: Greg Hather git_url: https://git.bioconductor.org/packages/geneRecommender git_branch: devel git_last_commit: 8b4b79c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/geneRecommender_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/geneRecommender_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/geneRecommender_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/geneRecommender_1.77.0.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: 6 Package: GeneRegionScan Version: 1.61.0 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: e173962d3c2e2e88a5e5c882a3546cfe 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 git_url: https://git.bioconductor.org/packages/GeneRegionScan git_branch: devel git_last_commit: 525824a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GeneRegionScan_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GeneRegionScan_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GeneRegionScan_1.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GeneRegionScan_1.61.0.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: 1.41.0 Depends: GenomicRanges,IRanges Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: ce238776c2e2101a93cbf28174dcdba9 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 git_url: https://git.bioconductor.org/packages/geneRxCluster git_branch: devel git_last_commit: bc03c0a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/geneRxCluster_1.41.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/geneRxCluster_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/geneRxCluster_1.41.0.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: 2.49.0 Depends: R (>= 2.13.2), Biobase Imports: MASS, graphics, stats, limma Suggests: ALL License: GPL (>= 2) Archs: x64 MD5sum: e66e09cb43a000633b51beec8b08ccfc 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 , Weiliang Qiu , Wenqing He , Xiaogang Wang , Ross Lazarus . Maintainer: Weiliang Qiu git_url: https://git.bioconductor.org/packages/GeneSelectMMD git_branch: devel git_last_commit: e11f54f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GeneSelectMMD_2.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GeneSelectMMD_2.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GeneSelectMMD_2.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GeneSelectMMD_2.49.0.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: 10 Package: GENESIS Version: 2.35.0 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 MD5sum: 216228982474ddea594e09a5f198490f 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 URL: https://github.com/UW-GAC/GENESIS VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GENESIS git_branch: devel git_last_commit: 420cdc6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GENESIS_2.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GENESIS_2.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GENESIS_2.35.0.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: 141 Package: GeneStructureTools Version: 1.25.0 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 MD5sum: b9efd4e7d729692ebea17dcd3bbabd2e 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeneStructureTools git_branch: devel git_last_commit: 59cccd6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GeneStructureTools_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GeneStructureTools_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GeneStructureTools_1.25.0.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: 160 Package: geNetClassifier Version: 1.45.0 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) MD5sum: 4bf43745b282498efa7fa25121f973fc 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 URL: http://www.cicancer.org git_url: https://git.bioconductor.org/packages/geNetClassifier git_branch: devel git_last_commit: 610a8f2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/geNetClassifier_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/geNetClassifier_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/geNetClassifier_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/geNetClassifier_1.45.0.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: 17 Package: GeneticsPed Version: 1.67.0 Depends: R (>= 2.4.0), MASS Imports: gdata, genetics Suggests: RUnit, gtools License: LGPL (>= 2.1) | file LICENSE Archs: x64 MD5sum: 0a1c3c3ba87c0f45bda85bcb71df0fc4 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 , with code contributions by Brian Kinghorn and Andrew Percy (see file COPYING) Maintainer: David Henderson URL: http://rgenetics.org git_url: https://git.bioconductor.org/packages/GeneticsPed git_branch: devel git_last_commit: 7ea12e9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GeneticsPed_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GeneticsPed_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GeneticsPed_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GeneticsPed_1.67.0.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: 2.9.0 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, 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: 595f5de537a82074c589d79a4a33371f 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] (), Annekathrin Ludt [aut] () Maintainer: Federico Marini 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: 9473081 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GeneTonic_2.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GeneTonic_2.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GeneTonic_2.9.0.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 dependencyCount: 168 Package: geneXtendeR Version: 1.31.0 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) MD5sum: 6e57f0138a71c089335c8e5d085b7eef 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 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: 0558d4d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/geneXtendeR_1.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/geneXtendeR_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/geneXtendeR_1.31.0.tgz vignettes: vignettes/geneXtendeR/inst/doc/geneXtendeR.pdf vignetteTitles: geneXtendeR.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 117 Package: GENIE3 Version: 1.27.0 Imports: stats, reshape2, dplyr Suggests: knitr, rmarkdown, foreach, doRNG, doParallel, Biobase, SummarizedExperiment, testthat, methods, BiocStyle License: GPL (>= 2) Archs: x64 MD5sum: b2144659b781ba47ea920566b23c1a57 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GENIE3 git_branch: devel git_last_commit: a693965 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GENIE3_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GENIE3_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GENIE3_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GENIE3_1.27.0.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 dependencyCount: 27 Package: genoCN Version: 1.57.0 Imports: graphics, stats, utils License: GPL (>=2) Archs: x64 MD5sum: 4e20d9be827d9ac5c4cd0019992341d4 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 git_url: https://git.bioconductor.org/packages/genoCN git_branch: devel git_last_commit: 5b71c0c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/genoCN_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/genoCN_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/genoCN_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/genoCN_1.57.0.tgz 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: 1.37.0 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 MD5sum: c8bcedf3457e067e082d9e716be431d0 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 , Vedran Franke , Katarzyna Wreczycka 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: 649b15f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/genomation_1.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/genomation_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/genomation_1.37.0.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, fCCAC, GenomicPlot, RCAS suggestsMe: methylKit dependencyCount: 106 Package: GenomAutomorphism Version: 1.7.0 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 MD5sum: 728b3be70ebb13448fb97496228f37a2 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] () Maintainer: Robersy Sanchez 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: 27e8dda git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GenomAutomorphism_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GenomAutomorphism_1.7.0.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: 57 Package: GenomeInfoDb Version: 1.41.0 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.25.12), IRanges (>= 2.13.12) 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 MD5sum: b902048f8d6a89c6d8f23f2ec07cc807 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 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: fa4ee29 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GenomeInfoDb_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GenomeInfoDb_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GenomeInfoDb_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GenomeInfoDb_1.41.0.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, groHMM, 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, BindingSiteFinder, biovizBase, biscuiteer, BiSeq, bnbc, branchpointer, breakpointR, bsseq, BUSpaRse, CAGEfightR, cageminer, CAGEr, cardelino, casper, cBioPortalData, CexoR, cfdnakit, cfDNAPro, chimeraviz, chipenrich, ChIPexoQual, ChIPpeakAnno, ChIPseeker, chromstaR, chromVAR, circRNAprofiler, cleanUpdTSeq, cn.mops, CNEr, CNVfilteR, CNVPanelizer, CNVRanger, Cogito, comapr, compEpiTools, consensusSeekeR, conumee, CopyNumberPlots, crisprBowtie, crisprBwa, crisprDesign, CRISPRseek, crisprShiny, CrispRVariants, crisprViz, csaw, customProDB, DAMEfinder, decompTumor2Sig, DegCre, demuxSNP, derfinder, derfinderPlot, DEScan2, DEWSeq, diffHic, diffUTR, DMRcate, DMRScan, dmrseq, DominoEffect, easylift, easyRNASeq, ELMER, enhancerHomologSearch, ensembldb, ensemblVEP, epialleleR, epigenomix, epigraHMM, EpiMix, epimutacions, epiregulon, EpiTxDb, epivizr, epivizrData, epivizrStandalone, erma, esATAC, EventPointer, extraChIPs, factR, FindIT2, FLAMES, FRASER, funtooNorm, GA4GHclient, GA4GHshiny, gcapc, gDNAx, geneAttribution, genomation, GenomAutomorphism, genomeIntervals, GenomicDistributions, GenomicFiles, GenomicInteractionNodes, GenomicInteractions, GenomicOZone, GenomicPlot, GenomicScores, GenVisR, ggbio, gmoviz, GOTHiC, GRaNIE, GreyListChIP, GUIDEseq, Gviz, gwascat, h5vc, heatmaps, HicAggR, HiCBricks, HiCDOC, HiCExperiment, HiContacts, hicVennDiagram, HiTC, HTSeqGenie, 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, motifTestR, MouseFM, msgbsR, multicrispr, multiHiCcompare, MungeSumstats, musicatk, MutationalPatterns, myvariant, NADfinder, nearBynding, normr, nucleR, nullranges, OGRE, OMICsPCA, ORFik, Organism.dplyr, panelcn.mops, periodicDNA, pipeFrame, plotgardener, plyinteractions, plyranges, podkat, pram, prebs, proActiv, profileplyr, ProteoDisco, PureCN, qpgraph, qsea, QuasR, R3CPET, r3Cseq, raer, RaggedExperiment, RareVariantVis, RCAS, RcisTarget, recoup, regioneR, regionReport, REMP, Repitools, RESOLVE, rfPred, RgnTX, rGREAT, RiboCrypt, RiboProfiling, riboSeqR, ribosomeProfilingQC, 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, 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.v3.1.2.GRCh38, 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, TCGAWorkflow, crispRdesignR, deconstructSigs, driveR, ICAMS, locuszoomr, MAAPER, Mega2R, MOCHA, SeedMatchR, Signac, simMP suggestsMe: AlphaMissenseR, AnnotationForge, AnnotationHub, BiocOncoTK, DiffBind, ExperimentHubData, fishpond, ldblock, megadepth, methrix, OUTRIDER, parglms, QDNAseq, RAIDS, regioneReloaded, scTreeViz, splatter, systemPipeR, TFutils, UCSC.utils, BioMartGOGeneSets, xcoredata, seqpac, gkmSVM, polyRAD, Seurat dependencyCount: 19 Package: genomeIntervals Version: 1.61.0 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: c7ff3b081443647132411e877087ca1d 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 , Joern Toedling, Richard Bourgon, Nicolas Delhomme Maintainer: Julien Gagneur git_url: https://git.bioconductor.org/packages/genomeIntervals git_branch: devel git_last_commit: 3d5323b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/genomeIntervals_1.61.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/genomeIntervals_1.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/genomeIntervals_1.61.0.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: 3.35.0 Depends: readr, curl License: GPL-3 MD5sum: d723a0aa7bdcb56498254456da16876b NeedsCompilation: no Title: Genome sequencing project metadata Description: Download genome and assembly reports from NCBI biocViews: Annotation, Genetics Author: Chris Stubben Maintainer: Chris Stubben git_url: https://git.bioconductor.org/packages/genomes git_branch: devel git_last_commit: f2acaee git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/genomes_3.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/genomes_3.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/genomes_3.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/genomes_3.35.0.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: 1.41.0 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: 68912c1f5a182cf909f6c6f15f660cb9 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 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: eb08522 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GenomicAlignments_1.41.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GenomicAlignments_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GenomicAlignments_1.41.0.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, groHMM, 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, chromstaR, CNEr, consensusDE, CoverageView, CrispRVariants, CSSQ, customProDB, DAMEfinder, DegNorm, derfinder, DEScan2, DiffBind, DNAfusion, easyRNASeq, esATAC, FLAMES, FRASER, gcapc, gDNAx, genomation, GenomicFiles, GenomicPlot, ggbio, gmapR, gmoviz, GreyListChIP, GUIDEseq, Gviz, HTSeqGenie, icetea, IMAS, INSPEcT, IntEREst, MADSEQ, MDTS, metagene2, metaseqR2, methylPipe, mosaics, Motif2Site, msgbsR, NADfinder, 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, ExperimentHub, 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: 1.29.0 Depends: R (>= 3.4.0), magrittr 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: 779c29f3b92d25450cf69500642d3e29 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 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: 47ac776 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GenomicDataCommons_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GenomicDataCommons_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GenomicDataCommons_1.29.0.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: 56 Package: GenomicDistributions Version: 1.13.0 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 MD5sum: 98941baf0d9ec9e111dc0023fa32bc44 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 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: 134482e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GenomicDistributions_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GenomicDistributions_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GenomicDistributions_1.13.0.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: 70 Package: GenomicFeatures Version: 1.57.0 Depends: R (>= 3.5.0), BiocGenerics (>= 0.1.0), 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: 4456e79c982cc5e4533104e33673b60c NeedsCompilation: no Title: Conveniently import and query gene models Description: A set of tools and methods for making and manipulating transcript centric annotations. With these tools the user can easily download the genomic locations of the transcripts, exons and cds of a given organism, from either the UCSC Genome Browser or a BioMart database (more sources will be supported in the future). This information is then stored in a local database that keeps track of the relationship between transcripts, exons, cds and genes. Flexible methods are provided for extracting the desired features in a convenient format. 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 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: 9d8d2d9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GenomicFeatures_1.57.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GenomicFeatures_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GenomicFeatures_1.57.0.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, 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, ATACCoGAPS, atena, bambu, BindingSiteFinder, BiocOncoTK, biovizBase, bumphunter, BUSpaRse, CAGEfightR, casper, ChIPpeakAnno, ChIPQC, ChIPseeker, compEpiTools, consensusDE, crisprDesign, crisprViz, CSSQ, customProDB, decompTumor2Sig, derfinder, derfinderPlot, DNAfusion, doubletrouble, EDASeq, ELMER, EpiMix, epimutacions, EpiTxDb, epivizrData, epivizrStandalone, esATAC, EventPointer, factR, FindIT2, FLAMES, FRASER, GA4GHshiny, gDNAx, geneAttribution, GenomicInteractionNodes, GenomicPlot, GenVisR, ggbio, gINTomics, gmapR, gmoviz, GUIDEseq, Gviz, gwascat, HiLDA, HTSeqGenie, 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, driveR, MAAPER, MOCHA, oncoPredict, SeedMatchR suggestsMe: BANDITS, biomvRCNS, Biostrings, BSgenomeForge, chipseq, chromPlot, CrispRVariants, csaw, cummeRbund, DEXSeq, eisaR, fishpond, GenomeInfoDb, GenomicAlignments, GenomicRanges, groHMM, HDF5Array, HiContacts, InteractiveComplexHeatmap, IRanges, MiRaGE, MutationalPatterns, pageRank, plotgardener, RNAmodR.ML, Rsamtools, rtracklayer, scPipe, ShortRead, SummarizedExperiment, systemPipeR, TFutils, tidyCoverage, 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: 1.41.0 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: 2757f202564b878215674d914628e0e1 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 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: 794aad2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GenomicFiles_1.41.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GenomicFiles_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GenomicFiles_1.41.0.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, VCFArray suggestsMe: ldblock, MungeSumstats, TFutils dependencyCount: 79 Package: genomicInstability Version: 1.11.0 Depends: R (>= 4.1.0), checkmate Imports: mixtools, SummarizedExperiment Suggests: SingleCellExperiment, ExperimentHub, pROC License: file LICENSE MD5sum: c45c6e6224754bd1f61149cf1c588a71 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 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: 6dfde84 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/genomicInstability_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/genomicInstability_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/genomicInstability_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/genomicInstability_1.11.0.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: 103 Package: GenomicInteractionNodes Version: 1.9.0 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: 9e084f848a3077c634a2d472c4488654 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], Yarui Diao [fnd] Maintainer: Jianhong Ou 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: 282b042 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GenomicInteractionNodes_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GenomicInteractionNodes_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GenomicInteractionNodes_1.9.0.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: 1.39.0 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: 2b64f75652013303d0f30f5df9037a6c 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 URL: https://github.com/ComputationalRegulatoryGenomicsICL/GenomicInteractions/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenomicInteractions git_branch: devel git_last_commit: 56932e9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GenomicInteractions_1.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GenomicInteractions_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GenomicInteractions_1.39.0.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, extraChIPs, spatzie, OHCA suggestsMe: Chicago, ELMER, sevenC, chicane dependencyCount: 159 Package: GenomicOZone Version: 1.19.0 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: 677ff85c65c19ed0aaa3e1c2c92bf9df 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, Mingzhou Song VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenomicOZone git_branch: devel git_last_commit: 7892b77 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GenomicOZone_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GenomicOZone_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GenomicOZone_1.19.0.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: 176 Package: GenomicPlot Version: 1.3.0 Depends: R (>= 4.3.0), 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 (>= 1.26.0), txdbmaker, ggplot2 (>= 3.3.5), BiocGenerics, dplyr, grid Suggests: knitr, rmarkdown, R.utils, Biobase, BiocStyle, testthat, AnnotationDbi License: GPL-2 MD5sum: 93ff401254a3a1ba47604561467fa836 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] () Maintainer: Shuye Pu 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: 42af486 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GenomicPlot_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GenomicPlot_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GenomicPlot_1.3.0.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: 219 Package: GenomicRanges Version: 1.57.0 Depends: R (>= 4.0.0), methods, stats4, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.37.1), GenomeInfoDb (>= 1.15.2) 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.hg19, BSgenome.Mmusculus.UCSC.mm10, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, RUnit, digest, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: ba1f9224dee03dea8868945b5b730434 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 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: d8c95f2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GenomicRanges_1.57.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GenomicRanges_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GenomicRanges_1.57.0.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, chromstaR, 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, ensemblVEP, epigenomix, 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trackViewer, transmogR, TransView, 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, comradesOO, PlasmaMutationDetector, PlasmaMutationDetector2, rnaCrosslinkOO, RTIGER importsMe: ACE, alabaster.se, ALDEx2, amplican, AnnotationFilter, annotatr, APAlyzer, apeglm, appreci8R, ASpli, AssessORF, ATACCoGAPS, ATACseqQC, ATACseqTFEA, atena, BadRegionFinder, ballgown, bambu, bamsignals, baySeq, BBCAnalyzer, beadarray, BEAT, BiFET, BiocOncoTK, 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, cliProfiler, CNEr, CNVfilteR, CNViz, CNVMetrics, comapr, coMethDMR, conumee, CopyNumberPlots, CoverageView, crisprBase, crisprBowtie, crisprDesign, CRISPRseek, CrispRVariants, crisprViz, CTexploreR, customProDB, DAMEfinder, debrowser, decompTumor2Sig, deconvR, DEFormats, DegCre, DegNorm, deltaCaptureC, derfinder, derfinderPlot, DEWSeq, diffUTR, dinoR, DMRcate, dmrseq, DNAfusion, DominoEffect, doubletrouble, DRIMSeq, DropletUtils, easyRNASeq, EDASeq, EDIRquery, eisaR, ELMER, enhancerHomologSearch, epialleleR, epidecodeR, epigraHMM, EpiMix, epimutacions, epiregulon, epistack, EpiTxDb, epivizr, epivizrData, erma, EventPointer, factR, fcScan, FilterFFPE, fishpond, FLAMES, FRASER, GA4GHclient, gcapc, gDNAx, geneAttribution, GeneGeneInteR, GENESIS, genomation, GenomAutomorphism, genomeIntervals, GenomicAlignments, GenomicDataCommons, GenomicInteractionNodes, GenomicInteractions, GenVisR, ggbio, gINTomics, GOfuncR, GrafGen, GRaNIE, gwascat, h5vc, heatmaps, hermes, HicAggR, HiCBricks, HiCcompare, HiCExperiment, HiContacts, HiCool, hicVennDiagram, HilbertCurve, HiLDA, hiReadsProcessor, HTSeqGenie, hummingbird, icetea, idr2d, IMAS, iNETgrate, INSPEcT, InterMineR, 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, 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, PICS, PING, PIPETS, plotgardener, plyinteractions, pqsfinder, pram, prebs, preciseTAD, primirTSS, proActiv, proBAMr, profileplyr, ProteoDisco, PureCN, Pviz, pwOmics, QDNAseq, qpgraph, qsea, Qtlizer, R3CPET, R453Plus1Toolbox, raer, RAIDS, RareVariantVis, RCAS, RcisTarget, recount3, regionalpcs, regioneR, regionReport, regutools, REMP, Repitools, RESOLVE, rfPred, rGADEM, RGMQL, RgnTX, Rhisat2, RiboCrypt, RiboDiPA, RiboProfiling, 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, 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.v3.1.2.GRCh38, 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, biscuiteerData, chipenrich.data, COSMIC.67, ELMER.data, fourDNData, GenomicDistributionsData, leeBamViews, mCSEAdata, MethylSeqData, pepDat, scMultiome, scRNAseq, sesameData, SomaticCancerAlterations, spatialLIBD, TumourMethData, VariantToolsData, ExpHunterSuite, recountWorkflow, seqpac, TCGAWorkflow, cinaR, crispRdesignR, driveR, geneHapR, geno2proteo, hahmmr, hoardeR, ICAMS, karyotapR, locuszoomr, lolliplot, LoopRig, MAAPER, MitoHEAR, MOCHA, noisyr, numbat, oncoPredict, PACVr, RapidoPGS, revert, scPloidy, Signac, simMP, VALERIE suggestsMe: AlphaMissenseR, AnnotationHub, autonomics, biobroom, BiocCheck, BiocGenerics, BiocParallel, Chicago, ComplexHeatmap, cummeRbund, epivizrChart, GenomeInfoDb, ggmanh, Glimma, GSReg, GWASTools, HDF5Array, InteractiveComplexHeatmap, interactiveDisplay, IRanges, lute, maftools, MiRaGE, MIRit, omicsPrint, parglms, recountmethylation, RTCGA, S4Vectors, SeqGSEA, shiny.gosling, splatter, TFutils, universalmotif, updateObject, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, CTCF, GenomicState, BeadArrayUseCases, GeuvadisTranscriptExpr, nanotubes, RNAmodR.Data, Single.mTEC.Transcriptomes, systemPipeRdata, xcoredata, CAGEWorkflow, chicane, DGEobj, gkmSVM, MARVEL, polyRAD, Rgff, rliger, seqmagick, Seurat, sigminer, SNPassoc, updog, valr dependencyCount: 22 Package: GenomicScores Version: 2.17.0 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: 7ee0783365c902505adbc3f7be547fa6 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 Rodríguez [ctb] Maintainer: Robert Castelo 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: b2a17dd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GenomicScores_2.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GenomicScores_2.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GenomicScores_2.17.0.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.v3.1.2.GRCh38, 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: 1.13.0 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 MD5sum: c759eccddc62f18de0198fbd8c0d86c2 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 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: 1f97d48 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GenomicSuperSignature_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GenomicSuperSignature_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GenomicSuperSignature_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GenomicSuperSignature_1.13.0.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: 182 Package: GenomicTuples Version: 1.39.0 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: 60bd8b68892a985196679f176c9f273a 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 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: 9a14fe7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GenomicTuples_1.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GenomicTuples_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GenomicTuples_1.39.0.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: 1.9.0 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: 3ce0b3c1d67cfe78100b8ba1ed2efc7d 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] () Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenProSeq git_branch: devel git_last_commit: 8049af5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GenProSeq_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GenProSeq_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GenProSeq_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GenProSeq_1.9.0.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: 1.37.0 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: bb40d2cb022281c612fd3cd59eaa277d 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 VignetteBuilder: knitr BugReports: https://github.com/griffithlab/GenVisR/issues git_url: https://git.bioconductor.org/packages/GenVisR git_branch: devel git_last_commit: 3b6df4f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GenVisR_1.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GenVisR_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GenVisR_1.37.0.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: 124 Package: GeoDiff Version: 1.11.0 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: 0ea7cf48dbe71a8e9dd7dcd0cc5a0b5a 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 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: c1d6199 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GeoDiff_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GeoDiff_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GeoDiff_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GeoDiff_1.11.0.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: 163 Package: GEOexplorer Version: 1.11.0 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: 99651b12528216456b2e6f5d56bd8aed 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] (), Rafael Henkin [ctb, ths] (), Alfredo Iacoangeli [ctb, ths] (), Fabrizio Smeraldi [ctb, ths] (), Michael Barnes [ctb, ths] () Maintainer: Guy Hunt 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: f631c1e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GEOexplorer_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GEOexplorer_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GEOexplorer_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GEOexplorer_1.11.0.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: 223 Package: GEOfastq Version: 1.13.0 Imports: xml2, rvest, stringr, RCurl, doParallel, foreach, plyr Suggests: BiocCheck, roxygen2, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 87f459ade5e62f0ff1eab540dcfba0cb 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] () Maintainer: Alex Pickering VignetteBuilder: knitr BugReports: https://github.com/alexvpickering/GEOfastq/issues git_url: https://git.bioconductor.org/packages/GEOfastq git_branch: devel git_last_commit: 2d075d0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GEOfastq_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GEOfastq_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GEOfastq_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GEOfastq_1.13.0.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: 1.67.0 Depends: GEOquery,RSQLite Suggests: knitr, rmarkdown, dplyr, dbplyr, tm, wordcloud License: Artistic-2.0 MD5sum: 3b7be588f1bcb7ca81876d5df546c55d 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GEOmetadb git_branch: devel git_last_commit: d8cdbed git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GEOmetadb_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GEOmetadb_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GEOmetadb_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GEOmetadb_1.67.0.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 importsMe: MetaIntegrator suggestsMe: antiProfilesData, maGUI dependencyCount: 55 Package: GeomxTools Version: 3.9.0 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: c155eb65dc2073ccc1b2fcab7390fd4f 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeomxTools git_branch: devel git_last_commit: 932e50a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GeomxTools_3.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GeomxTools_3.9.0.zip 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: 153 Package: GEOquery Version: 2.73.0 Depends: methods, Biobase Imports: readr (>= 1.3.1), xml2, dplyr, data.table, tidyr, magrittr, limma, curl, R.utils Suggests: knitr, rmarkdown, BiocGenerics, testthat, covr, markdown License: MIT MD5sum: 88a6ad36fb8972b7af402497c882444e 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] () Maintainer: Sean Davis URL: https://github.com/seandavi/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: 1e54ba1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-03 source.ver: src/contrib/GEOquery_2.73.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GEOquery_2.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GEOquery_2.73.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GEOquery_2.73.0.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, crossmeta, DExMA, EGAD, GEOexplorer, minfi, Moonlight2R, MoonlightR, phantasus, SRAdb, BeadArrayUseCases, BioPlex, GSE13015, healthyControlsPresenceChecker, easyDifferentialGeneCoexpression, geneExpressionFromGEO, MetaIntegrator, RCPA, seeker suggestsMe: AUCell, autonomics, CBNplot, 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, fdrci, maGUI, metaMA, MLML2R, NACHO, TcGSA dependencyCount: 47 Package: GEOsubmission Version: 1.57.0 Imports: affy, Biobase, utils License: GPL (>= 2) MD5sum: 12916a098dfe702626f83ffdad729c3e 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 Maintainer: Alexandre Kuhn git_url: https://git.bioconductor.org/packages/GEOsubmission git_branch: devel git_last_commit: 2e870ae git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GEOsubmission_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GEOsubmission_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GEOsubmission_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GEOsubmission_1.57.0.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: 2.5.0 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: cc7fc59680a78625e9d1c4cfa420b40d 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] () 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: ed9734d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GeoTcgaData_2.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GeoTcgaData_2.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GeoTcgaData_2.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GeoTcgaData_2.5.0.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: 75 Package: gep2pep Version: 1.25.0 Imports: repo (>= 2.1.1), foreach, stats, utils, GSEABase, methods, Biobase, XML, rhdf5, digest, iterators Suggests: WriteXLS, testthat, knitr, rmarkdown License: GPL-3 MD5sum: 719dfff9036799ff481adcf2e92dd315 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 Maintainer: Francesco Napolitano VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gep2pep git_branch: devel git_last_commit: 45570c7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gep2pep_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/gep2pep_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gep2pep_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gep2pep_1.25.0.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: 1.15.0 Depends: R (>= 4.0) Imports: stats, utils, SummarizedExperiment, htm2txt Suggests: knitr, testthat, rmarkdown License: GPL-3 MD5sum: cffabdde142e5e31c8cd8d8b47ece26f 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 URL: https://github.com/markziemann/getDEE2 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/getDEE2 git_branch: devel git_last_commit: ceeaae7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/getDEE2_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/getDEE2_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/getDEE2_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/getDEE2_1.15.0.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: 1.13.0 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: 5c794748e02db45924ef67fe42f7868a 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] (), Murilo Zanini David [ctb], Bruno César Feltes [ctb] (), Marcio Dorn [ctb] () Maintainer: Itamar José Guimarães Nunes URL: https://github.com/sbcblab/geva VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geva git_branch: devel git_last_commit: 17c46a7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/geva_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/geva_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/geva_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/geva_1.13.0.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: 9 Package: GEWIST Version: 1.49.0 Depends: R (>= 2.10), car License: GPL-2 MD5sum: 2ea5194fa4411c67ad3cdbd2fb10a92a 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 git_url: https://git.bioconductor.org/packages/GEWIST git_branch: devel git_last_commit: 21a333b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GEWIST_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GEWIST_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GEWIST_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GEWIST_1.49.0.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: 79 Package: gg4way Version: 1.3.0 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 License: MIT + file LICENSE MD5sum: b931145b4e404aadcd6a237a32397db5 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 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: 41c67f3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gg4way_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gg4way_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gg4way_1.3.0.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: 92 Package: ggbio Version: 1.53.0 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: f51934406d5b3e0cf85c7a87d34a5a3e 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 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: 6123478 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ggbio_1.53.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ggbio_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ggbio_1.53.0.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, derfinderPlot, FLAMES, GenomicOZone, msgbsR, R3CPET, ReportingTools, RiboProfiling, scruff, SomaticSignatures, OHCA, MOCHA suggestsMe: bambu, beadarray, ensembldb, FRASER, gwascat, interactiveDisplay, NanoStringNCTools, OUTRIDER, regionReport, RnBeads, shiny.gosling, StructuralVariantAnnotation, universalmotif, NanoporeRNASeq, Single.mTEC.Transcriptomes, SomaticCancerAlterations dependencyCount: 171 Package: ggcyto Version: 1.33.0 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: fbb39ac95ec32f7d433ed3ce74133753 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 URL: https://github.com/RGLab/ggcyto/issues VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ggcyto git_branch: devel git_last_commit: 09c942f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ggcyto_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ggcyto_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ggcyto_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ggcyto_1.33.0.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: 70 Package: ggkegg Version: 1.3.0 Depends: R (>= 4.3.0), ggplot2, ggraph, XML, igraph, tidygraph Imports: BiocFileCache, GetoptLong, data.table, dplyr, magick, patchwork, shadowtext, stringr, tibble, org.Hs.eg.db, methods, utils, stats, AnnotationDbi, grDevices, gtable Suggests: knitr, clusterProfiler, bnlearn, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 0c7c14bd6cd55f2e2f33c21ab17e74e9 NeedsCompilation: no Title: KEGG pathway visualization by ggplot2 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 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: 609d359 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ggkegg_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ggkegg_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ggkegg_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ggkegg_1.3.0.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: 101 Package: ggmanh Version: 1.9.0 Depends: methods, ggplot2 Imports: gdsfmt, ggrepel, grDevices, RColorBrewer, rlang, scales, SeqArray (>= 1.32.0), stats Suggests: BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0), markdown, GenomicRanges License: MIT + file LICENSE MD5sum: 0740e6f90b4593d1b6cbf3dcf1020aa4 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ggmanh git_branch: devel git_last_commit: ccb440c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ggmanh_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ggmanh_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ggmanh_1.9.0.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: 60 Package: ggmsa Version: 1.11.0 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: a9172a0e580668aeaad98774704b3dc9 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: Lang Zhou [aut, cre], Guangchuang Yu [aut, ths] (), Shuangbin Xu [ctb], Huina Huang [ctb] Maintainer: Lang Zhou 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: 6f21768 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ggmsa_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ggmsa_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ggmsa_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ggmsa_1.11.0.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: 116 Package: GGPA Version: 1.17.0 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) Archs: x64 MD5sum: c91fd264d0a3488f33589a7ee9085cfb 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 URL: https://github.com/dongjunchung/GGPA/ SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/GGPA git_branch: devel git_last_commit: 8efbddb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GGPA_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GGPA_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GGPA_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GGPA_1.17.0.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: 73 Package: ggsc Version: 1.3.0 Imports: Rcpp, RcppParallel, cli, dplyr, ggfun, 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, scuttle, shadowtext, sf, SeuratObject, SpatialExperiment, STexampleData, testthat (>= 3.0.0) License: Artistic-2.0 Archs: x64 MD5sum: 3ffdb6992ad3a41f072d2613ef206ae5 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] (), Shuangbin Xu [aut] (), Noriaki Sato [ctb] Maintainer: Guangchuang Yu 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: 77ee00c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ggsc_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ggsc_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ggsc_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ggsc_1.3.0.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 dependencyCount: 175 Package: ggtree Version: 3.13.0 Depends: R (>= 3.5.0) Imports: ape, aplot, dplyr, ggplot2 (> 3.3.6), grid, magrittr, methods, purrr, rlang, ggfun (>= 0.0.9), yulab.utils, 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: ea4a3a150fd11d8c4b692a0406adb0fa 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] (), Tommy Tsan-Yuk Lam [aut, ths], Shuangbin Xu [aut] (), Lin Li [ctb], Bradley Jones [ctb], Justin Silverman [ctb], Watal M. Iwasaki [ctb], Yonghe Xia [ctb], Ruizhu Huang [ctb] Maintainer: Guangchuang Yu 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: fc02e5a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ggtree_3.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ggtree_3.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ggtree_3.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ggtree_3.13.0.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, LinTInd, LymphoSeq, miaViz, microbiomeMarker, MicrobiotaProcess, orthogene, philr, scBubbletree, singleCellTK, sitePath, systemPipeTools, treeclimbR, treekoR, DAISIEprep, ddtlcm, dowser, EvoPhylo, genBaRcode, harrietr, numbat, Platypus, RevGadgets, scistreer, shinyTempSignal, STraTUS, Sysrecon suggestsMe: compcodeR, syntenet, TreeAndLeaf, treeio, TreeSummarizedExperiment, universalmotif, aplot, aplotExtra, CoOL, DAISIE, deeptime, ggimage, idiogramFISH, MetaNet, nosoi, oppr, PCMBase, pctax, RAINBOWR, rhierbaps, rphylopic dependencyCount: 62 Package: ggtreeDendro Version: 1.7.0 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: 3d8649de9a8ea2f3ef0f4efec8341e8a 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] (), Shuangbin Xu [ctb] (), Chuanjie Zhang [ctb] Maintainer: Guangchuang Yu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ggtreeDendro git_branch: devel git_last_commit: d2ae38f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ggtreeDendro_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ggtreeDendro_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ggtreeDendro_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ggtreeDendro_1.7.0.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: 63 Package: ggtreeExtra Version: 1.15.0 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: 3de134cdcc559468b58607d47e568dc7 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] (), Guangchuang Yu [aut, ctb] () Maintainer: Shuangbin Xu 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: eb59d96 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ggtreeExtra_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ggtreeExtra_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ggtreeExtra_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ggtreeExtra_1.15.0.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 dependencyCount: 64 Package: ggtreeSpace Version: 1.1.0 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: 83801d5516c60306de01d688845fb88a 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] (), Li Lin [ctb] Maintainer: Guangchuang Yu 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: c42ecdb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ggtreeSpace_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ggtreeSpace_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ggtreeSpace_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ggtreeSpace_1.1.0.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: 104 Package: GIGSEA Version: 1.23.0 Depends: R (>= 3.5), Matrix, MASS, locfdr, stats, utils Suggests: knitr, rmarkdown License: LGPL-3 MD5sum: eaeb5b48a18608812d7f50c825b0e46e 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GIGSEA git_branch: devel git_last_commit: 38b82ac git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GIGSEA_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GIGSEA_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GIGSEA_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GIGSEA_1.23.0.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: 1.1.0 Imports: KEGGREST, UniProt.ws, XML, rentrez, httr, utils, memoise, cachem Suggests: RUnit, BiocGenerics, markdown, knitr License: GPL-3 + file LICENSE MD5sum: 52d0734c7666fe2f55c90e12d662cf74 NeedsCompilation: no Title: Gene Identifier Mapper Description: Provides functionalities to translate gene or protein identifiers between state-of-art biological databases: CARD (), NCBI Protein, Nucleotide and Gene (), UniProt () and KEGG (). 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] (), Daniel Ayala [aut] (), Marina Pulido [aut] (), Rafael Ayala [aut] (), Lorena López-Cerero [aut] (), Inma Hernández [aut] (), David Ruiz [aut] () Maintainer: Fernando Sola VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ginmappeR git_branch: devel git_last_commit: be92c70 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-03 source.ver: src/contrib/ginmappeR_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ginmappeR_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ginmappeR_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ginmappeR_1.1.0.tgz vignettes: vignettes/ginmappeR/inst/doc/ginmappeR.html vignetteTitles: ginmappeR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ginmappeR/inst/doc/ginmappeR.R dependencyCount: 71 Package: girafe Version: 1.57.0 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: 607b7e56f11bd22970cdfec7b53f119e 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 git_url: https://git.bioconductor.org/packages/girafe git_branch: devel git_last_commit: 65a2882 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/girafe_1.57.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/girafe_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/girafe_1.57.0.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: 2.69.0 Depends: R (>= 2.10) Imports: aws License: GPL-2 Archs: x64 MD5sum: c92574789f5e2b2215065a13371caf53 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 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: 55fd28b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GLAD_2.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GLAD_2.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GLAD_2.69.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GLAD_2.69.0.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: ADaCGH2, ITALICS importsMe: ITALICS, MANOR suggestsMe: aroma.cn, aroma.core dependencyCount: 4 Package: GladiaTOX Version: 1.21.0 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 MD5sum: 21612cf49133eab34e99f4b7522de717 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GladiaTOX git_branch: devel git_last_commit: 57ecf15 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GladiaTOX_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GladiaTOX_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GladiaTOX_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GladiaTOX_1.21.0.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: 2.15.0 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 License: GPL-3 MD5sum: 5dcec86a186c3a684ced0a8248b6e2a9 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 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: fe089cb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Glimma_2.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Glimma_2.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Glimma_2.15.0.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: 1.17.0 Imports: Rcpp, DelayedMatrixStats, matrixStats, MatrixGenerics, DelayedArray, HDF5Array, SummarizedExperiment, SingleCellExperiment, BiocGenerics, methods, stats, utils, splines, rlang, vctrs LinkingTo: Rcpp, RcppArmadillo, beachmat Suggests: testthat (>= 2.1.0), zoo, DESeq2, edgeR, limma, beachmat, MASS, statmod, ggplot2, bench, BiocParallel, knitr, rmarkdown, BiocStyle, TENxPBMCData, muscData, scran, Matrix, dplyr License: GPL-3 Archs: x64 MD5sum: 9c57c48f25869c661c074875a8c4a18e 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] (), Nathan Lubock [ctb] (), Michael Love [ctb] Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/glmGamPoi SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/const-ae/glmGamPoi/issues git_url: https://git.bioconductor.org/packages/glmGamPoi git_branch: devel git_last_commit: d07de0f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/glmGamPoi_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/glmGamPoi_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/glmGamPoi_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/glmGamPoi_1.17.0.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 dependencyCount: 52 Package: glmSparseNet Version: 1.23.0 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: 7c08a7b57720cfe816208d6649615288 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 Veríssimo [aut, cre] (), Susana Vinga [aut], Eunice Carrasquinha [ctb], Marta Lopes [ctb] Maintainer: André Veríssimo 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: 63a16f5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/glmSparseNet_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/glmSparseNet_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/glmSparseNet_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/glmSparseNet_1.23.0.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 dependencyCount: 192 Package: GlobalAncova Version: 4.23.0 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: e2d38939a9b893dc5d854574ccd11449 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 git_url: https://git.bioconductor.org/packages/GlobalAncova git_branch: devel git_last_commit: bac6c17 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GlobalAncova_4.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GlobalAncova_4.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GlobalAncova_4.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GlobalAncova_4.23.0.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 dependencyCount: 81 Package: globalSeq Version: 1.33.0 Depends: R (>= 3.0.0) Suggests: knitr, testthat, SummarizedExperiment, S4Vectors License: GPL-3 MD5sum: 3840ab5be05973ff7972e428c1184ab1 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 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: 3ad762b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/globalSeq_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/globalSeq_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/globalSeq_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/globalSeq_1.33.0.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: 5.59.0 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) MD5sum: 15e394813bdef5c6c96d392065f5ce45 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 git_url: https://git.bioconductor.org/packages/globaltest git_branch: devel git_last_commit: 1bfa681 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/globaltest_5.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/globaltest_5.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/globaltest_5.59.0.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, SlaPMEG suggestsMe: topGO, penalized dependencyCount: 53 Package: GloScope Version: 1.3.0 Depends: R (>= 4.3.0) 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: 80fa3357e4843f21c5536fada959bfd4 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] (), Hao Wang [aut] (), Elizabeth Purdom [aut], Boying Gong [aut] Maintainer: William Torous VignetteBuilder: knitr BugReports: https://github.com/epurdom/GloScope/issues git_url: https://git.bioconductor.org/packages/GloScope git_branch: devel git_last_commit: 15b5dfa git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GloScope_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GloScope_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GloScope_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GloScope_1.3.0.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: 1.47.0 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: f1587cebdf43e5df21963a3f982cdf62 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 git_url: https://git.bioconductor.org/packages/gmapR git_branch: devel git_last_commit: 8bfcf6a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gmapR_1.47.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gmapR_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gmapR_1.47.0.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 dependsOnMe: HTSeqGenie suggestsMe: VariantTools, VariantToolsData dependencyCount: 79 Package: GmicR Version: 1.19.0 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: 44cc127bf304150fdc44fbdef2d0f4bb 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GmicR git_branch: devel git_last_commit: 304a4a7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GmicR_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GmicR_1.19.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GmicR_1.19.0.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: 156 Package: gmoviz Version: 1.17.0 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: dc7432b7e387bf44cc43ed451f89d98a 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] (), Constantinos Koutsakis [aut] Maintainer: Kathleen Zeglinski VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gmoviz git_branch: devel git_last_commit: 1a42947 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gmoviz_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gmoviz_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gmoviz_1.17.0.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: 1.33.0 Depends: R(>= 3.3.0),stats,utils,graphics, grDevices, diagram, plotrix, base,GenomicRanges Suggests: BiocStyle, BiocGenerics License: GPL (>= 2) MD5sum: b4fe0e9e3fc56cf13261e7e517943a52 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 git_url: https://git.bioconductor.org/packages/GMRP git_branch: devel git_last_commit: 0cc3fe0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GMRP_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GMRP_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GMRP_1.33.0.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: 1.21.0 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 Archs: x64 MD5sum: d2e0aaea1e370c838c8a8a73c2d82216 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 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: 07d4275 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GNET2_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GNET2_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GNET2_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GNET2_1.21.0.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: 108 Package: GNOSIS Version: 1.3.0 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: e325297a4f35690a619b7cdd6beda13d 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] (), Marcel Ramos [ctb] Maintainer: Lydia King 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: c79d867 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GNOSIS_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GNOSIS_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GNOSIS_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GNOSIS_1.3.0.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: 299 Package: GOexpress Version: 1.39.0 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) MD5sum: b67fba0394d716b3aa23b2aadc9264ee 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 URL: https://github.com/kevinrue/GOexpress git_url: https://git.bioconductor.org/packages/GOexpress git_branch: devel git_last_commit: a8aa379 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GOexpress_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GOexpress_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GOexpress_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GOexpress_1.39.0.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: 93 Package: GOfuncR Version: 1.25.0 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: fb6cdb3bf78256ac0da40307054e1d0d 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GOfuncR git_branch: devel git_last_commit: 89fed08 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GOfuncR_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GOfuncR_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GOfuncR_1.25.0.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: 1.31.0 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 Archs: x64 MD5sum: c5a34f1b0252df04a845e9c57fe8772a 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 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: 81a7080 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GOpro_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GOpro_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GOpro_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GOpro_1.31.0.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: 98 Package: goProfiles Version: 1.67.0 Depends: Biobase, AnnotationDbi, GO.db, CompQuadForm, stringr Suggests: org.Hs.eg.db License: GPL-2 MD5sum: f76e14ded5ee408ffaa023ecfa46ccae 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 git_url: https://git.bioconductor.org/packages/goProfiles git_branch: devel git_last_commit: a95e396 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/goProfiles_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/goProfiles_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/goProfiles_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/goProfiles_1.67.0.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: 2.31.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi, GO.db, methods, rlang, stats, utils, yulab.utils LinkingTo: Rcpp Suggests: AnnotationHub, BiocManager, clusterProfiler, DOSE, knitr, org.Hs.eg.db, prettydoc, readr, rmarkdown, testthat, tidyr, tidyselect, ROCR License: Artistic-2.0 Archs: x64 MD5sum: 4018d9cdb1f4b76045b7c055219dd7a7 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], Lluís Revilla Sancho [ctb] Maintainer: Guangchuang Yu 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: be50a16 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GOSemSim_2.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GOSemSim_2.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GOSemSim_2.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GOSemSim_2.31.0.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, simplifyEnrichment, ViSEAGO, BiSEp suggestsMe: BioCor, epiNEM, FELLA, SemDist, protr, rDNAse, scDiffCom dependencyCount: 50 Package: goSorensen Version: 1.7.0 Depends: R (>= 4.3.0) Imports: GO.db, org.Hs.eg.db, goProfiles, stats, clusterProfiler, parallel, stringr Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 64f2cb94b9bd0bfbc374d1df34eceef9 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] (), Jordi Ocana [aut, ctb] (0000-0002-4736-699), Alexandre Sanchez-Pla [ctb] (), Miquel Salicru [ctb] () Maintainer: Pablo Flores VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/goSorensen git_branch: devel git_last_commit: 2be7d70 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/goSorensen_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/goSorensen_1.7.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/goSorensen_1.7.0.tgz vignettes: vignettes/goSorensen/inst/doc/goSorensen_Introduction.html vignetteTitles: An introduction to equivalence test between feature lists using goSorensen. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goSorensen/inst/doc/goSorensen_Introduction.R dependencyCount: 132 Package: goSTAG Version: 1.29.0 Depends: R (>= 3.4) Imports: AnnotationDbi, biomaRt, GO.db, graphics, memoise, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: cd78a41d2b736dcf55c18b9054f46d20 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/goSTAG git_branch: devel git_last_commit: 89a3ad6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/goSTAG_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/goSTAG_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/goSTAG_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/goSTAG_1.29.0.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: 70 Package: GOstats Version: 2.71.0 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: ef3ec45f331dfa257e6bada331feb528 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GOstats git_branch: devel git_last_commit: f8c38a1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GOstats_2.71.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GOstats_2.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GOstats_2.71.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GOstats_2.71.0.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, miRLAB, netZooR, pcaExplorer, scTensor, DNLC suggestsMe: a4, Category, fastLiquidAssociation, fgga, GSEAlm, interactiveDisplay, MineICA, MLP, qpgraph, RnBeads, safe, maGUI, sand dependencyCount: 66 Package: GOTHiC Version: 1.41.0 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: d3290d9b3554f65ef08eec0a66a77ab7 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 git_url: https://git.bioconductor.org/packages/GOTHiC git_branch: devel git_last_commit: f8c5e87 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GOTHiC_1.41.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GOTHiC_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GOTHiC_1.41.0.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: 1.79.0 Depends: GO.db Imports: AnnotationDbi, GO.db, graphics, grDevices Suggests: hgu133a.db License: GPL-2 MD5sum: d39051053c75682b948e795a67a379cc 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 , Agnes Paquet Maintainer: Agnes Paquet git_url: https://git.bioconductor.org/packages/goTools git_branch: devel git_last_commit: cef4ddc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/goTools_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/goTools_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/goTools_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/goTools_1.79.0.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: 1.17.0 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) Archs: x64 MD5sum: 39240d16975fb1b87c724c592426d260 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 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: da898e0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GPA_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GPA_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GPA_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GPA_1.17.0.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: 1.77.0 Imports: stats Suggests: MASS License: Artistic-2.0 MD5sum: f3a9471043fa39189517fe40b1ed180f 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 git_url: https://git.bioconductor.org/packages/gpls git_branch: devel git_last_commit: 2f96863 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gpls_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/gpls_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gpls_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gpls_1.77.0.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: 1.21.0 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: 6ccf19038ecbb3d75f001774daa7c325 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 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: ed349fd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gpuMagic_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/gpuMagic_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gpuMagic_1.21.0.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: 61 Package: GrafGen Version: 1.1.0 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: 41f87c8a874af6f0837586e95243cea6 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 VignetteBuilder: knitr BugReports: https://github.com/wheelerb/GrafGen/issues git_url: https://git.bioconductor.org/packages/GrafGen git_branch: devel git_last_commit: 2dd071a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GrafGen_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GrafGen_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GrafGen_1.1.0.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: 135 Package: GRaNIE Version: 1.9.0 Depends: R (>= 4.2.0) Imports: futile.logger, checkmate, patchwork, 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, 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.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.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, TFBSTools, motifmatchr, rbioapi, LDlinkR License: Artistic-2.0 MD5sum: 91d769aae4cfd46ef5ae9973f4a2e27d 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 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: fe4f02a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GRaNIE_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GRaNIE_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GRaNIE_1.9.0.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: 156 Package: granulator Version: 1.13.0 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 MD5sum: 9850090fae112fecb1f4f259eb5f940f 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 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: e912228 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/granulator_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/granulator_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/granulator_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/granulator_1.13.0.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: 125 Package: graper Version: 1.21.0 Depends: R (>= 3.6) Imports: Matrix, Rcpp, stats, ggplot2, methods, cowplot, matrixStats LinkingTo: Rcpp, RcppArmadillo, BH Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL (>= 2) Archs: x64 MD5sum: df17bffbfed4c3e1652b4b43a1cd73f8 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/graper git_branch: devel git_last_commit: 7b45fcc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/graper_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/graper_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/graper_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/graper_1.21.0.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: 1.83.0 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 Archs: x64 MD5sum: 05dd6efa5eb3f8ad27ae9a84c0652a2d 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/graph git_branch: devel git_last_commit: 4602e26 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/graph_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/graph_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/graph_1.83.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/graph_1.83.0.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, MineICA, pathRender, Pigengene, RbcBook1, RBGL, RBioinf, RCyjs, Rgraphviz, ROntoTools, SRAdb, topGO, vtpnet, DLBCL, SNAData, yeastExpData, cyjShiny, dlsem, gridGraphviz, GUIProfiler, hasseDiagram, PairViz, PerfMeas, SubpathwayLNCE importsMe: AnnotationHubData, BgeeDB, BiocCheck, BiocFHIR, biocGraph, BiocOncoTK, BiocPkgTools, biocViews, bnem, CAMERA, Category, categoryCompare, chimeraviz, ChIPpeakAnno, CHRONOS, consICA, CytoML, dce, DEGraph, DEsubs, epiNEM, EventPointer, fgga, flowClust, flowWorkspace, gage, GeneNetworkBuilder, GenomicInteractionNodes, GraphAT, graphite, hyperdraw, KEGGgraph, keggorthology, MIRit, mnem, NCIgraph, netresponse, OncoSimulR, ontoProc, oposSOM, OrganismDbi, pathview, PhenStat, pwOmics, qpgraph, RCy3, RGraph2js, rsbml, Rtreemix, SplicingGraphs, Streamer, VariantFiltering, BioPlex, BayesNetBP, BCDAG, BiDAG, BNrich, ceg, CePa, classGraph, clustNet, CodeDepends, cogmapr, eulerian, ggm, gridDebug, HEMDAG, kpcalg, 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, rBiopaxParser, RCX, rTRM, S4Vectors, SPIA, VariantTools, arulesViz, bnlearn, bnstruct, bsub, ChoR, epoc, gbutils, GeneNet, gMCP, igraph, lava, loon, maGUI, psych, rEMM, rPref, sisal, textplot, tidygraph dependencyCount: 6 Package: GraphAlignment Version: 1.69.0 License: file LICENSE License_restricts_use: yes Archs: x64 MD5sum: 6bde1805efa06e345c462b39b7bd7714 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 , Michal Kolar, Ville Mustonen, Michael Laessig, and Johannes Berg. Maintainer: Joern P. Meier URL: http://www.thp.uni-koeln.de/~berg/GraphAlignment/ git_url: https://git.bioconductor.org/packages/GraphAlignment git_branch: devel git_last_commit: ad491a3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GraphAlignment_1.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GraphAlignment_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GraphAlignment_1.69.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GraphAlignment_1.69.0.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: 1.77.0 Depends: R (>= 2.10), graph, methods Imports: graph, MCMCpack, methods, stats License: LGPL MD5sum: 1075beb705658a4325e1bff0f33dff9e 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 git_url: https://git.bioconductor.org/packages/GraphAT git_branch: devel git_last_commit: 3bfb627 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GraphAT_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GraphAT_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GraphAT_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GraphAT_1.77.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 20 Package: graphite Version: 1.51.0 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 MD5sum: 03a46e50815bf00c14602b4ad399ff9a 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 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: f63b0c7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/graphite_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/graphite_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/graphite_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/graphite_1.51.0.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, multiGSEA, ReactomePA, sSNAPPY, ICDS, netgsa suggestsMe: clipper, InterCellar, metaboliteIDmapping dependencyCount: 49 Package: GraphPAC Version: 1.47.0 Depends: R(>= 2.15),iPAC, igraph, TSP, RMallow Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 6b7facd530d8beb396728d0ecc41259e 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 git_url: https://git.bioconductor.org/packages/GraphPAC git_branch: devel git_last_commit: 8c5789e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GraphPAC_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GraphPAC_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GraphPAC_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GraphPAC_1.47.0.tgz vignettes: vignettes/GraphPAC/inst/doc/GraphPAC.pdf vignetteTitles: iPAC: identification of Protein Amino acid Mutations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GraphPAC/inst/doc/GraphPAC.R dependsOnMe: QuartPAC dependencyCount: 53 Package: GRENITS Version: 1.57.0 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) Archs: x64 MD5sum: 673dad63770fd8d9bfd3742eea8eca6a 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 git_url: https://git.bioconductor.org/packages/GRENITS git_branch: devel git_last_commit: 436f5eb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GRENITS_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GRENITS_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GRENITS_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GRENITS_1.57.0.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: 1.37.0 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: b1fb74f0daac5d34bb535a620249d86c 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 git_url: https://git.bioconductor.org/packages/GreyListChIP git_branch: devel git_last_commit: 76e965f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GreyListChIP_1.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GreyListChIP_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GreyListChIP_1.37.0.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: 1.31.0 Depends: R (>= 4.0), SummarizedExperiment Imports: drc, plotly, ggplot2, S4Vectors, stats Suggests: knitr, rmarkdown, BiocStyle, tinytex License: GPL-3 MD5sum: e830152dfdff6b833a9fb4a875212cbe 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 , Mario Medvedovic 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: 7ac30d8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GRmetrics_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GRmetrics_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GRmetrics_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GRmetrics_1.31.0.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: 137 Package: groHMM Version: 1.39.0 Depends: R (>= 3.0.2), 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 MD5sum: e380343cc8c94d2cbd9df4a6d6ab78f9 NeedsCompilation: yes Title: GRO-seq Analysis Pipeline Description: A pipeline for the analysis of GRO-seq data. biocViews: Sequencing, Software Author: Charles G. Danko, Minho Chae, Andre Martins, W. Lee Kraus Maintainer: Tulip Nandu , W. Lee Kraus 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: d83ee48 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/groHMM_1.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/groHMM_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/groHMM_1.39.0.tgz vignettes: vignettes/groHMM/inst/doc/groHMM.pdf vignetteTitles: groHMM tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/groHMM/inst/doc/groHMM.R dependencyCount: 59 Package: GSALightning Version: 1.33.0 Depends: R (>= 3.3.0) Imports: Matrix, data.table, stats Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 7a7483d4f9323da5969d128c81777474 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 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: c5e3e48 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GSALightning_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GSALightning_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GSALightning_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GSALightning_1.33.0.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: 1.39.0 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: 49316f2dcb45af10859573509dc60f6a 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 , Galina Glazko Maintainer: Yasir Rahmatallah , Galina Glazko git_url: https://git.bioconductor.org/packages/GSAR git_branch: devel git_last_commit: 5678540 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GSAR_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GSAR_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GSAR_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GSAR_1.39.0.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: 2.35.0 Depends: shiny, sp, gplots, ggplot2, reshape2, RColorBrewer, rhdf5, R(>= 2.10.0) Imports: graphics Suggests: Affyhgu133aExpr, Affymoe4302Expr, Affyhgu133A2Expr, Affyhgu133Plus2Expr License: GPL(>=2) MD5sum: 5662d9db329bed4a75cf560c14e14a95 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 git_url: https://git.bioconductor.org/packages/GSCA git_branch: devel git_last_commit: 61085b0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GSCA_2.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GSCA_2.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GSCA_2.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GSCA_2.35.0.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: 1.19.0 Depends: R (>= 3.6) Imports: SummarizedExperiment, nloptr, fGarch, methods, BiocParallel, graphics Suggests: knitr, testthat, rmarkdown, BiocStyle License: GPL-3 MD5sum: 36b4081801cac9dc00c3511b9cece96d 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 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: 1f146b3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gscreend_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/gscreend_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gscreend_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gscreend_1.19.0.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: 88 Package: GSEABase Version: 1.67.0 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: d0abff757387aa743827ef5be986d478 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GSEABase git_branch: devel git_last_commit: 27ee159 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GSEABase_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GSEABase_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GSEABase_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GSEABase_1.67.0.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, cpvSNP, npGSEA, PROMISE, splineTimeR, GSVAdata, OSCA.basic importsMe: AUCell, BioCor, canceR, Category, categoryCompare, cellHTS2, 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, vissE, zenith, msigdb, SingscoreAMLMutations, clustermole, RVA suggestsMe: BiocSet, gage, globaltest, GOstats, GSAR, MAST, phenoTest, BaseSet dependencyCount: 49 Package: GSEABenchmarkeR Version: 1.25.0 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: 00c97d723f94cc2afa5d3fa3ca14e388 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 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: 8159afd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GSEABenchmarkeR_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GSEABenchmarkeR_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GSEABenchmarkeR_1.25.0.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: pareg, roastgsa dependencyCount: 112 Package: GSEAlm Version: 1.65.0 Depends: Biobase Suggests: GSEABase,Category, multtest, ALL, annotate, hgu95av2.db, genefilter, GOstats, RColorBrewer License: Artistic-2.0 MD5sum: 9b82c59812f751afebed5063a682d627 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 git_url: https://git.bioconductor.org/packages/GSEAlm git_branch: devel git_last_commit: 304352a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GSEAlm_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GSEAlm_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GSEAlm_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GSEAlm_1.65.0.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: 6 Package: GSEAmining Version: 1.15.0 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: 40e5d48292546fbc86b23c977189fc9b 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GSEAmining git_branch: devel git_last_commit: 24ced6b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GSEAmining_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GSEAmining_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GSEAmining_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GSEAmining_1.15.0.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: 60 Package: gsean Version: 1.25.0 Depends: R (>= 3.5), fgsea, PPInfer Suggests: SummarizedExperiment, pasilla, org.Dm.eg.db, AnnotationDbi, knitr, plotly, WGCNA, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 20e4ce687ef57462b0999c3a3b2b5481 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gsean git_branch: devel git_last_commit: aa9b841 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gsean_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/gsean_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gsean_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gsean_1.25.0.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: 118 Package: GSgalgoR Version: 1.15.0 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: bc3d03f326d1d172c5f170922880008e 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 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: 4009e05 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GSgalgoR_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GSgalgoR_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GSgalgoR_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GSgalgoR_1.15.0.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: 1.39.0 Depends: R (>= 2.13.1), Homo.sapiens, org.Hs.eg.db, GenomicFeatures, AnnotationDbi Suggests: GenomicRanges, GSBenchMark License: GPL-2 Archs: x64 MD5sum: 4abcc5f9237607d75541c329c29ad6ad 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 , Elana J. Fertig Maintainer: Bahman Afsari , Elana J. Fertig git_url: https://git.bioconductor.org/packages/GSReg git_branch: devel git_last_commit: 5f15cec git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GSReg_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GSReg_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GSReg_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GSReg_1.39.0.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: 110 Package: GSRI Version: 2.53.0 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 MD5sum: e3c8c00807e71c8c862fdabaa5201f7e 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 git_url: https://git.bioconductor.org/packages/GSRI git_branch: devel git_last_commit: 6624d4d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GSRI_2.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GSRI_2.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GSRI_2.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GSRI_2.53.0.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: 1.53.0 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 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) Archs: x64 MD5sum: 6d0ff448f48fa81d5a2d43524ab5bcda 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 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: 3280b36 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GSVA_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GSVA_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GSVA_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GSVA_1.53.1.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 importsMe: consensusOV, EGSEA, escape, octad, oppar, scFeatures, signifinder, singleCellTK, TBSignatureProfiler, autoGO, clustermole, DRviaSPCN, psSubpathway, scMappR, SIGN, sigQC, SMDIC suggestsMe: decoupleR, MCbiclust, sparrow, ReporterScore dependencyCount: 102 Package: gtrellis Version: 1.37.0 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 MD5sum: 180d438c95489cdcad49df807cc3e257 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] () Maintainer: Zuguang Gu URL: https://github.com/jokergoo/gtrellis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gtrellis git_branch: devel git_last_commit: 6bba4fa git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gtrellis_1.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gtrellis_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gtrellis_1.37.0.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: 1.35.0 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: 2cde6dd6ef55ece79952b202f02281b3 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GUIDEseq git_branch: devel git_last_commit: 306e99e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GUIDEseq_1.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GUIDEseq_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GUIDEseq_1.35.0.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: 2.21.0 Depends: GenomicFeatures, rtracklayer,AnnotationDbi, GenomicRanges, magrittr, ggplot2, methods, stats,utils ,knitr,dplyr License: GPL-2 MD5sum: 53da073c7b8b6cf6e01bb4f8dde5b704 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Guitar git_branch: devel git_last_commit: feed882 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Guitar_2.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Guitar_2.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Guitar_2.21.0.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: 104 Package: Gviz Version: 1.49.0 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: 5b173086d1b428f8a9c5c9f70adeb869 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] (), 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 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: 8df05b1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Gviz_1.49.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Gviz_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Gviz_1.49.0.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, methylationArrayAnalysis, rnaseqGene, csawBook importsMe: AllelicImbalance, ASpli, CAGEfightR, comapr, crisprViz, DMRcate, 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 suggestsMe: annmap, BindingSiteFinder, cellbaseR, CNEr, CNVRanger, ensembldb, extraChIPs, fishpond, GenomicRanges, gwascat, interactiveDisplay, InterMineR, MIRit, pqsfinder, QuasR, RnBeads, segmenter, SplicingGraphs, TFutils, Single.mTEC.Transcriptomes, CAGEWorkflow, chipseqDB, chicane, RTIGER dependencyCount: 156 Package: GWAS.BAYES Version: 1.15.0 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: 9d701b571f14454cf9bf4420fda75308 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 IEB. 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] (), Marco Ferreira [aut] (), Tieming Ji [aut] Maintainer: Jacob Williams VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GWAS.BAYES git_branch: devel git_last_commit: 963cbb5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GWAS.BAYES_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GWAS.BAYES_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GWAS.BAYES_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GWAS.BAYES_1.15.0.tgz vignettes: vignettes/GWAS.BAYES/inst/doc/Vignette_BICOSS.html, vignettes/GWAS.BAYES/inst/doc/Vignette_IEB.html vignetteTitles: BICOSS, IEB hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GWAS.BAYES/inst/doc/Vignette_BICOSS.R, vignettes/GWAS.BAYES/inst/doc/Vignette_IEB.R dependencyCount: 94 Package: gwascat Version: 2.37.0 Depends: R (>= 3.5.0), methods Imports: S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges (>= 1.29.6), GenomicFeatures, readr, Biostrings, AnnotationDbi, BiocFileCache, snpStats, VariantAnnotation, AnnotationHub 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: a288a1351cee0f8d1be44d7063f55e9d 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 Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gwascat git_branch: devel git_last_commit: 5c502fb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gwascat_2.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gwascat_2.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gwascat_2.37.0.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: 109 Package: GWASTools Version: 1.51.0 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: e95ad6f34c0af73d7a13921729639bf8 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 URL: https://github.com/smgogarten/GWASTools VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GWASTools git_branch: devel git_last_commit: cfd60b4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GWASTools_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GWASTools_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GWASTools_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GWASTools_1.51.0.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: 111 Package: gwasurvivr Version: 1.23.0 Depends: R (>= 3.4.0) Imports: GWASTools, survival, VariantAnnotation, parallel, matrixStats, SummarizedExperiment, stats, utils, SNPRelate Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: bb1c2d23ac7fee236ce88f15f8a398a1 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 URL: https://github.com/suchestoncampbelllab/gwasurvivr VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gwasurvivr git_branch: devel git_last_commit: a78fc0d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gwasurvivr_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gwasurvivr_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gwasurvivr_1.23.0.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: 160 Package: GWENA Version: 1.15.0 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: 4bf1f435fdb5f8e16b681ef1e0364ea2 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] (), Marie-Pier Scott-Boyer [ths], Arnaud Droit [fnd] Maintainer: Gwenaëlle Lemoine VignetteBuilder: knitr BugReports: https://github.com/Kumquatum/GWENA/issues git_url: https://git.bioconductor.org/packages/GWENA git_branch: devel git_last_commit: 3f995f0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/GWENA_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/GWENA_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/GWENA_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/GWENA_1.15.0.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: 142 Package: gypsum Version: 1.1.0 Imports: utils, tools, httr2, jsonlite, paws.storage, parallel, filelock Suggests: knitr, rmarkdown, testthat, BiocStyle, digest, jsonvalidate, DBI, RSQLite, S4Vectors, methods License: MIT + file LICENSE MD5sum: 2b44401be93ed982e51b8aa4601a6734 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 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: 45c0671 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/gypsum_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/gypsum_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/gypsum_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/gypsum_1.1.0.tgz vignettes: vignettes/gypsum/inst/doc/userguide.html vignetteTitles: Hitting the gypsum API hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gypsum/inst/doc/userguide.R importsMe: celldex, scRNAseq dependencyCount: 31 Package: h5vc Version: 2.39.0 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: d94ec6e4910d85387ce2f9102dfc9085 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 SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/h5vc git_branch: devel git_last_commit: a30ec4f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/h5vc_2.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/h5vc_2.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/h5vc_2.39.0.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: 1.47.0 Depends: R (>= 3.6.0), Biobase, fabia (>= 2.3.1) Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) Archs: x64 MD5sum: 4853cb9727d0326bbe813220e4051689 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 Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/hapFabia/hapFabia.html git_url: https://git.bioconductor.org/packages/hapFabia git_branch: devel git_last_commit: 1432c8a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/hapFabia_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/hapFabia_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/hapFabia_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/hapFabia_1.47.0.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: 8 Package: Harman Version: 1.33.0 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: 5d1208b526275206e47952f2a16a3981 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 URL: http://www.bioinformatics.csiro.au/harman/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Harman git_branch: devel git_last_commit: 83701c4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Harman_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Harman_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Harman_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Harman_1.33.0.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: 1.3.0 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 MD5sum: 3c4172ee57354171bc801565a8971662 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HarmonizR git_branch: devel git_last_commit: 67fe640 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HarmonizR_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/HarmonizR_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HarmonizR_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HarmonizR_1.3.0.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: 112 Package: Harshlight Version: 1.77.0 Depends: R (>= 2.10) Imports: affy, altcdfenvs, Biobase, stats, utils License: GPL (>= 2) Archs: x64 MD5sum: ca5f9b920c2cc29cdb7c92f6b202fbdd 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 URL: http://asterion.rockefeller.edu/Harshlight/ git_url: https://git.bioconductor.org/packages/Harshlight git_branch: devel git_last_commit: 7a42244 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Harshlight_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Harshlight_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Harshlight_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Harshlight_1.77.0.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: 1.13.0 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 MD5sum: d3fc33c175e70b81a667e8f4d2e1953e 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] (), Kayla Interdonato [ctb] Maintainer: Martin Morgan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hca git_branch: devel git_last_commit: 476f9ae git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/hca_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/hca_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/hca_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/hca_1.13.0.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: 1.33.0 Depends: R (>= 3.4), methods, DelayedArray (>= 0.27.2), rhdf5 (>= 2.31.6) Imports: utils, stats, tools, Matrix, rhdf5filters, BiocGenerics (>= 0.31.5), S4Vectors, IRanges, S4Arrays (>= 1.1.1) LinkingTo: S4Vectors (>= 0.27.13), Rhdf5lib Suggests: BiocParallel, GenomicRanges, SummarizedExperiment (>= 1.15.1), h5vcData, ExperimentHub, TENxBrainData, zellkonverter, GenomicFeatures, RUnit, SingleCellExperiment, DelayedMatrixStats, genefilter License: Artistic-2.0 Archs: x64 MD5sum: d74dd257eae105f01bbb0279140c9196 NeedsCompilation: yes Title: HDF5 backend for DelayedArray objects Description: Implement 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 Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/HDF5Array SystemRequirements: GNU make BugReports: https://github.com/Bioconductor/HDF5Array/issues git_url: https://git.bioconductor.org/packages/HDF5Array git_branch: devel git_last_commit: decd6b8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-03 source.ver: src/contrib/HDF5Array_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/HDF5Array_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HDF5Array_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HDF5Array_1.33.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: MAGAR, restfulSEData, TENxBrainData, TENxPBMCData importsMe: alabaster.matrix, beachmat.hdf5, biscuiteer, bsseq, chihaya, clusterExperiment, CuratedAtlasQueryR, cytomapper, DelayedTensor, DropletUtils, FRASER, GenomicScores, glmGamPoi, GSVA, lemur, LoomExperiment, mariner, methodical, methrix, minfi, MOFA2, netSmooth, orthos, recountmethylation, scmeth, signatureSearch, SpliceWiz, transformGamPoi, MafH5.gnomAD.v3.1.2.GRCh38, MafH5.gnomAD.v4.0.GRCh38, curatedTCGAData, HCAData, HCATonsilData, imcdatasets, MethylSeqData, orthosData, scMultiome, SingleCellMultiModal, TabulaMurisSenisData, TumourMethData, ebvcube suggestsMe: beachmat, BiocSklearn, cellxgenedp, DelayedArray, DelayedMatrixStats, iSEE, MAST, mbkmeans, metabolomicsWorkbenchR, MuData, MultiAssayExperiment, PDATK, QFeatures, SCArray, scMerge, scran, scry, spatialHeatmap, SummarizedExperiment, TENxIO, zellkonverter, spicyWorkflow, digitalDLSorteR, SeuratObject, SpatialDDLS dependencyCount: 25 Package: HDTD Version: 1.39.0 Depends: R (>= 4.1) Imports: stats, Rcpp (>= 1.0.1) LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 588356a31279c50d09b3c2bd74393500 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] (), John C. Marioni [aut] (), Simon Tavar\'{e} [aut] () Maintainer: Anestis Touloumis 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: 3009a50 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HDTD_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/HDTD_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HDTD_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HDTD_1.39.0.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: heatmaps Version: 1.29.0 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: 239b5b66b2ad1dd24df85f21a4aea706 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 Maintainer: Malcolm Perry VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/heatmaps git_branch: devel git_last_commit: 6974f83 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/heatmaps_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/heatmaps_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/heatmaps_1.29.0.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: 3.13.0 Imports: graphics, grDevices, stats, RColorBrewer Suggests: Biobase, hgu95av2.db, limma License: GPL (>= 2) MD5sum: 15b8032fd7312671aa42286bbaf0563c 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 Maintainer: Alexander Ploner 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: f65294f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Heatplus_3.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Heatplus_3.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Heatplus_3.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Heatplus_3.13.0.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: 1.31.0 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: d7fa7aebd8440b0c22004064c5e5ffb4 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 git_url: https://git.bioconductor.org/packages/HelloRanges git_branch: devel git_last_commit: 292d51c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HelloRanges_1.31.0.tar.gz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HelloRanges_1.31.0.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: 1.63.0 Depends: R (>= 2.8.0), stats, graphics, grDevices, Biobase, methods License: GPL (>= 2) MD5sum: e53950c3ef016237bdd28c2e1a5611f1 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 , John M. Greally , with contributions from Mark Reimers Maintainer: Reid F. Thompson git_url: https://git.bioconductor.org/packages/HELP git_branch: devel git_last_commit: 4a43015 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HELP_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/HELP_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HELP_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HELP_1.63.0.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: 7 Package: HEM Version: 1.77.0 Depends: R (>= 2.1.0) Imports: Biobase, grDevices, stats, utils License: GPL (>= 2) Archs: x64 MD5sum: 29328481bd0879cd87fb696e7074e83a 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 and Jae K. Lee Maintainer: HyungJun Cho URL: http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/ git_url: https://git.bioconductor.org/packages/HEM git_branch: devel git_last_commit: 6f42f42 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HEM_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/HEM_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HEM_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HEM_1.77.0.tgz vignettes: vignettes/HEM/inst/doc/HEM.pdf vignetteTitles: HEM Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 7 Package: hermes Version: 1.9.0 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: d0ce43a6b5d014bcc98110e0961b36d4 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 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: 7a6397b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/hermes_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/hermes_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/hermes_1.9.0.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: 134 Package: HERON Version: 1.3.0 Depends: R (>= 4.3.0), 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: fc4d1c39006a4f73c502b216b8b5be6c 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, Sequencing, Coverage Author: Sean McIlwain [aut, cre] (), Irene Ong [aut] () Maintainer: Sean McIlwain 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: 171bac7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HERON_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HERON_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HERON_1.3.0.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: 1.15.0 Depends: R (>= 4.0), reticulate Imports: utils, rjson, withr, stats Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 MD5sum: a819fdb7a6361c4bacab9c4d0c12ca5c 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] (), Thomas Carroll [aut, cre] (), Doug Barrows [aut], Kathryn Rozen-Gagnon [ctb] Maintainer: Thomas Carroll URL: https://github.com/RockefellerUniversity/Herper VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Herper git_branch: devel git_last_commit: 36e113e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Herper_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Herper_1.15.0.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: 1.13.0 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: 415ba2e115a3c4aa42d98f3f26b17487 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 SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HGC git_branch: devel git_last_commit: 9a01113 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HGC_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/HGC_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HGC_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HGC_1.13.0.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: 1.39.0 Depends: GenomicRanges, R (>= 2.10) Imports: foreach, iterators, rtracklayer, dplyr, BSgenome, ggplot2, scales, methods Suggests: knitr, doParallel, testthat, BiocGenerics, markdown License: GPL (>= 2) MD5sum: fa3ade62acd1480c3e492215158cfe08 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 Maintainer: Nirav V Malani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hiAnnotator git_branch: devel git_last_commit: 946eaa3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/hiAnnotator_1.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/hiAnnotator_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/hiAnnotator_1.39.0.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: 90 Package: HIBAG Version: 1.41.0 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 Archs: x64 MD5sum: 0e855c2fdab80151c35316b6d5cfd611 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] (), Bruce Weir [ctb, ths] () Maintainer: Xiuwen Zheng 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: c37d501 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HIBAG_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/HIBAG_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HIBAG_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HIBAG_1.41.0.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: 1.1.0 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 MD5sum: f553ca3f23196bbab8c8456abe99f536 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] (), David Depierre [aut], Naomi Schickele [ctb], Nicolas Chanard [aut], Refka Askri [ctb], Stéphane Schaak [aut, ctb], Pascal Martin [ctb], Olivier Cuvier [cre, ctb] () Maintainer: Olivier Cuvier 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: b5de74b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HicAggR_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HicAggR_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HicAggR_1.1.0.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: 103 Package: HiCBricks Version: 1.23.0 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: 5b53701bbd43fdfd8c24fc4001e12fc1 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HiCBricks git_branch: devel git_last_commit: f7d7911 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HiCBricks_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HiCBricks_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HiCBricks_1.23.0.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: 1.27.0 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 MD5sum: 21e2ccfed7800a1585296cba642ef084 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] (), Kellen Cresswell [aut], John Stansfield [aut] Maintainer: Mikhail Dozmorov 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: 51eae7a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HiCcompare_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HiCcompare_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HiCcompare_1.27.0.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: 85 Package: HiCDCPlus Version: 1.13.0 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: 81e4f46ecea75c0b525299406b09fb92 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] () Maintainer: Merve Sahin SystemRequirements: JRE 8+ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HiCDCPlus git_branch: devel git_last_commit: e393345 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HiCDCPlus_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HiCDCPlus_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HiCDCPlus_1.13.0.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: 169 Package: HiCDOC Version: 1.7.0 Depends: InteractionSet, GenomicRanges, SummarizedExperiment, R (>= 4.1.0) Imports: methods, zlibbioc, 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: adf93c1eeea145bff6a0b909e3efebb5 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 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: 7216e65 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HiCDOC_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HiCDOC_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HiCDOC_1.7.0.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: 96 Package: HiCExperiment Version: 1.5.0 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: 06b0ffb937ca67181bd5e6c802ba6974 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] () Maintainer: Jacques Serizay 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: 6d01259 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HiCExperiment_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HiCExperiment_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HiCExperiment_1.5.0.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: 75 Package: HiContacts Version: 1.7.0 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: 68104567b0d47614b0e497fc3d942aaa 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] () Maintainer: Jacques Serizay 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: a48dfc8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HiContacts_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HiContacts_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HiContacts_1.7.0.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: 107 Package: HiCool Version: 1.5.0 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: cf4de51debb1a08e856d57357395439d 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 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: fda0f1a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HiCool_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HiCool_1.5.0.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: 132 Package: hicVennDiagram Version: 1.3.0 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 MD5sum: 98e882602b40afcdc260d44dfc776ee7 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] () Maintainer: Jianhong Ou 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: e922b4d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/hicVennDiagram_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/hicVennDiagram_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/hicVennDiagram_1.3.0.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: 1.35.0 Depends: R (>= 3.2.0) Imports: fastcluster,glmnet, fmsb Suggests: BiocGenerics, RUnit, MASS License: GPL-3 MD5sum: 3e8b75ab2c8d3483a011cdb8a19f2284 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 git_url: https://git.bioconductor.org/packages/hierGWAS git_branch: devel git_last_commit: 3e78032 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/hierGWAS_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/hierGWAS_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/hierGWAS_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/hierGWAS_1.35.0.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: 1.23.0 Depends: R (>= 3.6.0) Imports: fmsb, glmnet, methods, parallel, stats Suggests: knitr, MASS, testthat License: GPL-3 | file LICENSE MD5sum: 1d5dd1d6707f3b3e45bb76f3e7c34832 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hierinf git_branch: devel git_last_commit: 31c94d1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/hierinf_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/hierinf_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/hierinf_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/hierinf_1.23.0.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: 1.35.0 Depends: R (>= 3.6.0), grid Imports: methods, utils, HilbertVis, png, grDevices, circlize (>= 0.3.3), IRanges, GenomicRanges, polylabelr Suggests: knitr, testthat (>= 1.0.0), ComplexHeatmap (>= 1.99.0), markdown, RColorBrewer, RCurl, GetoptLong, rmarkdown License: MIT + file LICENSE MD5sum: 9e8392679665cac01e6456c167d1e0ae NeedsCompilation: no 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] () Maintainer: Zuguang Gu URL: https://github.com/jokergoo/HilbertCurve VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HilbertCurve git_branch: devel git_last_commit: f14fb17 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HilbertCurve_1.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HilbertCurve_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HilbertCurve_1.35.0.tgz vignettes: vignettes/HilbertCurve/inst/doc/HilbertCurve.html vignetteTitles: Making 2D Hilbert Curve hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HilbertCurve/inst/doc/HilbertCurve.R suggestsMe: InteractiveComplexHeatmap dependencyCount: 34 Package: HilbertVis Version: 1.63.0 Depends: R (>= 2.6.0), grid, lattice Suggests: IRanges, EBImage License: GPL (>= 3) Archs: x64 MD5sum: 0ba0ccd2ff9f985644b2b514bb951a3c 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 Maintainer: Simon Anders URL: http://www.ebi.ac.uk/~anders/hilbert git_url: https://git.bioconductor.org/packages/HilbertVis git_branch: devel git_last_commit: 8f5a11c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HilbertVis_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/HilbertVis_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HilbertVis_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HilbertVis_1.63.0.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, HilbertCurve dependencyCount: 6 Package: HilbertVisGUI Version: 1.63.0 Depends: R (>= 2.6.0), HilbertVis (>= 1.1.6) Suggests: lattice, IRanges License: GPL (>= 3) MD5sum: 22b83d84304d126511bf4c288da368a2 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 Maintainer: Simon Anders 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: 817bff0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HilbertVisGUI_1.63.0.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: 1.19.0 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: c85431a8bc47a0dc1b40e7dd754d4cc9 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 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: e01b02e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HiLDA_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HiLDA_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HiLDA_1.19.0.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: 115 Package: hipathia Version: 3.5.0 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 MD5sum: e41ccf89438ac017012c4ba55c03c6af 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hipathia git_branch: devel git_last_commit: 8457cec git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/hipathia_3.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/hipathia_3.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/hipathia_3.5.0.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: 170 Package: HIPPO Version: 1.17.0 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) MD5sum: 23989d0c0bb9ab71e0b952a8fd8a40d2 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 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: c9135a6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HIPPO_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/HIPPO_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HIPPO_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HIPPO_1.17.0.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: 84 Package: hiReadsProcessor Version: 1.41.0 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: ddaf8391417bcc03dea12dac92d5aa6d 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 Maintainer: Nirav V Malani 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: b581021 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/hiReadsProcessor_1.41.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/hiReadsProcessor_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/hiReadsProcessor_1.41.0.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: 99 Package: HIREewas Version: 1.23.0 Depends: R (>= 3.5.0) Imports: quadprog, gplots, grDevices, stats Suggests: BiocStyle, knitr, BiocGenerics License: GPL (>= 2) Archs: x64 MD5sum: 09bc1dd9c585d537c223e2b16ec12c12 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 , Can Yang , Yingying Wei Maintainer: Xiangyu Luo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HIREewas git_branch: devel git_last_commit: 4821805 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HIREewas_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/HIREewas_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HIREewas_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HIREewas_1.23.0.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: 1.49.0 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: 91d6e26dc24f4c714d7664e93f3781d5 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 git_url: https://git.bioconductor.org/packages/HiTC git_branch: devel git_last_commit: 1cdc74d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HiTC_1.49.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HiTC_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HiTC_1.49.0.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: 1.25.0 Depends: R (>= 3.5), XML Imports: S4Vectors, methods, utils Suggests: knitr, annotate, gwascat, testthat, rmarkdown License: Artistic-2.0 MD5sum: 08c276c41487a8d398f7eb95c866073f 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 Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hmdbQuery git_branch: devel git_last_commit: 78b8b80 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/hmdbQuery_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/hmdbQuery_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/hmdbQuery_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/hmdbQuery_1.25.0.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: 8 Package: HMMcopy Version: 1.47.0 Depends: R (>= 2.10.0), data.table (>= 1.11.8) License: GPL-3 Archs: x64 MD5sum: 8f4c89cb006e876a6c48cba5d23f353f 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 git_url: https://git.bioconductor.org/packages/HMMcopy git_branch: devel git_last_commit: 3256bd7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HMMcopy_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/HMMcopy_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HMMcopy_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HMMcopy_1.47.0.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: hoodscanR Version: 1.3.0 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 Archs: x64 MD5sum: 7ba60f2e842fb7c90fb281fb98ce27a7 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] (), Jarryd Martin [aut] Maintainer: Ning Liu 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: 9a3a032 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/hoodscanR_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/hoodscanR_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/hoodscanR_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/hoodscanR_1.3.0.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: 118 Package: hopach Version: 2.65.0 Depends: R (>= 2.11.0), cluster, Biobase, methods Imports: graphics, grDevices, stats, utils, BiocGenerics License: GPL (>= 2) Archs: x64 MD5sum: 3df7dfbf695db5cfc5f7946bcff73ca5 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 and Greg Wall Maintainer: Katherine S. Pollard URL: http://www.stat.berkeley.edu/~laan/, http://docpollard.org/ git_url: https://git.bioconductor.org/packages/hopach git_branch: devel git_last_commit: 529f75d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/hopach_2.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/hopach_2.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/hopach_2.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/hopach_2.65.0.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, treekoR suggestsMe: MicrobiotaProcess, seqArchR dependencyCount: 8 Package: HPAanalyze Version: 1.23.0 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: e74b35d2c4f0b79b31780ba24966d562 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 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: fafa656 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HPAanalyze_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/HPAanalyze_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HPAanalyze_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HPAanalyze_1.23.0.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: 1.47.0 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 MD5sum: 80c0c63c8e0b0456bbb50b7e5156a525 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] (), Manon Martin [aut] Maintainer: Laurent Gatto VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hpar git_branch: devel git_last_commit: ef6f37a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/hpar_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/hpar_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/hpar_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/hpar_1.47.0.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: 67 Package: HPiP Version: 1.11.0 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 MD5sum: 4a9b89717f5c9bf15067a7ab2bce036d 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 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: 74e36f0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HPiP_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/HPiP_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HPiP_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HPiP_1.11.0.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: HTSeqGenie Version: 4.35.0 Depends: R (>= 3.5.0), 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 MD5sum: afb059d171a5dc4607c15c16243a0124 NeedsCompilation: no Title: A NGS analysis pipeline. Description: Libraries to perform NGS analysis. Author: Gregoire Pau, Jens Reeder Maintainer: Jens Reeder git_url: https://git.bioconductor.org/packages/HTSeqGenie git_branch: devel git_last_commit: 76b98c6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HTSeqGenie_4.35.0.tar.gz vignettes: vignettes/HTSeqGenie/inst/doc/HTSeqGenie.pdf vignetteTitles: HTSeqGenie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSeqGenie/inst/doc/HTSeqGenie.R dependencyCount: 94 Package: HTSFilter Version: 1.45.0 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: fa4c7c6ffb581c8710b8f0a5c771890f 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] (), Melina Gallopin [ctb], Gilles Celeux [ctb], Florence Jaffrézic [ctb] Maintainer: Andrea Rau VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HTSFilter git_branch: devel git_last_commit: a9515cc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HTSFilter_1.45.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HTSFilter_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HTSFilter_1.45.0.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: HubPub Version: 1.13.0 Imports: available, usethis, biocthis, dplyr, aws.s3, fs, BiocManager, utils Suggests: AnnotationHubData, ExperimentHubData, testthat, knitr, rmarkdown, BiocStyle, License: Artistic-2.0 MD5sum: 0d95893dc77419f61e1b42ceb91a146d 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] Maintainer: Kayla Interdonato VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/HubPub/issues git_url: https://git.bioconductor.org/packages/HubPub git_branch: devel git_last_commit: bbc4576 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HubPub_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/HubPub_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HubPub_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HubPub_1.13.0.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: 1.15.0 Depends: R (>= 4.0) Imports: Rcpp, graphics, GenomicRanges, SummarizedExperiment, IRanges LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle License: GPL (>=2) MD5sum: a7d13c197e013843ca3e493834d824b8 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hummingbird git_branch: devel git_last_commit: c13162b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/hummingbird_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/hummingbird_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/hummingbird_1.15.0.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: 1.1.0 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: 51e38c0300d7607f16488cff5852a21c 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] (), Lucas Prost-Boxoen [aut] (), Yves Van de Peer [aut] () Maintainer: Fabricio Almeida-Silva 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: 3fbdc87 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HybridExpress_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HybridExpress_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HybridExpress_1.1.0.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: 1.49.0 Depends: R (>= 2.9.0), Biobase, fdrtool, MASS, survival Imports: stats License: GPL Version 2 or later MD5sum: dbde9db7f22e8a211ca835f55c679402 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 , Demba Fofana Maintainer: Demba Fofana git_url: https://git.bioconductor.org/packages/HybridMTest git_branch: devel git_last_commit: 688edf3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/HybridMTest_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/HybridMTest_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/HybridMTest_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/HybridMTest_1.49.0.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: 14 Package: hypeR Version: 2.3.0 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: f7076b13bf66a6951a775b3aa4dd1153 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 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: c8695b3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/hypeR_2.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/hypeR_2.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/hypeR_2.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/hypeR_2.3.0.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: 101 Package: hyperdraw Version: 1.57.0 Depends: R (>= 2.9.0) Imports: methods, grid, graph, hypergraph, Rgraphviz, stats4 License: GPL (>= 2) MD5sum: a2ec7c40b27043055cb409fcdd9bf0e5 NeedsCompilation: no Title: Visualizing Hypergaphs Description: Functions for visualizing hypergraphs. biocViews: Visualization, GraphAndNetwork Author: Paul Murrell Maintainer: Paul Murrell SystemRequirements: graphviz git_url: https://git.bioconductor.org/packages/hyperdraw git_branch: devel git_last_commit: ea89127 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/hyperdraw_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/hyperdraw_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/hyperdraw_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/hyperdraw_1.57.0.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: 11 Package: hypergraph Version: 1.77.0 Depends: R (>= 2.1.0), methods, utils, graph Suggests: BiocGenerics, RUnit License: Artistic-2.0 MD5sum: 192848f41c7531f3db257862d561331f 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 git_url: https://git.bioconductor.org/packages/hypergraph git_branch: devel git_last_commit: f514fa6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/hypergraph_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/hypergraph_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/hypergraph_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/hypergraph_1.77.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: altcdfenvs importsMe: BiGGR, hyperdraw dependencyCount: 7 Package: iASeq Version: 1.49.0 Depends: R (>= 2.14.1) Imports: graphics, grDevices License: GPL-2 MD5sum: be8534417d1fece21f33a482e0f273ce 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 git_url: https://git.bioconductor.org/packages/iASeq git_branch: devel git_last_commit: 7cee083 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iASeq_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/iASeq_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iASeq_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iASeq_1.49.0.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: 1.23.0 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: 75f46ec61246a6fb988da8d9eba41219 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 , Anthony Cheng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/iasva git_branch: devel git_last_commit: 5f0aaf4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iasva_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/iasva_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iasva_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iasva_1.23.0.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: 1.49.0 Depends: biclust Imports: stats4,xtable,ade4 Suggests: methods License: Artistic-2.0 Archs: x64 MD5sum: 661d7207e94dfaf13946481f44e22a0f 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 URL: http://bcb.dfci.harvard.edu/~aedin/publications/ git_url: https://git.bioconductor.org/packages/iBBiG git_branch: devel git_last_commit: da6a437 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iBBiG_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/iBBiG_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iBBiG_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iBBiG_1.49.0.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: 1.53.0 Depends: simpIntLists Suggests: yeastCC, stats License: GPL (>= 2) MD5sum: 9c8d768d208a59ff67ef112f8ce5f051 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 git_url: https://git.bioconductor.org/packages/ibh git_branch: devel git_last_commit: 2adbd3e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ibh_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ibh_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ibh_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ibh_1.53.0.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: 1.45.0 Depends: R(>= 2.15.0),Biobase (>= 2.16.0), ggplot2 (>= 0.9.2) License: Artistic-2.0 Archs: x64 MD5sum: 618a59eacb6b04c3e35532a7a593a001 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 URL: http://www.rglab.org SystemRequirements: GSL and OpenMP git_url: https://git.bioconductor.org/packages/iBMQ git_branch: devel git_last_commit: 09641c2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iBMQ_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/iBMQ_1.45.0.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: 37 Package: iCARE Version: 1.33.0 Depends: R (>= 3.3.0), plotrix, gtools, Hmisc Suggests: RUnit, BiocGenerics License: GPL-3 + file LICENSE Archs: x64 MD5sum: 806ba2037d2217df51cf358b24dca05e NeedsCompilation: yes Title: A Tool for Individualized Coherent Absolute Risk Estimation (iCARE) Description: An R package to compute Individualized Coherent Absolute Risk Estimators. biocViews: Software, StatisticalMethod, GenomeWideAssociation Author: Paige Maas, Parichoy Pal Choudhury, Nilanjan Chatterjee and William Wheeler Maintainer: Bill Wheeler git_url: https://git.bioconductor.org/packages/iCARE git_branch: devel git_last_commit: eae1606 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iCARE_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/iCARE_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iCARE_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iCARE_1.33.0.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: 1.77.0 Depends: survival Imports: graphics License: Artistic-2.0 MD5sum: 19d6f23f7af96aea1798c5bb0ef00264 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 git_url: https://git.bioconductor.org/packages/Icens git_branch: devel git_last_commit: 047f925 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Icens_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Icens_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Icens_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Icens_1.77.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: PROcess, icensBKL, interval importsMe: PROcess, LTRCtrees suggestsMe: ReIns dependencyCount: 10 Package: icetea Version: 1.23.0 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 MD5sum: af28e0215878d7c9494dcaec7c377ab7 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 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: 8212f8c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/icetea_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/icetea_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/icetea_1.23.0.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: 1.35.0 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) MD5sum: 93af72c9e46da578ecbcb87b1e7067bf 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 git_url: https://git.bioconductor.org/packages/iCheck git_branch: devel git_last_commit: ba7c59d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iCheck_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/iCheck_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iCheck_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iCheck_1.35.0.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: 185 Package: iChip Version: 1.59.0 Depends: R (>= 2.10.0) Imports: limma License: GPL (>= 2) Archs: x64 MD5sum: 73bf72e9371c76b8ea25bfe70fc3d7ce 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 git_url: https://git.bioconductor.org/packages/iChip git_branch: devel git_last_commit: 6600966 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iChip_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/iChip_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iChip_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iChip_1.59.0.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: 1.41.0 Depends: R (>= 4.1.0), parallel Suggests: RUnit, BiocGenerics License: GPL (>= 2) Archs: x64 MD5sum: 6f6852ad511485112cc0b53d98733c06 NeedsCompilation: yes Title: Integrative clustering of multi-type genomic data Description: Integrative clustering of multiple genomic data using a joint latent variable model. biocViews: Microarray, Clustering Author: Qianxing Mo, Ronglai Shen Maintainer: Qianxing Mo , Ronglai Shen git_url: https://git.bioconductor.org/packages/iClusterPlus git_branch: devel git_last_commit: f64f975 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iClusterPlus_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/iClusterPlus_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iClusterPlus_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iClusterPlus_1.41.0.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: 1.25.0 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: ef04b133b57932157abd6b57d5c40d6f 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/iCNV git_branch: devel git_last_commit: ec76135 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iCNV_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/iCNV_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iCNV_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iCNV_1.25.0.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: 101 Package: iCOBRA Version: 1.33.0 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) MD5sum: 836aeb5b6e6c5138e9e020a0bebf52d8 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] () Maintainer: Charlotte Soneson 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: 7b42317 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iCOBRA_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/iCOBRA_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iCOBRA_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iCOBRA_1.33.0.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: 90 Package: IdeoViz Version: 1.41.0 Depends: R (>= 3.5.0), Biobase, IRanges, GenomicRanges, RColorBrewer, rtracklayer, graphics, GenomeInfoDb License: GPL-2 MD5sum: 759a7e626defbd39b067eb374fc571c6 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 , Jingliang Ren Maintainer: Shraddha Pai git_url: https://git.bioconductor.org/packages/IdeoViz git_branch: devel git_last_commit: 5bbcf00 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/IdeoViz_1.41.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/IdeoViz_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/IdeoViz_1.41.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 59 Package: idiogram Version: 1.81.0 Depends: R (>= 2.10), methods, Biobase, annotate, plotrix Suggests: hu6800.db, hgu95av2.db, golubEsets License: GPL-2 MD5sum: 97f79e541a964bbdd46c4bee5bf27eda NeedsCompilation: no Title: idiogram Description: A package for plotting genomic data by chromosomal location biocViews: Visualization Author: Karl J. Dykema Maintainer: Karl J. Dykema git_url: https://git.bioconductor.org/packages/idiogram git_branch: devel git_last_commit: d3a2975 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/idiogram_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/idiogram_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/idiogram_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/idiogram_1.81.0.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: 1.15.0 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: a2b8fdfa7e45091843518f9753c8d2b2 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. . biocViews: StructuralPrediction, Proteomics, CellBiology Author: William M. McFadden [cre, aut], Judith L. Yanowitz [aut, fnd], Michael Buszczak [ctb, fnd] Maintainer: William M. McFadden VignetteBuilder: knitr BugReports: https://github.com/wmm27/idpr/issues git_url: https://git.bioconductor.org/packages/idpr git_branch: devel git_last_commit: 39cef0b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/idpr_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/idpr_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/idpr_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/idpr_1.15.0.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: 59 Package: idr2d Version: 1.19.0 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: cd944d09a507a2d620d7b1bcefa56467 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] (), David Gifford [ths, cph] () Maintainer: Konstantin Krismer 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: 88845c6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/idr2d_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/idr2d_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/idr2d_1.19.0.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: 70 Package: IFAA Version: 1.7.0 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: f29229e7a51b8a5d0a38ec290c03def2 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 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: 3734fce git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/IFAA_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/IFAA_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/IFAA_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/IFAA_1.7.0.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: 93 Package: iGC Version: 1.35.0 Depends: R (>= 3.2.0) Imports: plyr, data.table Suggests: BiocStyle, knitr, rmarkdown Enhances: doMC License: GPL-2 MD5sum: 5caf175f13af4ec7ba0f0739cc7c0c6c 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 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: f22312a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iGC_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/iGC_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iGC_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iGC_1.35.0.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: 1.19.0 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 Archs: x64 MD5sum: be939f209a217584e63b8aa6d7b59583 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 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: a4f2b16 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/IgGeneUsage_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/IgGeneUsage_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/IgGeneUsage_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/IgGeneUsage_1.19.0.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: 95 Package: igvR Version: 1.25.0 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: a2ed44d0dff7bc717e839108851fe83f 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 URL: https://gladkia.github.io/igvR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/igvR git_branch: devel git_last_commit: dcc3575 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/igvR_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/igvR_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/igvR_1.25.0.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: 1.1.0 Depends: R (>= 3.5.0), GenomicRanges, methods, shiny Imports: BiocGenerics, checkmate, futile.logger, GenomeInfoDbData, htmlwidgets, httr, jsonlite, randomcoloR, RCurl, utils Suggests: BiocStyle, GenomicAlignments, knitr, Rsamtools, rtracklayer, RUnit, shinytest2, VariantAnnotation License: MIT + file LICENSE MD5sum: 7d0ef640b27a5e75d3ee4003fa0816d2 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] (), Karolina Scigocka [aut] Maintainer: Arkadiusz Gladki 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: ac9897c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/igvShiny_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/igvShiny_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/igvShiny_1.1.0.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: 81 Package: IHW Version: 1.33.0 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 MD5sum: b7cc2b6bba2626014ec076774769a1b7 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IHW git_branch: devel git_last_commit: 34fbda6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/IHW_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/IHW_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/IHW_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/IHW_1.33.0.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: scp suggestsMe: DEWSeq, GRaNIE, metagenomeSeq, BloodCancerMultiOmics2017, BisRNA, DGEobj.utils dependencyCount: 9 Package: illuminaio Version: 0.47.0 Imports: base64 Suggests: RUnit, BiocGenerics, IlluminaDataTestFiles (>= 1.0.2), BiocStyle License: GPL-2 Archs: x64 MD5sum: 406dfd17bac14a63f615fa0707bdabd6 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 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: 523b7f6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/illuminaio_0.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/illuminaio_0.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/illuminaio_0.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/illuminaio_0.47.0.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, sesame suggestsMe: limma dependencyCount: 4 Package: ILoReg Version: 1.15.0 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: 1353c15c03cfe45c4a53ad340c4be79a 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 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: c16069e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ILoReg_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ILoReg_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ILoReg_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ILoReg_1.15.0.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: 125 Package: IMAS Version: 1.29.0 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: 93e485ed751fecdbff051787d4c8e4b7 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 git_url: https://git.bioconductor.org/packages/IMAS git_branch: devel git_last_commit: 92913a4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/IMAS_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/IMAS_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/IMAS_1.29.0.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: 133 Package: imcRtools Version: 1.11.0 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 MD5sum: b166f1bbb858b1d80414a68cfbec34cb 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] (), Lasse Meyer [ctb] Maintainer: Daniel Schulz 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: ddbfcd9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/imcRtools_1.11.0.tar.gz 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: 187 Package: IMMAN Version: 1.25.0 Imports: STRINGdb, pwalign, igraph, graphics, utils, seqinr Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 04d4b2a28f2a010f9427657068a052ea 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IMMAN git_branch: devel git_last_commit: 9b3d3ef git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/IMMAN_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/IMMAN_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/IMMAN_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/IMMAN_1.25.0.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: immunoClust Version: 1.37.0 Depends: R(>= 3.6), flowCore Imports: methods, stats, graphics, grid, lattice, grDevices Suggests: BiocStyle, utils, testthat License: Artistic-2.0 Archs: x64 MD5sum: cc468c001c922442d76524d63e0c585a 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 git_url: https://git.bioconductor.org/packages/immunoClust git_branch: devel git_last_commit: f933c1b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/immunoClust_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/immunoClust_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/immunoClust_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/immunoClust_1.37.0.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: 19 Package: immunotation Version: 1.13.0 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: fe1cce6225e135f750e1a3f3c3a49c60 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 VignetteBuilder: knitr BugReports: https://github.com/imkeller/immunotation/issues git_url: https://git.bioconductor.org/packages/immunotation git_branch: devel git_last_commit: aebf955 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/immunotation_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/immunotation_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/immunotation_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/immunotation_1.13.0.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: 1.41.0 Depends: R (>= 2.3.0) Imports: rjson License: file LICENSE MD5sum: 8375631f24459d904ee4534c432bc8d4 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 git_url: https://git.bioconductor.org/packages/IMPCdata git_branch: devel git_last_commit: 92e0e19 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/IMPCdata_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/IMPCdata_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/IMPCdata_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/IMPCdata_1.41.0.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: 1.79.0 Depends: R (>= 2.10) License: GPL-2 Archs: x64 MD5sum: 8569667532c5ee2ecd0c035dc4467755 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 git_url: https://git.bioconductor.org/packages/impute git_branch: devel git_last_commit: 0513dba git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/impute_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/impute_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/impute_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/impute_1.79.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: AMARETTO, CGHcall, TIN, curatedBreastData, iC10, 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, Rnits, MetaGxBreast, MetaGxOvarian, MetaGxPancreas, armada, DIscBIO, ePCR, FAMT, lilikoi, mi4p, 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: 2.19.0 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: f939818b19d8f19aa5776c4be2b2687c 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 , Kian Ghaffari , Zhenzhi Li Maintainer: Ressom group , Yiming Zuo 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: 25eda6b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/INDEED_2.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/INDEED_2.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/INDEED_2.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/INDEED_2.19.0.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: 108 Package: iNETgrate Version: 1.3.0 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: 6d99d58996f3700f72ffa420bf741402 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] (), Ghazal Ebrahimi [aut], Hanie Samimi [aut], Habil Zare [aut, cre] () Maintainer: Habil Zare VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocManager/issues git_url: https://git.bioconductor.org/packages/iNETgrate git_branch: devel git_last_commit: 0966b8f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iNETgrate_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iNETgrate_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iNETgrate_1.3.0.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: 1.21.0 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: 968dadbc4173353eb1340eb730a56139 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 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: 6b73e12 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/infercnv_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/infercnv_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/infercnv_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/infercnv_1.21.0.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: 1.15.0 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 MD5sum: 74b0ad1619a7bf9490d7d6eb58c9efbd 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/infinityFlow git_branch: devel git_last_commit: f0b4892 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/infinityFlow_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/infinityFlow_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/infinityFlow_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/infinityFlow_1.15.0.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: 1.15.0 Depends: R (>= 4.0) Imports: entropy Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0), SummarizedExperiment License: Artistic-2.0 MD5sum: 4caaf13273b7056ffe2c29107db4f744 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 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: 0ae77ab git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Informeasure_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Informeasure_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Informeasure_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Informeasure_1.15.0.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: 2.13.0 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: 7b8f816d2b15bfeadc58ac3860ff7dfc 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InPAS git_branch: devel git_last_commit: db3f500 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/InPAS_2.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/InPAS_2.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/InPAS_2.13.0.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: 147 Package: INPower Version: 1.41.0 Depends: R (>= 3.1.0), mvtnorm Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: e645ed4c250627a3b0b7ad512d2377ef 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 git_url: https://git.bioconductor.org/packages/INPower git_branch: devel git_last_commit: 008c80d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/INPower_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/INPower_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/INPower_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/INPower_1.41.0.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: 1.35.0 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: 443b23c172a06d11d2523aac457e7e91 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 , Mattia Furlan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/INSPEcT git_branch: devel git_last_commit: 08b5f2f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/INSPEcT_1.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/INSPEcT_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/INSPEcT_1.35.0.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: 1.5.0 Depends: R (>= 4.3.0) Imports: SQUAREM, bdsmatrix, numDeriv, stats, tidyr Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 + file LICENSE MD5sum: a8b6a66a842a8508905c87653bf74200 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] (), Xiaoquan Wen [aut] () Maintainer: Jeffrey Okamoto 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: cb10773 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/INTACT_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/INTACT_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/INTACT_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/INTACT_1.5.0.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: 30 Package: InTAD Version: 1.25.0 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: d11e17d46fcfcda8a894bb29cb10b52d 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InTAD git_branch: devel git_last_commit: 5089245 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/InTAD_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/InTAD_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/InTAD_1.25.0.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: 140 Package: intansv Version: 1.45.0 Depends: R (>= 2.14.0), plyr, ggbio, GenomicRanges Imports: BiocGenerics, IRanges License: MIT + file LICENSE MD5sum: af53c18639d54355f2b1fe3442db7aa3 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 Maintainer: Wen Yao git_url: https://git.bioconductor.org/packages/intansv git_branch: devel git_last_commit: 0caedf8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/intansv_1.45.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/intansv_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/intansv_1.45.0.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: 172 Package: interacCircos Version: 1.15.0 Depends: R (>= 4.1) Imports: RColorBrewer, htmlwidgets, plyr, methods Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 42ed0d06b74fc600175f9f29bff3145c 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/interacCircos git_branch: devel git_last_commit: c9cab39 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/interacCircos_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/interacCircos_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/interacCircos_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/interacCircos_1.15.0.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: 1.33.0 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: 98882c671a36b91f2ff5d883bc785ed2 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 SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InteractionSet git_branch: devel git_last_commit: 46058bf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/InteractionSet_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/InteractionSet_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/InteractionSet_1.33.0.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, GenomicInteractions, HiCDOC, sevenC, nullrangesData importsMe: CAGEfightR, ChIPpeakAnno, DegCre, EDIRquery, extraChIPs, HicAggR, HiCcompare, HiCExperiment, HiContacts, HiCool, hicVennDiagram, mariner, nullranges, plyinteractions, trackViewer, treediff suggestsMe: plotgardener, transmogR, updateObject, CAGEWorkflow dependencyCount: 37 Package: InteractiveComplexHeatmap Version: 1.13.0 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: 1d46eac0a11bf0886252949c8bbed664 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] () Maintainer: Zuguang Gu 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: bd8471f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/InteractiveComplexHeatmap_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/InteractiveComplexHeatmap_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/InteractiveComplexHeatmap_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/InteractiveComplexHeatmap_1.13.0.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: simona, simplifyEnrichment dependencyCount: 80 Package: interactiveDisplay Version: 1.43.0 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: af3b393e88f27008dd94a864196b87e0 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/interactiveDisplay git_branch: devel git_last_commit: 21b31e0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/interactiveDisplay_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/interactiveDisplay_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/interactiveDisplay_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/interactiveDisplay_1.43.0.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: 1.43.0 Depends: R (>= 2.10), methods, BiocGenerics Imports: shiny, DT Suggests: knitr, markdown Enhances: rstudioapi License: Artistic-2.0 MD5sum: a402f80b2f1c0dfd03cd4df46eadef08 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/interactiveDisplayBase git_branch: devel git_last_commit: 4c333d5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/interactiveDisplayBase_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/interactiveDisplayBase_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/interactiveDisplayBase_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/interactiveDisplayBase_1.43.0.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: 48 Package: InterCellar Version: 2.11.0 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: a3b7cd4bd8c6d176c0f841755b549b35 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] () Maintainer: Marta Interlandi 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: 79aebf6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/InterCellar_2.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/InterCellar_2.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/InterCellar_2.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/InterCellar_2.11.0.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: 208 Package: IntEREst Version: 1.29.0 Depends: R (>= 3.5.0), GenomicRanges, Rsamtools, SummarizedExperiment, edgeR, S4Vectors, GenomicFiles Imports: seqLogo, Biostrings, GenomicFeatures, txdbmaker, IRanges, seqinr, graphics, grDevices, stats, utils, grid, methods, DBI, RMySQL, GenomicAlignments, BiocParallel, BiocGenerics, DEXSeq, DESeq2 Suggests: clinfun, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 License: GPL-2 MD5sum: 70f1c5ccd95cf6e2c035a4ee0721b651 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 , Dario Greco , Mikko Frilander Maintainer: Ali Oghabian , Mikko Frilander VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IntEREst git_branch: devel git_last_commit: c53a1fd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/IntEREst_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/IntEREst_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/IntEREst_1.29.0.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: 140 Package: IntramiRExploreR Version: 1.27.0 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: 180231f9d1f806ac4fd5807cf68bc442 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 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: ac65c7c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/IntramiRExploreR_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/IntramiRExploreR_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/IntramiRExploreR_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/IntramiRExploreR_1.27.0.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: 2.29.0 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: 3c15197ff7165bb1416e1dfddab87280 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IONiseR git_branch: devel git_last_commit: 356c193 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/IONiseR_2.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/IONiseR_2.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/IONiseR_2.29.0.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: 99 Package: iPAC Version: 1.49.0 Depends: R(>= 2.15), scatterplot3d, Biostrings, pwalign, multtest Imports: grDevices, graphics, stats, gdata License: GPL-2 MD5sum: 05edea462a146ef54127708415c80eee 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 git_url: https://git.bioconductor.org/packages/iPAC git_branch: devel git_last_commit: 900bc27 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iPAC_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/iPAC_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iPAC_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iPAC_1.49.0.tgz vignettes: vignettes/iPAC/inst/doc/iPAC.pdf vignetteTitles: iPAC: identification of Protein Amino acid Mutations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iPAC/inst/doc/iPAC.R dependsOnMe: QuartPAC dependencyCount: 37 Package: iPath Version: 1.11.0 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: b4af717fe6bd74099ba18950e2833611 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 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: 85b7801 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iPath_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iPath_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iPath_1.11.0.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: 124 Package: ipdDb Version: 1.23.0 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: 83b85110f7efb48aef39c79ef3ce42f9 NeedsCompilation: no Title: IPD IMGT/HLA and IPD KIR database for Homo sapiens Description: All alleles from the IPD IMGT/HLA and 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 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: aa21284 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ipdDb_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ipdDb_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ipdDb_1.23.0.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: 68 Package: IPO Version: 1.31.0 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: 24fd39ac07c0b28e1760f4c7d31652c5 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 , Christoph Magnes , Thomas Lieb Maintainer: Thomas Lieb 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: 8fdc697 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/IPO_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/IPO_1.31.0.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: 2.39.0 Depends: R (>= 4.0.0), methods, utils, stats, BiocGenerics (>= 0.39.2), S4Vectors (>= 0.33.3) Imports: stats4 LinkingTo: S4Vectors Suggests: XVector, GenomicRanges, Rsamtools, GenomicAlignments, GenomicFeatures, BSgenome.Celegans.UCSC.ce2, pasillaBamSubset, RUnit, BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: 7f635b5fffc249cb744877d4f816759e 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 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: 8eea1c1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/IRanges_2.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/IRanges_2.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/IRanges_2.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/IRanges_2.39.0.tgz vignettes: vignettes/IRanges/inst/doc/IRangesOverview.pdf vignetteTitles: An Overview of the IRanges package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE 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extraChIPs, factR, FastqCleaner, fastseg, fcScan, FilterFFPE, FindIT2, fishpond, FLAMES, FRASER, GA4GHclient, gcapc, gDNAx, geneAttribution, GeneGeneInteR, GENESIS, genomation, GenomAutomorphism, genomeIntervals, GenomicAlignments, GenomicDataCommons, GenomicFiles, GenomicInteractionNodes, GenomicInteractions, GenomicOZone, GenomicPlot, GenomicScores, GenomicTuples, GenVisR, ggbio, girafe, gmapR, gmoviz, GOfuncR, GOpro, GOTHiC, GSVA, GUIDEseq, gwascat, h5vc, HDF5Array, heatmaps, hermes, HicAggR, HiCBricks, HiCcompare, HiCExperiment, HiContacts, HiCool, hicVennDiagram, HilbertCurve, HTSeqGenie, hummingbird, icetea, idr2d, IMAS, InPAS, INSPEcT, intansv, InteractionSet, InteractiveComplexHeatmap, IntEREst, InterMineR, 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, microbiomeMarker, minfi, MinimumDistance, MIRA, missMethyl, mobileRNA, Modstrings, monaLisa, mosaics, MOSim, Motif2Site, motifbreakR, motifmatchr, 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, PICS, PING, plotgardener, plyinteractions, podkat, polyester, 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, recoup, REDseq, regioneR, regutools, REMP, Repitools, ReportingTools, RESOLVE, rfaRm, rfPred, RgnTX, RiboCrypt, RiboDiPA, RiboProfiling, riboSeqR, ribosomeProfilingQC, 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, snapcount, SNPhood, soGGi, SomaticSignatures, SOMNiBUS, SparseArray, SparseSignatures, spatzie, Spectra, spicyR, spiky, SpliceWiz, SplicingGraphs, SPLINTER, srnadiff, strandCheckR, StructuralVariantAnnotation, SummarizedExperiment, SynExtend, tadar, TAPseq, target, TCGAbiolinks, TCGAutils, TCseq, TFBSTools, TFEA.ChIP, TFHAZ, tidyCoverage, TitanCNA, tracktables, trackViewer, transcriptR, transmogR, TransView, 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.v3.1.2.GRCh38, 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, seqpac, alakazam, comradesOO, crispRdesignR, cubar, geneHapR, geno2proteo, hahmmr, hoardeR, ICAMS, iimi, karyotapR, locuszoomr, lolliplot, LoopRig, MAAPER, MitoHEAR, MOCHA, noisyr, numbat, oncoPredict, PACVr, RapidoPGS, revert, rnaCrosslinkOO, RTIGER, Signac, simMP, STRMPS, tidygenomics, VALERIE suggestsMe: annotate, AnnotationHub, BaseSpaceR, BiocGenerics, BREW3R.r, Chicago, ClassifyR, easylift, epivizrChart, gDRcore, gDRutils, Glimma, GWASTools, HilbertVis, HilbertVisGUI, maftools, martini, MiRaGE, multicrispr, partCNV, regionalpcs, regionReport, RTCGA, S4Vectors, SigsPack, splatter, svaNUMT, svaRetro, systemPipeR, TFutils, tidybulk, scMultiome, systemPipeRdata, xcoredata, yeastRNASeq, fuzzyjoin, gkmSVM, MARVEL, polyRAD, 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: 7 Package: ISAnalytics Version: 1.15.0 Depends: R (>= 4.3) 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: 159cde21b572517e9a58c7612e13ba36 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: Giulia Pais [aut, cre] (), Andrea Calabria [aut], Giulio Spinozzi [aut] Maintainer: Giulia Pais 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: 2ca4cc4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ISAnalytics_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ISAnalytics_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ISAnalytics_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ISAnalytics_1.15.0.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: 117 Package: iSEE Version: 2.17.0 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 License: MIT + file LICENSE MD5sum: dff11680f5a3ae044fa55b06ee66f85f 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] (), Federico Marini [aut] (), Charlotte Soneson [aut] (), Aaron Lun [aut] () Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEE VignetteBuilder: knitr BugReports: https://github.com/iSEE/iSEE/issues git_url: https://git.bioconductor.org/packages/iSEE git_branch: devel git_last_commit: 3663ffc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-03 source.ver: src/contrib/iSEE_2.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/iSEE_2.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iSEE_2.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iSEE_2.17.0.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, iSEEu importsMe: iSEEfier, iSEEhub, iSEEindex suggestsMe: schex, DuoClustering2018, HCAData, HCATonsilData, TabulaMurisData, TabulaMurisSenisData dependencyCount: 122 Package: iSEEde Version: 1.3.0 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: 1bf3785b43f2d4198f1f77391d56fea2 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] (), Thomas Sandmann [ctb] (), Denali Therapeutics [fnd] Maintainer: Kevin Rue-Albrecht 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: ed9ba77 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iSEEde_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iSEEde_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iSEEde_1.3.0.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: 135 Package: iSEEfier Version: 1.1.0 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 MD5sum: c9d506b8da7160fd4333478775d7b408 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] (), Federico Marini [aut] () Maintainer: Najla Abassi 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: c6001d4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iSEEfier_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iSEEfier_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iSEEfier_1.1.0.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: 128 Package: iSEEhex Version: 1.7.0 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: 63c04836aa6838e0c3584b079b8ce685 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] (), Charlotte Soneson [aut] (), Federico Marini [aut] (), Aaron Lun [aut] () Maintainer: Kevin Rue-Albrecht 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: 6ec521b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iSEEhex_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/iSEEhex_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iSEEhex_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iSEEhex_1.7.0.tgz vignettes: vignettes/iSEEhex/inst/doc/iSEEhex.html vignetteTitles: Panel universe hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSEEhex/inst/doc/iSEEhex.R dependsOnMe: iSEEu dependencyCount: 124 Package: iSEEhub Version: 1.7.0 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: cb1b2b65f04d09606315088a1a2a567f 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] () Maintainer: Kevin Rue-Albrecht 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: 3b25117 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iSEEhub_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iSEEhub_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iSEEhub_1.7.0.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: 146 Package: iSEEindex Version: 1.3.0 Depends: SummarizedExperiment, SingleCellExperiment Imports: BiocFileCache, DT, iSEE, methods, paws.storage, rintrojs, shiny, shinydashboard, shinyjs, stringr, urltools, utils Suggests: BiocStyle, covr, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0), yaml License: Artistic-2.0 MD5sum: 978e9295fa4d8c4f6d959dc905d32a1a 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] (), Thomas Sandmann [ctb] (), Federico Marini [aut] (), Denali Therapeutics [fnd] Maintainer: Kevin Rue-Albrecht 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: e3c18ce git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iSEEindex_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/iSEEindex_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iSEEindex_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iSEEindex_1.3.0.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: 144 Package: iSEEpathways Version: 1.3.0 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: 084afcac74d412ace93018cb356394b6 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] (), Thomas Sandmann [ctb] (), Charlotte Soneson [aut] (), Federico Marini [ctb] (), Denali Therapeutics [fnd] Maintainer: Kevin Rue-Albrecht 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: fbcecb0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iSEEpathways_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/iSEEpathways_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iSEEpathways_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iSEEpathways_1.3.0.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: 123 Package: iSEEu Version: 1.17.0 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: e72041ca9bcbb906702246933aa39187 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] (), Charlotte Soneson [aut] (), Federico Marini [aut] (), Aaron Lun [aut] (), Michael Stadler [ctb] Maintainer: Kevin Rue-Albrecht 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: 27624b8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iSEEu_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/iSEEu_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iSEEu_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iSEEu_1.17.0.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: 125 Package: iSeq Version: 1.57.0 Depends: R (>= 2.10.0) License: GPL (>= 2) Archs: x64 MD5sum: 5c68bc14cef4dad1c3832bad3cb1a4ea 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 git_url: https://git.bioconductor.org/packages/iSeq git_branch: devel git_last_commit: 7f94061 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iSeq_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/iSeq_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iSeq_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iSeq_1.57.0.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: 1.7.0 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: 1581ea1454091857afbaea34bb4f28c1 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] (), Qian Li [aut], Guanqun Meng [aut] Maintainer: Hao Feng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ISLET git_branch: devel git_last_commit: 34b1288 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ISLET_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ISLET_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ISLET_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ISLET_1.7.0.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: 85 Package: isobar Version: 1.51.0 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: cb8a9892f86ca2dbee66b0cd371e0720 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 and Jacques Colinge , with contributions from Alexey Stukalov , Xavier Robin and Florent Gluck Maintainer: Florian P Breitwieser 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: 96447b8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/isobar_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/isobar_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/isobar_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/isobar_1.51.0.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: 91 Package: IsoBayes Version: 1.3.0 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 Archs: x64 MD5sum: 780e4112def4af1b5d88b50f70d693d6 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] () Maintainer: Simone Tiberi 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: c883914 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/IsoBayes_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/IsoBayes_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/IsoBayes_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/IsoBayes_1.3.0.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: 1.23.0 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 MD5sum: b25df6a8ac393519ebfd4092e234075b 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 URL: https://genomics.ur.de/files/IsoCorrectoR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IsoCorrectoR git_branch: devel git_last_commit: a78040d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/IsoCorrectoR_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/IsoCorrectoR_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/IsoCorrectoR_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/IsoCorrectoR_1.23.0.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: 1.21.0 Depends: R (>= 3.6) Imports: IsoCorrectoR, readxl, tcltk2, tcltk, utils Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: b8a89e1b2b23a60d1d76609a8643d2fb 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 URL: https://genomics.ur.de/files/IsoCorrectoRGUI VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IsoCorrectoRGUI git_branch: devel git_last_commit: cea3de7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/IsoCorrectoRGUI_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/IsoCorrectoRGUI_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/IsoCorrectoRGUI_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/IsoCorrectoRGUI_1.21.0.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: 2.5.0 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: 891b1ac20c6bb0561057cbc84ccbe521 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] (), Jeroen Gilis [ctb] () Maintainer: Kristoffer Vitting-Seerup 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: 0aaa169 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/IsoformSwitchAnalyzeR_2.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/IsoformSwitchAnalyzeR_2.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/IsoformSwitchAnalyzeR_2.5.0.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: 155 Package: ISoLDE Version: 1.33.0 Depends: R (>= 3.3.0),graphics,grDevices,stats,utils License: GPL (>= 2.0) Archs: x64 MD5sum: 1506ebdbfb9694b3afa01d448cddc81a 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 URL: www.r-project.org git_url: https://git.bioconductor.org/packages/ISoLDE git_branch: devel git_last_commit: f798961 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ISoLDE_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ISoLDE_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ISoLDE_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ISoLDE_1.33.0.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 4 Package: isomiRs Version: 1.33.1 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: e7070b68b4db69ae1d2632c9c22a4bf7 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 VignetteBuilder: knitr BugReports: https://github.com/lpantano/isomiRs/issues git_url: https://git.bioconductor.org/packages/isomiRs git_branch: devel git_last_commit: 2f53652 git_last_commit_date: 2024-05-03 Date/Publication: 2024-05-06 source.ver: src/contrib/isomiRs_1.33.1.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/isomiRs_1.33.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/isomiRs_1.33.1.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: 153 Package: ITALICS Version: 2.65.0 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: 371dd28ea28ffa314726635442ad6d51 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 URL: http://bioinfo.curie.fr git_url: https://git.bioconductor.org/packages/ITALICS git_branch: devel git_last_commit: aa53459 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ITALICS_2.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ITALICS_2.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ITALICS_2.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ITALICS_2.65.0.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: 1.63.0 Depends: BMA, leaps, Biobase (>= 2.5.5) License: GPL (>= 2) MD5sum: 25dd23d6d452002cf927341f9e73efe7 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 URL: http://faculty.washington.edu/kayee/research.html git_url: https://git.bioconductor.org/packages/iterativeBMA git_branch: devel git_last_commit: 122a012 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iterativeBMA_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/iterativeBMA_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iterativeBMA_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iterativeBMA_1.63.0.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: 21 Package: iterativeBMAsurv Version: 1.63.0 Depends: BMA, leaps, survival, splines Imports: graphics, grDevices, stats, survival, utils License: GPL (>= 2) MD5sum: a3ee6b6c11d4f3d1dc42d3eae243d68f 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 URL: http://expression.washington.edu/ibmasurv/protected git_url: https://git.bioconductor.org/packages/iterativeBMAsurv git_branch: devel git_last_commit: d1b83b0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/iterativeBMAsurv_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/iterativeBMAsurv_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/iterativeBMAsurv_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/iterativeBMAsurv_1.63.0.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: 2.25.0 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: 3318cb652c3e46aba0afa3d1c2422a9d 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 git_url: https://git.bioconductor.org/packages/IVAS git_branch: devel git_last_commit: 6650399 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/IVAS_2.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/IVAS_2.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/IVAS_2.25.0.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: 131 Package: ivygapSE Version: 1.27.0 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: 2d35d937af19c7f284690c03315abe16 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ivygapSE git_branch: devel git_last_commit: 7c114af git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ivygapSE_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ivygapSE_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ivygapSE_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ivygapSE_1.27.0.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: 159 Package: IWTomics Version: 1.29.0 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: 6be7c7e15611b73ff7efdfb3cdb59df4 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IWTomics git_branch: devel git_last_commit: cb8d9ec git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/IWTomics_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/IWTomics_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/IWTomics_1.29.0.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: karyoploteR Version: 1.31.0 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: 6a9cbf17f46f2951fedca43d711eebf2 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] () Maintainer: Bernat Gel 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: 574fd7a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/karyoploteR_1.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/karyoploteR_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/karyoploteR_1.31.0.tgz 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: 140 Package: katdetectr Version: 1.7.0 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: 7b4669408ff4a27b112ec1b3da318c47 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] (), Job van Riet [aut] (), Harmen van de Werken [ths] () Maintainer: Daan Hazelaar 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: c0e0cc7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/katdetectr_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/katdetectr_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/katdetectr_1.7.0.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: 128 Package: KBoost Version: 1.13.0 Depends: R (>= 4.1), stats, utils Suggests: knitr, rmarkdown, testthat License: GPL-2 | GPL-3 MD5sum: eb36099f7c42d1e1653ff63f510689a1 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] (), Barbara de Kegel [aut], Walter Kolch [aut] Maintainer: Luis F. Iglesias-Martinez URL: https://github.com/Luisiglm/KBoost VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KBoost git_branch: devel git_last_commit: ed58a71 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/KBoost_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/KBoost_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/KBoost_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/KBoost_1.13.0.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: 2.63.0 Depends: siggenes, multtest, KernSmooth Imports: methods, BiocGenerics Enhances: Biobase, CGHbase License: GPL-3 MD5sum: c2836154097913d21ee0cc1b586d03c2 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 git_url: https://git.bioconductor.org/packages/KCsmart git_branch: devel git_last_commit: a3d15ea git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/KCsmart_2.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/KCsmart_2.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/KCsmart_2.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/KCsmart_2.63.0.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: 18 Package: kebabs Version: 1.39.0 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) Archs: x64 MD5sum: 134d00cea6920f86d0612c107edc3dae 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 URL: https://github.com/UBod/kebabs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/kebabs git_branch: devel git_last_commit: 2fcfc70 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/kebabs_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/kebabs_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/kebabs_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/kebabs_1.39.0.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: 1.65.0 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: a17656e48bb2961c5a95c5bfbdc94b47 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 URL: http://www.nextbiomotif.com git_url: https://git.bioconductor.org/packages/KEGGgraph git_branch: devel git_last_commit: beecd46 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/KEGGgraph_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/KEGGgraph_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/KEGGgraph_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/KEGGgraph_1.65.0.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: 13 Package: KEGGlincs Version: 1.31.0 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: a38c78f2face936ba41e7fc0907e50f7 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 , Mario Medvedovic SystemRequirements: Cytoscape (>= 3.3.0), Java (>= 8) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KEGGlincs git_branch: devel git_last_commit: 40748de git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/KEGGlincs_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/KEGGlincs_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/KEGGlincs_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/KEGGlincs_1.31.0.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: 2.57.0 Depends: R (>= 2.5.0),stats,graph,hgu95av2.db Imports: AnnotationDbi,graph,DBI, graph, grDevices, methods, stats, tools, utils Suggests: RBGL,ALL License: Artistic-2.0 MD5sum: 1e1dca687356e5f40859e9593a9f8f28 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 Maintainer: VJ Carey git_url: https://git.bioconductor.org/packages/keggorthology git_branch: devel git_last_commit: 6669643 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/keggorthology_2.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/keggorthology_2.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/keggorthology_2.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/keggorthology_2.57.0.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: 1.45.0 Depends: R (>= 3.5.0) Imports: methods, httr, png, Biostrings Suggests: RUnit, BiocGenerics, knitr, markdown License: Artistic-2.0 MD5sum: aecdee93196b39c2a8b9f2591343953f 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 ). 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 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: 1e2de52 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/KEGGREST_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/KEGGREST_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/KEGGREST_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/KEGGREST_1.45.0.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, Hiiragi2013 importsMe: ADAM, adSplit, AnnotationDbi, attract, BiocSet, ChIPpeakAnno, CNEr, EnrichmentBrowser, famat, FELLA, gage, ginmappeR, MetaboSignal, MWASTools, PADOG, pairkat, pathview, SBGNview, transomics2cytoscape, YAPSA suggestsMe: Category, categoryCompare, gatom, GenomicRanges, globaltest, iSEEu, MLP, padma, rGREAT, RTopper, SomaScan.db, CALANGO, ggpicrust2, maGUI, ptm, ReporterScore, scDiffCom dependencyCount: 26 Package: KinSwingR Version: 1.23.0 Depends: R (>= 3.5) Imports: data.table, BiocParallel, sqldf, stats, grid, grDevices Suggests: knitr, rmarkdown License: GPL-3 MD5sum: acd1142b5201168cfe786749c18717d3 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KinSwingR git_branch: devel git_last_commit: 81dce29 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/KinSwingR_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/KinSwingR_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/KinSwingR_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/KinSwingR_1.23.0.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: 1.25.0 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: ad7716b81c34e4b600ceba9296bf19f1 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 URL: https://github.com/lbbe-software/kissDE git_url: https://git.bioconductor.org/packages/kissDE git_branch: devel git_last_commit: a8b6659 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/kissDE_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/kissDE_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/kissDE_1.25.0.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: 208 Package: KnowSeq Version: 1.19.0 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: 02a19dcef91e0aa5e532588ef1a63287 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KnowSeq git_branch: devel git_last_commit: 8ad5145 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/KnowSeq_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/KnowSeq_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/KnowSeq_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/KnowSeq_1.19.0.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: 174 Package: knowYourCG Version: 1.1.0 Depends: R (>= 4.4.0) Imports: sesameData, dplyr, methods, rlang, GenomicRanges, IRanges, reshape2, S4Vectors, stats, stringr, utils Suggests: testthat (>= 3.0.0), SummarizedExperiment, rmarkdown, knitr, sesame, gprofiler2 License: MIT + file LICENSE MD5sum: 764e31391d1afadcaefced287ecc18fc NeedsCompilation: no Title: Functional analysis of DNA methylome datasets Description: knowYourCG automates the functional analysis of DNA methylation data. The package tests the enrichment of discrete CpG probes across thousands of curated biological and technical features. GSEA-like analysis can be performed on continuous methylation data query sets. knowYourCG can also take beta matrices as input to perform feature aggregation over the curated database sets. biocViews: Epigenetics, DNAMethylation, MethylationArray Author: Zhou Wanding [aut], Goldberg David [aut, cre] (), Moyer Ethan [ctb] Maintainer: Goldberg David 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: 4077bd9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/knowYourCG_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/knowYourCG_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/knowYourCG_1.1.0.tgz vignettes: vignettes/knowYourCG/inst/doc/KYCG.html vignetteTitles: "5. knowYourCG" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/knowYourCG/inst/doc/KYCG.R dependencyCount: 79 Package: LACE Version: 2.9.0 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: a3a2d54c2b8e78d3237964949a7fac46 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] (), Fabrizio Angaroni [aut], Davide Maspero [cre, aut], Alex Graudenzi [aut], Luca De Sano [aut] (), Gianluca Ascolani [aut] Maintainer: Davide Maspero 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: da83021 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/LACE_2.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/LACE_2.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/LACE_2.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/LACE_2.9.0.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: 166 Package: lapmix Version: 1.71.0 Depends: R (>= 2.6.0),stats Imports: Biobase, graphics, grDevices, methods, stats, tools, utils License: GPL (>= 2) MD5sum: e7f36979d031df92fec9726f369b9f21 NeedsCompilation: no 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 URL: http://www.r-project.org, http://www.bioconductor.org, http://stat.epfl.ch git_url: https://git.bioconductor.org/packages/lapmix git_branch: devel git_last_commit: e7294ba git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/lapmix_1.71.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/lapmix_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/lapmix_1.71.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/lapmix_1.71.0.tgz vignettes: vignettes/lapmix/inst/doc/lapmix-example.pdf vignetteTitles: lapmix example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lapmix/inst/doc/lapmix-example.R dependencyCount: 8 Package: LBE Version: 1.73.0 Depends: stats Imports: graphics, grDevices, methods, stats, utils Suggests: qvalue License: GPL-2 MD5sum: c959a5a96e8bdd15e3ddec964a957c68 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 git_url: https://git.bioconductor.org/packages/LBE git_branch: devel git_last_commit: a0a059b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/LBE_1.73.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/LBE_1.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/LBE_1.73.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/LBE_1.73.0.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 dependsOnMe: PhViD dependencyCount: 5 Package: ldblock Version: 1.35.0 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 MD5sum: e5fe19f86f71ac64effddcc481fb6bc7 NeedsCompilation: no Title: data structures for linkage disequilibrium measures in populations Description: Define data structures for linkage disequilibrium measures in populations. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ldblock git_branch: devel git_last_commit: ff23027 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ldblock_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ldblock_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ldblock_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ldblock_1.35.0.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: 19 Package: LEA Version: 3.17.0 Depends: R (>= 3.3.0), methods, stats, utils, graphics Suggests: knitr License: GPL-3 Archs: x64 MD5sum: 28457cbdf4be45401c649374fd487e9c 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 , Olivier Francois , Clement Gain Maintainer: Olivier Francois , Eric Frichot 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: 6bfecf9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/LEA_3.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/LEA_3.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/LEA_3.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/LEA_3.17.0.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: 1.39.0 Depends: R (>= 3.2.0), e1071 (>= 1.6) Imports: akima, ggplot2, irr, jsonlite, parallel, plot3D, plyr, RCurl, ROCR, testthat License: MIT | file LICENSE MD5sum: 3610efe6739c8c8b8754b32561e0b035 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 BugReports: https://github.com/aitgon/LedPred/issues git_url: https://git.bioconductor.org/packages/LedPred git_branch: devel git_last_commit: 7d6e85f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/LedPred_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/LedPred_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/LedPred_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/LedPred_1.39.0.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: 74 Package: lefser Version: 1.15.0 Depends: SummarizedExperiment, R (>= 4.0.0) Imports: coin, MASS, ggplot2, S4Vectors, stats, methods, utils, dplyr Suggests: knitr, rmarkdown, curatedMetagenomicData, BiocStyle, phyloseq, testthat, pkgdown, covr, withr License: Artistic-2.0 MD5sum: 86c0f6a790abe62de5ebde82b6859458 NeedsCompilation: no Title: R implementation of the LEfSE method for microbiome biomarker discovery Description: lefser is an implementation in R of the popular "LDA Effect Size (LEfSe)" method for microbiome biomarker discovery. It uses the Kruskal-Wallis test, Wilcoxon-Rank Sum test, and Linear Discriminant Analysis to find biomarkers of groups and sub-groups. biocViews: Software, Sequencing, DifferentialExpression, Microbiome, StatisticalMethod, Classification Author: Asya Khleborodova [cre, aut], Ludwig Geistlinger [ctb], Marcel Ramos [ctb] (), Samuel Gamboa-Tuz [ctb], Levi Waldron [ctb], Sehyun Oh [ctb] Maintainer: Asya Khleborodova 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: 3c49cb2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/lefser_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/lefser_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/lefser_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/lefser_1.15.0.tgz vignettes: vignettes/lefser/inst/doc/lefser.html vignetteTitles: Introduction to the lefser R implementation of the popular LEfSE software for biomarker discovery in microbiome analysis. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lefser/inst/doc/lefser.R importsMe: ggpicrust2 suggestsMe: dar dependencyCount: 76 Package: lemur Version: 1.3.0 Depends: R (>= 4.1) Imports: stats, utils, irlba, methods, SingleCellExperiment, SummarizedExperiment, rlang, vctrs, glmGamPoi (>= 1.12.0), BiocGenerics, S4Vectors, Matrix, DelayedMatrixStats, HDF5Array, MatrixGenerics, matrixStats, Rcpp, harmony (>= 1.0.3), limma, BiocNeighbors LinkingTo: Rcpp, RcppArmadillo Suggests: testthat (>= 3.0.0), tidyverse, uwot, dplyr, edgeR, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE Archs: x64 MD5sum: e159f85fe4aa9f6d6a3fa17febbc7ab6 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] () Maintainer: Constantin Ahlmann-Eltze 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: 78323e1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/lemur_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/lemur_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/lemur_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/lemur_1.3.0.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: 95 Package: les Version: 1.55.0 Depends: R (>= 2.13.2), methods, graphics, fdrtool Imports: boot, gplots, RColorBrewer Suggests: Biobase, limma Enhances: parallel License: GPL-3 MD5sum: 11407ad32469418a88954018e8f24255 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 git_url: https://git.bioconductor.org/packages/les git_branch: devel git_last_commit: 98165aa git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/les_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/les_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/les_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/les_1.55.0.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: 1.23.0 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) Archs: x64 MD5sum: d7960cd3b7e6feffb07dbf6e96f4ff10 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/levi git_branch: devel git_last_commit: 5838f5b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/levi_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/levi_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/levi_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/levi_1.23.0.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: 100 Package: lfa Version: 2.5.0 Depends: R (>= 4.0) Imports: utils, methods, corpcor, RSpectra Suggests: knitr, ggplot2, testthat, BEDMatrix, genio License: GPL (>= 3) Archs: x64 MD5sum: 012e4b179422064b10f949dd72317d9a 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] (), John D. Storey [aut] () Maintainer: Alejandro Ochoa 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: f2e7dbc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/lfa_2.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/lfa_2.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/lfa_2.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/lfa_2.5.0.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 dependencyCount: 12 Package: limma Version: 3.61.0 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: e66f7c98a827ce8e29b031e9fe198ab0 NeedsCompilation: yes Title: Linear Models for Microarray Data Description: Data analysis, linear models and differential expression for microarray and RNA-seq 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] Maintainer: Gordon Smyth URL: https://bioinf.wehi.edu.au/limma/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/limma git_branch: devel git_last_commit: 13b0eab git_last_commit_date: 2024-05-01 Date/Publication: 2024-05-01 source.ver: src/contrib/limma_3.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/limma_3.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/limma_3.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/limma_3.61.0.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, marray, metagenomeSeq, metaseqR2, mpra, NanoTube, NeuCA, octad, protGear, qpcrNorm, qusage, RBM, RnBeads, Rnits, splineTimeR, TOAST, tRanslatome, TurboNorm, variancePartition, wateRmelon, zenith, CCl4, Fletcher2013a, HD2013SGI, ReactomeGSA.data, EGSEA123, maEndToEnd, methylationArrayAnalysis, 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, clusterExperiment, CNVRanger, combi, compcodeR, consensusDE, consensusOV, crlmm, crossmeta, 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, InPAS, isomiRs, KnowSeq, lemur, limmaGUI, Linnorm, lipidr, lmdme, mastR, MatrixQCvis, MBECS, MBQN, mCSEA, MEAL, methylKit, MethylMix, microbiomeExplorer, microbiomeMarker, 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, pepStat, phantasus, phenomis, phenoTest, PhosR, polyester, POMA, POWSC, projectR, qmtools, qPLEXanalyzer, qsea, RegEnrich, regsplice, RNAinteract, RNAseqCovarImpute, roastgsa, ROSeq, RTN, RTopper, saseR, satuRn, scone, scran, ScreenR, scviR, seqsetvis, shinyepico, SingleCellSignalR, singleCellTK, sparrow, speckle, SPsimSeq, standR, STATegRa, Statial, sva, timecourse, TOP, ToxicoGx, TPP, TPP2D, transcriptogramer, TVTB, tweeDEseq, vsclust, vsn, weitrix, Wrench, yamss, yarn, BeadArrayUseCases, DmelSGI, signatureSearchData, spatialLIBD, ExpHunterSuite, ExpressionNormalizationWorkflow, recountWorkflow, aliases2entrez, batchtma, BPM, Cascade, cinaR, DiPALM, dsb, easyDifferentialGeneCoexpression, eLNNpairedCov, ggpicrust2, Grouphmap, GWASbyCluster, INCATome, lilikoi, limorhyde2, lipidomeR, metaMA, mi4p, MiDA, miRtest, MKmisc, MKomics, MSclassifR, newIMVC, nlcv, OncoSubtype, Patterns, plfMA, promor, RANKS, RCPA, RPPanalyzer, scBio, scGOclust, scRNAtools, scROSHI, ssizeRNA, statVisual, treediff, wrProteo suggestsMe: ABarray, ADaCGH2, Biobase, biobroom, BiocSet, BioNet, BioQC, Category, categoryCompare, celaref, CellBench, CellMixS, ChIPpeakAnno, ClassifyR, CMA, coGPS, CONSTANd, cydar, 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, GeuvadisTranscriptExpr, mammaPrintData, msigdb, seventyGeneData, arrays, CAGEWorkflow, fluentGenomics, simpleSingleCell, AnnoProbe, aroma.affymetrix, canvasXpress, COCONUT, corncob, DGEobj.utils, hexbin, limorhyde, LPS, maGUI, NACHO, pctax, Platypus, pmartR, protti, RepeatedHighDim, SCdeconR, seqgendiff, Seurat, simphony, st, volcano3D, wrGraph, wrMisc, wrTopDownFrag dependencyCount: 6 Package: limmaGUI Version: 1.81.0 Imports: methods, grDevices, graphics, limma, R2HTML, tcltk, tkrplot, xtable, utils License: GPL (>=2) MD5sum: 0482b5d1b2da055b1ad5fa96c3e7a59f 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 URL: http://bioinf.wehi.edu.au/limmaGUI/ git_url: https://git.bioconductor.org/packages/limmaGUI git_branch: devel git_last_commit: aa69e95 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/limmaGUI_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/limmaGUI_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/limmaGUI_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/limmaGUI_1.81.0.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: limpca Version: 1.1.0 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 MD5sum: d2d3f5c7e54f3d713a3cc2599041d4d2 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] (), Nadia Benaiche [ctb] Maintainer: Manon Martin 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: d7869a9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/limpca_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/limpca_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/limpca_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/limpca_1.1.0.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: 143 Package: lineagespot Version: 1.9.0 Imports: VariantAnnotation, MatrixGenerics, SummarizedExperiment, data.table, stringr, httr, utils Suggests: BiocStyle, RefManageR, rmarkdown, knitr, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: bb3c3d9e55206f9b55074b911752ae30 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] (), 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 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: ae4993e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/lineagespot_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/lineagespot_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/lineagespot_1.9.0.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: 1.19.0 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: 81a9d38bb08e3361407ebace3011fd75 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 Tableaux”), 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" VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LinkHD git_branch: devel git_last_commit: 23dd505 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/LinkHD_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/LinkHD_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/LinkHD_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/LinkHD_1.19.0.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: 145 Package: Linnorm Version: 2.29.0 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 Archs: x64 MD5sum: 7535ffe91e1834d2a23e0bad08c19898 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 Maintainer: Shun Hang Yip URL: https://doi.org/10.1093/nar/gkx828 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Linnorm git_branch: devel git_last_commit: b65f8e5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Linnorm_2.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Linnorm_2.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Linnorm_2.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Linnorm_2.29.0.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: 1.9.0 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: 5d7535fa2f94aff66175de8232305490 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LinTInd git_branch: devel git_last_commit: fb7ef03 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/LinTInd_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/LinTInd_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/LinTInd_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/LinTInd_1.9.0.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: 108 Package: lionessR Version: 1.19.0 Depends: R (>= 3.6.0) Imports: stats, SummarizedExperiment, S4Vectors Suggests: knitr, rmarkdown, igraph, reshape2, limma, License: MIT + file LICENSE MD5sum: 86ed8a945b02a4ebafdd5e064526c862 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] (), Ping-Han Hsieh [cre] () Maintainer: Ping-Han Hsieh 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: 919cb30 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/lionessR_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/lionessR_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/lionessR_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/lionessR_1.19.0.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: 2.19.0 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: 57c700ab3d9a195a677941d3ca25b5d1 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] (), Ahmed Mohamed [aut], Jeffrey Molendijk [aut] Maintainer: Ahmed Mohamed 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: 562b70e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/lipidr_2.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/lipidr_2.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/lipidr_2.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/lipidr_2.19.0.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: 128 Package: LiquidAssociation Version: 1.59.0 Depends: geepack, methods, yeastCC, org.Sc.sgd.db Imports: Biobase, graphics, grDevices, methods, stats License: GPL (>=3) MD5sum: 88b3b4e1be3365e9db1bb20619274c46 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 Maintainer: Yen-Yi Ho git_url: https://git.bioconductor.org/packages/LiquidAssociation git_branch: devel git_last_commit: 3e07664 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/LiquidAssociation_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/LiquidAssociation_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/LiquidAssociation_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/LiquidAssociation_1.59.0.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: 65 Package: lisaClust Version: 1.13.0 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 Suggests: BiocStyle, knitr, rmarkdown License: GPL (>=2) MD5sum: c8d196d413debb76c7263e11dc186427 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] Maintainer: Ellis Patrick 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: e6c5156 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/lisaClust_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/lisaClust_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/lisaClust_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/lisaClust_1.13.0.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, spicyWorkflow dependencyCount: 182 Package: lmdme Version: 1.47.0 Depends: R (>= 2.14.1), pls, stemHypoxia Imports: stats, methods, limma Enhances: parallel License: GPL (>=2) MD5sum: faad2ab93ca38b40783dd43da895e711 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 URL: http://www.bdmg.com.ar/?page_id=38 git_url: https://git.bioconductor.org/packages/lmdme git_branch: devel git_last_commit: dbd34da git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/lmdme_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/lmdme_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/lmdme_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/lmdme_1.47.0.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: 1.31.0 Depends: R (>= 3.4), xcms, CAMERA, methods Imports: utils Suggests: PtH2O2lipids, knitr, rmarkdown License: GPL (>= 3) + file LICENSE MD5sum: 6d326263fbdb03b1cbcaf4006fb1b9b7 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 , Daniel Lowenstein , James Collins 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: 305ce8d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/LOBSTAHS_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/LOBSTAHS_1.31.0.zip 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: 1.25.0 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: a2219bad8cfcbbfd251f8a4221f98ddc 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 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: c307a20 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/loci2path_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/loci2path_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/loci2path_1.25.0.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: 2.25.0 Depends: LogicReg, mcbiopi, survival Imports: graphics, methods, stats Suggests: genefilter, siggenes License: LGPL (>= 2) MD5sum: e259b64e77fa18dc503f8f5567b848e9 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 git_url: https://git.bioconductor.org/packages/logicFS git_branch: devel git_last_commit: 4f2d992 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/logicFS_2.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/logicFS_2.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/logicFS_2.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/logicFS_2.25.0.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: 1.35.0 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: 141815c15d7f66811aecb1b1a3d12506 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 [aut, cre], Christoph Bock [ctb] Maintainer: Nathan Sheffield 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: f821fd8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/LOLA_1.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/LOLA_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/LOLA_1.35.0.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: 1.23.0 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: ae232e45a83e670b9fc1e5df1ae121e9 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LoomExperiment git_branch: devel git_last_commit: 9dc016d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/LoomExperiment_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/LoomExperiment_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/LoomExperiment_1.23.0.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: 50 Package: LPE Version: 1.79.0 Depends: R (>= 2.10) Imports: stats License: LGPL MD5sum: 80ae4cd4519df41c1625e0f7c72e4e2b 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 , Michael O'Connell , Jae K. Lee . Includes R source code contributed by HyungJun Cho Maintainer: Nitin Jain 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: 66a7c70 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/LPE_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/LPE_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/LPE_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/LPE_1.79.0.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: 2.37.0 Depends: lpSolve, KEGGgraph License: Artistic License 2.0 MD5sum: ca7478d7e9f121e69a0fbd6d849bd6c9 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 git_url: https://git.bioconductor.org/packages/lpNet git_branch: devel git_last_commit: 9806e14 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/lpNet_2.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/lpNet_2.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/lpNet_2.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/lpNet_2.37.0.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: 15 Package: lpsymphony Version: 1.33.0 Depends: R (>= 3.0.0) Suggests: BiocStyle, knitr, testthat Enhances: slam License: EPL Archs: x64 MD5sum: bc0ec927c6c4d38d254b44ee76a278c7 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] Maintainer: Vladislav Kim 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 git_url: https://git.bioconductor.org/packages/lpsymphony git_branch: devel git_last_commit: 56997c7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/lpsymphony_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/lpsymphony_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/lpsymphony_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/lpsymphony_1.33.0.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: 2.15.0 Depends: R (>= 3.5.0) Imports: methods, stats, utils, AnnotationDbi, RSQLite, DBI, Biobase Suggests: testthat, BiocStyle, AnnotationHub License: Artistic-2.0 MD5sum: ef4d3451ff4543288dcf84c8a4a51d1c 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 VignetteBuilder: utils git_url: https://git.bioconductor.org/packages/LRBaseDbi git_branch: devel git_last_commit: b60c019 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/LRBaseDbi_2.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/LRBaseDbi_2.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/LRBaseDbi_2.15.0.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: 1.13.0 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: 57f59f3e315fe7f285d15bad2d4f805f 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] () Maintainer: Wenjing Ma VignetteBuilder: knitr BugReports: https://github.com/marvinquiet/LRcell/issues git_url: https://git.bioconductor.org/packages/LRcell git_branch: devel git_last_commit: 7922d2d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/LRcell_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/LRcell_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/LRcell_1.13.0.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: 95 Package: lumi Version: 2.57.0 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: 5660a7e2313472244fbf75ecb6afbd2e 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 git_url: https://git.bioconductor.org/packages/lumi git_branch: devel git_last_commit: 2833a3e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/lumi_2.57.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/lumi_2.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/lumi_2.57.0.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: 160 Package: lute Version: 1.1.0 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 MD5sum: b95ce6f467acf7e88b37374bd72324f6 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] (), Stephanie Hicks [aut] () Maintainer: Sean K Maden 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: 0986a8b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/lute_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/lute_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/lute_1.1.0.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: 98 Package: LymphoSeq Version: 1.33.0 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: 6074e51eccc032ea95577d19be0247a8 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 Maintainer: David Coffey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LymphoSeq git_branch: devel git_last_commit: fb0a3fc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/LymphoSeq_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/LymphoSeq_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/LymphoSeq_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/LymphoSeq_1.33.0.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: 101 Package: M3C Version: 1.27.0 Depends: R (>= 3.5.0) Imports: ggplot2, Matrix, doSNOW, cluster, parallel, foreach, doParallel, matrixcalc, Rtsne, corpcor, umap Suggests: knitr, rmarkdown License: AGPL-3 MD5sum: 348ab6fb6cdcb4d550e20bd523d6cfa9 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/M3C git_branch: devel git_last_commit: 8675ffc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/M3C_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/M3C_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/M3C_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/M3C_1.27.0.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: 1.31.0 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: 81e01d4684e5e0728b86464e1030a219 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 Maintainer: Tallulah Andrews 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: 65bab9c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/M3Drop_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/M3Drop_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/M3Drop_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/M3Drop_1.31.0.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: 170 Package: m6Aboost Version: 1.11.0 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: 2fac113d69f47dcc695e91cf5d699e34 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] (), Kathi Zarnack [aut] () Maintainer: You Zhou 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: 139401a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/m6Aboost_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/m6Aboost_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/m6Aboost_1.11.0.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: 171 Package: Maaslin2 Version: 1.19.0 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: ab613de4086e087295db1786114f6a34 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 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: 2f9ea7a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Maaslin2_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Maaslin2_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Maaslin2_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Maaslin2_1.19.0.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, ggpicrust2 suggestsMe: dar dependencyCount: 137 Package: Macarron Version: 1.9.0 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: 267f6e3ce20155e7003b2b34208b7852 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 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: f9fdaa6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Macarron_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Macarron_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Macarron_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Macarron_1.9.0.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: 210 Package: maCorrPlot Version: 1.75.0 Depends: lattice Imports: graphics, grDevices, lattice, stats License: GPL (>= 2) MD5sum: 571dd9abe15de6fd78c428a76861784c 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 Maintainer: Alexander Ploner 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: 72b8a8b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/maCorrPlot_1.75.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/maCorrPlot_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/maCorrPlot_1.75.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/maCorrPlot_1.75.0.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: 1.19.0 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: a740fe466e188a8f2281c267d3cb7321 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MACSQuantifyR git_branch: devel git_last_commit: 7e9c739 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MACSQuantifyR_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MACSQuantifyR_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MACSQuantifyR_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MACSQuantifyR_1.19.0.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: 1.13.0 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: 28f47c1ba6938f2f4c77fdc8c2ef3f36 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: Qiang Hu [aut, cre] Maintainer: Qiang Hu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MACSr git_branch: devel git_last_commit: d2e5752 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MACSr_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MACSr_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MACSr_1.13.0.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: 79 Package: made4 Version: 1.79.0 Depends: RColorBrewer,gplots,scatterplot3d, Biobase, SummarizedExperiment Imports: ade4 Suggests: affy, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 28cc060e51472aa35ea65d0416a83a61 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 URL: http://www.hsph.harvard.edu/aedin-culhane/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/made4 git_branch: devel git_last_commit: d732f8d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/made4_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/made4_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/made4_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/made4_1.79.0.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: 1.31.0 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: d2aae5630b7fa63f73c91ceac2961ca4 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 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: 7771b2a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MADSEQ_1.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MADSEQ_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MADSEQ_1.31.0.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: 2.21.0 Depends: R (>= 3.3) Imports: data.table, grDevices, methods, RColorBrewer, Rhtslib, survival, DNAcopy LinkingTo: Rhtslib, zlibbioc Suggests: berryFunctions, Biostrings, BSgenome, BSgenome.Hsapiens.UCSC.hg19, GenomicRanges, IRanges, knitr, mclust, MultiAssayExperiment, NMF, R.utils, RaggedExperiment, rmarkdown, S4Vectors, pheatmap, curl License: MIT + file LICENSE Archs: x64 MD5sum: 970613a90f2d76fe5e5a210a026e2b78 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] () Maintainer: Anand Mayakonda 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: 8584531 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/maftools_2.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/maftools_2.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/maftools_2.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/maftools_2.21.0.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, TCGAWorkflow, aplotExtra, pathwayTMB, PMAPscore, Rediscover, sigminer, SMDIC, ssMutPA suggestsMe: GenomicDataCommons, MultiAssayExperiment, survtype, TCGAbiolinks, oncoPredict dependencyCount: 16 Package: MAGAR Version: 1.13.0 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: ee8cdbae7dc3d93486709673b0072b9b 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] () Maintainer: Michael Scherer 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: 10815a6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MAGAR_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MAGAR_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MAGAR_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MAGAR_1.13.0.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: 195 Package: MAGeCKFlute Version: 2.9.0 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) MD5sum: db4370d1ed11d966195ad30266e7b6c3 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MAGeCKFlute git_branch: devel git_last_commit: 056f370 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MAGeCKFlute_2.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MAGeCKFlute_2.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MAGeCKFlute_2.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MAGeCKFlute_2.9.0.tgz vignettes: vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute_enrichment.html, vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute.html vignetteTitles: MAGeCKFlute_enrichment.Rmd, MAGeCKFlute.Rmd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute_enrichment.R, vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute.R importsMe: CRISPRball dependencyCount: 150 Package: magpie Version: 1.5.0 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: 3124ce69b6f8fe4237b6d716d3a00ece 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 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: b0da14f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/magpie_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/magpie_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/magpie_1.5.0.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: 1.7.0 Depends: R (>= 4.2.0) Imports: utils, stats, BiocParallel Suggests: BiocStyle, covr, knitr, rmarkdown, ggplot2, sessioninfo, testthat (>= 3.0.0) License: GPL-3 MD5sum: ed4b3202a70bcaf77420248055f517a4 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: Fabrício Almeida-Silva [aut, cre] (), Yves Van de Peer [aut] () Maintainer: Fabrício Almeida-Silva 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: a22e4b6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/magrene_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/magrene_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/magrene_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/magrene_1.7.0.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: 1.11.0 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 MD5sum: eea669d0e0e07d55e76eb6240ca7a39b 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 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: 8bb058f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MAI_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MAI_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MAI_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MAI_1.11.0.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: 175 Package: MAIT Version: 1.39.0 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: faa29c03bbbe73107253ec0a38b55f67 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 git_url: https://git.bioconductor.org/packages/MAIT git_branch: devel git_last_commit: c0b4563 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MAIT_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MAIT_1.39.0.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: 205 Package: makecdfenv Version: 1.81.0 Depends: R (>= 2.6.0), affyio Imports: Biobase, affy, methods, stats, utils, zlibbioc License: GPL (>= 2) Archs: x64 MD5sum: 6162c0147c9b594dd2ec36cbb5890f3d 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 , Laurent Gautier , Wolfgang Huber , Ben Bolstad Maintainer: James W. MacDonald git_url: https://git.bioconductor.org/packages/makecdfenv git_branch: devel git_last_commit: 7cb4573 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/makecdfenv_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/makecdfenv_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/makecdfenv_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/makecdfenv_1.81.0.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: 1.77.0 Depends: R (>= 2.10) Imports: GLAD, graphics, grDevices, stats, utils Suggests: knitr, rmarkdown, bookdown License: GPL-2 Archs: x64 MD5sum: 37394b3efe20549b276efe608cadbf87 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 , Philippe Hupé Maintainer: Pierre Neuvial 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: d6da044 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MANOR_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MANOR_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MANOR_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MANOR_1.77.0.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: 1.75.0 Depends: R (>= 2.10) Imports: stats License: GPL (>= 2) MD5sum: b21f6e075cb99b9abef01d35dec460c5 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 git_url: https://git.bioconductor.org/packages/MantelCorr git_branch: devel git_last_commit: 348ff1b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MantelCorr_1.75.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MantelCorr_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MantelCorr_1.75.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MantelCorr_1.75.0.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: 1.1.0 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: 7f40d02a0e3ba51a410451d022b9abd5 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] (), Agus Salim [ctb], infinityFlow [ctb] Maintainer: Hsiao-Chi Liao 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: cc748e3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MAPFX_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MAPFX_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MAPFX_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MAPFX_1.1.0.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: 196 Package: maPredictDSC Version: 1.43.0 Depends: R (>= 2.15.0), MASS,affy,limma,gcrma,ROC,class,e1071,caret,hgu133plus2.db,ROCR,AnnotationDbi,LungCancerACvsSCCGEO Suggests: parallel License: GPL-2 MD5sum: ee4df1ec8e32b06edf77fd1448a03bae 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 Maintainer: Adi Laurentiu Tarca URL: http://bioinformaticsprb.med.wayne.edu/maPredictDSC git_url: https://git.bioconductor.org/packages/maPredictDSC git_branch: devel git_last_commit: b552d05 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/maPredictDSC_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/maPredictDSC_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/maPredictDSC_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/maPredictDSC_1.43.0.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: 137 Package: mapscape Version: 1.29.0 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 MD5sum: 9610f0698435f43b11eb8de1c38c7bb2 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mapscape git_branch: devel git_last_commit: 68baf1c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mapscape_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mapscape_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mapscape_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mapscape_1.29.0.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: 1.5.0 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: 5a90ed605914dcb6cd1ed274f5c21687 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] () Maintainer: Eric Davis URL: http://ericscottdavis.com/mariner/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mariner git_branch: devel git_last_commit: e8791c8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mariner_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mariner_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mariner_1.5.0.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: 113 Package: marr Version: 1.15.0 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) Archs: x64 MD5sum: ae03fbdd97fab89368bc6ace4c581a70 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 VignetteBuilder: knitr BugReports: https://github.com/Ghoshlab/marr/issues git_url: https://git.bioconductor.org/packages/marr git_branch: devel git_last_commit: 50c45eb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/marr_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/marr_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/marr_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/marr_1.15.0.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: 66 Package: marray Version: 1.83.0 Depends: R (>= 2.10.0), limma, methods Suggests: tkWidgets License: LGPL MD5sum: 9978b9073bcb13917fb40345069abf85 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 with contributions from Agnes Paquet and Sandrine Dudoit. Maintainer: Yee Hwa (Jean) Yang URL: http://www.maths.usyd.edu.au/u/jeany/ git_url: https://git.bioconductor.org/packages/marray git_branch: devel git_last_commit: 57d60e9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/marray_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/marray_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/marray_1.83.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/marray_1.83.0.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, methylPipe, MSstats, MSstatsShiny, nnNorm, OLIN, OLINgui, piano, stepNorm, timecourse suggestsMe: DEGraph, Mfuzz, hexbin dependencyCount: 7 Package: martini Version: 1.25.0 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 Archs: x64 MD5sum: f66775de5dffd5017afe4fcc79401d51 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] (), Chloe-Agathe Azencott [aut] () Maintainer: Hector Climente-Gonzalez 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: 4e7b18c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/martini_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/martini_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/martini_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/martini_1.25.0.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: 27 Package: maser Version: 1.23.0 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: cd00f1ddb899b9139ad9f69e9198bab4 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 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: ae8e9eb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/maser_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/maser_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/maser_1.23.0.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: 164 Package: maSigPro Version: 1.77.0 Depends: R (>= 2.3.1) Imports: Biobase, graphics, grDevices, venn, mclust, stats, MASS License: GPL (>= 2) MD5sum: 082f96cebeffe9a2c3a6d9ac4767da41 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 git_url: https://git.bioconductor.org/packages/maSigPro git_branch: devel git_last_commit: 6d12731 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/maSigPro_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/maSigPro_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/maSigPro_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/maSigPro_1.77.0.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: 11 Package: maskBAD Version: 1.49.0 Depends: R (>= 2.10), gcrma (>= 2.27.1), affy Suggests: hgu95av2probe, hgu95av2cdf License: GPL (>= 2) MD5sum: 1b3685d5bfc016d1e8b569531e52b6f0 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 Maintainer: Michael Dannemann git_url: https://git.bioconductor.org/packages/maskBAD git_branch: devel git_last_commit: 4ae4138 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/maskBAD_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/maskBAD_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/maskBAD_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/maskBAD_1.49.0.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: 1.57.0 Depends: R (>= 2.10.0), methods Imports: graphics, grDevices, stats, utils License: GPL (>=2) MD5sum: 17b3319f515f0f5d174be943c69aa0d3 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 , John M. Greally Maintainer: Reid F. Thompson git_url: https://git.bioconductor.org/packages/MassArray git_branch: devel git_last_commit: 7a61a98 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MassArray_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MassArray_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MassArray_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MassArray_1.57.0.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: 1.41.0 Depends: cluster, gplots, diptest, Biobase, R (>= 3.0.2) Suggests: biomaRt, RUnit, BiocGenerics License: GPL-3 MD5sum: f9b304c3f03a7bd2114c7d953c6241e2 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 git_url: https://git.bioconductor.org/packages/massiR git_branch: devel git_last_commit: fc10d28 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/massiR_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/massiR_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/massiR_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/massiR_1.41.0.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: 14 Package: MassSpecWavelet Version: 1.71.0 Suggests: signal, waveslim, BiocStyle, knitr, rmarkdown, RUnit, bench License: LGPL (>= 2) Archs: x64 MD5sum: a397d7366f6619d0099cb6699306d928 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 ) Maintainer: Sergio Oller Moreno 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: 81f9073 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MassSpecWavelet_1.71.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MassSpecWavelet_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MassSpecWavelet_1.71.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MassSpecWavelet_1.71.0.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: 1.31.0 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: 404470835b284718bdcc79677311cd40 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 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: a6dde6d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MAST_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MAST_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MAST_1.31.0.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 dependencyCount: 73 Package: mastR Version: 1.5.0 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: bcc48a09ceeba99ba130a8674ebc203e 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] (), Ahmed Mohamed [aut, ctb] (), Chin Wee Tan [ctb] () Maintainer: Jinjin Chen 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: f99d37b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mastR_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mastR_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mastR_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mastR_1.5.0.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: 161 Package: matchBox Version: 1.47.0 Depends: R (>= 2.8.0) License: Artistic-2.0 MD5sum: 1e52393511f6850dab924785b626abdf 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 , Anuj Gupta Maintainer: Luigi Marchionni , Anuj Gupta git_url: https://git.bioconductor.org/packages/matchBox git_branch: devel git_last_commit: de7a7ca git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/matchBox_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/matchBox_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/matchBox_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/matchBox_1.47.0.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: 1.17.0 Depends: matrixStats (>= 1.0.0) Imports: methods Suggests: Matrix, sparseMatrixStats, SparseArray, DelayedArray, DelayedMatrixStats, SummarizedExperiment, testthat (>= 2.1.0) License: Artistic-2.0 MD5sum: 81dd01b07523f1785bf6a44826f296d9 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] (), Peter Hickey [aut, cre] (), Hervé Pagès [aut] Maintainer: Peter Hickey 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: de9be73 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MatrixGenerics_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MatrixGenerics_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MatrixGenerics_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MatrixGenerics_1.17.0.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, genefilter, glmGamPoi, imcRtools, lemur, lineagespot, mia, miaSim, miloR, MinimumDistance, PDATK, RaggedExperiment, RCSL, saseR, scater, scFeatures, scone, scPCA, scviR, shinyMethyl, spatzie, tadar, TENxIO, tLOH, tpSVG, transformGamPoi, universalmotif, VanillaICE, homosapienDEE2CellScore, spatialLIBD suggestsMe: bnem, cypress, MungeSumstats dependencyCount: 2 Package: MatrixQCvis Version: 1.13.0 Depends: 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), 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 MD5sum: e39f717372169a0c8740b6b9e73a353f 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] (), Wolfgang Huber [aut] () Maintainer: Thomas Naake VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MatrixQCvis git_branch: devel git_last_commit: 7500052 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MatrixQCvis_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MatrixQCvis_1.13.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MatrixQCvis_1.13.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MatrixQCvis_1.13.1.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: 174 Package: MatrixRider Version: 1.37.0 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: 757e2a0e574845b2f21e14d15b46b391 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 git_url: https://git.bioconductor.org/packages/MatrixRider git_branch: devel git_last_commit: e362023 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MatrixRider_1.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MatrixRider_1.37.0.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: 126 Package: matter Version: 2.7.0 Depends: R (>= 4.0), BiocParallel, Matrix, methods Imports: BiocGenerics, ProtGenerics, digest, irlba, biglm, stats, stats4, graphics, grDevices, utils Suggests: BiocStyle, knitr, testthat, plotly License: Artistic-2.0 Archs: x64 MD5sum: dff38de65832ff29073a4ffef635ed2d NeedsCompilation: yes Title: Scientific computing for out-of-memory signals and images Description: Toolbox for out-of-memory scientific computing and data visualization, providing memory-efficient file-based data structures 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 Maintainer: Kylie A. Bemis 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: 5712f98 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/matter_2.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/matter_2.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/matter_2.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/matter_2.7.0.tgz vignettes: vignettes/matter/inst/doc/matter-2-guide.html vignetteTitles: 1. Matter 2: User guide for flexible out-of-memory data structures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/matter/inst/doc/matter-2-guide.R dependsOnMe: CardinalIO importsMe: Cardinal dependencyCount: 25 Package: MBAmethyl Version: 1.39.0 Depends: R (>= 2.15) License: Artistic-2.0 MD5sum: 8ecc5eca65f375248cf2ab16b6546f66 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 git_url: https://git.bioconductor.org/packages/MBAmethyl git_branch: devel git_last_commit: 2092220 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MBAmethyl_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MBAmethyl_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MBAmethyl_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MBAmethyl_1.39.0.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: 1.39.0 Depends: RUnit, BiocGenerics, BiocParallel, GenomicRanges, SummarizedExperiment Suggests: BiocStyle License: Artistic-2.0 MD5sum: 74402f4d9393219bef5f7f0b437f5b32 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 git_url: https://git.bioconductor.org/packages/MBASED git_branch: devel git_last_commit: 31af69a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MBASED_1.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MBASED_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MBASED_1.39.0.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: 1.59.0 Depends: R (>= 2.9.0), tcltk, tcltk2 Imports: preprocessCore, stats, utils License: GPL (>=2) MD5sum: 7a5734ae732f3a01be47d16f8f42a513 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 Maintainer: Bo Yao URL: https://qbrc.swmed.edu/ git_url: https://git.bioconductor.org/packages/MBCB git_branch: devel git_last_commit: c34c207 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MBCB_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MBCB_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MBCB_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MBCB_1.59.0.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: 1.9.0 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: f21eed405363a505395eb77c5117dcf6 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] () Maintainer: Michael Olbrich 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: 7d5cdaa git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MBECS_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MBECS_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MBECS_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MBECS_1.9.0.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: 156 Package: mbkmeans Version: 1.21.0 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 Archs: x64 MD5sum: 9f5c8bfaebabe616b99a90833199a272 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 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: 8980ffa git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mbkmeans_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mbkmeans_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mbkmeans_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mbkmeans_1.21.0.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, scDblFinder dependencyCount: 90 Package: mBPCR Version: 1.59.0 Depends: oligoClasses, GWASTools Imports: Biobase, graphics, methods, utils, grDevices Suggests: xtable License: GPL (>= 2) MD5sum: 9568d4c143752e0f16c251a0eb37e472 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 , with contributions from M. Hutter Maintainer: P.M.V. Rancoita URL: http://www.idsia.ch/~paola/mBPCR git_url: https://git.bioconductor.org/packages/mBPCR git_branch: devel git_last_commit: 139da23 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mBPCR_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mBPCR_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mBPCR_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mBPCR_1.59.0.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: 139 Package: MBQN Version: 2.17.0 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: 9d9a4cda7ca696918943e6a05dd2c168 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] (), Clemens Kreutz [aut, ctb] (), Ariane Schad [aut, ctb] () Maintainer: Eva Brombacher 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: 44188d4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MBQN_2.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MBQN_2.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MBQN_2.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MBQN_2.17.0.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: 111 Package: mbQTL Version: 1.5.0 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: ba394366583d580aad422fcedbef1442 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] (), Steven Schiff [aut], Joseph N Paulson [aut] Maintainer: Mercedeh Movassagh URL: "https://github.com/Mercedeh66/mbQTL" VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mbQTL git_branch: devel git_last_commit: 4fe6777 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mbQTL_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mbQTL_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mbQTL_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mbQTL_1.5.0.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: 78 Package: MBttest Version: 1.33.0 Depends: R (>= 3.3.0), stats, gplots, gtools,graphics,base, utils,grDevices Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: 17d3fbc121a35946b13486a3505df185 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 git_url: https://git.bioconductor.org/packages/MBttest git_branch: devel git_last_commit: 46a7e7a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MBttest_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MBttest_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MBttest_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MBttest_1.33.0.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: 1.29.0 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: ee9dcdb55ecbd3334ac813f71d8d5838 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MCbiclust git_branch: devel git_last_commit: 1a3df25 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MCbiclust_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MCbiclust_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MCbiclust_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MCbiclust_1.29.0.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: 144 Package: mCSEA Version: 1.25.0 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: 23f4a6df9b1d6848ffdfef7c56f24c37 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mCSEA git_branch: devel git_last_commit: 8cf2444 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mCSEA_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mCSEA_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mCSEA_1.25.0.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: 172 Package: mdp Version: 1.25.0 Depends: R (>= 4.0) Imports: ggplot2, gridExtra, grid, stats, utils Suggests: testthat, knitr, rmarkdown, fgsea, BiocManager License: GPL-3 MD5sum: c90281a366ecd834f08283d94ec74100 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 URL: https://mdp.sysbio.tools/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mdp git_branch: devel git_last_commit: 08b5bed git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mdp_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mdp_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mdp_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mdp_1.25.0.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: 1.67.0 Depends: R (>= 2.2.1), cluster, MASS License: LGPL (>= 2) MD5sum: af5940cfb1f9a51bd3fa2d47761d22de 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 git_url: https://git.bioconductor.org/packages/mdqc git_branch: devel git_last_commit: 7ad63ad git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mdqc_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mdqc_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mdqc_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mdqc_1.67.0.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: 1.25.0 Depends: R (>= 3.5.0) Imports: GenomicAlignments, GenomicRanges, IRanges, Biostrings, DNAcopy, Rsamtools, parallel, stringr Suggests: testthat, knitr License: Artistic-2.0 MD5sum: 05e11d392ab13fe4fff4a26284b08b05 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MDTS git_branch: devel git_last_commit: 1c2937e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MDTS_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MDTS_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MDTS_1.25.0.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: 1.35.0 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: 287bcd907d3860ab05fc7161d061be49 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEAL git_branch: devel git_last_commit: 16f66a8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MEAL_1.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MEAL_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MEAL_1.35.0.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: 224 Package: MeasurementError.cor Version: 1.77.0 License: LGPL MD5sum: c672d1f7bbbd1fb591e604747d37b7da 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 git_url: https://git.bioconductor.org/packages/MeasurementError.cor git_branch: devel git_last_commit: f731521 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MeasurementError.cor_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MeasurementError.cor_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MeasurementError.cor_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MeasurementError.cor_1.77.0.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: 1.17.0 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: defa89db01e726e357cac47b78a771ea 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] (), Steve Horvath [ctb] () Maintainer: Sarah Voisin 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: 38799c0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MEAT_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MEAT_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MEAT_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MEAT_1.17.0.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: 175 Package: MEB Version: 1.19.0 Depends: R (>= 3.6.0) Imports: e1071, edgeR, scater, stats, wrswoR, SummarizedExperiment, SingleCellExperiment Suggests: knitr,rmarkdown,BiocStyle License: GPL-2 MD5sum: 74127a10232c693ec73ac27607b09eb6 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEB git_branch: devel git_last_commit: 9e57f37 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MEB_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MEB_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MEB_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MEB_1.19.0.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: 117 Package: MEDIPS Version: 1.57.0 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: adee540736890bd3d436b59bf87c91c9 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 git_url: https://git.bioconductor.org/packages/MEDIPS git_branch: devel git_last_commit: 19ba4a5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MEDIPS_1.57.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MEDIPS_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MEDIPS_1.57.0.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: 109 Package: MEDME Version: 1.65.0 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) Archs: x64 MD5sum: f7dd536b9a9f328c71ec10476fdfa305 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 git_url: https://git.bioconductor.org/packages/MEDME git_branch: devel git_last_commit: 3c269cd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MEDME_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MEDME_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MEDME_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MEDME_1.65.0.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: 104 Package: megadepth Version: 1.15.0 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: 1aaa4f3c344c0a9a2dfc628d561d374f 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] (), David Zhang [aut, cre] () Maintainer: David Zhang URL: https://github.com/LieberInstitute/megadepth SystemRequirements: megadepth () VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/megadepth git_url: https://git.bioconductor.org/packages/megadepth git_branch: devel git_last_commit: 28434b8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/megadepth_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/megadepth_1.15.0.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 dependencyCount: 84 Package: MEIGOR Version: 1.39.0 Depends: R (>= 4.0), Rsolnp, snowfall, deSolve, CNORode Suggests: CellNOptR, knitr, BiocStyle License: GPL-3 MD5sum: 2539c3bcaab4038f1eb6465c9a265bb5 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEIGOR git_branch: devel git_last_commit: e62dc16 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MEIGOR_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MEIGOR_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MEIGOR_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MEIGOR_1.39.0.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: 79 Package: Melissa Version: 1.21.0 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: c30ecb17db76ddf7f23ae988e5aaea8a 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Melissa git_branch: devel git_last_commit: b2edeb7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Melissa_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Melissa_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Melissa_1.21.0.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: 1.13.0 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: 173a9e9bf3fabf36c6750f2eac5ad5c4 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] () Maintainer: Spencer Nystrom URL: https://snystrom.github.io/memes/, https://github.com/snystrom/memes SystemRequirements: Meme Suite (v5.3.3 or above) VignetteBuilder: knitr BugReports: https://github.com/snystrom/memes/issues git_url: https://git.bioconductor.org/packages/memes git_branch: devel git_last_commit: 10e69e6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/memes_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/memes_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/memes_1.13.0.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 dependencyCount: 112 Package: Mergeomics Version: 1.33.0 Depends: R (>= 3.0.1) Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 0a2cbcd2c8227557ef2e67711870b794 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 git_url: https://git.bioconductor.org/packages/Mergeomics git_branch: devel git_last_commit: df484e2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Mergeomics_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Mergeomics_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Mergeomics_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Mergeomics_1.33.0.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: 1.41.0 Depends: R (>= 3.0.1) Imports: methods, AnnotationDbi (>= 1.31.19), RSQLite, Biobase Suggests: testthat License: Artistic-2.0 MD5sum: 485bd04e1060566d5d961b1b4c58aa47 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 git_url: https://git.bioconductor.org/packages/MeSHDbi git_branch: devel git_last_commit: 9a4933b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MeSHDbi_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MeSHDbi_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MeSHDbi_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MeSHDbi_1.41.0.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: 1.31.0 Depends: R (>= 4.1.0) Imports: AnnotationDbi, DOSE, enrichplot, GOSemSim, methods, utils, AnnotationHub, MeSHDbi, yulab.utils Suggests: knitr, rmarkdown, prettydoc License: Artistic-2.0 MD5sum: 47d94792c8ab5475c81b2f552ee06137 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], Erqiang Hu [ctb] Maintainer: Guangchuang Yu 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: 77d300a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/meshes_1.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/meshes_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/meshes_1.31.0.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: 135 Package: meshr Version: 2.11.0 Depends: R (>= 4.1.0) Imports: markdown, rmarkdown, BiocStyle, knitr, methods, stats, utils, fdrtool, MeSHDbi, Category, S4Vectors, BiocGenerics, RSQLite License: Artistic-2.0 MD5sum: dfa3d867577004f448e5431365c3110e 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 VignetteBuilder: knitr BugReports: https://github.com/rikenbit/meshr/issues git_url: https://git.bioconductor.org/packages/meshr git_branch: devel git_last_commit: ce0f2de git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/meshr_2.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/meshr_2.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/meshr_2.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/meshr_2.11.0.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: 84 Package: MesKit Version: 1.15.0 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: ed4c9e9a83c6091eb38715bb164d1325 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] (), Jianyu Chen [aut, ctb] (), Xin Wang [aut, ctb] () Maintainer: Mengni Liu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MesKit git_branch: devel git_last_commit: 7ed263d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MesKit_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MesKit_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MesKit_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MesKit_1.15.0.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: 102 Package: messina Version: 1.41.0 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) Archs: x64 MD5sum: 2b1b99608e831c8868febce4318608b3 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/messina git_branch: devel git_last_commit: 69fe8ef git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/messina_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/messina_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/messina_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/messina_1.41.0.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: 1.15.0 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 Archs: x64 MD5sum: 448392a17c24ebb31524acccd431cfd6 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 VignetteBuilder: knitr BugReports: https://www.github.com/hhabra/metabCombiner/issues git_url: https://git.bioconductor.org/packages/metabCombiner git_branch: devel git_last_commit: 35f5ef4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/metabCombiner_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/metabCombiner_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/metabCombiner_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/metabCombiner_1.15.0.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: 1.7.0 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: 2029160c514ec461e5ad69ed38ca238d 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] () Maintainer: Anestis Gkanogiannis 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: 611ad69 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/metabinR_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/metabinR_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/metabinR_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/metabinR_1.7.0.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: 1.9.0 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: d4d5b63e055df66e84fe32f2eaf33a1d 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] (), Johannes Rainer [aut, cre] (), Andrea Vicini [aut] (), Carolin Huber [aut] (), Philippine Louail [aut] (), Nir Shachaf [ctb] Maintainer: Johannes Rainer 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: 0c8b461 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MetaboAnnotation_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MetaboAnnotation_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MetaboAnnotation_1.9.0.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: 143 Package: MetaboCoreUtils Version: 1.13.0 Depends: R (>= 4.0) Imports: utils, MsCoreUtils, BiocParallel, methods, stats Suggests: BiocStyle, testthat, knitr, rmarkdown, robustbase License: Artistic-2.0 MD5sum: a7eca5f28afe26db5bf5a5641d243f5a 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] (), Michael Witting [aut] (), Andrea Vicini [aut], Liesa Salzer [ctb] (), Sebastian Gibb [aut] (), Michael Stravs [ctb] (), Roger Gine [aut] (), Philippine Louail [aut] () Maintainer: Johannes Rainer 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: 257c5ad git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MetaboCoreUtils_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MetaboCoreUtils_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MetaboCoreUtils_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MetaboCoreUtils_1.13.0.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: 23 Package: metabolomicsWorkbenchR Version: 1.15.0 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: 6bdf95a756a140347219b81fbf51fd5f 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metabolomicsWorkbenchR git_branch: devel git_last_commit: d29c876 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/metabolomicsWorkbenchR_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/metabolomicsWorkbenchR_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/metabolomicsWorkbenchR_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/metabolomicsWorkbenchR_1.15.0.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 dependencyCount: 70 Package: metabomxtr Version: 1.39.0 Depends: methods,Biobase Imports: optimx, Formula, plyr, multtest, BiocParallel, ggplot2 Suggests: xtable, reshape2 License: GPL-2 MD5sum: 284923e2199eed8ec94db68f9696af48 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 git_url: https://git.bioconductor.org/packages/metabomxtr git_branch: devel git_last_commit: 42a4a10 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/metabomxtr_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/metabomxtr_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/metabomxtr_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/metabomxtr_1.39.0.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: 76 Package: MetaboSignal Version: 1.35.0 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: 8d8fe886ff409b26e1f02bcbff3d7f84 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 , Rafael Ayala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaboSignal git_branch: devel git_last_commit: 4f711a3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MetaboSignal_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MetaboSignal_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MetaboSignal_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MetaboSignal_1.35.0.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: 207 Package: metaCCA Version: 1.33.0 Suggests: knitr License: MIT + file LICENSE MD5sum: 9d9c58bbe8474a0d96d8e16cf8651bdd 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 Maintainer: Anna Cichonska URL: https://doi.org/10.1093/bioinformatics/btw052 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metaCCA git_branch: devel git_last_commit: 14372c6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/metaCCA_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/metaCCA_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/metaCCA_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/metaCCA_1.33.0.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: 1.27.0 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: b9d387c21df8b607b22ceb24d12e9e5f 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 VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/MetaCyto git_branch: devel git_last_commit: fc2aedc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MetaCyto_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MetaCyto_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MetaCyto_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MetaCyto_1.27.0.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: 119 Package: metagene2 Version: 1.21.0 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: c0f8f218bcc04dfe628f72cba70dba76 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 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: c842835 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/metagene2_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/metagene2_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/metagene2_1.21.0.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: 94 Package: metagenomeSeq Version: 1.47.0 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: fd28cc8f5285c5db6899e9e2fa3b3173 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 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: 7a5a260 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/metagenomeSeq_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/metagenomeSeq_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/metagenomeSeq_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/metagenomeSeq_1.47.0.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, microbiomeMarker, ggpicrust2, MetaLonDA suggestsMe: dar, interactiveDisplay, phyloseq, scTreeViz, Wrench dependencyCount: 31 Package: metahdep Version: 1.63.0 Depends: R (>= 2.10), methods Suggests: affyPLM License: GPL-3 Archs: x64 MD5sum: 59cbec24c6c4ddad635b707adce00b59 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 git_url: https://git.bioconductor.org/packages/metahdep git_branch: devel git_last_commit: 585628f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/metahdep_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/metahdep_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/metahdep_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/metahdep_1.63.0.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: 1.41.0 Depends: R (>= 4.0), methods, CAMERA, xcms (>= 1.35) Imports: Matrix, tools, robustbase, BiocGenerics, graphics, stats, utils Suggests: metaMSdata, RUnit License: GPL (>= 2) MD5sum: 562cd5364278ef3c944ad1f48fc6d0c8 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] (), Julien Saint-Vanne [ctb] Maintainer: Yann Guitton URL: https://github.com/yguitton/metaMS git_url: https://git.bioconductor.org/packages/metaMS git_branch: devel git_last_commit: 7d396f3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/metaMS_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/metaMS_1.41.0.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: 1.25.0 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: e89ec0c20ac0166e2430bd30bccb0fbb 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaNeighbor git_branch: devel git_last_commit: f7aeeff git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MetaNeighbor_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MetaNeighbor_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MetaNeighbor_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MetaNeighbor_1.25.0.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: 78 Package: MetaPhOR Version: 1.7.0 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: 5adae0e86ff8602a01c9d0594ddffb86 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 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: bd8ea30 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MetaPhOR_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MetaPhOR_1.7.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MetaPhOR_1.7.0.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: 174 Package: metapod Version: 1.13.0 Imports: Rcpp LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown License: GPL-3 Archs: x64 MD5sum: b97410749728ee27962d5145f49ce90e 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 SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metapod git_branch: devel git_last_commit: 1071825 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/metapod_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/metapod_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/metapod_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/metapod_1.13.0.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: 1.11.0 Depends: R (>= 4.1.0), BiocParallel, fields, markdown, fdrtool, fgsea, ggplot2, ggrepel Imports: methods Suggests: rmarkdown, knitr License: Artistic-2.0 MD5sum: caae917f084395e9d0cd95f81c03ac56 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metapone git_branch: devel git_last_commit: 80daf9f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/metapone_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/metapone_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/metapone_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/metapone_1.11.0.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: 60 Package: metaSeq Version: 1.45.0 Depends: R (>= 2.13.0), NOISeq, snow, Rcpp License: Artistic-2.0 MD5sum: 2123e99d5b497ddbb61783dcfe763cd0 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 git_url: https://git.bioconductor.org/packages/metaSeq git_branch: devel git_last_commit: 7dfe2b8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/metaSeq_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/metaSeq_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/metaSeq_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/metaSeq_1.45.0.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: 14 Package: metaseqR2 Version: 1.17.0 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: b1deca6f92af1f9fc06ee6ab850dcdf5 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 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: 03f3b0b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/metaseqR2_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/metaseqR2_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/metaseqR2_1.17.0.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: 238 Package: MetCirc Version: 1.35.0 Depends: R (>= 3.5), amap (>= 0.8), circlize (>= 0.3.9), scales (>= 0.3.0), shiny (>= 1.0.0), Spectra (>= 1.4.3) Imports: ggplot2 (>= 3.2.1), MsCoreUtils (>= 1.9.2), S4Vectors (>= 0.22.0) Suggests: BiocGenerics, graphics (>= 3.5), grDevices (>= 3.5), knitr (>= 1.11), testthat (>= 2.2.1) License: GPL (>= 3) MD5sum: 618304a06f8c948a57868440632d377c 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 , Johannes Rainer and Emmanuel Gaquerel Maintainer: Thomas Naake VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetCirc git_branch: devel git_last_commit: 2699812 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MetCirc_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MetCirc_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MetCirc_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MetCirc_1.35.0.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: 83 Package: methimpute Version: 1.27.0 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: 4f17aeda6c54ed7c973123583beb849b 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methimpute git_branch: devel git_last_commit: 2e2fc1f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/methimpute_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/methimpute_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/methimpute_1.27.0.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: 1.27.0 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: d81f8f6961c6a8a61301368d930ed0a5 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 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: 85df361 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/methInheritSim_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/methInheritSim_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/methInheritSim_1.27.0.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: 1.1.0 Depends: GenomicRanges, ggplot2, R (>= 4.2.0), SummarizedExperiment Imports: BiocParallel, Biostrings, BSgenome, cowplot, data.table, DelayedArray, dplyr, ExperimentHub, foreach, GenomeInfoDb, HDF5Array, IRanges, R.utils, RcppRoll, rhdf5, rtracklayer, S4Vectors, scales, tibble, tidyr Suggests: AnnotationHub, annotatr, BiocStyle, BSgenome.Athaliana.TAIR.TAIR9, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, DESeq2, knitr, methrix, rmarkdown, TumourMethData License: GPL (>= 3) MD5sum: 23156e95d795886dd4d829ca4bb70002 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] () Maintainer: Richard Heery 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: 3aab960 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/methodical_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/methodical_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/methodical_1.1.0.tgz vignettes: vignettes/methodical/inst/doc/calculating_methylation_transcription_correlations.html, vignettes/methodical/inst/doc/identifying_tmrs.html, vignettes/methodical/inst/doc/working_with_meth_rses.html vignetteTitles: calculating_methylation_transcription_correlations, identifying_tmrs, 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/identifying_tmrs.R, vignettes/methodical/inst/doc/working_with_meth_rses.R dependencyCount: 125 Package: MethPed Version: 1.33.0 Depends: R (>= 3.0.0), Biobase Imports: randomForest, grDevices, graphics, stats Suggests: BiocStyle, knitr, markdown, impute License: GPL-2 MD5sum: e739a72a808256eb9fa526b4623b7b71 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethPed git_branch: devel git_last_commit: aa6e410 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MethPed_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MethPed_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MethPed_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MethPed_1.33.0.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: 8 Package: MethReg Version: 1.15.0 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: d726c5c9957499e392bc18461eb15481 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] (), Lily Wang [aut] Maintainer: Tiago Silva VignetteBuilder: knitr BugReports: https://github.com/TransBioInfoLab/MethReg/issues/ git_url: https://git.bioconductor.org/packages/MethReg git_branch: devel git_last_commit: aeb2a7e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MethReg_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MethReg_1.15.0.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: 192 Package: methrix Version: 1.19.0 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 MD5sum: 0193da666cc1b21c62ea36987f3128da 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] (), Reka Toth [aut] (), Rajbir Batra [ctb], Clarissa Feuerstein-Akgöz [ctb], Joschka Hey [ctb], Maximilian Schönung [ctb], Pavlo Lutsik [ctb] Maintainer: Anand Mayakonda 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: 34add75 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/methrix_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/methrix_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/methrix_1.19.0.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: 93 Package: MethTargetedNGS Version: 1.37.0 Depends: R (>= 3.1.2), stringr, seqinr, gplots, Biostrings, pwalign Imports: utils, graphics, stats License: Artistic-2.0 MD5sum: a232b30eca7449e3ab09693e94f8b7ab 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 SystemRequirements: HMMER3 git_url: https://git.bioconductor.org/packages/MethTargetedNGS git_branch: devel git_last_commit: 37e7483 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MethTargetedNGS_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MethTargetedNGS_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MethTargetedNGS_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MethTargetedNGS_1.37.0.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: 1.39.0 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: 738beafbc445a738ab363ed14768f0a9 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethylAid git_branch: devel git_last_commit: 6685685 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MethylAid_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MethylAid_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MethylAid_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MethylAid_1.39.0.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: 166 Package: methylCC Version: 1.19.0 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: 223534bb45a0d67719d63c18e0342a72 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] (), Rafael Irizarry [aut] () Maintainer: Stephanie C. Hicks 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: 9e2f073 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/methylCC_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/methylCC_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/methylCC_1.19.0.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: 155 Package: methylclock Version: 1.11.0 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 Archs: x64 MD5sum: c25183725e88edac0a19b8c7f9635eb0 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] (), Juan R. Gonzalez [aut] () Maintainer: Dolors Pelegri-Siso 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: bb5ccfa git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/methylclock_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/methylclock_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/methylclock_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/methylclock_1.11.0.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: 293 Package: methylGSA Version: 1.23.0 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: 820f8a190cf42d7526121316bf42d67a 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 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: 54e1b7f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/methylGSA_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/methylGSA_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/methylGSA_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/methylGSA_1.23.0.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: 217 Package: methyLImp2 Version: 1.1.0 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: 2d549ded59981a4ec97286cda68d63b4 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] (), Anna Plaksienko [aut, cre] (), Claudia Angelini [aut] (), Christine Nardini [aut] () Maintainer: Anna Plaksienko 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: f5755fe git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/methyLImp2_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/methyLImp2_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/methyLImp2_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/methyLImp2_1.1.0.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: 1.29.0 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: 5de2695cfb825fac26598691ea1bd138 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] (), Pascal Belleau [aut] (), Arnaud Droit [aut] Maintainer: Astrid Deschênes 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: 9bfdd08 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/methylInheritance_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/methylInheritance_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/methylInheritance_1.29.0.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: 1.31.0 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), zlibbioc Suggests: testthat (>= 2.1.0), knitr, rmarkdown, genomation, BiocManager License: Artistic-2.0 MD5sum: 17bd322107a2c3eaed9e0a3268144c56 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 , Alexander Blume 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: 9edbc66 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/methylKit_1.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/methylKit_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/methylKit_1.31.0.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: 2.35.0 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: 78db6cc1566cd8665b35de3d166a3714 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethylMix git_branch: devel git_last_commit: a544e1b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MethylMix_2.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MethylMix_2.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MethylMix_2.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MethylMix_2.35.0.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: 1.43.0 Depends: R (>= 2.12.1), edgeR, statmod License: GPL-3 Archs: x64 MD5sum: 3da5cc17d6c644e1cf05c6adb5a36cc2 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 git_url: https://git.bioconductor.org/packages/methylMnM git_branch: devel git_last_commit: 1ac891a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/methylMnM_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/methylMnM_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/methylMnM_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/methylMnM_1.43.0.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: 12 Package: methylPipe Version: 1.39.0 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: 0a369f7ee79d8a10478c246a0b307273 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methylPipe git_branch: devel git_last_commit: 7fec8ef git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/methylPipe_1.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/methylPipe_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/methylPipe_1.39.0.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: 164 Package: methylscaper Version: 1.13.0 Depends: R (>= 4.1.0) 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 MD5sum: 4b35c37729ee3376c2b13b7fca884738 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: Rhonda Bacher VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methylscaper git_branch: devel git_last_commit: 9bdf1c7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/methylscaper_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/methylscaper_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/methylscaper_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/methylscaper_1.13.0.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: 109 Package: MethylSeekR Version: 1.45.0 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: 18c22317b10f649739f80de66bbd5f8d 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 git_url: https://git.bioconductor.org/packages/MethylSeekR git_branch: devel git_last_commit: 94e0371 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MethylSeekR_1.45.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MethylSeekR_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MethylSeekR_1.45.0.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: 1.17.0 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 MD5sum: e7f3faee4fc1434ac40c2c4d556c8dd7 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 VignetteBuilder: knitr BugReports: https://github.com/sartorlab/methylSig/issues git_url: https://git.bioconductor.org/packages/methylSig git_branch: devel git_last_commit: 0003bd7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/methylSig_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/methylSig_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/methylSig_1.17.0.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: 90 Package: methylumi Version: 2.51.0 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 MD5sum: 6fd9ee74fbdaa9550d8ea0ab2045c96f 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 VignetteBuilder: knitr BugReports: https://github.com/seandavi/methylumi/issues/new git_url: https://git.bioconductor.org/packages/methylumi git_branch: devel git_last_commit: 53b30af git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/methylumi_2.51.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/methylumi_2.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/methylumi_2.51.0.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: 154 Package: MetID Version: 1.23.0 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: 850af76bfe49dfcf3941a28792b4cdde 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 Maintainer: Zhenzhi Li URL: https://github.com/ressomlab/MetID VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetID git_branch: devel git_last_commit: 7c0801f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MetID_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MetID_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MetID_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MetID_1.23.0.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: 133 Package: MetNet Version: 1.23.0 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: 4cff67608b92e3a6bba4b38de6e54db9 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetNet git_branch: devel git_last_commit: 2cea676 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MetNet_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MetNet_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MetNet_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MetNet_1.23.0.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: 1.27.0 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) Archs: x64 MD5sum: dafb928c4c86ba5e873b275d8997977c 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mfa git_branch: devel git_last_commit: 9505ca7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mfa_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mfa_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mfa_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mfa_1.27.0.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: 83 Package: Mfuzz Version: 2.65.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), e1071 Imports: tcltk, tkWidgets Suggests: marray License: GPL-2 MD5sum: 8cb02415fce17ab9ac54083d509d1480 NeedsCompilation: no Title: Soft clustering of time series gene expression data Description: Package for noise-robust soft clustering of gene expression time-series data (including a graphical user interface) biocViews: Microarray, Clustering, TimeCourse, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://mfuzz.sysbiolab.eu/ git_url: https://git.bioconductor.org/packages/Mfuzz git_branch: devel git_last_commit: 0fadc63 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Mfuzz_2.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Mfuzz_2.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Mfuzz_2.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Mfuzz_2.65.0.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, TOmicsVis suggestsMe: DAPAR, pwOmics, pctax dependencyCount: 16 Package: MGFM Version: 1.39.0 Depends: AnnotationDbi,annotate Suggests: hgu133a.db License: GPL-3 MD5sum: e1b40d1be43b50a4fdd355662bce743f 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 git_url: https://git.bioconductor.org/packages/MGFM git_branch: devel git_last_commit: aca356c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MGFM_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MGFM_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MGFM_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MGFM_1.39.0.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: 1.31.0 Depends: R (>= 3.5) Imports: biomaRt, annotate License: GPL-3 MD5sum: e24a0a2107520a48286ea7f1882e5b5c 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 git_url: https://git.bioconductor.org/packages/MGFR git_branch: devel git_last_commit: 7d1990f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MGFR_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MGFR_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MGFR_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MGFR_1.31.0.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: 72 Package: MGnifyR Version: 1.1.0 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 MD5sum: b9509d48610e3edd2db691195060fa8a 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] (), Ben Allen [aut], Leo Lahti [aut] () Maintainer: Tuomas Borman 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: e8c773f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MGnifyR_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MGnifyR_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MGnifyR_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MGnifyR_1.1.0.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 dependencyCount: 145 Package: mgsa Version: 1.53.0 Depends: R (>= 2.14.0), methods, gplots Imports: graphics, stats, utils Suggests: DBI, RSQLite, GO.db, testthat License: Artistic-2.0 Archs: x64 MD5sum: d4dc9be5c3796be05d1dcd5af34248c4 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 , Julien Gagneur Maintainer: Sebastian Bauer URL: https://github.com/sba1/mgsa-bioc git_url: https://git.bioconductor.org/packages/mgsa git_branch: devel git_last_commit: 238e320 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mgsa_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mgsa_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mgsa_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mgsa_1.53.0.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 suggestsMe: pareg dependencyCount: 9 Package: mia Version: 1.13.0 Depends: R (>= 4.0), SummarizedExperiment, SingleCellExperiment, TreeSummarizedExperiment (>= 1.99.3), MultiAssayExperiment Imports: methods, stats, utils, MASS, ape, decontam, vegan, BiocGenerics, S4Vectors, IRanges, Biostrings, DECIPHER, BiocParallel, DelayedArray, DelayedMatrixStats, scuttle, scater, DirichletMultinomial, rlang, dplyr, tibble, tidyr, bluster, MatrixGenerics Suggests: testthat, knitr, patchwork, BiocStyle, yaml, phyloseq, dada2, stringr, biomformat, reldist, ade4, microbiomeDataSets, rmarkdown License: Artistic-2.0 | file LICENSE MD5sum: a714b70e06c4e448aff20ede2e3f57e2 NeedsCompilation: no 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: Felix G.M. Ernst [aut] (), Sudarshan A. Shetty [aut] (), Tuomas Borman [aut, cre] (), Leo Lahti [aut] (), 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] Maintainer: Tuomas Borman 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: f257732 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mia_1.13.0.tar.gz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mia_1.13.0.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: ANCOMBC, dar, MGnifyR, curatedMetagenomicData suggestsMe: CBEA, miaSim, philr, MicrobiomeBenchmarkData, MiscMetabar dependencyCount: 140 Package: miaSim Version: 1.11.0 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: a403b7d8c1eff01cb58c9141b9b03ed1 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] (), Leo Lahti [aut] () Maintainer: Yagmur Simsek 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: 20f6cb7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/miaSim_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/miaSim_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/miaSim_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/miaSim_1.11.0.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: 83 Package: miaViz Version: 1.13.0 Depends: R (>= 4.0), SummarizedExperiment, TreeSummarizedExperiment, mia (>= 0.99), ggplot2, ggraph (>= 2.0) Imports: methods, stats, S4Vectors, BiocGenerics, BiocParallel, DelayedArray, scater, ggtree, ggnewscale, viridis, tibble, tidytree, tidygraph, rlang, purrr, tidyr, dplyr, ape, DirichletMultinomial, ggrepel, SingleCellExperiment Suggests: knitr, rmarkdown, BiocStyle, testthat, patchwork, vegan, microbiomeDataSets, bluster License: Artistic-2.0 | file LICENSE MD5sum: 3beb02203408208e5413b968b05028fe 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] (), Felix G.M. Ernst [aut] (), Leo Lahti [aut] (), Basil Courbayre [ctb], Giulio Benedetti [ctb] () Maintainer: Tuomas Borman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miaViz git_branch: devel git_last_commit: 47ad304 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/miaViz_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/miaViz_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/miaViz_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/miaViz_1.13.0.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 suggestsMe: MGnifyR, miaSim dependencyCount: 155 Package: MiChip Version: 1.59.0 Depends: R (>= 2.3.0), Biobase Imports: Biobase License: GPL (>= 2) MD5sum: 8b2024482ae7bc8961d0bb17abd5175c 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 Maintainer: Jonathon Blake git_url: https://git.bioconductor.org/packages/MiChip git_branch: devel git_last_commit: 6c54628 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MiChip_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MiChip_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MiChip_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MiChip_1.59.0.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: 6 Package: microbiome Version: 1.27.0 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 MD5sum: 481dd030ee5a19a168bdb747a4f37326 NeedsCompilation: no Title: Microbiome Analytics Description: Utilities for microbiome analysis. biocViews: Metagenomics,Microbiome,Sequencing,SystemsBiology Author: Leo Lahti [aut, cre] (), Sudarshan Shetty [aut] () Maintainer: Leo Lahti 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: b216965 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/microbiome_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/microbiome_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/microbiome_1.27.0.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: MicrobiomeSurv suggestsMe: ANCOMBC, dar dependencyCount: 94 Package: microbiomeDASim Version: 1.19.0 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: 38ea779ba84bbf40f89e9e901113393b 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 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: c98c3a6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/microbiomeDASim_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/microbiomeDASim_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/microbiomeDASim_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/microbiomeDASim_1.19.0.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: 1.15.0 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: e14f6405b36574173ced22e29ffe8b6a 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/microbiomeExplorer git_branch: devel git_last_commit: 3036201 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/microbiomeExplorer_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/microbiomeExplorer_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/microbiomeExplorer_1.15.0.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: 198 Package: microbiomeMarker Version: 1.11.0 Depends: R (>= 4.1.0) Imports: dplyr, phyloseq, magrittr, purrr, MASS, utils, ggplot2, tibble, rlang, stats, coin, ggtree, tidytree, methods, IRanges, tidyr, patchwork, ggsignif, metagenomeSeq, DESeq2, edgeR, BiocGenerics, Biostrings, yaml, biomformat, S4Vectors, Biobase, ComplexHeatmap, ANCOMBC, caret, limma, ALDEx2, multtest, plotROC, vegan, pROC, BiocParallel Suggests: testthat, covr, glmnet, Matrix, kernlab, e1071, ranger, knitr, rmarkdown, BiocStyle, withr License: GPL-3 MD5sum: ec02d8143ecc10c7c0c8dcb6067da620 NeedsCompilation: no Title: microbiome biomarker analysis toolkit Description: To date, a number of methods have been developed for microbiome marker discovery based on metagenomic profiles, e.g. LEfSe. However, all of these methods have its own advantages and disadvantages, and none of them is considered standard or universal. Moreover, different programs or softwares may be development using different programming languages, even in different operating systems. Here, we have developed an all-in-one R package microbiomeMarker that integrates commonly used differential analysis methods as well as three machine learning-based approaches, including Logistic regression, Random forest, and Support vector machine, to facilitate the identification of microbiome markers. biocViews: Metagenomics, Microbiome, DifferentialExpression Author: Yang Cao [aut, cre] Maintainer: Yang Cao URL: https://github.com/yiluheihei/microbiomeMarker VignetteBuilder: knitr BugReports: https://github.com/yiluheihei/microbiomeMarker/issues git_url: https://git.bioconductor.org/packages/microbiomeMarker git_branch: devel git_last_commit: 0197460 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/microbiomeMarker_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/microbiomeMarker_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/microbiomeMarker_1.11.0.tgz vignettes: vignettes/microbiomeMarker/inst/doc/microbiomeMarker-vignette.html vignetteTitles: Tools for microbiome marker identification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/microbiomeMarker/inst/doc/microbiomeMarker-vignette.R dependencyCount: 312 Package: MicrobiomeProfiler Version: 1.11.0 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 Suggests: rmarkdown, knitr, testthat (>= 3.0.0), prettydoc License: GPL-2 MD5sum: 52ca77737a83dc6d1db984fe26ba01b0 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 [aut, ths] (), Meijun Chen [aut, cre] () Maintainer: Meijun Chen URL: https://github.com/YuLab-SMU/MicrobiomeProfiler/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/MicrobiomeProfiler/issues git_url: https://git.bioconductor.org/packages/MicrobiomeProfiler git_branch: devel git_last_commit: 0b41aed git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MicrobiomeProfiler_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MicrobiomeProfiler_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MicrobiomeProfiler_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MicrobiomeProfiler_1.11.0.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: 161 Package: MicrobiotaProcess Version: 1.17.0 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: d3d832872a73d41f923accea7957fefd 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] (), Guangchuang Yu [aut, ctb] () Maintainer: Shuangbin Xu 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: b7c0468 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MicrobiotaProcess_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MicrobiotaProcess_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MicrobiotaProcess_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MicrobiotaProcess_1.17.0.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 dependencyCount: 113 Package: microRNA Version: 1.63.0 Depends: R (>= 2.10) Imports: Biostrings (>= 2.11.32) License: Artistic-2.0 Archs: x64 MD5sum: 477c995b1250ef05a485509f1a5c7b10 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: "James F. Reid" git_url: https://git.bioconductor.org/packages/microRNA git_branch: devel git_last_commit: f57ed04 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/microRNA_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/microRNA_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/microRNA_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/microRNA_1.63.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: rtracklayer dependencyCount: 25 Package: microSTASIS Version: 1.5.0 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: ca9dfb5b0bb66d7fc80ab37526ec8f25 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] (), Alfonso Benítez-Páez [aut] () Maintainer: Pedro Sánchez-Sánchez 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: 00fe97a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/microSTASIS_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/microSTASIS_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/microSTASIS_1.5.0.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: 94 Package: MICSQTL Version: 1.3.0 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: 51672946f5bd28b033b310a64bbc949d 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, cre] (), Qian Li [aut], Iain Carmichael [ctb] Maintainer: Yue Pan 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: 4f19661 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MICSQTL_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MICSQTL_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MICSQTL_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MICSQTL_1.3.0.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: 166 Package: midasHLA Version: 1.13.0 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: ccdedd5d5b619dfef6b15df8d49ba045 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/midasHLA git_branch: devel git_last_commit: 1e62016 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/midasHLA_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/midasHLA_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/midasHLA_1.13.0.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: 153 Package: miloR Version: 2.1.0 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, numDeriv LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, mvtnorm, scater, scran, covr, knitr, rmarkdown, uwot, scuttle, BiocStyle, MouseGastrulationData, MouseThymusAgeing, magick, RCurl, MASS, curl, scRNAseq, graphics License: GPL-3 + file LICENSE MD5sum: 11182a1f2395d8c29a4c07e0390a14c9 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] (), Emma Dann [aut, ctb] Maintainer: Mike Morgan 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: 15c14c9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/miloR_2.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/miloR_2.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/miloR_2.1.0.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 dependencyCount: 115 Package: mimager Version: 1.29.0 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 MD5sum: e7c3d394293c9362bb13b8fc7594dc38 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 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: 087a076 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mimager_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mimager_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mimager_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mimager_1.29.0.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: 1.13.0 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 Archs: x64 MD5sum: bc49f315378861e1583e159e7bb167ae 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 VignetteBuilder: knitr BugReports: https://github.com/Guan06/mina git_url: https://git.bioconductor.org/packages/mina git_branch: devel git_last_commit: 07cc1c7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mina_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mina_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mina_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mina_1.13.0.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: 1.45.0 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: 5c38b89bfa3c24e06da3b4d9fee76b18 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 git_url: https://git.bioconductor.org/packages/MineICA git_branch: devel git_last_commit: 678d64f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MineICA_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MineICA_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MineICA_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MineICA_1.45.0.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: 217 Package: minet Version: 3.63.0 Imports: infotheo License: Artistic-2.0 Archs: x64 MD5sum: 001f864ffcb6dbc589aed1cfaf374ae4 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 URL: http://minet.meyerp.com git_url: https://git.bioconductor.org/packages/minet git_branch: devel git_last_commit: ec1ccbc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/minet_3.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/minet_3.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/minet_3.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/minet_3.63.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BUS, geNetClassifier, netresponse importsMe: BioNERO, epiNEM, RTN, TCGAWorkflow, TGS suggestsMe: CNORfeeder, TCGAbiolinks, WGCNA dependencyCount: 1 Package: minfi Version: 1.51.0 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: 46731dfc49ab4cb793263f4c03a4c02c 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 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: eeda660 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/minfi_1.51.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/minfi_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/minfi_1.51.0.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, 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, BeadSorted.Saliva.EPIC, FlowSorted.Blood.450k, FlowSorted.Blood.EPIC, FlowSorted.CordBlood.450k, FlowSorted.CordBloodCombined.450k, FlowSorted.CordBloodNorway.450k, FlowSorted.DLPFC.450k, minfiData, minfiDataEPIC, methylationArrayAnalysis 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: 139 Package: MinimumDistance Version: 1.49.0 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 MD5sum: d00075ed93abdca143dc25d9ae5fe13c 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 git_url: https://git.bioconductor.org/packages/MinimumDistance git_branch: devel git_last_commit: 9a42cf5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MinimumDistance_1.49.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MinimumDistance_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MinimumDistance_1.49.0.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: 1.77.0 Depends: R (>= 2.4) Imports: Biobase, e1071, MASS, stats License: GPL (>= 2) MD5sum: 7a1d11f0a53fcbe83f0b19f9d7ed8d95 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 , Sukwoo Kim , Mat Soukup , and Jae K. Lee Maintainer: Sukwoo Kim URL: http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/ git_url: https://git.bioconductor.org/packages/MiPP git_branch: devel git_last_commit: 4d82439 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MiPP_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MiPP_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MiPP_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MiPP_1.77.0.tgz vignettes: vignettes/MiPP/inst/doc/MiPP.pdf vignetteTitles: MiPP Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 11 Package: miQC Version: 1.13.0 Depends: R (>= 3.5.0) Imports: SingleCellExperiment, flexmix, ggplot2, splines Suggests: scRNAseq, scater, BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 2391a01550ec9b59b9d26173553cec04 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 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: 384be38 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/miQC_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/miQC_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/miQC_1.13.0.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: 1.27.0 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: 4ba889cad40f14abe4eac13c7e4d779c 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 [aut], Christoph Bock [ctb], John Lawson [aut, cre] Maintainer: John Lawson 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: 0586e79 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MIRA_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MIRA_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MIRA_1.27.0.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: 103 Package: MiRaGE Version: 1.47.0 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: e9bd0fe4c6bee3b94996f69a12eece31 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 Maintainer: Y-h. Taguchi git_url: https://git.bioconductor.org/packages/MiRaGE git_branch: devel git_last_commit: c2461b2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MiRaGE_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MiRaGE_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MiRaGE_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MiRaGE_1.47.0.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: miRcomp Version: 1.35.0 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: f0ec76d4d0b07f5024fa8c0ce4c1017c 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 , Lauren Kemperman Maintainer: Matthew N. McCall VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRcomp git_branch: devel git_last_commit: 54a639a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/miRcomp_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/miRcomp_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/miRcomp_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/miRcomp_1.35.0.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: 8 Package: mirIntegrator Version: 1.35.0 Depends: R (>= 3.3) Imports: graph,ROntoTools, ggplot2, org.Hs.eg.db, AnnotationDbi, Rgraphviz Suggests: RUnit, BiocGenerics License: GPL (>=3) MD5sum: f378906b0cb6bbf31616e1e9ee5131ab 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 Maintainer: Diana Diaz URL: http://datad.github.io/mirIntegrator/ git_url: https://git.bioconductor.org/packages/mirIntegrator git_branch: devel git_last_commit: 64c6ce0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mirIntegrator_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mirIntegrator_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mirIntegrator_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mirIntegrator_1.35.0.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: 1.1.0 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, readxl, 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: 7eefa400f561dc2ab21d0fda309f8517 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] (), Maria Foti [fnd] () Maintainer: Jacopo Ronchi 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: 3abb821 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MIRit_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MIRit_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MIRit_0.99.13.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: 165 Package: miRLAB Version: 1.35.0 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: 1f2fac703b1b8c46ccfd7cb9f77c5ce5 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 URL: https://github.com/pvvhoang/miRLAB VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRLAB git_branch: devel git_last_commit: 71301a0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/miRLAB_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/miRLAB_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/miRLAB_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/miRLAB_1.35.0.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: 197 Package: miRNAmeConverter Version: 1.33.0 Depends: miRBaseVersions.db Imports: DBI, AnnotationDbi, reshape2 Suggests: methods, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: 9129043a58e3eb4d69a0c1b09ef211e0 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRNAmeConverter git_branch: devel git_last_commit: b9b0f1b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/miRNAmeConverter_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/miRNAmeConverter_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/miRNAmeConverter_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/miRNAmeConverter_1.33.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 53 Package: miRNApath Version: 1.65.0 Depends: methods, R(>= 2.7.0) License: LGPL-2.1 MD5sum: 49df2d36ea94c51fa2ff1b21c8718223 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 with contributions from Yunling Shi, Cindy Richards, John P. Cogswell Maintainer: James M. Ward git_url: https://git.bioconductor.org/packages/miRNApath git_branch: devel git_last_commit: 33c05f0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/miRNApath_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/miRNApath_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/miRNApath_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/miRNApath_1.65.0.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: 1.39.0 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 MD5sum: 4aa6a1d8719e36d103833cb0173acfe2 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 git_url: https://git.bioconductor.org/packages/miRNAtap git_branch: devel git_last_commit: 603e51b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/miRNAtap_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/miRNAtap_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/miRNAtap_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/miRNAtap_1.39.0.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: 2.1.0 Depends: R (>= 3.5.0) 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: dd33a44c981aced58158bbc371df0e3e 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 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: 059eed1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/miRSM_2.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/miRSM_2.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/miRSM_2.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/miRSM_2.1.0.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: 261 Package: mirTarRnaSeq Version: 1.13.0 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 Archs: x64 MD5sum: 00fd026e362bb6055481c77a9500c74d 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] (), Sarah Morton [aut], Rafael Irizarry [aut], Jeffrey Bailey [aut], Joseph N Paulson [aut] Maintainer: Mercedeh Movassagh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mirTarRnaSeq git_branch: devel git_last_commit: faa833a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mirTarRnaSeq_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mirTarRnaSeq_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mirTarRnaSeq_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mirTarRnaSeq_1.13.0.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: 1.39.0 Depends: R (>= 3.6.0), IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 Imports: AnnotationDbi, BiasedUrn, Biobase, BiocGenerics, GenomicRanges, GO.db, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICmanifest, 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: e5c94035d568efd484e13f1b24a5613a 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 , Jovana Maksimovic , Andrew Lonsdale VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/missMethyl git_branch: devel git_last_commit: f9bce79 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/missMethyl_1.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/missMethyl_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/missMethyl_1.39.0.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 dependsOnMe: methylationArrayAnalysis importsMe: DMRcate, MEAL, methylGSA suggestsMe: RnBeads dependencyCount: 163 Package: missRows Version: 1.25.0 Depends: R (>= 3.5), methods, ggplot2, grDevices, MultiAssayExperiment Imports: plyr, stats, gtools, S4Vectors Suggests: BiocStyle, knitr, testthat License: Artistic-2.0 Archs: x64 MD5sum: c28bbd5cda2cd67a02ecfe592fe97654 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/missRows git_branch: devel git_last_commit: 3e94be6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/missRows_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/missRows_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/missRows_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/missRows_1.25.0.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: 75 Package: mistyR Version: 1.13.0 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: 04f81c98ec612d5b97f9d386111af20d 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] (), Ricardo Omar Ramirez Flores [ctb] (), Philipp Schäfer [ctb] Maintainer: Jovan Tanevski 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: 142139d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mistyR_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mistyR_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mistyR_1.13.0.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: 1.17.1 Depends: R (>= 4.3) 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: 33ff5276650ac542a5c2a3da0ddbcce3 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] (), Antony Kaspi [aut, cph] Maintainer: Mark Ziemann URL: https://github.com/markziemann/mitch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mitch git_branch: devel git_last_commit: 702c79a git_last_commit_date: 2024-05-03 Date/Publication: 2024-05-03 source.ver: src/contrib/mitch_1.17.1.tar.gz win.binary.ver: bin/windows/contrib/4.4/mitch_1.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mitch_1.17.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mitch_1.17.1.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: 112 Package: mitoClone2 Version: 1.11.0 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: 03f79e899def791ed1fa50e32b37553c 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 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: 191fc47 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mitoClone2_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mitoClone2_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mitoClone2_1.11.0.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: 6.29.0 Depends: R (>= 3.5.0), MASS, lattice, ggplot2 Imports: igraph, ellipse, corpcor, RColorBrewer, parallel, dplyr, tidyr, reshape2, methods, matrixStats, rARPACK, gridExtra, grDevices, graphics, stats, ggrepel, BiocParallel, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, rgl License: GPL (>= 2) MD5sum: 383db87762fa5b6a0bab01441853a872 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, cre], Max Bladen [ctb], Benoit Gautier [ctb], Francois Bartolo [ctb], Pierre Monget [ctb], Jeff Coquery [ctb], FangZou Yao [ctb], Benoit Liquet [ctb] Maintainer: Max Bladen 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: 3eefa3c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mixOmics_6.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mixOmics_6.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mixOmics_6.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mixOmics_6.29.0.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, MetabolomicsBasics, pctax, RVAideMemoire, SelectBoost, sharp dependencyCount: 65 Package: MLInterfaces Version: 1.85.0 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: 207049374ca994e4ac0ffd46e9d24cf0 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] (), Jess Mar [aut], Jason Vertrees [ctb], Laurent Gatto [ctb], Phylis Atieno [ctb] (Translated vignettes from Sweave to Rmarkdown / HTML.) Maintainer: Vincent Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MLInterfaces git_branch: devel git_last_commit: 8cc43e4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MLInterfaces_1.85.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MLInterfaces_1.85.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MLInterfaces_1.85.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MLInterfaces_1.85.0.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: 124 Package: MLP Version: 1.53.0 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: 9e154b3a9eb3f7fc09cd4194494814b8 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 git_url: https://git.bioconductor.org/packages/MLP git_branch: devel git_last_commit: 1ea2edc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MLP_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MLP_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MLP_1.53.0.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: 2.23.0 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) MD5sum: 2f4bdea99c88fa1479cfe57545061bfd 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MLSeq git_branch: devel git_last_commit: 695f40c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MLSeq_2.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MLSeq_2.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MLSeq_2.23.0.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: 146 Package: MMDiff2 Version: 1.33.0 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: e255b6bfba6f7c57889772d8b3ec00aa 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MMDiff2 git_branch: devel git_last_commit: dff78f4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MMDiff2_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MMDiff2_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MMDiff2_1.33.0.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: 1.19.0 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: d7cb6294a938a47c5d4e6854e68e3e68 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 SystemRequirements: glpk (>= 4.57) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MMUPHin git_branch: devel git_last_commit: c7fd998 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MMUPHin_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MMUPHin_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MMUPHin_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MMUPHin_1.19.0.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: 149 Package: mnem Version: 1.21.0 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: d5eef27c190a12189925a6a8fb677ea5 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 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: 4056d13 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mnem_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mnem_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mnem_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mnem_1.21.0.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: 82 Package: moanin Version: 1.13.0 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: 1f2fea78a91693db628815d17e823cd0 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] (), Nelle Varoquaux [aut, cre] () Maintainer: Nelle Varoquaux VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/moanin git_branch: devel git_last_commit: 05b8ab7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/moanin_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/moanin_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/moanin_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/moanin_1.13.0.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: 1.1.0 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: 16d3cf8690153fa78f6ba3f3675b4c8c 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] (), Marco Catoni [aut] () Maintainer: Katie Jeynes-Cupper 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: 8406245 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mobileRNA_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mobileRNA_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mobileRNA_1.1.0.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: 137 Package: MODA Version: 1.31.0 Depends: R (>= 3.3) Imports: grDevices, graphics, stats, utils, WGCNA, dynamicTreeCut, igraph, cluster, AMOUNTAIN, RColorBrewer Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) Archs: x64 MD5sum: 3dc38d58a6a41e46f04b3f1fe84c084c 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 git_url: https://git.bioconductor.org/packages/MODA git_branch: devel git_last_commit: c5caa1c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MODA_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MODA_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MODA_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MODA_1.31.0.tgz vignettes: vignettes/MODA/inst/doc/MODA.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 116 Package: ModCon Version: 1.13.0 Depends: data.table, parallel, utils, stats, R (>= 4.1) Suggests: testthat, knitr, rmarkdown, dplyr, shinycssloaders, shiny, shinyFiles, shinydashboard, shinyjs License: GPL-3 + file LICENSE MD5sum: 0b7c96239b6a600027f6921ba77ad497 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] () Maintainer: Johannes Ptok URL: https://github.com/caggtaagtat/ModCon SystemRequirements: Perl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ModCon git_branch: devel git_last_commit: eec0043 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ModCon_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ModCon_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ModCon_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ModCon_1.13.0.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: 1.21.0 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: d90044b22240d8c993b667c0ede49b0b 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] (), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/Modstrings/issues git_url: https://git.bioconductor.org/packages/Modstrings git_branch: devel git_last_commit: 9f311d1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Modstrings_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Modstrings_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Modstrings_1.21.0.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: 1.15.0 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: 14f7642ec741258a0890e93d929cb379 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] (), Damien Arnol [aut] (), Danila Bredikhin [aut] (), Britta Velten [aut] () Maintainer: Ricard Argelaguet 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: 591d80c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MOFA2_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MOFA2_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MOFA2_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MOFA2_1.15.0.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 dependencyCount: 92 Package: MOGAMUN Version: 1.15.0 Imports: stats, utils, RCy3, stringr, graphics, grDevices, RUnit, BiocParallel, igraph Suggests: knitr, markdown License: GPL-3 + file LICENSE MD5sum: 87cef22a2eb5663443b465cc25081028 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-María Novoa-del-Toro [aut, cre] () Maintainer: Elva-María Novoa-del-Toro URL: https://github.com/elvanov/MOGAMUN VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MOGAMUN git_branch: devel git_last_commit: 07870a5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MOGAMUN_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MOGAMUN_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MOGAMUN_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MOGAMUN_1.15.0.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: 1.39.0 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: 4259cf0cefb6af40a312f3adb7b25adf 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mogsa git_branch: devel git_last_commit: f4d1b5d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mogsa_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mogsa_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mogsa_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mogsa_1.39.0.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: 1.5.0 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 Suggests: knitr, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: f9e232c797270d095c69f8aec8a1cf46 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 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: 27e270d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MoleculeExperiment_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MoleculeExperiment_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MoleculeExperiment_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MoleculeExperiment_1.5.0.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: 126 Package: MOMA Version: 1.17.0 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: c129fb7930b4103d0a4c9bc1be48b44b 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 VignetteBuilder: knitr BugReports: https://github.com/califano-lab/MOMA/issues git_url: https://git.bioconductor.org/packages/MOMA git_branch: devel git_last_commit: f2d9ee4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MOMA_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MOMA_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MOMA_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MOMA_1.17.0.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: 105 Package: monaLisa Version: 1.11.0 Depends: R (>= 4.1) Imports: methods, stats, utils, grDevices, graphics, BiocGenerics, GenomicRanges, TFBSTools, Biostrings, IRanges, stabs, BSgenome, glmnet, S4Vectors, SummarizedExperiment, BiocParallel, grid, circlize, ComplexHeatmap (>= 2.11.1), XVector, GenomeInfoDb, tools, vioplot, RSQLite Suggests: JASPAR2020, JASPAR2024, BSgenome.Mmusculus.UCSC.mm10, TxDb.Mmusculus.UCSC.mm10.knownGene, knitr, rmarkdown, testthat, BiocStyle, gridExtra License: GPL (>= 3) MD5sum: 169edb4cc316278d1c5d40ddc5091f6e 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] (), Lukas Burger [aut], Charlotte Soneson [aut] (), Michael Stadler [aut, cre] () Maintainer: Michael Stadler 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: bc6b292 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/monaLisa_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/monaLisa_1.11.0.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: 144 Package: monocle Version: 2.33.0 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: 460bd1729037bf3a30b37a933ef1ce84 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/monocle git_branch: devel git_last_commit: 440f0f6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/monocle_2.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/monocle_2.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/monocle_2.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/monocle_2.33.0.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, grandR, Seurat dependencyCount: 78 Package: Moonlight2R Version: 1.3.0 Depends: R (>= 4.3), 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 Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), devtools, roxygen2, png License: GPL-3 MD5sum: 350c59cc6afb7ea6c66016f9c587b4b4 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. biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Pathways, Network, Survival, GeneSetEnrichment, NetworkEnrichment Author: Mona Nourbakhsh [aut], Astrid Saksager [aut], Nikola Tom [aut], Xi Steven Chen [aut], Antonio Colaprico [aut], Catharina Olsen [aut], Matteo Tiberti [cre, aut], Elena Papaleo [aut] Maintainer: Matteo Tiberti 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: d5b0796 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Moonlight2R_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Moonlight2R_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Moonlight2R_1.3.0.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: 225 Package: MoonlightR Version: 1.31.0 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: 9b2f65bfed1b7dead4a04fa8a1c0556e 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 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: bba1442 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MoonlightR_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MoonlightR_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MoonlightR_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MoonlightR_1.31.0.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: 194 Package: mosaics Version: 2.43.0 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: ba20983280ee7b5d788c6ca13ce2f202 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 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: 9ffe768 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mosaics_2.43.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mosaics_2.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mosaics_2.43.0.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: 1.11.0 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: 3b0bd5b58a8f8db644c73367b9ed2521 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 SystemRequirements: C++17, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mosbi git_branch: devel git_last_commit: 7d8c0c3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mosbi_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mosbi_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mosbi_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mosbi_1.11.0.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: MOSim Version: 2.1.0 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 MD5sum: e7b681762e888b6a929c7774b0a478bf 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 Martínez [aut], Sonia Tarazona [cre, aut] Maintainer: Sonia Tarazona 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: 4a2f421 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MOSim_2.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MOSim_2.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MOSim_2.1.0.tgz vignettes: vignettes/MOSim/inst/doc/MOSim.pdf, vignettes/MOSim/inst/doc/scMOSim.html vignetteTitles: MOSim, Wiki of how to use scMOSim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MOSim/inst/doc/MOSim.R, vignettes/MOSim/inst/doc/scMOSim.R dependencyCount: 199 Package: Motif2Site Version: 1.9.0 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: 3073acb0dbf38cee9e6836aaa6e0718b 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] () Maintainer: Peyman Zarrineh VignetteBuilder: knitr BugReports: https://github.com/ManchesterBioinference/Motif2Site/issues git_url: https://git.bioconductor.org/packages/Motif2Site git_branch: devel git_last_commit: 69abc37 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Motif2Site_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Motif2Site_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Motif2Site_1.9.0.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: 126 Package: motifbreakR Version: 2.19.0 Depends: R (>= 4.3.0), 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 Suggests: BSgenome.Hsapiens.UCSC.hg19, SNPlocs.Hsapiens.dbSNP155.GRCh37, knitr, rmarkdown, BSgenome.Drerio.UCSC.danRer7, BiocStyle License: GPL-2 MD5sum: 23453fc1720d44fd786c24d7ee84e6a1 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], Dennis J. Hazelett [aut] Maintainer: Simon Gert Coetzee VignetteBuilder: knitr BugReports: https://github.com/Simon-Coetzee/motifbreakR/issues git_url: https://git.bioconductor.org/packages/motifbreakR git_branch: devel git_last_commit: b35745f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/motifbreakR_2.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/motifbreakR_2.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/motifbreakR_2.19.0.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: 186 Package: motifcounter Version: 1.29.0 Depends: R(>= 3.0) Imports: Biostrings, methods Suggests: knitr, rmarkdown, testthat, MotifDb, seqLogo, prettydoc License: GPL-2 MD5sum: 70aba027c681f9fb3a7c1f71018cb056 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifcounter git_branch: devel git_last_commit: a85abf5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/motifcounter_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/motifcounter_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/motifcounter_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/motifcounter_1.29.0.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: 1.47.0 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: c8c3af0f68874ceb0a4f2cddf3229dcc 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 VignetteBuilder: knitr, rmarkdown, formatR, markdown git_url: https://git.bioconductor.org/packages/MotifDb git_branch: devel git_last_commit: eff8f41 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MotifDb_1.47.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MotifDb_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MotifDb_1.47.0.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, TFutils, universalmotif, vtpnet dependencyCount: 60 Package: motifmatchr Version: 1.27.0 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 MD5sum: 4bcf531e65b92512b42032b851b0352e 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 SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifmatchr git_branch: devel git_last_commit: dec735a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/motifmatchr_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/motifmatchr_1.27.0.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: ATACCoGAPS, ATACseqTFEA, enhancerHomologSearch, epiregulon, esATAC, pageRank, spatzie suggestsMe: chromVAR, GRaNIE, MethReg, CAGEWorkflow, MOCHA, Signac dependencyCount: 127 Package: motifStack Version: 1.49.1 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: 6edcc483d647b39124c7cb38ed856c02 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifStack git_branch: devel git_last_commit: c30ea4e git_last_commit_date: 2024-05-06 Date/Publication: 2024-05-06 source.ver: src/contrib/motifStack_1.49.1.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/motifStack_1.49.1.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, TCGAWorkflow suggestsMe: ChIPpeakAnno, TFutils, trackViewer, tripr, universalmotif dependencyCount: 146 Package: motifTestR Version: 1.1.0 Depends: Biostrings, GenomicRanges, ggplot2 (>= 3.5.0), R (>= 4.3.0), Imports: GenomeInfoDb, harmonicmeanp, IRanges, matrixStats, methods, parallel, patchwork, rlang, S4Vectors, stats, universalmotif Suggests: AnnotationHub, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, extraChIPs, ggdendro, knitr, MotifDb, rmarkdown, rtracklayer, testthat (>= 3.0.0) License: GPL-3 MD5sum: 789d17e0168a5d278b6468024c27f224 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] () Maintainer: Stevie Pederson 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: 750f691 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/motifTestR_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/motifTestR_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/motifTestR_1.1.0.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: 1.15.0 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: a167494b2780a9aab2e122ac3f30ccd2 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] (), Inken Wohlers [aut] (), Hauke Busch [aut] () Maintainer: Matthias Munz VignetteBuilder: knitr BugReports: https://github.com/matmu/MouseFM/issues git_url: https://git.bioconductor.org/packages/MouseFM git_branch: devel git_last_commit: 120d04a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MouseFM_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MouseFM_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MouseFM_1.15.0.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: 95 Package: MPFE Version: 1.41.0 License: GPL (>= 3) Archs: x64 MD5sum: 3f5c7d383938fe01528e7309884444f0 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 git_url: https://git.bioconductor.org/packages/MPFE git_branch: devel git_last_commit: f730497 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MPFE_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MPFE_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MPFE_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MPFE_1.41.0.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: 1.27.0 Depends: R (>= 3.5.0), methods, BiocGenerics, SummarizedExperiment, limma Imports: S4Vectors, scales, stats, graphics, statmod Suggests: BiocStyle, knitr, rmarkdown, RUnit License: Artistic-2.0 Archs: x64 MD5sum: 5032c5f50d89cff1275c1f7e5aba03b5 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 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: 70b460f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mpra_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mpra_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mpra_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mpra_1.27.0.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: 1.23.0 Imports: BiocParallel, methods, progress, stats, SummarizedExperiment Suggests: knitr License: GPL-3 MD5sum: a6d056ac7c05ce3b160561da633efc16 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 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: ac38efc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MPRAnalyze_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MPRAnalyze_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MPRAnalyze_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MPRAnalyze_1.23.0.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: 1.37.0 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: a8c83bd8cc81e28a6ebddfe6e68846fa 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 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: dc8b00f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/msa_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/msa_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/msa_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/msa_1.37.0.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, bio3d, datelife dependencyCount: 26 Package: MSA2dist Version: 1.9.0 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: 146f0c27a51c3cd51f7cf8d68cfcdc9d 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 calcualtions which can be adapted to any scoring for DNA or AA characters. E.g. by using literal distances MSA2dist calculates pairwise IUPAC distances. biocViews: Alignment, Sequencing, Genetics, GO Author: Kristian K Ullrich [aut, cre] () Maintainer: Kristian K Ullrich 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: 98bd32e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MSA2dist_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MSA2dist_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MSA2dist_1.9.0.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: 66 Package: MsBackendMassbank Version: 1.13.0 Depends: R (>= 4.0), Spectra (>= 1.9.12) 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: 33e7495edb419153ff519a1d03809a23 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] (), Johannes Rainer [aut] (), Michael Stravs [ctb] Maintainer: RforMassSpectrometry Package Maintainer 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: a55a389 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MsBackendMassbank_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MsBackendMassbank_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MsBackendMassbank_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MsBackendMassbank_1.13.0.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: 30 Package: MsBackendMgf Version: 1.13.0 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: ec545303874627342c4cebe0fce58fb0 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] (), Johannes Rainer [aut] (), Sebastian Gibb [aut] (), Michael Witting [ctb] (), Adriano Rutz [ctb] () Maintainer: RforMassSpectrometry Package Maintainer 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: f1bdb81 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MsBackendMgf_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MsBackendMgf_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MsBackendMgf_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MsBackendMgf_1.13.0.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: 29 Package: MsBackendMsp Version: 1.9.0 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 MD5sum: e82cf57a03f3f58d8cc9c8cb0b648197 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] (), Johannes Rainer [aut, cre] (), Michael Witting [ctb] () Maintainer: Johannes Rainer 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: ebb4196 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MsBackendMsp_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MsBackendMsp_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MsBackendMsp_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MsBackendMsp_1.9.0.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: 29 Package: MsBackendRawFileReader Version: 1.11.0 Depends: R (>= 4.1), methods, Spectra (>= 1.5.8) Imports: ProtGenerics (>= 1.35.3), MsCoreUtils, S4Vectors, IRanges, rawrr (>= 1.10.1), utils, BiocParallel Suggests: BiocStyle (>= 2.5), ExperimentHub, MsBackendMgf, knitr, lattice, mzR, protViz (>= 0.7), rmarkdown, tartare (>= 1.5), testthat License: GPL-3 MD5sum: b84e7853eba7b259b425cf3fb984d307 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] (), Tobias Kockmann [aut] (), Roger Gine Bertomeu [ctb] () Maintainer: Christian Panse 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: e53879a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-06 source.ver: src/contrib/MsBackendRawFileReader_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MsBackendRawFileReader_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MsBackendRawFileReader_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MsBackendRawFileReader_1.11.0.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: 30 Package: MsBackendSql Version: 1.5.0 Depends: R (>= 4.2.0), Spectra (>= 1.9.12) Imports: BiocParallel, S4Vectors, methods, ProtGenerics (>= 1.35.3), DBI, MsCoreUtils, IRanges, data.table, progress, BiocGenerics Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), RSQLite, msdata, rmarkdown, microbenchmark, mzR License: Artistic-2.0 MD5sum: 9d35e8e1c413751799e7a2e5ac82a77e 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] (), Chong Tang [ctb], Laurent Gatto [ctb] () Maintainer: Johannes Rainer 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: dd49c9e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MsBackendSql_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MsBackendSql_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MsBackendSql_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MsBackendSql_1.5.0.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: 42 Package: MsCoreUtils Version: 1.17.0 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: da6c72eb0bea60fe6c2c17205c2f989c 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] (), Johannes Rainer [aut] (), Sebastian Gibb [aut] (), Philippine Louail [aut] (), Adriaan Sticker [ctb], Sigurdur Smarason [ctb], Thomas Naake [ctb], Josep Maria Badia Aparicio [ctb] (), Michael Witting [ctb] (), Samuel Wieczorek [ctb], Roger Gine Bertomeu [ctb] (), Mar Garcia-Aloy [ctb] () Maintainer: RforMassSpectrometry Package Maintainer 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: 313127d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MsCoreUtils_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MsCoreUtils_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MsCoreUtils_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MsCoreUtils_1.17.0.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, MetaboAnnotation, MetaboCoreUtils, MetCirc, MsBackendMassbank, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, MsBackendSql, MsFeatures, MSnbase, PSMatch, QFeatures, qmtools, scp, Spectra, xcms suggestsMe: MetNet, msqrob2 dependencyCount: 12 Package: MsDataHub Version: 1.5.0 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: 787cf592bfc6b5d712ec5978b6780ea6 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] (), Kristina Gomoryova [ctb] (), Johannes Rainer [aut] () Maintainer: Laurent Gatto 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: ecb2ec0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MsDataHub_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MsDataHub_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MsDataHub_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MsDataHub_1.5.0.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: QFeatures, scp dependencyCount: 67 Package: MsExperiment Version: 1.7.0 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 Archs: x64 MD5sum: d7abe0deb3645c68b430bb2e67050a40 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] (), Johannes Rainer [aut] (), Sebastian Gibb [aut] () Maintainer: Laurent Gatto 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: 6fd2ba7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MsExperiment_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MsExperiment_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MsExperiment_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MsExperiment_1.7.0.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, xcms dependencyCount: 118 Package: MsFeatures Version: 1.13.0 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: b9fc022a1074e8007631a44430e148cb 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] () Maintainer: Johannes Rainer 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: 8e8ebfa git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MsFeatures_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MsFeatures_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MsFeatures_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MsFeatures_1.13.0.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: msImpute Version: 1.15.0 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: a3bebbbae8a0003a50d12fa6117c217b 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] () Maintainer: Soroor Hediyeh-zadeh 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: 6de7ce5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/msImpute_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/msImpute_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/msImpute_1.15.0.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: 99 Package: mslp Version: 1.7.0 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: 2591b161db10d781cb23568a3065db7e 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mslp git_branch: devel git_last_commit: 847986e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mslp_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mslp_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mslp_1.7.0.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: 1.43.0 Depends: R (>= 3.0.1), MSnbase Imports: MASS, gplots, RColorBrewer License: GPL-2 MD5sum: 4cf7d724b1e541170e30827201456f0d 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 git_url: https://git.bioconductor.org/packages/msmsEDA git_branch: devel git_last_commit: 64c5c64 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/msmsEDA_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/msmsEDA_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/msmsEDA_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/msmsEDA_1.43.0.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: 1.43.0 Depends: R (>= 3.0.1), MSnbase, msmsEDA Imports: edgeR, qvalue Suggests: xtable License: GPL-2 MD5sum: 38022936fb73d140d9dc100e5cbc1037 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 git_url: https://git.bioconductor.org/packages/msmsTests git_branch: devel git_last_commit: d8fe456 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/msmsTests_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/msmsTests_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/msmsTests_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/msmsTests_1.43.0.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: 146 Package: MSnbase Version: 2.31.0 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 (>= 1.7.1), msdata (>= 0.19.3), roxygen2, rgl, rpx, AnnotationHub, BiocStyle (>= 2.5.19), rmarkdown, imputeLCMD, norm, gplots, XML, shiny, magrittr, SummarizedExperiment License: Artistic-2.0 MD5sum: 192f21a46fe636f49cd9b846e66ddc97 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 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: 9f167c9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MSnbase_2.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MSnbase_2.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MSnbase_2.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MSnbase_2.31.0.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: CluMSID, DAPAR, DEP, MSnID, MSstatsQC, peakPantheR, PrInCE, ptairMS, topdownr, xcms, qPLEXdata suggestsMe: AnnotationHub, biobroom, BiocGenerics, isobar, msqrob2, proDA, qcmetrics, wpm, msdata, enviGCMS, LCMSQA, pmd, RAMClustR dependencyCount: 135 Package: MSnID Version: 1.39.0 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: de7899eed0f55784bfbaff447949e70a 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 git_url: https://git.bioconductor.org/packages/MSnID git_branch: devel git_last_commit: 2286916 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MSnID_1.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MSnID_1.39.0.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: 170 Package: MSPrep Version: 1.15.0 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: ae5717c0f171dca81412296d958cfb7d 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 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: 4263b68 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MSPrep_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MSPrep_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MSPrep_1.15.0.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: 155 Package: msqrob2 Version: 1.13.0 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, msdata, MSnbase, matrixStats, MsCoreUtils, covr License: Artistic-2.0 Archs: x64 MD5sum: d061546dc8a037ee6b0291940f85ac5c 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] (), Laurent Gatto [aut] (), Oliver M. Crook [aut] (), Adriaan Sticker [ctb], Ludger Goeminne [ctb], Milan Malfait [ctb] (), Stijn Vandenbulcke [aut] Maintainer: Lieven Clement 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: f9276cf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/msqrob2_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/msqrob2_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/msqrob2_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/msqrob2_1.13.0.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: 135 Package: MsQuality Version: 1.5.0 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: c596539b1b4032b8d48142fc776a28ce 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] (), Johannes Rainer [aut] () Maintainer: Thomas Naake URL: https://www.github.com/tnaake/MsQuality/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MsQuality git_branch: devel git_last_commit: 8e3581c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MsQuality_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MsQuality_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MsQuality_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MsQuality_1.5.0.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: 4.13.0 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 License: Artistic-2.0 MD5sum: 1205da431864895ac2f260764b04e5ca 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], Tsung-Heng Tsai [aut], Ting Huang [aut], Olga Vitek [aut] Maintainer: Meena Choi 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: 1d9e1ed git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MSstats_4.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MSstats_4.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MSstats_4.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MSstats_4.13.0.tgz vignettes: vignettes/MSstats/inst/doc/MSstats.html vignetteTitles: MSstats: Protein/Peptide significance analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstats/inst/doc/MSstats.R importsMe: artMS, MSstatsBig, MSstatsLiP, MSstatsPTM, MSstatsShiny, MSstatsTMT dependencyCount: 114 Package: MSstatsBig Version: 1.3.0 Imports: arrow, DBI, dplyr, MSstats, MSstatsConvert, readr, sparklyr, utils Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: 78e3dc7fac32978f2a2a7d14ba8ef396 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MSstatsBig git_branch: devel git_last_commit: 4531cf8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MSstatsBig_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MSstatsBig_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MSstatsBig_1.3.0.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: 136 Package: MSstatsConvert Version: 1.15.1 Depends: R (>= 4.0) Imports: data.table, log4r, methods, checkmate, utils, stringi Suggests: tinytest, covr, knitr, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 31bf9e978a751bb60238275c6fd4748b 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], Meena Choi [aut], Ting Huang [aut], Olga Vitek [aut] Maintainer: Mateusz Staniak VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MSstatsConvert git_branch: devel git_last_commit: b4db270 git_last_commit_date: 2024-05-06 Date/Publication: 2024-05-06 source.ver: src/contrib/MSstatsConvert_1.15.1.tar.gz win.binary.ver: bin/windows/contrib/4.4/MSstatsConvert_1.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MSstatsConvert_1.15.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MSstatsConvert_1.15.1.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 dependencyCount: 9 Package: MSstatsLiP Version: 1.11.0 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: b546833dd2f3633b21eb61ab7db002fd 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 VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsLiP/issues git_url: https://git.bioconductor.org/packages/MSstatsLiP git_branch: devel git_last_commit: dda6d80 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MSstatsLiP_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MSstatsLiP_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MSstatsLiP_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MSstatsLiP_1.11.0.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: 201 Package: MSstatsLOBD Version: 1.13.0 Depends: R (>= 4.0) Imports: minpack.lm, ggplot2, utils, stats, grDevices LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, covr, tinytest, dplyr License: Artistic-2.0 MD5sum: 03efc9674bb968a6abde69086beeaab9 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) . 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 VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsLODQ/issues git_url: https://git.bioconductor.org/packages/MSstatsLOBD git_branch: devel git_last_commit: 2582c17 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MSstatsLOBD_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MSstatsLOBD_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MSstatsLOBD_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MSstatsLOBD_1.13.0.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: 2.7.0 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 License: Artistic-2.0 Archs: x64 MD5sum: 6e442658dd94b1c0475ae987e2f275ee 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 VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsPTM/issues git_url: https://git.bioconductor.org/packages/MSstatsPTM git_branch: devel git_last_commit: 6b3c442 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MSstatsPTM_2.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MSstatsPTM_2.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MSstatsPTM_2.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MSstatsPTM_2.7.0.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: 129 Package: MSstatsQC Version: 2.23.0 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: 01788c778f4a056cbd7be1ec33550890 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 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: 0aa68de git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MSstatsQC_2.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MSstatsQC_2.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MSstatsQC_2.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MSstatsQC_2.23.0.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: 1.25.0 Imports: shiny, MSstatsQC, ggExtra, gridExtra, plotly, dplyr, grid Suggests: knitr License: Artistic License 2.0 Archs: x64 MD5sum: 429b0ede1ad8347624d389c2a995b6df 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 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: 98df101 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MSstatsQCgui_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MSstatsQCgui_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MSstatsQCgui_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MSstatsQCgui_1.25.0.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: 1.7.0 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: 2084600fec87ce1f3843ccd31dab3804 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 VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsShiny/issues git_url: https://git.bioconductor.org/packages/MSstatsShiny git_branch: devel git_last_commit: 79acab3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MSstatsShiny_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MSstatsShiny_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MSstatsShiny_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MSstatsShiny_1.7.0.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: 156 Package: MSstatsTMT Version: 2.13.0 Depends: R (>= 4.2) Imports: limma, lme4, lmerTest, methods, data.table, stats, utils, ggplot2, grDevices, graphics, MSstats, MSstatsConvert, checkmate Suggests: BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: bbab246889fc4279996826be3a038070 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], Sicheng Hao [aut], Olga Vitek [aut] Maintainer: Devon Kohler 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: 01ebf80 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MSstatsTMT_2.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MSstatsTMT_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MSstatsTMT_2.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MSstatsTMT_2.13.0.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: 117 Package: MuData Version: 1.9.0 Depends: Matrix, S4Vectors, rhdf5 Imports: methods, stats, MultiAssayExperiment, SingleCellExperiment, SummarizedExperiment, DelayedArray Suggests: HDF5Array, rmarkdown, knitr, fs, testthat, BiocStyle, covr, SingleCellMultiModal, CiteFuse, scater License: GPL-3 MD5sum: 36f0ad33f64b0745f8dacdd62a042df0 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, cre] (), Ilia Kats [aut] () Maintainer: Danila Bredikhin 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: 9ec354a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MuData_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MuData_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MuData_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MuData_1.9.0.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: 62 Package: Mulcom Version: 1.55.0 Depends: R (>= 2.10), Biobase Imports: graphics, grDevices, stats, methods, fields License: GPL-2 MD5sum: b57b8b5b134a8375f547195a19795d81 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 git_url: https://git.bioconductor.org/packages/Mulcom git_branch: devel git_last_commit: 80b4bae git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Mulcom_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Mulcom_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Mulcom_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Mulcom_1.55.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 14 Package: MultiAssayExperiment Version: 1.31.1 Depends: SummarizedExperiment (>= 1.3.81), R (>= 3.5.0) Imports: Biobase, BiocBaseUtils, BiocGenerics, DelayedArray, GenomicRanges (>= 1.25.93), IRanges, methods, S4Vectors (>= 0.23.19), tidyr, utils Suggests: BiocStyle, HDF5Array (>= 1.19.17), knitr, maftools (>= 2.7.10), R.rsp, RaggedExperiment, reshape2, rmarkdown, survival, survminer, testthat, UpSetR License: Artistic-2.0 MD5sum: 4b163059f2363e2db62f0e3826958d0f 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] (), Martin Morgan [aut, ctb], Lori Shepherd [ctb], Hervé Pagès [ctb], Vincent J Carey [aut, ctb], Levi Waldron [aut], MultiAssay SIG [ctb] Maintainer: Marcel Ramos 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: 768e96e git_last_commit_date: 2024-05-01 Date/Publication: 2024-05-03 source.ver: src/contrib/MultiAssayExperiment_1.31.1.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MultiAssayExperiment_1.31.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MultiAssayExperiment_1.31.1.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, InTAD, MGnifyR, mia, midasHLA, MIRit, missRows, QFeatures, terraTCGAdata, curatedPCaData, curatedTCGAData, microbiomeDataSets, OMICsPCAdata, scMultiome, SingleCellMultiModal importsMe: AffiXcan, AMARETTO, animalcules, autonomics, biosigner, CoreGx, corral, ELMER, FindIT2, FLAMES, gDRcore, gDRimport, gDRutils, gINTomics, glmSparseNet, GOpro, hermes, LinkHD, metabolomicsWorkbenchR, MOMA, msqrob2, MuData, MultiBaC, MultimodalExperiment, nipalsMCIA, OMICsPCA, omicsPrint, padma, PDATK, PharmacoGx, phenomis, ropls, scp, scPipe, TCGAutils, vsclust, xcore, curatedTBData, HMP2Data, MetaScope, TCGAWorkflow, MOCHA suggestsMe: BiocOncoTK, CNVRanger, maftools, MOFA2, MultiDataSet, RaggedExperiment, updateObject, brgedata, MOFAdata, teal, teal.slice dependencyCount: 57 Package: MultiBaC Version: 1.15.0 Imports: Matrix, ggplot2, MultiAssayExperiment, ropls, graphics, methods, plotrix, grDevices, pcaMethods Suggests: knitr, rmarkdown, BiocStyle, devtools License: GPL-3 MD5sum: 43ba2aac7865ccc75a625c644f152702 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("María José", "Nueda", email = "mjnueda@ua.es") Maintainer: The package maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MultiBaC git_branch: devel git_last_commit: fa65482 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MultiBaC_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MultiBaC_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MultiBaC_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MultiBaC_1.15.0.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: 108 Package: multiClust Version: 1.35.0 Imports: mclust, ctc, survival, cluster, dendextend, amap, graphics, grDevices Suggests: knitr, rmarkdown, gplots, RUnit, BiocGenerics, preprocessCore, Biobase, GEOquery License: GPL (>= 2) Archs: x64 MD5sum: 42e30647d28ae2f6509d66c4d72c0711 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/multiClust git_branch: devel git_last_commit: 9845a58 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/multiClust_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/multiClust_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/multiClust_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/multiClust_1.35.0.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: 1.15.0 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 MD5sum: f241f0f94b92ec7a8d3b673044ea3494 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 URL: https://github.com/loosolab/multicrispr VignetteBuilder: knitr BugReports: https://github.com/loosolab/multicrispr/issues git_url: https://git.bioconductor.org/packages/multicrispr git_branch: devel git_last_commit: b15b9a0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/multicrispr_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/multicrispr_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/multicrispr_1.15.0.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: 173 Package: MultiDataSet Version: 1.33.0 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: 43fd5e2904750429b07dbbac0657a419 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 Montagut VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MultiDataSet git_branch: devel git_last_commit: 6edc287 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MultiDataSet_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MultiDataSet_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MultiDataSet_1.33.0.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: 1.15.0 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: 43b1cf1c95d1ef129a7ff3b38f9391ca 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] (), Jörg Hackermüller [aut] () Maintainer: Sebastian Canzler 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: f1d6c36 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-03 source.ver: src/contrib/multiGSEA_1.15.0.tar.gz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/multiGSEA_1.15.0.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: 123 Package: multiHiCcompare Version: 1.23.0 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: ea9203140ccb0608710af032459f3622 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] (), John Stansfield [aut] Maintainer: Mikhail Dozmorov 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: ff8e880 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/multiHiCcompare_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/multiHiCcompare_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/multiHiCcompare_1.23.0.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: 94 Package: MultiMed Version: 2.27.0 Depends: R (>= 3.1.0) Suggests: RUnit, BiocGenerics License: GPL (>= 2) + file LICENSE MD5sum: 0a55c4a214167dce1462be13d5fa07d1 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 git_url: https://git.bioconductor.org/packages/MultiMed git_branch: devel git_last_commit: 8137556 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MultiMed_2.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MultiMed_2.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MultiMed_2.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MultiMed_2.27.0.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: 1.27.0 Depends: R (>= 3.4) Imports: stats, XML, RCurl, purrr (>= 0.2.2), tibble (>= 1.2), methods, BiocGenerics, AnnotationDbi, dplyr, Suggests: BiocStyle, edgeR, knitr, rmarkdown, testthat (>= 1.0.2) License: MIT + file LICENSE MD5sum: 1f75a9fb9ff37baa42639584a84ff63e 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 [cre, aut], Spencer Mahaffey [aut], Katerina Kechris [aut, cph, ths] Maintainer: Matt Mulvahill 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: 6522400 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/multiMiR_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/multiMiR_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/multiMiR_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/multiMiR_1.27.0.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: 58 Package: MultimodalExperiment Version: 1.5.0 Depends: R (>= 4.3.0), IRanges, S4Vectors Imports: BiocGenerics, MultiAssayExperiment, methods, utils Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 0c77a231781454a6595540342d7adb4c 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] () Maintainer: Lucas Schiffer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MultimodalExperiment git_branch: devel git_last_commit: c5feb0e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MultimodalExperiment_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MultimodalExperiment_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MultimodalExperiment_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MultimodalExperiment_1.5.0.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: 58 Package: MultiRNAflow Version: 1.3.0 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: 554928b1f1d747f60315d299fb257ab4 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] (), Nicolas Champagnat [aut, ths] (), Laurent Vallat [aut, ths] (), Pierre Vallois [aut] (), Région Grand Est [fnd], Cancéropôle Est [fnd] Maintainer: Rodolphe Loubaton 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: 850819a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MultiRNAflow_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MultiRNAflow_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MultiRNAflow_1.3.0.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: 197 Package: multiscan Version: 1.65.0 Depends: R (>= 2.3.0) Imports: Biobase, utils License: GPL (>= 2) MD5sum: 57539a06f5619106900857cdd42fcee0 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 , Chris Glasbey, Bruce Worton. Maintainer: Mizanur Khondoker git_url: https://git.bioconductor.org/packages/multiscan git_branch: devel git_last_commit: 3d81a47 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/multiscan_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/multiscan_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/multiscan_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/multiscan_1.65.0.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: 6 Package: multistateQTL Version: 1.1.0 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: 63305fc00468c224011d4b675dd347dd 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] () Maintainer: Amelia Dunstone 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: c5015dd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/multistateQTL_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/multistateQTL_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/multistateQTL_1.1.0.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: 117 Package: multiWGCNA Version: 1.3.1 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 Archs: x64 MD5sum: 5ce1ebedc7699f14bc94d78ec8c03055 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] (), Brent Fogel [aut, ctb] Maintainer: Dario Tommasini VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/multiWGCNA git_branch: devel git_last_commit: 6e4e30e git_last_commit_date: 2024-05-04 Date/Publication: 2024-05-06 source.ver: src/contrib/multiWGCNA_1.3.1.tar.gz win.binary.ver: bin/windows/contrib/4.4/multiWGCNA_1.3.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/multiWGCNA_1.3.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/multiWGCNA_1.3.1.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: 148 Package: multtest Version: 2.61.0 Depends: R (>= 2.10), methods, BiocGenerics, Biobase Imports: survival, MASS, stats4 Suggests: snow License: LGPL MD5sum: 73349c211a44520ee1482b3860ef97fc 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 git_url: https://git.bioconductor.org/packages/multtest git_branch: devel git_last_commit: 40bc39d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/multtest_2.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/multtest_2.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/multtest_2.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/multtest_2.61.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: aCGH, BicARE, iPAC, KCsmart, PREDA, rain, REDseq, siggenes, webbioc, cp4p, DiffCorr, PCS importsMe: a4Base, ABarray, adSplit, ALDEx2, anota, ChIPpeakAnno, GUIDEseq, metabomxtr, microbiomeMarker, nethet, OCplus, phyloseq, RTopper, SARC, SingleCellSignalR, singleCellTK, synapter, webbioc, hddplot, INCATome, MetaIntegrator, mutoss, nlcv, pRF, TcGSA suggestsMe: annaffy, CAMERA, ecolitk, factDesign, GOstats, GSEAlm, ropls, topGO, xcms, cherry, POSTm dependencyCount: 14 Package: mumosa Version: 1.13.0 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 MD5sum: c58936ab9b84113cdaf53b770a79dfd7 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 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: f558a92 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mumosa_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mumosa_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mumosa_1.13.0.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: 1.13.1 Depends: R(>= 4.1) Imports: magrittr, data.table, utils, R.utils, dplyr, stats, GenomicRanges, IRanges, GenomeInfoDb, BSgenome, Biostrings, stringr, VariantAnnotation, googleAuthR, httr, jsonlite, 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: 2a47be4f9f3e472dfac37d8977450911 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] (), Brian Schilder [aut, ctb] (), Nathan Skene [aut] () Maintainer: Alan Murphy URL: https://github.com/neurogenomics/MungeSumstats VignetteBuilder: knitr BugReports: https://github.com/neurogenomics/MungeSumstats/issues git_url: https://git.bioconductor.org/packages/MungeSumstats git_branch: devel git_last_commit: 08bb31d git_last_commit_date: 2024-05-02 Date/Publication: 2024-05-03 source.ver: src/contrib/MungeSumstats_1.13.1.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MungeSumstats_1.11.11.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MungeSumstats_1.13.1.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: 100 Package: muscat Version: 1.19.0 Depends: R (>= 4.3) Imports: BiocParallel, blme, ComplexHeatmap, data.table, DESeq2, dplyr, edgeR, ggplot2, glmmTMB, grDevices, grid, limma, lmerTest, lme4, Matrix, matrixStats, methods, progress, purrr, S4Vectors, scales, scater, scuttle, sctransform, stats, SingleCellExperiment, SummarizedExperiment, variancePartition, viridis Suggests: BiocStyle, countsimQC, cowplot, ExperimentHub, iCOBRA, knitr, phylogram, RColorBrewer, reshape2, rmarkdown, statmod, testthat, UpSetR License: GPL-3 MD5sum: b43e515ba9934b34fdc9cc89f1a6be37 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] (), Pierre-Luc Germain [aut], Charlotte Soneson [aut], Anthony Sonrel [aut], Mark D. Robinson [aut, fnd] Maintainer: Helena L. Crowell 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: e015e1d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/muscat_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/muscat_1.19.0.tgz vignettes: vignettes/muscat/inst/doc/analysis.html, vignettes/muscat/inst/doc/simulation.html vignetteTitles: "1. DS analysis", "2. Data simulation" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/muscat/inst/doc/analysis.R, vignettes/muscat/inst/doc/simulation.R suggestsMe: dreamlet, muscData dependencyCount: 190 Package: muscle Version: 3.47.0 Depends: Biostrings License: Unlimited MD5sum: 8ddee2e6029ff234b98df24815e9fb8c 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 URL: http://www.drive5.com/muscle/ git_url: https://git.bioconductor.org/packages/muscle git_branch: devel git_last_commit: c4013fb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/muscle_3.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/muscle_3.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/muscle_3.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/muscle_3.47.0.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: ptm, seqmagick dependencyCount: 25 Package: musicatk Version: 1.15.0 Depends: R (>= 4.0.0), 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, deconstructSigs, decompTumor2Sig, topicmodels, ggrepel, plotly, utils, factoextra, cluster, ComplexHeatmap, philentropy, maftools, shiny, stringi, tidyverse, ggpubr, Matrix (>= 1.6.1) Suggests: TCGAbiolinks, shinyBS, shinyalert, shinybusy, shinydashboard, shinyjs, shinyjqui, sortable, testthat, BiocStyle, knitr, rmarkdown, survival, XVector, qpdf, covr, shinyWidgets, cowplot, withr License: LGPL-3 MD5sum: d170b76c7c2f63b9bea58b616be3cab6 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] (0000-0002-3968-9250), Joshua D. Campbell [aut, cre] () Maintainer: Joshua D. Campbell VignetteBuilder: knitr BugReports: https://github.com/campbio/musicatk/issues git_url: https://git.bioconductor.org/packages/musicatk git_branch: devel git_last_commit: 15fc322 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/musicatk_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/musicatk_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/musicatk_1.15.0.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: 270 Package: MutationalPatterns Version: 3.15.0 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: 763232ae21615fec0882f100b9a0d460 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] (), Francis Blokzijl [aut] (), Roel Janssen [aut] (), Jurrian de Kanter [ctb] (), Rurika Oka [ctb] (), Mark van Roosmalen [cre], Ruben van Boxtel [aut, cph] (), Edwin Cuppen [aut] () Maintainer: Mark van Roosmalen 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: 3141f44 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MutationalPatterns_3.15.0.tar.gz 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: 124 Package: MVCClass Version: 1.79.0 Depends: R (>= 2.1.0), methods License: LGPL Archs: x64 MD5sum: 80f1aa606c9b7bd8ab0d2e7e111fbe0e 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 git_url: https://git.bioconductor.org/packages/MVCClass git_branch: devel git_last_commit: bbb1066 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MVCClass_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MVCClass_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MVCClass_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MVCClass_1.79.0.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: 1.29.0 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: 23b5b5f05ad61b43042550d7943dbeb5 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 , Rafael Ayala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MWASTools git_branch: devel git_last_commit: abf4ef0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/MWASTools_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/MWASTools_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/MWASTools_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/MWASTools_1.29.0.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: 133 Package: mygene Version: 1.41.0 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: 848a06375177387a909bdca3423e9301 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 git_url: https://git.bioconductor.org/packages/mygene git_branch: devel git_last_commit: 486275d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mygene_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mygene_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mygene_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mygene_1.41.0.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: 149 Package: myvariant Version: 1.35.0 Depends: R (>= 3.2.1), VariantAnnotation Imports: httr, jsonlite, S4Vectors, Hmisc, plyr, magrittr, GenomeInfoDb Suggests: BiocStyle License: Artistic-2.0 MD5sum: a18748188a471a3b4c30489bd4dde45d 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 git_url: https://git.bioconductor.org/packages/myvariant git_branch: devel git_last_commit: ff9a783 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/myvariant_1.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/myvariant_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/myvariant_1.35.0.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: 1.43.0 Depends: methods Imports: XML, plyr, parallel, doParallel, foreach, iterators, ProtGenerics Suggests: knitr, testthat License: GPL (>= 2) MD5sum: 26fb085f8d653a1aad799e7bb6906bbe 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] (), Thomas Pedersen [aut] (), Vladislav Petyuk [ctb] Maintainer: Laurent Gatto VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mzID git_branch: devel git_last_commit: b4b5489 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mzID_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mzID_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mzID_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mzID_1.43.0.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: 2.39.0 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: cc5aa4de598b7d80a493fa5792ae7969 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 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: b2b8884 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/mzR_2.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/mzR_2.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/mzR_2.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/mzR_2.39.0.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, DIAlignR, MSnID, peakPantheR, SIMAT, TargetDecoy, topdownr, xcms, yamss suggestsMe: AnnotationHub, MetaboAnnotation, MsBackendRawFileReader, MsBackendSql, MsDataHub, MsExperiment, MsQuality, PSMatch, qcmetrics, Spectra, msdata, RforProteomics, chromConverter, erah dependencyCount: 10 Package: NADfinder Version: 1.29.0 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: a1f168256ab0c0dacb5c357ed29d53c0 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 , Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NADfinder git_branch: devel git_last_commit: e8ed28e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/NADfinder_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/NADfinder_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/NADfinder_1.29.0.tgz vignettes: vignettes/NADfinder/inst/doc/NADfinder.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NADfinder/inst/doc/NADfinder.R dependencyCount: 246 Package: NanoMethViz Version: 3.1.1 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, data.table, dbscan, e1071, fs, GenomicRanges, Biostrings, ggrastr, glue, graphics, IRanges, limma (>= 3.44.0), patchwork, purrr, rlang, R.utils, Rsamtools, scales (>= 1.2.0), scico, stats, stringr, tibble, tidyr, utils, withr, zlibbioc 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: 008c9fdf22f5f333220160b2553414c2 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 URL: https://github.com/shians/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: 69dde2c git_last_commit_date: 2024-05-06 Date/Publication: 2024-05-06 source.ver: src/contrib/NanoMethViz_3.1.1.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/NanoMethViz_3.1.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/NanoMethViz_3.1.3.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: 1.35.0 Depends: Biobase Imports: matrixStats, methods, Rcpp LinkingTo: Rcpp Suggests: testthat, BiocStyle License: GPL MD5sum: 0c367ceebd437e851dfef2b3a9407db9 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 , tingting zhai , chi wang Maintainer: tingting zhai ,hong wang git_url: https://git.bioconductor.org/packages/NanoStringDiff git_branch: devel git_last_commit: b3fd0a0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/NanoStringDiff_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/NanoStringDiff_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/NanoStringDiff_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/NanoStringDiff_1.35.0.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: 8 Package: NanoStringNCTools Version: 1.13.0 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: 825cd1ae0df4934a454f9cf998f67832 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NanoStringNCTools git_branch: devel git_last_commit: 0e46bd3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/NanoStringNCTools_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/NanoStringNCTools_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/NanoStringNCTools_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/NanoStringNCTools_1.13.0.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: 1.11.0 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 Archs: x64 MD5sum: 03cbb1083640daa1d739e140841a07e7 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] (), Caiden Lukan [ctb] Maintainer: Caleb Class VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NanoTube git_branch: devel git_last_commit: 3e8759e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/NanoTube_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/NanoTube_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/NanoTube_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/NanoTube_1.11.0.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: 56 Package: NBAMSeq Version: 1.21.0 Depends: R (>= 3.6), SummarizedExperiment, S4Vectors Imports: DESeq2, mgcv(>= 1.8-24), BiocParallel, genefilter, methods, stats, Suggests: knitr, rmarkdown, testthat, ggplot2 License: GPL-2 MD5sum: c19596ca29ff0a253a495a48bdd9bbf0 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 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: bb2f728 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/NBAMSeq_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/NBAMSeq_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/NBAMSeq_1.21.0.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: 2.51.0 Depends: R (>= 2.14.0), flowCore(>= 1.51.7), methods, BH Imports: Biobase,BiocGenerics,flowCore,zlibbioc LinkingTo: cpp11,BH, Rhdf5lib Suggests: testthat,parallel,flowStats,knitr License: AGPL-3.0-only MD5sum: 565cb0eac0f830f01a8fe7e2d9458bc5 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ncdfFlow git_branch: devel git_last_commit: 1ef00b3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ncdfFlow_2.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ncdfFlow_2.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ncdfFlow_2.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ncdfFlow_2.51.0.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 suggestsMe: COMPASS, cydar dependencyCount: 18 Package: ncGTW Version: 1.19.0 Depends: methods, BiocParallel, xcms Imports: Rcpp, grDevices, graphics, stats LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat, rmarkdown License: GPL-2 MD5sum: f7b1748c9afebc6501bd936786543663 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 Maintainer: Chiung-Ting Wu VignetteBuilder: knitr BugReports: https://github.com/ChiungTingWu/ncGTW/issues git_url: https://git.bioconductor.org/packages/ncGTW git_branch: devel git_last_commit: 526b2e6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ncGTW_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ncGTW_1.19.0.zip 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: 1.53.0 Depends: R (>= 4.0.0) Imports: graph, KEGGgraph, methods, RBGL, RCy3, R.oo Suggests: Rgraphviz Enhances: DEGraph License: GPL-3 MD5sum: 327edec62cdcbda461ea70a857490f1a 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 git_url: https://git.bioconductor.org/packages/NCIgraph git_branch: devel git_last_commit: be43d65 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/NCIgraph_1.53.0.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: 1.15.0 Imports: httr, xml2, utils, methods, grDevices, ggplot2, IRanges, GenomicRanges, S4Vectors Suggests: knitr, BiocStyle, rmarkdown, RUnit, BiocGenerics License: GPL-3 MD5sum: 4ce9ecc7b6fc316d3fe0f04aa231314a 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] (), Rafael Ayala [aut] (), Guy-Bart Stan [aut] (), Rodrigo Ledesma-Amaro [aut] () Maintainer: Lara Selles Vidal VignetteBuilder: knitr BugReports: https://github.com/LaraSellesVidal/ncRNAtools/issues git_url: https://git.bioconductor.org/packages/ncRNAtools git_branch: devel git_last_commit: 87725c1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ncRNAtools_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ncRNAtools_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ncRNAtools_1.15.0.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: 1.27.0 Depends: RCX Imports: httr, jsonlite, plyr, tidyr Suggests: BiocStyle, testthat, knitr, rmarkdown License: BSD_3_clause + file LICENSE Archs: x64 MD5sum: 8a0d1ec64846db012661273c67096e9d 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] (), Frank Kramer [ctb], Alex Ishkin [ctb], Dexter Pratt [ctb] Maintainer: Florian Auer 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: 0875689 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ndexr_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ndexr_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ndexr_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ndexr_1.27.0.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: 1.15.0 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 MD5sum: 4437cfc7883a0b58b91ba515fb873b68 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 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: 521f662 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/nearBynding_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/nearBynding_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/nearBynding_1.15.0.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: 114 Package: Nebulosa Version: 1.15.0 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: e019a24f72a38409cd36554251b57240 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] () Maintainer: Jose Alquicira-Hernandez 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: 4e6afd0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Nebulosa_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Nebulosa_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Nebulosa_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Nebulosa_1.15.0.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: 1.13.0 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: dc536f47877b3ccc086d6871d2627093 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 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: 7e0620f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/nempi_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/nempi_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/nempi_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/nempi_1.13.0.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: 111 Package: NetActivity Version: 1.7.0 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: 519e59a0ee0b1a7ae29706388b2abfe2 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NetActivity git_branch: devel git_last_commit: a57b155 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/NetActivity_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/NetActivity_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/NetActivity_1.7.0.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: 2.13.0 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: 27fde7b9473f2c297a7b3a56c34cc7d2 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 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: 65f9df2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/netboost_2.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/netboost_2.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/netboost_2.13.0.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: netDx Version: 1.17.0 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 MD5sum: 808e76f57bdd1e991e5d1ba14869978d NeedsCompilation: no 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] (), 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 URL: http://netdx.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/netDx git_branch: devel git_last_commit: 827f9ce git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/netDx_1.17.0.tar.gz vignettes: vignettes/netDx/inst/doc/ThreeWayClassifier.html vignetteTitles: 01. Build & test classifier with clinical and multi-omic data & pathway features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/netDx/inst/doc/ThreeWayClassifier.R dependencyCount: 112 Package: nethet Version: 1.37.0 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: fede67d918d2be5fc767b4cdc08cae94 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 , Frank Dondelinger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nethet git_branch: devel git_last_commit: c252a0d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/nethet_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/nethet_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/nethet_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/nethet_1.37.0.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: 77 Package: netprioR Version: 1.31.0 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: e2173da758a5bb9aa25ff22e74cbef88 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 URL: http://bioconductor.org/packages/netprioR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/netprioR git_branch: devel git_last_commit: 55bc415 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/netprioR_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/netprioR_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/netprioR_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/netprioR_1.31.0.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: 1.65.0 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) Archs: x64 MD5sum: f876b6d8f0406c784892422cb54bc1bd 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 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: 88d87f5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/netresponse_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/netresponse_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/netresponse_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/netresponse_1.65.0.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: 76 Package: NetSAM Version: 1.45.0 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: fb650ab0c24759ce103c538e5397fa50 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 Maintainer: Zhiao Shi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NetSAM git_branch: devel git_last_commit: 643d2c4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/NetSAM_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/NetSAM_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/NetSAM_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/NetSAM_1.45.0.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: 138 Package: netSmooth Version: 1.25.0 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 Archs: x64 MD5sum: 15d276795d2f82b6907593c67acbf71a 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 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: ee9993b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/netSmooth_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/netSmooth_1.25.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/netSmooth_1.25.0.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 suggestsMe: netDx dependencyCount: 179 Package: netZooR Version: 1.9.0 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: e11db67a7061b2fb44e6fc48340ac518 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] (), Tian Wang [aut] (), John Platig [aut], Marieke Kuijjer [aut] (), Megha Padi [aut] (), Rebekka Burkholz [aut], Des Weighill [aut] (), Kate Shutta [aut] () Maintainer: Marouen Ben Guebila 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: 39d320e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/netZooR_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/netZooR_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/netZooR_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/netZooR_1.9.0.tgz vignettes: vignettes/netZooR/inst/doc/CONDOR.html vignetteTitles: CONDOR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netZooR/inst/doc/CONDOR.R dependencyCount: 212 Package: NeuCA Version: 1.11.0 Depends: R(>= 3.5.0), keras, limma, e1071, SingleCellExperiment, kableExtra Suggests: BiocStyle, knitr, rmarkdown, networkD3 License: GPL-2 MD5sum: 4727787a6f4887ff6f397e997223c992 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NeuCA git_branch: devel git_last_commit: 06cc722 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/NeuCA_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/NeuCA_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/NeuCA_1.11.0.tgz vignettes: vignettes/NeuCA/inst/doc/NeuCA.html vignetteTitles: NeuCA Package User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NeuCA/inst/doc/NeuCA.R dependencyCount: 102 Package: NewWave Version: 1.15.0 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 MD5sum: 83a2b07e21f03f9008fe9bff2850498e 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 VignetteBuilder: knitr BugReports: https://github.com/fedeago/NewWave/issues git_url: https://git.bioconductor.org/packages/NewWave git_branch: devel git_last_commit: 04d6ef8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/NewWave_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/NewWave_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/NewWave_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/NewWave_1.15.0.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: 54 Package: ngsReports Version: 2.7.0 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: 5e4c75e1b0f778009372db70b3f55b84 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] (), Christopher Ward [aut], Thu-Hien To [aut] Maintainer: Stevie Pederson 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: e3a8ecb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ngsReports_2.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ngsReports_2.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ngsReports_2.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ngsReports_2.7.0.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: 99 Package: nipalsMCIA Version: 1.3.0 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 MD5sum: 541453abde592c07c74478615deead9e 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] (), Joaquin Reyna [aut] (), Edel Aron [aut] (), Anna Konstorum [aut] () Maintainer: Maximilian Mattessich 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: 3ebb28f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/nipalsMCIA_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/nipalsMCIA_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/nipalsMCIA_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/nipalsMCIA_1.3.0.tgz vignettes: vignettes/nipalsMCIA/inst/doc/Vignette1.Analysis-of-MCIA-Decomposition.html, vignettes/nipalsMCIA/inst/doc/Vignette2.Single-Cell-Analysis.html, vignettes/nipalsMCIA/inst/doc/Vignette3.Predicting-New-Scores.html vignetteTitles: Analysis of MCIA Decomposition, Single Cell Analysis, Predicting New MCIA scores hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nipalsMCIA/inst/doc/Vignette1.Analysis-of-MCIA-Decomposition.R, vignettes/nipalsMCIA/inst/doc/Vignette2.Single-Cell-Analysis.R, vignettes/nipalsMCIA/inst/doc/Vignette3.Predicting-New-Scores.R dependencyCount: 102 Package: nnNorm Version: 2.69.0 Depends: R(>= 2.2.0), marray Imports: graphics, grDevices, marray, methods, nnet, stats License: LGPL MD5sum: 6b05d2eeb15d5586da9ba9bd2be57d2c 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 Maintainer: Adi Laurentiu Tarca URL: http://bioinformaticsprb.med.wayne.edu/tarca/ git_url: https://git.bioconductor.org/packages/nnNorm git_branch: devel git_last_commit: 60acf3c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/nnNorm_2.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/nnNorm_2.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/nnNorm_2.69.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/nnNorm_2.69.0.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: NOISeq Version: 2.49.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.13.11), splines (>= 3.0.1), Matrix (>= 1.2) License: Artistic-2.0 MD5sum: 77ed048b030fe1dca329b0cfda9d34c5 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 git_url: https://git.bioconductor.org/packages/NOISeq git_branch: devel git_last_commit: 50f1aa7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/NOISeq_2.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/NOISeq_2.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/NOISeq_2.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/NOISeq_2.49.0.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, CNVPanelizer, ExpHunterSuite suggestsMe: compcodeR, GeoTcgaData dependencyCount: 11 Package: NoRCE Version: 1.17.0 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: 97c037b427e1b694cc381b57bde167a4 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 VignetteBuilder: knitr BugReports: https://github.com/guldenolgun/NoRCE/issues git_url: https://git.bioconductor.org/packages/NoRCE git_branch: devel git_last_commit: 421e9b8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/NoRCE_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/NoRCE_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/NoRCE_1.17.0.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: 1.33.0 Depends: R (>= 3.3), Biobase, illuminaio, quadprog Imports: utils License: BSD_2_clause + file LICENSE MD5sum: ff6665703a52ec97c7ce0cc0a371df87 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 git_url: https://git.bioconductor.org/packages/normalize450K git_branch: devel git_last_commit: f408a63 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/normalize450K_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/normalize450K_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/normalize450K_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/normalize450K_1.33.0.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: 12 Package: NormalyzerDE Version: 1.23.0 Depends: R (>= 4.1.0) Imports: vsn, preprocessCore, limma, MASS, ape, car, ggplot2, methods, Biobase, utils, stats, SummarizedExperiment, matrixStats, ggforce Suggests: knitr, testthat, rmarkdown, roxygen2, hexbin, BiocStyle License: Artistic-2.0 MD5sum: ecbe78ad1674cce25f4e7b6677bd1999 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 URL: https://github.com/ComputationalProteomics/NormalyzerDE VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NormalyzerDE git_branch: devel git_last_commit: 861595f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/NormalyzerDE_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/NormalyzerDE_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/NormalyzerDE_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/NormalyzerDE_1.23.0.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 dependencyCount: 117 Package: NormqPCR Version: 1.51.0 Depends: R(>= 2.14.0), stats, RColorBrewer, Biobase, methods, ReadqPCR, qpcR License: LGPL-3 MD5sum: 72df303346834bea0a12363e74ca244a 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 URL: www.bioconductor.org/packages/release/bioc/html/NormqPCR.html git_url: https://git.bioconductor.org/packages/NormqPCR git_branch: devel git_last_commit: 6ae5430 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/NormqPCR_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/NormqPCR_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/NormqPCR_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/NormqPCR_1.51.0.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: 47 Package: normr Version: 1.31.0 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: dab185ec2d63ba21a03780f5cc0c3e18 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 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: 6240dd6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/normr_1.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/normr_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/normr_1.31.0.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: 91 Package: NPARC Version: 1.17.0 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: bd13347e6fada8d822bf31f5cc4c56e2 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NPARC git_branch: devel git_last_commit: 13315da git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/NPARC_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/NPARC_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/NPARC_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/NPARC_1.17.0.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: 40 Package: npGSEA Version: 1.41.0 Depends: GSEABase (>= 1.24.0) Imports: Biobase, methods, BiocGenerics, graphics, stats Suggests: ALL, genefilter, limma, hgu95av2.db, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: 5032f050747a039359c47e45ed4dc21d 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 git_url: https://git.bioconductor.org/packages/npGSEA git_branch: devel git_last_commit: 70fb995 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/npGSEA_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/npGSEA_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/npGSEA_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/npGSEA_1.41.0.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: 1.55.0 Depends: R (>= 2.3.0) Imports: mvtnorm, stats, utils License: GPL-2 MD5sum: c29f101bf37de5ed8403bef4d8da90ef 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 git_url: https://git.bioconductor.org/packages/NTW git_branch: devel git_last_commit: 0d80922 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/NTW_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/NTW_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/NTW_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/NTW_1.55.0.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: 1.33.0 Imports: stats, IRanges, S4Vectors, graphics, methods Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 47a80ab7f940b37bb12d6f2c4323ea84 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] (), Pascal Belleau [aut] (), Arnaud Droit [aut] Maintainer: Astrid Deschênes 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: 1db3ecb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/nucleoSim_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/nucleoSim_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/nucleoSim_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/nucleoSim_1.33.0.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: 8 Package: nucleR Version: 2.37.0 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: 2e9851f8407cc59e3c13eb92b5e8f851 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nucleR git_branch: devel git_last_commit: dc7f0ad git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/nucleR_2.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/nucleR_2.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/nucleR_2.37.0.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: 90 Package: nuCpos Version: 1.23.0 Depends: R (>= 4.2.0) Imports: graphics, methods Suggests: NuPoP, Biostrings, testthat License: GPL-2 MD5sum: 9aa0c6ac86bbd8cba7a79ba4924e4c08 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 git_url: https://git.bioconductor.org/packages/nuCpos git_branch: devel git_last_commit: 1b7524d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/nuCpos_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/nuCpos_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/nuCpos_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/nuCpos_1.23.0.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: 1.11.0 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: accef70ffc71c80137733d80b785551e 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] (), Wancen Mu [aut] (), Eric Davis [aut] (), Douglas Phanstiel [aut] (), Stuart Lee [aut] (), Mikhail Dozmorov [ctb], Tim Triche [ctb], CZI [fnd] Maintainer: Michael Love 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: c161668 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/nullranges_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/nullranges_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/nullranges_1.11.0.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: 95 Package: NuPoP Version: 2.13.0 Depends: R (>= 4.0) Imports: graphics, utils Suggests: knitr, rmarkdown License: GPL-2 MD5sum: ada294380d391a49d9707c0cf088708c 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 ; Liqun Xi ; Oscar Zarate Maintainer: Ji-Ping Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NuPoP git_branch: devel git_last_commit: 70959c9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/NuPoP_2.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/NuPoP_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/NuPoP_2.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/NuPoP_2.13.0.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: 1.65.0 Depends: R (>= 2.0.0) License: GPL (>= 2) MD5sum: 9369d8e4d0a64d3079880ef7b1a87e0f 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 Maintainer: Oliver Will git_url: https://git.bioconductor.org/packages/occugene git_branch: devel git_last_commit: d8b1e9c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/occugene_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/occugene_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/occugene_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/occugene_1.65.0.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: 1.79.0 Depends: R (>= 2.1.0) Imports: multtest (>= 1.7.3), graphics, grDevices, stats, interp License: LGPL Archs: x64 MD5sum: 5cc9f1a5c8491eb50ce00319b2450485 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 and Alexander Ploner Maintainer: Alexander Ploner git_url: https://git.bioconductor.org/packages/OCplus git_branch: devel git_last_commit: 0559f22 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/OCplus_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/OCplus_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/OCplus_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/OCplus_1.79.0.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: 19 Package: octad Version: 1.7.0 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: 1e8bd04e0501ecfb7bb04e7b2e869a72 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/octad git_branch: devel git_last_commit: 639b685 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/octad_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/octad_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/octad_1.7.0.tgz 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: 1.33.0 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: 9817877d3399e01fef9ff6ccb409673f 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/odseq git_branch: devel git_last_commit: 4ec507f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/odseq_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/odseq_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/odseq_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/odseq_1.33.0.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: 1.9.0 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 MD5sum: 80386f51629b05ac245061e26de4bfc7 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 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: b2033ca git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/OGRE_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/OGRE_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/OGRE_1.9.0.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: 173 Package: oligo Version: 1.69.0 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, zlibbioc 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: 0b494bc6b4d73937dcada7c309620365 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oligo git_branch: devel git_last_commit: 87cd053 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/oligo_1.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/oligo_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/oligo_1.69.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/oligo_1.69.0.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, crossmeta, frma, ITALICS, mimager suggestsMe: fastseg, frmaTools, hapmap100khind, hapmap100kxba, hapmap500knsp, hapmap500ksty, hapmapsnp5, hapmapsnp6, maqcExpression4plex, aroma.affymetrix, maGUI, RCPA dependencyCount: 63 Package: oligoClasses Version: 1.67.0 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: c30bb768e1fb72dd940e9cb9c02f18de 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 and Robert Scharpf git_url: https://git.bioconductor.org/packages/oligoClasses git_branch: devel git_last_commit: 46ee6a9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/oligoClasses_1.67.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/oligoClasses_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/oligoClasses_1.67.0.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: 1.83.0 Depends: R (>= 2.10), methods, locfit, marray Imports: graphics, grDevices, limma, marray, methods, stats Suggests: convert License: GPL-2 MD5sum: 93259c7a2ad41a66a5e1afb2bd979cd2 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 Maintainer: Matthias Futschik URL: http://olin.sysbiolab.eu git_url: https://git.bioconductor.org/packages/OLIN git_branch: devel git_last_commit: a968b1b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/OLIN_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/OLIN_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/OLIN_1.83.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/OLIN_1.83.0.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: 1.79.0 Depends: R (>= 2.0.0), OLIN (>= 1.4.0) Imports: graphics, marray, OLIN, tcltk, tkWidgets, widgetTools License: GPL-2 Archs: x64 MD5sum: 55f091370447ffde1f0fbe259af0d08d 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 Maintainer: Matthias Futschik URL: http://olin.sysbiolab.eu git_url: https://git.bioconductor.org/packages/OLINgui git_branch: devel git_last_commit: 1e733bf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/OLINgui_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/OLINgui_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/OLINgui_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/OLINgui_1.79.0.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: 1.7.0 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 MD5sum: e1d56b82022bbbb12cd5c32154797998 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] () Maintainer: Sokratis Kariotis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omada git_branch: devel git_last_commit: 3d3c329 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/omada_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/omada_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/omada_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/omada_1.7.0.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: 152 Package: OmaDB Version: 2.21.0 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: daeb1798a44e1a351a4c26c70feaf7bc 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 , Adrian Altenhoff 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: 8a1c338 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/OmaDB_2.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/OmaDB_2.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/OmaDB_2.21.0.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 importsMe: PhyloProfile suggestsMe: orthogene dependencyCount: 59 Package: omicade4 Version: 1.45.0 Depends: R (>= 3.0.0), ade4 Imports: made4, Biobase Suggests: BiocStyle License: GPL-2 MD5sum: ce009f184b37f4b298e25a6ad1f6a420 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 git_url: https://git.bioconductor.org/packages/omicade4 git_branch: devel git_last_commit: 1791858 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/omicade4_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/omicade4_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/omicade4_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/omicade4_1.45.0.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: 1.43.0 Depends: R (>= 2.14.0), methods,GenomicRanges License: GPL-2 MD5sum: 06b6385916abd8a7a280589aef0a8215 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 Chunhua Yan Maintainer: Ying Hu git_url: https://git.bioconductor.org/packages/OmicCircos git_branch: devel git_last_commit: 849095e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/OmicCircos_1.43.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/OmicCircos_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/OmicCircos_1.43.0.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: 1.25.0 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: 03d52c1bdac43154db6eb5fd50e911b6 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicplotR git_branch: devel git_last_commit: 013a922 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/omicplotR_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/omicplotR_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/omicplotR_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/omicplotR_1.25.0.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: 115 Package: omicRexposome Version: 1.27.0 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: e727ff0229c662ae949e2d28f0f81829 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicRexposome git_branch: devel git_last_commit: 33b689f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/omicRexposome_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/omicRexposome_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/omicRexposome_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/omicRexposome_1.27.0.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: 235 Package: OMICsPCA Version: 1.23.0 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: 81bc507fc90ce3d141be343dea664827 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OMICsPCA git_branch: devel git_last_commit: 648484c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/OMICsPCA_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/OMICsPCA_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/OMICsPCA_1.23.0.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: 215 Package: omicsPrint Version: 1.25.0 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: 6bc9c095d30f00adf1b5d96745a9671f 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicsPrint git_branch: devel git_last_commit: 84d1e02 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/omicsPrint_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/omicsPrint_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/omicsPrint_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/omicsPrint_1.25.0.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: 60 Package: omicsViewer Version: 1.9.0 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: fc796d4742509e02d3cbf6942eca8082 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 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: d6bd4ab git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/omicsViewer_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/omicsViewer_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/omicsViewer_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/omicsViewer_1.9.0.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: 202 Package: Omixer Version: 1.15.0 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: e42ef017206e9c167900880e50765ffd 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 VignetteBuilder: knitr BugReports: https://github.com/molepi/Omixer/issues git_url: https://git.bioconductor.org/packages/Omixer git_branch: devel git_last_commit: 2880530 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Omixer_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Omixer_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Omixer_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Omixer_1.15.0.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: ompBAM Version: 1.9.0 Imports: utils, Rcpp, zlibbioc Suggests: RcppProgress, knitr, rmarkdown, roxygen2, devtools, usethis, desc, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: c8233653565535eb824c375e2351b84a 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 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: a14035e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ompBAM_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ompBAM_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ompBAM_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ompBAM_1.9.0.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: 4 Package: oncomix Version: 1.27.0 Depends: R (>= 3.4.0) Imports: ggplot2, ggrepel, RColorBrewer, mclust, stats, SummarizedExperiment Suggests: knitr, rmarkdown, testthat, RMySQL License: GPL-3 MD5sum: 0c9d252ce75a69247619f57386227ea0 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oncomix git_branch: devel git_last_commit: 8f2ad6e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/oncomix_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/oncomix_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/oncomix_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/oncomix_1.27.0.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: 1.7.0 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: 243fc60948ea2ff0c1458eb93e64c934 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 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: 7d61d05 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/oncoscanR_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/oncoscanR_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/oncoscanR_1.7.0.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: 1.33.0 Depends: R (>= 4.1.0), Imports: biomaRt, grDevices, graphics, utils, methods, Suggests: BiocGenerics, BiocStyle, knitr, testthat, License: file LICENSE MD5sum: 87a521fbcb2dcf43ae866d8ccc1b68ff 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] (), Carlo Gambacorti Passerini [ctb], Rocco Piazza [ctb], Daniele Ramazzotti [aut] (), Roberta Spinelli [ctb] Maintainer: Luca De Sano 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: b7415ab git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/OncoScore_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/OncoScore_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/OncoScore_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/OncoScore_1.33.0.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: 69 Package: OncoSimulR Version: 4.7.0 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: 1b0045b938d465cbe2b989ab5ed3767e 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 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: c3dd5c6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/OncoSimulR_4.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/OncoSimulR_4.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/OncoSimulR_4.7.0.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: 91 Package: onlineFDR Version: 2.13.0 Imports: stats, Rcpp, progress LinkingTo: Rcpp, RcppProgress Suggests: knitr, rmarkdown, testthat, covr License: GPL-3 MD5sum: d7eb2415a5ccb1249c1d39b416685f2e 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 URL: https://dsrobertson.github.io/onlineFDR/index.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/onlineFDR git_branch: devel git_last_commit: 86fcecd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/onlineFDR_2.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/onlineFDR_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/onlineFDR_2.13.0.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: openCyto Version: 2.17.0 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 LinkingTo: cpp11, BH Suggests: flowWorkspaceData, knitr, rmarkdown, markdown, testthat, utils, tools, parallel, ggcyto, CytoML, flowStats(>= 4.5.2), MASS License: AGPL-3.0-only Archs: x64 MD5sum: c0821bfe3d35a38677a65ecca99785c2 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/openCyto git_branch: devel git_last_commit: d4e45d3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/openCyto_2.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/openCyto_2.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/openCyto_2.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/openCyto_2.17.0.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: 79 Package: openPrimeR Version: 1.27.0 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: e56f40e213b762612455c8a9e3ec9df4 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 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: de3b9f2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/openPrimeR_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/openPrimeR_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/openPrimeR_1.27.0.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 dependsOnMe: openPrimeRui dependencyCount: 116 Package: OpenStats Version: 1.17.0 Depends: nlme Imports: MASS, jsonlite, Hmisc, methods, knitr, AICcmodavg, car, rlist, summarytools, graphics, stats, utils Suggests: rmarkdown License: GPL (>= 2) Archs: x64 MD5sum: 0a5b84c2f9940ae077302cd34b4e5416 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 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: 9a7a5dd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/OpenStats_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/OpenStats_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/OpenStats_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/OpenStats_1.17.0.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: 135 Package: oposSOM Version: 2.23.0 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: b982bc453142de1ebdae5c840ee05d79 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 , Hoang Thanh Le and Martin Kalcher Maintainer: Henry Loeffler-Wirth URL: http://som.izbi.uni-leipzig.de git_url: https://git.bioconductor.org/packages/oposSOM git_branch: devel git_last_commit: 2fb8704 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/oposSOM_2.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/oposSOM_2.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/oposSOM_2.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/oposSOM_2.23.0.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: 87 Package: oppar Version: 1.33.0 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: 1f42081273db5d2f9b0e2bb661125012 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oppar git_branch: devel git_last_commit: 0170bdd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/oppar_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/oppar_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/oppar_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/oppar_1.33.0.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: 103 Package: oppti Version: 1.19.0 Depends: R (>= 3.5) Imports: limma, stats, reshape, ggplot2, grDevices, RColorBrewer, pheatmap, knitr, methods, devtools, parallelDist, Suggests: markdown License: MIT Archs: x64 MD5sum: 539238524115a5eeb129dca3329c69eb 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 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: 5d78373 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/oppti_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/oppti_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/oppti_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/oppti_1.19.0.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: 127 Package: optimalFlow Version: 1.17.0 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: c88ea6068cfcc47742cc7573d2784c5e NeedsCompilation: no Title: optimalFlow Description: Optimal-transport techniques applied to supervised flow cytometry gating. biocViews: Software, FlowCytometry, Technology Author: Hristo Inouzhe Maintainer: Hristo Inouzhe VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/optimalFlow git_branch: devel git_last_commit: 90befc8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/optimalFlow_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/optimalFlow_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/optimalFlow_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/optimalFlow_1.17.0.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: 97 Package: OPWeight Version: 1.27.0 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: 60afddfc938ecdbd480e5151aaec0d49 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 URL: https://github.com/mshasan/OPWeight VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OPWeight git_branch: devel git_last_commit: e2fb967 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/OPWeight_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/OPWeight_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/OPWeight_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/OPWeight_1.27.0.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: 1.77.0 Depends: R (>= 3.6.1), Biobase, twilight Imports: methods License: GPL (>= 2) MD5sum: 01260401149133d0280f01fd30be8f46 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 URL: http://compdiag.molgen.mpg.de/software/OrderedList.shtml git_url: https://git.bioconductor.org/packages/OrderedList git_branch: devel git_last_commit: fda4e2f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/OrderedList_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/OrderedList_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/OrderedList_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/OrderedList_1.77.0.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: 9 Package: ORFhunteR Version: 1.13.0 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 MD5sum: 16f38627f8127110845cb85f40db1cc0 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] (), Mikalai M. Yatskou [aut], Victor V. Skakun [aut], Maryna Chepeleva [aut] (), Petr V. Nazarov [aut] () Maintainer: Vasily V. Grinev VignetteBuilder: knitr BugReports: https://github.com/rfctbio-bsu/ORFhunteR/issues git_url: https://git.bioconductor.org/packages/ORFhunteR git_branch: devel git_last_commit: ba3699d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ORFhunteR_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ORFhunteR_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ORFhunteR_1.13.0.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: 1.25.0 Depends: R (>= 4.4.0), 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), curl, RCurl, data.table (>= 1.11.8), DESeq2 (>= 1.24.0), downloader, 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: 6d914c00b0e47bce6e9def51c63f5d3f 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 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: 39b5ee1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ORFik_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ORFik_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ORFik_1.25.0.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: 139 Package: Organism.dplyr Version: 1.33.0 Depends: R (>= 3.4), 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: 93c2c0654e633570181873f64adf72b2 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 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: 35ff40b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Organism.dplyr_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Organism.dplyr_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Organism.dplyr_1.33.0.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 dependsOnMe: annotation importsMe: Ularcirc dependencyCount: 95 Package: OrganismDbi Version: 1.47.0 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: 922d69a5ff24b60fc653248165ac35e8 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OrganismDbi git_branch: devel git_last_commit: a1b0b14 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/OrganismDbi_1.47.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/OrganismDbi_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/OrganismDbi_1.47.0.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: 105 Package: orthogene Version: 1.11.0 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 MD5sum: 6e4ee07f310d868ad1212d08e10798df 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] () Maintainer: Brian Schilder 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: 37712f2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/orthogene_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/orthogene_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/orthogene_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/orthogene_1.11.0.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: EWCE dependencyCount: 162 Package: orthos Version: 1.3.0 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 MD5sum: 31957e3a0b20a7a6c8ade7f9b5595933 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] (), Charlotte Soneson [aut] (), Michael Stadler [aut] (), Friedrich Miescher Institute for Biomedical Research [cph] Maintainer: Panagiotis Papasaikas VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/orthos git_branch: devel git_last_commit: ef3a3ea git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/orthos_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/orthos_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/orthos_1.3.0.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: 165 Package: OSAT Version: 1.53.0 Depends: methods,stats Suggests: xtable, Biobase License: Artistic-2.0 MD5sum: 4f411643649397c4cf4d9b8375274ad3 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 URL: http://www.biomedcentral.com/1471-2164/13/689 git_url: https://git.bioconductor.org/packages/OSAT git_branch: devel git_last_commit: 11dd51e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/OSAT_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/OSAT_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/OSAT_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/OSAT_1.53.0.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: 1.35.0 Depends: EBSeq, cluster, testthat, BiocParallel Suggests: BiocStyle License: Artistic-2.0 MD5sum: 8146d3c68b23ffad08030900a05a1386 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 git_url: https://git.bioconductor.org/packages/Oscope git_branch: devel git_last_commit: 31fb426 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Oscope_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Oscope_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Oscope_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Oscope_1.35.0.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: 60 Package: OTUbase Version: 1.55.0 Depends: R (>= 2.9.0), methods, S4Vectors, IRanges, ShortRead (>= 1.23.15), Biobase, vegan Imports: Biostrings License: Artistic-2.0 MD5sum: 342437b4e1e93103d514278e1ff93562 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 git_url: https://git.bioconductor.org/packages/OTUbase git_branch: devel git_last_commit: 341051b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/OTUbase_1.55.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/OTUbase_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/OTUbase_1.55.0.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: 1.23.0 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: 185f3ebe8dd182c127df1660b11c4db9 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] (), Agne Matuseviciute [aut], Michaela Fee Müller [ctb], Vicente Yepez [aut], Julien Gagneur [aut] Maintainer: Christian Mertes 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: 87c9a44 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/OUTRIDER_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/OUTRIDER_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/OUTRIDER_1.23.0.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: 171 Package: OutSplice Version: 1.5.0 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: 91094230940aade9bb05a5105f5b5729 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] (), Sandhya Kalavacherla [aut] (), Michael Considine [aut] (), Bahman Afsari [aut] (), Michael F. Ochs [aut], Joseph Califano [aut] (), Daria A. Gaykalova [aut] (), Elana Fertig [aut] (), Theresa Guo [cre, aut] () Maintainer: Theresa Guo 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: f691869 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/OutSplice_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/OutSplice_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/OutSplice_1.5.0.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: 1.21.0 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: 6663b0d204b94568c8fb27a9c79084d7 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 Maintainer: Lulu Chen 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: c3d58a3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/OVESEG_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/OVESEG_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/OVESEG_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/OVESEG_1.21.0.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: 1.39.0 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: 53e8dd1d7aaa0b4e7c9aa0b04f6ebb99 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 , Martin Eisenacher 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: ac79f3e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PAA_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PAA_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PAA_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PAA_1.39.0.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: 1.17.0 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 MD5sum: 3f1b59c869ff96d41d115182681dcd3d 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] (), Marco Catoni [aut] () Maintainer: Jack Gisby 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: 639edef git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/packFinder_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/packFinder_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/packFinder_1.17.0.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: 1.15.0 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 License: GPL (>=3) Archs: x64 MD5sum: 1d02d491f8790402b01625119568369a 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] (), Regina Manansala [aut], Florence Jaffrézic [ctb], Denis Laloë [aut], Paul Auer [aut] Maintainer: Andrea Rau URL: https://github.com/andreamrau/padma VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/padma git_branch: devel git_last_commit: abc7f40 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/padma_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/padma_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/padma_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/padma_1.15.0.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: 141 Package: PADOG Version: 1.47.0 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: cc3104e99bedb636af94b90035768766 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 ; Zhonghui Xu Maintainer: Adi L. Tarca git_url: https://git.bioconductor.org/packages/PADOG git_branch: devel git_last_commit: d43e5b7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PADOG_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PADOG_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PADOG_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PADOG_1.47.0.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: 1.15.0 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 MD5sum: 9a9136a5add206107af8b57c3eb636c9 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 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: 36e0576 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pageRank_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pageRank_1.15.0.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: 129 Package: PAIRADISE Version: 1.21.0 Depends: R (>= 3.6), nloptr Imports: SummarizedExperiment, S4Vectors, stats, methods, abind, BiocParallel Suggests: testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 33d3516acef1fde12f8412a2d9856c6c 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 , Levon Demirdjian VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PAIRADISE git_branch: devel git_last_commit: abe5ab0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PAIRADISE_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PAIRADISE_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PAIRADISE_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PAIRADISE_1.21.0.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: 75 Package: paircompviz Version: 1.43.0 Depends: R (>= 2.10), Rgraphviz Imports: Rgraphviz Suggests: multcomp, reshape, rpart, plyr, xtable License: GPL (>=3.0) MD5sum: a9b990c5caac8a7692808a88699155dd 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 git_url: https://git.bioconductor.org/packages/paircompviz git_branch: devel git_last_commit: b2ee5de git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/paircompviz_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/paircompviz_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/paircompviz_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/paircompviz_1.43.0.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: 10 Package: pairedGSEA Version: 1.5.0 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: 9a9da2fc4f8fe40f555397594c2099ab 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] (), Lars Rønn Olsen [aut] (), Kristoffer Vitting-Seerup [aut] () Maintainer: Søren Helweg Dam 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: 749f173 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pairedGSEA_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pairedGSEA_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pairedGSEA_1.5.0.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: 127 Package: pairkat Version: 1.11.0 Depends: R (>= 4.1) Imports: SummarizedExperiment, KEGGREST, igraph, data.table, methods, stats, magrittr, CompQuadForm, tibble Suggests: rmarkdown, knitr, BiocStyle, dplyr License: GPL-3 MD5sum: e0257b158637926d3a89f9b0c8d42d07 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 VignetteBuilder: knitr BugReports: https://github.com/Ghoshlab/pairkat/issues git_url: https://git.bioconductor.org/packages/pairkat git_branch: devel git_last_commit: 1abe175 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pairkat_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pairkat_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pairkat_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pairkat_1.11.0.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: 1.37.0 Depends: R (>= 3.0.0), methods, Biobase, BiocGenerics, Imports: matrixStats, igraph, ggplot2, grid, reshape, plyr, RUnit, hexbin Suggests: knitr, rmarkdown License: GPL-2 MD5sum: c2f896102db113f31fae03ecccd9f9e1 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 , Dan Schlauch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pandaR git_branch: devel git_last_commit: 9d69a49 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pandaR_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pandaR_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pandaR_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pandaR_1.37.0.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 dependsOnMe: netZooR dependencyCount: 45 Package: panelcn.mops Version: 1.27.0 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: c624f552fc4afe2a8986dddea40b5ea7 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/panelcn.mops git_branch: devel git_last_commit: b787b21 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/panelcn.mops_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/panelcn.mops_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/panelcn.mops_1.27.0.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: 1.9.0 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: 678999b1bb18d647a131bdea379f93d8 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 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: b2155ce git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PanomiR_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PanomiR_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PanomiR_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PanomiR_1.9.0.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: 162 Package: panp Version: 1.75.0 Depends: R (>= 2.10), affy (>= 1.23.4), Biobase (>= 2.5.5) Imports: Biobase, methods, stats, utils Suggests: gcrma License: GPL (>= 2) MD5sum: de3fc875b488f3c8e7f5768295377258 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 git_url: https://git.bioconductor.org/packages/panp git_branch: devel git_last_commit: 108d3b4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/panp_1.75.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/panp_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/panp_1.75.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/panp_1.75.0.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: 1.51.0 Depends: R (>= 2.14), igraph Imports: graphics, grDevices, MASS, methods, pvclust, stats, utils, RedeR Suggests: snow License: Artistic-2.0 MD5sum: a4ae46cf17b0099836eb0fc0b26138b1 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 Maintainer: Xin Wang git_url: https://git.bioconductor.org/packages/PANR git_branch: devel git_last_commit: 6fa36ec git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PANR_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PANR_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PANR_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PANR_1.51.0.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: PanViz Version: 1.7.0 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 MD5sum: 10d48cf0ef82c9334b55542a6936a44c NeedsCompilation: no 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 URL: https://github.com/LucaAnholt/PanViz VignetteBuilder: knitr BugReports: https://github.com/LucaAnholt/PanViz/issues git_url: https://git.bioconductor.org/packages/PanViz git_branch: devel git_last_commit: e40b683 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PanViz_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PanViz_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PanViz_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PanViz_1.7.0.tgz vignettes: vignettes/PanViz/inst/doc/my-vignette.html vignetteTitles: PanViz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PanViz/inst/doc/my-vignette.R dependencyCount: 48 Package: pareg Version: 1.9.0 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 MD5sum: db035c442f18aa85f07092286e1d0ca3 NeedsCompilation: no 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] () Maintainer: Kim Philipp Jablonski URL: https://github.com/cbg-ethz/pareg VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/pareg/issues git_url: https://git.bioconductor.org/packages/pareg git_branch: devel git_last_commit: c9db169 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pareg_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pareg_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pareg_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pareg_1.9.0.tgz vignettes: vignettes/pareg/inst/doc/pareg.html, vignettes/pareg/inst/doc/pathway_similarities.html vignetteTitles: Get started, Pathway similarities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pareg/inst/doc/pareg.R, vignettes/pareg/inst/doc/pathway_similarities.R dependencyCount: 206 Package: parglms Version: 1.37.0 Depends: methods Imports: BiocGenerics, BatchJobs, foreach, doParallel Suggests: RUnit, sandwich, MASS, knitr, GenomeInfoDb, GenomicRanges, gwascat, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 00ba7bd3ca2e9c25810d2330763a9127 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 Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/parglms git_branch: devel git_last_commit: f6cc821 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/parglms_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/parglms_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/parglms_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/parglms_1.37.0.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: 37 Package: parody Version: 1.63.0 Depends: R (>= 3.5.0), tools, utils Suggests: knitr, BiocStyle, testthat, rmarkdown License: Artistic-2.0 MD5sum: 0ae970b963b5e5fff7b00ed6d4309a74 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] () Maintainer: Vince Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/parody git_branch: devel git_last_commit: 7f8089d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/parody_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/parody_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/parody_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/parody_1.63.0.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: 1.3.0 Depends: R (>= 3.5.0) Imports: stats, data.table, depmixS4, Seurat, SingleCellExperiment, AnnotationHub, magrittr, GenomicRanges Suggests: BiocStyle, rmarkdown, knitr, IRanges, testthat (>= 3.0.0) License: GPL-2 MD5sum: b64d4a42dba7715fa26e6f72b5b57407 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/partCNV git_branch: devel git_last_commit: 8f48593 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/partCNV_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/partCNV_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/partCNV_1.3.0.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: 190 Package: PAST Version: 1.21.0 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: c1ac58f090da808bea23cd54a0737a51 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 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: b99ea77 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PAST_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PAST_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PAST_1.21.0.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: 96 Package: Path2PPI Version: 1.35.0 Depends: R (>= 3.2.1), igraph (>= 1.0.1), methods Suggests: knitr, rmarkdown, RUnit, BiocGenerics, BiocStyle License: GPL (>= 2) MD5sum: 0ef4d2b9ecb035dd69586a9b9528cdcc 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 URL: http://www.bioinformatik.uni-frankfurt.de/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Path2PPI git_branch: devel git_last_commit: 07a3c56 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Path2PPI_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Path2PPI_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Path2PPI_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Path2PPI_1.35.0.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: 1.43.0 Imports: R.oo, princurve (>= 2.0.4) License: Artistic-1.0 MD5sum: 5352cf4717f22dfbe3c67af45e915748 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 git_url: https://git.bioconductor.org/packages/pathifier git_branch: devel git_last_commit: 9db33da git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pathifier_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pathifier_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pathifier_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pathifier_1.43.0.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 dependencyCount: 9 Package: pathlinkR Version: 1.1.0 Depends: R (>= 4.3.0) Imports: circlize, clusterProfiler, ComplexHeatmap, dplyr, 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: a24bf834f2f68facf578bb5480d88e32 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] (), Andy An [aut] Maintainer: Travis Blimkie 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: 896684f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pathlinkR_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pathlinkR_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pathlinkR_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pathlinkR_1.1.0.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: 197 Package: PathNet Version: 1.45.0 Suggests: PathNetData, RUnit, BiocGenerics License: GPL-3 MD5sum: 6a701c074210a4737b45db25e3953cb1 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 , Anders Wallqvist , and Jaques Reifman Maintainer: Ludwig Geistlinger git_url: https://git.bioconductor.org/packages/PathNet git_branch: devel git_last_commit: d75f06b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PathNet_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PathNet_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PathNet_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PathNet_1.45.0.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: 1.31.0 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: 9aac8b0ff5e07d85bde0fb6e48ddd0aa 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 , Matthew Bendall , Sandro Valenzuela Diaz , Eduardo Castro , Tyler Faits , Yue Zhao , Anthony Nicholas Federico , W. Evan Johnson Maintainer: Solaiappan Manimaran , Yue Zhao 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: abc2a55 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PathoStat_1.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PathoStat_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PathoStat_1.31.0.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: 208 Package: pathRender Version: 1.73.0 Depends: graph, Rgraphviz, RColorBrewer, cMAP, AnnotationDbi, methods, stats4 Suggests: ALL, hgu95av2.db License: LGPL MD5sum: b09fb70eb367999beec0ad7d01cdd716 NeedsCompilation: no Title: Render molecular pathways Description: build graphs from pathway databases, render them by Rgraphviz. biocViews: GraphAndNetwork, Pathways, Visualization Author: Li Long Maintainer: Vince Carey URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/pathRender git_branch: devel git_last_commit: 420cb5c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pathRender_1.73.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pathRender_1.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pathRender_1.73.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pathRender_1.73.0.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: 1.45.0 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: 7d65fde7bfabae2c4c559e9af99eeb63 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 URL: https://github.com/datapplab/pathview, https://pathview.uncc.edu/ git_url: https://git.bioconductor.org/packages/pathview git_branch: devel git_last_commit: 4961e0c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pathview_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pathview_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pathview_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pathview_1.45.0.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, MAGeCKFlute, TCGAWorkflow, lilikoi, SQMtools suggestsMe: gage, TCGAbiolinks, gageData, CAGEWorkflow, ReporterScore dependencyCount: 53 Package: pathwayPCA Version: 1.21.0 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: 67dceb54dfa47b3bf4634e0d475e0a75 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) ; Chen et al. (2010) ; and Chen (2011) . 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 URL: VignetteBuilder: knitr BugReports: https://github.com/gabrielodom/pathwayPCA/issues git_url: https://git.bioconductor.org/packages/pathwayPCA git_branch: devel git_last_commit: b37e7a4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pathwayPCA_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pathwayPCA_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pathwayPCA_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pathwayPCA_1.21.0.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: 2.31.0 Imports: DESeq2, SummarizedExperiment, 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: 157c2535b2697a8236a626c3e279279e 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] () Maintainer: Federico Marini 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: 883a346 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pcaExplorer_2.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pcaExplorer_2.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pcaExplorer_2.31.0.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: 188 Package: pcaMethods Version: 1.97.0 Depends: Biobase, methods Imports: BiocGenerics, Rcpp (>= 0.11.3), MASS LinkingTo: Rcpp Suggests: matrixStats, lattice, ggplot2 License: GPL (>= 3) MD5sum: 6bd7aaff1848aaeb855b35a772b3d864 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 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: 5cb7432 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pcaMethods_1.97.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pcaMethods_1.97.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pcaMethods_1.97.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pcaMethods_1.97.0.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, LOST, MetabolomicsBasics, missCompare, multiDimBio, pmartR, polyRAD, promor, RAMClustR, santaR, scMappR suggestsMe: autonomics, cardelino, MsCoreUtils, QFeatures, qmtools, mtbls2, pagoda2, rsvddpd dependencyCount: 9 Package: PCAN Version: 1.33.0 Depends: R (>= 3.3), BiocParallel Imports: grDevices, stats Suggests: BiocStyle, knitr, rmarkdown, reactome.db, STRINGdb License: CC BY-NC-ND 4.0 MD5sum: 10b730fd827cbedc823d9112a8ecea6b 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 and Patrice Godard VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PCAN git_branch: devel git_last_commit: 1cd058a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PCAN_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PCAN_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PCAN_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PCAN_1.33.0.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: 2.17.0 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 Archs: x64 MD5sum: 4dc198f8f5780fb5af1f87455472ab78 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 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: f7b5819 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PCAtools_2.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PCAtools_2.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PCAtools_2.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PCAtools_2.17.0.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: COTAN, CRISPRball, regionalpcs suggestsMe: RNAseqCovarImpute, scDataviz dependencyCount: 75 Package: PDATK Version: 1.13.0 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: eb1916b3fbc0d14f77cb858acc97fc1c 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 VignetteBuilder: knitr BugReports: https://github.com/bhklab/PDATK/issues git_url: https://git.bioconductor.org/packages/PDATK git_branch: devel git_last_commit: e15f1cf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PDATK_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PDATK_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PDATK_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PDATK_1.13.0.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: 270 Package: pdInfoBuilder Version: 1.69.0 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: 21f1639584279d7585cdd0224774519c 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 git_url: https://git.bioconductor.org/packages/pdInfoBuilder git_branch: devel git_last_commit: d7b9304 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pdInfoBuilder_1.69.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pdInfoBuilder_1.69.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pdInfoBuilder_1.69.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pdInfoBuilder_1.69.0.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: 1.15.0 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: 9fc10465b7ad85abe86d881fb5edb142 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 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: c8c47c5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PeacoQC_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PeacoQC_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PeacoQC_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PeacoQC_1.15.0.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: 84 Package: peakPantheR Version: 1.19.0 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: 5921b67b4ab8525c6cfcda3fe8c7b925 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] (), Goncalo Correia [aut] (), Jake Pearce [ctb], Caroline Sands [ctb] Maintainer: Arnaud Wolfer 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: aefa25b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/peakPantheR_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/peakPantheR_1.19.0.zip 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: 1.41.0 Depends: R (>= 3.3) Imports: ROTS, limma, affy, genefilter, preprocessCore, aroma.affymetrix, aroma.core Suggests: SpikeIn License: GPL (>= 2) MD5sum: 02a36462cbdbafccd7cc8f8fc75c7038 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 git_url: https://git.bioconductor.org/packages/PECA git_branch: devel git_last_commit: c3ba487 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PECA_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PECA_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PECA_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PECA_1.41.0.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: 88 Package: peco Version: 1.17.0 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) MD5sum: 03d6dd10876146f2096580a3d45bbeee 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 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: 9ceb0d7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/peco_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/peco_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/peco_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/peco_1.17.0.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: 120 Package: Pedixplorer Version: 1.1.0 Depends: R (>= 4.3.0) Imports: graphics, stats, methods, ggplot2, utils, grDevices, stringr, plyr, dplyr, tidyr, quadprog, Matrix, S4Vectors, testthat Suggests: diffviewer, vdiffr, rmarkdown, BiocStyle, knitr, withr, magick License: Artistic-2.0 MD5sum: bba8f3cd88106aff1d2c9c17690f039e 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, Alignment Author: Louis Le Nézet [aut, cre] (), Jason Sinnwell [aut], Terry Therneau [aut], Daniel Schaid [ctb], Elizabeth Atkinson [ctb], Louis Le Nezet [ctb] Maintainer: Louis Le Nézet URL: https://github.com/LouisLeNezet/Pedixplorer VignetteBuilder: knitr BugReports: https://github.com/LouisLeLezet/Pedixplorer/issues git_url: https://git.bioconductor.org/packages/Pedixplorer git_branch: devel git_last_commit: 569e94f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Pedixplorer_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Pedixplorer_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Pedixplorer_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Pedixplorer_1.1.0.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/pedigree.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/pedigree.R dependencyCount: 68 Package: pengls Version: 1.11.0 Depends: R (>= 4.2.0) Imports: glmnet, nlme, stats, BiocParallel Suggests: knitr,rmarkdown,testthat License: GPL-2 MD5sum: af75c1d4e4dc667c535c66082cd1d479 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] () Maintainer: Stijn Hawinkel VignetteBuilder: knitr BugReports: https://github.com/sthawinke/pengls git_url: https://git.bioconductor.org/packages/pengls git_branch: devel git_last_commit: 0b774d4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pengls_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pengls_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pengls_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pengls_1.11.0.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: PepsNMR Version: 1.23.0 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: 7d0d866efd6f1d7564812871afbec05b 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 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: 9040871 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PepsNMR_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PepsNMR_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PepsNMR_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PepsNMR_1.23.0.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: 1.39.0 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: 828cf1295e27fbae3ad9bd1dadea1fef 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 URL: https://github.com/RGLab/pepStat VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pepStat git_branch: devel git_last_commit: 3b4c2dd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pepStat_1.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pepStat_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pepStat_1.39.0.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: 1.39.0 Depends: R (>= 3.0.1) Imports: XML(>= 3.98-1.1) Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: b3795acb0e1b7557e21c29dd0cb84c94 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 git_url: https://git.bioconductor.org/packages/pepXMLTab git_branch: devel git_last_commit: 9fc7d2e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pepXMLTab_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pepXMLTab_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pepXMLTab_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pepXMLTab_1.39.0.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: 1.15.0 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: 4c7eee780b1f6630795e1355445330a1 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] () Maintainer: Jacques Serizay 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: cf66ae5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/periodicDNA_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/periodicDNA_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/periodicDNA_1.15.0.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: 1.5.0 Depends: R (>= 4.3.0), readr, stringr, dplyr Imports: utils, tibble, magrittr Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 30cae337aad5130aca0d3fe1f9c5d1d6 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] () Maintainer: Kristoffer Vitting-Seerup VignetteBuilder: knitr BugReports: https://github.com/kvittingseerup/pfamAnalyzeR/issues git_url: https://git.bioconductor.org/packages/pfamAnalyzeR git_branch: devel git_last_commit: b38e61b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pfamAnalyzeR_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pfamAnalyzeR_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pfamAnalyzeR_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pfamAnalyzeR_1.5.0.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: 1.29.0 Imports: utils, stats Suggests: knitr, testthat, rmarkdown License: GPL (>= 2) MD5sum: aa1626ef75e15bc4592dd495d4d7c4e5 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 Maintainer: Gabriela Cohen-Freue VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pgca git_branch: devel git_last_commit: 6ce7c15 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pgca_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pgca_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pgca_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pgca_1.29.0.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: 1.1.2 Depends: R (>= 4.2) Imports: utils, methods, grDevices, graphics, circlize, httr, dplyr, attempt, lubridate, survival, survminer, ggplot2, plyr, GenomicRanges, SummarizedExperiment, S4Vectors Suggests: BiocStyle, rmarkdown, knitr, testthat License: Artistic-2.0 MD5sum: 7651c1c6b7dd675ef3297762243c8a77 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] (), Michael Baudis [aut] () Maintainer: Hangjia Zhao 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: 833ff02 git_last_commit_date: 2024-05-03 Date/Publication: 2024-05-03 source.ver: src/contrib/pgxRpi_1.1.2.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pgxRpi_1.1.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pgxRpi_1.1.2.tgz vignettes: vignettes/pgxRpi/inst/doc/Introduction_1_loadmetadata.html, vignettes/pgxRpi/inst/doc/Introduction_2_loadvariants.html, vignettes/pgxRpi/inst/doc/Introduction_3_loadfrequency.html, vignettes/pgxRpi/inst/doc/Introduction_4_process_pgxseg.html vignetteTitles: Introduction_1_loadmetadata, Introduction_2_loadvariants, Introduction_3_loadfrequency, Introduction_4_process_pgxseg hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pgxRpi/inst/doc/Introduction_1_loadmetadata.R, vignettes/pgxRpi/inst/doc/Introduction_2_loadvariants.R, vignettes/pgxRpi/inst/doc/Introduction_3_loadfrequency.R, vignettes/pgxRpi/inst/doc/Introduction_4_process_pgxseg.R dependencyCount: 142 Package: phantasus Version: 1.25.0 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, 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: 19339ed8fe07b8904cec326fb66b4e13 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: Daria Zenkova [aut], Vladislav Kamenev [aut], Rita Sablina [ctb], Maxim Kleverov [ctb], Maxim Artyomov [aut], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev 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: 1a75bcc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/phantasus_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/phantasus_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/phantasus_1.25.0.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: 152 Package: phantasusLite Version: 1.3.0 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: 7f906ed8882700829dec67aa8e5ce1f9 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 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: 236c8d2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/phantasusLite_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/phantasusLite_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/phantasusLite_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/phantasusLite_1.3.0.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: 3.9.0 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: f767055a3276aadc50daf57c8abd20fd 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 VignetteBuilder: knitr BugReports: https://github.com/bhklab/PharmacoGx/issues git_url: https://git.bioconductor.org/packages/PharmacoGx git_branch: devel git_last_commit: e7ed1c4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PharmacoGx_3.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PharmacoGx_3.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PharmacoGx_3.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PharmacoGx_3.9.0.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: 155 Package: PhenoGeneRanker Version: 1.13.0 Imports: igraph, Matrix, foreach, doParallel, dplyr, stats, utils, parallel Suggests: knitr, rmarkdown License: Creative Commons Attribution 4.0 International License MD5sum: 3c93b662aeed283486816dd99c4ce2cb 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PhenoGeneRanker git_branch: devel git_last_commit: 091ee2d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PhenoGeneRanker_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PhenoGeneRanker_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PhenoGeneRanker_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PhenoGeneRanker_1.13.0.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: 1.7.0 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 Archs: x64 MD5sum: fedfcc481097b450af430c4bf0cee939 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] (), Natacha Lenuzza [ctb], Marie Tremblay-Franco [ctb], Alyssa Imbert [ctb], Pierrick Roger [ctb], Eric Venot [ctb], Sylvain Dechaumet [ctb] Maintainer: Etienne A. Thevenot 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: d35e2b6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/phenomis_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/phenomis_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/phenomis_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/phenomis_1.7.0.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 dependencyCount: 156 Package: phenopath Version: 1.29.0 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: bbc745e0e6469e13702f21171ae985b1 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phenopath git_branch: devel git_last_commit: 92c7800 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/phenopath_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/phenopath_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/phenopath_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/phenopath_1.29.0.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: 71 Package: phenoTest Version: 1.53.0 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: ed75f4491e98feccf8c203be43673a2c 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 git_url: https://git.bioconductor.org/packages/phenoTest git_branch: devel git_last_commit: 6054a5d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/phenoTest_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/phenoTest_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/phenoTest_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/phenoTest_1.53.0.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: 146 Package: PhenStat Version: 2.41.0 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 Archs: x64 MD5sum: 17de650cf062fa2e5d497845681ed07f 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 git_url: https://git.bioconductor.org/packages/PhenStat git_branch: devel git_last_commit: 0171dda git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PhenStat_2.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PhenStat_2.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PhenStat_2.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PhenStat_2.41.0.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: 127 Package: philr Version: 1.31.0 Imports: ape, phangorn, tidyr, ggplot2, ggtree, methods Suggests: testthat, knitr, ecodist, rmarkdown, BiocStyle, phyloseq, SummarizedExperiment, TreeSummarizedExperiment, glmnet, dplyr, mia License: GPL-3 MD5sum: 0e4767901c5ad971c18a5ccf13474035 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] () Maintainer: Justin Silverman 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: a51727c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/philr_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/philr_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/philr_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/philr_1.31.0.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 dependencyCount: 67 Package: PhIPData Version: 1.13.0 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 MD5sum: cee8654c74737d4da36fd5dfcb0c11e6 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] (), Rob Scharpf [aut], Ingo Ruczinski [aut] Maintainer: Athena Chen VignetteBuilder: knitr BugReports: https://github.com/athchen/PhIPData/issues git_url: https://git.bioconductor.org/packages/PhIPData git_branch: devel git_last_commit: a362430 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PhIPData_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PhIPData_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PhIPData_1.13.0.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: 73 Package: phosphonormalizer Version: 1.29.0 Depends: R (>= 4.0) Imports: plyr, stats, graphics, matrixStats, methods Suggests: knitr, rmarkdown, testthat Enhances: MSnbase License: GPL (>= 2) MD5sum: 9617cd870dcd70ca8711481d31b22c04 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phosphonormalizer git_branch: devel git_last_commit: 1aed6bf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/phosphonormalizer_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/phosphonormalizer_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/phosphonormalizer_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/phosphonormalizer_1.29.0.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: 1.15.0 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: a8ee54e612b83bbdae4d71991482582c 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PhosR git_branch: devel git_last_commit: 9a43a09 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PhosR_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PhosR_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PhosR_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PhosR_1.15.0.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: 160 Package: PhyloProfile Version: 1.19.0 Depends: R (>= 4.3.0) Imports: ape, bioDist, BiocStyle, Biostrings, colourpicker, data.table, DT, energy, ExperimentHub, ggplot2, gridExtra, pbapply, RColorBrewer, RCurl, shiny, shinyBS, shinycssloaders, shinyFiles, shinyjs, stringr, OmaDB, plyr, xml2, zoo, yaml Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 34ccde1a0c2bd6b7aa8c56ec3f4672b0 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 Author: Vinh Tran [aut, cre] (), Bastian Greshake Tzovaras [aut], Ingo Ebersberger [aut], Carla Mölbert [ctb] Maintainer: Vinh Tran 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: a4a2f9d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PhyloProfile_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PhyloProfile_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PhyloProfile_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PhyloProfile_1.19.0.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: 140 Package: phyloseq Version: 1.49.0 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: a189d7ac654e0555fae423943d375fb9 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 , Susan Holmes , with contributions from Gregory Jordan and Scott Chamberlain Maintainer: Paul J. McMurdie 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: 6bb7e90 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/phyloseq_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/phyloseq_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/phyloseq_1.49.0.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: benchdamic, combi, dar, MBECS, microbiomeDASim, microbiomeMarker, PathoStat, RCM, reconsi, RPA, SimBu, SPsimSeq, HMP2Data, adaptiveGPCA, breakaway, chem16S, HTSSIP, HybridMicrobiomes, microbial, mixKernel, SigTree, SIPmg, speedytax, TaxaNorm, treeDA suggestsMe: CBEA, decontam, lefser, MGnifyR, mia, MicrobiotaProcess, MMUPHin, philr, HMP16SData, corncob, fido, file2meco, metacoder, pctax, phyloregion dependencyCount: 82 Package: piano Version: 2.21.0 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: fd93d69cf27eb3be116b06a09da958c0 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 and Intawat Nookaew Maintainer: Leif Varemo Wigge 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: 7b16144 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/piano_2.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/piano_2.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/piano_2.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/piano_2.21.0.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: 102 Package: pickgene Version: 1.77.0 Imports: graphics, grDevices, MASS, stats, utils License: GPL (>= 2) MD5sum: 8ad7b80aec24d9b03f6e3eff9e4bcbe0 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 Maintainer: Brian S. Yandell URL: http://www.stat.wisc.edu/~yandell/statgen git_url: https://git.bioconductor.org/packages/pickgene git_branch: devel git_last_commit: 4ee579b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pickgene_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pickgene_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pickgene_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pickgene_1.77.0.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: 2.49.0 Depends: R (>= 3.0.0) Imports: utils, stats, graphics, grDevices, methods, IRanges, GenomicRanges, Rsamtools, GenomicAlignments Suggests: rtracklayer, parallel, knitr License: Artistic-2.0 MD5sum: eeb8bad62de005af54d8bdc6e22bc780 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 , Raphael Gottardo Maintainer: Renan Sauteraud 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: 5328ba5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PICS_2.49.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PICS_2.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PICS_2.49.0.tgz 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: 1.31.0 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: 9d759dbf6540ce9f099b416cf7181147 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Pigengene git_branch: devel git_last_commit: 864b6c9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Pigengene_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Pigengene_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Pigengene_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Pigengene_1.31.0.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: 2.49.0 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: a190484c46d98fd900ba5ec676501ff1 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 , Raphael Gottardo , Sangsoon Woo Maintainer: Renan Sauteraud git_url: https://git.bioconductor.org/packages/PING git_branch: devel git_last_commit: 4eff825 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PING_2.49.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PING_2.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PING_2.49.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 174 Package: pipeComp Version: 1.15.0 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: 0fb80e18d70a4bb575a605704eea02e8 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] (), Anthony Sonrel [aut] (), Mark D. Robinson [aut, fnd] () Maintainer: Pierre-Luc Germain 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: 1356fef git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pipeComp_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pipeComp_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pipeComp_1.15.0.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: 219 Package: pipeFrame Version: 1.21.0 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: 2f1e2a31c8a8a299b39119fdc3173b50 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 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: e97ea2e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pipeFrame_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pipeFrame_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pipeFrame_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pipeFrame_1.21.0.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: 1.1.0 Depends: R (>= 4.4.0) Imports: dplyr, utils, stats, GenomicRanges, BiocGenerics, methods Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: fc8c86700d4b1b6fa71e84813f1bfa6f 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] () Maintainer: Quinlan Furumo 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: 0eda05f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PIPETS_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PIPETS_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PIPETS_1.1.0.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: 39 Package: PIUMA Version: 1.1.0 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: 3300bb2887c229ff44eb4195e0544e14 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] (), Arianna Dagliati [aut] (), Alessia Gerbasi [aut] (), Giuseppe Albi [aut], Laura Ballarini [aut], Luca Piacentini [aut] () Maintainer: Mattia Chiesa 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: f13d9a2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PIUMA_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PIUMA_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PIUMA_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PIUMA_1.1.0.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: 1.13.0 Depends: R (>= 4.3) Imports: methods, tibble, magrittr, dplyr Suggests: ggplot2, testthat, tidyr, scales, minfi, EpiDISH, knitr, rmarkdown License: GPL-2 MD5sum: ebb61962d31c16c14c13138f599ce0d1 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 [ctb] Maintainer: Victor Yuan URL: https://victor.rbind.io/planet, http://github.com/wvictor14/planet VignetteBuilder: knitr BugReports: http://github.com/wvictor14/planet/issues git_url: https://git.bioconductor.org/packages/planet git_branch: devel git_last_commit: 6513ac4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/planet_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/planet_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/planet_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/planet_1.13.0.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 dependencyCount: 20 Package: planttfhunter Version: 1.5.0 Depends: R (>= 4.2.0) Imports: Biostrings, SummarizedExperiment, utils, methods Suggests: BiocStyle, covr, sessioninfo, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: 3bf1e2b9d117b30167ba74de303ad350 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: Fabrício Almeida-Silva [aut, cre] (), Yves Van de Peer [aut] () Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/planttfhunter SystemRequirements: HMMER VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/planttfhunter git_url: https://git.bioconductor.org/packages/planttfhunter git_branch: devel git_last_commit: 7fcb973 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/planttfhunter_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/planttfhunter_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/planttfhunter_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/planttfhunter_1.5.0.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: 1.3.0 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: 40d3d4361e1c05431d2e01b8a3440a74 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/plasmut git_branch: devel git_last_commit: 816f8b0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/plasmut_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/plasmut_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/plasmut_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/plasmut_1.3.0.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: 1.77.0 Depends: R (>= 2.10) Imports: utils, Biobase (>= 2.5.5), MASS, methods License: GPL-2 MD5sum: 34264a07f8bd4accfb98c4af44776231 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 and Norman Pavelka Maintainer: Norman Pavelka URL: http://www.genopolis.it git_url: https://git.bioconductor.org/packages/plgem git_branch: devel git_last_commit: 148a1b3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/plgem_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/plgem_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/plgem_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/plgem_1.77.0.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: 8 Package: plier Version: 1.75.0 Depends: R (>= 2.0), methods Imports: affy, Biobase, methods License: GPL (>= 2) MD5sum: 6cae8acef9aa61480b7936b18157b929 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 git_url: https://git.bioconductor.org/packages/plier git_branch: devel git_last_commit: ccc26f1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/plier_1.75.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/plier_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/plier_1.75.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/plier_1.75.0.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: piano dependencyCount: 12 Package: plotgardener Version: 1.11.0 Depends: R (>= 4.1.0) Imports: curl, data.table, dplyr, GenomeInfoDb, GenomicRanges, grDevices, grid, ggplotify, IRanges, methods, plyranges, purrr, Rcpp, RColorBrewer, 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: 644df0ca65ed22b5da12975f9cb20ec7 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, cph] Maintainer: Nicole Kramer 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: afa8d3a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/plotgardener_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/plotgardener_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/plotgardener_1.11.0.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 dependencyCount: 100 Package: plotGrouper Version: 1.23.0 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: 857a77fa3c96c91c19a84b66b0c89a07 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 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: 6ea75e2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/plotGrouper_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/plotGrouper_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/plotGrouper_1.23.0.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: 143 Package: PLPE Version: 1.65.0 Depends: R (>= 2.6.2), Biobase (>= 2.5.5), LPE, MASS, methods License: GPL (>= 2) MD5sum: f40691e71ba48e2deff14aa7c76e43cd 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 and Jae K. Lee Maintainer: Soo-heang Eo URL: http://www.korea.ac.kr/~stat2242/ git_url: https://git.bioconductor.org/packages/PLPE git_branch: devel git_last_commit: b67b910 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PLPE_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PLPE_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PLPE_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PLPE_1.65.0.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: 9 Package: PLSDAbatch Version: 1.1.0 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 Archs: x64 MD5sum: 94061442c0c7a53614f07dbeac8b88aa 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-batch”. 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] (), Kim-Anh Le Cao [aut] Maintainer: Yiwen (Eva) Wang 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: cea9d4e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PLSDAbatch_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PLSDAbatch_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PLSDAbatch_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PLSDAbatch_1.1.0.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: 168 Package: plyinteractions Version: 1.3.0 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 MD5sum: 204308f2f921d58a4eb662b773b917e6 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 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: 08914c7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/plyinteractions_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/plyinteractions_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/plyinteractions_1.3.0.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: 76 Package: plyranges Version: 1.25.0 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: 8b565da925e0af23c16eb403aec79629 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] (), Michael Lawrence [aut, ctb], Dianne Cook [aut, ctb], Spencer Nystrom [ctb] (), Pierre-Paul Axisa [ctb], Michael Love [ctb, cre] Maintainer: Michael Love VignetteBuilder: knitr BugReports: https://github.com/tidyomics/plyranges git_url: https://git.bioconductor.org/packages/plyranges git_branch: devel git_last_commit: 0dc7403 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/plyranges_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/plyranges_1.25.0.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, GenomicPlot, InPAS, katdetectr, mariner, methylCC, multicrispr, nearBynding, nullranges, plotgardener, plyinteractions, SARC, tidyomics, fluentGenomics, MOCHA suggestsMe: extraChIPs, memes, svaNUMT, svaRetro, tidyCoverage, CTCF dependencyCount: 73 Package: pmm Version: 1.37.0 Depends: R (>= 2.10) Imports: lme4, splines License: GPL-3 MD5sum: d7202163c969e04e99d806a024f0049d 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 git_url: https://git.bioconductor.org/packages/pmm git_branch: devel git_last_commit: 0d59076 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pmm_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pmm_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pmm_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pmm_1.37.0.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: 50 Package: pmp Version: 1.17.0 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: ab27be2e9e6b80bfdba91b1ddf16e8e9 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pmp git_branch: devel git_last_commit: e7acb46 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pmp_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pmp_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pmp_1.17.0.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: 1.13.0 Depends: R (>= 4.4) Imports: ggplot2, gridExtra, mclust, diptest, rlist, shiny, DT, LaplacesDemon, purrr, shinyjs, readr, grDevices, stats, utils Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: 2e8a92bee4c46aac56762305f4720449 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PoDCall git_branch: devel git_last_commit: febd185 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PoDCall_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PoDCall_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PoDCall_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PoDCall_1.13.0.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: 1.37.0 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: 3b40ac5a80decd5ed4c87c4505e56e12 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 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: 3200a3e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/podkat_1.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/podkat_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/podkat_1.37.0.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: POMA Version: 1.15.0 Depends: R (>= 4.0) Imports: broom, caret, ComplexHeatmap, dbscan, dplyr, DESeq2, FSA, ggplot2, ggrepel, glmnet, impute, janitor, limma, lme4, magrittr, MASS, mixOmics, 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: f422e65f16bcaacdf517337effad36be 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) for more details. biocViews: BatchEffect, Classification, Clustering, DecisionTree, DimensionReduction, MultidimensionalScaling, Normalization, Preprocessing, PrincipalComponent, Regression, RNASeq, Software, StatisticalMethod, Visualization Author: Pol Castellano-Escuder [aut, cre] () Maintainer: Pol Castellano-Escuder 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: 7623cc5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/POMA_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/POMA_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/POMA_1.15.0.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 suggestsMe: fobitools dependencyCount: 210 Package: powerTCR Version: 1.25.0 Imports: cubature, doParallel, evmix, foreach, magrittr, methods, parallel, purrr, stats, truncdist, vegan, VGAM Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: a01fd827dbe02d571086afb75943fda0 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/powerTCR git_branch: devel git_last_commit: 8566201 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/powerTCR_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/powerTCR_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/powerTCR_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/powerTCR_1.25.0.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: 1.13.0 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: f80ae56db15a11b020e77880b543bb59 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/POWSC git_branch: devel git_last_commit: 2cefe0c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/POWSC_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/POWSC_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/POWSC_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/POWSC_1.13.0.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: 1.13.0 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: bedb989260cced5f573ce13e9458309c 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] () Maintainer: Stefano Mangiola 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: 9168270 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ppcseq_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ppcseq_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ppcseq_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ppcseq_1.13.0.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: 1.31.0 Depends: biomaRt, fgsea, kernlab, ggplot2, igraph, STRINGdb, yeastExpData Imports: httr, grDevices, graphics, stats, utils License: Artistic-2.0 MD5sum: 9ea8d61ff1e8d33cbd30d922623da171 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 git_url: https://git.bioconductor.org/packages/PPInfer git_branch: devel git_last_commit: 789e930 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PPInfer_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PPInfer_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PPInfer_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PPInfer_1.31.0.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: 117 Package: pqsfinder Version: 2.21.0 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: 197ac8775d2b2584eb154e3aec859acb 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 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: e618113 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pqsfinder_2.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pqsfinder_2.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pqsfinder_2.21.0.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: 1.21.0 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: bf794c470ca7a7b43f13a3338da99ae7 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 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: e2a15b4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pram_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pram_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pram_1.21.0.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: 1.45.0 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 MD5sum: 8e40fcff93162564da1c89cfd031dc6d 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 git_url: https://git.bioconductor.org/packages/prebs git_branch: devel git_last_commit: fa046cf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/prebs_1.45.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/prebs_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/prebs_1.45.0.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: 1.15.0 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: 5b3a5f282703523b3d757f5cbe8b6711 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 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: 01a9559 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/preciseTAD_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/preciseTAD_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/preciseTAD_1.15.0.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: 180 Package: PREDA Version: 1.51.0 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: 47269a614ad8cca5b43988245ec76eaf 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 Maintainer: Francesco Ferrari git_url: https://git.bioconductor.org/packages/PREDA git_branch: devel git_last_commit: 6a76d46 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PREDA_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PREDA_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PREDA_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PREDA_1.51.0.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: 1.67.0 Imports: stats License: LGPL (>= 2) MD5sum: 1ae573c47aed4fe08f0c4e793f217b3e NeedsCompilation: yes Title: A collection of pre-processing functions Description: A library of core preprocessing routines. biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/preprocessCore git_url: https://git.bioconductor.org/packages/preprocessCore git_branch: devel git_last_commit: 3a8599b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/preprocessCore_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/preprocessCore_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/preprocessCore_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/preprocessCore_1.67.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyPLM, cqn, crlmm importsMe: affy, BloodGen3Module, bnbc, cn.farms, 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, qPLEXanalyzer, quantiseqr, sesame, soGGi, tidybulk, yarn, GSE13015, ADAPTS, bulkAnalyseR, cinaR, FARDEEP, HEMDAG, lilikoi, MetaIntegrator, MiDA, noise, noisyr, oncoPredict, retriever, SMDIC, WGCNA suggestsMe: DAPAR, MsCoreUtils, multiClust, QFeatures, roastgsa, scp, splatter, wateRmelon, aroma.affymetrix, aroma.core, glycanr, SCdeconR, wrMisc, wrTopDownFrag linksToMe: affy, affyPLM, crlmm, oligo dependencyCount: 1 Package: primirTSS Version: 1.23.0 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: f11b20b9919d02f6b00d0a85d1e79ddc 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 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: e1f8021 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/primirTSS_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/primirTSS_1.23.0.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: 196 Package: PrInCE Version: 1.21.0 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: ac6f6f3c22ea2d5e27c880ceb375c50e 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 VignetteBuilder: knitr BugReports: https://github.com/fosterlab/PrInCE/issues git_url: https://git.bioconductor.org/packages/PrInCE git_branch: devel git_last_commit: 85124c5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PrInCE_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PrInCE_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PrInCE_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PrInCE_1.21.0.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: 1.15.0 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: 3e79a628e5abeff8b94786c32f4b6e9f 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] (), Jonathan Göke [aut], Joseph Lee [cre] Maintainer: Joseph Lee URL: https://github.com/GoekeLab/proActiv VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/proActiv git_branch: devel git_last_commit: 6a63792 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/proActiv_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/proActiv_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/proActiv_1.15.0.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: 125 Package: proBAMr Version: 1.39.0 Depends: R (>= 3.0.1), IRanges, AnnotationDbi Imports: GenomicRanges, Biostrings, GenomicFeatures, txdbmaker, rtracklayer Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 300e8579f3723d0816abdefc06f16525 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 git_url: https://git.bioconductor.org/packages/proBAMr git_branch: devel git_last_commit: e5b7be3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/proBAMr_1.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/proBAMr_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/proBAMr_1.39.0.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: 102 Package: PROcess Version: 1.81.0 Depends: Icens Imports: graphics, grDevices, Icens, stats, utils License: Artistic-2.0 Archs: x64 MD5sum: 67be4bc66563e61b29456b2b7c166a9c 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 git_url: https://git.bioconductor.org/packages/PROcess git_branch: devel git_last_commit: 181d5b3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PROcess_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PROcess_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PROcess_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PROcess_1.81.0.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: 2.33.0 Depends: R (>= 3.3.0), kebabs Imports: methods, stats, graphics, S4Vectors, Biostrings, utils Suggests: knitr License: GPL (>= 2) MD5sum: 7e53fb2d4776415747d8801f59cad835 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 URL: https://github.com/UBod/procoil VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/procoil git_branch: devel git_last_commit: d0e596e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/procoil_2.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/procoil_2.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/procoil_2.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/procoil_2.33.0.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: 1.19.0 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: fd93a6b03f1c8e14618eb336e5f9f95f 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] (), Simon Anders [ths] () Maintainer: Constantin Ahlmann-Eltze 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: 82055d0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/proDA_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/proDA_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/proDA_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/proDA_1.19.0.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: 1.21.0 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: af2c0b021a3403addd9bf243695a7be4 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 , Doug Barrows VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/profileplyr git_branch: devel git_last_commit: c54d5bd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/profileplyr_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/profileplyr_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/profileplyr_1.21.0.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: 213 Package: profileScoreDist Version: 1.33.0 Depends: R(>= 3.3) Imports: Rcpp, BiocGenerics, methods, graphics LinkingTo: Rcpp Suggests: BiocStyle, knitr, MotifDb License: MIT + file LICENSE MD5sum: d954e00ef7e79cede32d69536ebe5675 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/profileScoreDist git_branch: devel git_last_commit: d842805 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/profileScoreDist_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/profileScoreDist_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/profileScoreDist_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/profileScoreDist_1.33.0.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: 6 Package: projectR Version: 1.21.0 Depends: R (>= 4.0.0) Imports: 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: 11688806083e1a320884461d31bf8f97 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 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: a687daf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/projectR_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/projectR_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/projectR_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/projectR_1.21.0.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 importsMe: ATACCoGAPS dependencyCount: 102 Package: pRoloc Version: 1.45.0 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 LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, rmarkdown, pRolocdata (>= 1.9.4), roxygen2, xtable, rgl, BiocStyle (>= 2.5.19), hpar (>= 1.41.0), dplyr, akima, fields, vegan, GO.db, AnnotationDbi, Rtsne (>= 0.13), nipals, reshape, magick License: GPL-2 Archs: x64 MD5sum: 896b470aa352cc2f16b6fed362d1c700 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, Oliver Crook and Lisa M. Breckels with contributions from Thomas Burger and Samuel Wieczorek Maintainer: Laurent Gatto 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: 69cdf26 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pRoloc_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pRoloc_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pRoloc_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pRoloc_1.45.0.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/v04-pRoloc-goannotations.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, Annotating spatial proteomics data, 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/v04-pRoloc-goannotations.R, vignettes/pRoloc/inst/doc/v05-pRoloc-transfer-learning.R dependsOnMe: bandle, pRolocGUI suggestsMe: MSnbase, pRolocdata, RforProteomics dependencyCount: 230 Package: pRolocGUI Version: 2.15.0 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 MD5sum: 58c0a69ce5d98a4fbf25092596d8e16f 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] (), Thomas Naake [aut], Laurent Gatto [aut] () Maintainer: Lisa Breckels 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: 3cb5ab4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pRolocGUI_2.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pRolocGUI_2.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pRolocGUI_2.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pRolocGUI_2.15.0.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: 244 Package: PROMISE Version: 1.57.0 Depends: R (>= 3.1.0), Biobase, GSEABase Imports: Biobase, GSEABase, stats License: GPL (>= 2) Archs: x64 MD5sum: 78dcf88192257bb4211704cac94272ac 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 , Xueyuan Cao Maintainer: Stan Pounds , Xueyuan Cao git_url: https://git.bioconductor.org/packages/PROMISE git_branch: devel git_last_commit: 11ce379 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PROMISE_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PROMISE_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PROMISE_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PROMISE_1.57.0.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: PROPER Version: 1.37.0 Depends: R (>= 3.3) Imports: edgeR Suggests: BiocStyle,DESeq2,DSS,knitr License: GPL MD5sum: bf5c359b9fdbba846143a79d91010925 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PROPER git_branch: devel git_last_commit: b8d9d3c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PROPER_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PROPER_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PROPER_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PROPER_1.37.0.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: 12 Package: PROPS Version: 1.27.0 Imports: bnlearn, reshape2, sva, stats, utils, Biobase Suggests: knitr, rmarkdown License: GPL-2 MD5sum: f2eb7bbb92f6aff236b58f03e135c99e 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PROPS git_branch: devel git_last_commit: f303bd6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PROPS_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PROPS_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PROPS_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PROPS_1.27.0.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: 1.37.0 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: ce9bfc3eb4e90f2e5d5b2a9a7f783bb3 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 URL: http://www.prostar-proteomics.org/ VignetteBuilder: knitr BugReports: https://github.com/prostarproteomics/Prostar/issues git_url: https://git.bioconductor.org/packages/Prostar git_branch: devel git_last_commit: 59afcf8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Prostar_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Prostar_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Prostar_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Prostar_1.37.0.tgz vignettes: vignettes/Prostar/inst/doc/Prostar_UserManual.pdf vignetteTitles: Prostar User Manual hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Prostar/inst/doc/Prostar_UserManual.R dependencyCount: 189 Package: proteinProfiles Version: 1.45.0 Depends: R (>= 2.15.2) Imports: graphics, stats Suggests: testthat License: GPL-3 MD5sum: 3ee38115f5ec44de78fa9594e19ba24c NeedsCompilation: no Title: Protein Profiling Description: Significance assessment for distance measures of time-course protein profiles Author: Julian Gehring Maintainer: Julian Gehring git_url: https://git.bioconductor.org/packages/proteinProfiles git_branch: devel git_last_commit: efea14c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/proteinProfiles_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/proteinProfiles_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/proteinProfiles_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/proteinProfiles_1.45.0.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: 1.11.0 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: d2fcee4141e91d7fa9f87dac65b7ef53 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 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: ca1c984 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ProteoDisco_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ProteoDisco_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ProteoDisco_1.11.0.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: 1.23.0 Depends: R (>= 3.5) Imports: gdata, biomaRt, ggplot2, ggrepel, gtools, stats, matrixStats, graphics Suggests: BiocStyle, knitr, rmarkdown License: MIT MD5sum: 0b0593236ed4f45f751a2beccb330b12 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ProteoMM git_branch: devel git_last_commit: 0473b9a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ProteoMM_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ProteoMM_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ProteoMM_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ProteoMM_1.23.0.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: 91 Package: protGear Version: 1.9.0 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: d03fba468a4263d23be5165d72aba899 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 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: 05ea7bc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/protGear_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/protGear_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/protGear_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/protGear_1.9.0.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: 199 Package: ProtGenerics Version: 1.37.0 Depends: methods Suggests: testthat License: Artistic-2.0 MD5sum: 0d059a48dbe71be61d7d630ab16b6814 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 , Johannes Rainer Maintainer: Laurent Gatto URL: https://github.com/RforMassSpectrometry/ProtGenerics git_url: https://git.bioconductor.org/packages/ProtGenerics git_branch: devel git_last_commit: 063f61b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ProtGenerics_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ProtGenerics_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ProtGenerics_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ProtGenerics_1.37.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Cardinal, MsExperiment, MSnbase, Spectra, topdownr importsMe: CompoundDb, ensembldb, matter, MetaboAnnotation, MsBackendMassbank, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, MsBackendSql, MsFeatures, MSnID, MsQuality, mzID, mzR, PSMatch, QFeatures, xcms dependencyCount: 1 Package: PSMatch Version: 1.9.0 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: 849d4e0286e11b29c3bacd7c9a83e252 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] (), Johannes Rainer [aut] (), Sebastian Gibb [aut] (), Samuel Wieczorek [ctb], Thomas Burger [ctb] Maintainer: Laurent Gatto 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: f2b7063 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PSMatch_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PSMatch_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PSMatch_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PSMatch_1.9.0.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, topdownr suggestsMe: MsDataHub dependencyCount: 115 Package: psygenet2r Version: 1.37.0 Depends: R (>= 3.4) Imports: stringr, RCurl, igraph, ggplot2, reshape2, grid, parallel, biomaRt, BgeeDB, topGO, Biobase, labeling, GO.db Suggests: testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 1a52c669cc5db5a8ed05207941dc1185 NeedsCompilation: no Title: psygenet2r - An R package for querying PsyGeNET and to perform comorbidity studies in psychiatric disorders Description: Package to retrieve data from PsyGeNET database (www.psygenet.org) and to perform comorbidity studies with PsyGeNET's and user's data. biocViews: Software, BiomedicalInformatics, Genetics, Infrastructure, DataImport, DataRepresentation Author: Alba Gutierrez-Sacristan [aut, cre], Carles Hernandez-Ferrer [aut], Jaun R. Gonzalez [aut], Laura I. Furlong [aut] Maintainer: Alba Gutierrez-Sacristan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/psygenet2r git_branch: devel git_last_commit: 4240a61 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/psygenet2r_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/psygenet2r_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/psygenet2r_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/psygenet2r_1.37.0.tgz vignettes: vignettes/psygenet2r/inst/doc/case_study.html, vignettes/psygenet2r/inst/doc/general_overview.html vignetteTitles: psygenet2r: Case study on GWAS on bipolar disorder, psygenet2r: An R package for querying PsyGeNET and to perform comorbidity studies in psychiatric disorders hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/psygenet2r/inst/doc/case_study.R, vignettes/psygenet2r/inst/doc/general_overview.R dependencyCount: 103 Package: ptairMS Version: 1.13.0 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: 6fa57424ef54e39dfff753525fd98e2a 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 VignetteBuilder: knitr BugReports: https://github.com/camilleroquencourt/ptairMS/issues git_url: https://git.bioconductor.org/packages/ptairMS git_branch: devel git_last_commit: f9cbcc5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ptairMS_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ptairMS_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ptairMS_1.13.0.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: 199 Package: puma Version: 3.47.0 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: 8a9009bc0c176d8a0cf420915996cdbd 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 URL: http://umber.sbs.man.ac.uk/resources/puma git_url: https://git.bioconductor.org/packages/puma git_branch: devel git_last_commit: e7a258c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/puma_3.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/puma_3.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/puma_3.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/puma_3.47.0.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: 2.11.0 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 MD5sum: 9de10837bcca5500cc79218f4c7780d7 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] (), Angad P. Singh [aut] Maintainer: Markus Riester URL: https://github.com/lima1/PureCN VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PureCN git_branch: devel git_last_commit: 8829f97 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PureCN_2.11.0.tar.gz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PureCN_2.11.0.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: 1.53.0 Depends: R (>= 2.8.0) Imports: affy (>= 1.20.0), stats, Biobase Suggests: pbapply, affydata, ALLMLL, genefilter License: LGPL (>= 2.0) Archs: x64 MD5sum: 8527ea6973a8dca3b11153cd6a1bb337 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 , Pierre R. Bushel git_url: https://git.bioconductor.org/packages/pvac git_branch: devel git_last_commit: 979e7cd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pvac_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pvac_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pvac_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pvac_1.53.0.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: 1.45.0 Depends: R (>= 2.15.1) Imports: Matrix, Biobase, vsn, stats, lme4 Suggests: golubEsets License: LGPL (>= 2.0) Archs: x64 MD5sum: d294af4fb86dbeb5539d7f349429d0c1 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 Maintainer: Jianying LI git_url: https://git.bioconductor.org/packages/pvca git_branch: devel git_last_commit: 28552ab git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pvca_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pvca_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pvca_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pvca_1.45.0.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: 71 Package: Pviz Version: 1.39.0 Depends: R(>= 3.0.0), Gviz(>= 1.7.10) Imports: biovizBase, Biostrings, GenomicRanges, IRanges, data.table, methods Suggests: knitr, pepDat License: Artistic-2.0 MD5sum: a89354afceee224c30f699978c1dbd02 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Pviz git_branch: devel git_last_commit: a5c503e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Pviz_1.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Pviz_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Pviz_1.39.0.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: 157 Package: pwalign Version: 1.1.0 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: 4118658834fcb0247cbc3d8c6928c5cf 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] Maintainer: Hervé Pagès 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: 402ad83 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/pwalign_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/pwalign_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pwalign_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/pwalign_1.1.0.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, iPAC, MethTargetedNGS, QSutils, R453Plus1Toolbox, sangeranalyseR, sangerseqR importsMe: ChIPpeakAnno, ClustIRR, CNEr, crisprShiny, DominoEffect, enhancerHomologSearch, girafe, GUIDEseq, HTSeqGenie, IMMAN, IsoformSwitchAnalyzeR, LinTInd, methylscaper, motifbreakR, MSA2dist, openPrimeR, QuartPAC, Rcpi, scanMiR, ShortRead, SPLINTER, StructuralVariantAnnotation, svaNUMT, TFBSTools, XNAString suggestsMe: Biostrings, idpr, msa, RSVSim, seqtrie dependencyCount: 25 Package: PWMEnrich Version: 4.41.0 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: a7c005241649dba2586b3db1656b3bbf 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PWMEnrich git_branch: devel git_last_commit: 2998058 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/PWMEnrich_4.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/PWMEnrich_4.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/PWMEnrich_4.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/PWMEnrich_4.41.0.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: 1.21.0 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: 21f13164ffd6f3a79137aae2fd50dd1e 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 SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qckitfastq git_branch: devel git_last_commit: 1e671c4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/qckitfastq_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/qckitfastq_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/qckitfastq_1.21.0.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: 1.43.0 Depends: R (>= 3.3) Imports: Biobase, methods, knitr, tools, xtable, pander, S4Vectors Suggests: affy, MSnbase, ggplot2, lattice, mzR, BiocStyle, rmarkdown, markdown License: GPL-2 Archs: x64 MD5sum: 3ac921ccc382744d1c55a66b608a63d8 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 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: efceb13 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/qcmetrics_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/qcmetrics_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/qcmetrics_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/qcmetrics_1.43.0.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: 19 Package: QDNAseq Version: 1.41.0 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: 0afd2226ca9736fbebd664163b42006c 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 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: c25e2b9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/QDNAseq_1.41.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/QDNAseq_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/QDNAseq_1.41.0.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: 1.15.0 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 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 MD5sum: a35e836c3ad487c2e7cb9d7239355f99 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] (), Christophe Vanderaa [aut] () Maintainer: Laurent Gatto 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: ff668ef git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/QFeatures_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/QFeatures_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/QFeatures_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/QFeatures_1.15.0.tgz vignettes: vignettes/QFeatures/inst/doc/Processing.html, vignettes/QFeatures/inst/doc/QFeatures.html, vignettes/QFeatures/inst/doc/Visualization.html vignetteTitles: Processing quantitative proteomics data with QFeatures, Quantitative features for mass spectrometry data, 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/Visualization.R dependsOnMe: msqrob2, scp, scpdata importsMe: MetaboAnnotation, MsExperiment, PSMatch suggestsMe: MsDataHub dependencyCount: 105 Package: qmtools Version: 1.9.0 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: f8e3bb01147fab87964aa9606fc20ee8 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 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: 0cf44a1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/qmtools_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/qmtools_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/qmtools_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/qmtools_1.9.0.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: 168 Package: qpcrNorm Version: 1.63.0 Depends: methods, Biobase, limma, affy License: LGPL (>= 2) MD5sum: 8b404c5438960c8f12f638739d679f4a 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 git_url: https://git.bioconductor.org/packages/qpcrNorm git_branch: devel git_last_commit: 35ef4c3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/qpcrNorm_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/qpcrNorm_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/qpcrNorm_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/qpcrNorm_1.63.0.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: 2.39.0 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: 1fa92da0a2708e82f3fc926a957b95c6 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 URL: https://github.com/rcastelo/qpgraph BugReports: https://github.com/rcastelo/rcastelo/issues git_url: https://git.bioconductor.org/packages/qpgraph git_branch: devel git_last_commit: 9740db9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/qpgraph_2.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/qpgraph_2.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/qpgraph_2.39.0.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, topologyGSA dependencyCount: 83 Package: qPLEXanalyzer Version: 1.23.0 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: gridExtra, knitr, qPLEXdata, rmarkdown, statmod, testthat, UniProt.ws, vdiffr License: GPL-2 MD5sum: 0a14c8b81625a845addf5dd647ff2258 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 VignetteBuilder: knitr BugReports: https://github.com/crukci-bioinformatics/qPLEXanalyzer/issues git_url: https://git.bioconductor.org/packages/qPLEXanalyzer git_branch: devel git_last_commit: 51a1218 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/qPLEXanalyzer_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/qPLEXanalyzer_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/qPLEXanalyzer_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/qPLEXanalyzer_1.23.0.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: 1.31.0 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: 8e8dc48a57dfe858a0ee5972b305c0a4 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] (), Lukas Chavez [aut] (), Ralf Herwig [aut] () Maintainer: Matthias Lienhard VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qsea git_branch: devel git_last_commit: 6c29f2d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/qsea_1.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/qsea_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/qsea_1.31.0.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: 1.21.0 Depends: R (>= 4.0) Imports: SummarizedExperiment, utils, sva, stats, methods, graphics, Hmisc Suggests: bodymapRat, quantro, knitr, rmarkdown, BiocStyle, testthat License: GPL-3 MD5sum: 771b54a51da6ed0823c8cbccd8daa104 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] (), Kwame Okrah [aut], Koen Van den Berge [ctb], Hector Corrada Bravo [aut] (), Rafael Irizarry [aut] () Maintainer: Stephanie C. Hicks VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qsmooth git_branch: devel git_last_commit: cbbedd7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/qsmooth_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/qsmooth_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/qsmooth_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/qsmooth_1.21.0.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 dependencyCount: 128 Package: QSutils Version: 1.23.0 Depends: R (>= 3.5), Biostrings, pwalign, BiocGenerics, methods Imports: ape, stats, psych Suggests: BiocStyle, knitr, rmarkdown, ggplot2 License: GPL-2 MD5sum: 258ba16ab82eb0f4fbe4fe6be1a5ab8e 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] (), Josep Gregori i Font [aut] () Maintainer: Mercedes Guerrero-Murillo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/QSutils git_branch: devel git_last_commit: 52c3795 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/QSutils_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/QSutils_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/QSutils_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/QSutils_1.23.0.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: 35 Package: qsvaR Version: 1.9.0 Depends: R (>= 4.2), SummarizedExperiment Imports: sva, stats, ggplot2, methods Suggests: BiocFileCache, BiocStyle, covr, knitr, limma, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: a109262a6d76b50ae0b742014a754ee8 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] (), Hedia Tnani [ctb, cre] (), Leonardo Collado-Torres [ctb] () Maintainer: Hedia Tnani 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: e867a41 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/qsvaR_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/qsvaR_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/qsvaR_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/qsvaR_1.9.0.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: 96 Package: QTLExperiment Version: 1.3.0 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 MD5sum: 1d90811dc07fabc3c950fa0ffcbc3dc7 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] () Maintainer: Amelia Dunstone 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: ea448bf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/QTLExperiment_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/QTLExperiment_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/QTLExperiment_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/QTLExperiment_1.3.0.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: 75 Package: Qtlizer Version: 1.19.0 Depends: R (>= 3.6.0) Imports: httr, curl, GenomicRanges, stringi Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 MD5sum: 1f6c0a360c110a350bf6be6da265abdd 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] (), Julia Remes [aut] Maintainer: Matthias Munz VignetteBuilder: knitr BugReports: https://github.com/matmu/Qtlizer/issues git_url: https://git.bioconductor.org/packages/Qtlizer git_branch: devel git_last_commit: 250001b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Qtlizer_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Qtlizer_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Qtlizer_1.19.0.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: 1.13.0 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: 3e3c9d85cc66466dcf17a3fd087c845e 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] (), Francesca Finotello [aut] () Maintainer: Federico Marini VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/quantiseqr git_branch: devel git_last_commit: 389003d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/quantiseqr_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/quantiseqr_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/quantiseqr_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/quantiseqr_1.13.0.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: 74 Package: quantro Version: 1.39.0 Depends: R (>= 4.0) Imports: Biobase, minfi, doParallel, foreach, iterators, ggplot2, methods, RColorBrewer Suggests: rmarkdown, knitr, RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: 85b3249afdb2f77d168d016f5b6ff1f7 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] (), Rafael Irizarry [aut] () Maintainer: Stephanie Hicks VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/quantro git_branch: devel git_last_commit: f8b5d9d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/quantro_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/quantro_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/quantro_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/quantro_1.39.0.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: 151 Package: quantsmooth Version: 1.71.0 Depends: R(>= 2.10.0), quantreg, grid License: GPL-2 MD5sum: f554d12b25e961c8ebf83165d2f4c2bd 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 git_url: https://git.bioconductor.org/packages/quantsmooth git_branch: devel git_last_commit: f89daf7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/quantsmooth_1.71.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/quantsmooth_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/quantsmooth_1.71.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/quantsmooth_1.71.0.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: QuartPAC Version: 1.37.0 Depends: iPAC, GraphPAC, SpacePAC, data.table Imports: Biostrings, pwalign Suggests: RUnit, BiocGenerics, rgl License: GPL-2 MD5sum: 77e7fa3b24611d64c742822e762811a1 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 git_url: https://git.bioconductor.org/packages/QuartPAC git_branch: devel git_last_commit: 4e3b998 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/QuartPAC_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/QuartPAC_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/QuartPAC_1.37.0.tgz vignettes: vignettes/QuartPAC/inst/doc/QuartPAC.pdf vignetteTitles: SpacePAC: Identifying mutational clusters in 3D protein space using simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuartPAC/inst/doc/QuartPAC.R dependencyCount: 56 Package: QuasR Version: 1.45.0 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 Archs: x64 MD5sum: 75527aac4805c44d09629a32162dc8e8 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] (), Charlotte Soneson [aut] (), Dimos Gaidatzis [aut], Michael Stadler [aut, cre] () Maintainer: Michael Stadler 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: 58e0c51 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/QuasR_1.45.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/QuasR_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/QuasR_1.45.0.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: 117 Package: QuaternaryProd Version: 1.39.0 Depends: R (>= 3.2.0), Rcpp (>= 0.11.3), dplyr, yaml (>= 2.1.18) LinkingTo: Rcpp Suggests: knitr License: GPL (>=3) MD5sum: e4006f05404b260587bb10d44f49331d 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/QuaternaryProd git_branch: devel git_last_commit: 68e3c4b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/QuaternaryProd_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/QuaternaryProd_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/QuaternaryProd_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/QuaternaryProd_1.39.0.tgz vignettes: vignettes/QuaternaryProd/inst/doc/QuaternaryProdVignette.pdf vignetteTitles: QuaternaryProdVignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuaternaryProd/inst/doc/QuaternaryProdVignette.R dependencyCount: 22 Package: QUBIC Version: 1.33.0 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: 5ed517f460a2fa5863ddc5c1e4cf2dce 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 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: 4b9a64e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/QUBIC_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/QUBIC_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/QUBIC_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/QUBIC_1.33.0.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: 2.39.0 Depends: R (>= 2.10), limma (>= 3.14), methods Imports: utils, Biobase, nlme, emmeans, fftw License: GPL (>= 2) Archs: x64 MD5sum: 3461a9d1849049469d72efa27eb93353 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 URL: http://clip.med.yale.edu/qusage git_url: https://git.bioconductor.org/packages/qusage git_branch: devel git_last_commit: 587f5ba git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/qusage_2.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/qusage_2.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/qusage_2.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/qusage_2.39.0.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: 17 Package: qvalue Version: 2.37.0 Depends: R(>= 2.10) Imports: splines, ggplot2, grid, reshape2 Suggests: knitr License: LGPL Archs: x64 MD5sum: 33bb325a7176b1e5a3f53083a41105c2 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 , Andrew J. Bass URL: http://github.com/jdstorey/qvalue VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qvalue git_branch: devel git_last_commit: e517ebf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/qvalue_2.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/qvalue_2.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/qvalue_2.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/qvalue_2.37.0.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, derfinder, DOSE, edge, erccdashboard, EventPointer, FindIT2, fishpond, metaseqR2, methylKit, MOMA, msmsTests, MWASTools, netresponse, normr, OPWeight, PAST, RiboDiPA, RNAsense, Rnits, RolDE, SDAMS, sights, signatureSearch, SpaceMarkers, subSeq, synapter, trigger, vsclust, webbioc, IHWpaper, AEenrich, armada, cancerGI, fdrDiscreteNull, glmmSeq, groupedSurv, HDMT, jaccard, jackstraw, medScan, MetAlyzer, MOCHA, NBPSeq, qch, SeqFeatR, shinyExprPortal, ssizeRNA, TFactSR suggestsMe: biobroom, LBE, PREDA, RnBeads, swfdr, RNAinteractMAPK, BootstrapQTL, CpGassoc, dartR, dartR.base, dartR.popgen, DGEobj.utils, easylabel, familiar, mutoss, Rediscover, seqgendiff, volcano3D, wrMisc dependencyCount: 41 Package: R3CPET Version: 1.37.0 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) MD5sum: e20c4f321df345f70c49836189f8f591 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 VignetteBuilder: knitr BugReports: https://github.com/sirusb/R3CPET/issues git_url: https://git.bioconductor.org/packages/R3CPET git_branch: devel git_last_commit: 42ebf46 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/R3CPET_1.37.0.tar.gz 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: 176 Package: r3Cseq Version: 1.51.0 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 MD5sum: f7217ad12afc4c96b46dc820d83949fa 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 Maintainer: Supat Thongjuea or 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: 30b9006 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/r3Cseq_1.51.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/r3Cseq_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/r3Cseq_1.51.0.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: 1.55.0 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 MD5sum: 218a5a123db90ae8d14b0f888208b0b1 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 git_url: https://git.bioconductor.org/packages/R453Plus1Toolbox git_branch: devel git_last_commit: aaba7c4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/R453Plus1Toolbox_1.55.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/R453Plus1Toolbox_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/R453Plus1Toolbox_1.55.0.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: 117 Package: R4RNA Version: 1.33.0 Depends: R (>= 3.2.0), Biostrings (>= 2.38.0) License: GPL-3 MD5sum: dea37e4169d07941b6ab4b180e97cf44 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 Maintainer: Daniel Lai URL: http://www.e-rna.org/r-chie/ git_url: https://git.bioconductor.org/packages/R4RNA git_branch: devel git_last_commit: 6cc9085 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/R4RNA_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/R4RNA_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/R4RNA_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/R4RNA_1.33.0.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, comradesOO, rnaCrosslinkOO suggestsMe: rfaRm dependencyCount: 25 Package: RadioGx Version: 2.9.0 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: 988fcd28f1b76e3c81e409233d1bd79c 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) . 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RadioGx git_branch: devel git_last_commit: d44281d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RadioGx_2.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RadioGx_2.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RadioGx_2.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RadioGx_2.9.0.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: 154 Package: raer Version: 1.3.0 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 MD5sum: 8d88d712cdb5f559cae4ba8c4fdfd6ed 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] (), Kristen Wells-Wrasman [aut] (), Ryan Sheridan [ctb] (), Jay Hesselberth [ctb] (), RNA Bioscience Initiative [cph, fnd] Maintainer: Kent Riemondy 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: 27290f6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/raer_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/raer_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/raer_1.3.0.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: 1.29.0 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: 649402496fef0c9d42593f65105b840c 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] (), Lydia King [ctb] Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/RaggedExperiment/issues git_url: https://git.bioconductor.org/packages/RaggedExperiment git_branch: devel git_last_commit: 5c3ad3b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RaggedExperiment_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RaggedExperiment_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RaggedExperiment_1.29.0.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, MOCHA suggestsMe: maftools, MultiAssayExperiment, MultiDataSet, TENxIO, curatedTCGAData, SingleCellMultiModal dependencyCount: 37 Package: RAIDS Version: 1.3.0 Depends: R (>= 4.2.0), gdsfmt, SNPRelate, stats, utils, GENESIS Imports: S4Vectors, GenomicRanges, ensembldb, BSgenome, AnnotationDbi, methods, class, pROC, IRanges, AnnotationFilter, rlang, VariantAnnotation, MatrixGenerics, Suggests: testthat, knitr, rmarkdown, BiocStyle, withr, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg38, EnsDb.Hsapiens.v86 License: Apache License (>= 2) MD5sum: db3ea1bb5ec37da68701bda82d4b6511 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 Author: Pascal Belleau [cre, aut] (), Astrid Deschênes [aut] (), David A. Tuveson [aut] (), Alexander Krasnitz [aut] Maintainer: Pascal Belleau 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: 8c31b43 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RAIDS_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RAIDS_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RAIDS_1.3.0.tgz vignettes: vignettes/RAIDS/inst/doc/Create_Reference_GDS_File.html, vignettes/RAIDS/inst/doc/RAIDS.html vignetteTitles: Population reference dataset GDS files, Accurate Inference of Genetic Ancestry from Cancer-derived Sequences hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RAIDS/inst/doc/Create_Reference_GDS_File.R, vignettes/RAIDS/inst/doc/RAIDS.R dependencyCount: 172 Package: rain Version: 1.39.0 Depends: R (>= 2.10), gmp, multtest Suggests: lattice, BiocStyle License: GPL-2 MD5sum: 498eb58f5af834465785a1f66ff282f2 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, Pål O. Westermark Maintainer: Paul F. Thaben git_url: https://git.bioconductor.org/packages/rain git_branch: devel git_last_commit: 751011b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rain_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rain_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rain_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rain_1.39.0.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: 16 Package: ramr Version: 1.13.0 Depends: R (>= 4.1), GenomicRanges, parallel, doParallel, foreach, doRNG, methods Imports: IRanges, BiocGenerics, ggplot2, reshape2, EnvStats, ExtDist, matrixStats, S4Vectors Suggests: RUnit, knitr, rmarkdown, gridExtra, annotatr, LOLA, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: b39d4735111dc3276f9141d190da197e NeedsCompilation: no Title: Detection of Rare Aberrantly Methylated Regions in Array and NGS Data Description: ramr is an R package for detection of low-frequency aberrant methylation events in large data sets obtained by methylation profiling using array or high-throughput bisulfite 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] () Maintainer: Oleksii Nikolaienko URL: https://github.com/BBCG/ramr VignetteBuilder: knitr BugReports: https://github.com/BBCG/ramr/issues git_url: https://git.bioconductor.org/packages/ramr git_branch: devel git_last_commit: 751437b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ramr_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ramr_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ramr_1.13.0.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: 90 Package: ramwas Version: 1.29.0 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 MD5sum: cfcfa7430da66f85427eac8b4a56885b 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) . biocViews: DNAMethylation, Sequencing, QualityControl, Coverage, Preprocessing, Normalization, BatchEffect, PrincipalComponent, DifferentialMethylation, Visualization Author: Andrey A Shabalin [aut, cre] (), Shaunna L Clark [aut], Mohammad W Hattab [aut], Karolina A Aberg [aut], Edwin J C G van den Oord [aut] Maintainer: Andrey A Shabalin 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: 4914fc5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ramwas_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ramwas_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ramwas_1.29.0.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: 103 Package: randPack Version: 1.51.0 Depends: methods Imports: Biobase License: Artistic 2.0 MD5sum: 9646513fbef9aa15e2b7ac007e85295d 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 and Robert Gentleman Maintainer: Robert Gentleman git_url: https://git.bioconductor.org/packages/randPack git_branch: devel git_last_commit: 38ed46e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/randPack_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/randPack_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/randPack_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/randPack_1.51.0.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: 6 Package: randRotation Version: 1.17.0 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: ff76c9230fcca3800d6d29a7eae706b0 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] () Maintainer: Peter Hettegger 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: 4dd63c8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/randRotation_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/randRotation_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/randRotation_1.17.0.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: 3.31.0 Depends: R (>= 3.2.1), stats, methods, Rmpfr, gmp Imports: graphics License: file LICENSE License_restricts_use: yes MD5sum: d296a553e534d517150f864372347602 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 , Andris Jankevics Fangxin Hong , Ben Wittner , Rainer Breitling , and Florian Battke Maintainer: Francesco Del Carratore git_url: https://git.bioconductor.org/packages/RankProd git_branch: devel git_last_commit: 01d1800 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RankProd_3.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RankProd_3.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RankProd_3.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RankProd_3.31.0.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, INCATome suggestsMe: sigQC dependencyCount: 6 Package: RAREsim Version: 1.9.0 Depends: R (>= 4.1.0) Imports: nloptr Suggests: markdown, ggplot2, BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0) License: GPL-3 MD5sum: 79d2630769f0dcb3fdcb037af639c5cb 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 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: 1b744ad git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RAREsim_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RAREsim_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RAREsim_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RAREsim_1.9.0.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: 38 Package: RareVariantVis Version: 2.33.0 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 MD5sum: ffcde4cff8f36848b466b4485d7cd244 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RareVariantVis git_branch: devel git_last_commit: 566c90b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RareVariantVis_2.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RareVariantVis_2.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RareVariantVis_2.33.0.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: 1.5.0 Imports: jsonlite, httr, stringr, R.utils, utils, paws.storage, methods Suggests: BiocStyle, covr, knitr, tinytest, mockery License: MIT + file LICENSE MD5sum: b8d6817a044aa9a349cb65893c790485 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] () Maintainer: Mike Smith 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: cc17907 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Rarr_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Rarr_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Rarr_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Rarr_1.5.0.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: 29 Package: rawrr Version: 1.13.0 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: 9e30c9c46ccae39b0de6aedda5894368 NeedsCompilation: no Title: Direct Access to Orbitrap Data and Beyond Description: This package wraps the functionality of the RawFileReader .NET 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] (), Tobias Kockmann [aut] () Maintainer: Christian Panse URL: https://github.com/fgcz/rawrr/ 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/rawrr/issues git_url: https://git.bioconductor.org/packages/rawrr git_branch: devel git_last_commit: 1667b6d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-06 source.ver: src/contrib/rawrr_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rawrr_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rawrr_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rawrr_1.13.0.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 dependencyCount: 4 Package: RbcBook1 Version: 1.73.0 Depends: R (>= 2.10), Biobase, graph, rpart License: Artistic-2.0 Archs: x64 MD5sum: 69f72632b134a8102648e347e55088c8 NeedsCompilation: no Title: Support for Springer monograph on Bioconductor Description: tools for building book biocViews: Software Author: Vince Carey and Wolfgang Huber Maintainer: Vince Carey URL: http://www.biostat.harvard.edu/~carey git_url: https://git.bioconductor.org/packages/RbcBook1 git_branch: devel git_last_commit: 882d9bb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RbcBook1_1.73.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RbcBook1_1.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RbcBook1_1.73.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RbcBook1_1.73.0.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: 10 Package: Rbec Version: 1.13.0 Imports: Rcpp (>= 1.0.6), dada2, ggplot2, readr, doParallel, foreach, grDevices, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: LGPL-3 MD5sum: f7af90a02df3b26c4dd2ed75245eebf4 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rbec git_branch: devel git_last_commit: ae70268 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Rbec_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Rbec_1.13.0.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: 1.81.0 Depends: graph, methods Imports: methods LinkingTo: BH Suggests: Rgraphviz, XML, RUnit, BiocGenerics, BiocStyle, knitr License: Artistic-2.0 MD5sum: b2308f82dd0c79d6d9444e541de32e0b 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 URL: http://www.bioconductor.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RBGL git_branch: devel git_last_commit: a26cb23 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RBGL_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RBGL_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RBGL_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RBGL_1.81.0.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, OrganismDbi, Streamer, VariantFiltering, BiDAG, clustNet, eff2, HEMDAG, micd, pcalg, rags2ridges, RANKS, SEMgraph, SID suggestsMe: DEGraph, GeneNetworkBuilder, graph, gwascat, KEGGgraph, rBiopaxParser, VariantTools, yeastExpData, archeofrag, maGUI dependencyCount: 8 Package: RBioFormats Version: 1.5.1 Imports: EBImage, methods, rJava (>= 0.9-6), S4Vectors, stats Suggests: BiocStyle, knitr, testthat, xml2 License: GPL-3 MD5sum: 81f41a7b63b01416d020966bc5b821a0 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 Oleś [aut, cre] (), John Lee [ctb] () Maintainer: Andrzej Oleś 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: 08f56a4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RBioFormats_1.5.1.tar.gz win.binary.ver: bin/windows/contrib/4.4/RBioFormats_1.5.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RBioFormats_1.5.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RBioFormats_1.5.1.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: SpatialOmicsOverlay dependencyCount: 48 Package: RBioinf Version: 1.65.0 Depends: graph, methods Suggests: Rgraphviz License: Artistic-2.0 MD5sum: 743a573c24ba10fdbd4b13e7f8ce6c2e 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 git_url: https://git.bioconductor.org/packages/RBioinf git_branch: devel git_last_commit: 4f82f3f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RBioinf_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RBioinf_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RBioinf_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RBioinf_1.65.0.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: 7 Package: rBiopaxParser Version: 2.45.0 Depends: R (>= 4.0), data.table Imports: XML Suggests: Rgraphviz, RCurl, graph, RUnit, BiocGenerics, RBGL, igraph License: GPL (>= 2) Archs: x64 MD5sum: 9f3a105340906c5e7c196acfea275769 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 URL: https://github.com/frankkramer-lab/rBiopaxParser git_url: https://git.bioconductor.org/packages/rBiopaxParser git_branch: devel git_last_commit: 2fec4ac git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rBiopaxParser_2.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rBiopaxParser_2.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rBiopaxParser_2.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rBiopaxParser_2.45.0.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 importsMe: pwOmics suggestsMe: AnnotationHub, NetPathMiner dependencyCount: 4 Package: rBLAST Version: 1.1.1 Depends: Biostrings (>= 2.26.2) Imports: methods, utils, BiocFileCache Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 384bc7ef44fd0281708f19fddfc7d6b7 NeedsCompilation: no Title: R Interface for the Basic Local Alignment Search Tool Description: Seamlessly interfaces the Basic Local Alignment Search Tool (BLAST) 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] (), Nagar Anurag [aut] Maintainer: Michael Hahsler 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: 39e28ce git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rBLAST_1.1.1.tar.gz win.binary.ver: bin/windows/contrib/4.4/rBLAST_1.1.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rBLAST_1.1.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rBLAST_1.1.1.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: 57 Package: RBM Version: 1.37.0 Depends: R (>= 3.2.0), limma, marray License: GPL (>= 2) Archs: x64 MD5sum: 0e3b04f8e91680730c71c24c71f6a7df 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 git_url: https://git.bioconductor.org/packages/RBM git_branch: devel git_last_commit: e03704c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RBM_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RBM_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RBM_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RBM_1.37.0.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: 1.45.0 Imports: utils Suggests: testthat, parallel, BiocStyle, knitr, rmarkdown License: Artistic-2.0 | file LICENSE MD5sum: cf8c8b6db3bb7f21648113d444136823 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] () Maintainer: Michael Stadler 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: 46a1763 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Rbowtie_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Rbowtie_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Rbowtie_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Rbowtie_1.45.0.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, seqpac suggestsMe: crisprDesign, eisaR dependencyCount: 1 Package: Rbowtie2 Version: 2.11.0 Depends: R (>= 4.1.0) Imports: magrittr, Rsamtools Suggests: knitr, testthat (>= 3.0.0), rmarkdown License: GPL (>= 3) Archs: x64 MD5sum: 8516f5f23f4af664b9cd9b31e40bd450 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 SystemRequirements: C++11, GNU make, samtools VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rbowtie2 git_branch: devel git_last_commit: ba11e07 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Rbowtie2_2.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Rbowtie2_2.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Rbowtie2_2.11.0.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: 2.63.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), survival License: GPL (>= 2) MD5sum: 07a8e7f3a8807abcd1fd20535e89f9c9 NeedsCompilation: no Title: Robust likelihood-based survival modeling with microarray data Description: This package selects genes associated with survival. biocViews: Microarray Author: HyungJun Cho , Sukwoo Kim , Soo-heang Eo , Jaewoo Kang Maintainer: Soo-heang Eo URL: http://www.korea.ac.kr/~stat2242/ git_url: https://git.bioconductor.org/packages/rbsurv git_branch: devel git_last_commit: 3db9f96 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rbsurv_2.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rbsurv_2.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rbsurv_2.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rbsurv_2.63.0.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: 12 Package: Rbwa Version: 1.9.0 Depends: R (>= 4.1) Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE OS_type: unix MD5sum: ca5ba5a9f6ba8fb4f657a3ab7fe4182d 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 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: 27c7017 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Rbwa_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Rbwa_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Rbwa_1.9.0.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: 1.31.0 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: 64180c029809b88674166a1d03523280 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 SystemRequirements: pandoc (>= 1.12.3) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RCAS git_branch: devel git_last_commit: 12f9502 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RCAS_1.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RCAS_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RCAS_1.31.0.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: 162 Package: RCASPAR Version: 1.51.0 License: GPL (>=3) MD5sum: e485829815a71afc896bd9821bc63bf6 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 , Lars Kaderali git_url: https://git.bioconductor.org/packages/RCASPAR git_branch: devel git_last_commit: b1f6adc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RCASPAR_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RCASPAR_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RCASPAR_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RCASPAR_1.51.0.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: 2.27.0 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: bd1f8b0f05263b98c8050327b447e51e 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 , Vinodh Rajapakse , Fathi Elloumi URL: http://discover.nci.nih.gov/cellminer/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rcellminer git_branch: devel git_last_commit: 48e3a34 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rcellminer_2.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rcellminer_2.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rcellminer_2.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rcellminer_2.27.0.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: 69 Package: rCGH Version: 1.35.0 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 MD5sum: bb3c39e506ee31307006d44a1c42a30c 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 URL: https://github.com/fredcommo/rCGH VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rCGH git_branch: devel git_last_commit: 00d92b9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rCGH_1.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rCGH_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rCGH_1.35.0.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: 1.25.0 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: d996e92380a0493fa6d7cc329b15e6dd 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 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: 0e77059 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RcisTarget_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RcisTarget_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RcisTarget_1.25.0.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: 125 Package: RCM Version: 1.21.0 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: 2f475c7169df7990b08d01a26137e500 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] () Maintainer: Stijn Hawinkel 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: 192270f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RCM_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RCM_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RCM_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RCM_1.21.0.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: 1.5.0 Imports: utils, ggplot2, lubridate, processx Suggests: knitr, BiocStyle, knitcitations, sessioninfo, rmarkdown, testthat, covr License: Artistic-2.0 MD5sum: 699b44dafaf135b8a70d506a0e7a21bf 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] (), Yubo Cheng [aut] Maintainer: Vincent Carey 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: 388fb52 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Rcollectl_1.5.0.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: 1.41.0 Depends: R (>= 3.0.2) Imports: Biostrings, GOSemSim, RCurl, doParallel, foreach, methods, pwalign, rjson, rlang, stats, utils Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 | file LICENSE MD5sum: 42d4e0903055a88ccdb13ec9344a7ed8 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], Dong-Sheng Cao [aut], Qing-Song Xu [aut] Maintainer: Nan Xiao 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: 2012074 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Rcpi_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Rcpi_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Rcpi_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Rcpi_1.41.0.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: 60 Package: RCSL Version: 1.13.0 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: 9f01c0cd53cc6b69cdac383a60fe8977 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 URL: https://github.com/QinglinMei/RCSL VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RCSL git_branch: devel git_last_commit: 124b95a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RCSL_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RCSL_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RCSL_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RCSL_1.13.0.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: 1.21.0 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: 29665d4794ce292e013bcc521638d8eb 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rcwl git_branch: devel git_last_commit: 7e88084 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Rcwl_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Rcwl_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Rcwl_1.21.0.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: 1.21.0 Depends: R (>= 3.6), Rcwl, BiocFileCache Imports: rappdirs, methods, utils, git2r, httr, S4Vectors Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: b8be5a0b3f7ee53d5590f77b944e0322 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 SystemRequirements: nodejs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RcwlPipelines git_branch: devel git_last_commit: d9f4010 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RcwlPipelines_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RcwlPipelines_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RcwlPipelines_1.21.0.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: 1.9.0 Depends: R (>= 4.2.0) Imports: jsonlite, plyr, igraph, methods Suggests: BiocStyle, testthat, knitr, rmarkdown, base64enc, graph License: MIT + file LICENSE MD5sum: a3d44bd7b62f2604477a4dc4a5211de1 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] () Maintainer: Florian Auer 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: eaf522c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RCX_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RCX_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RCX_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RCX_1.9.0.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: 2.25.0 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: 105a3969131a4e57da0451f9098fd1fc 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] (), Tanja Muetze [aut], Paul Shannon [aut], Ruth Isserlin [ctb], Shraddha Pai [ctb], Julia Gustavsen [ctb], Georgi Kolishovski [ctb], Yihang Xin [ctb] Maintainer: Alex Pico 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: 47b2922 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RCy3_2.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RCy3_2.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RCy3_2.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RCy3_2.25.0.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, MetaPhOR, MOGAMUN, NCIgraph, netZooR, regutools, transomics2cytoscape, dendroNetwork, lilikoi, netgsa, ScriptMapR suggestsMe: graphite, netDx, rScudo, sharp dependencyCount: 49 Package: RCyjs Version: 2.27.1 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: 5501c4ad534f77252b4e95fe868f6dfc 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RCyjs git_branch: devel git_last_commit: 42d87ad git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RCyjs_2.27.1.tar.gz win.binary.ver: bin/windows/contrib/4.4/RCyjs_2.27.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RCyjs_2.27.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RCyjs_2.27.1.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: 18 Package: rDGIdb Version: 1.31.0 Imports: jsonlite,httr,methods,graphics Suggests: BiocStyle,knitr,testthat License: MIT + file LICENSE MD5sum: 09b3c2b4e750cd1623adac701eb39bf5 NeedsCompilation: no Title: R Wrapper for DGIdb Description: The rDGIdb package provides a wrapper for the Drug Gene Interaction Database (DGIdb). For simplicity, the wrapper query function and output resembles the user interface and results format provided on the DGIdb website (https://www.dgidb.org/). biocViews: Software,ResearchField,Pharmacogenetics,Pharmacogenomics, FunctionalGenomics,WorkflowStep,Annotation Author: Thomas Thurnherr, Franziska Singer, Daniel J. Stekhoven, and Niko Beerenwinkel Maintainer: Lars Bosshard VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rDGIdb git_branch: devel git_last_commit: 15c07ad git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rDGIdb_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rDGIdb_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rDGIdb_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rDGIdb_1.31.0.tgz vignettes: vignettes/rDGIdb/inst/doc/vignette.pdf vignetteTitles: Query DGIdb using R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rDGIdb/inst/doc/vignette.R dependencyCount: 11 Package: Rdisop Version: 1.65.0 Depends: R (>= 2.0.0), Rcpp LinkingTo: Rcpp Suggests: RUnit License: GPL-2 MD5sum: 6d27322af78d69c1971300cbdaffd9dc NeedsCompilation: yes Title: Decomposition of Isotopic Patterns Description: Identification of metabolites using high precision mass spectrometry. MS Peaks are used to derive a ranked list of sum formulae, alternatively for a given sum formula the theoretical isotope distribution can be calculated to search in MS peak lists. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Anton Pervukhin , Steffen Neumann Maintainer: Steffen Neumann URL: https://github.com/sneumann/Rdisop SystemRequirements: None BugReports: https://github.com/sneumann/Rdisop/issues/new git_url: https://git.bioconductor.org/packages/Rdisop git_branch: devel git_last_commit: b0de150 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Rdisop_1.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Rdisop_1.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Rdisop_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Rdisop_1.65.0.tgz vignettes: vignettes/Rdisop/inst/doc/Rdisop.pdf vignetteTitles: Molecule Identification with Rdisop hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE importsMe: CorMID, enviGCMS suggestsMe: adductomicsR, MSnbase, RforProteomics, InterpretMSSpectrum dependencyCount: 3 Package: RDRToolbox Version: 1.55.0 Depends: R (>= 2.9.0) Imports: graphics, grDevices, methods, stats, MASS, rgl Suggests: golubEsets License: GPL (>= 2) MD5sum: 0b785dc66d48a090cee816ee57069bda 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 git_url: https://git.bioconductor.org/packages/RDRToolbox git_branch: devel git_last_commit: d47c092 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RDRToolbox_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RDRToolbox_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RDRToolbox_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RDRToolbox_1.55.0.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: ReactomeContentService4R Version: 1.13.0 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 MD5sum: 671578a78a474609efb357e42e4b9a9e NeedsCompilation: no 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] (), Reactome [cph] Maintainer: Chi-Lam Poon URL: https://github.com/reactome/ReactomeContentService4R VignetteBuilder: knitr BugReports: https://github.com/reactome/ReactomeContentService4R/issues git_url: https://git.bioconductor.org/packages/ReactomeContentService4R git_branch: devel git_last_commit: c21c04e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-06 source.ver: src/contrib/ReactomeContentService4R_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ReactomeContentService4R_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ReactomeContentService4R_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ReactomeContentService4R_1.13.0.tgz vignettes: vignettes/ReactomeContentService4R/inst/doc/ReactomeContentService4R.html vignetteTitles: ReactomeContentService4R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ReactomeContentService4R/inst/doc/ReactomeContentService4R.R importsMe: ReactomeGraph4R dependencyCount: 20 Package: ReactomeGraph4R Version: 1.13.0 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) Archs: x64 MD5sum: 5ebcc75dbd7778eed9a41f936611adde NeedsCompilation: no 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] (), Reactome [cph] Maintainer: Chi-Lam Poon URL: https://github.com/reactome/ReactomeGraph4R VignetteBuilder: knitr BugReports: https://github.com/reactome/ReactomeGraph4R/issues git_url: https://git.bioconductor.org/packages/ReactomeGraph4R git_branch: devel git_last_commit: e6da7e9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-06 source.ver: src/contrib/ReactomeGraph4R_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ReactomeGraph4R_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ReactomeGraph4R_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ReactomeGraph4R_1.13.0.tgz vignettes: vignettes/ReactomeGraph4R/inst/doc/Introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReactomeGraph4R/inst/doc/Introduction.R dependencyCount: 71 Package: ReactomeGSA Version: 1.19.0 Imports: jsonlite, httr, progress, ggplot2, methods, gplots, RColorBrewer, dplyr, tidyr, Biobase Suggests: testthat, knitr, rmarkdown, ReactomeGSA.data, devtools Enhances: limma, edgeR, Seurat (>= 3.0), scater License: MIT + file LICENSE MD5sum: f65175573d58ffa0a8644e83d00d19bf 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] () Maintainer: Johannes Griss 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: d4a071f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ReactomeGSA_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ReactomeGSA_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ReactomeGSA_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ReactomeGSA_1.19.0.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 dependencyCount: 62 Package: ReactomePA Version: 1.49.0 Depends: R (>= 3.4.0) Imports: AnnotationDbi, DOSE (>= 3.5.1), enrichplot, ggplot2 (>= 3.3.5), ggraph, reactome.db, igraph, graphite, gson Suggests: BiocStyle, clusterProfiler, knitr, rmarkdown, org.Hs.eg.db, prettydoc, testthat License: GPL-2 MD5sum: 1524c35dd77b83ab8ec059269d59ef3e 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 URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/ReactomePA/issues git_url: https://git.bioconductor.org/packages/ReactomePA git_branch: devel git_last_commit: 6ac0ae2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ReactomePA_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ReactomePA_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ReactomePA_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ReactomePA_1.49.0.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, Pigengene, scTensor, ExpHunterSuite suggestsMe: CBNplot, ChIPseeker, CINdex, cola, GeDi, GRaNIE, scGPS dependencyCount: 131 Package: ReadqPCR Version: 1.51.0 Depends: R(>= 2.14.0), Biobase, methods Suggests: qpcR License: LGPL-3 MD5sum: 24977538a8034392d4e175e9175c846a 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 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: e84b608 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ReadqPCR_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ReadqPCR_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ReadqPCR_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ReadqPCR_1.51.0.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: 6 Package: REBET Version: 1.23.0 Depends: ASSET Imports: stats, utils Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 5d4f1330753a26f2fb84c30383291852 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 git_url: https://git.bioconductor.org/packages/REBET git_branch: devel git_last_commit: 70a42bd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/REBET_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/REBET_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/REBET_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/REBET_1.23.0.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: 16 Package: rebook Version: 1.15.0 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: beba50df5b87b5590d7713bb03efd78a 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rebook git_branch: devel git_last_commit: 1e4823e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rebook_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rebook_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rebook_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rebook_1.15.0.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: 43 Package: receptLoss Version: 1.17.0 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: a4452a0040a5f78f44cc256b127dc0fa 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/receptLoss git_branch: devel git_last_commit: 1df9a05 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/receptLoss_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/receptLoss_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/receptLoss_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/receptLoss_1.17.0.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: 70 Package: reconsi Version: 1.17.0 Imports: phyloseq, ks, reshape2, ggplot2, stats, methods, graphics, grDevices, matrixStats, Matrix Suggests: knitr, rmarkdown, testthat License: GPL-2 Archs: x64 MD5sum: 7f28dc8e95ea4a417c09620fb730bbdb 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] () Maintainer: Stijn Hawinkel VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/reconsi/issues git_url: https://git.bioconductor.org/packages/reconsi git_branch: devel git_last_commit: 5e0d65c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/reconsi_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/reconsi_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/reconsi_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/reconsi_1.17.0.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: recount3 Version: 1.15.0 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 MD5sum: 3d056e93bde88298123a62944aad28ab 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] () Maintainer: Leonardo Collado-Torres 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: cbdf974 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/recount3_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/recount3_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/recount3_1.15.0.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: RNAseqQC dependencyCount: 94 Package: recountmethylation Version: 1.15.0 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: 7d2c6beb5655d49c9cf5793dbf503095 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] (), Brian Walsh [aut] (), Kyle Ellrott [aut] (), Kasper D Hansen [aut] (), Reid F Thompson [aut] (), Abhinav Nellore [aut] () Maintainer: Sean K Maden 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: c029431 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/recountmethylation_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/recountmethylation_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/recountmethylation_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/recountmethylation_1.15.0.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: 151 Package: recoup Version: 1.33.0 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: 2b8dbd90d361dc26e1cf91941d7575b0 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 Maintainer: Panagiotis Moulos URL: https://github.com/pmoulos/recoup VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/recoup git_branch: devel git_last_commit: 351206b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/recoup_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/recoup_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/recoup_1.33.0.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: 126 Package: RedeR Version: 3.1.0 Depends: R (>= 4.0), methods Imports: scales, igraph Suggests: knitr, rmarkdown, markdown, BiocStyle, TreeAndLeaf License: GPL-3 MD5sum: 971d44e6766314069327f9c8e170a8d3 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] () Maintainer: Mauro Castro 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: f181d97 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RedeR_3.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RedeR_3.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RedeR_3.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RedeR_3.1.0.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: RGraphSpace dependencyCount: 25 Package: RedisParam Version: 1.7.0 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: a584fdd9bd6ab95992edd6e011eebd2e 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] (), Jiefei Wang [aut] Maintainer: Martin Morgan SystemRequirements: hiredis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RedisParam git_branch: devel git_last_commit: cbd7681 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RedisParam_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RedisParam_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RedisParam_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RedisParam_1.7.0.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: 1.51.0 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: 71a15d3e2cc5055a886f179e2b2075f7 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 git_url: https://git.bioconductor.org/packages/REDseq git_branch: devel git_last_commit: 6192333 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/REDseq_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/REDseq_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/REDseq_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/REDseq_1.51.0.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: 134 Package: RegEnrich Version: 1.15.0 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: 17e923b1e9f1d39a43e98d35d9f2d253 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RegEnrich git_branch: devel git_last_commit: bba0b4e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RegEnrich_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RegEnrich_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RegEnrich_1.15.0.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: 155 Package: regionalpcs Version: 1.3.0 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: 17978a5d45843042d573396fd401add3 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] () Maintainer: Tiffany Eulalio 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: 0129b26 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/regionalpcs_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/regionalpcs_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/regionalpcs_1.3.0.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: 1.3.0 Depends: R (>= 4.3.0) Imports: stats, grDevices, utils, ggplot2, dplyr, scater, gridExtra, BayesSpace, fgsea, magrittr, SingleCellExperiment, RColorBrewer, Seurat, S4Vectors, tibble, TOAST, assertthat, colorspace, shiny, SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, gplots, testthat (>= 3.0.0) License: GPL-3 MD5sum: abdc6fa9dd3dadaac55d4c5f705626ae NeedsCompilation: no Title: Investigating regions of interest and performing cross-regional analysis with spatial transcriptomics data Description: This package analyze spatial transcriptomics data through cross-regional 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RegionalST git_branch: devel git_last_commit: 889b990 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RegionalST_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RegionalST_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RegionalST_1.3.0.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: 258 Package: regioneR Version: 1.37.0 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: 7c787105ce0c4a3c89d3a4f89393a706 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 , Roberto Malinverni and Bernat Gel Maintainer: Bernat Gel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/regioneR git_branch: devel git_last_commit: d6368f7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/regioneR_1.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/regioneR_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/regioneR_1.37.0.tgz 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: 1.7.0 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: 008d1927416145989739f587198e5553 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] (), David Corujo [aut], Bernat Gel [aut] Maintainer: Roberto Malinverni URL: https://github.com/RMalinverni/regioneReload VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/regioneReloaded git_branch: devel git_last_commit: c1a79e4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/regioneReloaded_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/regioneReloaded_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/regioneReloaded_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/regioneReloaded_1.7.0.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: 1.39.0 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: d3cdf4abb1bc9140af6897e8543eec13 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] (), Andrew E. Jaffe [aut] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres 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: 1155bb9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/regionReport_1.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/regionReport_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/regionReport_1.39.0.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 dependencyCount: 161 Package: regsplice Version: 1.31.0 Imports: glmnet, SummarizedExperiment, S4Vectors, limma, edgeR, stats, pbapply, utils, methods Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: fd166f022500044234093cd90c8966ab 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 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: e502e49 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/regsplice_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/regsplice_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/regsplice_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/regsplice_1.31.0.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: 1.17.0 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: 400aa1fd22acf2b00a952eb278f9d6e9 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] (), Carmina Barberena-Jonas [aut] (), Jesus E. Sotelo-Fonseca [aut] (), Jose Alquicira-Hernandez [ctb] (), Heladia Salgado [ctb] (), Leonardo Collado-Torres [aut] (), Alejandro Reyes [aut] () Maintainer: Joselyn Chavez 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: 0e8dd20 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/regutools_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/regutools_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/regutools_1.17.0.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: 173 Package: REMP Version: 1.29.0 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: c92cdab6011dddbd7740dadabaaa2562 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 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: 8751068 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/REMP_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/REMP_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/REMP_1.29.0.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: 196 Package: Repitools Version: 1.51.0 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: b2416e16404871bd4e0b7503707a282d 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 , Dario Strbenac , Aaron Statham , Andrea Riebler Maintainer: Mark Robinson git_url: https://git.bioconductor.org/packages/Repitools git_branch: devel git_last_commit: 62a6cd3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Repitools_1.51.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Repitools_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Repitools_1.51.0.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: 74 Package: ReportingTools Version: 2.45.0 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: 75121245635c5cb45d362b9e6dfafcd8 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 , Gabriel Becker , Jessica L. Larson VignetteBuilder: utils, rmarkdown git_url: https://git.bioconductor.org/packages/ReportingTools git_branch: devel git_last_commit: 1690137 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ReportingTools_2.45.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ReportingTools_2.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ReportingTools_2.45.0.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: 193 Package: RepViz Version: 1.21.0 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: 78e8466bdaf733a3056c47860e2ed7a8 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RepViz git_branch: devel git_last_commit: 325bf8e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RepViz_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RepViz_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RepViz_1.21.0.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: 82 Package: ResidualMatrix Version: 1.15.0 Imports: methods, Matrix, S4Vectors, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, BiocSingular License: GPL-3 MD5sum: 912a6aa62201f89470836de4c34aeb0a 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 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: 9ae2dfa git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ResidualMatrix_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ResidualMatrix_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ResidualMatrix_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ResidualMatrix_1.15.0.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: 1.7.0 Depends: R (>= 4.1.0) Imports: Biostrings, BSgenome, BSgenome.Hsapiens.1000genomes.hs37d5, data.table, GenomeInfoDb, GenomicRanges, glmnet, ggplot2, gridExtra, IRanges, lsa, MutationalPatterns, nnls, parallel, reshape2, S4Vectors Suggests: BiocGenerics, BiocStyle, testthat, knitr License: file LICENSE MD5sum: 61dd2fe07f062b93bb2c3dd61e4bbac2 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] (), Luca De Sano [cre, aut] () Maintainer: Luca De Sano 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: fe17157 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RESOLVE_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RESOLVE_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RESOLVE_1.7.0.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: 1.5.0 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 MD5sum: a7f02ce50dc034d0103cad98644b0ec2 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] (), Xiang Zhu [aut] (), Qunhua Li [aut] () Maintainer: Adam Park 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: 7dc1ce2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/retrofit_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/retrofit_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/retrofit_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/retrofit_1.5.0.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: 1.5.0 Imports: Rcwl, RcwlPipelines, BiocFileCache, S4Vectors, stats, tools, utils, methods, jsonlite, yaml, basilisk Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle License: GPL-3 MD5sum: 1787900251ef79cd8af66e841a4218d4 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] () Maintainer: Qian Liu 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: cbe4e73 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ReUseData_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ReUseData_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ReUseData_1.5.0.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: 1.27.0 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: ddd71c9be21ba203593525eb58f963eb 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rexposome git_branch: devel git_last_commit: 03f5c48 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rexposome_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rexposome_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rexposome_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rexposome_1.27.0.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: 175 Package: rfaRm Version: 1.17.0 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: db408dee161b45bd77b975d457a34457 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 , Rafael Ayala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rfaRm git_branch: devel git_last_commit: 5de5040 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rfaRm_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rfaRm_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rfaRm_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rfaRm_1.17.0.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: 1.15.0 Imports: Rcpp, rjson, ggplot2, reshape2 LinkingTo: Rcpp, Rhtslib, zlibbioc Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 + file LICENSE Archs: x64 MD5sum: bc611f6d7ecf9947868a05e6d09dee9a 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] (), Ji-Dung Luo [ctb] (), Thomas Carroll [cre, aut] () Maintainer: Thomas Carroll SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rfastp git_branch: devel git_last_commit: d358573 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Rfastp_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Rfastp_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Rfastp_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Rfastp_1.15.0.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: 1.43.0 Depends: R (>= 3.5.0), methods Imports: utils, GenomeInfoDb, data.table, IRanges, GenomicRanges, parallel, Rsamtools Suggests: BiocStyle License: GPL (>=2 ) MD5sum: aae94be37bfc58ad7ccbf45a3ff6594d 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 git_url: https://git.bioconductor.org/packages/rfPred git_branch: devel git_last_commit: 78fb5a9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rfPred_1.43.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rfPred_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rfPred_1.43.0.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: 2.53.0 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 MD5sum: b206a3dcffd7ada0f45b3f1d1f50cc72 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 git_url: https://git.bioconductor.org/packages/rGADEM git_branch: devel git_last_commit: 192d081 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rGADEM_2.53.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rGADEM_2.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rGADEM_2.53.0.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 importsMe: TCGAWorkflow dependencyCount: 60 Package: rGenomeTracks Version: 1.11.0 Depends: R (>= 4.1.0), Imports: imager, reticulate, methods, rGenomeTracksData Suggests: rmarkdown, knitr, testthat (>= 3.0.0) License: GPL-3 MD5sum: 5b09495c64c2c927193701dae58f1d85 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] () Maintainer: Omar Elashkar SystemRequirements: pyGenomeTracks (prefered to use install_pyGenomeTracks()) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rGenomeTracks git_branch: devel git_last_commit: f5a6b1b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rGenomeTracks_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rGenomeTracks_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rGenomeTracks_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rGenomeTracks_1.11.0.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: 83 Package: RGMQL Version: 1.25.0 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 MD5sum: 73eca1b38e34f16668e01561a85a58bf NeedsCompilation: no 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 URL: http://www.bioinformatics.deib.polimi.it/genomic_computing/GMQL/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RGMQL git_branch: devel git_last_commit: f7f3046 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RGMQL_1.25.0.tar.gz vignettes: vignettes/RGMQL/inst/doc/RGMQL-vignette.html vignetteTitles: RGMQL: GenoMetric Query Language for R/Bioconductor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGMQL/inst/doc/RGMQL-vignette.R dependencyCount: 79 Package: RgnTX Version: 1.7.0 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: ab2db234f131defb03ce1e62fac335b0 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RgnTX git_branch: devel git_last_commit: eae84f4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RgnTX_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RgnTX_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RgnTX_1.7.0.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: 1.9.0 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 MD5sum: e93284a4441e4f6b67107c3936529a45 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] (), Dominik Kopczynski [aut] () Maintainer: Nils Hoffmann 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: c2c9bc9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rgoslin_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rgoslin_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rgoslin_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rgoslin_1.9.0.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 dependencyCount: 21 Package: RGraph2js Version: 1.33.0 Imports: utils, whisker, rjson, digest, graph Suggests: RUnit, BiocStyle, BiocGenerics, xtable, sna License: GPL-2 Archs: x64 MD5sum: 0ed830a1106d57ed8c5037c6067dd34c 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 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: 2a5abe4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RGraph2js_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RGraph2js_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RGraph2js_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RGraph2js_1.33.0.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: 10 Package: Rgraphviz Version: 2.49.0 Depends: R (>= 2.6.0), methods, utils, graph, grid Imports: stats4, graphics, grDevices Suggests: RUnit, BiocGenerics, XML License: EPL MD5sum: 80aa9fd3fb94f65152eef7d2afec6482 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 SystemRequirements: optionally Graphviz (>= 2.16) git_url: https://git.bioconductor.org/packages/Rgraphviz git_branch: devel git_last_commit: 1ca69df git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Rgraphviz_2.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Rgraphviz_2.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Rgraphviz_2.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Rgraphviz_2.49.0.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, hasseDiagram importsMe: apComplex, biocGraph, BiocOncoTK, bnem, chimeraviz, CytoML, dce, DEGraph, EnrichmentBrowser, flowWorkspace, GeneNetworkBuilder, GOstats, hyperdraw, KEGGgraph, mirIntegrator, MIRit, mnem, OncoSimulR, ontoProc, paircompviz, pathview, Pigengene, qpgraph, TRONCO, agena.ai, BCDAG, BiDAG, bnpa, 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, kpcalg, kst, lava, loon, maGUI, micd, multiplex, ParallelPC, pcalg, pchc, pks, psych, relations, rEMM, rPref, rSpectral, SCCI, sisal, textplot, tm, topologyGSA, tpc, unifDAG, zenplots dependencyCount: 9 Package: rGREAT Version: 2.7.0 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: 92c2b0c1b9da55190065e51fb0fdd7a3 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] () Maintainer: Zuguang Gu 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: a6da6b1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rGREAT_2.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rGREAT_2.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rGREAT_2.7.0.tgz vignettes: vignettes/rGREAT/inst/doc/rGREAT.html vignetteTitles: The rGREAT package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: ATACCoGAPS dependencyCount: 122 Package: RGSEA Version: 1.39.0 Depends: R(>= 2.10.0) Imports: BiocGenerics Suggests: BiocStyle, GEOquery, knitr, RUnit License: GPL(>=3) Archs: x64 MD5sum: a0313cde22aad055679f55b454c3b75f 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RGSEA git_branch: devel git_last_commit: 47f2de2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RGSEA_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RGSEA_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RGSEA_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RGSEA_1.39.0.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: 5 Package: rhdf5 Version: 2.49.0 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: 69493e014bb4694f5c0896c4a47e87c1 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] (), Gregoire Pau [aut], Martin Morgan [ctb], Daniel van Twisk [ctb] Maintainer: Mike Smith 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: cb3b4a4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rhdf5_2.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rhdf5_2.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rhdf5_2.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rhdf5_2.49.0.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, HDF5Array, HiCBricks, LoomExperiment, MuData, octad importsMe: alabaster.base, alabaster.bumpy, alabaster.mae, alabaster.matrix, alabaster.ranges, alabaster.spatial, BayesSpace, biomformat, bnbc, bsseq, chihaya, CiteFuse, cmapR, CoGAPS, CopyNumberPlots, cTRAP, cytomapper, diffHic, DropletUtils, epigraHMM, EventPointer, FRASER, GenomicScores, gep2pep, h5vc, HicAggR, HiCcompare, HiCExperiment, IONiseR, mariner, methodical, MOFA2, MoleculeExperiment, phantasus, ptairMS, PureCN, recountmethylation, ribor, scCB2, scMitoMut, scone, scRNAseqApp, signatureSearch, SpliceWiz, SpotClean, SurfR, trackViewer, MafH5.gnomAD.v3.1.2.GRCh38, MafH5.gnomAD.v4.0.GRCh38, DmelSGI, MethylSeqData, ptairData, scMultiome, signatureSearchData, TumourMethData, bioRad, ebvcube, file2meco, karyotapR, LOMAR, NEONiso, ondisc, rDataPipeline suggestsMe: beachmat.hdf5, edgeR, HiCDOC, pairedGSEA, phantasusLite, rhdf5filters, SCArray, scviR, slalom, spatialHeatmap, Spectra, SummarizedExperiment, TENxIO, tximport, zellkonverter, antaresProcessing, antaresRead, antaresViz, conos, CRMetrics, digitalDLSorteR, io, MplusAutomation, neonstore, neonUtilities, rbiom, SignacX, SpatialDDLS dependencyCount: 3 Package: rhdf5client Version: 1.27.1 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: 4d5b5b4c2a89106a607cfdfc34ea29d2 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rhdf5client git_branch: devel git_last_commit: 6e27c0c git_last_commit_date: 2024-05-02 Date/Publication: 2024-05-03 source.ver: src/contrib/rhdf5client_1.27.1.tar.gz win.binary.ver: bin/windows/contrib/4.4/rhdf5client_1.27.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rhdf5client_1.27.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rhdf5client_1.27.1.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 suggestsMe: BiocOncoTK, HumanTranscriptomeCompendium, restfulSEData dependencyCount: 32 Package: rhdf5filters Version: 1.17.0 LinkingTo: Rhdf5lib Suggests: BiocStyle, knitr, rmarkdown, tinytest, rhdf5 (>= 2.47.7) License: BSD_2_clause + file LICENSE MD5sum: 0e732c8bd305173926e9f7b763488107 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] () Maintainer: Mike Smith 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: 5f72c8f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rhdf5filters_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rhdf5filters_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rhdf5filters_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rhdf5filters_1.17.0.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: HDF5Array, rhdf5 dependencyCount: 1 Package: Rhdf5lib Version: 1.27.0 Depends: R (>= 4.2.0) Suggests: BiocStyle, knitr, rmarkdown, tinytest, mockery License: Artistic-2.0 Archs: x64 MD5sum: e480a3bfda1f61ed1158d26df8ede435 NeedsCompilation: yes Title: hdf5 library as an R package Description: Provides C and C++ hdf5 libraries. biocViews: Infrastructure Author: Mike Smith [ctb, cre] (), The HDF Group [cph] Maintainer: Mike Smith URL: https://github.com/grimbough/Rhdf5lib SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/grimbough/Rhdf5lib git_url: https://git.bioconductor.org/packages/Rhdf5lib git_branch: devel git_last_commit: 84f4182 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Rhdf5lib_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Rhdf5lib_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Rhdf5lib_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Rhdf5lib_1.27.0.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, HDF5Array, mbkmeans, mzR, ncdfFlow, rhdf5, rhdf5filters, ondisc dependencyCount: 0 Package: Rhisat2 Version: 1.21.0 Depends: R (>= 4.4.0) Imports: txdbmaker, SGSeq, GenomicRanges, methods, utils Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 Archs: x64 MD5sum: 2d8c481fb1806fca97a2be062f8b45e7 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] () Maintainer: Charlotte Soneson 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: d4554a0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Rhisat2_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Rhisat2_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Rhisat2_1.21.0.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: 105 Package: Rhtslib Version: 3.1.0 Imports: tools, zlibbioc LinkingTo: zlibbioc Suggests: knitr, rmarkdown, BiocStyle License: LGPL (>= 2) Archs: x64 MD5sum: a3bb874b8816d5bb621b70eedb06cfda 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 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: 31b99ff git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Rhtslib_3.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Rhtslib_3.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Rhtslib_3.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Rhtslib_3.1.0.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, TransView, VariantAnnotation, jackalope dependencyCount: 2 Package: RiboCrypt Version: 1.11.0 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: 93c6bb0b980c1e80587b2b6b7266844c 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 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: 6debd28 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RiboCrypt_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RiboCrypt_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RiboCrypt_1.11.0.tgz 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: 1.13.0 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) MD5sum: 0fa4772623279a6194081843c8686894 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RiboDiPA git_branch: devel git_last_commit: 0e3a457 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RiboDiPA_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RiboDiPA_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RiboDiPA_1.13.0.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: 150 Package: RiboProfiling Version: 1.35.0 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 MD5sum: 5fd372d919b9c08f445f15bbf5b2d1fc 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RiboProfiling git_branch: devel git_last_commit: 0a5feaa git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RiboProfiling_1.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RiboProfiling_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RiboProfiling_1.35.0.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: 176 Package: ribor Version: 1.17.0 Depends: R (>= 3.6.0) Imports: dplyr, ggplot2, hash, methods, rhdf5, rlang, stats, S4Vectors, tidyr, tools, yaml Suggests: testthat, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 4c40d8c14160aaa40bba0419ae472283 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ribor git_branch: devel git_last_commit: 2fce63f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ribor_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ribor_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ribor_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ribor_1.17.0.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: 1.39.0 Depends: R (>= 3.0.2), methods, GenomicRanges, abind Imports: Rsamtools, IRanges, S4Vectors, baySeq, GenomeInfoDb, seqLogo Suggests: BiocStyle, RUnit, BiocGenerics License: GPL-3 MD5sum: a040a7d45e76b27550d76b74943dfd30 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] () Maintainer: Samuel Granjeaud 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: 707b159 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/riboSeqR_1.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/riboSeqR_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/riboSeqR_1.39.0.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: 49 Package: ribosomeProfilingQC Version: 1.17.0 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 MD5sum: 569d699232674b803598e7e8096c3613 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] (), Mariah Hoye [aut] Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ribosomeProfilingQC git_branch: devel git_last_commit: df792aa git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ribosomeProfilingQC_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ribosomeProfilingQC_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ribosomeProfilingQC_1.17.0.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: 191 Package: rifi Version: 1.9.0 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: 3c46b492585d70e760f5c5427c4a1d0a 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 VignetteBuilder: knitr BugReports: https://github.com/CyanolabFreiburg/rifi git_url: https://git.bioconductor.org/packages/rifi git_branch: devel git_last_commit: 4a8fea2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rifi_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rifi_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rifi_1.9.0.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: 135 Package: rifiComparative Version: 1.5.0 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: 76d551cbcde9532ad7f34445d2275a76 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 VignetteBuilder: knitr BugReports: https://github.com/CyanolabFreiburg/rifiComparative git_url: https://git.bioconductor.org/packages/rifiComparative git_branch: devel git_last_commit: 99524ff git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rifiComparative_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rifiComparative_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rifiComparative_1.5.0.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: 174 Package: RImmPort Version: 1.33.0 Imports: plyr, dplyr, DBI, data.table, reshape2, methods, sqldf, tools, utils, RSQLite Suggests: knitr License: GPL-3 Archs: x64 MD5sum: 766a668bfc7ea0d156537308fa5e7395 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 Maintainer: Zicheng Hu , Ravi Shankar URL: http://bioconductor.org/packages/RImmPort/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RImmPort git_branch: devel git_last_commit: 76f6d0c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RImmPort_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RImmPort_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RImmPort_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RImmPort_1.33.0.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: 1.29.0 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 Archs: x64 MD5sum: 8c23eb07b8b83775322bc7cae49d1798 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RITAN git_branch: devel git_last_commit: b1d9bc3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RITAN_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RITAN_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RITAN_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RITAN_1.29.0.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: 144 Package: RIVER Version: 1.29.0 Depends: R (>= 3.3.2) Imports: glmnet, pROC, ggplot2, graphics, stats, Biobase, methods, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools License: GPL (>= 2) MD5sum: 88cdd57260aaf19c9b5f1f39ff80c7ab 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 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: 6265e75 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RIVER_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RIVER_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RIVER_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RIVER_1.29.0.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: 47 Package: RJMCMCNucleosomes Version: 1.29.0 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: d29fc890f2c7e6dda6e3f07ee838284d 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 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: 46dc35f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RJMCMCNucleosomes_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RJMCMCNucleosomes_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RJMCMCNucleosomes_1.29.0.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: 1.13.0 Depends: R (>= 4.1), glmnet Imports: Matrix, igraph, survival, stats Suggests: knitr License: Artistic-2.0 MD5sum: d1ea08ebe2b3a574a673a17c58179b68 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] () Maintainer: Wei Liu VignetteBuilder: knitr BugReports: https://github.com/weiliu123/RLassoCox/issues git_url: https://git.bioconductor.org/packages/RLassoCox git_branch: devel git_last_commit: 04330c0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RLassoCox_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RLassoCox_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RLassoCox_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RLassoCox_1.13.0.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: 1.67.0 Depends: R (>= 2.1.0) Imports: graphics, grDevices, MASS, stats, utils License: LGPL (>= 2) MD5sum: 091e9b3623bc180448c2c214d6b88d70 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 , Gary Wong Maintainer: Nusrat Rabbee 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: 2bf3fba git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RLMM_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RLMM_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RLMM_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RLMM_1.67.0.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: 1.61.0 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: a0ed59135bdcbbd66949c3d0382bfdfc 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 , with contributions from C. Ambroise J. Zhu Maintainer: Camille Maumet URL: http://www.bioconductor.org/ git_url: https://git.bioconductor.org/packages/Rmagpie git_branch: devel git_last_commit: d731d21 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Rmagpie_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Rmagpie_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Rmagpie_1.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Rmagpie_1.61.0.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: 19 Package: RMassBank Version: 3.15.1 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: efe7b809ba0467bf379234802d3c8f5d 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 SystemRequirements: OpenBabel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RMassBank git_branch: devel git_last_commit: 8af9a1d git_last_commit_date: 2024-05-02 Date/Publication: 2024-05-03 source.ver: src/contrib/RMassBank_3.15.1.tar.gz win.binary.ver: bin/windows/contrib/4.4/RMassBank_3.15.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RMassBank_3.15.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RMassBank_3.15.1.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: 164 Package: rmelting Version: 1.21.0 Depends: R (>= 3.6) Imports: Rdpack, rJava (>= 0.9-8) Suggests: readxl, knitr, rmarkdown, reshape2, pander, testthat License: GPL-2 | GPL-3 MD5sum: b1aad93a195b3aae369f12c67d8500f4 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] (), 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 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: 0199c50 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rmelting_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rmelting_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rmelting_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rmelting_1.21.0.tgz vignettes: vignettes/rmelting/inst/doc/Tutorial.pdf vignetteTitles: Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: Rmmquant Version: 1.23.0 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: ec8b46b032fb28d884a34672dd1b13d8 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 SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rmmquant git_branch: devel git_last_commit: d9d5410 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Rmmquant_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Rmmquant_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Rmmquant_1.23.0.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: 186 Package: rmspc Version: 1.11.0 Imports: processx, BiocManager, rtracklayer, stats, tools, methods, GenomicRanges, stringr Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL-3 MD5sum: 4fd170a1be92875866e7b4d40e0bd6dd 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 URL: https://genometric.github.io/MSPC/ SystemRequirements: .NET 6.0 VignetteBuilder: knitr BugReports: https://github.com/Genometric/MSPC/issues git_url: https://git.bioconductor.org/packages/rmspc git_branch: devel git_last_commit: 1ae78a6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rmspc_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rmspc_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rmspc_1.11.0.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: RNAdecay Version: 1.25.0 Depends: R (>= 3.5) Imports: stats, grDevices, grid, ggplot2, gplots, utils, TMB, nloptr, scales Suggests: parallel, knitr, reshape2, rmarkdown License: GPL-2 MD5sum: 713e01612703828ab3ff43ec7869de3d 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RNAdecay git_branch: devel git_last_commit: 95e2395 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RNAdecay_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RNAdecay_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RNAdecay_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RNAdecay_1.25.0.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: 63 Package: rnaEditr Version: 1.15.0 Depends: R (>= 4.0) Imports: GenomicRanges, IRanges, BiocGenerics, GenomeInfoDb, bumphunter, S4Vectors, stats, survival, logistf, plyr, corrplot Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: d961159618da1f48fb50bc4bba497c86 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 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: 0d74af7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rnaEditr_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rnaEditr_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rnaEditr_1.15.0.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: 153 Package: RNAmodR Version: 1.19.0 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 MD5sum: 3c4d003936dd2549e61d629f9c5feef7 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] (), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst 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: ac36c21 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RNAmodR_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RNAmodR_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RNAmodR_1.19.0.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: 167 Package: RNAmodR.AlkAnilineSeq Version: 1.19.0 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: 9e43fb6a913849ea230ca57a39f50371 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] (), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst 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: b5002dd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RNAmodR.AlkAnilineSeq_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RNAmodR.AlkAnilineSeq_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RNAmodR.AlkAnilineSeq_1.19.0.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: 168 Package: RNAmodR.ML Version: 1.19.0 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: 35a9e35972577b0b0093f8ed8e63979b 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] (), Denis L.J. Lafontaine [ctb] Maintainer: Felix G.M. Ernst 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: f3125af git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RNAmodR.ML_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RNAmodR.ML_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RNAmodR.ML_1.19.0.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: 169 Package: RNAmodR.RiboMethSeq Version: 1.19.0 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 MD5sum: a8e6ba3acf646f918610678baafaefa1 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] (), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst 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: 25b69c1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RNAmodR.RiboMethSeq_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RNAmodR.RiboMethSeq_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RNAmodR.RiboMethSeq_1.19.0.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: 168 Package: RNAsense Version: 1.19.0 Depends: R (>= 3.6) Imports: ggplot2, parallel, NBPSeq, qvalue, SummarizedExperiment, stats, utils, methods Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 21e180998483b7a9cf13959fd10d7a3d 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 VignetteBuilder: knitr BugReports: https://github.com/marcusrosenblatt/RNAsense git_url: https://git.bioconductor.org/packages/RNAsense git_branch: devel git_last_commit: 9b5181d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RNAsense_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RNAsense_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RNAsense_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RNAsense_1.19.0.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: 1.35.0 Depends: R (>= 3.2.0) Imports: RColorBrewer, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 153274efb495b1bd71a8cb231ae2911c 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 URL: https://github.com/tengmx/rnaseqcomp VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rnaseqcomp git_branch: devel git_last_commit: c4a6a4e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rnaseqcomp_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rnaseqcomp_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rnaseqcomp_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rnaseqcomp_1.35.0.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: 1.3.0 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 Archs: x64 MD5sum: 8705ad1922440ef17db0623c436ddc30 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] (), Sheela Sathyanarayana [aut], Adam Szpiro [aut], James MacDonald [aut], Alison Paquette [aut] Maintainer: Brennan Baker 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: 930fa0b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RNAseqCovarImpute_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RNAseqCovarImpute_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RNAseqCovarImpute_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RNAseqCovarImpute_1.3.0.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: 100 Package: RNASeqPower Version: 1.45.0 License: LGPL (>=2) MD5sum: 98452a9a520060ac85d552e755fd77f0 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 git_url: https://git.bioconductor.org/packages/RNASeqPower git_branch: devel git_last_commit: 417915a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RNASeqPower_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RNASeqPower_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RNASeqPower_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RNASeqPower_1.45.0.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: RnBeads Version: 2.23.0 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: 7511f57ab0bfe23d5392e9ea730303a9 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 git_url: https://git.bioconductor.org/packages/RnBeads git_branch: devel git_last_commit: e5dd13d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RnBeads_2.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RnBeads_2.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RnBeads_2.23.0.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: 166 Package: Rnits Version: 1.39.0 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: 92b05d2bf93d20d530f636961f711edc 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 Maintainer: Dipen P. Sangurdekar VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rnits git_branch: devel git_last_commit: d4219b6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-03 source.ver: src/contrib/Rnits_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Rnits_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Rnits_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Rnits_1.39.0.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: 1.41.0 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: 6e0223d9d73f16b3469639d143385275 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 URL: https://github.com/vodkatad/roar/ git_url: https://git.bioconductor.org/packages/roar git_branch: devel git_last_commit: 2aafb4d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/roar_1.41.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/roar_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/roar_1.41.0.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: 1.3.0 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: 300ab748750fdfbc90977281ea887a04 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] () Maintainer: Adria Caballe VignetteBuilder: knitr BugReports: https://github.com/adricaba/roastgsa/issues git_url: https://git.bioconductor.org/packages/roastgsa git_branch: devel git_last_commit: 1ebc54f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/roastgsa_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/roastgsa_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/roastgsa_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/roastgsa_1.3.0.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: 45 Package: ROC Version: 1.81.0 Depends: R (>= 1.9.0), utils, methods Imports: knitr Suggests: rmarkdown, Biobase, BiocStyle License: Artistic-2.0 MD5sum: f82f85f5b14f91a6b0e5e6fcf5230bb3 NeedsCompilation: yes Title: utilities for ROC, with microarray focus Description: Provide utilities for ROC, with microarray focus. biocViews: DifferentialExpression Author: Vince Carey , Henning Redestig for C++ language enhancements Maintainer: Vince Carey URL: http://www.bioconductor.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ROC git_branch: devel git_last_commit: 162b83a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ROC_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ROC_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ROC_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ROC_1.81.0.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: 1.17.0 Depends: boot, SummarizedExperiment, fission, knitr, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 18516fb77e6c6b2a9fa1608467afa1f7 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-María Vivo [aut] Maintainer: Juan-Pedro Garcia 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: f0060fe git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ROCpAI_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ROCpAI_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ROCpAI_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ROCpAI_1.17.0.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: 1.9.0 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: 411201de6ad17044c96961fb5d73f309 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 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: 20ebf6b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RolDE_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RolDE_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RolDE_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RolDE_1.9.0.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: 77 Package: rols Version: 3.1.0 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 Archs: x64 MD5sum: 1048875dee47eca9ec2e368624a3d2d8 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] (), Tiage Chedraoui Silva [ctb], Andrew Clugston [ctb] Maintainer: Laurent Gatto 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: 1973f5d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rols_3.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rols_3.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rols_3.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rols_3.1.0.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: struct suggestsMe: MSnbase, spatialHeatmap, RforProteomics dependencyCount: 22 Package: ROntoTools Version: 2.33.0 Depends: methods, graph, boot, KEGGREST, KEGGgraph, Rgraphviz Suggests: RUnit, BiocGenerics License: CC BY-NC-ND 4.0 + file LICENSE MD5sum: b309a60d7e0dff186524544084597d40 NeedsCompilation: no Title: R Onto-Tools suite Description: Suite of tools for functional analysis. biocViews: NetworkAnalysis, Microarray, GraphsAndNetworks Author: Calin Voichita and Sahar Ansari and Sorin Draghici Maintainer: Sorin Draghici git_url: https://git.bioconductor.org/packages/ROntoTools git_branch: devel git_last_commit: 3a8bf4e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ROntoTools_2.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ROntoTools_2.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ROntoTools_2.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ROntoTools_2.33.0.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: 1.37.0 Depends: R (>= 3.5.0) Imports: Biobase, ggplot2, graphics, grDevices, methods, plotly, stats, MultiAssayExperiment, MultiDataSet, SummarizedExperiment, utils Suggests: BiocGenerics, BiocStyle, knitr, multtest, omicade4, rmarkdown, testthat License: CeCILL MD5sum: 5a985854624425712231405356e0b5cc 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] () Maintainer: Etienne A. Thevenot 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: c62124f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ropls_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ropls_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ropls_1.37.0.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: 105 Package: ROSeq Version: 1.17.0 Depends: R (>= 4.0) Imports: pbmcapply, edgeR, limma Suggests: knitr, rmarkdown, testthat, RUnit, BiocGenerics License: GPL-3 MD5sum: 4bb14c5b5f430b6ae1bad1f5d1e3737b 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 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: 72d00a4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ROSeq_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ROSeq_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ROSeq_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ROSeq_1.17.0.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: 14 Package: ROTS Version: 1.33.0 Depends: R (>= 3.3) Imports: Rcpp, stats, Biobase, methods LinkingTo: Rcpp Suggests: testthat License: GPL (>= 2) MD5sum: b582a50a0f00bced50acd2f3c7be4e24 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 git_url: https://git.bioconductor.org/packages/ROTS git_branch: devel git_last_commit: 339a228 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ROTS_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ROTS_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ROTS_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ROTS_1.33.0.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, RolDE suggestsMe: wrProteo dependencyCount: 7 Package: RPA Version: 1.61.0 Depends: R (>= 3.1.1), affy, BiocGenerics, BiocStyle, methods, rmarkdown Imports: phyloseq Suggests: knitr, parallel License: BSD_2_clause + file LICENSE MD5sum: 5323e16fd9579241fefce0b36453b949 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] () Maintainer: Leo Lahti 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: 7b6d209 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RPA_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RPA_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RPA_1.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RPA_1.61.0.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: 1.9.0 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: 19d7ba25849b55ab107ae9f0820c43de 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] () Maintainer: Sofia Persson 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: dd88cec git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rprimer_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rprimer_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rprimer_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rprimer_1.9.0.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: 2.17.0 Suggests: knitr, rmarkdown License: BSD_3_clause Archs: x64 MD5sum: 195193ab4c606aec8cc4da73129c7d93 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 SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RProtoBufLib git_branch: devel git_last_commit: c38964a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RProtoBufLib_2.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RProtoBufLib_2.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RProtoBufLib_2.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RProtoBufLib_2.17.0.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: 2.13.0 Depends: R (>= 3.5.0), methods Imports: BiocFileCache, jsonlite, xml2, RCurl, curl, utils Suggests: Biostrings, BiocStyle, testthat, knitr, tibble, rmarkdown License: GPL-2 MD5sum: eaec3f78d552d1bbbc244142d36f8ec8 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 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: 4ffc9c8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rpx_2.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rpx_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rpx_2.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rpx_2.13.0.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: 1.39.0 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) MD5sum: 2f0a292207b6d7439b0bc9f0cf861e51 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 Maintainer: Welliton Souza 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: 7237106 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Rqc_1.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Rqc_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Rqc_1.39.0.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: 159 Package: rqubic Version: 1.51.0 Imports: methods, Biobase, BiocGenerics, biclust Suggests: RColorBrewer License: GPL-2 MD5sum: 2a64319dbbc563264e0819aa7ffbc961 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] () Maintainer: Jitao David Zhang git_url: https://git.bioconductor.org/packages/rqubic git_branch: devel git_last_commit: fdcc059 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rqubic_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rqubic_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rqubic_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rqubic_1.51.0.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: 1.39.0 Depends: Biostrings (>= 2.26.2) Imports: rJava, utils Suggests: rRDPData, knitr, rmarkdown License: GPL-2 + file LICENSE Archs: x64 MD5sum: 50567a98d251d040ab21c273df7d17fc 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] (), Nagar Anurag [aut] Maintainer: Michael Hahsler 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: 1d3c5fd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rRDP_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rRDP_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rRDP_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rRDP_1.39.0.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: 1.45.0 Depends: R (>= 2.10), grid Imports: VennDiagram Suggests: lattice License: GPL-2 MD5sum: 2fc99131c41a206259c48724f6fe1686 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 git_url: https://git.bioconductor.org/packages/RRHO git_branch: devel git_last_commit: b94784c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RRHO_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RRHO_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RRHO_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RRHO_1.45.0.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: 1.17.0 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: cd1ee9414f703fecce3cc2ea9860b0f7 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 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: 4facf4b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rrvgo_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rrvgo_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rrvgo_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rrvgo_1.17.0.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: scDiffCom dependencyCount: 108 Package: Rsamtools Version: 2.21.0 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), zlibbioc, bitops, BiocParallel, stats LinkingTo: Rhtslib (>= 2.99.1), 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: 089c3f875a36d2482d2dff2ae0f1e67d 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 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: 17b1859 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Rsamtools_2.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Rsamtools_2.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Rsamtools_2.21.0.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, chromstaR, chromVAR, CircSeqAlignTk, cn.mops, CNVfilteR, CNVPanelizer, CNVrd2, compEpiTools, consensusDE, CopyNumberPlots, CrispRVariants, csaw, CSSQ, customProDB, DAMEfinder, 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, HTSeqGenie, icetea, IMAS, INSPEcT, karyoploteR, MADSEQ, magpie, MDTS, metagene2, metaseqR2, methylKit, mosaics, motifmatchr, msgbsR, NADfinder, NanoMethViz, nearBynding, nucleR, ORFik, panelcn.mops, 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, single, 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, MMAPPR2data, raerdata, hoardeR, iimi, kibior, MAAPER, NIPTeR, noisyr, PlasmaMutationDetector, PlasmaMutationDetector2, revert, scPloidy, Signac, umiAnalyzer, VALERIE suggestsMe: AnnotationHub, bamsignals, BaseSpaceR, BiocGenerics, BiocParallel, biomvRCNS, BSgenomeForge, Chicago, 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: 2.63.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), methods, utils Imports: BiocGenerics, graph, utils License: Artistic-2.0 MD5sum: 1c9bc2d2cf1d17a0e9f5fb968b81042f 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 Maintainer: Michael Lawrence URL: http://www.sbml.org SystemRequirements: libsbml (==5.10.2) git_url: https://git.bioconductor.org/packages/rsbml git_branch: devel git_last_commit: 588dff4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rsbml_2.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rsbml_2.63.0.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: 7 Package: rScudo Version: 1.21.0 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: 02d1f895710933addfe7e73833690e26 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 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: f66cee9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rScudo_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rScudo_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rScudo_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rScudo_1.21.0.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: 1.15.0 Depends: R (>= 4.0), igraph Imports: methods, magrittr, stringr, dplyr Suggests: testthat, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: c612ab667678c47f6ab8e30a343898c5 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] () Maintainer: Leslie Myint 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: 1928f14 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rsemmed_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rsemmed_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rsemmed_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rsemmed_1.15.0.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: 1.25.0 Imports: Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE Archs: x64 MD5sum: 5d8c927616a073ae2b2188ab5df27e22 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 VignetteBuilder: knitr BugReports: https://github.com/compbiocore/RSeqAn/issues git_url: https://git.bioconductor.org/packages/RSeqAn git_branch: devel git_last_commit: 11f0625 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RSeqAn_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RSeqAn_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RSeqAn_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RSeqAn_1.25.0.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: 2.19.0 Imports: grDevices, stats, utils, Matrix License: GPL (>=3) MD5sum: e23ecd1bdb338ea9057819e69e643eed 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 , Yang Liao and Gordon K Smyth URL: http://bioconductor.org/packages/Rsubread git_url: https://git.bioconductor.org/packages/Rsubread git_branch: devel git_last_commit: ff20753 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Rsubread_2.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Rsubread_2.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Rsubread_2.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Rsubread_2.19.0.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, diffUTR, dupRadar, FRASER, ribosomeProfilingQC, scPipe, scruff suggestsMe: autonomics, icetea, singleCellTK, SpliceWiz, tidybulk, MetaScope dependencyCount: 8 Package: RSVSim Version: 1.45.0 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 MD5sum: af3966d535730c743f4cf97fb48d2164 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 git_url: https://git.bioconductor.org/packages/RSVSim git_branch: devel git_last_commit: 8366fd9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RSVSim_1.45.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RSVSim_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RSVSim_1.45.0.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: 1.17.0 Depends: R (>= 4.0) Imports: pracma, stats Suggests: Biostrings, methods, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 5b149c8a62d6e69ef3a197bf1c4501ad NeedsCompilation: no Title: Functions to creation of low dimensional comparative matrices of Amino Acid Sequence occurrences Description: The SWeeP method was developed to favor the analizes between amino acids sequences and to assist alignment free phylogenetic studies. This method is based on the concept of sparse words, which is applied in the scan of biological sequences and its the conversion in a matrix of ocurrences. Aiming the generation of low dimensional matrices of Amino Acid Sequence occurrences. biocViews: Software,StatisticalMethod,SupportVectorMachine,Technology,Sequencing,Genetics, Alignment Author: Danrley R. Fernandes [com, cre, aut] Maintainer: Danrley R. Fernandes VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rSWeeP git_branch: devel git_last_commit: 2437a5b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rSWeeP_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rSWeeP_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rSWeeP_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rSWeeP_1.17.0.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: 5 Package: RTCA Version: 1.57.0 Depends: methods,stats,graphics,Biobase,RColorBrewer, gtools Suggests: xtable License: LGPL-3 MD5sum: 568ef71a34a19b2ae56682bea150add7 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 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: bd4a00f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RTCA_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RTCA_1.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RTCA_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RTCA_1.57.0.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: 8 Package: RTCGA Version: 1.35.0 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 Archs: x64 MD5sum: 97ffbe453d7e6f8677a55701fa5285f5 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 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: 9237b12 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RTCGA_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RTCGA_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RTCGA_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RTCGA_1.35.0.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: 138 Package: RTCGAToolbox Version: 2.35.0 Depends: R (>= 4.3.0) Imports: BiocGenerics, data.table, DelayedArray, GenomicRanges, GenomeInfoDb, httr, methods, RaggedExperiment, RCurl, RJSONIO, rvest, S4Vectors (>= 0.23.10), stats, stringr, SummarizedExperiment, TCGAutils (>= 1.9.4), utils Suggests: BiocStyle, Homo.sapiens, knitr, readr, rmarkdown License: GPL-2 MD5sum: 0d8185a2a3e3d75387f2c024bf050564 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] (), Ludwig Geistlinger [ctb] Maintainer: Marcel Ramos 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: e922ad1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RTCGAToolbox_2.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RTCGAToolbox_2.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RTCGAToolbox_2.35.0.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, TCGAWorkflow suggestsMe: TCGAutils dependencyCount: 108 Package: RTN Version: 2.29.0 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: 452daab0c9fdfcebde1b49cdf99ae190 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 URL: http://dx.doi.org/10.1038/ncomms3464 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTN git_branch: devel git_last_commit: 11f2811 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RTN_2.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RTN_2.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RTN_2.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RTN_2.29.0.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: 145 Package: RTNduals Version: 1.29.0 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: ea3b0978f204f3e05d628bdb36ba7315 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 , Clarice Groeneveld VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTNduals git_branch: devel git_last_commit: 4f1b41d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RTNduals_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RTNduals_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RTNduals_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RTNduals_1.29.0.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: 146 Package: RTNsurvival Version: 1.29.0 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: f2bec2a1d894f5951c7b5b770685059b 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 , Mauro A. A. Castro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTNsurvival git_branch: devel git_last_commit: 4a2c66c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RTNsurvival_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RTNsurvival_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RTNsurvival_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RTNsurvival_1.29.0.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: 150 Package: RTopper Version: 1.51.0 Depends: R (>= 2.12.0), Biobase Imports: limma, multtest Suggests: org.Hs.eg.db, KEGGREST, GO.db License: GPL (>= 3) + file LICENSE MD5sum: 42a25781ccb5da54b0b76bf81345aa7c 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 , Svitlana Tyekucheva Maintainer: Luigi Marchionni git_url: https://git.bioconductor.org/packages/RTopper git_branch: devel git_last_commit: 2fe7b0c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RTopper_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RTopper_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RTopper_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RTopper_1.51.0.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: 17 Package: Rtpca Version: 1.15.0 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: dab55fa405bf3017a858c6864a8a8665 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 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/Rtpca git_branch: devel git_last_commit: e46be1b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Rtpca_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Rtpca_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Rtpca_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Rtpca_1.15.0.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: 1.65.0 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), zlibbioc, 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: 72d86ef4af1e8eb685904a7202c0cffa 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 git_url: https://git.bioconductor.org/packages/rtracklayer git_branch: devel git_last_commit: 6e92ec6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rtracklayer_1.65.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rtracklayer_1.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rtracklayer_1.65.0.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, groHMM, HelloRanges, IdeoViz, MethylSeekR, ORFhunteR, r3Cseq, StructuralVariantAnnotation, svaNUMT, svaRetro, EatonEtAlChIPseq, liftOver, sequencing, csawBook, OSCA.intro importsMe: AnnotationHubData, annotatr, APAlyzer, ATACseqQC, ATACseqTFEA, ballgown, 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, easylift, ELMER, enhancerHomologSearch, ensembldb, epidecodeR, epigraHMM, epimutacions, erma, esATAC, extraChIPs, factR, fcScan, FindIT2, FLAMES, geneAttribution, genomation, GenomicFeatures, GenomicInteractions, GenomicPlot, ggbio, gmapR, gmoviz, GOTHiC, GreyListChIP, Gviz, hiAnnotator, HicAggR, hicVennDiagram, HiTC, HTSeqGenie, icetea, igvR, INSPEcT, IsoformSwitchAnalyzeR, karyoploteR, m6Aboost, MADSEQ, magpie, maser, MEDIPS, metagene2, metaseqR2, methodical, methrix, methylKit, mobileRNA, Moonlight2R, motifbreakR, MotifDb, multicrispr, MungeSumstats, NADfinder, nearBynding, normr, OGRE, OMICsPCA, ORFik, PAST, periodicDNA, plyranges, pram, primirTSS, proBAMr, profileplyr, PureCN, qsea, QuasR, raer, RCAS, recount3, recoup, regioneR, REMP, Repitools, RGMQL, 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, TFBSTools, tidyCoverage, trackViewer, transcriptR, TRESS, tRNAscanImport, txcutr, txdbmaker, VariantAnnotation, VariantTools, wavClusteR, wiggleplotr, GenomicState, chipenrich.data, DMRcatedata, geneLenDataBase, NxtIRFdata, raerdata, spatialLIBD, seqpac, SingscoreAMLMutations, crispRdesignR, GALLO, geneHapR, kibior, locuszoomr, PlasmaMutationDetector, PlasmaMutationDetector2, valr suggestsMe: alabaster.files, AnnotationHub, autonomics, BiocFileCache, biovizBase, BREW3R.r, bsseq, cicero, CINdex, compEpiTools, CrispRVariants, crisprViz, DAMEfinder, DiffBind, eisaR, epistack, epivizrChart, epivizrData, FRASER, geneXtendeR, GenomicAlignments, GenomicDistributions, GenomicInteractionNodes, GenomicRanges, gwascat, HiCExperiment, HiContacts, igvShiny, InPAS, interactiveDisplay, megadepth, methylumi, motifTestR, MutationalPatterns, NanoMethViz, OrganismDbi, 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, MOCHA, Rgff, RTIGER, Seurat, Signac dependencyCount: 57 Package: Rtreemix Version: 1.67.0 Depends: R (>= 2.5.0) Imports: methods, graph, Biobase, Hmisc Suggests: Rgraphviz License: LGPL MD5sum: 39653e2cc9d5c570e1ab7bb60f9074fd 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 git_url: https://git.bioconductor.org/packages/Rtreemix git_branch: devel git_last_commit: e608f1e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Rtreemix_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Rtreemix_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Rtreemix_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Rtreemix_1.67.0.tgz 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: 77 Package: rTRM Version: 1.43.0 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: a58c77adde55e589878c87ff58a9a451 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 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: f94a5e6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rTRM_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rTRM_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rTRM_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rTRM_1.43.0.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: 1.43.0 Imports: shiny (>= 0.9), rTRM, MotifDb, org.Hs.eg.db, org.Mm.eg.db License: GPL-3 MD5sum: 49b8d738a3eafbe6d1188bb2060781ab 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 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: ea09ddc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rTRMui_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rTRMui_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rTRMui_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rTRMui_1.43.0.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: 1.27.0 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 Archs: x64 MD5sum: 6a541e17b2e72a9b6236981f3a0f3baf 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 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: 7694c09 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/runibic_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/runibic_1.27.0.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: 93 Package: RUVcorr Version: 1.37.0 Imports: corrplot, MASS, stats, lattice, grDevices, gridExtra, snowfall, psych, BiocParallel, grid, bladderbatch, reshape2, graphics Suggests: knitr, hgu133a2.db, rmarkdown License: GPL-2 MD5sum: a8e488c8e01262fbb57657713a5e7447 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RUVcorr git_branch: devel git_last_commit: 99f0f15 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RUVcorr_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RUVcorr_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RUVcorr_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RUVcorr_1.37.0.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: 40 Package: RUVnormalize Version: 1.39.0 Depends: R (>= 2.10.0) Imports: RUVnormalizeData, Biobase Enhances: spams License: GPL-3 MD5sum: 2b8eb118170ae828f5c193b6c267a7c3 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 git_url: https://git.bioconductor.org/packages/RUVnormalize git_branch: devel git_last_commit: 3552f3e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RUVnormalize_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RUVnormalize_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RUVnormalize_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RUVnormalize_1.39.0.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: 7 Package: RUVSeq Version: 1.39.0 Depends: Biobase, EDASeq (>= 1.99.1), edgeR Imports: methods, MASS Suggests: BiocStyle, knitr, RColorBrewer, zebrafishRNASeq, DESeq2 License: Artistic-2.0 Archs: x64 MD5sum: 2d7ed38910f9f3b3bd01cc982aee3192 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 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: c91b567 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RUVSeq_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RUVSeq_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RUVSeq_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RUVSeq_1.39.0.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: 122 Package: Rvisdiff Version: 1.3.0 Depends: R (>= 4.3.0) Imports: edgeR, utils Suggests: knitr, rmarkdown, DESeq2, limma, SummarizedExperiment, airway, BiocStyle, matrixTests, BiocManager License: GPL-2 | GPL-3 MD5sum: 22e3c23fd37de8137c0cd4e1e90f890b 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] (), David Barrios [cre, aut] () Maintainer: David Barrios 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: c640ce4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Rvisdiff_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Rvisdiff_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Rvisdiff_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Rvisdiff_1.3.0.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: 12 Package: RVS Version: 1.27.0 Depends: R (>= 3.5.0) Imports: GENLIB, gRain, snpStats, kinship2, methods, stats, utils, R.utils Suggests: knitr, testthat, rmarkdown, BiocStyle, VariantAnnotation License: GPL-2 MD5sum: cae0c1ed4e77f903184397f0f003f391 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RVS git_branch: devel git_last_commit: 3a75dc6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/RVS_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/RVS_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/RVS_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/RVS_1.27.0.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: 63 Package: rWikiPathways Version: 1.25.0 Imports: httr, utils, XML, rjson, data.table, RCurl, dplyr, tidyr, readr, stringr, purrr, lubridate Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 01fe028502eb53ba31479ce59033c484 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] (), Alex Pico [aut] () Maintainer: Egon Willighagen 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: 8dabeff git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/rWikiPathways_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/rWikiPathways_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rWikiPathways_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rWikiPathways_1.25.0.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: 1.5.0 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, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 71df1bc79bbd328232aff5777742bda3 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], Jacques Serizay [ctb] Maintainer: Hervé Pagès 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: 4962838 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/S4Arrays_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/S4Arrays_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/S4Arrays_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/S4Arrays_1.5.0.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, HDF5Array, SummarizedExperiment dependencyCount: 14 Package: S4Vectors Version: 0.43.0 Depends: R (>= 4.0.0), methods, utils, stats, stats4, BiocGenerics (>= 0.37.0) Suggests: IRanges, GenomicRanges, SummarizedExperiment, Matrix, DelayedArray, ShortRead, graph, data.table, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: ac9569c8497c7bad274a733ced875dc8 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. 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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.v3.1.2.GRCh38, 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, celldex, chipenrich.data, chipseqDBData, curatedMetagenomicData, curatedTCGAData, DNAZooData, DropletTestFiles, FlowSorted.Blood.EPIC, fourDNData, HCATonsilData, HighlyReplicatedRNASeq, HMP16SData, HMP2Data, homosapienDEE2CellScore, imcdatasets, leeBamViews, MerfishData, MetaGxPancreas, MetaScope, MethylSeqData, MicrobiomeBenchmarkData, MouseGastrulationData, MouseThymusAgeing, pd.atdschip.tiling, scMultiome, scpdata, scRNAseq, sesameData, SimBenchData, SingleCellMultiModal, SomaticCancerAlterations, spatialLIBD, TransOmicsData, tuberculosis, GeoMxWorkflows, seqpac, comradesOO, crispRdesignR, digitalDLSorteR, DR.SC, driveR, genBaRcode, geno2proteo, hoardeR, imcExperiment, karyotapR, LoopRig, MetAlyzer, microbial, MOCHA, NIPTeR, oncoPredict, PlasmaMutationDetector, PlasmaMutationDetector2, restfulr, rliger, rnaCrosslinkOO, rsolr, SC.MEB, SCRIP, scROSHI, Signac, SpatialDDLS, TaxaNorm, toxpiR suggestsMe: AlpsNMR, BiocGenerics, chihaya, dearseq, epiregulon.extra, epivizrChart, GeoTcgaData, globalSeq, GWASTools, GWENA, gypsum, hca, maftools, martini, MicrobiotaProcess, MsQuality, MungeSumstats, RTCGA, scFeatures, SPOTlight, TFEA.ChIP, TFutils, traviz, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, BioPlex, curatedAdipoChIP, curatedAdipoRNA, ObMiTi, xcoredata, gkmSVM, grandR, MARVEL, pmartR, polyRAD, RCPA, Rgff, Seurat, SNPassoc, updog, valr linksToMe: Biostrings, CNEr, DECIPHER, DelayedArray, GenomicAlignments, GenomicRanges, HDF5Array, IRanges, kebabs, MatrixRider, pwalign, Rsamtools, rtracklayer, S4Arrays, ShortRead, SparseArray, Structstrings, triplex, VariantAnnotation, VariantFiltering, XVector dependencyCount: 6 Package: safe Version: 3.45.0 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: 78936f4ae7f4cfb0a706b6569882b89f 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 git_url: https://git.bioconductor.org/packages/safe git_branch: devel git_last_commit: 0dddf91 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/safe_3.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/safe_3.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/safe_3.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/safe_3.45.0.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: 1.75.0 Depends: R (>= 2.10), SparseM (>= 0.73), methods Imports: graphics, stats, utils License: GPL (>= 2) Archs: x64 MD5sum: 9cc0d2c09d17ff81cfe3402310f37867 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 , with contributions from Gordon Smyth Maintainer: Tim Beissbarth URL: http://www.bioinf.med.uni-goettingen.de git_url: https://git.bioconductor.org/packages/sagenhaft git_branch: devel git_last_commit: 80cfe21 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sagenhaft_1.75.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/sagenhaft_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sagenhaft_1.75.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sagenhaft_1.75.0.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: 2.5.0 Depends: R (>= 3.5.0), gdsfmt (>= 1.28.0), SeqArray (>= 1.42.3), 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: ece6c3d8c9ae2f656882ad0deba5d669 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] (), Wei Zhou [ctb] (the original author of the SAIGE R package), J. Wade Davis [ctb] Maintainer: Xiuwen Zheng 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: fb07dcb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SAIGEgds_2.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SAIGEgds_2.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SAIGEgds_2.5.0.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: 43 Package: sampleClassifier Version: 1.29.0 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: d02a2d5c41c532bc65c429d302a07719 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 git_url: https://git.bioconductor.org/packages/sampleClassifier git_branch: devel git_last_commit: ad2e404 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sampleClassifier_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/sampleClassifier_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sampleClassifier_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sampleClassifier_1.29.0.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: 94 Package: SamSPECTRAL Version: 1.59.0 Depends: R (>= 3.3.3) Imports: methods License: GPL (>= 2) MD5sum: 782e07d7f485a479ead4ddc856475bf8 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 git_url: https://git.bioconductor.org/packages/SamSPECTRAL git_branch: devel git_last_commit: 0709b86 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SamSPECTRAL_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SamSPECTRAL_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SamSPECTRAL_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SamSPECTRAL_1.59.0.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: 1.15.0 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: ce434b7d7f76962079637565819f17d2 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 , Kuan-Hao Chao Maintainer: Kuan-Hao Chao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sangeranalyseR git_branch: devel git_last_commit: 6e226b0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sangeranalyseR_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/sangeranalyseR_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sangeranalyseR_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sangeranalyseR_1.15.0.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: 125 Package: sangerseqR Version: 1.41.0 Depends: R (>= 3.5.0), Biostrings, pwalign, stringr Imports: methods, shiny Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL-2 MD5sum: 3cad9005bcf029e1c71c4a07486c83ed 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sangerseqR git_branch: devel git_last_commit: eb76dc8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sangerseqR_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/sangerseqR_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sangerseqR_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sangerseqR_1.41.0.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, bold dependencyCount: 55 Package: SARC Version: 1.3.0 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: ddd5dd782fd8e2cc487b9259814c2d5a 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] () Maintainer: Krutik Patel 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: a50c621 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SARC_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SARC_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SARC_1.3.0.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: 206 Package: sarks Version: 1.17.0 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: 7b7fc3a8c4206cae12987f86a763581c 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] () Maintainer: Dennis Wylie 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: 6b3684f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sarks_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/sarks_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sarks_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sarks_1.17.0.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: 1.1.0 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: cbded7bbcd3cb84312817970f089356c 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 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: a1754de git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/saseR_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/saseR_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/saseR_1.1.0.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: 193 Package: satuRn Version: 1.13.0 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: 1747d30723a5b7d59871ffa5065ac3ff 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 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: 21c5c1e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/satuRn_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/satuRn_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/satuRn_1.13.0.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: 1.19.0 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 MD5sum: f9ae155d2467f03a074e4aebd6157eb1 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 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: 666bbc2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SBGNview_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SBGNview_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SBGNview_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SBGNview_1.19.0.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: 2.1.0 Depends: XML, deSolve Suggests: rsbml License: GPL-2 MD5sum: 26166f52f7a79bf900dedd63ac3a0eb3 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 URL: http://epbi-radivot.cwru.edu/SBMLR/SBMLR.html git_url: https://git.bioconductor.org/packages/SBMLR git_branch: devel git_last_commit: 7b986e2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SBMLR_2.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SBMLR_2.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SBMLR_2.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SBMLR_2.1.0.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: 1.33.0 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: e528f14d4efd6e12f40f7d49f4e73a29 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 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: 4e4070e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SC3_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SC3_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SC3_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SC3_1.33.0.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: 1.27.0 Depends: R (>= 3.4), smoothie, GenomicRanges, IRanges, SummarizedExperiment Imports: methods, grDevices, graphics, utils License: LGPL-3 MD5sum: 33783d2fb10a6624c08c60e49733bcf5 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 git_url: https://git.bioconductor.org/packages/Scale4C git_branch: devel git_last_commit: 8571982 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Scale4C_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Scale4C_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Scale4C_1.27.0.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: 1.13.0 Imports: methods, Matrix, S4Vectors, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, BiocSingular, DelayedMatrixStats License: GPL-3 MD5sum: c37ee278d3282c909a85ebb80976beda 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 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: b4bf8cb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ScaledMatrix_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ScaledMatrix_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ScaledMatrix_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ScaledMatrix_1.13.0.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: 2.47.0 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: 97bbcf8369b6fcc56a9c195867521c6e 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 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: fee46f7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SCAN.UPC_2.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SCAN.UPC_2.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SCAN.UPC_2.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SCAN.UPC_2.47.0.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: 116 Package: scanMiR Version: 1.11.0 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: ddc5d1ef5b72d7fe17ec73f74ac18a37 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] (), Michael Soutschek [aut], Fridolin Gross [aut] Maintainer: Pierre-Luc Germain VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scanMiR git_branch: devel git_last_commit: 9ca55dd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scanMiR_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scanMiR_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scanMiR_1.11.0.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: 1.11.0 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 MD5sum: 511ba79164f977e83a8a3d01c7604494 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] (), Michael Soutschek [aut], Fridolin Gross [ctb] Maintainer: Pierre-Luc Germain VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scanMiRApp git_branch: devel git_last_commit: dd36d70 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scanMiRApp_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scanMiRApp_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scanMiRApp_1.11.0.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: 161 Package: scAnnotatR Version: 1.11.0 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: ab244921797d9a8d88c44056b055e6dc 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] (), Johannes Griss [cre] () Maintainer: Johannes Griss 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: 77000fc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scAnnotatR_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scAnnotatR_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scAnnotatR_1.11.0.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: 1.19.0 Depends: R (>= 3.6) Imports: IRanges,plotrix,RCurl,rtracklayer Suggests: knitr, rmarkdown License: file LICENSE MD5sum: 534c4fb55dc29f6ab51a4cdc6fce1515 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 Maintainer: Phaedra Agius VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCANVIS git_branch: devel git_last_commit: f8a646f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SCANVIS_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SCANVIS_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SCANVIS_1.19.0.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: 1.13.0 Depends: R (>= 3.5.0), gdsfmt (>= 1.36.0), methods, DelayedArray (>= 0.28.0) Imports: S4Vectors, utils, Matrix, BiocParallel, DelayedMatrixStats, SummarizedExperiment, SingleCellExperiment, BiocSingular Suggests: BiocGenerics, scater, scuttle, uwot, RUnit, knitr, markdown, rmarkdown, rhdf5, HDF5Array License: GPL-3 Archs: x64 MD5sum: 7d3ac2ce0f58de309348824ae1e6676d 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] () Maintainer: Xiuwen Zheng URL: https://github.com/AbbVie-ComputationalGenomics/SCArray VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCArray git_branch: devel git_last_commit: 5df3190 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SCArray_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SCArray_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SCArray_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SCArray_1.13.0.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: 56 Package: SCArray.sat Version: 1.5.0 Depends: methods, SCArray (>= 1.7.13), 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: 937f1fa246046b2de9ddf7a9bc38bd3e 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] (), Seurat contributors [ctb] (for the classes and methods defined in Seurat) Maintainer: Xiuwen Zheng 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: 17b97a4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SCArray.sat_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SCArray.sat_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SCArray.sat_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SCArray.sat_1.5.0.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: 185 Package: scater Version: 1.33.0 Depends: SingleCellExperiment, scuttle, ggplot2 Imports: stats, utils, methods, Matrix, BiocGenerics, S4Vectors, SummarizedExperiment, DelayedArray, MatrixGenerics, beachmat, BiocNeighbors, BiocSingular, BiocParallel, rlang, ggbeeswarm, viridis, Rtsne, RColorBrewer, RcppML, uwot, pheatmap, ggrepel, ggrastr Suggests: BiocStyle, snifter, densvis, cowplot, biomaRt, knitr, scRNAseq, robustbase, rmarkdown, testthat, Biobase, scattermore License: GPL-3 MD5sum: c9e2134a892e99d4a0b492f9c451d390 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] () Maintainer: Alan O'Callaghan 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: 483f942 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scater_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scater_1.33.0.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: netSmooth, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows, SingleRBook importsMe: airpart, BayesSpace, CATALYST, celda, CelliD, CellMixS, ChromSCape, decontX, distinct, epiregulon.extra, FLAMES, M3Drop, MEB, mia, miaViz, muscat, peco, pipeComp, RegionalST, scDblFinder, scMerge, scTreeViz, scviR, singleCellTK, Spaniel, spatialHeatmap, tricycle, VAExprs, spatialLIBD, PRECAST, SC.MEB suggestsMe: APL, Banksy, batchelor, bluster, ccImpute, CellTrails, CiteFuse, concordexR, corral, dittoSeq, dreamlet, epiregulon, escheR, ExperimentSubset, ggsc, ggspavis, Glimma, InteractiveComplexHeatmap, iSEE, iSEEfier, iSEEhex, iSEEpathways, iSEEu, MAST, mbkmeans, MGnifyR, miloR, miQC, monocle, MOSim, MuData, mumosa, Nebulosa, netDx, raer, SC3, SCArray, scds, schex, scHOT, scone, scp, scPipe, scran, scRepertoire, SingleCellAlleleExperiment, SingleR, sketchR, slalom, smartid, smoothclust, speckle, splatter, SPOTlight, standR, tidySingleCellExperiment, tidySpatialExperiment, traviz, UCell, velociraptor, waddR, curatedMetagenomicData, DuoClustering2018, HCAData, HCATonsilData, MouseAgingData, muscData, SingleCellMultiModal, TabulaMurisData, tuberculosis, simpleSingleCell, spicyWorkflow, Canek, ProFAST, SCdeconR, scellpam dependencyCount: 107 Package: scatterHatch Version: 1.11.0 Depends: R (>= 4.1) Imports: grid, ggplot2, plyr, spatstat.geom, stats, grDevices Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: e762ecd023200e57aa2254d08c46c137 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] () Maintainer: Atul Deshpande 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: 3dd2ec6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scatterHatch_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scatterHatch_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scatterHatch_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scatterHatch_1.11.0.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: 42 Package: scBFA Version: 1.19.0 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: 3017334f2a8847fd9c76d2db8dadc7c0 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 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: 96c5a69 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scBFA_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scBFA_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scBFA_1.19.0.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: 1.23.0 Depends: R (>= 3.5.0) Imports: stats Suggests: knitr,rmarkdown,BiocStyle,BiocManager License: GPL-2 Archs: x64 MD5sum: b7baa9912029798d90e7b8730488993f 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: 43fd99b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SCBN_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SCBN_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SCBN_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SCBN_1.23.0.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: 1.7.0 Depends: R (>= 4.2.0) Imports: reshape2, future, future.apply, ape, scales, Seurat, ggplot2, ggtree, patchwork, proxy, methods, stats, base, utils Suggests: BiocStyle, knitr, testthat, cluster, SingleCellExperiment License: GPL-3 + file LICENSE MD5sum: 3f7a3b18de2ec8b5c86b637482ce9748 NeedsCompilation: no Title: Quantitative visual exploration of scRNA-seq data Description: scBubbletree is a quantitative method for visual exploration of scRNA-seq data. It preserves biologically meaningful properties of scRNA-seq data, such as local and global cell distances, as well as the density distribution of cells across the sample. scBubbletree is scalable and avoids the overplotting problem, and is able to visualize diverse cell attributes derived from multiomic single-cell experiments. Importantly, Importantly, scBubbletree is easy to use and to integrate with popular approaches for scRNA-seq data analysis. biocViews: Visualization,Clustering, SingleCell,Transcriptomics,RNASeq Author: Simo Kitanovski [aut, cre] Maintainer: Simo Kitanovski 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: 3f6bdc7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scBubbletree_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scBubbletree_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scBubbletree_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scBubbletree_1.7.0.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: 165 Package: scCB2 Version: 1.15.0 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: e9081c0f1dbc7831e0f555c8453dda28 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 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: 35426ba git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scCB2_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scCB2_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scCB2_1.15.0.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: 196 Package: sccomp Version: 1.9.0 Depends: R (>= 4.2.0) Imports: methods, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstantools (>= 2.1.1), rstan (>= 2.26.0), SeuratObject, SingleCellExperiment, parallel, dplyr, tidyr, purrr, magrittr, rlang, tibble, boot, lifecycle, stats, tidyselect, utils, ggplot2, ggrepel, patchwork, forcats, readr, scales, stringr, glue LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.26.0), StanHeaders (>= 2.26.0) Suggests: BiocStyle, testthat (>= 3.0.0), markdown, knitr, loo, tidyseurat, tidySingleCellExperiment, prettydoc Enhances: furrr, extraDistr License: GPL-3 MD5sum: 6460c12dcd9738b42a29f0e05e389787 NeedsCompilation: yes Title: Robust Outlier-aware Estimation of Composition and Heterogeneity for Single-cell Data Description: A robust and outlier-aware method for testing differential tissue composition from 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: ImmunoOncology, Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, Clustering Author: Stefano Mangiola [aut, cre] Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/sccomp SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/stemangiola/sccomp/issues git_url: https://git.bioconductor.org/packages/sccomp git_branch: devel git_last_commit: 13c5322 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sccomp_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/sccomp_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sccomp_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sccomp_1.9.0.tgz vignettes: vignettes/sccomp/inst/doc/introduction.html vignetteTitles: sccomp hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sccomp/inst/doc/introduction.R dependencyCount: 119 Package: scDataviz Version: 1.15.0 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: f974f2c04b510dea4e1b353840dc2eab 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 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: b219c0f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scDataviz_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scDataviz_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scDataviz_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scDataviz_1.15.0.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: 1.19.0 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: b385da51cc6f41fd7fd0fde53ecef09c 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] (), Aaron Lun [ctb] Maintainer: Pierre-Luc Germain 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: 53c2715 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scDblFinder_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scDblFinder_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scDblFinder_1.19.0.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: 131 Package: scDD Version: 1.29.0 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: 3e3aaf4cf584a4169d283dcb231603a6 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] () Maintainer: Keegan Korthauer 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: 36ce326 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scDD_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scDD_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scDD_1.29.0.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: 1.7.0 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: 289c9e92b475fbd5fb771e89b180661e 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 URL: https://github.com/wiscstatman/scDDboost SystemRequirements: c++11 VignetteBuilder: knitr BugReports: https://github.com/wiscstatman/scDDboost/issues git_url: https://git.bioconductor.org/packages/scDDboost git_branch: devel git_last_commit: 68a6e0d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scDDboost_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scDDboost_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scDDboost_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scDDboost_1.7.0.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: 101 Package: scde Version: 2.33.0 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 Archs: x64 MD5sum: 40bd4b92577e5803e02b1cac35594c4e 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 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: c4214c1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scde_2.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scde_2.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scde_2.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scde_2.33.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: pagoda2 dependencyCount: 49 Package: scDesign3 Version: 1.3.0 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: a5ec41029832d31a96963735b89b1ddb 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] (), Qingyang Wang [aut] () Maintainer: Dongyuan Song 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: f375fe1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scDesign3_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scDesign3_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scDesign3_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scDesign3_1.3.0.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: 105 Package: scds Version: 1.21.0 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: cbe3e31f102854c2210be68f327531ee 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scds git_branch: devel git_last_commit: 4fa3a66 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scds_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scds_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scds_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scds_1.21.0.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: 57 Package: SCFA Version: 1.15.0 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: d28b147a283b0976e7ebdb9617c41c24 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 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: 3fcfca9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SCFA_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SCFA_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SCFA_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SCFA_1.15.0.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: 54 Package: scFeatureFilter Version: 1.25.0 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: 4fcba82fb99a7a77b926331c8e10383a 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scFeatureFilter git_branch: devel git_last_commit: 14c90ee git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scFeatureFilter_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scFeatureFilter_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scFeatureFilter_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scFeatureFilter_1.25.0.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: 1.5.0 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: 2d8f7d4315f072ece1011899392a3109 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 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: 030b7be git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scFeatures_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scFeatures_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scFeatures_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scFeatures_1.5.0.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: 250 Package: scGPS Version: 1.19.0 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: c77d66598c76dc5f2ab8af7e1b184eef 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 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: f97750b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scGPS_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scGPS_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scGPS_1.19.0.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: 127 Package: schex Version: 1.19.0 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: fe7bdf58522394badd7add33670eb801 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 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: 0915fb4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/schex_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/schex_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/schex_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/schex_1.19.0.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: 88 Package: scHOT Version: 1.17.0 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: c6f5afd2cd3e62dfd648d45c66b8b23b 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scHOT git_branch: devel git_last_commit: e762c77 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scHOT_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scHOT_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scHOT_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scHOT_1.17.0.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: 1.3.0 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: d75d2e4d159fab4960789e0a52376ec2 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] (), Mengbo Li [aut] (), Yunshun Chen [aut, cre] () Maintainer: Yunshun Chen 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: 639189a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scider_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scider_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scider_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scider_1.3.0.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: 1.7.0 Imports: dplyr, rmarkdown, data.table, Biostrings, parallel, stats, plyr, knitr, ggplot2, gridExtra, DECIPHER, stringr, sangerseqR, kableExtra, tibble, scales, rlang, flowCore, methods Suggests: fs, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 861f10e9d33688413f8953f2f1ed9742 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] (), Sebastian Ols [aut, dtc] (), Karin Loré [dtc, ths] () Maintainer: Rodrigo Arcoverde Cerveira 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: 83dd5a4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scifer_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scifer_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scifer_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scifer_1.7.0.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: 107 Package: scmap Version: 1.27.0 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 MD5sum: fc6f30e4ddaca696158551ef579dd74c 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 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: 6f5609a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scmap_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scmap_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scmap_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scmap_1.27.0.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: 77 Package: scMerge Version: 1.21.0 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: 655d68fba1a2b009eb9dd2678eafdecd 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 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: 4b2e3ee git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scMerge_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scMerge_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scMerge_1.21.0.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 dependencyCount: 188 Package: scMET Version: 1.7.0 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: 6ec8702a0c1f29e8c4b1ee43aaad0304 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] (), John Riddell [ctb] Maintainer: Andreas C. Kapourani 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: 494c37a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scMET_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scMET_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scMET_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scMET_1.7.0.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: 118 Package: scmeth Version: 1.25.0 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: bc5c8b3f768111b464809f559cf88492 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 Maintainer: Divy Kangeyan VignetteBuilder: knitr BugReports: https://github.com/aryeelab/scmeth/issues git_url: https://git.bioconductor.org/packages/scmeth git_branch: devel git_last_commit: 363cbc5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scmeth_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scmeth_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scmeth_1.25.0.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: 162 Package: scMitoMut Version: 1.1.0 Depends: R (>= 4.3.0) Imports: data.table, Rcpp, magrittr, plyr, stringr, utils, stats, methods, ggplot2, pheatmap, zlibbioc, RColorBrewer, rhdf5, readr, parallel, grDevices LinkingTo: Rcpp, RcppArmadillo Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown, VGAM, R.utils License: Artistic-2.0 MD5sum: f805693a98eed8e9490fb3043d61bac5 NeedsCompilation: yes Title: Single-cell Mitochondrial Mutation Analysis Tool Description: This package is designed for analyzing 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 filtering and visualization. In the future, the visualization tool will become an independent package. Mutation filtering is performed by fitting a statistical model to account for various sources of noise, including PCR error, sequencing error, mtDNA sampling and/or heteroplasmy dynamics. The model tests whether the observed allele frequency of a locus in a cell can be explained by the noise model. If not, we classify it as a mutation. The input for this analysis is the allele frequency. The noise model consists of three independent models: binomial, binomial-mixture, and beta-binomial models. biocViews: Preprocessing, Sequencing, SingleCell Author: Wenjie Sun [cre, aut] (), Leila Perie [ctb] Maintainer: Wenjie Sun 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: 954512a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scMitoMut_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scMitoMut_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scMitoMut_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scMitoMut_1.1.0.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: 60 Package: scMultiSim Version: 1.1.0 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 Suggests: knitr, rmarkdown, roxygen2, testthat (>= 3.0.0) License: Artistic-2.0 Archs: x64 MD5sum: 05b8ec74016cbb7f4afa6c39dc870c5d 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 i [aut, cre] (), Xiuwei Zhang [aut], Michael Squires [aut] Maintainer: Hechen i URL: https://github.com/ZhangLabGT/scMultiSim VignetteBuilder: knitr BugReports: https://github.com/ZhangLabGT/scMultiSim/issues git_url: https://git.bioconductor.org/packages/scMultiSim git_branch: devel git_last_commit: 18614db git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scMultiSim_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scMultiSim_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scMultiSim_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scMultiSim_1.1.0.tgz vignettes: vignettes/scMultiSim/inst/doc/basics.html, vignettes/scMultiSim/inst/doc/spatialCCI.html vignetteTitles: scMultiSim Basics, spatialCCI.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scMultiSim/inst/doc/basics.R, vignettes/scMultiSim/inst/doc/spatialCCI.R dependencyCount: 101 Package: SCnorm Version: 1.27.0 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) Archs: x64 MD5sum: 3adffbbeb6d63badaf42de5b8903d680 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 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: c114876 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SCnorm_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SCnorm_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SCnorm_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SCnorm_1.27.0.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: 1.29.0 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 Suggests: BiocStyle, DT, ggplot2, knitr, miniUI, NMF, plotly, reshape2, rmarkdown, scran, scRNAseq, shiny, testthat, visNetwork, doParallel, batchtools, splatter, scater, kableExtra, mclust, TENxPBMCData License: Artistic-2.0 Archs: x64 MD5sum: a1e8dcc146b517114941be3bf885758b 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 VignetteBuilder: knitr BugReports: https://github.com/YosefLab/scone/issues git_url: https://git.bioconductor.org/packages/scone git_branch: devel git_last_commit: 91519d5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scone_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scone_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scone_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scone_1.29.0.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: 186 Package: Sconify Version: 1.25.0 Depends: R (>= 3.5) Imports: tibble, dplyr, FNN, flowCore, Rtsne, ggplot2, magrittr, utils, stats, readr Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 0557c56d7e444ef72e99d03914c09751 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Sconify git_branch: devel git_last_commit: 49aeaa1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Sconify_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Sconify_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Sconify_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Sconify_1.25.0.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: 1.17.0 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 MD5sum: 1a9805d7402a3f9c398d08f6f7a97bac 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCOPE git_branch: devel git_last_commit: 9989494 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SCOPE_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SCOPE_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SCOPE_1.17.0.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: 103 Package: scoreInvHap Version: 1.27.0 Depends: R (>= 3.6.0) Imports: Biostrings, methods, snpStats, VariantAnnotation, GenomicRanges, BiocParallel, graphics, SummarizedExperiment Suggests: testthat, knitr, BiocStyle, rmarkdown License: file LICENSE MD5sum: de0615189f97113b8e2c8a0817b4dc4d 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 Pelegrí [aut], Juan R. Gonzalez [aut, cre] Maintainer: Dolors Pelegri-Siso VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scoreInvHap git_branch: devel git_last_commit: 509fec2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scoreInvHap_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scoreInvHap_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scoreInvHap_1.27.0.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: 82 Package: scp Version: 1.15.0 Depends: R (>= 4.3.0), QFeatures (>= 1.13.5) Imports: dplyr, IHW, ggplot2, ggrepel, matrixStats, metapod, methods, MsCoreUtils, MultiAssayExperiment, nipals, RColorBrewer, S4Vectors, SingleCellExperiment, SummarizedExperiment, stats, utils Suggests: BiocStyle, MsDataHub (>= 1.3.3), impute, knitr, patchwork, preprocessCore, rmarkdown, scater, scpdata, sva, testthat, vsn, uwot License: Artistic-2.0 MD5sum: e2181fb93abd5170202ab5c07cf017b6 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] (), Laurent Gatto [aut] () Maintainer: Christophe Vanderaa 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: b9838a0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scp_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scp_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scp_1.15.0.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: 114 Package: scPCA Version: 1.19.0 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: e060b062f7d2a6d9e24696ccfb27cbf6 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] (), Nima Hejazi [aut] (), Sandrine Dudoit [ctb, ths] () Maintainer: Philippe Boileau 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: dd18d37 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scPCA_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scPCA_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scPCA_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scPCA_1.19.0.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: 73 Package: scPipe Version: 2.5.0 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), zlibbioc, 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: 199dd64e2822860f01580ebf7aec041a 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 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: 54cf2d1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scPipe_2.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scPipe_2.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scPipe_2.5.0.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: 184 Package: scran Version: 1.33.0 Depends: SingleCellExperiment, scuttle Imports: SummarizedExperiment, S4Vectors, BiocGenerics, BiocParallel, Rcpp, stats, methods, utils, Matrix, edgeR, limma, igraph, statmod, DelayedArray, DelayedMatrixStats, BiocSingular, bluster, metapod, dqrng, beachmat LinkingTo: Rcpp, beachmat, BH, dqrng, scuttle Suggests: testthat, BiocStyle, knitr, rmarkdown, HDF5Array, scRNAseq, dynamicTreeCut, ResidualMatrix, ScaledMatrix, DESeq2, pheatmap, scater License: GPL-3 MD5sum: 2b64d5256486678971cf11ae77d1f1c3 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 SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scran git_branch: devel git_last_commit: edd02ba git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scran_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scran_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scran_1.33.0.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, celda, ChromSCape, CiteFuse, Dino, epiregulon, epiregulon.extra, FLAMES, lute, MOSim, msImpute, mumosa, pipeComp, scDblFinder, scDD, scMerge, scTreeViz, SingleCellSignalR, singleCellTK, Spaniel, spatialHeatmap, digitalDLSorteR, mixhvg, SC.MEB, SpatialDDLS suggestsMe: APL, Banksy, batchelor, bluster, CellTrails, clusterExperiment, decontX, destiny, dittoSeq, escape, escheR, ExperimentSubset, ggsc, ggspavis, Glimma, glmGamPoi, iSEEu, miloR, Nebulosa, nnSVG, PCAtools, raer, schex, scone, scuttle, SingleCellAlleleExperiment, SingleR, sketchR, smoothclust, splatter, SPOTlight, tidySingleCellExperiment, tidySpatialExperiment, tpSVG, transformGamPoi, TSCAN, velociraptor, HCAData, SingleCellMultiModal, TabulaMurisData, simpleSingleCell, Canek, SCdeconR dependencyCount: 75 Package: scReClassify Version: 1.11.0 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: 3a4e4158780d70eec95f77b77a648496 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] (), Taiyun Kim [aut, cre] () Maintainer: Taiyun Kim 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: 5fa98cf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scReClassify_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scReClassify_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scReClassify_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scReClassify_1.11.0.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: 1.21.0 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 MD5sum: e30029a616afc369579eef37ae048957 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 Maintainer: Zhun Miao 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: c7c6ede git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scRecover_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scRecover_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scRecover_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scRecover_1.21.0.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: 1.5.0 Depends: S4Vectors, SummarizedExperiment Imports: Rcpp, zlibbioc, BiocParallel LinkingTo: Rcpp Suggests: BiocGenerics, Biostrings, BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 6d8bb74880bab2415c6f76d96581c449 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] () Maintainer: Aaron Lun 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: d34bb07 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/screenCounter_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/screenCounter_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/screenCounter_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/screenCounter_1.5.0.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: 1.7.0 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 MD5sum: 7b608e95a8f8c4535cf5b188c7889441 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] (0000-0002-2301-6465), Elena Ceccacci [aut] (0000-0002-2285-8994), Saverio Minucci [fnd, ths] (0000-0001-5678-536X) Maintainer: Emanuel Michele Soda 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: 1468977 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ScreenR_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ScreenR_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ScreenR_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ScreenR_1.7.0.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: 51 Package: scRepertoire Version: 2.1.0 Depends: ggplot2, R (>= 4.0) Imports: cubature, dplyr, evmix, ggalluvial, ggdendro, ggraph, grDevices, igraph, iNEXT, methods, plyr, quantreg, Rcpp, reshape2, rjson, rlang, S4Vectors, SeuratObject, SingleCellExperiment, stats, stringr, stringdist, SummarizedExperiment, tidygraph, truncdist, utils, VGAM, hash LinkingTo: Rcpp Suggests: BiocManager, BiocStyle, circlize, knitr, rmarkdown, scales, scater, Seurat, spelling, testthat (>= 3.0.0), vdiffr License: MIT + file LICENSE MD5sum: 3c4f44e210b1a9bea9bcb816b317a4ef 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 URL: https://www.borch.dev/uploads/screpertoire/ VignetteBuilder: knitr BugReports: https://github.com/ncborcherding/scRepertoire/issues git_url: https://git.bioconductor.org/packages/scRepertoire git_branch: devel git_last_commit: 9061c35 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scRepertoire_2.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scRepertoire_2.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scRepertoire_2.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scRepertoire_2.1.0.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 dependencyCount: 120 Package: scRNAseqApp Version: 1.5.0 Depends: R (>= 4.3.0) Imports: bibtex, bslib, circlize, ComplexHeatmap, data.table, DBI, DT, GenomicRanges, GenomeInfoDb, ggdendro, ggforce, ggplot2, ggrepel, ggridges, grDevices, grid, gridExtra, htmltools, IRanges, jsonlite, 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: ba4f9d435ff13bee7426e9243ceeb609 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] () Maintainer: Jianhong Ou 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: 7ab3935 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scRNAseqApp_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scRNAseqApp_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scRNAseqApp_1.5.0.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: 233 Package: scruff Version: 1.23.0 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: b8a9c4ceaa98a291252d2a67bb907703 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 VignetteBuilder: knitr BugReports: https://github.com/campbio/scruff/issues git_url: https://git.bioconductor.org/packages/scruff git_branch: devel git_last_commit: 53bf075 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scruff_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scruff_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scruff_1.23.0.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: 185 Package: scry Version: 1.17.0 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: 91582c6ea9afc72987a1b76deb93b417 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 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: 82669ca git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scry_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scry_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scry_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scry_1.17.0.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: 55 Package: scShapes Version: 1.11.0 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: 7f16b3e0f1e290c3f813da9fbb29f201 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] () Maintainer: Malindrie Dharmaratne 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: 821013c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scShapes_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scShapes_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scShapes_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scShapes_1.11.0.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: 2.15.0 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 MD5sum: fe58df375aeeccf0afc00cc79019d3f9 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scTensor git_branch: devel git_last_commit: 29f39d9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scTensor_2.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scTensor_2.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scTensor_2.15.0.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: 235 Package: scTGIF Version: 1.19.0 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 Archs: x64 MD5sum: 59a4ea8670cc0f1bf76a4f281e03cd22 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scTGIF git_branch: devel git_last_commit: 24ef24e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scTGIF_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scTGIF_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scTGIF_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scTGIF_1.19.0.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: 1.17.0 Depends: R (>= 4.0) Imports: BiocParallel, Rtsne, grDevices, graphics, stats Suggests: scTHI.data, knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: c79007c29a5bd462cc24983e4b474650 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 VignetteBuilder: knitr BugReports: https://github.com/miccec/scTHI/issues git_url: https://git.bioconductor.org/packages/scTHI git_branch: devel git_last_commit: dacba00 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scTHI_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/scTHI_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scTHI_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scTHI_1.17.0.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: 1.11.0 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: 5cefa640b4031f1187edba94f82b8e46 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scTreeViz git_branch: devel git_last_commit: ce68a1d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scTreeViz_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scTreeViz_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scTreeViz_1.11.0.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: 256 Package: scuttle Version: 1.15.0 Depends: SingleCellExperiment Imports: methods, utils, stats, Matrix, Rcpp, BiocGenerics, S4Vectors, BiocParallel, GenomicRanges, SummarizedExperiment, DelayedArray, DelayedMatrixStats, beachmat LinkingTo: Rcpp, beachmat Suggests: BiocStyle, knitr, scRNAseq, rmarkdown, testthat, scran License: GPL-3 MD5sum: 4586e9909866ca3b081c5afcef2ff2dd 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 SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scuttle git_branch: devel git_last_commit: fd7d1ad git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scuttle_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/scuttle_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scuttle_1.15.0.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, DropletUtils, epiregulon, FLAMES, imcRtools, mia, mumosa, muscat, scDblFinder, simPIC, singleCellTK, spatialHeatmap, splatter, spoon, velociraptor, spatialLIBD, digitalDLSorteR, mixhvg, SpatialDDLS suggestsMe: Banksy, bluster, DESpace, dreamlet, epiregulon.extra, escheR, ggsc, iSEEde, iSEEfier, iSEEpathways, mastR, miloR, raer, SCArray, schex, SingleCellAlleleExperiment, SingleR, sketchR, smoothclust, SpotSweeper, tpSVG, TSCAN, HCAData, MouseThymusAgeing, scCustomize linksToMe: DropletUtils, scran dependencyCount: 51 Package: scviR Version: 1.5.0 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: 42fa2e836960f955b6bd978ad0b371ae 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] () Maintainer: Vincent Carey 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: ff7fb36 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/scviR_1.5.0.tar.gz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/scviR_1.5.0.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: 152 Package: SDAMS Version: 1.25.0 Depends: R(>= 3.5), SummarizedExperiment Imports: trust, qvalue, methods, stats, utils Suggests: testthat License: GPL MD5sum: 06fb4cd86d6e4b06dfbc64e411d7d7bf 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 , Chi Wang , Li Chen Maintainer: Yuntong Li git_url: https://git.bioconductor.org/packages/SDAMS git_branch: devel git_last_commit: 06b53de git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SDAMS_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SDAMS_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SDAMS_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SDAMS_1.25.0.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: sechm Version: 1.13.0 Depends: R (>= 4.0), SummarizedExperiment, ComplexHeatmap Imports: S4Vectors, seriation, circlize, methods, randomcoloR, stats, grid, grDevices, matrixStats Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 26f295f7bed492d2b8c56ca152330b00 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] () Maintainer: Pierre-Luc Germain VignetteBuilder: knitr BugReports: https://github.com/plger/sechm git_url: https://git.bioconductor.org/packages/sechm git_branch: devel git_last_commit: a3ad8d9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sechm_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/sechm_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sechm_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sechm_1.13.0.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 dependencyCount: 82 Package: segmenter Version: 1.11.0 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: fd1032404fa726a2f3d4d6a50f877330 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] () Maintainer: Mahmoud Ahmed VignetteBuilder: knitr BugReports: https://github.com/MahShaaban/segmenter/issues git_url: https://git.bioconductor.org/packages/segmenter git_branch: devel git_last_commit: f47ee7c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/segmenter_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/segmenter_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/segmenter_1.11.0.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: 166 Package: segmentSeq Version: 2.39.0 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 License: GPL-3 MD5sum: 8b1aac9c95e74438389471c5ae2e7997 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] () Maintainer: Samuel Granjeaud URL: https://github.com/samgg/segmentSeq BugReports: https://github.com/samgg/segmentSeq/issues git_url: https://git.bioconductor.org/packages/segmentSeq git_branch: devel git_last_commit: 84a3e10 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/segmentSeq_2.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/segmentSeq_2.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/segmentSeq_2.39.0.tgz vignettes: vignettes/segmentSeq/inst/doc/methylationAnalysis.pdf, vignettes/segmentSeq/inst/doc/segmentSeq.pdf 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: 1.17.0 Depends: R(>= 3.6) Imports: HiLDA, magrittr, gtools, methods, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, dplyr, tidyr License: GPL-3 MD5sum: 9675159818c7481bf45a12d9ff06a2d2 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 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: c06f0d4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/selectKSigs_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/selectKSigs_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/selectKSigs_1.17.0.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: 117 Package: SELEX Version: 1.37.0 Depends: rJava (>= 0.5-0), Biostrings (>= 2.26.0) Imports: stats, utils License: GPL (>=2) MD5sum: f0ff3bf29be9fc0d72d251a9292a9dbb 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 URL: https://bussemakerlab.org/site/software/ SystemRequirements: Java (>= 1.5) git_url: https://git.bioconductor.org/packages/SELEX git_branch: devel git_last_commit: 3b39f6a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SELEX_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SELEX_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SELEX_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SELEX_1.37.0.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: 1.39.0 Depends: R (>= 3.1), AnnotationDbi, GO.db, annotate Suggests: GOSemSim License: GPL (>= 2) MD5sum: f3f313203c1c9d503bf087a08533b67d 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 URL: http://github.com/iangonzalez/SemDist git_url: https://git.bioconductor.org/packages/SemDist git_branch: devel git_last_commit: 0577e29 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SemDist_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SemDist_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SemDist_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SemDist_1.39.0.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: 1.29.0 Depends: R (>= 3.0.0) Imports: VGAM Suggests: knitr, testthat, SummarizedExperiment License: GPL-3 MD5sum: 7170b3c7d5a30741aed9d7d98da3a1be 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 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: 2856f5c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/semisup_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/semisup_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/semisup_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/semisup_1.29.0.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: 1.5.0 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: d10b0b7b810fb770686a938190f679b7 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 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: 04130c6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/seq.hotSPOT_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/seq.hotSPOT_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/seq.hotSPOT_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/seq.hotSPOT_1.5.0.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: 1.37.0 Depends: R (>= 3.6.2) Imports: nnet, WGCNA, GSA, biomaRt, GenomicRanges, seq2pathway.data License: GPL-2 MD5sum: 818445a0503069750490d4c7844f6136 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 ; Bin Wang Maintainer: Arjun Kinstlick git_url: https://git.bioconductor.org/packages/seq2pathway git_branch: devel git_last_commit: 31d3c8a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/seq2pathway_1.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/seq2pathway_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/seq2pathway_1.37.0.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: 131 Package: seqArchR Version: 1.9.0 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: 8e8718f125ba65c87182b30c05a70cf3 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] () Maintainer: Sarvesh Nikumbh 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: 2fbbdc7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/seqArchR_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/seqArchR_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/seqArchR_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/seqArchR_1.9.0.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: 1.5.0 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: 8230fc345c90197096ee6c32ac4ab380 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] () Maintainer: Sarvesh Nikumbh 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: 67eccab git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/seqArchRplus_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/seqArchRplus_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/seqArchRplus_1.5.0.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: 250 Package: SeqArray Version: 1.45.0 Depends: R (>= 3.5.0), gdsfmt (>= 1.31.1) Imports: methods, parallel, IRanges, GenomicRanges, GenomeInfoDb, Biostrings, S4Vectors LinkingTo: gdsfmt Suggests: Biobase, BiocGenerics, BiocParallel, RUnit, Rcpp, SNPRelate, digest, crayon, knitr, markdown, rmarkdown, Rsamtools, VariantAnnotation License: GPL-3 MD5sum: 41607060495503f2825a1d252c4cadac NeedsCompilation: yes Title: Data Management of Large-Scale Whole-Genome Sequence Variant Calls 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] (), Stephanie Gogarten [aut], David Levine [ctb], Cathy Laurie [ctb] Maintainer: Xiuwen Zheng 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: 54b8445 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SeqArray_1.45.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SeqArray_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SeqArray_1.45.0.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: 28 Package: seqCAT Version: 1.27.0 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: 1f1178f2b41b717b5e3259f5155f04b1 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/seqCAT git_branch: devel git_last_commit: ffb24b8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/seqCAT_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/seqCAT_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/seqCAT_1.27.0.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: 106 Package: seqcombo Version: 1.27.0 Depends: R (>= 3.4.0) Imports: ggplot2, grid, igraph, utils, yulab.utils Suggests: emojifont, knitr, rmarkdown, prettydoc, tibble License: Artistic-2.0 MD5sum: 6c9cb7e8c3798f725c84cf631e695827 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 VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/seqcombo/issues git_url: https://git.bioconductor.org/packages/seqcombo git_branch: devel git_last_commit: 4bd359b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/seqcombo_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/seqcombo_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/seqcombo_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/seqcombo_1.27.0.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: 44 Package: SeqGate Version: 1.15.0 Depends: S4Vectors, SummarizedExperiment, GenomicRanges Imports: stats, methods, BiocManager Suggests: testthat (>= 3.0.0), edgeR, BiocStyle, knitr, rmarkdown License: GPL (>= 2.0) MD5sum: e37d7d245b9c7caf1587af15dd824c63 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 VignetteBuilder: knitr BugReports: https://github.com/srialle/SeqGate/issues git_url: https://git.bioconductor.org/packages/SeqGate git_branch: devel git_last_commit: b888e6d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SeqGate_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SeqGate_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SeqGate_1.15.0.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: 1.45.0 Depends: Biobase, doParallel, DESeq2 Imports: methods, biomaRt Suggests: GenomicRanges License: GPL (>= 3) MD5sum: 7c3840060132d1e857cd460000877c04 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 Maintainer: Xi Wang git_url: https://git.bioconductor.org/packages/SeqGSEA git_branch: devel git_last_commit: fd1d162 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SeqGSEA_1.45.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SeqGSEA_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SeqGSEA_1.45.0.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: 110 Package: seqLogo Version: 1.71.0 Depends: R (>= 4.2), methods, grid Imports: stats4, grDevices Suggests: knitr, BiocStyle, rmarkdown, testthat License: LGPL (>= 2) MD5sum: 19a95b43c2b0826e4329a493ab6f0f50 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] () Maintainer: Robert Ivanek VignetteBuilder: knitr BugReports: https://github.com/ivanek/seqLogo/issues git_url: https://git.bioconductor.org/packages/seqLogo git_branch: devel git_last_commit: 5c2c889 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/seqLogo_1.71.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/seqLogo_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/seqLogo_1.71.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/seqLogo_1.71.0.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, TFBSTools suggestsMe: BCRANK, DiffLogo, igvR, MAGAR, motifcounter, MotifDb, universalmotif, phangorn dependencyCount: 4 Package: seqPattern Version: 1.37.0 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: 2d66e4e3196d022ac08bfdeb931643a1 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 Maintainer: Vanja Haberle git_url: https://git.bioconductor.org/packages/seqPattern git_branch: devel git_last_commit: 03617d6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/seqPattern_1.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/seqPattern_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/seqPattern_1.37.0.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: 1.25.0 Depends: R (>= 3.6), 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: 2000ceddde6f297a27e71e21d02231c9 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). biocViews: Software, ChIPSeq, MultipleComparison, Sequencing, Visualization Author: Joseph R Boyd [aut, cre] Maintainer: Joseph R Boyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/seqsetvis git_branch: devel git_last_commit: 9e472c2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/seqsetvis_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/seqsetvis_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/seqsetvis_1.25.0.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: 108 Package: SeqSQC Version: 1.27.0 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: 806fdadf40683e4db4850a159fe9cbbe 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 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: 06f6f25 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SeqSQC_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SeqSQC_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SeqSQC_1.27.0.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: 131 Package: seqTools Version: 1.39.0 Depends: methods,utils,zlibbioc LinkingTo: zlibbioc Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: cbb11cbb3c4c5e89c8303d67ce25838a 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 git_url: https://git.bioconductor.org/packages/seqTools git_branch: devel git_last_commit: 67624cf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/seqTools_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/seqTools_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/seqTools_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/seqTools_1.39.0.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: 1.43.0 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: 4aff9fe1545dc15bffaed0ff172c6e8f 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 URL: https://github.com/smgogarten/SeqVarTools git_url: https://git.bioconductor.org/packages/SeqVarTools git_branch: devel git_last_commit: dbb2b6c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SeqVarTools_1.43.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SeqVarTools_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SeqVarTools_1.43.0.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: 113 Package: sesame Version: 1.23.0 Depends: R (>= 4.3.0), sesameData Imports: graphics, BiocParallel, utils, methods, stringr, readr, tibble, illuminaio, 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: 34f085e91bfe78255e980ce5bbc0ea4e 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] (), Wubin Ding [ctb], David Goldberg [ctb], Ethan Moyer [ctb], Bret Barnes [ctb], Timothy Triche [ctb], Hui Shen [aut] Maintainer: Wanding Zhou 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: 787d45b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sesame_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sesame_1.23.0.tgz vignettes: vignettes/sesame/inst/doc/inferences.html, vignettes/sesame/inst/doc/KYCG.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", "5. knowYourCG", 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/KYCG.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, CytoMethIC suggestsMe: knowYourCG, RnBeads, TCGAbiolinks, sesameData dependencyCount: 116 Package: SEtools Version: 1.19.0 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: 3c66b11a82b52ad03fe0c07f9b4e0dc7 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] () Maintainer: Pierre-Luc Germain VignetteBuilder: knitr BugReports: https://github.com/plger/SEtools git_url: https://git.bioconductor.org/packages/SEtools git_branch: devel git_last_commit: dc6b961 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SEtools_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SEtools_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SEtools_1.19.0.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: 1.35.0 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: 0c200858625116bf23e8d419c91ca079 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] (), Nan Xiao [aut], Tengfei Yin [aut], Dusan Randjelovic [ctb], Emile Young [ctb], Velsera [cph, fnd] Maintainer: Phil Webster 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: 842f68a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sevenbridges_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/sevenbridges_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sevenbridges_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sevenbridges_1.35.0.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: 30 Package: sevenC Version: 1.25.0 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 MD5sum: c0cfee0b7ca6ca35cce006fb9f93a4ef 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 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: dda512d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sevenC_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sevenC_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sevenC_1.25.0.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: SGSeq Version: 1.39.0 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: 070880a2e374731ff2dad180258cadc1 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SGSeq git_branch: devel git_last_commit: aee7b9a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SGSeq_1.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SGSeq_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SGSeq_1.39.0.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: 1.19.0 Depends: R (>= 3.6.0) Imports: Rcpp, methods, stats, BiocGenerics LinkingTo: BH, Rcpp Suggests: testthat, parallel, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: df6d3df333d1d59c2f28b25827cecedf 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 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: 12f3ae5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SharedObject_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SharedObject_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SharedObject_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SharedObject_1.19.0.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 dependencyCount: 7 Package: shiny.gosling Version: 1.1.0 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 Archs: x64 MD5sum: 9c49a38bb37bdc855b4425a7fbaefaac 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] (), Federico Rivadeneira [aut] () Maintainer: Appsilon VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/shiny.gosling git_branch: devel git_last_commit: 7f26c69 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/shiny.gosling_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/shiny.gosling_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/shiny.gosling_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/shiny.gosling_1.1.0.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 dependencyCount: 41 Package: shinyepico Version: 1.13.0 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: b1a8c00c7d9e0469206cd750e12d24aa 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 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: 605d58a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/shinyepico_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/shinyepico_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/shinyepico_1.13.0.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: 207 Package: shinyMethyl Version: 1.41.0 Imports: Biobase, BiocGenerics, graphics, grDevices, htmltools, MatrixGenerics, methods, minfi, RColorBrewer, shiny, stats, utils Suggests: shinyMethylData, minfiData, BiocStyle, knitr, testthat License: Artistic-2.0 MD5sum: ea326032fb8df72685597e1720bc03bb 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 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: 43d63c9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/shinyMethyl_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/shinyMethyl_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/shinyMethyl_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/shinyMethyl_1.41.0.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: 154 Package: ShortRead Version: 1.63.0 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, zlibbioc, lattice, latticeExtra, LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rhtslib, zlibbioc Suggests: BiocStyle, RUnit, biomaRt, GenomicFeatures, yeastNagalakshmi, knitr License: Artistic-2.0 MD5sum: a1a3aeb6ba6ff822c30758fda52004cd 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 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: 6ceea06 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ShortRead_1.63.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ShortRead_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ShortRead_1.63.0.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, HTSeqGenie, OTUbase, Rqc, segmentSeq, systemPipeR, EatonEtAlChIPseq, NestLink, sequencing, STRMPS importsMe: amplican, basecallQC, BEAT, CellBarcode, chipseq, ChIPseqR, ChIPsim, CircSeqAlignTk, dada2, easyRNASeq, FastqCleaner, GOTHiC, icetea, IONiseR, nucleR, QuasR, R453Plus1Toolbox, RSVSim, scruff, UMI4Cats, seqpac, DBTC, genBaRcode suggestsMe: BiocParallel, CSAR, FLAMES, GenomicAlignments, PING, Repitools, Rsamtools, S4Vectors, HiCDataLymphoblast, systemPipeRdata, yeastRNASeq dependencyCount: 62 Package: SIAMCAT Version: 2.9.0 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: 90d2578675d8c61d4d6af33565c2d667 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] (), Jakob Wirbel [aut, cre] (), Georg Zeller [aut] (), Morgan Essex [ctb], Nicolai Karcher [ctb], Kersten Breuer [ctb] Maintainer: Jakob Wirbel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SIAMCAT git_branch: devel git_last_commit: 665aeef git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SIAMCAT_2.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SIAMCAT_2.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SIAMCAT_2.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SIAMCAT_2.9.0.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: 138 Package: SICtools Version: 1.35.0 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: 1a439d37b2d36deee2a81142e24a052b 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SICtools git_branch: devel git_last_commit: b1d0c2f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SICtools_1.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SICtools_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SICtools_1.35.0.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: 2.37.0 Depends: R (>= 4.0.0), MLInterfaces, Biobase, e1071, BiocParallel, survival Imports: graphics, stats, utils, methods Suggests: BiocStyle, breastCancerNKI, qusage License: Artistic-2.0 MD5sum: 2cd5b91f82679f631ae2f6ded6dc0802 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 and Justin Norden Maintainer: Rory Stark git_url: https://git.bioconductor.org/packages/SigCheck git_branch: devel git_last_commit: 20d4426 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SigCheck_2.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SigCheck_2.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SigCheck_2.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SigCheck_2.37.0.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: 135 Package: sigFeature Version: 1.23.0 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: de71846f21aa8b4f5e1d0c402d4c272b 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sigFeature git_branch: devel git_last_commit: ac0e07e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sigFeature_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/sigFeature_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sigFeature_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sigFeature_1.23.0.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: 1.43.0 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: e8498bfd926708e9c0d4210626a5bffb 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 git_url: https://git.bioconductor.org/packages/SigFuge git_branch: devel git_last_commit: 2ed6c60 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SigFuge_1.43.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SigFuge_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SigFuge_1.43.0.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: 1.79.0 Depends: Biobase, multtest, splines, methods Imports: stats4, grDevices, graphics, stats, scrime (>= 1.2.5) Suggests: affy, annotate, genefilter, KernSmooth License: LGPL (>= 2) MD5sum: ebf9044c7ae1fe487e0c05720519c703 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 git_url: https://git.bioconductor.org/packages/siggenes git_branch: devel git_last_commit: f2ada96 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/siggenes_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/siggenes_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/siggenes_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/siggenes_1.79.0.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, INCATome suggestsMe: logicFS dependencyCount: 16 Package: sights Version: 1.31.0 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: fc6e8994320d9cde1ea9ce74fbd7778a 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 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: e0770f1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sights_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/sights_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sights_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sights_1.31.0.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: 1.19.0 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: 09c6ed2ac81f18db54e81c777e16ad6f 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 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: d3e13a4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/signatureSearch_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/signatureSearch_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/signatureSearch_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/signatureSearch_1.19.0.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 importsMe: DFD dependencyCount: 178 Package: signeR Version: 2.7.0 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: 46b500509994f6a8274e4ed972b9526c 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 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: 1b7d598 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/signeR_2.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/signeR_2.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/signeR_2.7.0.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: 252 Package: SigsPack Version: 1.19.0 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: d6b5c0b55e8573bd5b522c639a10d1c3 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 Maintainer: Franziska Schumann 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: 62811a2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SigsPack_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SigsPack_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SigsPack_1.19.0.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: 1.37.0 Depends: R (>= 3.2.0), methods Imports: Biobase, survival Suggests: RUnit, BiocGenerics License: GPL version 3 MD5sum: d7cbe3843d7ee67a98a15446a9476816 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 git_url: https://git.bioconductor.org/packages/sigsquared git_branch: devel git_last_commit: 82eb979 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sigsquared_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/sigsquared_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sigsquared_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sigsquared_1.37.0.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: 12 Package: SIM Version: 1.75.0 Depends: R (>= 3.5), quantreg Imports: graphics, stats, globaltest, quantsmooth Suggests: biomaRt, RColorBrewer License: GPL (>= 2) MD5sum: 7d179829376488a2815396f12d469089 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 git_url: https://git.bioconductor.org/packages/SIM git_branch: devel git_last_commit: e7c61ef git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SIM_1.75.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SIM_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SIM_1.75.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SIM_1.75.0.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: 1.37.0 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: 757808ffe02b9d35d07bd42bb6cf9955 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 Maintainer: M. R. Nezami Ranjbar URL: http://omics.georgetown.edu/SIMAT.html git_url: https://git.bioconductor.org/packages/SIMAT git_branch: devel git_last_commit: f034495 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SIMAT_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SIMAT_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SIMAT_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SIMAT_1.37.0.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: 47 Package: SimBu Version: 1.7.0 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: cb4ad69512bbb2053798d62e3e7f2001 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 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: b412ad3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SimBu_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SimBu_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SimBu_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SimBu_1.7.0.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: 117 Package: SIMD Version: 1.23.0 Depends: R (>= 3.5.0) Imports: edgeR, statmod, methylMnM, stats, utils Suggests: BiocStyle, knitr,rmarkdown License: GPL-3 Archs: x64 MD5sum: 07446984caa05f734c0f18dd633a11ac 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: dff3bb3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SIMD_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SIMD_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SIMD_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SIMD_1.23.0.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: 13 Package: SimFFPE Version: 1.17.0 Depends: Biostrings Imports: dplyr, foreach, doParallel, truncnorm, GenomicRanges, IRanges, Rsamtools, parallel, graphics, stats, utils, methods Suggests: BiocStyle License: LGPL-3 MD5sum: b69286f1701856d7355e88befba3b87a 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] () Maintainer: Lanying Wei git_url: https://git.bioconductor.org/packages/SimFFPE git_branch: devel git_last_commit: d913736 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SimFFPE_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SimFFPE_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SimFFPE_1.17.0.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: 58 Package: similaRpeak Version: 1.37.0 Depends: R6 (>= 2.0) Imports: stats Suggests: RUnit, BiocGenerics, knitr, Rsamtools, GenomicAlignments, rtracklayer, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 1b13d1eee4a86c8b07f658ea42282ae0 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 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: 7322f17 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/similaRpeak_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/similaRpeak_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/similaRpeak_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/similaRpeak_1.37.0.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: 1.31.0 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 MD5sum: 9d9caeffcf2b05edb44eb3b58f0c4fab 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] (), Bo Wang [aut], Luca De Sano [cre, aut] (), Serafim Batzoglou [ctb] Maintainer: Luca De Sano 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: 8cf2039 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SIMLR_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SIMLR_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SIMLR_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SIMLR_1.31.0.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 importsMe: ccImpute dependencyCount: 14 Package: simona Version: 1.3.0 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 Archs: x64 MD5sum: 28da8f330ecdf878778e5b857c76b868 NeedsCompilation: yes Title: Semantic Similarity in 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] () Maintainer: Zuguang Gu 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: b148115 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/simona_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/simona_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/simona_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/simona_1.3.0.tgz vignettes: vignettes/simona/inst/doc/simona.html vignetteTitles: The simona package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 67 Package: simPIC Version: 1.1.0 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 MD5sum: ec52c46292c378aedd272d59956b62f6 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] (), Davis McCarthy [aut], Heejung Shim [aut] Maintainer: Sagrika Chugh 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: a7c9c39 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/simPIC_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/simPIC_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/simPIC_1.1.0.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: 71 Package: simplifyEnrichment Version: 1.15.0 Depends: R (>= 3.6.0), BiocGenerics, grid Imports: GOSemSim, ComplexHeatmap (>= 2.7.4), circlize, GetoptLong, digest, tm, GO.db, org.Hs.eg.db, AnnotationDbi, slam, methods, clue, grDevices, graphics, stats, utils, proxyC, Matrix, cluster (>= 1.14.2), colorspace, GlobalOptions (>= 0.1.0) Suggests: knitr, ggplot2, cowplot, mclust, apcluster, MCL, dbscan, igraph, gridExtra, dynamicTreeCut, testthat, gridGraphics, clusterProfiler, msigdbr, DOSE, DO.db, reactome.db, flexclust, BiocManager, InteractiveComplexHeatmap (>= 0.99.11), shiny, shinydashboard, cola, hu6800.db, rmarkdown, genefilter, gridtext, fpc License: MIT + file LICENSE MD5sum: a86497cdb67e264d00c6d1bfb7fde375 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] () Maintainer: Zuguang Gu 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: ffc1a5a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/simplifyEnrichment_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/simplifyEnrichment_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/simplifyEnrichment_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/simplifyEnrichment_1.15.0.tgz vignettes: vignettes/simplifyEnrichment/inst/doc/interactive.html, vignettes/simplifyEnrichment/inst/doc/simplifyEnrichment.html, vignettes/simplifyEnrichment/inst/doc/word_cloud_anno.html vignetteTitles: A Shiny app to interactively visualize clustering results, Simplify Functional Enrichment Results, Word Cloud Annotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/simplifyEnrichment/inst/doc/interactive.R, vignettes/simplifyEnrichment/inst/doc/simplifyEnrichment.R, vignettes/simplifyEnrichment/inst/doc/word_cloud_anno.R suggestsMe: cola, InteractiveComplexHeatmap, pareg, simona, scITD dependencyCount: 78 Package: sincell Version: 1.37.0 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: 75f290f74ac76a715573cf90933946b5 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 , Amalio Telenti , Antonio Rausell Maintainer: Miguel Julia , Antonio Rausell URL: http://bioconductor.org/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sincell git_branch: devel git_last_commit: b0b7ebd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sincell_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/sincell_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sincell_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sincell_1.37.0.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: 1.1.0 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: e6e1969522e86657fc65f65000676424 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] (), Ahmad Al Ajami [aut] (), Federico Marini [aut] (), Katharina Imkeller [aut] () Maintainer: Jonas Schuck 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: 23e0f9f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SingleCellAlleleExperiment_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SingleCellAlleleExperiment_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SingleCellAlleleExperiment_1.1.0.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: 1.27.0 Depends: SummarizedExperiment Imports: methods, utils, stats, S4Vectors, BiocGenerics, GenomicRanges, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, Matrix, scRNAseq (>= 2.9.1), Rtsne License: GPL-3 MD5sum: 1529ace4297709459755a926a4b63c18 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] (, lazappi) Maintainer: Davide Risso VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SingleCellExperiment git_branch: devel git_last_commit: 9a26b66 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SingleCellExperiment_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SingleCellExperiment_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SingleCellExperiment_1.27.0.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, clusterExperiment, cydar, cytomapper, demuxSNP, dreamlet, DropletUtils, epiregulon, epiregulon.extra, ExperimentSubset, iSEE, iSEEhub, iSEEindex, LoomExperiment, lute, MAST, mia, mumosa, NeuCA, 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, TENxBrainData, TENxPBMCData, TMExplorer, WeberDivechaLCdata, OSCA.intro, DIscBIO, imcExperiment, karyotapR importsMe: ADImpute, aggregateBioVar, airpart, ANCOMBC, APL, ASURAT, Banksy, BASiCStan, bayNorm, BEARscc, BUSseq, ccfindR, CDI, CellMixS, ChromSCape, CiteFuse, ClusterFoldSimilarity, clustifyr, CoGAPS, condiments, corral, CTexploreR, CuratedAtlasQueryR, cytofQC, cytoviewer, decontX, destiny, DifferentialRegulation, Dino, distinct, dittoSeq, escape, escheR, EWCE, FEAST, fishpond, FLAMES, ggsc, ggspavis, glmGamPoi, GloScope, GSVA, HIPPO, ILoReg, imcRtools, infercnv, iSEEfier, iSEEu, lemur, lisaClust, mastR, mbkmeans, MEB, MetaNeighbor, miaViz, miloR, miQC, MuData, muscat, Nebulosa, netSmooth, NewWave, nnSVG, partCNV, peco, pipeComp, raer, RCSL, RegionalST, SC3, SCArray, scBFA, scCB2, sccomp, scDD, scDDboost, scDesign3, scds, scHOT, scmap, scMerge, scMET, SCnorm, scone, scp, scReClassify, scRepertoire, scRNAseqApp, scruff, scry, scTensor, scTGIF, scTreeViz, slalom, slingshot, Spaniel, spatialHeatmap, speckle, spicyR, SPOTlight, SpotSweeper, SPsimSeq, standR, Statial, tidySpatialExperiment, tpSVG, tradeSeq, traviz, treekoR, UCell, VAExprs, VDJdive, velociraptor, waddR, zellkonverter, HCATonsilData, MerfishData, raerdata, scpdata, SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData, digitalDLSorteR, mixhvg, nebula, SC.MEB, SCRIP, scROSHI, SpatialDDLS suggestsMe: ccImpute, cellxgenedp, CTdata, DEsingle, FuseSOM, genomicInstability, hca, HDF5Array, InteractiveComplexHeatmap, M3Drop, microSTASIS, MOFA2, MOSim, ontoProc, phenopath, progeny, QFeatures, scBubbletree, scFeatureFilter, scPCA, scRecover, SingleR, sketchR, TREG, updateObject, dorothea, DuoClustering2018, GSE103322, microbiomeDataSets, TabulaMurisData, simpleSingleCell, Canek, clustree, CytoSimplex, dyngen, harmony, Platypus, RaceID, rliger, SCdeconR, SCORPIUS, Seurat, singleCellHaystack, tidydr dependencyCount: 36 Package: SingleCellSignalR Version: 1.17.0 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: feb58ab5cc2c7bd8403d3eef6a3b90eb 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SingleCellSignalR git_branch: devel git_last_commit: 51012cb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SingleCellSignalR_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SingleCellSignalR_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SingleCellSignalR_1.17.0.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, tidySpatialExperiment, scDiffCom dependencyCount: 104 Package: singleCellTK Version: 2.15.0 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, 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, SoupX, sva, reshape2, shinyalert, circlize, enrichR (>= 3.2), celda, shinycssloaders, DropletUtils, scds (>= 1.2.0), reticulate (>= 1.14), tools, tximport, 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, stringr, kableExtra, shinythemes, shinyBS, shinyjqui, shinyWidgets, shinyFiles, BiocGenerics, RColorBrewer, fastmap (>= 1.1.0), harmony, SeuratObject, optparse License: MIT + file LICENSE MD5sum: ee36481b27106f64afb90ce59e6316a2 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] (), Irzam Sarfraz [aut] (), Rui Hong [aut], Yusuke Koga [aut], Salam Alabdullatif [aut], Nida Pervaiz [aut], David Jenkins [aut] (), 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] (), Ming Liu [aut], Joshua David Campbell [aut, cre] () Maintainer: Joshua David Campbell 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: 4d7a515 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/singleCellTK_2.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/singleCellTK_2.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/singleCellTK_2.15.0.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: 387 Package: SingleMoleculeFootprinting Version: 1.13.0 Depends: R (>= 4.1.0) Imports: BiocGenerics, Biostrings, BSgenome, GenomeInfoDb, GenomicRanges, data.table, grDevices, plyr, IRanges, RColorBrewer, stats, QuasR Suggests: BSgenome.Mmusculus.UCSC.mm10, devtools, ExperimentHub, knitr, parallel, rmarkdown, readr, SingleMoleculeFootprintingData, testthat (>= 3.0.0) License: GPL-3 MD5sum: a3e5af18d4a4fa0b9008582da9b265a9 NeedsCompilation: no Title: Analysis tools for Single Molecule Footprinting (SMF) data Description: SingleMoleculeFootprinting is an R package providing 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 Author: Guido Barzaghi [aut, cre] (), Arnaud Krebs [aut] (), Mike Smith [ctb] () Maintainer: Guido Barzaghi VignetteBuilder: knitr BugReports: https://github.com/Krebslabrep/SingleMoleculeFootprinting/issues git_url: https://git.bioconductor.org/packages/SingleMoleculeFootprinting git_branch: devel git_last_commit: 25363eb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SingleMoleculeFootprinting_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SingleMoleculeFootprinting_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SingleMoleculeFootprinting_1.13.0.tgz vignettes: vignettes/SingleMoleculeFootprinting/inst/doc/SingleMoleculeFootprinting.html vignetteTitles: SingleMoleculeFootprinting hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingleMoleculeFootprinting/inst/doc/SingleMoleculeFootprinting.R dependencyCount: 120 Package: SingleR Version: 2.7.0 Depends: SummarizedExperiment Imports: methods, Matrix, S4Vectors, DelayedArray, DelayedMatrixStats, BiocParallel, BiocSingular, stats, utils, Rcpp, beachmat, parallel LinkingTo: Rcpp, beachmat, BiocNeighbors Suggests: testthat, knitr, rmarkdown, BiocStyle, BiocGenerics, SingleCellExperiment, scuttle, scater, scran, scRNAseq, ggplot2, pheatmap, grDevices, gridExtra, viridis, celldex License: GPL-3 + file LICENSE Archs: x64 MD5sum: 26d4f00b07914faf1514f08afd2db640 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 URL: https://github.com/LTLA/SingleR SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/SingleR git_branch: devel git_last_commit: db9c9be git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SingleR_2.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SingleR_2.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SingleR_2.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SingleR_2.7.0.tgz vignettes: vignettes/SingleR/inst/doc/SingleR.html vignetteTitles: Annotating scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SingleR/inst/doc/SingleR.R dependsOnMe: OSCA.basic, OSCA.multisample, OSCA.workflows, SingleRBook importsMe: singleCellTK suggestsMe: sketchR, tidySingleCellExperiment, tidySpatialExperiment, tidyseurat dependencyCount: 56 Package: singscore Version: 1.25.0 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 MD5sum: 2543824b8e26f06804345a4a31b3a9e2 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] (), Ruqian Lyu [aut, ctb], Momeneh Foroutan [aut, ctb] (), Malvika Kharbanda [aut, cre] () Maintainer: Malvika Kharbanda 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: 7eeaa47 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/singscore_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/singscore_1.25.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/singscore_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/singscore_1.25.0.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, SingscoreAMLMutations, clustermole suggestsMe: mastR, vissE, msigdb dependencyCount: 129 Package: SiPSiC Version: 1.5.0 Depends: Matrix, SingleCellExperiment Suggests: knitr, rmarkdown, BiocStyle License: file LICENSE Archs: x64 MD5sum: 2667b1a1f5c09c170f406a51ff673fe7 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] (), Yotam Drier [aut] Maintainer: Daniel Davis URL: https://doi.org/10.1101/2023.03.27.534310 VignetteBuilder: knitr BugReports: https://github.com/DanielDavis12/SiPSiC/issues git_url: https://git.bioconductor.org/packages/SiPSiC git_branch: devel git_last_commit: 8388adc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SiPSiC_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SiPSiC_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SiPSiC_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SiPSiC_1.5.0.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: 1.13.0 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: 9a307d1ee1fd83a76592b8da2e2bf70e 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 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: 407ddcf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sitadela_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sitadela_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sitadela_1.13.0.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: 102 Package: sitePath Version: 1.21.0 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 Archs: x64 MD5sum: 0a25da61b9b86715d0055a79b4c6fcd4 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] (), Hangyu Zhou [ths], Aiping Wu [ths] Maintainer: Chengyang Ji 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: fb3104b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sitePath_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/sitePath_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sitePath_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sitePath_1.21.0.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: 71 Package: sizepower Version: 1.75.0 Depends: stats License: LGPL MD5sum: 77881670358b85503aa22f8a644e2921 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 and Mei-Ling Ting Lee and George Alex Whitmore Maintainer: Weiliang Qiu git_url: https://git.bioconductor.org/packages/sizepower git_branch: devel git_last_commit: 16393eb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sizepower_1.75.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/sizepower_1.75.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sizepower_1.75.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sizepower_1.75.0.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: 1.1.0 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: ffdd9facee16aac2c2883f46fb802ffd 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] (), Michael Stadler [aut] (), Friedrich Miescher Institute for Biomedical Research [cph] Maintainer: Charlotte Soneson 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: de35d60 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sketchR_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/sketchR_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sketchR_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sketchR_1.1.0.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: 66 Package: skewr Version: 1.37.0 Depends: R (>= 3.1.1), methylumi, wateRmelon, mixsmsn, IlluminaHumanMethylation450kmanifest Imports: minfi, S4Vectors (>= 0.19.1), RColorBrewer Suggests: GEOquery, knitr, minfiData License: GPL-2 Archs: x64 MD5sum: b6bab6db226eca6b414e46cf63e3db9f 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/skewr git_branch: devel git_last_commit: af6e1a6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/skewr_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/skewr_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/skewr_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/skewr_1.37.0.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: 171 Package: slalom Version: 1.27.0 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: ffb7b3a41f2f851ac37f7e3fff9d9322 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/slalom git_branch: devel git_last_commit: b673002 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/slalom_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/slalom_1.27.0.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: 2.13.0 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 MD5sum: 88af66432fc639860728a08b547ca042 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] (, rcannood) Maintainer: Kelly Street VignetteBuilder: knitr BugReports: https://github.com/kstreet13/slingshot/issues git_url: https://git.bioconductor.org/packages/slingshot git_branch: devel git_last_commit: 9105069 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/slingshot_2.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/slingshot_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/slingshot_2.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/slingshot_2.13.0.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, traviz suggestsMe: Platypus, RaceID dependencyCount: 49 Package: SLqPCR Version: 1.71.0 Depends: R(>= 2.4.0) Imports: stats Suggests: RColorBrewer License: GPL (>= 2) MD5sum: 03bbf2752769a81d609e93706e4097ad 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 git_url: https://git.bioconductor.org/packages/SLqPCR git_branch: devel git_last_commit: 38c38ba git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SLqPCR_1.71.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SLqPCR_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SLqPCR_1.71.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SLqPCR_1.71.0.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: 1.21.0 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 Archs: x64 MD5sum: 41ad8f5cf492c1c88acf0e1db6274af7 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SMAD git_branch: devel git_last_commit: 84f1bf1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SMAD_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SMAD_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SMAD_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SMAD_1.21.0.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: 1.1.0 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: 7b13188067dd3db3b5f611dbe432f245 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] () Maintainer: Jinjin Chen 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: 9b61cfd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/smartid_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/smartid_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/smartid_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/smartid_1.1.0.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: 103 Package: SNAGEE Version: 1.45.0 Depends: R (>= 2.6.0), SNAGEEdata Suggests: ALL, hgu95av2.db Enhances: parallel License: Artistic-2.0 MD5sum: bd1c92598fb8d51acb3b21d99ee2c59a 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 Maintainer: David Venet URL: http://bioconductor.org/ git_url: https://git.bioconductor.org/packages/SNAGEE git_branch: devel git_last_commit: 866515e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SNAGEE_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SNAGEE_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SNAGEE_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SNAGEE_1.45.0.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: 1.17.0 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 MD5sum: 2da028d3185d87f52d36ae7fec0fa9b9 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 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: 2e3db16 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/snapcount_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/snapcount_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/snapcount_1.17.0.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: 1.15.0 Depends: R (>= 4.0.0) Imports: basilisk, reticulate, irlba, stats, assertthat Suggests: knitr, rmarkdown, BiocStyle, ggplot2, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: 525d4639dcf9672c0cd308f696dcee9d 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 Poličar et al. (2019) and the algorithm described by Linderman et al. (2018) . biocViews: DimensionReduction, Visualization, Software, SingleCell, Sequencing Author: Alan O'Callaghan [aut, cre], Aaron Lun [aut] Maintainer: Alan O'Callaghan 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: 93b8c3b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/snifter_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/snifter_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/snifter_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/snifter_1.15.0.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: 1.53.0 Depends: R (>= 2.12.0) Imports: corpcor, lme4 (>= 1.0), splines License: LGPL MD5sum: 0060138f1e113bf47271693616735f48 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 Maintainer: John D. Storey git_url: https://git.bioconductor.org/packages/snm git_branch: devel git_last_commit: f0f73dc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/snm_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/snm_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/snm_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/snm_1.53.0.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: 51 Package: SNPediaR Version: 1.31.0 Depends: R (>= 3.0.0) Imports: RCurl, jsonlite Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: 5e58e518bbcaafdff845ce37443a3264 NeedsCompilation: no Title: Query data from SNPedia Description: SNPediaR provides some tools for downloading and parsing data from the SNPedia web site . 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 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: 7501d8c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-03 source.ver: src/contrib/SNPediaR_1.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SNPediaR_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SNPediaR_1.31.0.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: 1.35.0 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: e35459e056ccf704e7b2429bb6f5d465 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 URL: https://bioconductor.org/packages/SNPhood VignetteBuilder: knitr BugReports: mailto: git_url: https://git.bioconductor.org/packages/SNPhood git_branch: devel git_last_commit: 147ba41 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SNPhood_1.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SNPhood_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SNPhood_1.35.0.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: 1.39.0 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: e8a788d692b61c733baa5fbff842238f 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] (), Stephanie Gogarten [ctb], Cathy Laurie [ctb], Bruce Weir [ctb, ths] () Maintainer: Xiuwen Zheng 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: 9da19ab git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SNPRelate_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SNPRelate_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SNPRelate_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SNPRelate_1.39.0.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 suggestsMe: GWASTools, HIBAG, SAIGEgds, SeqArray, snplinkage dependencyCount: 2 Package: snpStats Version: 1.55.0 Depends: R(>= 2.10.0), survival, Matrix, methods Imports: graphics, grDevices, stats, utils, BiocGenerics, zlibbioc Suggests: hexbin License: GPL-3 MD5sum: cea08aa916da6c8d56c8a7736e2975dd 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 Maintainer: David Clayton git_url: https://git.bioconductor.org/packages/snpStats git_branch: devel git_last_commit: 6be473e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/snpStats_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/snpStats_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/snpStats_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/snpStats_1.55.0.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, GenomicTools.fileHandler, GWASbyCluster, PhenotypeSimulator, TriadSim suggestsMe: crlmm, GenomicFiles, GWASTools, ldblock, omicRexposome, omicsPrint, VariantAnnotation, adjclust, dartR, dartR.base, dartR.popgen, genio, pegas, statgenGWAS dependencyCount: 12 Package: soGGi Version: 1.37.0 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: 401552401b23205288c2c82d48f947a4 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/soGGi git_branch: devel git_last_commit: f0e0c41 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/soGGi_1.37.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/soGGi_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/soGGi_1.37.0.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: 2.41.0 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: e0701fba9810bd2692033db4245050ff 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 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: 894a05c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SomaticSignatures_2.41.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SomaticSignatures_2.41.0.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: 181 Package: SOMNiBUS Version: 1.13.0 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: ed5dde359e2ffbdf22d3c8d50a3b75cb 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 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: ad81567 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SOMNiBUS_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SOMNiBUS_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SOMNiBUS_1.13.0.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: 142 Package: SpaceMarkers Version: 1.1.0 Depends: R (>= 4.4.0) Imports: matrixStats, matrixTests, rstatix, spatstat.explore, spatstat.geom, ape, hdf5r, jsonlite, Matrix, qvalue, stats, utils, methods Suggests: data.table, devtools, dplyr, ggplot2, hrbrthemes, knitr, RColorBrewer, cowplot, readbitmap, rjson, rmarkdown, BiocStyle, testthat (>= 3.0.0), viridis, CoGAPS License: MIT + file LICENSE MD5sum: c259d7ad7f805f76f6ad55f479d97322 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] (), Ludmila Danilova [ctb], Dimitrijs Lvovs [ctb] Maintainer: Atul Deshpande URL: https://github.com/atuldeshpande/SpaceMarkers VignetteBuilder: knitr BugReports: https://github.com/atuldeshpande/SpaceMarkers/issues git_url: https://git.bioconductor.org/packages/SpaceMarkers git_branch: devel git_last_commit: 306f2b7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SpaceMarkers_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SpaceMarkers_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SpaceMarkers_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SpaceMarkers_1.1.0.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: 103 Package: SpacePAC Version: 1.43.0 Depends: R(>= 2.15),iPAC Suggests: RUnit, BiocGenerics, rgl License: GPL-2 Archs: x64 MD5sum: 4a45e17be3434a4c3f9738b937fad873 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 git_url: https://git.bioconductor.org/packages/SpacePAC git_branch: devel git_last_commit: 0496197 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SpacePAC_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SpacePAC_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SpacePAC_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SpacePAC_1.43.0.tgz vignettes: vignettes/SpacePAC/inst/doc/SpacePAC.pdf vignetteTitles: SpacePAC: Identifying mutational clusters in 3D protein space using simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpacePAC/inst/doc/SpacePAC.R dependsOnMe: QuartPAC dependencyCount: 38 Package: Spaniel Version: 1.19.0 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: ed2abbfdaa0af0bbe03837529aeee110 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Spaniel git_branch: devel git_last_commit: c27a0c1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Spaniel_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Spaniel_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Spaniel_1.19.0.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: 213 Package: SparseArray Version: 1.5.1 Depends: R (>= 4.3.0), methods, Matrix, BiocGenerics (>= 0.43.1), MatrixGenerics (>= 1.11.1), S4Vectors, S4Arrays (>= 1.1.6) Imports: utils, stats, matrixStats, IRanges, XVector LinkingTo: S4Vectors, IRanges, XVector Suggests: DelayedArray, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: 8af741bac14d8418a38449358fd5f4ba 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], Vince Carey [fnd], Rafael A. Irizarry [fnd], Jacques Serizay [ctb] Maintainer: Hervé Pagès 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: 2fc4998 git_last_commit_date: 2024-05-06 Date/Publication: 2024-05-06 source.ver: src/contrib/SparseArray_1.5.1.tar.gz win.binary.ver: bin/windows/contrib/4.4/SparseArray_1.5.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SparseArray_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SparseArray_1.5.0.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 importsMe: alabaster.matrix, beachmat, scRNAseq suggestsMe: MatrixGenerics, S4Arrays dependencyCount: 20 Package: sparseMatrixStats Version: 1.17.0 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: dea8935fe42355ad269c8c161a0d663a 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] () Maintainer: Constantin Ahlmann-Eltze 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: 18cae8f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sparseMatrixStats_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/sparseMatrixStats_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sparseMatrixStats_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sparseMatrixStats_1.17.0.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, DelayedMatrixStats, dreamlet, GSVA, SimBu, smartid, smoothclust, SPOTlight, adjclust, CRMetrics, GrabSVG, mombf, scBSP suggestsMe: MatrixGenerics, scPCA, zinbwave, singleCellHaystack dependencyCount: 11 Package: sparsenetgls Version: 1.23.0 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: 0abb0b6bfb4cf168a8df6f87ccfc9ce9 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 SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sparsenetgls git_branch: devel git_last_commit: fe7ca0c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sparsenetgls_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/sparsenetgls_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sparsenetgls_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sparsenetgls_1.23.0.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: 2.15.0 Depends: R (>= 4.1.0), NMF Imports: nnlasso, nnls, parallel, data.table, Biostrings, GenomicRanges, IRanges, BSgenome, GenomeInfoDb, ggplot2, gridExtra, reshape2 Suggests: BiocGenerics, BSgenome.Hsapiens.1000genomes.hs37d5, BiocStyle, testthat, knitr, License: file LICENSE MD5sum: cc492a2f44a398607125da0b1e5b9c43 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] (), Avantika Lal [aut], Keli Liu [ctb], Luca De Sano [cre, aut] (), Robert Tibshirani [ctb], Arend Sidow [aut] Maintainer: Luca De Sano 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: 47ee1a3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SparseSignatures_2.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SparseSignatures_2.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SparseSignatures_2.15.0.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: 104 Package: spaSim Version: 1.7.0 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 Archs: x64 MD5sum: 7859fc39a7d382ce2880574e84325122 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] (), Anna Trigos [aut] () Maintainer: Yuzhou Feng 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: ddcef2b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/spaSim_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/spaSim_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/spaSim_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/spaSim_1.7.0.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: 1.21.0 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 Archs: x64 MD5sum: 5fef6fd7ce6a3891947de61879ac78f5 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SpatialCPie git_branch: devel git_last_commit: 4eacf42 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SpatialCPie_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SpatialCPie_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SpatialCPie_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SpatialCPie_1.21.0.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: 124 Package: spatialDE Version: 1.11.0 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: b1636ffff39d52d7f168150b8e1a535c 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] (), Milan Malfait [aut] (), Lambda Moses [aut] (), Gabriele Sales [cre] Maintainer: Gabriele Sales 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: 8c1ba06 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/spatialDE_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/spatialDE_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/spatialDE_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/spatialDE_1.11.0.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: 101 Package: SpatialDecon Version: 1.15.0 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 MD5sum: d7f624a34d1f3155f29d734ff68fe271 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 VignetteBuilder: knitr BugReports: https://github.com/Nanostring-Biostats/SpatialDecon/issues git_url: https://git.bioconductor.org/packages/SpatialDecon git_branch: devel git_last_commit: 091cec3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SpatialDecon_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SpatialDecon_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SpatialDecon_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SpatialDecon_1.15.0.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: 160 Package: SpatialExperiment Version: 1.15.0 Depends: methods, SingleCellExperiment Imports: rjson, grDevices, magick, utils, S4Vectors, SummarizedExperiment, BiocGenerics, BiocFileCache Suggests: knitr, rmarkdown, testthat, BiocStyle, BumpyMatrix, DropletUtils License: GPL-3 MD5sum: 73095782e0cde9646ea48683b6e48c5d 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 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: 2621d1d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SpatialExperiment_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SpatialExperiment_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SpatialExperiment_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SpatialExperiment_1.15.0.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, imcdatasets, MerfishData, MouseGastrulationData, spatialLIBD, STexampleData, TENxVisiumData, VectraPolarisData, WeberDivechaLCdata importsMe: Banksy, CTSV, cytomapper, DESpace, escheR, ggspavis, GSVA, hoodscanR, lisaClust, MoleculeExperiment, nnSVG, scider, signifinder, smoothclust, spaSim, spatialDE, spicyR, spoon, SpotClean, SpotSweeper, standR, Statial, stJoincount, tpSVG, VisiumIO, HCATonsilData, SingleCellMultiModal, SubcellularSpatialData, TENxXeniumData, SpatialDDLS suggestsMe: GeomxTools, ggsc, SPOTlight dependencyCount: 72 Package: spatialHeatmap Version: 2.11.0 Depends: R (>= 3.5.0) Imports: data.table, dplyr, edgeR, genefilter, ggplot2, grImport, grid, gridExtra, gplots, igraph, methods, Matrix, rsvg, shiny, grDevices, graphics, ggplotify, parallel, reshape2, scater, scuttle, scran, 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, 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, shinyWidgets, shinyjs, shinyBS, sortable, Seurat, sparkline, spsUtil, uwot, UpSetR, visNetwork, WGCNA, xlsx, yaml License: Artistic-2.0 MD5sum: 44accacae092425412c29d5c12ca7c68 NeedsCompilation: no Title: spatialHeatmap 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. 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 URL: 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: 5909972 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/spatialHeatmap_2.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/spatialHeatmap_2.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/spatialHeatmap_2.11.0.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: 173 Package: SpatialOmicsOverlay Version: 1.5.0 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: 4becf1b64e52238c8463e5a894a474c6 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SpatialOmicsOverlay git_branch: devel git_last_commit: 7b62e7e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SpatialOmicsOverlay_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SpatialOmicsOverlay_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SpatialOmicsOverlay_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SpatialOmicsOverlay_1.5.0.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: 182 Package: spatzie Version: 1.11.0 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: 094b4beaf0d03bd497513377a67718bb 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] (), Konstantin Krismer [aut] (), David Gifford [ths, cph] () Maintainer: Jennifer Hammelman URL: https://spatzie.mit.edu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/spatzie git_branch: devel git_last_commit: dbb1e32 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/spatzie_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/spatzie_1.11.0.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: 184 Package: speckle Version: 1.5.0 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: 7b957a0198f014e8de4f7386558e8bec 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/speckle git_branch: devel git_last_commit: dd5fa17 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/speckle_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/speckle_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/speckle_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/speckle_1.5.0.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: 1.39.0 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: b8d71045e89e2066c33203b8f5997d77 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 . biocViews: MassSpectrometry, Proteomics Author: Christian Panse [aut, cre] (), Jonas Grossmann [aut] (), Christian Trachsel [aut], Witold E. Wolski [ctb] Maintainer: Christian Panse 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: 0c76db6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/specL_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/specL_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/specL_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/specL_1.39.0.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: 1.59.0 Depends: R (>= 2.10.0), mclust (>= 3.3.1), Biobase (>= 1.15.13), fields, hwriter (>= 1.1), RColorBrewer, methods License: LGPL (>=2) MD5sum: 744fc73ac6ca2301452d4d6dc95edb85 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 git_url: https://git.bioconductor.org/packages/SpeCond git_branch: devel git_last_commit: 0224a84 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SpeCond_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SpeCond_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SpeCond_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SpeCond_1.59.0.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: 17 Package: Spectra Version: 1.15.0 Depends: R (>= 4.0.0), S4Vectors, BiocParallel, ProtGenerics (>= 1.35.4) Imports: 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: ce2fc2076f0912680ee9fe3dc4f9ee46 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] (), Johannes Rainer [aut] (), Sebastian Gibb [aut] (), Philippine Louail [aut] (), Jan Stanstrup [ctb] (), Nir Shahaf [ctb], Mar Garcia-Aloy [ctb] () Maintainer: RforMassSpectrometry Package Maintainer 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: 051567e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Spectra_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Spectra_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Spectra_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Spectra_1.15.0.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: MetCirc, MsBackendMassbank, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, MsBackendSql importsMe: CompoundDb, MetaboAnnotation, MsExperiment, MsQuality, xcms suggestsMe: MetNet, MsDataHub, PSMatch, RaMS dependencyCount: 28 Package: SpectralTAD Version: 1.21.0 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: 6712912a79a505ff1e377bdc0f549458 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] (), Kellen Cresswell [aut], John Stansfield [aut] Maintainer: Mikhail Dozmorov 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: 94d70c5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SpectralTAD_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SpectralTAD_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SpectralTAD_1.21.0.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: 88 Package: SPEM Version: 1.45.0 Depends: R (>= 2.15.1), Rsolnp, Biobase, methods License: GPL-2 MD5sum: 2cc06be2bc88605313fd25bca44f9673 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 git_url: https://git.bioconductor.org/packages/SPEM git_branch: devel git_last_commit: 0104e9e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SPEM_1.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SPEM_1.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SPEM_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SPEM_1.45.0.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: 9 Package: SPIA Version: 2.57.0 Depends: R (>= 2.14.0), graphics, KEGGgraph Imports: graphics Suggests: graph, Rgraphviz, hgu133plus2.db License: file LICENSE License_restricts_use: yes MD5sum: 5076e85c3c7b67331135d419393a94a6 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 , Purvesh Kathri and Sorin Draghici Maintainer: Adi Laurentiu Tarca URL: http://bioinformatics.oxfordjournals.org/cgi/reprint/btn577v1 git_url: https://git.bioconductor.org/packages/SPIA git_branch: devel git_last_commit: 40a790f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SPIA_2.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SPIA_2.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SPIA_2.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SPIA_2.57.0.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: 14 Package: SPIAT Version: 1.7.0 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: 1d1398f8700829c90b80aa0a292a4c72 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] (), Yuzhou Feng [aut, cre] (), Tianpei Yang [aut], Mabel Li [aut], John Zhu [aut], Volkan Ozcoban [aut], Maria Doyle [aut] Maintainer: Yuzhou Feng 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: 92c1bf5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SPIAT_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SPIAT_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SPIAT_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SPIAT_1.7.0.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: 1.17.0 Depends: R (>= 4.1) Imports: ggplot2, concaveman, BiocParallel, spatstat.explore, spatstat.geom, lmerTest, BiocGenerics, S4Vectors, methods, mgcv, pheatmap, rlang, grDevices, IRanges, stats, data.table, dplyr, tidyr, scam, SingleCellExperiment, SpatialExperiment, SummarizedExperiment, ggforce, ClassifyR, tibble Suggests: BiocStyle, knitr, rmarkdown, pkgdown, imcRtools License: GPL (>=2) MD5sum: 3749f324d5e13d91c18671afe7f9b115 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] Maintainer: Ellis Patrick URL: https://ellispatrick.github.io/spicyR/ https://github.com/SydneyBioX/spicyR VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/spicyR/issues git_url: https://git.bioconductor.org/packages/spicyR git_branch: devel git_last_commit: 27413e8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/spicyR_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/spicyR_1.17.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/spicyR_1.17.0.tgz vignettes: vignettes/spicyR/inst/doc/spicyR.html vignetteTitles: "Introduction to spicy" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spicyR/inst/doc/spicyR.R importsMe: lisaClust suggestsMe: Statial, spicyWorkflow dependencyCount: 181 Package: spikeLI Version: 2.65.0 Imports: graphics, grDevices, stats, utils License: GPL-2 MD5sum: d9b4d4966bb9a73242ce8325d424f87f 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 , Sarah Ternisien, Thomas Heim, Enrico Carlon Maintainer: Enrico Carlon git_url: https://git.bioconductor.org/packages/spikeLI git_branch: devel git_last_commit: 044f531 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/spikeLI_2.65.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/spikeLI_2.65.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/spikeLI_2.65.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/spikeLI_2.65.0.tgz vignettes: vignettes/spikeLI/inst/doc/spikeLI.pdf vignetteTitles: spikeLI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 4 Package: spiky Version: 1.11.0 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: 16a9cf0c63ab7b3ec68d9876b8c1bfe7 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 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: a8a55d7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/spiky_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/spiky_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/spiky_1.11.0.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: 95 Package: spillR Version: 1.1.0 Depends: R (>= 4.3.0), SummarizedExperiment, CATALYST Imports: dplyr, tibble, tidyselect, stats, ggplot2, tidyr, spatstat.geom, S4Vectors, parallel Suggests: knitr, rmarkdown, cowplot, testthat (>= 3.0.0), BiocStyle, hexbin License: LGPL-3 MD5sum: ffbbc9ee2c7eb5cc7fad894153dc3842 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] (), Alexander G. Reisach [aut], Sebastian Weichwald [aut] (), Christof Seiler [aut] () Maintainer: Marco Guazzini VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/spillR git_branch: devel git_last_commit: d4dea65 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/spillR_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/spillR_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/spillR_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/spillR_1.1.0.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: 197 Package: spkTools Version: 1.61.0 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: 9bc3f70f0891a98d3d3fb929aa884c6e 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 , Rafael A Irizarry Maintainer: Matthew N McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/spkTools git_branch: devel git_last_commit: 746b103 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/spkTools_1.61.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/spkTools_1.61.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/spkTools_1.61.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/spkTools_1.61.0.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: 9 Package: splatter Version: 1.29.0 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), IRanges, igraph, knitr, limSolve, lme4, magick, mfa, phenopath, progress, preprocessCore, pscl, rmarkdown, scales, scater (>= 1.15.16), scDD, scran, SparseDC, spelling, testthat, VariantAnnotation, zinbwave, License: GPL-3 + file LICENSE MD5sum: 2ceda3e14949384573ba8bbddfebd362 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] (, lazappi), Belinda Phipson [aut] (, bphipson), Christina Azodi [ctb] (, azodichr), Alicia Oshlack [aut] () Maintainer: Luke Zappia URL: https://bioconductor.org/packages/splatter/, https://github.com/Oshlack/splatter VignetteBuilder: knitr BugReports: https://github.com/Oshlack/splatter/issues git_url: https://git.bioconductor.org/packages/splatter git_branch: devel git_last_commit: bb957f9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/splatter_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/splatter_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/splatter_1.29.0.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: mastR, NewWave, scone, scPCA, smartid, scellpam dependencyCount: 64 Package: SpliceWiz Version: 1.7.0 Depends: R (>= 3.5.0), NxtIRFdata Imports: ompBAM, methods, stats, utils, tools, parallel, scales, magrittr, Rcpp (>= 1.0.5), data.table, fst, ggplot2, AnnotationHub, BiocFileCache, BiocGenerics, BiocParallel, Biostrings, BSgenome, DelayedArray, DelayedMatrixStats, genefilter, GenomeInfoDb, GenomicRanges, HDF5Array, htmltools, IRanges, patchwork, pheatmap, progress, plotly, R.utils, rhdf5, rtracklayer, SummarizedExperiment, S4Vectors, shiny, shinyFiles, shinyWidgets, shinydashboard, stringi, rhandsontable, DT, grDevices, heatmaply, matrixStats, RColorBrewer, rvest LinkingTo: ompBAM, Rcpp, zlibbioc, 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: f624e28655d09a415f87d43c9ffd427f 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 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: 609e0e1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SpliceWiz_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SpliceWiz_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SpliceWiz_1.7.0.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: 194 Package: SplicingFactory Version: 1.13.0 Depends: R (>= 4.1) Imports: SummarizedExperiment, methods, stats Suggests: testthat, knitr, rmarkdown, ggplot2, tidyr License: GPL-3 + file LICENSE MD5sum: 36152d9fe263de4d1e177b2e7a876055 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] () Maintainer: Endre Sebestyen 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: cb30fa4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SplicingFactory_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SplicingFactory_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SplicingFactory_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SplicingFactory_1.13.0.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: 1.45.0 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: 83da21e5705da0818db3a7c95606f3d8 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 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: d44efd6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SplicingGraphs_1.45.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SplicingGraphs_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SplicingGraphs_1.45.0.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: splineTimeR Version: 1.33.0 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: 77efa77f99fe84a9c5ce422a557ba67a 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 , Martin Selmansberger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/splineTimeR git_branch: devel git_last_commit: 5dfc68a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/splineTimeR_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/splineTimeR_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/splineTimeR_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/splineTimeR_1.33.0.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: 1.31.0 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: 7bcb44b6fa0c8048894fe1b8ca119008 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 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: f70b793 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SPLINTER_1.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SPLINTER_1.31.0.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: 162 Package: splots Version: 1.71.0 Imports: grid, RColorBrewer Suggests: BiocStyle, knitr, rmarkdown, assertthat, HD2013SGI, dplyr, ggplot2 License: LGPL MD5sum: d9ef03c4c4163aab260bb27098759866 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/splots git_branch: devel git_last_commit: 59ec74e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/splots_1.71.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/splots_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/splots_1.71.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/splots_1.71.0.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: cellHTS2, HD2013SGI importsMe: RNAinteract dependencyCount: 2 Package: SpotClean Version: 1.7.0 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: c3e02f502d26f6bfbbaf5e6d2c7c7fd3 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] (), Christina Kendziorski [ctb] Maintainer: Zijian Ni 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: c0a0533 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SpotClean_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SpotClean_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SpotClean_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SpotClean_1.7.0.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: 1.9.0 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: b4bc14ea8339b360b31245400d5efb6f 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 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: 2d04d8a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SPOTlight_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SPOTlight_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SPOTlight_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SPOTlight_1.9.0.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: spqn Version: 1.17.0 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: e8bca6cccaf2f5747a6465e1bb32a305 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 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: 6e3843d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/spqn_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/spqn_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/spqn_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/spqn_1.17.0.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: 1.15.0 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: f94f9eff1abc0b34fa83a26e3d526a6f 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 URL: https://github.com/CenterForStatistics-UGent/SPsimSeq VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SPsimSeq git_branch: devel git_last_commit: a578ee9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SPsimSeq_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SPsimSeq_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SPsimSeq_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SPsimSeq_1.15.0.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: 1.19.0 Depends: DelayedArray, S4Vectors Imports: stats, utils, methods, BiocGenerics, RSQLite, duckdb, DBI Suggests: knitr, rmarkdown, BiocStyle, testthat License: LGPL (>= 3); File LICENSE MD5sum: 7b1132258c4a35ba52023c6ceb3e8ee8 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] (), Aaron Lun [aut], Martin Morgan [aut] Maintainer: Qian Liu 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: ee7374d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SQLDataFrame_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SQLDataFrame_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SQLDataFrame_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SQLDataFrame_1.19.0.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: sRACIPE Version: 1.21.0 Depends: R (>= 3.6.0),SummarizedExperiment, methods, Rcpp Imports: ggplot2, reshape2, MASS, RColorBrewer, gridExtra,visNetwork, gplots, umap, htmlwidgets, S4Vectors, BiocGenerics, grDevices, stats, utils, graphics LinkingTo: Rcpp Suggests: knitr, BiocStyle, rmarkdown, tinytest, doFuture License: MIT + file LICENSE MD5sum: 2cccc001d845e8d8d3127d56b34930bf 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: Vivek Kohar [aut, cre] (), Mingyang Lu [aut] Maintainer: Vivek Kohar URL: https://vivekkohar.github.io/sRACIPE/, https://github.com/vivekkohar/sRACIPE, https://geneex.jax.org/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sRACIPE git_branch: devel git_last_commit: 7c85e5b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sRACIPE_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/sRACIPE_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sRACIPE_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sRACIPE_1.21.0.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: 102 Package: SRAdb Version: 1.67.0 Depends: RSQLite, graph, RCurl Imports: GEOquery Suggests: Rgraphviz License: Artistic-2.0 MD5sum: 2c9afc2b9c4efa84a8e30cd3c508d455 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 BugReports: https://github.com/zhujack/SRAdb/issues/new git_url: https://git.bioconductor.org/packages/SRAdb git_branch: devel git_last_commit: 3b62473 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SRAdb_1.67.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SRAdb_1.67.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SRAdb_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SRAdb_1.67.0.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: 59 Package: srnadiff Version: 1.25.0 Depends: R (>= 3.6) Imports: Rcpp (>= 0.12.8), stats, methods, devtools, 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: c272fa7efc120061d420503012bea95b 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 SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/srnadiff git_branch: devel git_last_commit: 7519e6e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/srnadiff_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/srnadiff_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/srnadiff_1.25.0.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: 212 Package: sscu Version: 2.35.0 Depends: R (>= 3.3) Imports: Biostrings (>= 2.36.4), seqinr (>= 3.1-3), BiocGenerics (>= 0.16.1) Suggests: knitr, rmarkdown License: GPL (>= 2) MD5sum: 5dadd4c05ea1cf42c410da953cf4e651 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sscu git_branch: devel git_last_commit: 5ebec20 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sscu_2.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/sscu_2.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sscu_2.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sscu_2.35.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 36 Package: sSeq Version: 1.43.0 Depends: R (>= 3.0), caTools, RColorBrewer License: GPL (>= 3) MD5sum: f2d4908c4260be324f81231acfcb2f23 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 , Wolfgang Huber and Olga Vitek Maintainer: Danni Yu git_url: https://git.bioconductor.org/packages/sSeq git_branch: devel git_last_commit: 48a8461 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sSeq_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/sSeq_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sSeq_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sSeq_1.43.0.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: 1.79.0 Depends: gdata, xtable License: LGPL Archs: x64 MD5sum: 78188e603d659e275c7550aed3378a52 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 git_url: https://git.bioconductor.org/packages/ssize git_branch: devel git_last_commit: f909dcd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ssize_1.79.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ssize_1.79.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ssize_1.79.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ssize_1.79.0.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: 1.9.0 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 MD5sum: 08ebea84d5b8141fb639e9f3541c4b68 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] (), Stephen Pederson [aut] () Maintainer: Wenjun Liu 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: bfe1a63 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sSNAPPY_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/sSNAPPY_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sSNAPPY_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sSNAPPY_1.9.0.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: 109 Package: ssPATHS Version: 1.19.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: ROCR, dml, MESS Suggests: ggplot2, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: ed92fdfd75f18660d30cdde9319fc32b 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 git_url: https://git.bioconductor.org/packages/ssPATHS git_branch: devel git_last_commit: 79d568d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ssPATHS_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ssPATHS_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ssPATHS_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ssPATHS_1.19.0.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: 120 Package: ssrch Version: 1.21.0 Depends: R (>= 3.6), methods Imports: shiny, DT, utils Suggests: knitr, testthat, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 62c7e0181b136ca35894bd31499bf2ea 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ssrch git_branch: devel git_last_commit: 47dc6e1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ssrch_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ssrch_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ssrch_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ssrch_1.21.0.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 importsMe: HumanTranscriptomeCompendium dependencyCount: 47 Package: ssviz Version: 1.39.0 Depends: R (>= 3.5.0), methods, Rsamtools, Biostrings, reshape, ggplot2, RColorBrewer, stats Suggests: knitr License: GPL-2 MD5sum: 3523af0fd7c38cae311980226ff54a0e 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ssviz git_branch: devel git_last_commit: 56a57c3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ssviz_1.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ssviz_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ssviz_1.39.0.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: stageR Version: 1.27.0 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: 267f6cc869d68c65e03f8c3ca5df2ac6 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/stageR git_branch: devel git_last_commit: 5a03d8b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/stageR_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/stageR_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/stageR_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/stageR_1.27.0.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, satuRn dependencyCount: 36 Package: standR Version: 1.9.0 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: cdf28de953835f7e4ddb0a8d265fb3f7 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] (), Dharmesh D Bhuva [aut] (), Ahmed Mohamed [aut] Maintainer: Ning Liu 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: 62c1f0d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/standR_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/standR_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/standR_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/standR_1.9.0.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: 151 Package: STATegRa Version: 1.41.0 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: bc2a12402aacbd12d6ea90223fbea0f0 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 , Núria Planell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/STATegRa git_branch: devel git_last_commit: 9860b00 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/STATegRa_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/STATegRa_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/STATegRa_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/STATegRa_1.41.0.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: 57 Package: Statial Version: 1.7.0 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 Suggests: BiocStyle, knitr, testthat (>= 3.0.0), ClassifyR, spicyR, ggsurvfit, lisaClust, survival License: GPL-3 MD5sum: 790669814cd0babcebecd85c1b4b508f 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 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: a8188d2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Statial_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Statial_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Statial_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Statial_1.7.0.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: 144 Package: statTarget Version: 1.35.0 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: c03c0ca1facb9bbe2b2691982ae269f8 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 URL: https://stattarget.github.io VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/statTarget git_branch: devel git_last_commit: 1278f44 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/statTarget_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/statTarget_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/statTarget_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/statTarget_1.35.0.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: STdeconvolve Version: 1.9.0 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 MD5sum: bb7e80b85c597a9b438ce669bf09c006 NeedsCompilation: no 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] (), Jean Fan [aut] () Maintainer: Brendan Miller URL: https://jef.works/STdeconvolve/ VignetteBuilder: knitr BugReports: https://github.com/JEFworks-Lab/STdeconvolve/issues git_url: https://git.bioconductor.org/packages/STdeconvolve git_branch: devel git_last_commit: 2b5edb8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/STdeconvolve_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/STdeconvolve_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/STdeconvolve_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/STdeconvolve_1.9.0.tgz vignettes: vignettes/STdeconvolve/inst/doc/vignette.html vignetteTitles: STdeconvolve Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STdeconvolve/inst/doc/vignette.R dependencyCount: 77 Package: stepNorm Version: 1.77.0 Depends: R (>= 1.8.0), marray, methods Imports: marray, MASS, methods, stats License: LGPL MD5sum: 18d79e9da00fa3cb96666ab0c9814c87 NeedsCompilation: no Title: Stepwise normalization functions for cDNA microarrays Description: Stepwise normalization functions for cDNA microarray data. biocViews: Microarray, TwoChannel, Preprocessing Author: Yuanyuan Xiao , Yee Hwa (Jean) Yang Maintainer: Yuanyuan Xiao URL: http://www.biostat.ucsf.edu/jean/ git_url: https://git.bioconductor.org/packages/stepNorm git_branch: devel git_last_commit: 5c49bc8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/stepNorm_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/stepNorm_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/stepNorm_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/stepNorm_1.77.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 9 Package: stJoincount Version: 1.7.0 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: edba260cee9847ec5ae13268251da306 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] (), Rania Bassiouni [aut], David Craig [aut] Maintainer: Jiarong Song URL: https://github.com/Nina-Song/stJoincount VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/stJoincount git_branch: devel git_last_commit: 1b562c6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/stJoincount_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/stJoincount_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/stJoincount_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/stJoincount_1.7.0.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: 1.23.0 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: 74df44bf8c871aabeb2673d9bdef6fad 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 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: 5ef1745 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/strandCheckR_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/strandCheckR_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/strandCheckR_1.23.0.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: 122 Package: Streamer Version: 1.51.0 Imports: methods, graph, RBGL, parallel, BiocGenerics Suggests: RUnit, Rsamtools (>= 1.5.53), GenomicAlignments, Rgraphviz License: Artistic-2.0 MD5sum: 375c83d14059a28082bea0b8b3171cf5 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 git_url: https://git.bioconductor.org/packages/Streamer git_branch: devel git_last_commit: 8ab9389 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Streamer_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Streamer_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Streamer_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Streamer_1.51.0.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: 10 Package: STRINGdb Version: 2.17.0 Depends: R (>= 2.14.0) Imports: png, sqldf, plyr, igraph, httr, methods, RColorBrewer, gplots, hash, plotrix Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: ee3c92f83e831ec7feea869b224ab7df 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 Maintainer: Damian Szklarczyk git_url: https://git.bioconductor.org/packages/STRINGdb git_branch: devel git_last_commit: 329e846 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/STRINGdb_2.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/STRINGdb_2.17.1.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/STRINGdb_2.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/STRINGdb_2.17.0.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, netZooR, pwOmics, RITAN, TDbasedUFEadv, XINA suggestsMe: epiNEM, GeneNetworkBuilder, martini, netSmooth, PCAN, protti dependencyCount: 50 Package: struct Version: 1.17.0 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: ddf0a267ff3347b54fad67edb690a904 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/struct git_branch: devel git_last_commit: 7c7d02a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/struct_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/struct_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/struct_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/struct_1.17.0.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: structToolbox importsMe: metabolomicsWorkbenchR dependencyCount: 53 Package: Structstrings Version: 1.21.0 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: dd8cfedd11713992311af9455d3d1439 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] () Maintainer: Felix G.M. Ernst 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: 3bba55a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Structstrings_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Structstrings_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Structstrings_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Structstrings_1.21.0.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: 1.17.0 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 Archs: x64 MD5sum: 1dbcb0f48a0ea26315085e065ada7a6c 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] (), Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd 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: beb4b7b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/structToolbox_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/structToolbox_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/structToolbox_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/structToolbox_1.17.0.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 dependencyCount: 79 Package: StructuralVariantAnnotation Version: 1.21.0 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: 4ece328e6839052708591b74c427f55f 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] (), Ruining Dong [aut] () Maintainer: Daniel Cameron VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/StructuralVariantAnnotation git_branch: devel git_last_commit: 25660e7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/StructuralVariantAnnotation_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/StructuralVariantAnnotation_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/StructuralVariantAnnotation_1.21.0.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: 92 Package: SubCellBarCode Version: 1.21.0 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: da48cbcad254d5f844664cce5b6fa1b0 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SubCellBarCode git_branch: devel git_last_commit: ae184dd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SubCellBarCode_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SubCellBarCode_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SubCellBarCode_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SubCellBarCode_1.21.0.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: 139 Package: subSeq Version: 1.35.0 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 MD5sum: 61c7b0d7d30c5bd054265df5adab4c10 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 , John D. Storey URL: http://github.com/StoreyLab/subSeq VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/subSeq git_branch: devel git_last_commit: d82b3c9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/subSeq_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/subSeq_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/subSeq_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/subSeq_1.35.0.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: 1.7.0 Depends: R (>= 4.2.0) Imports: stats, utils, graphics, ggplot2, BiocParallel Suggests: devtools, MutationalPatterns, RUnit, BiocManager, BiocGenerics, BiocStyle, knitr, rmarkdown License: GPL-2 Archs: x64 MD5sum: d8bf4ff733816cf29c6c88a263a50ec3 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 VignetteBuilder: knitr BugReports: https://github.com/wheelerb/SUITOR/issues git_url: https://git.bioconductor.org/packages/SUITOR git_branch: devel git_last_commit: 382dc6e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SUITOR_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SUITOR_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SUITOR_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SUITOR_1.7.0.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: 1.35.0 Depends: R (>= 4.0.0), methods, MatrixGenerics (>= 1.1.3), GenomicRanges (>= 1.55.2), Biobase Imports: utils, stats, tools, Matrix, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.33.7), IRanges (>= 2.23.9), GenomeInfoDb (>= 1.13.1), S4Arrays (>= 1.1.1), DelayedArray (>= 0.27.1) Suggests: HDF5Array (>= 1.7.5), annotate, AnnotationDbi, hgu95av2.db, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, jsonlite, rhdf5, airway (>= 1.15.1), BiocStyle, knitr, rmarkdown, RUnit, testthat, digest License: Artistic-2.0 MD5sum: 3921563b7271464237c6932def6c53bb NeedsCompilation: no Title: SummarizedExperiment container 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 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: b5b7041 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SummarizedExperiment_1.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SummarizedExperiment_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SummarizedExperiment_1.35.0.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, 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, lipidr, LoomExperiment, lute, Macarron, made4, MatrixQCvis, MBASED, methodical, 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quantiseqr, R453Plus1Toolbox, RadioGx, raer, RaggedExperiment, RareVariantVis, RcisTarget, receptLoss, RegionalST, regionReport, regsplice, rifi, rifiComparative, Rmmquant, RNAsense, roar, RolDE, ropls, rScudo, RTCGAToolbox, RTN, saseR, satuRn, SBGNview, SC3, SCArray, SCArray.sat, scater, scBFA, scCB2, scDblFinder, scDD, scDDboost, scDesign3, scds, scHOT, scider, scmap, scMerge, scMET, scmeth, scMultiSim, SCnorm, scoreInvHap, scp, scPipe, scran, scReClassify, scRepertoire, scruff, scry, scTensor, scTGIF, scuttle, scviR, segmenter, seqCAT, sesame, sigFeature, signifinder, SigsPack, SimBu, simPIC, SingleCellAlleleExperiment, singscore, slalom, slingshot, smartid, smoothclust, snapcount, SNPhood, Spaniel, spaSim, SpatialCPie, spatialDE, SpatialExperiment, spatialHeatmap, spatzie, SPIAT, spicyR, splatter, SpliceWiz, SplicingFactory, spoon, SpotClean, SpotSweeper, srnadiff, sSNAPPY, standR, Statial, stJoincount, struct, StructuralVariantAnnotation, supersigs, SurfR, SVMDO, switchde, systemPipeR, systemPipeTools, TBSignatureProfiler, TCGAbiolinks, TCGAutils, TCseq, tidybulk, tidySingleCellExperiment, tidySpatialExperiment, TOAST, tomoda, ToxicoGx, tpSVG, tradeSeq, TrajectoryUtils, transformGamPoi, transmogR, traviz, treeclimbR, TreeSummarizedExperiment, Trendy, tricycle, TSCAN, TTMap, TVTB, tximeta, UCell, VAExprs, VariantFiltering, VDJdive, vidger, VisiumIO, wpm, xcms, zellkonverter, zFPKM, BloodCancerMultiOmics2017, brgedata, CLLmethylation, COSMIC.67, curatedTCGAData, easierData, emtdata, FieldEffectCrc, FlowSorted.Blood.EPIC, FlowSorted.CordBloodCombined.450k, GSE13015, HCATonsilData, HiBED, HMP2Data, homosapienDEE2CellScore, IHWpaper, MerfishData, MetaGxBreast, MetaScope, orthosData, scRNAseq, SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData, TCGAWorkflowData, TENxXeniumData, ExpHunterSuite, fluentGenomics, SingscoreAMLMutations, TCGAWorkflow, autoGO, digitalDLSorteR, DWLS, ggpicrust2, HeritSeq, imcExperiment, karyotapR, MetAlyzer, microbial, MOCHA, PlasmaMutationDetector, PlasmaMutationDetector2, RCPA, RNAseqQC, SC.MEB, SCRIP, scROSHI, SpatialDDLS, treediff, VSOLassoBag suggestsMe: alabaster.mae, AlpsNMR, AnnotationHub, BindingSiteFinder, biobroom, BiocPkgTools, cageminer, CTdata, dar, dcanr, dce, dearseq, decoupleR, DelayedArray, easier, edgeR, EnMCB, epivizr, epivizrChart, esetVis, fobitools, gDR, GENIE3, GenomicRanges, globalSeq, gsean, hca, HDF5Array, HPiP, Informeasure, InteractiveComplexHeatmap, interactiveDisplay, knowYourCG, MatrixGenerics, microSTASIS, MOFA2, MSnbase, pathwayPCA, philr, podkat, PSMatch, RiboProfiling, Rvisdiff, S4Vectors, scFeatureFilter, semisup, sketchR, sparrow, SPOTlight, svaNUMT, svaRetro, systemPipeShiny, TFutils, updateObject, biotmleData, curatedAdipoArray, curatedTBData, dorothea, DuoClustering2018, gDRtestData, GSE103322, multiWGCNAdata, pRolocdata, RforProteomics, SBGNview.data, tissueTreg, CAGEWorkflow, Canek, clustree, conos, CytoSimplex, dyngen, lfc, MiscMetabar, polyRAD, RaceID, rliger, seqgendiff, Seurat, Signac, singleCellHaystack, teal.slice, tidydr, volcano3D dependencyCount: 35 Package: Summix Version: 2.11.0 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: f9886471d694874d1ef4c7391e66844b 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 VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Summix/issues git_url: https://git.bioconductor.org/packages/Summix git_branch: devel git_last_commit: 0ee06a7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Summix_2.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Summix_2.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Summix_2.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Summix_2.11.0.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: 89 Package: supersigs Version: 1.13.0 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: 59cbdbd4daea37edca9b3097deaa7d29 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] (), Yifan Zhang [aut], Bahman Afsari [aut], Cristian Tomasetti [aut] Maintainer: Albert Kuo 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: cfee96f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/supersigs_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/supersigs_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/supersigs_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/supersigs_1.13.0.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: 117 Package: supraHex Version: 1.43.0 Depends: R (>= 3.6), hexbin Imports: ape, MASS, grDevices, graphics, stats, readr, tibble, tidyr, dplyr, stringr, purrr, magrittr, igraph, methods License: GPL-2 MD5sum: 9947cb9bb78a81362085dd1d67debbc2 NeedsCompilation: no 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 URL: http://suprahex.r-forge.r-project.org git_url: https://git.bioconductor.org/packages/supraHex git_branch: devel git_last_commit: 7c4793e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/supraHex_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/supraHex_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/supraHex_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/supraHex_1.43.0.tgz vignettes: vignettes/supraHex/inst/doc/supraHex_vignettes.pdf vignetteTitles: supraHex User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/supraHex/inst/doc/supraHex_vignettes.R suggestsMe: OmnipathR, TCGAbiolinks dependencyCount: 48 Package: surfaltr Version: 1.11.0 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 Archs: x64 MD5sum: 13fdf88dc39f1b0d8a777084febd21a9 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] (), Aditi Merchant [aut] Maintainer: Pooja Gangras VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/surfaltr git_branch: devel git_last_commit: f4c0d77 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/surfaltr_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/surfaltr_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/surfaltr_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/surfaltr_1.11.0.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: 114 Package: SurfR Version: 1.1.0 Depends: R (>= 4.3.0) Imports: httr, BiocFileCache, BiocStyle, SPsimSeq, DESeq2, edgeR, openxlsx, stringr, rhdf5, ggplot2, ggrepel, stats, magrittr, assertr, tidyr, dplyr, TCGAbiolinks, biomaRt, metaRNASeq, enrichR, scales, venn, gridExtra, SummarizedExperiment, knitr, grDevices, graphics, utils Suggests: testthat (>= 3.0.0) License: GPL-3 + file LICENSE MD5sum: a88af79125da90cfb3f4b25d7b439b84 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] (), Anna Sofia Tascini [aut, ctb] () Maintainer: Aurora Maurizio 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: b43b6d4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SurfR_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SurfR_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SurfR_1.1.0.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: 193 Package: survcomp Version: 1.55.0 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: 796ef3cbcafd2714b5121ac818ef9dc4 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 URL: http://www.pmgenomics.ca/bhklab/ git_url: https://git.bioconductor.org/packages/survcomp git_branch: devel git_last_commit: 2fe84c2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/survcomp_1.55.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/survcomp_1.55.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/survcomp_1.55.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/survcomp_1.55.0.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: 1.21.0 Depends: SummarizedExperiment, pheatmap, survival, survminer, clustvarsel, stats, utils Suggests: maftools, scales, knitr, rmarkdown License: Artistic-2.0 MD5sum: 305286099f2d608afa4228035bd4cf29 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/survtype git_branch: devel git_last_commit: 5584c2f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/survtype_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/survtype_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/survtype_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/survtype_1.21.0.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: 148 Package: sva Version: 3.53.0 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: cefcad85c8634311ea168914b94deb61 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 , W. Evan Johnson , Hilary S. Parker , Elana J. Fertig , Andrew E. Jaffe , Yuqing Zhang , John D. Storey , Leonardo Collado Torres Maintainer: Jeffrey T. Leek , John D. Storey , W. Evan Johnson git_url: https://git.bioconductor.org/packages/sva git_branch: devel git_last_commit: 7e792c3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/sva_3.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/sva_3.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/sva_3.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/sva_3.53.0.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, crossmeta, DaMiRseq, debrowser, DExMA, doppelgangR, edge, GEOexplorer, HarmonizR, KnowSeq, MBECS, MSPrep, omicRexposome, PAA, pairedGSEA, POMA, PROPS, qsmooth, qsvaR, SEtools, singleCellTK, trigger, DeSousa2013, ExpressionNormalizationWorkflow, cate, causalBatch, cinaR, dSVA, oncoPredict, scITD, seqgendiff suggestsMe: compcodeR, Harman, iasva, MAGeCKFlute, randRotation, RnBeads, scp, SomaticSignatures, TBSignatureProfiler, TCGAbiolinks, tidybulk, curatedBladderData, curatedCRCData, curatedOvarianData, curatedTBData, FieldEffectCrc, CAGEWorkflow, DGEobj.utils, DRomics, SuperLearner dependencyCount: 72 Package: svaNUMT Version: 1.11.0 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: 69cbfe126dce010f68a6a3fbd1fb3faa 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] () Maintainer: Ruining Dong VignetteBuilder: knitr BugReports: https://github.com/PapenfussLab/svaNUMT/issues git_url: https://git.bioconductor.org/packages/svaNUMT git_branch: devel git_last_commit: b92b6fa git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/svaNUMT_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/svaNUMT_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/svaNUMT_1.11.0.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: 93 Package: svaRetro Version: 1.11.0 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: 3bd147e55464169c2f32f9dc00cc2565 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] () Maintainer: Ruining Dong VignetteBuilder: knitr BugReports: https://github.com/PapenfussLab/svaRetro/issues git_url: https://git.bioconductor.org/packages/svaRetro git_branch: devel git_last_commit: 466c145 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/svaRetro_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/svaRetro_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/svaRetro_1.11.0.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: 93 Package: SVMDO Version: 1.5.0 Depends: R(>= 4.3), 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), 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 MD5sum: 6a2fcbbff4d19ef470846cfb1cfd0567 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 wilk’s 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] (), Pemra Ozbek Sarica [aut], Kazim Yalcin Arga [aut] Maintainer: Mustafa Erhan Ozer VignetteBuilder: knitr BugReports: https://github.com/robogeno/SVMDO/issues git_url: https://git.bioconductor.org/packages/SVMDO git_branch: devel git_last_commit: b5eba63 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SVMDO_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SVMDO_1.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SVMDO_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SVMDO_1.5.0.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: 194 Package: SWATH2stats Version: 1.35.0 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 Archs: x64 MD5sum: e75809cfbbb18ae8f753862b4b3c129e 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 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: 5634a0b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SWATH2stats_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SWATH2stats_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SWATH2stats_1.35.0.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: 90 Package: SwathXtend Version: 2.27.0 Depends: e1071, openxlsx, VennDiagram, lattice License: GPL-2 MD5sum: 900480650e215c8829f326de872babbc 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 git_url: https://git.bioconductor.org/packages/SwathXtend git_branch: devel git_last_commit: d56e98f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SwathXtend_2.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SwathXtend_2.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SwathXtend_2.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SwathXtend_2.27.0.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: 1.31.0 Depends: R (>= 3.4) Imports: methods, splines, stats4, stats Suggests: dplyr, ggplot2, BiocStyle, knitr, qvalue, reshape2, rmarkdown, testthat License: GPL (>= 3) Archs: x64 MD5sum: 396660df242377d561a0fdc467e54dc5 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 , Jeffrey T. Leek 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: 1ba4d3b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/swfdr_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/swfdr_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/swfdr_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/swfdr_1.31.0.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: 1.41.0 Depends: R (>= 2.13.1), pROC, gplots License: GPL-2 MD5sum: bd70c4a2248a257527fef3919221147b 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 , Luigi Marchionni , Wikum Dinalankara Maintainer: Bahman Afsari , Luigi Marchionni , Wikum Dinalankara git_url: https://git.bioconductor.org/packages/switchBox git_branch: devel git_last_commit: 857b4af git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/switchBox_1.41.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/switchBox_1.41.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/switchBox_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/switchBox_1.41.0.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: 1.31.0 Depends: R (>= 3.4), SingleCellExperiment Imports: SummarizedExperiment, dplyr, ggplot2, methods, stats Suggests: knitr, rmarkdown, BiocStyle, testthat, numDeriv, tidyr License: GPL (>= 2) MD5sum: 34f1525f3ad21f7cd7c46ff7dd0a30b2 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 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: 38887c6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/switchde_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/switchde_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/switchde_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/switchde_1.31.0.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: 66 Package: synapsis Version: 1.11.0 Depends: R (>= 4.1) Imports: EBImage, stats, utils, graphics Suggests: knitr, rmarkdown, testthat (>= 3.0.0), ggplot2, tidyverse, BiocStyle License: MIT + file LICENSE MD5sum: 75d12e5444c90bfe484329dd0551f6db 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] (), Wayne Crismani [rev, ctb] () Maintainer: Lucy McNeill VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synapsis git_branch: devel git_last_commit: 38535ef git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/synapsis_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/synapsis_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/synapsis_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/synapsis_1.11.0.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: 45 Package: synapter Version: 2.29.0 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: 56a1c355e37eb4134320bc262fa7e974 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 Sebastian Gibb URL: https://lgatto.github.io/synapter/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synapter git_branch: devel git_last_commit: 57f1bcb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/synapter_2.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/synapter_2.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/synapter_2.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/synapter_2.29.0.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: 151 Package: synergyfinder Version: 3.13.0 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 Archs: x64 MD5sum: 1cff13df805d1bd4fe82cc5611a00448 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 URL: http://www.synergyfinder.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synergyfinder git_branch: devel git_last_commit: 98b3790 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/synergyfinder_3.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/synergyfinder_3.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/synergyfinder_3.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/synergyfinder_3.13.0.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: 211 Package: SynExtend Version: 1.17.0 Depends: R (>= 4.3.0), 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: 97739f798f2499ab9ce8c9a32f1cbf7e 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] (), Aidan Lakshman [aut, ctb] (), Adelle Fernando [ctb], Erik Wright [aut] Maintainer: Nicholas Cooley VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SynExtend git_branch: devel git_last_commit: 7cdfb94 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SynExtend_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SynExtend_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SynExtend_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SynExtend_1.17.0.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: 2.5.0 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: e8f403dd9ad073c15edb8cc84633f4c8 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synlet git_branch: devel git_last_commit: b705d00 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/synlet_2.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/synlet_2.5.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/synlet_2.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/synlet_2.5.0.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: 1.21.0 Imports: seqinr, methods, Biostrings, stringr, BiocGenerics Suggests: BiocManager, knitr, rmarkdown, testthat, devtools, prettydoc, glue License: GPL-2 MD5sum: 55d403ea6f7315e1a2d8d835a71ef689 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 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: dc19ea3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/SynMut_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/SynMut_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SynMut_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SynMut_1.21.0.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: 1.7.0 Depends: R (>= 4.2) Imports: Rcpp (>= 1.0.8), BiocParallel, GenomicRanges, rlang, Biostrings, rtracklayer, utils, methods, igraph, stats, grDevices, RColorBrewer, pheatmap, ggplot2, ggnetwork, intergraph, networkD3 LinkingTo: Rcpp, testthat Suggests: BiocStyle, ggtree, labdsv, covr, knitr, rmarkdown, testthat (>= 3.0.0), xml2 License: GPL-3 MD5sum: e880affac02155c4452398c5bb49451f 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: Fabrício Almeida-Silva [aut, cre] (), Tao Zhao [aut] (), Kristian K Ullrich [aut] (), Yves Van de Peer [aut] () Maintainer: Fabrício Almeida-Silva 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: 2b17e15 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/syntenet_1.7.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/syntenet_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/syntenet_1.7.0.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: 127 Package: systemPipeR Version: 2.11.0 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: 37a6364802053e2860979553210825a7 NeedsCompilation: no Title: systemPipeR: workflow management and report generation environment 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 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: 55f5649 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/systemPipeR_2.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/systemPipeR_2.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/systemPipeR_2.11.0.tgz vignettes: vignettes/systemPipeR/inst/doc/systemPipeR_workflows.html, vignettes/systemPipeR/inst/doc/systemPipeR.html vignetteTitles: systemPipeR: Workflows collection, 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: 1.15.0 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: d050c2f7d8c197893100cc1ff369be62 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 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: 147deff git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/systemPipeShiny_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/systemPipeShiny_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/systemPipeShiny_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/systemPipeShiny_1.15.0.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: 122 Package: systemPipeTools Version: 1.13.0 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 MD5sum: 97403c32a5431023d05ac1afb772ad26 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/systemPipeTools git_branch: devel git_last_commit: a2e0c3b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/systemPipeTools_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/systemPipeTools_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/systemPipeTools_1.13.0.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: 144 Package: tadar Version: 1.3.0 Depends: GenomicRanges, ggplot2, R (>= 4.3.0) Imports: BiocGenerics, GenomeInfoDb, Gviz, IRanges, MatrixGenerics, methods, rlang, Rsamtools, S4Vectors, stats, VariantAnnotation Suggests: BiocStyle, covr, knitr, limma, rmarkdown, testthat (>= 3.0.0), tidyverse License: GPL-3 MD5sum: 56e06ee39379a0710f153e4eccbde7fd 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] (), Stevie Pederson [aut] () Maintainer: Lachlan Baer 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: 61ecdbe git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tadar_1.3.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tadar_1.3.0.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: 157 Package: TADCompare Version: 1.15.0 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 Archs: x64 MD5sum: 467e4d21d170c3b1fa3c6b1ff962343f 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] (), Kellen Cresswell [aut] Maintainer: Mikhail Dozmorov 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: 40787bd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TADCompare_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/TADCompare_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TADCompare_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TADCompare_1.15.0.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: 135 Package: tanggle Version: 1.11.0 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 MD5sum: a7ad7d79b381e7aee4cb700feec1f835 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) and 'phangorn' Schliep (2011) . biocViews: Software, Visualization, Phylogenetics, Alignment, Clustering, MultipleSequenceAlignment, DataImport Author: Klaus Schliep [aut, cre] (), Marta Vidal-Garcia [aut], Claudia Solis-Lemus [aut] (), Leann Biancani [aut], Eren Ada [aut], L. Francisco Henao Diaz [aut], Guangchuang Yu [ctb] Maintainer: Klaus Schliep 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: e66c1bc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tanggle_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/tanggle_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tanggle_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tanggle_1.11.0.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: 67 Package: TAPseq Version: 1.17.0 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: 0709be695375fb41fb93ef85ef2abfcb 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] (), Lars Velten [aut] (), Lars M. Steinmetz [aut] Maintainer: Andreas R. Gschwind 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: 9e5cbf0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TAPseq_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TAPseq_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TAPseq_1.17.0.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: 91 Package: target Version: 1.19.0 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: ceae4f54dd276b509d3a5957fddedbe0 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) . 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 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: 626e503 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/target_1.19.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/target_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/target_1.19.0.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: 1.11.0 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: eabd4b7ebcb3cee921afe5aef25b2574 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] (), Milan Malfait [aut] () Maintainer: Elke Debrie 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: 07518ff git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TargetDecoy_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/TargetDecoy_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TargetDecoy_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TargetDecoy_1.11.0.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: 123 Package: TargetScore Version: 1.43.0 Depends: pracma, Matrix Suggests: TargetScoreData, gplots, Biobase, GEOquery License: GPL-2 MD5sum: 563065321d3d4a532b3a1a2b7a749fa2 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 URL: http://www.cs.utoronto.ca/~yueli/software.html git_url: https://git.bioconductor.org/packages/TargetScore git_branch: devel git_last_commit: ca32e07 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TargetScore_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/TargetScore_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TargetScore_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TargetScore_1.43.0.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: 2.7.0 Imports: graphics, grDevices, methods, ncdf4, stats, utils, assertthat Suggests: TargetSearchData, BiocStyle, knitr, tinytest License: GPL (>= 2) MD5sum: e245411a1e5ccfcacb97f4deb0b28fc4 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 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: 2252393 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TargetSearch_2.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/TargetSearch_2.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TargetSearch_2.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TargetSearch_2.7.0.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: 1.17.0 Depends: R (>= 4.2.0) Imports: ASSIGN (>= 1.23.1), BiocParallel, ComplexHeatmap, DESeq2, DT, edgeR, gdata, ggplot2, GSVA (>= 1.51.3), magrittr, methods, RColorBrewer, reshape2, rlang, ROCit, S4Vectors, singscore, stats, SummarizedExperiment Suggests: BiocStyle, caret, circlize, class, covr, dplyr, e1071, glmnet, HGNChelper, impute, knitr, lintr, MASS, plyr, pROC, randomForest, rmarkdown, shiny, spelling, sva, testthat License: MIT + file LICENSE MD5sum: 222b49e4141b52064f9e27de6a0372a6 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] (), David Jenkins [aut, org] (), Xutao Wang [aut], Yue Zhao [ctb] (), Christian Love [ctb], W. Evan Johnson [aut] Maintainer: Aubrey R. Odom 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: 57d9064 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TBSignatureProfiler_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TBSignatureProfiler_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TBSignatureProfiler_1.17.0.tgz vignettes: vignettes/TBSignatureProfiler/inst/doc/tbspVignette.html vignetteTitles: "Introduction to the TBSignatureProfiler" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TBSignatureProfiler/inst/doc/tbspVignette.R dependencyCount: 178 Package: TCC Version: 1.45.0 Depends: R (>= 3.0), methods, DESeq2, edgeR, ROC Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 3e13db5a223078ef33d8e84ad1c9aafc 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 , Tomoaki Nishiyama git_url: https://git.bioconductor.org/packages/TCC git_branch: devel git_last_commit: 200654e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TCC_1.45.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TCC_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TCC_1.45.0.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: 2.33.0 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: fcf5d415e382f25aa70e5294690761b5 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 , Antonio Colaprico 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: 0ce7e26 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TCGAbiolinks_2.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TCGAbiolinks_2.33.0.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, SingscoreAMLMutations, TCGAWorkflow, CureAuxSP, oncoPredict suggestsMe: GeoTcgaData, iNETgrate, musicatk dependencyCount: 114 Package: TCGAutils Version: 1.25.0 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: 170291f3cc9117619d4b39bed3d2bfcb 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] (), Lucas Schiffer [aut], Sean Davis [ctb], Levi Waldron [aut] Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/waldronlab/TCGAutils/issues git_url: https://git.bioconductor.org/packages/TCGAutils git_branch: devel git_last_commit: aa708e4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TCGAutils_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TCGAutils_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TCGAutils_1.25.0.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: 105 Package: TCseq Version: 1.29.0 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: 748416ea3bdba4793500b30eec80fa83 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 , Lei Gu Maintainer: Mengjun Wu git_url: https://git.bioconductor.org/packages/TCseq git_branch: devel git_last_commit: 1de767b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TCseq_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TCseq_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TCseq_1.29.0.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: 1.5.0 Imports: GenomicRanges, rTensor, readr, methods, MOFAdata, tximport, tximportData, graphics, stats, utils, shiny Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: 0ce3b691fbac64a2f4a96e085abde782 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] () Maintainer: Y-h. Taguchi 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: ae64311 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TDbasedUFE_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TDbasedUFE_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TDbasedUFE_1.5.0.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: 1.5.0 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: 74a415fe11d2a5dba47befa3db4f2fa5 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] () Maintainer: Y-h. Taguchi 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: 3b6739a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TDbasedUFEadv_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TDbasedUFEadv_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TDbasedUFEadv_1.5.0.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: 231 Package: TEKRABber Version: 1.9.0 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: af25275b8d447d2d1f509eaaea9b3e2b 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] (), Katja Nowick [aut] () Maintainer: Yao-Chung Chen 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: 39d89f1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TEKRABber_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TEKRABber_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TEKRABber_1.9.0.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: 132 Package: TENxIO Version: 1.7.0 Depends: R (>= 4.2.0), SingleCellExperiment, SummarizedExperiment Imports: BiocBaseUtils, BiocGenerics, BiocIO, GenomeInfoDb, GenomicRanges, Matrix, MatrixGenerics, methods, RCurl, readr, R.utils, S4Vectors, utils Suggests: BiocStyle, DropletTestFiles, ExperimentHub, HDF5Array, knitr, RaggedExperiment, rhdf5, rmarkdown, Rsamtools, tinytest License: Artistic-2.0 MD5sum: 57a28cfc926dd402726fb102129ccb95 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] () Maintainer: Marcel Ramos 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: 79a7378 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TENxIO_1.7.0.tar.gz 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 dependencyCount: 67 Package: TEQC Version: 4.27.0 Depends: methods, BiocGenerics (>= 0.1.0), IRanges (>= 1.13.5), Rsamtools, hwriter Imports: Biobase (>= 2.15.1) License: GPL (>= 2) MD5sum: 603e629746bf161ee48e75a433a54db5 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 git_url: https://git.bioconductor.org/packages/TEQC git_branch: devel git_last_commit: 6574302 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TEQC_4.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TEQC_4.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TEQC_4.27.0.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: ternarynet Version: 1.49.0 Depends: R (>= 4.0) Imports: utils, igraph, methods, graphics, stats, BiocParallel Suggests: testthat Enhances: Rmpi, snow License: GPL (>= 2) MD5sum: 089e531a83f93b63e1bc519ac793fa8d 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 , Anthony Almudevar , David Burton , Harry Stern Maintainer: McCall N. Matthew git_url: https://git.bioconductor.org/packages/ternarynet git_branch: devel git_last_commit: 4ec3c81 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ternarynet_1.49.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ternarynet_1.49.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ternarynet_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ternarynet_1.49.0.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: 1.9.0 Depends: R (>= 4.2.0), AnVIL, MultiAssayExperiment Imports: BiocFileCache, dplyr, GenomicRanges, methods, RaggedExperiment, readr, S4Vectors, stats, tidyr, TCGAutils, utils Suggests: knitr, rmarkdown, BiocStyle, withr, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: f429529322e30a68c0305f785643257d 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] () Maintainer: Marcel Ramos 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: bb03f37 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/terraTCGAdata_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/terraTCGAdata_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/terraTCGAdata_1.9.0.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: 139 Package: TFARM Version: 1.27.0 Depends: R (>= 3.5.0) Imports: arules, fields, GenomicRanges, graphics, stringr, methods, stats, gplots Suggests: BiocStyle, knitr, plyr License: Artistic-2.0 MD5sum: a06bc8e3da8ffe5328ee37e5e70f8a3b 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFARM git_branch: devel git_last_commit: fc7b6f7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TFARM_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TFARM_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TFARM_1.27.0.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: 48 Package: TFBSTools Version: 1.43.0 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), CNEr(>= 1.4.0), 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: 3daf69e4a750d24f01b8685808c7170d 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 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: 141a2f1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TFBSTools_1.43.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TFBSTools_1.43.0.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: ATACCoGAPS, ATACseqTFEA, chromVAR, esATAC, MatrixRider, MethReg, monaLisa, motifmatchr, motifStack, primirTSS, spatzie suggestsMe: enhancerHomologSearch, GRaNIE, MAGAR, pageRank, universalmotif, JASPAR2018, JASPAR2020, JASPAR2022, CAGEWorkflow, Signac dependencyCount: 125 Package: TFEA.ChIP Version: 1.25.0 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: 260b11c1ba884122a7650a2b9381f67d 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 Santamaría, Luis del Peso Maintainer: Laura Puente Santamaría VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFEA.ChIP git_branch: devel git_last_commit: 17c9186 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TFEA.ChIP_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TFEA.ChIP_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TFEA.ChIP_1.25.0.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: 105 Package: TFHAZ Version: 1.27.0 Depends: R (>= 3.5.0) Imports: GenomicRanges, S4Vectors, grDevices, graphics, stats, utils, IRanges, methods, ORFik Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 50937f08050fb83fde362bc768a720be 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFHAZ git_branch: devel git_last_commit: cffe561 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TFHAZ_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TFHAZ_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TFHAZ_1.27.0.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: 140 Package: TFutils Version: 1.25.0 Depends: R (>= 4.1.0) Imports: methods, dplyr, magrittr, miniUI, shiny, Rsamtools, GSEABase, rjson, BiocFileCache, DT, httr, readxl, AnnotationDbi, org.Hs.eg.db, utils Suggests: knitr, data.table, testthat, AnnotationFilter, Biobase, GenomicFeatures, GenomicRanges, Gviz, IRanges, S4Vectors, EnsDb.Hsapiens.v75, BiocParallel, BiocStyle, GO.db, GenomicFiles, GenomeInfoDb, SummarizedExperiment, UpSetR, ggplot2, png, gwascat, MotifDb, motifStack, RColorBrewer, rmarkdown License: Artistic-2.0 MD5sum: 589989001f077527e25f79453363fe47 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFutils git_branch: devel git_last_commit: 8d8fcc3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TFutils_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TFutils_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TFutils_1.25.0.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: 115 Package: tidybulk Version: 1.17.0 Depends: R (>= 4.1.0), 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: 120f1f2d0632dadf1c8bf0b8bad4db07 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 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: 0c757df git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tidybulk_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tidybulk_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tidybulk_1.17.0.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: 109 Package: tidyCoverage Version: 1.1.0 Depends: R (>= 4.3.0), SummarizedExperiment Imports: S4Vectors, IRanges, GenomicRanges, GenomeInfoDb, BiocParallel, BiocIO, rtracklayer, methods, tidyr, dplyr, fansi, pillar, rlang, cli, purrr, vctrs, stats Suggests: tidySummarizedExperiment, plyranges, ggplot2, TxDb.Hsapiens.UCSC.hg19.knownGene, AnnotationHub, GenomicFeatures, BiocStyle, hues, knitr, rmarkdown, sessioninfo, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: ed38b5ab7978c069bf6fef4e65afde89 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 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: 4ff03fe git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tidyCoverage_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tidyCoverage_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tidyCoverage_1.1.0.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: 77 Package: tidyomics Version: 1.1.0 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: 02e708a73695d147133b90b7b6ff470c 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] (), Michael Love [aut] (), William Hutchison [aut] () Maintainer: Stefano Mangiola 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: 37001b5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tidyomics_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/tidyomics_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tidyomics_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tidyomics_1.1.0.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: tidySingleCellExperiment Version: 1.15.0 Depends: R (>= 4.3.0), 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, rmarkdown, SingleCellSignalR, SingleR, scater, scran, tidyHeatmap, igraph, GGally, uwot, celldex, dittoSeq, plotly License: GPL-3 MD5sum: e9c0bf3c8bd0f9ed84450b50a154c861 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] () Maintainer: Stefano Mangiola 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: 33737b0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tidySingleCellExperiment_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/tidySingleCellExperiment_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tidySingleCellExperiment_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tidySingleCellExperiment_1.15.0.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, spicyWorkflow dependencyCount: 100 Package: tidySpatialExperiment Version: 1.1.0 Depends: R (>= 4.3.0), SpatialExperiment, tidySingleCellExperiment Imports: ttservice, SummarizedExperiment, SingleCellExperiment, BiocGenerics, Matrix, S4Vectors, methods, utils, pkgconfig, tibble, dplyr, tidyr, ggplot2, magrittr, rlang, purrr, stringr, vctrs, tidyselect, pillar, cli, fansi, lifecycle Suggests: BiocStyle, testthat, knitr, markdown, SingleCellSignalR, SingleR, scater, scran, igraph, GGally, celldex, dittoSeq, cowplot, DropletUtils, plotly, tidySummarizedExperiment License: GPL-3 Archs: x64 MD5sum: 2c3df2bfeb65e7e1081eaf658352c08f NeedsCompilation: no Title: Brings SpatialExperiment to the tidyverse 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] (), Stefano Mangiola [aut] Maintainer: William Hutchison 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: 55489cc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tidySpatialExperiment_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/tidySpatialExperiment_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tidySpatialExperiment_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tidySpatialExperiment_1.1.0.tgz vignettes: vignettes/tidySpatialExperiment/inst/doc/overview.html vignetteTitles: Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidySpatialExperiment/inst/doc/overview.R dependencyCount: 113 Package: tidySummarizedExperiment Version: 1.15.0 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, plotly License: GPL-3 Archs: x64 MD5sum: 7271fbb1b326dac96c0c05d81be3e284 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 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: 319343c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tidySummarizedExperiment_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/tidySummarizedExperiment_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tidySummarizedExperiment_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tidySummarizedExperiment_1.15.0.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: 99 Package: tigre Version: 1.59.0 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: 7c53d1e6f4c19a893f956164e70b1681 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 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: 59d8655 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tigre_1.59.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/tigre_1.59.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tigre_1.59.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tigre_1.59.0.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: 1.15.0 Depends: DelayedArray (>= 0.27.2) Imports: methods, Rcpp, tiledb, S4Vectors LinkingTo: Rcpp Suggests: knitr, Matrix, rmarkdown, BiocStyle, BiocParallel, testthat License: MIT + file LICENSE MD5sum: 8363cd723e4f5c4b8179ffd50cdd476f NeedsCompilation: yes 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 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: 81f0708 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TileDBArray_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/TileDBArray_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TileDBArray_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TileDBArray_1.15.0.tgz vignettes: vignettes/TileDBArray/inst/doc/userguide.html vignetteTitles: User guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TileDBArray/inst/doc/userguide.R dependencyCount: 34 Package: tilingArray Version: 1.83.0 Depends: R (>= 2.11.0), Biobase, methods, pixmap Imports: strucchange, affy, vsn, genefilter, RColorBrewer, grid, stats4 License: Artistic-2.0 MD5sum: 53da6957967481805b8bc69779025aa7 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 git_url: https://git.bioconductor.org/packages/tilingArray git_branch: devel git_last_commit: 1dbbbbd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tilingArray_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/tilingArray_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tilingArray_1.83.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tilingArray_1.83.0.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: 1.77.0 Depends: R (>= 2.1.1), MASS, methods Imports: Biobase, graphics, limma (>= 1.8.6), MASS, marray, methods, stats License: LGPL MD5sum: 69f86601f1d4d0939956ce073a09bd44 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 URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/timecourse git_branch: devel git_last_commit: 921bf87 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/timecourse_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/timecourse_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/timecourse_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/timecourse_1.77.0.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: 11 Package: timeOmics Version: 1.17.0 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 Archs: x64 MD5sum: bcead0cc9d1a484a943da0379eefb130 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 VignetteBuilder: knitr BugReports: https://github.com/abodein/timeOmics/issues git_url: https://git.bioconductor.org/packages/timeOmics git_branch: devel git_last_commit: edd3537 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/timeOmics_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/timeOmics_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/timeOmics_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/timeOmics_1.17.0.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: 70 Package: timescape Version: 1.29.0 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: a1072707564df052f0d670b1e10a64f9 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/timescape git_branch: devel git_last_commit: b335594 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/timescape_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/timescape_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/timescape_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/timescape_1.29.0.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: 1.37.0 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: 9efeb4becf4e1abeb0271b4decb7d3a3 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TIN git_branch: devel git_last_commit: 584f810 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TIN_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/TIN_1.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TIN_1.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TIN_1.37.0.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: TitanCNA Version: 1.43.0 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: 87d94702bdb01df02ba9c9b1bd35d622 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 URL: https://github.com/gavinha/TitanCNA git_url: https://git.bioconductor.org/packages/TitanCNA git_branch: devel git_last_commit: 4965a10 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TitanCNA_1.43.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TitanCNA_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TitanCNA_1.43.0.tgz vignettes: vignettes/TitanCNA/inst/doc/TitanCNA.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TitanCNA/inst/doc/TitanCNA.R dependencyCount: 91 Package: tkWidgets Version: 1.83.0 Depends: R (>= 2.0.0), methods, widgetTools (>= 1.1.7), DynDoc (>= 1.3.0), tools Suggests: Biobase, hgu95av2 License: Artistic-2.0 MD5sum: f98059a112c905d11c56d38a0ea3359a 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 Maintainer: J. Zhang git_url: https://git.bioconductor.org/packages/tkWidgets git_branch: devel git_last_commit: 6e6205a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tkWidgets_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/tkWidgets_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tkWidgets_1.83.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tkWidgets_1.83.0.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: 1.13.0 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: e090927a0d15639092ae735222cb21e3 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 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: 37d6f12 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tLOH_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tLOH_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tLOH_1.13.0.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: 166 Package: TMixClust Version: 1.27.0 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: 9bdb1250ce4008aba648add0fd9093be 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 Maintainer: Monica Golumbeanu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TMixClust git_branch: devel git_last_commit: 41dbf66 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TMixClust_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/TMixClust_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TMixClust_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TMixClust_1.27.0.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: 31 Package: TOAST Version: 1.19.0 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: e5e379602a0b2de5d47512d72723c180 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 VignetteBuilder: knitr BugReports: https://github.com/ziyili20/TOAST/issues git_url: https://git.bioconductor.org/packages/TOAST git_branch: devel git_last_commit: 81d1be1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TOAST_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/TOAST_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TOAST_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TOAST_1.19.0.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: 106 Package: tomoda Version: 1.15.0 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: 8dce689c0058cbad86ad1b007a78bd98 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] () Maintainer: Wendao Liu 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: 8071a47 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tomoda_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/tomoda_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tomoda_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tomoda_1.15.0.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: 1.9.0 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: e23681b8fa1da361bb1d2c14ce8c697f 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] () Maintainer: Ryosuke Matsuzawa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tomoseqr git_branch: devel git_last_commit: a1dd707 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tomoseqr_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/tomoseqr_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tomoseqr_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tomoseqr_1.9.0.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: 1.5.0 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: 3c3d1e9c29bde57720badd123142f7f5 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] (), Nicholas Robertson [aut] Maintainer: Harry Robertson 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: 3d414c4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TOP_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/TOP_1.5.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TOP_1.5.0.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 dependencyCount: 223 Package: topconfects Version: 1.21.0 Depends: R (>= 3.6.0) Imports: methods, utils, stats, assertthat, ggplot2 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: 89df3a162f0f30133a51733b02c9c6db 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] () Maintainer: Paul Harrison 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: 724d850 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/topconfects_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/topconfects_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/topconfects_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/topconfects_1.21.0.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: 1.27.0 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: 4afafb27d6e63c0ed61b9d8deff73b2d 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] (), Pavel Shliaha [aut] (), Ole Nørregaard Jensen [aut] () Maintainer: Sebastian Gibb 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: f5a82bf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/topdownr_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/topdownr_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/topdownr_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/topdownr_1.27.0.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: 2.57.0 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: 6074fdafe84a984167fd9a09f2140f84 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 git_url: https://git.bioconductor.org/packages/topGO git_branch: devel git_last_commit: e7cc100 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/topGO_2.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/topGO_2.57.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/topGO_2.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/topGO_2.57.0.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, moanin, tRanslatome, maEndToEnd importsMe: APL, cellity, consICA, GRaNIE, OmaDB, pcaExplorer, psygenet2r, transcriptogramer, ViSEAGO, ExpHunterSuite suggestsMe: fenr, FGNet, GeDi, geva, IntramiRExploreR, miRNAtap, pareg dependencyCount: 51 Package: ToxicoGx Version: 2.9.0 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: 7c5b48a32dbd46f0f32b54531a3e8296 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ToxicoGx git_branch: devel git_last_commit: bff4f5f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ToxicoGx_2.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ToxicoGx_2.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ToxicoGx_2.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ToxicoGx_2.9.0.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: 145 Package: TPP Version: 3.33.0 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: 3adb9421c5c07dc3b1254e381dc571be 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TPP git_branch: devel git_last_commit: ba0b6b8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TPP_3.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/TPP_3.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TPP_3.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TPP_3.33.0.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: 97 Package: TPP2D Version: 1.21.0 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: 30134ceb6518895f85f721198bc15f9b 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 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: 7a3f225 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TPP2D_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/TPP2D_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TPP2D_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TPP2D_1.21.0.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: tracktables Version: 1.39.0 Depends: R (>= 3.5.0) Imports: IRanges, GenomicRanges, XVector, Rsamtools, XML, tractor.base, stringr, RColorBrewer, methods Suggests: knitr, BiocStyle License: GPL (>= 3) MD5sum: 58c47ecccfdaea0d89aaaf1b40879b62 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tracktables git_branch: devel git_last_commit: 1756bbc git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tracktables_1.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tracktables_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tracktables_1.39.0.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: 1.41.0 Depends: R (>= 3.5.0), grDevices, methods, GenomicRanges, grid Imports: GenomeInfoDb, GenomicAlignments, GenomicFeatures, Gviz, Rsamtools, S4Vectors, rtracklayer, BiocGenerics, scales, tools, IRanges, AnnotationDbi, grImport, htmlwidgets, plotrix, InteractionSet, igraph, 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: dfeb93b331148bb191088c405e9387ff 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] (), Julie Lihua Zhu [aut] Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/trackViewer git_branch: devel git_last_commit: b3acaa4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/trackViewer_1.41.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/trackViewer_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/trackViewer_1.41.0.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: NADfinder suggestsMe: ATACseqQC, ChIPpeakAnno dependencyCount: 166 Package: tradeSeq Version: 1.19.0 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: c24ba50c9560df31a22a44bbd9f076be 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] (), Kelly Street [aut, ctb], Lieven Clement [aut, ctb], Sandrine Dudoit [ctb] Maintainer: Hector Roux de Bezieux 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: e85d386 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tradeSeq_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/tradeSeq_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tradeSeq_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tradeSeq_1.19.0.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: 1.13.0 Depends: R (>= 4.1) Imports: pracma, rgl, ggplot2, stats, methods Suggests: dplyr, knitr, RColorBrewer, rmarkdown License: MIT + file LICENSE MD5sum: 82220219612fa180e31d8afaedb900f1 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] () Maintainer: Michael Shapiro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TrajectoryGeometry git_branch: devel git_last_commit: af67d6b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TrajectoryGeometry_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/TrajectoryGeometry_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TrajectoryGeometry_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TrajectoryGeometry_1.13.0.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: 1.13.0 Depends: SingleCellExperiment Imports: methods, stats, Matrix, igraph, S4Vectors, SummarizedExperiment Suggests: BiocNeighbors, DelayedArray, DelayedMatrixStats, BiocParallel, testthat, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: f1704bf9c8f2d673627f44adccf5ba4f 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 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: 01b5a90 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TrajectoryUtils_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/TrajectoryUtils_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TrajectoryUtils_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TrajectoryUtils_1.13.0.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: 1.27.0 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: d04d5cde84c99eb30c329c905d6b2ae0 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 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: 9ca2baf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/transcriptogramer_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/transcriptogramer_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/transcriptogramer_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/transcriptogramer_1.27.0.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: 102 Package: transcriptR Version: 1.33.0 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: 352bdd0939eb59ebaba368f99d60c282 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 Maintainer: Armen R. Karapetyan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transcriptR git_branch: devel git_last_commit: 2b3a1a8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/transcriptR_1.33.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/transcriptR_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/transcriptR_1.33.0.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: 152 Package: transformGamPoi Version: 1.11.0 Imports: glmGamPoi, DelayedArray, Matrix, MatrixGenerics, SummarizedExperiment, HDF5Array, methods, utils, Rcpp LinkingTo: Rcpp Suggests: testthat, TENxPBMCData, scran, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: fab942074e166077dfaf54866598c6f5 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] () Maintainer: Constantin Ahlmann-Eltze 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: 281d3ea git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/transformGamPoi_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/transformGamPoi_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/transformGamPoi_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/transformGamPoi_1.11.0.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: 53 Package: transite Version: 1.23.0 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: 45672efe1ec592a5ce6e490da823b090 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] (), Anna Gattinger [aut] (), Michael Yaffe [ths, cph] (), Ian Cannell [ths] () Maintainer: Konstantin Krismer URL: https://transite.mit.edu SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transite git_branch: devel git_last_commit: 1dd208c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/transite_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/transite_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/transite_1.23.0.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: 62 Package: tRanslatome Version: 1.43.0 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: 54dff95a76910b181c29e689c421509b 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 , Erik Dassi git_url: https://git.bioconductor.org/packages/tRanslatome git_branch: devel git_last_commit: e1ff782 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tRanslatome_1.43.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tRanslatome_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tRanslatome_1.43.0.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: 119 Package: transmogR Version: 1.1.0 Depends: Biostrings, GenomicRanges Imports: BSgenome, GenomeInfoDb, GenomicFeatures, ggplot2 (>= 3.5.0), IRanges, methods, parallel, rlang, scales, stats, S4Vectors, SummarizedExperiment, VariantAnnotation Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg38, ComplexUpset, extraChIPs, InteractionSet, knitr, rmarkdown, rtracklayer, testthat (>= 3.0.0) License: GPL-3 MD5sum: 890ba0d195395fc4d61142f5b6082905 NeedsCompilation: no 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 Author: Stevie Pederson [aut, cre] () Maintainer: Stevie Pederson 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: ba33d38 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/transmogR_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/transmogR_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/transmogR_1.1.0.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: 99 Package: transomics2cytoscape Version: 1.15.0 Imports: RCy3, KEGGREST, dplyr, purrr, tibble, pbapply Suggests: testthat, roxygen2, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 404f080b440d812963e6a42ecd5db4b6 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] (), Katsuyuki Yugi [aut] () Maintainer: Kozo Nishida SystemRequirements: Cytoscape >= 3.10.0 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transomics2cytoscape git_branch: devel git_last_commit: 084e793 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/transomics2cytoscape_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/transomics2cytoscape_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/transomics2cytoscape_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/transomics2cytoscape_1.15.0.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: 70 Package: TransView Version: 1.49.0 Depends: methods, GenomicRanges Imports: BiocGenerics, S4Vectors (>= 0.9.25), IRanges, zlibbioc, gplots LinkingTo: Rhtslib (>= 1.99.1) Suggests: RUnit, pasillaBamSubset, BiocManager License: GPL-3 MD5sum: 8642724f6d736f05892be3fca88ac44d 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 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: 8895e63 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TransView_1.49.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TransView_1.49.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TransView_1.49.0.tgz vignettes: vignettes/TransView/inst/doc/TransView.pdf vignetteTitles: An introduction to TransView hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TransView/inst/doc/TransView.R dependencyCount: 29 Package: traseR Version: 1.35.0 Depends: R (>= 3.5.0), GenomicRanges, IRanges, BSgenome.Hsapiens.UCSC.hg19 Suggests: BiocStyle,RUnit, BiocGenerics License: GPL MD5sum: 4182315dc84c7346772774698ebfc6f2 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 git_url: https://git.bioconductor.org/packages/traseR git_branch: devel git_last_commit: d84c01b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/traseR_1.35.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/traseR_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/traseR_1.35.0.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: traviz Version: 1.11.0 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 MD5sum: 80bddd58628348c081b7d0c034d30050 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/traviz git_branch: devel git_last_commit: 0b9debe git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/traviz_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/traviz_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/traviz_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/traviz_1.11.0.tgz vignettes: vignettes/traviz/inst/doc/slingshot.html, vignettes/traviz/inst/doc/traviz.html vignetteTitles: ggplot2 + slingshot, traviz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/traviz/inst/doc/slingshot.R, vignettes/traviz/inst/doc/traviz.R dependencyCount: 92 Package: TreeAndLeaf Version: 1.17.0 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 MD5sum: c79f4a0790fb985704805d2b529f05fa 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TreeAndLeaf git_branch: devel git_last_commit: c3c52f0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TreeAndLeaf_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/TreeAndLeaf_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TreeAndLeaf_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TreeAndLeaf_1.17.0.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: 1.1.0 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: 603b09c35a2d00f7e1668edce5024103 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] (), Charlotte Soneson [aut, cre] () Maintainer: Charlotte Soneson 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: 3c3e1a3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/treeclimbR_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/treeclimbR_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/treeclimbR_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/treeclimbR_1.1.0.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: 181 Package: treeio Version: 1.29.0 Depends: R (>= 3.6.0) Imports: ape, dplyr, jsonlite, magrittr, methods, rlang, stats, tibble, tidytree (>= 0.4.5), utils, yulab.utils (> 0.1.1) Suggests: Biostrings, cli, ggplot2, ggtree, igraph, knitr, rmarkdown, phangorn, prettydoc, purrr, testthat, tidyr, vroom, xml2, yaml License: Artistic-2.0 MD5sum: 52fcfbe70aa5c7237947d46d2cef29c5 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] (), Tommy Tsan-Yuk Lam [ctb, ths], Shuangbin Xu [ctb] (), Bradley Jones [ctb], Casey Dunn [ctb], Tyler Bradley [ctb], Konstantinos Geles [ctb] Maintainer: Guangchuang Yu URL: https://github.com/YuLab-SMU/treeio (devel), https://docs.ropensci.org/treeio/ (docs), https://www.amazon.com/Integration-Manipulation-Visualization-Phylogenetic-Computational-ebook/dp/B0B5NLZR1Z/ (book), https://doi.org/10.1093/molbev/msz240 (paper) VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/treeio/issues git_url: https://git.bioconductor.org/packages/treeio git_branch: devel git_last_commit: 70e8a71 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/treeio_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/treeio_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/treeio_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/treeio_1.29.0.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, MicrobiotaProcess, TreeSummarizedExperiment, geneplast.data, EvoPhylo, RevGadgets, shinyTempSignal suggestsMe: ggtreeDendro, ggtreeExtra, rfaRm, idiogramFISH, MetaNet, nosoi dependencyCount: 42 Package: treekoR Version: 1.13.0 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 MD5sum: 7386310da60e9a242988c6364d27fed9 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/treekoR git_branch: devel git_last_commit: 7c56cd9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/treekoR_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/treekoR_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/treekoR_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/treekoR_1.13.0.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 dependencyCount: 189 Package: TreeSummarizedExperiment Version: 2.13.0 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: e1082233b7be896685b108239a7a52d3 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] (), Felix G.M. Ernst [ctb] () Maintainer: Ruizhu Huang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TreeSummarizedExperiment git_branch: devel git_last_commit: b6da78c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TreeSummarizedExperiment_2.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/TreeSummarizedExperiment_2.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TreeSummarizedExperiment_2.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TreeSummarizedExperiment_2.13.0.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, MGnifyR, mia, miaSim, miaViz, curatedMetagenomicData, MicrobiomeBenchmarkData, microbiomeDataSets importsMe: ANCOMBC, benchdamic, CBEA, microSTASIS, PLSDAbatch, treeclimbR suggestsMe: dar, philr dependencyCount: 79 Package: TREG Version: 1.9.0 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 MD5sum: 833f1b25530694249fb164e405b8a0da 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] (), Leonardo Collado-Torres [ctb] () Maintainer: Louise Huuki-Myers 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: 7ec9e45 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TREG_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/TREG_1.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TREG_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TREG_1.9.0.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: 45 Package: Trendy Version: 1.27.0 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: e2e629eea9064dff5fc1dd94662ded5d 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 URL: https://github.com/rhondabacher/Trendy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Trendy git_branch: devel git_last_commit: 2f09aad git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Trendy_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Trendy_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Trendy_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Trendy_1.27.0.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: 1.11.0 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: 6972c10ddf1d5f02dc94c4432bb850ee 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TRESS git_branch: devel git_last_commit: a4dd67a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TRESS_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TRESS_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TRESS_1.11.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: magpie dependencyCount: 77 Package: tricycle Version: 1.13.0 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: f98b6fb260b7ad76d86643e566a47c81 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 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: f3ce911 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tricycle_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tricycle_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tricycle_1.13.0.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: 128 Package: trigger Version: 1.51.0 Depends: R (>= 2.14.0), corpcor, qtl Imports: qvalue, methods, graphics, sva License: GPL-3 MD5sum: 75453361956bbfebd1f6e2f281c97b40 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 , Dipen P. Sangurdekar and John D. Storey Maintainer: John D. Storey git_url: https://git.bioconductor.org/packages/trigger git_branch: devel git_last_commit: eca1cb4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/trigger_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/trigger_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/trigger_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/trigger_1.51.0.tgz 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: 3.43.0 Depends: R (>= 3.0.1) Imports: grDevices, graphics, methods, stats, survival, utils, siggenes, LogicReg (>= 1.6.1) Suggests: haplo.stats, mcbiopi, splines, logicFS (>= 1.28.1), KernSmooth, VariantAnnotation License: LGPL-2 MD5sum: ef0900c06e12fc140b58ad22f0e8e6f2 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 git_url: https://git.bioconductor.org/packages/trio git_branch: devel git_last_commit: 159397e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/trio_3.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/trio_3.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/trio_3.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/trio_3.43.0.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: 18 Package: triplex Version: 1.45.0 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: c84866a4f5234ccada7a45caf6977a0f 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 URL: http://www.fi.muni.cz/~lexa/triplex/ git_url: https://git.bioconductor.org/packages/triplex git_branch: devel git_last_commit: 9ce9251 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/triplex_1.45.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/triplex_1.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/triplex_1.45.0.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: 1.11.0 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 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: c3006b9237c2bbc1ab4bc1f86970d572 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 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: 32a5a99 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tripr_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/tripr_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tripr_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tripr_1.11.0.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: 100 Package: tRNA Version: 1.23.0 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: 08572ad0c5a14e1a0e916165282001af 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] () Maintainer: Felix GM Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNA/issues git_url: https://git.bioconductor.org/packages/tRNA git_branch: devel git_last_commit: d2ba0fd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tRNA_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tRNA_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tRNA_1.23.0.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: 1.23.0 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: 7d39f2d722800045098d3976fa635012 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] () Maintainer: Felix G.M. Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNAdbImport/issues git_url: https://git.bioconductor.org/packages/tRNAdbImport git_branch: devel git_last_commit: c6599e9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tRNAdbImport_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tRNAdbImport_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tRNAdbImport_1.23.0.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: 1.25.0 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: ba0b244be42e8ea2e2c01c127b37072a 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] () Maintainer: Felix G.M. Ernst 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: ab3c45c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tRNAscanImport_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tRNAscanImport_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tRNAscanImport_1.25.0.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: 2.37.0 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: ed29e4eb16de8f6e387277e9fb7ef609 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] (), Alex Graudenzi [aut], Giancarlo Mauri [ctb], Bud Mishra [ctb], Daniele Ramazzotti [aut] () Maintainer: Luca De Sano 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: 3152235 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TRONCO_2.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/TRONCO_2.37.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TRONCO_2.37.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TRONCO_2.37.0.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: 47 Package: TSAR Version: 1.3.0 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 Archs: x64 MD5sum: b213cd9dadb4de6c4a66508ec98b7bb0 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] (), William M. McFadden [aut, fnd] (), Stefan G. Sarafianos [fnd, aut, ths] () Maintainer: Xinlin Gao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TSAR git_branch: devel git_last_commit: dfc1f3d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TSAR_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/TSAR_1.3.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TSAR_1.3.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TSAR_1.3.0.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: 139 Package: TSCAN Version: 1.43.0 Depends: SingleCellExperiment, TrajectoryUtils Imports: ggplot2, shiny, plyr, grid, fastICA, igraph, combinat, mgcv, mclust, gplots, methods, stats, Matrix, SummarizedExperiment, DelayedArray, S4Vectors Suggests: knitr, testthat, scuttle, scran, metapod, BiocParallel, BiocNeighbors, batchelor License: GPL(>=2) MD5sum: 27b87cffb23e84b4d75690bec0761931 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TSCAN git_branch: devel git_last_commit: a2c4e7c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TSCAN_1.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/TSCAN_1.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TSCAN_1.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TSCAN_1.43.0.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: 1.13.0 Depends: keras Imports: tm, text2vec, tokenizers, textstem, stopwords, data.table, purrr, DiagrammeR, stats Suggests: fgsea, knitr, testthat, reticulate, rmarkdown License: Artistic-2.0 MD5sum: 0a216551bffc8bda0c33c68bc9aa6f72 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] () Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ttgsea git_branch: devel git_last_commit: 23e12ac git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ttgsea_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ttgsea_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ttgsea_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ttgsea_1.13.0.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: 1.27.0 Depends: rgl, colorRamps Imports: grDevices,graphics,stats,utils, methods, SummarizedExperiment, Biobase Suggests: BiocStyle, airway License: GPL-2 MD5sum: e048494b77d7bbfa243d4d862bae401b 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 git_url: https://git.bioconductor.org/packages/TTMap git_branch: devel git_last_commit: 09d64ca git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TTMap_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/TTMap_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TTMap_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TTMap_1.27.0.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: 1.53.0 Depends: R (>= 2.12.0), convert, limma (>= 1.7.0), marray Imports: stats, grDevices, affy, lattice Suggests: BiocStyle, affydata, hgu95av2cdf License: LGPL MD5sum: abc081e27a33fdc48c54e5935067ae0c 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 URL: http://www.humgen.nl/MicroarrayAnalysisGroup.html git_url: https://git.bioconductor.org/packages/TurboNorm git_branch: devel git_last_commit: 49068df git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TurboNorm_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/TurboNorm_1.53.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/TurboNorm_1.53.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/TurboNorm_1.53.0.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: tweeDEseq Version: 1.51.0 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: 5cf56880fe79f0b616f0f1493a6505ce 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] (), Juan R. Gonzalez [aut] (), Mikel Esnaola [aut], Robert Castelo [aut] Maintainer: Dolors Pelegri-Siso 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: 2cf0a28 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tweeDEseq_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/tweeDEseq_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tweeDEseq_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tweeDEseq_1.51.0.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: 24 Package: twilight Version: 1.81.0 Depends: R (>= 2.10), splines (>= 2.2.0), stats (>= 2.2.0), Biobase(>= 1.12.0) Imports: Biobase, graphics, grDevices, stats Suggests: golubEsets (>= 1.4.2), vsn (>= 1.7.2) License: GPL (>= 2) MD5sum: c1a8ba3e9f29379f4de57fa7b931c2b9 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 Scheid Maintainer: Stefanie Scheid URL: http://compdiag.molgen.mpg.de/software/twilight.shtml git_url: https://git.bioconductor.org/packages/twilight git_branch: devel git_last_commit: 463ab03 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/twilight_1.81.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/twilight_1.81.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/twilight_1.81.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/twilight_1.81.0.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: 8 Package: twoddpcr Version: 1.29.0 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: 6f33f59e5759601687b707106dd7948c 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 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: 94372ba git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/twoddpcr_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/twoddpcr_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/twoddpcr_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/twoddpcr_1.29.0.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: 64 Package: txcutr Version: 1.11.0 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: b7cad17081717d46cb3b1912db246f48 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] () Maintainer: Mervin Fansler VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/txcutr git_branch: devel git_last_commit: 4e59583 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/txcutr_1.11.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/txcutr_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/txcutr_1.11.0.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: 102 Package: txdbmaker Version: 1.1.0 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: fb9d6728db149fbd7f5efac690a3281c 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], M. Lawrence [ctb], J. MacDonald [ctb], M. Ramos [ctb], S. Saini [ctb], L. Shepherd [ctb] Maintainer: H. Pagès 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: 8d2ced3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/txdbmaker_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/txdbmaker_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/txdbmaker_1.1.0.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, crisprDesign, crisprViz, customProDB, DegNorm, EpiTxDb, FLAMES, GenomicPlot, IntEREst, metaseqR2, ORFik, OrganismDbi, OUTRIDER, proActiv, proBAMr, QuasR, RCAS, recoup, Rhisat2, RiboDiPA, ribosomeProfilingQC, RNAmodR, scanMiRApp, scruff, sitadela, trackViewer, txcutr, tximeta suggestsMe: AnnotationHub, bumphunter, BUSpaRse, DEXSeq, doubletrouble, eisaR, FLAMES, GenomicFeatures, GenomicRanges, raer, SplicingGraphs, SPLINTER, systemPipeR dependencyCount: 101 Package: tximeta Version: 1.23.0 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: 55eda9707183bd1b2adff14a31b7bdf1 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 URL: https://github.com/thelovelab/tximeta VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tximeta git_branch: devel git_last_commit: 5efa5c6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tximeta_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tximeta_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tximeta_1.23.0.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: 110 Package: tximport Version: 1.33.0 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) Archs: x64 MD5sum: 9ed0bbd64a73ee385f32df34eb4e2af7 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 URL: https://github.com/thelovelab/tximport VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tximport git_branch: devel git_last_commit: ec99ce8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/tximport_1.33.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/tximport_1.33.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/tximport_1.33.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/tximport_1.33.0.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, DifferentialRegulation, EventPointer, IsoformSwitchAnalyzeR, singleCellTK, TDbasedUFE, tximeta, ExpHunterSuite, seeker suggestsMe: BANDITS, DESeq2, HumanTranscriptomeCompendium, variancePartition dependencyCount: 3 Package: TypeInfo Version: 1.71.0 Depends: methods Suggests: Biobase License: BSD_2_clause MD5sum: b3314fd6fea248ec42c7fc74bef8205d 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 () Maintainer: Duncan Temple Lang git_url: https://git.bioconductor.org/packages/TypeInfo git_branch: devel git_last_commit: ce9b3b7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/TypeInfo_1.71.0.tar.gz vignettes: vignettes/TypeInfo/inst/doc/TypeInfoNews.pdf vignetteTitles: TypeInfo R News hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TypeInfo/inst/doc/TypeInfoNews.R dependencyCount: 1 Package: UCell Version: 2.9.0 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: d423ba1352a17f5e031ada48d445affe 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] (), Santiago Carmona [aut] () Maintainer: Massimo Andreatta 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: f62e7a2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/UCell_2.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/UCell_2.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/UCell_2.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/UCell_2.9.0.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: 1.1.0 Imports: methods, stats, httr, jsonlite, S4Vectors Suggests: DBI, RMariaDB, GenomeInfoDb, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: 94a9883e29a4a8be460b1e1e8ac0b0eb 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 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: e7cd20f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/UCSC.utils_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/UCSC.utils_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/UCSC.utils_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/UCSC.utils_1.1.0.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: 16 Package: Ularcirc Version: 1.23.0 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: f8b17ffeea180ad42c2db72cfb47a79f 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Ularcirc git_branch: devel git_last_commit: 8ca4b0f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Ularcirc_1.23.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Ularcirc_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Ularcirc_1.23.0.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: 158 Package: UMI4Cats Version: 1.15.0 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: 1386fe4c6afb57c7d0e1ea734602b629 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] (), Marc Subirana-Granes [aut], Lorenzo Pasquali [aut] Maintainer: Mireia Ramos-Rodriguez 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: 75019e7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/UMI4Cats_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/UMI4Cats_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/UMI4Cats_1.15.0.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: 151 Package: uncoverappLib Version: 1.15.0 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 MD5sum: 1ef178fe8866e590f5d6f8b093c1dcbe 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 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: 5881aab git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/uncoverappLib_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/uncoverappLib_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/uncoverappLib_1.15.0.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: 190 Package: UNDO Version: 1.47.0 Depends: R (>= 2.15.2), methods, BiocGenerics, Biobase Imports: MASS, boot, nnls, stats, utils License: GPL-2 MD5sum: d178eef4d9c24d38083eb64f67ca265a 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 Maintainer: Niya Wang git_url: https://git.bioconductor.org/packages/UNDO git_branch: devel git_last_commit: 62fc5fe git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/UNDO_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/UNDO_1.47.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/UNDO_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/UNDO_1.47.0.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: 10 Package: UniProt.ws Version: 2.45.0 Depends: BiocGenerics, methods, RSQLite, utils Imports: AnnotationDbi, BiocFileCache, BiocBaseUtils, httr, httpcache, jsonlite, progress, rjsoncons Suggests: BiocStyle, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: bed4dc2b8f5fbf2f202380b842f6c84a 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] () Maintainer: Marcel Ramos 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: fce5a4e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-03 source.ver: src/contrib/UniProt.ws_2.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/UniProt.ws_2.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/UniProt.ws_2.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/UniProt.ws_2.45.0.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 suggestsMe: autonomics, cleaver, qPLEXanalyzer dependencyCount: 68 Package: Uniquorn Version: 2.25.0 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: d4faa2dc5312cfd1a816a93a51771826 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' VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Uniquorn git_branch: devel git_last_commit: bcacad3 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Uniquorn_2.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Uniquorn_2.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Uniquorn_2.25.0.tgz vignettes: vignettes/Uniquorn/inst/doc/Uniquorn.html vignetteTitles: Uniquorn vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 90 Package: universalmotif Version: 1.23.0 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: 9d5aa9c75a81364357e096e9e08e86a8 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] (), Spencer Nystrom [ctb] () Maintainer: Benjamin Jean-Marie Tremblay 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: 34eb367 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/universalmotif_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/universalmotif_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/universalmotif_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/universalmotif_1.23.0.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, motifTestR dependencyCount: 59 Package: UPDhmm Version: 1.1.0 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: d7a013b9797e9586145c63568da1862f 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] (), Carlos Ruiz-Arenas [aut] () Maintainer: Marta Sevilla 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: a88d0ff git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/UPDhmm_1.1.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/UPDhmm_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/UPDhmm_1.1.0.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: 1.31.0 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: 90e8fda8dc74c5cc98b6f2dd4f2577d7 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/uSORT git_branch: devel git_last_commit: dbd8244 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/uSORT_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/uSORT_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/uSORT_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/uSORT_1.31.0.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: 1.11.0 Depends: keras, mclust Imports: SingleCellExperiment, SummarizedExperiment, tensorflow, scater, CatEncoders, DeepPINCS, purrr, DiagrammeR, stats Suggests: SC3, knitr, testthat, reticulate, rmarkdown License: Artistic-2.0 MD5sum: a5dcb210892cfb93623f535fb6728763 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] () Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/VAExprs git_branch: devel git_last_commit: 9cb8dad git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/VAExprs_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/VAExprs_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/VAExprs_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/VAExprs_1.11.0.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: 213 Package: VanillaICE Version: 1.67.0 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: e16a39848a13369f65d14d3e08a2cba9 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 git_url: https://git.bioconductor.org/packages/VanillaICE git_branch: devel git_last_commit: 7613876 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/VanillaICE_1.67.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/VanillaICE_1.67.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/VanillaICE_1.67.0.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: 1.13.0 Depends: Biostrings, BSgenome, GenomicRanges, R (>= 4.1) Imports: methods, stats, IRanges, shiny, shinycssloaders, shinyFiles, ggplot2 Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: 171e16ec3be224cd8fcb7ee7656274f1 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/VarCon git_branch: devel git_last_commit: 4109260 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/VarCon_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/VarCon_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/VarCon_1.13.0.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: 1.35.0 Depends: R (>= 4.3.0), ggplot2, limma, 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: e3a4d2fd9f195f5b2b0a6bc094f9aec5 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] () Maintainer: Gabriel E. Hoffman 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: aa5bed2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/variancePartition_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/variancePartition_1.35.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/variancePartition_1.35.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/variancePartition_1.35.0.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: 108 Package: VariantAnnotation Version: 1.51.0 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, zlibbioc, 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: a2c0c1c2a234fdefada9c625e98555f2 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 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: 6d549f2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/VariantAnnotation_1.51.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/VariantAnnotation_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/VariantAnnotation_1.51.0.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, ensemblVEP, HelloRanges, HTSeqGenie, myvariant, PureCN, R453Plus1Toolbox, RareVariantVis, seqCAT, signeR, SomaticSignatures, StructuralVariantAnnotation, svaNUMT, VariantFiltering, VariantTools, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, VariantToolsData, annotation, sequencing, variants, PlasmaMutationDetector, PlasmaMutationDetector2 importsMe: AllelicImbalance, APAlyzer, appreci8R, BadRegionFinder, BBCAnalyzer, biovizBase, biscuiteer, cardelino, CNVfilteR, CopyNumberPlots, crisprDesign, customProDB, DAMEfinder, decompTumor2Sig, DominoEffect, epialleleR, 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 suggestsMe: alabaster.files, AnnotationHub, BiocParallel, cellbaseR, CrispRVariants, GenomicDataCommons, GenomicRanges, GenomicScores, GWASTools, igvShiny, ldblock, omicsPrint, podkat, RVS, SeqArray, shiny.gosling, splatter, supersigs, systemPipeR, trackViewer, trio, vtpnet, AshkenazimSonChr21, GeuvadisTranscriptExpr, deconstructSigs, ldsep, polyRAD, SNPassoc, updog dependencyCount: 78 Package: VariantExperiment Version: 1.19.0 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: 34ab273221a0ab6e36376eb16e79672d 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 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: 141c6df git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/VariantExperiment_1.19.0.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: 43 Package: VariantFiltering Version: 1.41.0 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 MD5sum: f8301dc38761cdedc99ec52aed1ea43a 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 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: e32d025 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/VariantFiltering_1.41.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/VariantFiltering_1.41.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/VariantFiltering_1.41.0.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: 1.47.0 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: 796e448f73c355ff1bf624a9d599a285 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 git_url: https://git.bioconductor.org/packages/VariantTools git_branch: devel git_last_commit: 6ce1d54 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/VariantTools_1.47.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/VariantTools_1.47.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/VariantTools_1.47.0.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 importsMe: HTSeqGenie suggestsMe: VariantToolsData dependencyCount: 79 Package: VaSP Version: 1.17.0 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: ff2e1096781c1fc282993a2e650ba888 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] (), Qian Du [aut] (), Chi Zhang [aut] () Maintainer: Huihui Yu 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: 69a8fc9 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/VaSP_1.17.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/VaSP_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/VaSP_1.17.0.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: 92 Package: vbmp Version: 1.73.0 Depends: R (>= 2.10) Suggests: Biobase (>= 2.5.5), statmod License: GPL (>= 2) Archs: x64 MD5sum: 97760fbeb6d491d5060f78ed5ebca493 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 , Mark Girolami Maintainer: Nicola Lama URL: http://bioinformatics.oxfordjournals.org/cgi/content/short/btm535v1 git_url: https://git.bioconductor.org/packages/vbmp git_branch: devel git_last_commit: ae8fed1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/vbmp_1.73.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/vbmp_1.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/vbmp_1.73.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/vbmp_1.73.0.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: 1.21.0 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 MD5sum: 7ad410ec193649bb22386d9333923d99 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 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: b7efef1 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/VCFArray_1.21.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/VCFArray_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/VCFArray_1.21.0.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: 1.7.0 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: 6abc2920206a9de151c080bd2b74cace 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] (), Mercedeh Movassagh [aut] (), Jill Lundell [aut] (), Jared Brown [ctb], Linglin Huang [ctb], Mingzhi Ye [ctb] Maintainer: Kelly Street 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: b85878d git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/VDJdive_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/VDJdive_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/VDJdive_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/VDJdive_1.7.0.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: 3.43.0 Depends: R (>= 2.10.0), biomaRt, Biobase Imports: methods License: GPL-2 MD5sum: 92633de5481bbfe1a7800ffced82e3a5 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 git_url: https://git.bioconductor.org/packages/VegaMC git_branch: devel git_last_commit: 0692c25 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/VegaMC_3.43.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/VegaMC_3.43.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/VegaMC_3.43.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/VegaMC_3.43.0.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: 69 Package: velociraptor Version: 1.15.0 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 MD5sum: 97702e0095050439cf9325048f1c444a 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] (), Aaron Lun [aut] (), Charlotte Soneson [aut] (), Michael Stadler [aut] () Maintainer: Kevin Rue-Albrecht 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: dbc6491 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/velociraptor_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/velociraptor_1.15.0.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: 70 Package: veloviz Version: 1.11.0 Depends: R (>= 4.1) Imports: Rcpp, Matrix, igraph, mgcv, RSpectra, grDevices, graphics, stats LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 1bbf542937e74ddcdd41df6d26907bf6 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] (), Jean Fan [aut] (), Arpan Sahoo [aut] () Maintainer: Lyla Atta VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/veloviz git_branch: devel git_last_commit: 13cad1e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/veloviz_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/veloviz_1.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/veloviz_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/veloviz_1.11.0.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: 1.21.0 Imports: utils, grDevices, stats, methods, dplyr, purrr, tibble, magrittr, ggplot2, UpSetR, VennDiagram, grid, futile.logger Suggests: knitr, rmarkdown, testthat, markdown License: GPL-2 MD5sum: 063625526e7170fc3a169df0b8709487 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 URL: https://github.com/guokai8/VennDetail VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/VennDetail git_branch: devel git_last_commit: 8684352 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/VennDetail_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/VennDetail_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/VennDetail_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/VennDetail_1.21.0.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: 1.15.0 Depends: R (>= 4.1.0) Imports: utils, data.tree, ape, parallel, Rfast, stats Suggests: BiocGenerics, BiocStyle, testthat, knitr License: file LICENSE Archs: x64 MD5sum: 74dd05eec01a344450bae3d31ede602c 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] (), Fabrizio Angaroni [aut], Davide Maspero [cre, aut], Alex Graudenzi [aut], Luca De Sano [aut] () Maintainer: Davide Maspero 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: 908c33c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/VERSO_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/VERSO_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/VERSO_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/VERSO_1.15.0.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: 21 Package: vidger Version: 1.25.0 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: 4451a9ec949c397349ae252b9087d0bf 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 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: 4230b76 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/vidger_1.25.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/vidger_1.25.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/vidger_1.25.0.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: 126 Package: viper Version: 1.39.0 Depends: R (>= 2.14.0), Biobase, methods Imports: mixtools, stats, parallel, e1071, KernSmooth Suggests: bcellViper License: file LICENSE Archs: x64 MD5sum: f87dca4e78c1afa6a4f5b4e5d099c900 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 Maintainer: Mariano J Alvarez git_url: https://git.bioconductor.org/packages/viper git_branch: devel git_last_commit: d2d0de2 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/viper_1.39.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/viper_1.39.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/viper_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/viper_1.39.0.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: 1.19.0 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 Archs: x64 MD5sum: 25ba047dcf70ee89240738935e1e3122 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 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: f2c9bdb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ViSEAGO_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/ViSEAGO_1.19.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ViSEAGO_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ViSEAGO_1.19.0.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: 170 Package: VisiumIO Version: 1.1.0 Depends: R (>= 4.4.0), TENxIO Imports: BiocBaseUtils, BiocGenerics, BiocIO, jsonlite, methods, S4Vectors, SpatialExperiment, SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, tinytest License: Artistic-2.0 MD5sum: 307dd15805168e686e8dd73b3d94d817 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] () Maintainer: Marcel Ramos 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: 212381f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/VisiumIO_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/VisiumIO_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/VisiumIO_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/VisiumIO_1.1.0.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 dependencyCount: 88 Package: vissE Version: 1.13.0 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: 4dd536f7d161aa859efdbece0cb45143 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] (), Ahmed Mohamed [ctb] Maintainer: Dharmesh D. Bhuva 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: 418c085 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/vissE_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/vissE_1.13.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/vissE_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/vissE_1.13.0.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: VplotR Version: 1.15.0 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: f1d9ae665cc4c8e83c8c523222108387 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] () Maintainer: Jacques Serizay 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: 6b2e049 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-03 source.ver: src/contrib/VplotR_1.15.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/VplotR_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/VplotR_1.15.0.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: 1.7.0 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 License: GPL-2 MD5sum: 0244034a5d4b4227129cb18dbcc8758e 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 Schwaemmle [aut, cre] Maintainer: Veit Schwaemmle VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/vsclust git_branch: devel git_last_commit: 42dd5bf git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/vsclust_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/vsclust_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/vsclust_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/vsclust_1.7.0.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: 98 Package: vsn Version: 3.73.0 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: f27f2232b5b1fa803c89b4fc20a0b76d 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 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: 59a091a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/vsn_3.73.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/vsn_3.73.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/vsn_3.73.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/vsn_3.73.0.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: cellHTS2, webbioc, rnaseqGene importsMe: arrayQualityMetrics, autonomics, bnem, DEP, Doscheda, MatrixQCvis, metaseqR2, MSnbase, NormalyzerDE, pvca, tilingArray, ExpressionNormalizationWorkflow, RNAseqQC suggestsMe: adSplit, beadarray, DAPAR, DESeq2, ggbio, GlobalAncova, globaltest, limma, lumi, MsCoreUtils, PAA, QFeatures, qmtools, ribosomeProfilingQC, scp, twilight, estrogen, wrMisc dependencyCount: 44 Package: vtpnet Version: 0.45.0 Depends: R (>= 3.0.0), graph, GenomicRanges, gwascat, doParallel, foreach Suggests: MotifDb, VariantAnnotation, Rgraphviz License: Artistic-2.0 MD5sum: 88262b2b57000d0d80f66d3e6b274e9e 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 Maintainer: VJ Carey git_url: https://git.bioconductor.org/packages/vtpnet git_branch: devel git_last_commit: 55ee11c git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/vtpnet_0.45.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/vtpnet_0.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/vtpnet_0.45.0.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: 114 Package: vulcan Version: 1.27.0 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: 149d70575b43a77723db0eca0c9bc6a2 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 git_url: https://git.bioconductor.org/packages/vulcan git_branch: devel git_last_commit: cb667e0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/vulcan_1.27.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/vulcan_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/vulcan_1.27.0.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: 201 Package: waddR Version: 1.19.0 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: 7a10af1142f93c4431b7e353c8cf4f6f 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 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: fa897d5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/waddR_1.19.0.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: 120 Package: wateRmelon Version: 2.11.0 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: 381e5356e0cc9b2564aecf438ccfcb47 NeedsCompilation: no Title: Illumina 450 and EPIC 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/wateRmelon git_branch: devel git_last_commit: 78159a5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/wateRmelon_2.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/wateRmelon_2.11.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/wateRmelon_2.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/wateRmelon_2.11.0.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: MEAT suggestsMe: RnBeads dependencyCount: 167 Package: wavClusteR Version: 2.39.0 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: 1183d5645ed1df36a142a518ad965ff0 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/wavClusteR git_branch: devel git_last_commit: 949b87b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/wavClusteR_2.39.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/wavClusteR_2.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/wavClusteR_2.39.0.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: 1.71.0 Depends: R (>= 2.5.0), digest, tools, utils, codetools Suggests: codetools License: GPL-2 MD5sum: 2d475605f1b4d96feafcd666d3da29c9 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 git_url: https://git.bioconductor.org/packages/weaver git_branch: devel git_last_commit: 11b6f92 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/weaver_1.71.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/weaver_1.71.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/weaver_1.71.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/weaver_1.71.0.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: 1.77.0 Depends: R (>= 1.8.0), Biobase, affy, multtest, annaffy, vsn, gcrma, qvalue Imports: multtest, qvalue, stats, utils, BiocManager License: GPL (>= 2) Archs: x64 MD5sum: b6158a3f529880765256b3826b4604cc 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 Maintainer: Colin A. Smith 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: fe751f5 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/webbioc_1.77.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/webbioc_1.77.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/webbioc_1.77.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/webbioc_1.77.0.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: 1.17.0 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: 1999c3c122bec5bff3776bcde99c52f0 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] () Maintainer: Paul Harrison VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/weitrix git_branch: devel git_last_commit: d230708 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/weitrix_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/weitrix_1.17.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/weitrix_1.17.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/weitrix_1.17.0.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: 92 Package: widgetTools Version: 1.83.0 Depends: R (>= 2.4.0), methods, utils, tcltk Suggests: Biobase License: LGPL MD5sum: da1ddbbc21fb954dcda42bfc5f3cff68 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 git_url: https://git.bioconductor.org/packages/widgetTools git_branch: devel git_last_commit: aab4ef7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/widgetTools_1.83.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/widgetTools_1.83.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/widgetTools_1.83.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/widgetTools_1.83.0.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: 1.29.0 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: 23e4b08470edb7b34f13e953df5f8042 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/wiggleplotr git_branch: devel git_last_commit: 4444892 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/wiggleplotr_1.29.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/wiggleplotr_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/wiggleplotr_1.29.0.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: factR suggestsMe: MARVEL dependencyCount: 90 Package: wpm Version: 1.15.0 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: 2abb807438be7935ab54353f1a847119 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 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: 761777a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/wpm_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/wpm_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/wpm_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/wpm_1.15.0.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: 109 Package: Wrench Version: 1.23.0 Depends: R (>= 3.5.0) Imports: limma, matrixStats, locfit, stats, graphics Suggests: knitr, rmarkdown, metagenomeSeq, DESeq2, edgeR License: Artistic-2.0 MD5sum: a5bd8c1670f97d54cf2dbae129d7e009 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 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: 70bb7e4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Wrench_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Wrench_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Wrench_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Wrench_1.23.0.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: xcms Version: 4.3.0 Depends: R (>= 4.0.0), BiocParallel (>= 1.8.0) Imports: MSnbase (>= 2.29.3), mzR (>= 2.25.3), methods, Biobase, BiocGenerics, ProtGenerics (>= 1.35.4), lattice, MassSpecWavelet (>= 1.66.0), S4Vectors, IRanges, SummarizedExperiment, MsCoreUtils (>= 1.15.5), MsFeatures, MsExperiment (>= 1.5.4), Spectra (>= 1.13.7), progress, jsonlite, 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: b1fb10fd3b478ad39bf2564b8b0c7314 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] (), Paul Benton [aut], Christopher Conley [aut], Johannes Rainer [aut] (), Michael Witting [ctb], William Kumler [aut] (), Philippine Louail [aut] (), Pablo Vangeenderhuysen [ctb] (), Carl Brunius [ctb] () Maintainer: Steffen Neumann 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: 56cebbd git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/xcms_4.3.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/xcms_4.3.0.zip 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, faahKO, PtH2O2lipids importsMe: CAMERA, cosmiq suggestsMe: CluMSID, RMassBank, msdata, mtbls2, RforProteomics, CorrectOverloadedPeaks, enviGCMS, isatabr, LCMSQA, MetabolomicsBasics, RAMClustR dependencyCount: 145 Package: xcore Version: 1.9.0 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: f0b1fd128431ac91c55d8d09c8505958 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] (), Bogumił Kaczkowski [aut] () Maintainer: Maciej Migdał VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/xcore git_branch: devel git_last_commit: e1f2e82 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/xcore_1.9.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/xcore_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/xcore_1.9.0.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: 71 Package: XDE Version: 2.51.0 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: 59dc27feaf4d7f56d374972dd14fb069 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 git_url: https://git.bioconductor.org/packages/XDE git_branch: devel git_last_commit: 1752dce git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/XDE_2.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/XDE_2.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/XDE_2.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/XDE_2.51.0.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: Xeva Version: 1.21.0 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: dc3f81c77aa757389dd5fe6405325af4 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 VignetteBuilder: knitr BugReports: https://github.com/bhklab/Xeva/issues git_url: https://git.bioconductor.org/packages/Xeva git_branch: devel git_last_commit: 97baee8 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/Xeva_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/Xeva_1.21.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/Xeva_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/Xeva_1.21.0.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: 169 Package: XINA Version: 1.23.0 Depends: R (>= 3.5) Imports: mclust, plyr, alluvial, ggplot2, igraph, gridExtra, tools, grDevices, graphics, utils, STRINGdb Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 3d607ec172cbd16f8a45c2bfc5f61483 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 and Sasha A. Singh Maintainer: Lang Ho Lee and Sasha A. Singh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/XINA git_branch: devel git_last_commit: 0538436 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/XINA_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/XINA_1.23.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/XINA_1.23.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/XINA_1.23.0.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: 1.63.0 Depends: R (>= 2.0), methods Suggests: RUnit, RColorBrewer License: LGPL-3 Archs: x64 MD5sum: d4211aef84a8d6aa71abf620ddc1c5d3 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 and Crispin J Miller Maintainer: Chris Wirth 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: 89aa796 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/xmapbridge_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/xmapbridge_1.63.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/xmapbridge_1.63.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/xmapbridge_1.63.0.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: 1.13.0 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: d1b20c59fd96f8e0e352e26b7d713939 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/XNAString git_branch: devel git_last_commit: 1a0e1a0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/XNAString_1.13.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/XNAString_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/XNAString_1.13.0.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: 0.45.0 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9) Imports: methods, utils, tools, zlibbioc, BiocGenerics, S4Vectors, IRanges LinkingTo: S4Vectors, IRanges Suggests: Biostrings, drosophila2probe, RUnit License: Artistic-2.0 Archs: x64 MD5sum: 296a3aed9dc1f706b3bc190db3f3b375 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 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: a73c4b6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/XVector_0.45.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/XVector_0.45.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/XVector_0.45.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/XVector_0.45.0.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: 1.31.0 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 MD5sum: b5930b96c06b77147fa0d04c4eb83b9a 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] (), Kasper Daniel Hansen [aut] Maintainer: Leslie Myint 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: 7072a74 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/yamss_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/yamss_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/yamss_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/yamss_1.31.0.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: 1.31.0 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: 101adb287958b02363fbf3c51edf647a 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/YAPSA git_branch: devel git_last_commit: 02bb798 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/YAPSA_1.31.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/YAPSA_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/YAPSA_1.31.0.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: 208 Package: yarn Version: 1.31.0 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 MD5sum: 9148be93db74afeed92d912caa012759 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 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/yarn git_branch: devel git_last_commit: 59a1448 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/yarn_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/yarn_1.31.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/yarn_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/yarn_1.31.0.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 dependsOnMe: netZooR dependencyCount: 164 Package: zellkonverter Version: 1.15.0 Imports: Matrix, basilisk, reticulate, SingleCellExperiment (>= 1.11.6), SummarizedExperiment, DelayedArray, methods, S4Vectors, utils, cli Suggests: anndata, BiocFileCache, BiocStyle, covr, HDF5Array, knitr, pkgload, rmarkdown, rhdf5 (>= 2.45.1), scRNAseq, spelling, testthat, withr License: MIT + file LICENSE MD5sum: 0c5e68858a5857a32bb01d89bb804a5c 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] (), Aaron Lun [aut] (), Jack Kamm [ctb] (), Robrecht Cannoodt [ctb] (, rcannood) Maintainer: Luke Zappia 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: 159b5ae git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/zellkonverter_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/zellkonverter_1.15.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/zellkonverter_1.15.0.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: OSCA.intro importsMe: singleCellTK, velociraptor suggestsMe: cellxgenedp, CuratedAtlasQueryR, GloScope, HDF5Array, HCATonsilData dependencyCount: 52 Package: zenith Version: 1.7.0 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 Archs: x64 MD5sum: 0f33c0a1c99986f0df1704a013b20586 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 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: dfff8f6 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/zenith_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/zenith_1.7.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/zenith_1.7.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/zenith_1.7.0.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: 176 Package: zFPKM Version: 1.27.0 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: 2ded5f4376b1dcf8c02082f13f471508 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 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: cd3941b git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/zFPKM_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/zFPKM_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/zFPKM_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/zFPKM_1.27.0.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: 72 Package: zinbwave Version: 1.27.0 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: bda5142c973a7b28de7d0d74ada022d7 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 VignetteBuilder: knitr BugReports: https://github.com/drisso/zinbwave/issues git_url: https://git.bioconductor.org/packages/zinbwave git_branch: devel git_last_commit: 82cf6db git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/zinbwave_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/zinbwave_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/zinbwave_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/zinbwave_1.27.0.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, digitalDLSorteR, SpatialDDLS suggestsMe: MAST dependencyCount: 78 Package: zlibbioc Version: 1.51.0 Suggests: BiocStyle, knitr License: Artistic-2.0 + file LICENSE MD5sum: f893307224d209d96b372361cdaee379 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 URL: https://bioconductor.org/packages/zlibbioc VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/zlibbioc/issues git_url: https://git.bioconductor.org/packages/zlibbioc git_branch: devel git_last_commit: 6278fd7 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/zlibbioc_1.51.0.tar.gz win.binary.ver: bin/windows/contrib/4.4/zlibbioc_1.51.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/zlibbioc_1.51.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/zlibbioc_1.51.0.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: affy, affyio, affyPLM, bamsignals, CellBarcode, ChemmineOB, FLAMES, GrafGen, HiCDOC, MADSEQ, makecdfenv, NanoMethViz, oligo, ompBAM, polyester, qckitfastq, Rhtslib, Rsamtools, rtracklayer, scMitoMut, screenCounter, ShortRead, snpStats, TransView, VariantAnnotation, XVector, jackalope suggestsMe: metacoder linksToMe: bamsignals, ChemmineOB, csaw, diffHic, epialleleR, FLAMES, maftools, methylKit, Rfastp, Rhtslib, scPipe, seqTools, ShortRead, SpliceWiz, jackalope dependencyCount: 0 Package: ZygosityPredictor Version: 1.5.0 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: 6101527f444d8e36d857de3a5f75d018 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] (), Marc Ruebsam [aut], Daniel Huebschmann [aut], Martina Froehlich [aut], Barbara Hutter [aut] Maintainer: Marco Rheinnecker VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ZygosityPredictor git_branch: devel git_last_commit: 4445bbb git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 source.ver: src/contrib/ZygosityPredictor_1.5.0.tar.gz mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ZygosityPredictor_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/ZygosityPredictor_1.5.0.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: 103 Package: miRBaseConverter Version: 1.29.0 Depends: R (>= 3.4) Imports: stats Suggests: BiocGenerics, RUnit, knitr, rtracklayer, utils, rmarkdown License: GPL (>= 2) 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, Thuc Le Maintainer: Taosheng Xu 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: 3b748a4 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 win.binary.ver: bin/windows/contrib/4.4/miRBaseConverter_1.29.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/miRBaseConverter_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/miRBaseConverter_1.29.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: miRspongeR Version: 2.9.0 Depends: R (>= 3.5.0) 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 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 Maintainer: Junpeng Zhang URL: VignetteBuilder: knitr BugReports: https://github.com/zhangjunpeng411/miRspongeR/issues git_url: https://git.bioconductor.org/packages/miRspongeR git_branch: devel git_last_commit: 6e3455f git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 win.binary.ver: bin/windows/contrib/4.4/miRspongeR_2.9.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/miRspongeR_2.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/miRspongeR_2.9.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: rawDiag Version: 1.1.0 Depends: R (>= 4.3) Imports: dplyr, ggplot2 (>= 3.4), grDevices, hexbin, htmltools, BiocManager, BiocParallel, rawrr (>= 1.10), rlang, reshape2, scales, shiny (>= 1.5), stats, utils Suggests: BiocStyle (>= 2.28), ExperimentHub, tartare, knitr, testthat License: GPL-3 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] (), Christian Trachsel [aut], Tobias Kockmann [aut] Maintainer: Christian Panse URL: https://github.com/fgcz/rawDiag/ SystemRequirements: mono 4.x or higher on OSX / Linux, .NET 4.x or higher on Windows, 'msbuild' and 'nuget' available in the path VignetteBuilder: knitr BugReports: https://github.com/fgcz/rawDiag/issues git_url: https://git.bioconductor.org/packages/rawDiag git_branch: devel git_last_commit: 3edc385 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-03 win.binary.ver: bin/windows/contrib/4.4/rawDiag_1.1.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/rawDiag_1.1.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/rawDiag_1.1.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SPONGE Version: 1.27.0 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, miRBaseConverter, ComplexHeatmap, ggplot2, MetBrewer, rlang, tnet, ggpubr, stringr, tidyr Suggests: testthat, knitr, rmarkdown, visNetwork, ggrepel, gridExtra, digest, doParallel, bigmemory, GSVA License: GPL (>=3) Archs: x64 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] (), Markus Hoffmann [aut] () Maintainer: Markus List VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SPONGE git_branch: devel git_last_commit: 485926e git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 win.binary.ver: bin/windows/contrib/4.4/SPONGE_1.27.0.zip mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/SPONGE_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/SPONGE_1.27.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: BgeeCall Version: 1.21.0 Depends: R (>= 3.6) Imports: GenomicFeatures, tximport, Biostrings, rtracklayer, biomaRt, jsonlite, methods, dplyr, data.table, sjmisc, grDevices, graphics, stats, utils, rslurm, rhdf5 Suggests: knitr, testthat, rmarkdown, AnnotationHub, httr License: GPL-3 + file LICENSE MD5sum: 14be0afacfa85d5bd23eba01123957b2 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 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: 7441e73 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BgeeCall_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BgeeCall_1.21.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: BioTIP Version: 1.19.0 Depends: R (>= 3.6) Imports: igraph, cluster, psych, stringr, GenomicRanges, MASS, scran Suggests: knitr, markdown, base, rmarkdown, ggplot2 License: GPL-2 MD5sum: efee67d3e40c68214d60a18bd87c85f2 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 , Zhezhen Wang , and X Holly Yang URL: https://github.com/xyang2uchicago/BioTIP VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BioTIP git_branch: devel git_last_commit: 67694a0 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/BioTIP_1.19.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/BioTIP_1.19.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ontoProc Version: 1.27.0 Depends: R (>= 4.0), ontologyIndex Imports: Biobase, S4Vectors, methods, stats, utils, BiocFileCache, shiny, graph, Rgraphviz, ontologyPlot, dplyr, magrittr, DT, igraph, AnnotationHub, SummarizedExperiment, reticulate, R.utils, httr Suggests: knitr, org.Hs.eg.db, org.Mm.eg.db, testthat, BiocStyle, SingleCellExperiment, celldex, rmarkdown, AnnotationDbi License: Artistic-2.0 MD5sum: 128a08fa7bb9d0fd6ae7fddea4687fef 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] (), Sara Stankiewicz [ctb] Maintainer: Vincent Carey URL: https://github.com/vjcitn/ontoProc SystemRequirements: owlready2 VignetteBuilder: knitr BugReports: https://github.com/vjcitn/ontoProc/issues git_url: https://git.bioconductor.org/packages/ontoProc git_branch: devel git_last_commit: 0111f05 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/ontoProc_1.27.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: SingleRBook importsMe: pogos suggestsMe: BiocOncoTK, scDiffCom dependencyCount: 116 Package: pogos Version: 1.25.0 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: a2f0682272c000ad329a2986cd099faf 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 Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pogos git_branch: devel git_last_commit: 3204864 git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/pogos_1.25.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: BiocOncoTK dependencyCount: 132 Package: single Version: 1.9.0 Depends: R (>= 4.0) Imports: Biostrings, BiocGenerics, dplyr, GenomicAlignments,IRanges, methods, reshape2, rlang, Rsamtools, stats, stringr, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 4606427db28d3bb663e701657402770b NeedsCompilation: no Title: Accurate consensus sequence from nanopore reads of a gene library Description: Accurate consensus sequence from nanopore reads of a DNA gene library. SINGLe corrects for systematic errors in nanopore sequencing reads of gene libraries and it retrieves true consensus sequences of variants identified by a barcode, needing only a few reads per variant. More information in preprint doi: https://doi.org/10.1101/2020.03.25.007146. biocViews: Software, Sequencing Author: Rocio Espada [aut, cre] () Maintainer: Rocio Espada VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/single git_branch: devel git_last_commit: e9ba58a git_last_commit_date: 2024-04-30 Date/Publication: 2024-05-01 mac.binary.big-sur-x86_64.ver: bin/macosx/big-sur-x86_64/contrib/4.4/single_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.4/single_1.9.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 73 Package: HTqPCR Version: 1.59.0 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: Heidi Dvinge URL: http://www.ebi.ac.uk/bertone/software Package: goseq Version: 1.57.0 Depends: R (>= 2.11.0), BiasedUrn, geneLenDataBase (>= 1.9.2) Imports: mgcv, graphics, stats, utils, AnnotationDbi, GO.db,BiocGenerics Suggests: edgeR, org.Hs.eg.db, rtracklayer License: LGPL (>= 2) 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 Author: Matthew Young Maintainer: Matthew Young , Nadia Davidson Package: RNAinteract Version: 1.53.0 Depends: R (>= 2.12.0), Imports: RColorBrewer, ICS, ICSNP, cellHTS2, geneplotter, gplots, grid, hwriter, lattice, latticeExtra, limma, methods, splots (>= 1.13.12), abind, locfit, Biobase License: Artistic-2.0 Title: Estimate Pairwise Interactions from multidimensional features Description: RNAinteract estimates genetic interactions from multi-dimensional read-outs like features extracted from images. The screen is assumed to be performed in multi-well plates or similar designs. Starting from a list of features (e.g. cell number, area, fluorescence intensity) per well, genetic interactions are estimated. The packages provides functions for reporting interacting gene pairs, plotting heatmaps and double RNAi plots. An HTML report can be written for quality control and analysis. biocViews: ImmunoOncology, CellBasedAssays, QualityControl, Preprocessing, Visualization Author: Bernd Fischer [aut], Wolfgang Huber [ctb], Mike Smith [cre] Maintainer: Mike Smith Package: easyRNASeq Version: 2.41.0 Imports: Biobase (>= 2.50.0), BiocFileCache (>= 1.14.0), BiocGenerics (>= 0.36.0), BiocParallel (>= 1.24.1), biomaRt (>= 2.46.0), Biostrings (>= 2.58.0), edgeR (>= 3.32.0), GenomeInfoDb (>= 1.26.0), genomeIntervals (>= 1.46.0), GenomicAlignments (>= 1.26.0), GenomicRanges (>= 1.42.0), SummarizedExperiment (>= 1.20.0), graphics, IRanges (>= 2.24.0), LSD (>= 4.1-0), locfit, methods, parallel, rappdirs (>= 0.3.1), Rsamtools (>= 2.6.0), S4Vectors (>= 0.28.0), ShortRead (>= 1.48.0), utils Suggests: BiocStyle (>= 2.18.0), BSgenome (>= 1.58.0), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.4.0), curl, knitr, rmarkdown, RUnit (>= 0.4.32) License: Artistic-2.0 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 VignetteBuilder: knitr Package: staRank Version: 1.47.0 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 Package: Risa Version: 1.47.0 Depends: R (>= 2.0.9), Biobase (>= 2.4.0), methods, Rcpp (>= 0.9.13), biocViews, affy Imports: xcms Suggests: faahKO (>= 1.2.11) License: LGPL Title: Converting experimental metadata from ISA-tab into Bioconductor data structures Description: The Investigation / Study / Assay (ISA) tab-delimited format is a general purpose framework with which to collect and communicate complex metadata (i.e. sample characteristics, technologies used, type of measurements made) from experiments employing a combination of technologies, spanning from traditional approaches to high-throughput techniques. Risa allows to access metadata/data in ISA-Tab format and build Bioconductor data structures. Currently, data generated from microarray, flow cytometry and metabolomics-based (i.e. mass spectrometry) assays are supported. The package is extendable and efforts are undergoing to support metadata associated to proteomics assays. biocViews: Annotation, DataImport, MassSpectrometry Author: Alejandra Gonzalez-Beltran, Audrey Kauffmann, Steffen Neumann, Gabriella Rustici, ISA Team Maintainer: Alejandra Gonzalez-Beltran URL: http://www.isa-tools.org/ BugReports: https://github.com/ISA-tools/Risa/issues Package: ChAMP Version: 2.35.0 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 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 VignetteBuilder: knitr Package: unifiedWMWqPCR Version: 1.41.0 Depends: methods Imports: BiocGenerics, stats, graphics, HTqPCR License: GPL (>=2) 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 Package: nondetects Version: 2.35.0 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 , Valeriia Sherina Maintainer: Valeriia Sherina VignetteBuilder: knitr Package: polyester Version: 1.41.0 Depends: R (>= 3.0.0) Imports: Biostrings (>= 2.32.0), IRanges, S4Vectors, logspline, limma, zlibbioc Suggests: knitr, ballgown, markdown License: Artistic-2.0 Title: Simulate RNA-seq reads Description: This package can be used to simulate RNA-seq reads from differential expression experiments with replicates. The reads can then be aligned and used to perform comparisons of methods for differential expression. biocViews: Sequencing, DifferentialExpression Author: Alyssa C. Frazee, Andrew E. Jaffe, Rory Kirchner, Jeffrey T. Leek Maintainer: Jack Fu , Jeff Leek VignetteBuilder: knitr Package: paxtoolsr Version: 1.39.0 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 URL: https://github.com/BioPAX/paxtoolsr SystemRequirements: Java (>= 1.6) VignetteBuilder: knitr Package: rgsepd Version: 1.37.0 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 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 VignetteBuilder: knitr Package: gespeR Version: 1.37.0 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 URL: http://www.cbg.ethz.ch/software/gespeR VignetteBuilder: knitr Package: SMITE Version: 1.33.0 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) 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 , Andrew Damon Johnston URL: https://github.com/GreallyLab/SMITE VignetteBuilder: knitr BugReports: https://github.com/GreallyLab/SMITE/issues Package: msPurity Version: 1.31.0 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 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] (), Ralf Weber [ctb], Martin Jones [ctb], Julien Saint-Vanne [ctb], Andris Jankevics [ctb], Mark Viant [ths], Warwick Dunn [ths] Maintainer: Thomas N. Lawson URL: https://github.com/computational-metabolomics/msPurity/ VignetteBuilder: knitr BugReports: https://github.com/computational-metabolomics/msPurity/issues/new Package: recount Version: 1.31.0 Depends: R (>= 3.3.0), 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 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] (), Abhinav Nellore [ctb], Andrew E. Jaffe [ctb] (), Margaret A. Taub [ctb], Kai Kammers [ctb], Shannon E. Ellis [ctb] (), Kasper Daniel Hansen [ctb] (), Ben Langmead [ctb] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/recount VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/recount/ Package: Pi Version: 2.17.0 Depends: igraph, dnet, ggplot2, graphics Imports: Matrix, GenomicRanges, GenomeInfoDb, supraHex, scales, grDevices, ggrepel, ROCR, randomForest, glmnet, lattice, caret, plot3D, stats, methods, MASS, IRanges, BiocGenerics, dplyr, tidyr, ggnetwork, osfr, RCircos, purrr, readr, tibble Suggests: foreach, doParallel, BiocStyle, knitr, rmarkdown, png, GGally, gridExtra, ggforce, fgsea, RColorBrewer, ggpubr, rtracklayer, ggbio, Gviz, data.tree, jsonlite License: GPL-3 Title: Leveraging Genetic Evidence to Prioritise Drug Targets at the Gene and Pathway Level Description: Priority index or Pi is developed as a genomic-led target prioritisation system. It integrates functional genomic predictors, knowledge of network connectivity and immune ontologies to prioritise potential drug targets at the gene and pathway level. biocViews: Software, Genetics, GraphAndNetwork, Pathways, GeneExpression, GeneTarget, GenomeWideAssociation, LinkageDisequilibrium, Network, HiC Author: Hai Fang, the ULTRA-DD Consortium, Julian C Knight Maintainer: Hai Fang URL: http://pi314.r-forge.r-project.org VignetteBuilder: knitr BugReports: https://github.com/hfang-bristol/Pi/issues Package: psichomics Version: 1.31.0 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 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] (), Nuno Luís Barbosa-Morais [aut, led, ths] (), 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 URL: https://nuno-agostinho.github.io/psichomics/, https://github.com/nuno-agostinho/psichomics/ VignetteBuilder: knitr BugReports: https://github.com/nuno-agostinho/psichomics/issues Package: TVTB Version: 1.31.0 Depends: R (>= 3.4), methods, utils, stats Imports: AnnotationFilter, BiocGenerics (>= 0.25.1), BiocParallel, Biostrings, ensembldb, ensemblVEP, 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 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 URL: https://github.com/kevinrue/TVTB VignetteBuilder: knitr BugReports: https://github.com/kevinrue/TVTB/issues Package: rqt Version: 1.31.0 Depends: R (>= 3.4), SummarizedExperiment Imports: stats,Matrix,ropls,methods,car,RUnit,metap,CompQuadForm,glmnet,utils,pls Suggests: BiocStyle, knitr, rmarkdown License: GPL 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, . biocViews: GenomeWideAssociation, Regression, Survival, PrincipalComponent, StatisticalMethod, Sequencing Author: I. Y. Zhbannikov, K. G. Arbeev, A. I. Yashin. Maintainer: Ilya Y. Zhbannikov URL: https://github.com/izhbannikov/rqt VignetteBuilder: knitr BugReports: https://github.com/izhbannikov/rqt/issues Package: ideal Version: 1.29.0 Depends: topGO Imports: DESeq2, SummarizedExperiment, GenomicRanges, IRanges, S4Vectors, ggplot2 (>= 2.0.0), heatmaply, plotly, pheatmap, pcaExplorer, 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, base64enc, methods Suggests: testthat, BiocStyle, markdown, airway, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, DEFormats, edgeR License: MIT + file LICENSE 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. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, RNASeq, Sequencing, Visualization, QualityControl, GUI, GeneSetEnrichment, ReportWriting, ShinyApps Author: Federico Marini [aut, cre] () Maintainer: Federico Marini URL: https://github.com/federicomarini/ideal, https://federicomarini.github.io/ideal/ VignetteBuilder: knitr BugReports: https://github.com/federicomarini/ideal/issues Package: msgbsR Version: 1.29.0 Depends: R (>= 3.4), 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 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 Package: BiocSklearn Version: 1.27.0 Depends: R (>= 4.0), reticulate, methods, SummarizedExperiment Imports: basilisk Suggests: testthat, restfulSE, HDF5Array, BiocStyle, rmarkdown, knitr License: Artistic-2.0 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 SystemRequirements: python (>= 2.7), sklearn, numpy, pandas, h5py VignetteBuilder: knitr Package: progeny Version: 1.27.0 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 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] (), Christian H. Holland [ctb] (), Igor Bulanov [ctb], Aurélien Dugourd [cre, ctb] Maintainer: Aurélien Dugourd URL: https://github.com/saezlab/progeny VignetteBuilder: knitr BugReports: https://github.com/saezlab/progeny/issues Package: tenXplore Version: 1.27.0 Depends: R (>= 4.0), shiny, restfulSE (>= 0.99.12) Imports: methods, ontoProc (>= 0.99.7), SummarizedExperiment, AnnotationDbi, matrixStats, org.Mm.eg.db, stats, utils Suggests: org.Hs.eg.db, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 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 VignetteBuilder: knitr Package: SummarizedBenchmark Version: 2.23.0 Depends: R (>= 3.6), tidyr, SummarizedExperiment, S4Vectors, BiocGenerics, methods, UpSetR, rlang, stringr, utils, BiocParallel, ggplot2, mclust, dplyr, digest, sessioninfo, crayon, tibble Suggests: iCOBRA, BiocStyle, rmarkdown, knitr, magrittr, IHW, qvalue, testthat, DESeq2, edgeR, limma, tximport, readr, scRNAseq, splatter, scater, rnaseqcomp, biomaRt License: GPL (>= 3) Title: Classes and methods for performing benchmark comparisons Description: This package defines the BenchDesign and SummarizedBenchmark classes for building, executing, and evaluating benchmark experiments of computational methods. The SummarizedBenchmark class extends the RangedSummarizedExperiment object, and is designed to provide infrastructure to store and compare the results of applying different methods to a shared data set. This class provides an integrated interface to store metadata such as method parameters and software versions as well as ground truths (when these are available) and evaluation metrics. biocViews: Software, Infrastructure Author: Alejandro Reyes [aut] (), Patrick Kimes [aut, cre] () Maintainer: Patrick Kimes URL: https://github.com/areyesq89/SummarizedBenchmark, http://bioconductor.org/packages/SummarizedBenchmark/ VignetteBuilder: knitr BugReports: https://github.com/areyesq89/SummarizedBenchmark/issues Package: TissueEnrich Version: 1.25.0 Depends: R (>= 3.5), ensurer (>= 1.1.0), 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 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 VignetteBuilder: knitr Package: RandomWalkRestartMH Version: 1.25.0 Depends: R(>= 3.5.0) Imports: igraph, Matrix, dnet, methods Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL (>= 2) NeedsCompilation: no Title: Random walk with restart on multiplex and heterogeneous Networks Description: This package performs Random Walk with Restart on multiplex and heterogeneous networks. It is described in the following article: "Random Walk With Restart On Multiplex And Heterogeneous Biological Networks" . biocViews: GenePrediction, NetworkInference, SomaticMutation, BiomedicalInformatics, MathematicalBiology, SystemsBiology, GraphAndNetwork, Pathways, BioCarta, KEGG, Reactome, Network Author: Alberto Valdeolivas [cre, aut, ctb] () Maintainer: Alberto Valdeolivas URL: https://github.com/alberto-valdeolivas/RandomWalkRestartMH VignetteBuilder: knitr BugReports: https://github.com/alberto-valdeolivas/RandomWalkRestartMH/issues Package: BiocOncoTK Version: 1.25.0 Depends: R (>= 3.6.0), methods, utils Imports: ComplexHeatmap, S4Vectors, bigrquery, shiny, stats, httr, rjson, dplyr, magrittr, grid, DT, GenomicRanges, IRanges, ggplot2, SummarizedExperiment, DBI, GenomicFeatures, curatedTCGAData, scales, ggpubr, plyr, car, graph, Rgraphviz, MASS, grDevices Suggests: knitr, dbplyr, org.Hs.eg.db, MultiAssayExperiment, BiocStyle, ontoProc, ontologyPlot, pogos, GenomeInfoDb, restfulSE (>= 1.3.7), BiocFileCache, TxDb.Hsapiens.UCSC.hg19.knownGene, Biobase, TxDb.Hsapiens.UCSC.hg18.knownGene, reshape2, testthat, AnnotationDbi, FDb.InfiniumMethylation.hg19, EnsDb.Hsapiens.v75, rmarkdown, rhdf5client, AnnotationHub License: Artistic-2.0 Title: Bioconductor components for general cancer genomics Description: Provide a central interface to various tools for genome-scale analysis of cancer studies. biocViews: CopyNumberVariation, CpGIsland, DNAMethylation, GeneExpression, GeneticVariability, SNP, Transcription, ImmunoOncology Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr Package: DEqMS Version: 1.23.0 Depends: R(>= 3.5),graphics,stats,ggplot2,matrixStats,limma(>= 3.34) Suggests: BiocStyle,knitr,rmarkdown,markdown,plyr,reshape2,farms,utils,ggrepel,ExperimentHub,LSD License: LGPL 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 VignetteBuilder: knitr BugReports: https://github.com/yafeng/DEqMS/issues Package: DNABarcodeCompatibility Version: 1.21.0 Depends: R (>= 3.6.0) Imports: dplyr, tidyr, numbers, purrr, stringr, DNABarcodes, stats, utils, methods Suggests: knitr, rmarkdown, BiocStyle, testthat License: file LICENSE 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] (), Jacques Boutet de Monvel [aut] (), Fabienne Wong Jun Tai [ctb], Raphaël Etournay [aut] () Maintainer: ERROR VignetteBuilder: knitr BugReports: https://github.com/comoto-pasteur-fr/DNABarcodeCompatibility/issues Package: nanotatoR Version: 1.21.0 Depends: R (>= 4.1), Imports: hash(>= 2.2.6), openxlsx(>= 4.0.17), rentrez(>= 1.1.0), stats, rlang, stringr, knitr, testthat, utils, AnnotationDbi, httr, GenomicRanges, tidyverse, VarfromPDB, org.Hs.eg.db, curl, dplyr, XML, XML2R Suggests: rmarkdown, yaml License: file LICENSE Title: Next generation structural variant annotation and classification Description: Whole genome sequencing (WGS) has successfully been used to identify single-nucleotide variants (SNV), small insertions and deletions (INDELs) and, more recently, small copy number variants (CNVs). However, due to utilization of short reads, it is not well suited for identification of structural variants (SV). Optical mapping (OM) from Bionano Genomics, utilizes long fluorescently labeled megabase size DNA molecules for de novo genome assembly and identification of SVs with a much higher sensitivity than WGS. Nevertheless, currently available SV annotation tools have limited number of functions. NanotatoR is an R package written to provide a set of annotations for SVs identified by OM. It uses Database of Genomic Variants (DGV), Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources (DECIPHER) as well as a subset (154 samples) of 1000 Genome Project to calculate the population frequencies of the SVs (an optional internal cohort SV frequency calculation is also available). NanotatoR creates a primary gene list (PG) from NCBI databases based on proband’s phenotype specific keywords and compares the list to the set of genes overlapping/near SVs. The output is given in an Excel file format, which is subdivided into multiple sheets based on SV type (e.g., INDELs, Inversions, Translocations). Users then have a choice to filter SVs using the provided annotations for de novo (if parental samples are available) or inherited rare variants. biocViews: Software, WorkflowStep, GenomeAssembly, VariantAnnotation Maintainer: Surajit Bhattacharya URL: https://github.com/VilainLab/nanotatoR VignetteBuilder: knitr BugReports: https://github.com/VilainLab/nanotatoR/issues Package: OmnipathR Version: 3.13.0 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, rvest, stats, stringi, stringr, tibble, tidyr, tidyselect, tools, utils, withr, xml2, yaml Suggests: BiocStyle, biomaRt, bookdown, ggplot2, ggraph, gprofiler2, knitr, mlrMBO, parallelMap, ParamHelpers, Rgraphviz, sigmajs, smoof, supraHex, testthat License: MIT + file LICENSE 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] (), Denes Turei [cre, aut] (), Attila Gabor [aut] () Maintainer: Denes Turei URL: https://r.omnipathdb.org/ VignetteBuilder: knitr BugReports: https://github.com/saezlab/OmnipathR/issues Package: brainflowprobes Version: 1.19.0 Depends: R (>= 3.6.0) Imports: Biostrings (>= 2.52.0), BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.0), bumphunter (>= 1.26.0), cowplot (>= 1.0.0), derfinder (>= 1.18.1), derfinderPlot (>= 1.18.1), GenomicRanges (>= 1.36.0), ggplot2 (>= 3.1.1), RColorBrewer (>= 1.1), utils, grDevices, GenomicState (>= 0.99.7) Suggests: BiocStyle, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 2.1.0), covr License: Artistic-2.0 Title: Plots and annotation for choosing BrainFlow target probe sequence Description: Use these functions to characterize genomic regions for BrainFlow target probe design. biocViews: Coverage, Visualization, ExperimentalDesign, Transcriptomics, FlowCytometry, GeneTarget Author: Amanda Price [aut, cre] (), Leonardo Collado-Torres [ctb] () Maintainer: Amanda Price URL: https://github.com/LieberInstitute/brainflowprobes VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/brainflowprobes Package: RNAAgeCalc Version: 1.17.0 Depends: R (>= 3.6) Imports: ggplot2, recount, impute, AnnotationDbi, org.Hs.eg.db, stats, SummarizedExperiment, methods Suggests: knitr, rmarkdown, testthat License: GPL-2 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 URL: https://github.com/reese3928/RNAAgeCalc VignetteBuilder: knitr BugReports: https://github.com/reese3928/RNAAgeCalc/issues Package: BRGenomics Version: 1.17.0 Depends: R (>= 4.0), rtracklayer, GenomeInfoDb, S4Vectors Imports: GenomicRanges, parallel, IRanges, stats, Rsamtools, GenomicAlignments, DESeq2, SummarizedExperiment, utils, methods Suggests: BiocStyle, knitr, rmarkdown, testthat, apeglm, remotes, ggplot2, reshape2, Biostrings License: Artistic-2.0 Title: Tools for the Efficient Analysis of High-Resolution Genomics Data Description: This package provides useful and efficient utilites for the analysis of high-resolution genomic data using standard Bioconductor methods and classes. BRGenomics is feature-rich and simplifies a number of post-alignment processing steps and data handling. Emphasis is on efficient analysis of multiple datasets, with support for normalization and blacklisting. Included are functions for: spike-in normalizing data; generating basepair-resolution readcounts and coverage data (e.g. for heatmaps); importing and processing bam files (e.g. for conversion to bigWig files); generating metaplots/metaprofiles (bootstrapped mean profiles) with confidence intervals; conveniently calling DESeq2 without using sample-blind estimates of genewise dispersion; among other features. biocViews: Software, DataImport, Sequencing, Coverage, RNASeq, ATACSeq, ChIPSeq, Transcription, GeneRegulation, GeneExpression, Normalization Author: Mike DeBerardine [aut, cre] Maintainer: Mike DeBerardine URL: https://mdeber.github.io VignetteBuilder: knitr BugReports: https://github.com/mdeber/BRGenomics/issues Package: scClassify Version: 1.17.0 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 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 VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scClassify/issues Package: RnaSeqSampleSize Version: 2.15.0 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) 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 VignetteBuilder: knitr Package: CellaRepertorium Version: 1.15.0 Depends: R (>= 4.0) Imports: dplyr, tibble, stringr, Biostrings, Rcpp, reshape2, methods, rlang (>= 0.3), purrr, Matrix, S4Vectors, BiocGenerics, tidyr, forcats, progress, stats, utils, generics, glue LinkingTo: Rcpp Suggests: testthat, readr, knitr, rmarkdown, ggplot2, BiocStyle, ggdendro, broom, lme4, RColorBrewer, SingleCellExperiment, scater, broom.mixed, cowplot, igraph, ggraph License: GPL-3 NeedsCompilation: yes Title: Data structures, clustering and testing for single cell immune receptor repertoires (scRNAseq RepSeq/AIRR-seq) Description: Methods to cluster and analyze high-throughput single cell immune cell repertoires, especially from the 10X Genomics VDJ solution. Contains an R interface to CD-HIT (Li and Godzik 2006). Methods to visualize and analyze paired heavy-light chain data. Tests for specific expansion, as well as omnibus oligoclonality under hypergeometric models. biocViews: RNASeq, Transcriptomics, SingleCell, TargetedResequencing, Technology, ImmunoOncology, Clustering Author: Andrew McDavid [aut, cre], Yu Gu [aut], Erik VonKaenel [aut], Aaron Wagner [aut], Thomas Lin Pedersen [ctb] Maintainer: Andrew McDavid URL: https://github.com/amcdavid/CellaRepertorium VignetteBuilder: knitr BugReports: https://github.com/amcdavid/CellaRepertorium/issues Package: SANTA Version: 2.41.0 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) 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 VignetteBuilder: knitr BugReports: https://github.com/alexjcornish/SANTA Package: wppi Version: 1.13.0 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 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] (), Denes Turei [aut] (), Michael P. Menden [aut] (), Albert Krewinkel [ctb, cph] (pagebreak Lua filter) Maintainer: Ana Galhoz URL: https://github.com/AnaGalhoz37/wppi VignetteBuilder: knitr BugReports: https://github.com/AnaGalhoz37/wppi/issues Package: MQmetrics Version: 1.13.0 Imports: ggplot2, readr, magrittr, dplyr, purrr, reshape2, gridExtra, utils, stringr, ggpubr, stats, cowplot, RColorBrewer, tidyr, scales, grid, rlang, ggforce, grDevices, gtable, plyr, knitr, rmarkdown, ggrepel, gghalves, tools Suggests: testthat (>= 3.0.0), BiocStyle License: GPL-3 Title: Quality Control of Protemics Data Description: The package MQmetrics (MaxQuant metrics) provides a workflow to analyze the quality and reproducibility of your proteomics mass spectrometry analysis from MaxQuant.Input data are extracted from several MaxQuant output tables and produces a pdf report. It includes several visualization tools to check numerous parameters regarding the quality of the runs. It also includes two functions to visualize the iRT peptides from Biognosys in case they were spiked in the samples. biocViews: Infrastructure, Proteomics, MassSpectrometry, QualityControl, DataImport Author: Alvaro Sanchez-Villalba [aut, cre], Thomas Stehrer [aut], Marek Vrbacky [aut] Maintainer: Alvaro Sanchez-Villalba VignetteBuilder: knitr Package: cosmosR Version: 1.13.0 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 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] (), Attila Gabor [cre] (), Katharina Zirngibl [aut] () Maintainer: Attila Gabor URL: https://github.com/saezlab/COSMOSR VignetteBuilder: knitr BugReports: https://github.com/saezlab/COSMOSR/issues Package: decoupleR Version: 2.11.0 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 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] (), Jesús Vélez-Santiago [aut] (), Jana Braunger [aut] (), Celina Geiss [aut] (), Daniel Dimitrov [aut] (), Sophia Müller-Dott [aut] (), Petr Taus [aut] (), Aurélien Dugourd [aut] (), Christian H. Holland [aut] (), Ricardo O. Ramirez Flores [aut] (), Julio Saez-Rodriguez [aut] () Maintainer: Pau Badia-i-Mompel URL: https://saezlab.github.io/decoupleR/ VignetteBuilder: knitr BugReports: https://github.com/saezlab/decoupleR/issues Package: ggspavis Version: 1.11.0 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 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] (), Helena L. Crowell [aut] (), Yixing E. Dong [aut] () Maintainer: Lukas M. Weber URL: https://github.com/lmweber/ggspavis VignetteBuilder: knitr BugReports: https://github.com/lmweber/ggspavis/issues Package: sparrow Version: 1.11.0 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 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] (), Arkadiusz Gladki [ctb], Aratus Informatics, LLC [fnd] (2023+), Denali Therapeutics [fnd] (2018-2022), Genentech [fnd] (2014 - 2017) Maintainer: Steve Lianoglou URL: https://github.com/lianos/sparrow VignetteBuilder: knitr BugReports: https://github.com/lianos/sparrow/issues Package: Cepo Version: 1.11.0 Depends: GSEABase, R (>= 4.1) Imports: DelayedMatrixStats, DelayedArray, HDF5Array, S4Vectors, methods, SingleCellExperiment, SummarizedExperiment, ggplot2, rlang, grDevices, patchwork, reshape2, BiocParallel, stats, dplyr Suggests: knitr, rmarkdown, BiocStyle, testthat, covr, UpSetR, scater, scMerge, fgsea, escape, pheatmap, patchwork License: MIT + file LICENSE 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] (), Kevin Wang [aut] () Maintainer: Hani Jieun Kim VignetteBuilder: knitr Package: netOmics Version: 1.11.0 Depends: R (>= 4.1) Imports: dplyr, ggplot2, igraph, magrittr, minet, purrr, tibble, tidyr, AnnotationDbi, GO.db, gprofiler2, methods, Matrix, stats Suggests: mixOmics, timeOmics, tidyverse, BiocStyle, testthat, covr, rmarkdown, knitr License: GPL-3 Title: Multi-Omics (time-course) network-based integration and interpretation Description: netOmics is a multi-omics networks builder and explorer. It uses a combination of network inference algorithms and and knowledge-based graphs to build multi-layered networks. The package can be combined with timeOmics to incorporate time-course expression data and build sub-networks from multi-omics kinetic clusters. Finally, from the generated multi-omics networks, propagation analyses allow the identification of missing biological functions (1), multi-omics mechanisms (2) and molecules between kinetic clusters (3). This helps to resolve complex regulatory mechanisms. biocViews: GraphAndNetwork, Software, TimeCourse, WorkflowStep, SystemsBiology, NetworkInference, Network Author: Antoine Bodein [aut, cre] Maintainer: Antoine Bodein URL: https://github.com/abodein/netOmics VignetteBuilder: knitr BugReports: https://github.com/abodein/netOmics/issues Package: easier Version: 1.11.0 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 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] (), Federico Marini [aut] (), Arsenij Ustjanzew [aut] (), Francesca Finotello [aut] (), Federica Eduati [aut] () Maintainer: Oscar Lapuente-Santana VignetteBuilder: knitr Package: updateObject Version: 1.9.0 Depends: R (>= 4.2.0), methods, BiocGenerics (>= 0.47.1), S4Vectors Imports: utils, digest Suggests: GenomicRanges, SummarizedExperiment, InteractionSet, SingleCellExperiment, MultiAssayExperiment, TimiRGeN, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 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 URL: https://bioconductor.org/packages/updateObject SystemRequirements: git VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/updateObject/issues Package: nnSVG Version: 1.9.0 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 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] (), Stephanie C. Hicks [aut] () Maintainer: Lukas M. Weber URL: https://github.com/lmweber/nnSVG VignetteBuilder: knitr BugReports: https://github.com/lmweber/nnSVG/issues Package: EpiCompare Version: 1.9.0 Depends: R (>= 4.2.0) Imports: AnnotationHub, BRGenomics, ChIPseeker, data.table, genomation, GenomicRanges, IRanges, 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 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] (), Brian Schilder [aut] (), Leyla Abbasova [aut], Alan Murphy [aut] (), Nathan Skene [aut] (), Thomas Roberts [cre] Maintainer: Thomas Roberts URL: https://github.com/neurogenomics/EpiCompare VignetteBuilder: knitr BugReports: https://github.com/neurogenomics/EpiCompare/issues Package: SpatialFeatureExperiment Version: 1.7.0 Depends: R (>= 4.2.0) Imports: BiocGenerics, BiocNeighbors, BiocParallel, data.table, DropletUtils, EBImage, grDevices, lifecycle, Matrix, methods, rjson, rlang, S4Vectors, sf, sfheaders, SingleCellExperiment, SpatialExperiment, spdep (>= 1.1-7), SummarizedExperiment, stats, terra, utils Suggests: arrow, BiocStyle, knitr, RBioFormats, rhdf5, rmarkdown, sfarrow, SFEData (>= 1.5.3), testthat (>= 3.0.0), Voyager, xml2, scater, Seurat, SeuratObject, dplyr, tidyr License: Artistic-2.0 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] (), Alik Huseynov [aut] (), Lior Pachter [aut, ths] () Maintainer: Lambda Moses URL: https://github.com/pachterlab/SpatialFeatureExperiment VignetteBuilder: knitr BugReports: https://github.com/pachterlab/SpatialFeatureExperiment/issues Package: Voyager Version: 1.7.0 Depends: R (>= 4.2.0), SpatialFeatureExperiment (>= 1.5.2) Imports: BiocParallel, bluster, ggnewscale, ggplot2 (>= 3.4.0), grDevices, grid, lifecycle, Matrix, matrixStats, memuse, methods, patchwork, rlang, RSpectra, S4Vectors, scales, scico, sf, SingleCellExperiment, SpatialExperiment, spdep, stats, SummarizedExperiment, terra, utils Suggests: automap, BiocSingular, BiocStyle, cowplot, EBImage, ExperimentHub, ggh4x, gstat, hexbin, knitr, pheatmap, RBioFormats, rhdf5, rmarkdown, scater, scattermore, scran, sfarrow, SFEData, testthat (>= 3.0.0), vdiffr, vroom, xml2 License: Artistic-2.0 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] (), Alik Huseynov [aut] (), Kayla Jackson [aut] (), Laura Luebbert [aut] (), Lior Pachter [aut, rev] () Maintainer: Lambda Moses URL: https://github.com/pachterlab/voyager VignetteBuilder: knitr BugReports: https://github.com/pachterlab/voyager/issues Package: signifinder Version: 1.7.0 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, sparrow, SpatialExperiment, stats, SummarizedExperiment, survival, survminer, viridis Suggests: BiocStyle, edgeR, grid, kableExtra, knitr, limma, testthat (>= 3.0.0) License: AGPL-3 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] (), Enrica Calura [aut] () Maintainer: Stefania Pirrotta URL: https://github.com/CaluraLab/signifinder VignetteBuilder: knitr BugReports: https://github.com/CaluraLab/signifinder/issues Package: simpleSeg Version: 1.7.0 Imports: BiocParallel, EBImage, terra, stats, spatstat.geom, S4Vectors, grDevices, SummarizedExperiment, methods, cytomapper Suggests: BiocStyle, testthat (>= 3.0.0), knitr, ggplot2 License: GPL-3 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 URL: https://sydneybiox.github.io/simpleSeg/ https://github.com/SydneyBioX/simpleSeg VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/simpleSeg/issues Package: SGCP Version: 1.5.0 Depends: R (>= 4.3.0) 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, BiocManager License: GPL-3 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] (), Ioannis Koutis [aut] Maintainer: Niloofar AghaieAbiane URL: https://github.com/na396/SGCP VignetteBuilder: knitr Date/Publication: 2022-12-10 Package: escheR Version: 1.5.0 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 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] (), Stephanie C. Hicks [aut] (), Erik D. Nelson [ctb] () Maintainer: Boyi Guo URL: https://github.com/boyiguo1/escheR VignetteBuilder: knitr BugReports: https://github.com/boyiguo1/escheR/issues Package: spoon Version: 1.1.0 Depends: R (>= 4.4) Imports: SpatialExperiment, BRISC, nnSVG, BiocParallel, Matrix, methods, SummarizedExperiment, stats, utils, scuttle Suggests: testthat, STexampleData, knitr License: MIT + file LICENSE 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] (), Boyi Guo [aut] (), Stephanie C. Hicks [aut] () Maintainer: Kinnary Shah URL: https://github.com/kinnaryshah/spoon VignetteBuilder: knitr BugReports: https://github.com/kinnaryshah/spoon/issues Package: tpSVG Version: 1.1.0 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 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] (), Lukas M. Weber [ctb] (), Stephanie C. Hicks [aut] () Maintainer: Boyi Guo URL: https://github.com/boyiguo1/tpSVG VignetteBuilder: knitr BugReports: https://github.com/boyiguo1/tpSVG/issues Package: smoothclust Version: 1.1.1 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 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] () Maintainer: Lukas M. Weber URL: https://github.com/lmweber/smoothclust VignetteBuilder: knitr BugReports: https://github.com/lmweber/smoothclust/issues Package: hdxmsqc Version: 1.1.0 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 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] () Maintainer: Oliver M. Crook URL: http://github.com/ococrook/hdxmsqc VignetteBuilder: knitr BugReports: https://github.com/ococrook/hdxmsqc/issues Package: Damsel Version: 1.1.0 Depends: R (>= 4.4.0) Imports: AnnotationDbi, Biostrings, 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 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] () Maintainer: Caitlin Page URL: https://github.com/Oshlack/Damsel VignetteBuilder: knitr BugReports: https://github.com/Oshlack/Damsel Package: SpotSweeper Version: 1.1.0 Depends: R (>= 4.3.0) Imports: SpatialExperiment, SummarizedExperiment, BiocNeighbors, SingleCellExperiment, stats, escheR, MASS, ggplot2, spatialEco, grDevices Suggests: knitr, BiocStyle, rmarkdown, scuttle, STexampleData, ggpubr, testthat (>= 3.0.0) License: MIT + file LICENSE 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. Scales linearly with the number of spots and is designed to be used with 'SpatialExperiment' objects. biocViews: Software, Spatial, Transcriptomics, QualityControl, GeneExpression, Author: Michael Totty [aut, cre] (), Boyi Guo [aut] () Maintainer: Michael Totty URL: https://github.com/MicTott/SpotSweeper VignetteBuilder: knitr BugReports: https://support.bioconductor.org/tag/SpotSweeper Package: mosdef Version: 1.1.0 Depends: R (>= 4.4.0) Imports: DT, ggplot2, ggforce, ggrepel, 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 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] (), Federico Marini [aut, cre] () Maintainer: Federico Marini URL: https://github.com/imbeimainz/mosdef VignetteBuilder: knitr BugReports: https://github.com/imbeimainz/mosdef/issues Package: gINTomics Version: 1.1.0 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 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] (), Francesco Patane' [aut] (), Chiara Romualdi [aut] () Maintainer: Angelo Velle URL: https://github.com/angelovelle96/gINTomics VignetteBuilder: knitr BugReports: https://github.com/angelovelle96/gINTomics/issues